CARTOGRAPHY AND GEOINFORMATION
DEPARTMENT OF GEOGRAPHY AND REGIONAL RESEARCH, UNIVERSITY OF VIENNA
Geographic Information
Science (GIS)
Wolfgang Kainz
© Wolfgang Kainz
Kartographie und Geoinformation
Institut für Geographie und Regionalforschung
Universität Wien
Universitätsstraße 7, A-1010 Wien
Tel. +43 (1) 4277 48640, Fax +43 (1) 4277 9486
wolfgang.kainz@univie.ac.at
http://www.univie.ac.at/geographie
Version 2.0 (25 February 2004)
I
Contents
0. Preface............................................................................ ix
1. Geographic Information Systems................................. 11
1.1 Geoinformatics and GIS ........................................................................12
1.1.1 Terminology...................................................................................................13
1.1.2 Spatial Information Theory and the Geoinformatics Context ................14
1.2 History of GIS .........................................................................................15
1.2.1 The Pioneers..................................................................................................15
1.2.2 The Breakthrough........................................................................................16
1.2.3 The Ubiquitous GIS and the Road Ahead.................................................17
1.3 Characteristics of Spatial Data.............................................................17
1.4 Functional Components of a GIS..........................................................18
2. Spatial Data ................................................................... 21
2.1 Metadata .................................................................................................22
2.2 Data Quality ...........................................................................................23
2.3 Sources of Error......................................................................................24
3. Data Models and Databases ......................................... 25
3.1 Introduction to Information Processing...............................................26
3.1.1 Real World Phenomena and Their Abstractions......................................26
3.1.2 Spatial Data and Information.....................................................................27
3.2 Concepts of Space and Time..................................................................27
3.2.1 Pre-Newtonian Concepts of Space and Time ............................................28
3.2.2 Classical Concepts of Space and Time .......................................................29
3.2.3 Contemporary Concepts of Space and Time..............................................30
3.2.4 Concepts of Space and Time in Spatial Information Systems................30
3.3 The Real World and Its Models.............................................................31
3.3.1 Maps...............................................................................................................31
3.3.2 Databases ......................................................................................................32
3.3.3 Space and Time in Real World Models ......................................................33
3.4 Real World Models and Their Representation....................................34
3.4.1 Database Design...........................................................................................36
3.4.2 Topology and Spatial Relationships...........................................................37
3.4.3 Spatial Data Models.....................................................................................40
3.4.3.1 Field-based Models..............................................................................................................40
3.4.3.2 Object-based Models............................................................................................................42
3.4.4 Spatiotemporal Data Models.......................................................................42
3.4.4.1 Space-Time Cube Model.....................................................................................................43
3.4.4.2 Snapshot Model....................................................................................................................44
3.4.4.3 Space-Time Composite Model............................................................................................44
3.4.4.4 Event-Based Model..............................................................................................................44
3.4.4.5 Spatiotemporal Object Model..............................................................................................44
3.5 Summary.................................................................................................44
4. Spatial Data Structures................................................. 47
4.1 Point Data ...............................................................................................48
4.1.1 Cell Method ...................................................................................................48
II CONTENTS
4.1.2 Point Quadtrees............................................................................................49
4.1.3 K-D Trees.......................................................................................................50
4.1.4 PR Quadtrees................................................................................................51
4.1.5 Grid Files.......................................................................................................51
4.2 Line Data.................................................................................................52
4.2.1 Arc/Node Structure.......................................................................................52
4.2.2 Chain Codes...................................................................................................53
4.3 Area Data ................................................................................................53
4.3.1 Boundary Model............................................................................................54
4.3.2 Region Quadtrees .........................................................................................54
4.4 Volume Data ...........................................................................................55
4.5 Classification of Spatial Data Structures ............................................55
5. Spatial Analysis ............................................................. 57
5.1 Data Organization and Spatial Queries ..............................................58
5.2 Maintenance and Analysis of the Spatial Data...................................58
5.3 Maintenance and Analysis of the Attribute Data...............................59
5.4 Integrated Analysis of Spatial and Attribute Data ............................59
5.4.1 Retrieval, classification, and measurement ..............................................59
5.4.2 Overlay Operations ......................................................................................59
5.4.3 Neighborhood Operations............................................................................59
5.4.4 Connectivity Operations..............................................................................60
6. Technical Infrastructure ............................................... 61
6.1 Introduction.............................................................................................62
6.2 Basic Hardware And Software..............................................................62
6.2.1 Computer Hardware ....................................................................................62
6.2.2 Peripheral Devices........................................................................................63
6.2.3 Software.........................................................................................................66
6.2.4 Computer Networks.....................................................................................67
6.2.5 Trends ............................................................................................................68
6.3 System architecture ...............................................................................68
6.4 Computer networks................................................................................70
6.4.1 Transmission media.....................................................................................70
6.4.1.1 Twisted pair..........................................................................................................................70
6.4.1.2 Coaxial cable........................................................................................................................71
6.4.1.3 Fiber optics...........................................................................................................................71
6.4.2 Wireless transmission..................................................................................72
6.4.3 Network hardware .......................................................................................73
6.4.3.1 Local area networks (LANs)...............................................................................................73
6.4.3.2 Metropolitan area networks (MANs).................................................................................74
6.4.3.3 Wide area networks (WANs)..............................................................................................74
6.4.3.4 Internetworks .......................................................................................................................74
6.4.4 Network software .........................................................................................75
6.4.4.1 Layer Services......................................................................................................................76
6.4.4.2 The OSI Reference Model...................................................................................................76
6.4.4.3 The TCP/IP Reference Model.............................................................................................78
6.4.4.4 Comparison between the OSI and TCP/IP Reference Models..........................................80
6.5 The World Wide Web .............................................................................80
7. Organizational Infrastructure ....................................... 83
7.1 Database Technology .............................................................................84
7.1.1 Database architectures ................................................................................85
CONTENTS III
III
7.1.2 Data models...................................................................................................85
7.1.3 Distributed databases..................................................................................86
7.2 Clearinghouse.........................................................................................86
7.3 Spatial Data Transfer............................................................................88
7.3.1 Spatial Data Transfer Process....................................................................88
7.3.2 Examples of Spatial Data Transfer Standards.........................................89
8. GIS Technology in Organizations ................................. 91
8.1 Prerequisites and Problems ..................................................................92
8.2 Implementation Strategy ......................................................................92
9. References and Bibliography........................................ 95
10. Index ............................................................................ 97
V
List of Figures
Figure 1: Geoinformatics context.......................................................................................14
Figure 2: Functional modules of a GIS..............................................................................18
Figure 3: Platonic solids as building blocks of matter.....................................................29
Figure 4: Spatial modeling is a structure preserving mapping from the real world to
a spatial model...............................................................................................................34
Figure 5: Data modeling from the real world to a database, and from there to digital
cartographic models and analogue products for visualization. ..............................35
Figure 6: Rubber sheet transformation.............................................................................37
Figure 7: Simplexes and simplicial complex. Features are approximated by a set of
points, line segments, triangles, and tetrahedrons..................................................38
Figure 8: Cells and a cell complex. Spatial features are represented as topological
images of cells. ...............................................................................................................39
Figure 9: Spatial relationships between two regions derived from the topological
invariants of boundary and interior...........................................................................40
Figure 10: Two data layers in a field-based model..........................................................41
Figure 11: Layers in an object based model......................................................................42
Figure 12: Grid representation for data in Table 7 (after SAMET 1990a).....................49
Figure 13: Point Quadtree obtained from insertion indicated in the text....................49
Figure 14: A k-d tree for the data in Table 7 (after SAMET 1990a)................................50
Figure 15: A PR quadtree of the data points in Table 7 (after SAMET 1990a).............51
Figure 16: Grid file partition and linear scales for the data of Table 7 (after SAMET
1990a)..............................................................................................................................52
Figure 17: An arc/node structure .......................................................................................53
Figure 18: Chain code and sample line.............................................................................53
Figure 19: Sample polygons and their topological data model. The outside region
(background or world polygon) is denoted as O........................................................54
Figure 20: Sample (a) region, (b) its binary array, (c) its maximal blocks, and (d) the
corresponding quadtree (after SAMET 1990a) ...........................................................54
Figure 21: Locational codes corresponding to the quadtree in Figure 20 (after SAMET
1990b)..............................................................................................................................55
Figure 22: General computer architecture .......................................................................63
Figure 23: Client/Server concept in computer networks ................................................67
Figure 24: Stand-alone computers without cooperation.................................................69
Figure 25. Client-server architecture...............................................................................70
Figure 26: Coaxial cable ......................................................................................................71
Figure 27: Fiber cable ..........................................................................................................71
Figure 28: Network topologies............................................................................................73
Figure 29: Wide area network............................................................................................74
Figure 30: Layers, protocols and interfaces......................................................................75
Figure 31: ISO/OSI reference model..................................................................................76
Figure 32: ISO/OSI and TCP/IP layers.............................................................................78
Figure 33: Database architecture.......................................................................................84
Figure 34: Models in database design...............................................................................85
Figure 35: Top-down distributed database design ..........................................................86
Figure 36: Federated database design ..............................................................................87
VI CONTENTS
Figure 37: Clearinghouse concept......................................................................................87
Figure 38: No standard versus a transfer standard........................................................88
Figure 39 Spatial data transfer process............................................................................89
VII
List of Tables
Table 1: Disciplines involved in spatial data handling...................................................12
Table 2: Characteristics of spatial data.............................................................................17
Table 3: Data input ..............................................................................................................18
Table 4: Data output and visualization.............................................................................19
Table 5: Processing steps and sources of error (after ARONOFF, 1989).........................24
Table 6: Data models and schemas in database design (the ANSI/SPARC
architecture)...................................................................................................................36
Table 7: Sample point dataset (after SAMET 1990a)........................................................48
Table 8: Classification of data structures..........................................................................56
Table 9: Types of queries.....................................................................................................58
Table 10: Peripheral input and output devices overview ...............................................66
Table 11: Examples of spatial data transfer standards..................................................89
IX
he first textbook on GIS was published in 1986. Today, we can
find dozens of books dealing with GIS. Yet, it is difficult to select
the right book that satisfies all needs. This lecture notes are the
author’’s view on what the basic scientific principles of GIS are that
everybody should know who wants to use and understand today’’s
geographic information system software. They are the result of more
than 20 years involvement in GIS and spatial data handling.
It is the intention to teach the principles of GIS independent of any
particular software system. Only in this way, the student can abstract
from the peculiarities of a particular system and open his/her mind to
the fundamental principles of geoinformatics.
Any theory needs to be demonstrated with practical examples. This is
also true for geographic information science. This lecture notes are
designed in such a way that they can be used together with any
reasonable GIS software to practice the principles learnt.
The notes start with a general introduction to the geoinformatics
context and the design and structure of a GIS The following chapter
deals with metadata, data quality and sources of error in spatial data.
Chapter 3 introduces spatial data models and databases, which are
followed by chapter 4 on spatial data structures. Experience shows that
particularly with data models and data structures we often find a lot of
confusion of terms and sloppy terminology. Therefore, much emphasis
has been put to provide the student with a clear and clean treatment of
these crucial concepts. Part of the material presented in chapters 4 and
5 come from an earlier lecture note on spatial data structures and
algorithms that I have provided for ITC students. They were later
revised and expanded by Rolf A. de By.
Chapter 5 describes in major lines the spatial analysis functions that we
can expect in today’’s GIS software. Issues on spatial data transfer and
the introduction of GIS technology in organizations are presented in
0 Preface
“Though this be madness,
Yet there’s method in it.”
.
The Binary Bible of Saint $ilicon
T
X PREFACE
chapters 6 and 7. This text is accompanied by slides that cover more
and advanced subjects.
WOLFGANG KAINZ
Vienna, February 2004
11
he handling of spatial data usually involves processes of
data acquisition, storage and maintenance, analysis and
output. For many years, this has been done using
analogue data sources, manual processing and the production of
paper maps. The introduction of modern technologies has led to
an increased use of computers and information technology in all
aspects of spatial data handling. The software technology used in
this domain is geographic information systems (GIS).
A general motivation for the use of GIS can be illustrated with the
following example. For a planning task usually different maps
and other data sources are needed. Assuming a conventional
analogue procedure we would have to collect all the maps and
documents needed before we can start the analysis. The first
problem we encounter is that the maps and data have to be
collected from different sources at different locations (e.g.,
mapping agency, geological survey, soil survey, forest survey,
census bureau, etc.), and that they are in different scales and
projections. In order to combine data from maps they have to be
converted into working documents of the same scale and
projection. This has to be done manually, and it requires much
time and money.
CHAPTER
1 Geographic Information
Systems
T
12 GEOGRAPHICINFORMATIONSCIENCE(GIS)
With the help of a GIS, the maps can be stored in digital form in a
database in world coordinates (meters or feet). This makes scale
transformations unnecessary, and the conversion between map
projections can be done easily with the software. The spatial
analysis functions of the GIS are then applied to perform the
planning tasks. This can speed up the process and allows for easy
modifications to the analysis approach.
This chapter illustrates the geoinformatics context, reviews the
historic development of GIS, explains the characteristics of spatial
data, and describes the functional components of a geographic
information system. The technology of geographic information
systems was first developed in Canada in the 1960’’s. In only 25
years, it became a major business from large-scale applications
down to the local level.
1.1 Geoinformatics and GIS
Spatial data handling involves many disciplines. We can distinguish
disciplines that develop spatial concepts, provide means for capturing
and processing of spatial data, provide a formal and theoretical
foundation, are application-oriented, and support spatial data handling
in legal and management aspects. Table 1 shows a classification of
some of these disciplines. They are grouped according to how they deal
with spatial information. The list is not meant to be exhaustive.
Table 1: Disciplines involved in spatial data handling
Characteristics of disciplines Sample disciplines
Development of spatial concepts Geography
Cognitive Science
Linguistics
Psychology
Means for capturing and processing spatial data Remote Sensing
Surveying Engineering
Cartography
Photogrammetry
Formal and theoretical foundation Computer Science
Expert Systems
Mathematics
Statistics
Applications Archaeology
Architecture
Forestry
Geo-Sciences
Regional and Urban Planning
Surveying
Support Legal Sciences
Economy
GEOGRAPHIC INFORMATION SYSTEMS 13
The discipline that deals with all aspects of spatial data handling is
called geoinformatics. It is defined as:
Geoinformatics is the integration of different disciplines dealing
with spatial information.
Geoinformatics has also been described as ““the science and technology
dealing with the structure and character of spatial information, its
capture, its classification and qualification, its storage, processing,
portrayal and dissemination, including the infrastructure necessary to
secure optimal use of this information”” (GROOT, 1989). EHLERS and
AMER (1991) define it as ““the art, science or technology dealing with the
acquisition, storage, processing production, presentation and
dissemination of geoinformation.””
A related term that is sometimes used synonymously with
geoinformatics is geomatics. It was originally introduced in Canada,
and became very popular in French speaking countries. LAURINI and
THOMPSON (1992) describe it as ““the fusion of ideas from geo-sciences
and informatics.”” The term geomatics, however, was never fully
accepted in the United States where the term geographic information
science is preferred. GOODCHILD (1992) defines GIS research as
““research on the generic issues that surround the use of GIS technology,
impede its successful implementation, or emerge from an understanding
of its potential capabilities.””
1.1.1 Terminology
Frequently used technical terms in spatial data handling related to GIS
are:
• geographic (or geographical) information system (GIS),
• geo-information system,
• spatial information system (SIS),
• land information system (LIS), and
• multi-purpose cadastre.
Geographic information systems are used by various disciplines as tools
for spatial data handling in a geoinformatics environment.
There are many definitions for a geographic information system. The
main characteristics, however, are the analytical functions that provide
means for deriving new information from existing data. A GIS is
defined as follows (ARONOFF, 1989):
A GIS is a computer-based system that provides the following four
sets of capabilities to handle geo-referenced data:
1. input,
2. data management (data storage and retrieval),
3. manipulation and analysis, and
4. output.
Depending on the interest of a particular application, a GIS can be
considered to be a data store (application of a spatial database), a tool(box),
a technology, an information source or a science (spatial
information science).
14 GEOGRAPHICINFORMATIONSCIENCE(GIS)
Like in any other discipline, the use of tools for problem solving is one
thing; to produce these tools is something different. Not all are equally
well suited for a particular application. Tools can be improved and
perfected to better serve a particular need or application. The discipline
that provides the background for the production of the tools in spatial
data handling is spatial information theory (or SIT) or geographic
information science.
1.1.2 Spatial Information Theory and the Geoinformatics Context
Geographic information technology is used to manipulate objects in
geographic space, and to acquire knowledge from spatial facts. Spatial
information theory provides a basis for GIS by bringing together fields
that deal with spatial reasoning, the representation of space, and
human understanding of space:
• Spatial reasoning addresses the inference of spatial information from
spatial facts. It deals with the framework and models for space and
time, and the relationships that can be identified between objects in
a spatiotemporal model of real world phenomena.
• Scientific methods for the representation of space are important for
the development of data models and data structures to represent
objects in spatial databases. Spatial databases are distinguished
from standard databases by their capability to store and manage
data with an extent in space and time (spatial data types).
• The human understanding of space, influenced by language and
culture, plays an important role in how people design and use GIS.
Figure 1 shows a general framework for geo-information. Here, spatial
information theory is built on fundamental disciplines that provide the
basis for the understanding of spatial phenomena, spatial cognition,
concepts of space, as well as technical and formal tools, such as
mathematics and computer science.
Cultural Background
Spatial Models
(Spatial Information Theory)
Spatial Models
(Spatial Information Theory)
GIS and Spatial Decision Support Systems (SDSS)
Management and Infrastructure
Theoretical
Foundation
Theoretical
Foundation
Data InputData Input Database
Output
Analysis
Geomatics Context
Figure 1: Geoinformatics context
The theory is used for the design of high-level models of spatial
phenomena and processes. They are then mapped into conceptual,
GEOGRAPHIC INFORMATION SYSTEMS 15
logical and physical models for spatial databases. The database stands
central in the geoinformatics environment. It is the database that holds
the data; without it, no useful function can be performed. Data are
entered into the database in input processes. Later, they are extracted
from the database for spatial analysis and display.
The processes of data management, analysis and display are often
supported by rules that are derived from domain experts. Systems that
apply stored rules to arrive at conclusions are called rule-based or
knowledge-based systems. Those that support decision making for
space-related problems are known as spatial decision support systems
(or SDSS). They are becoming increasingly popular in planning
agencies and management of natural resources.
All these activities happen in a social, economic and legal context. It is
generally referred to as spatial information infrastructure. Everything
within this infrastructure and all concepts of space and time in turn are
shaped and determined by the cultural background of the individuals
and organizations involved.
1.2 History of GIS
When in the mid 1960s ROGER TOMLINSON developed the Canada
Geographic Information System (CGIS), terms like quadtree, topology,
object-orientation, workstation, personal computer, or geoinformatics
were not known. For many of them this is true, because the objects that
they would refer to, simply did not exist yet, or they did exist (as in the
case of topology), but not in the context of spatial data handling. The
efforts in building CGIS were driven by the need to overcome technical
problems in getting maps into the computer. On the hardware side, a
large-scale drum scanner had to be invented, and the well-known
Morton order, a spatial indexing method, was developed.
1.2.1 The Pioneers
In the 1970s, developments mainly took place at universities in the
United States, in Canada and the United Kingdom. The Laboratory for
Computer Graphics and Spatial Analysis of the Harvard Graduate
School of Design, or the Geographic Information Systems Laboratory at
SUNY Buffalo achieved a worldwide recognition. Commercial agencies
started to develop and offer GIS software. Among them were today’’s
market leaders ESRI and Intergraph.
A growing awareness of the need for sound and stable structures to
store and analyze map data became dominant in the late 1970s. This
was the start of the successful series of Auto Carto conferences. The
emphasis, however, was on map data rather than on spatial data. The
idea of storing graphical features that are displayed on maps, in
computer files was predominant. This is also reflected in the
terminology used in the scientific literature of that time: map data
model, map data structure or cartographic data structure are
widespread. Abstract data types and spatial data models came later.
They are children of the 1980s.
The search for stable and consistent representation structures of map
data lead to the introduction of topology into GIS. Topology and the
related graph theory proved to be effective and efficient tools to provide
logically consistent 2-dimensional data representations. This success
made many people believe that topology was invented by GIS
researchers for GIS. In fact, topology is a branch of mathematics that
16 GEOGRAPHICINFORMATIONSCIENCE(GIS)
existed long before GIS was invented. The landmark paper by JAMES
CORBETT on Topological Principles in Cartography became a milestone
in research on spatial relationships (CORBETT, 1979).
1.2.2 The Breakthrough
The 1980s were shaped by the introduction and spread of the personal
computer. For the first time in computing history, it was possible to
have a computer on the desk that was able to execute programs that
previously could only be run on mainframes. At the same time
minicomputers, and later workstations, were widely available.
Relational database technology became the standard.
Research on spatial data structures, indexing methods, and spatial
databases made tremendous progress. This lead to better and more
reliable systems. Operational GIS software entered the market and was
readily adopted mainly by government agencies for mapping, planning
and design.
The first textbook on GIS appeared in 1986 (BURROUGH, 1986) and the
International Journal of Geographical Information Systems was
launched in 1987. The biennial series of Symposia on Spatial Data
handling was started in 1984, and a similar one on Large Spatial
Databases in 1989.
Despite all these promising developments, the need for a theoretical
foundation of GIS became increasingly obvious. At the first
International Symposium on Spatial Data Handling in Zurich, in 1984,
JACK DANGERMOND, president of the Environmental Systems Research
Institute, reported on the results of a workshop on GIS development
that had been held one year before (DANGERMOND, 1984):
““There is at present no coherent mathematical theory of spatial
relations. A small group of knowledgeable persons should be selected
to define the problem thus created. Then a group of mathematicians
should be selected to meet and refine the problem statement and to
identify what means are best adapted to its solution. Research in this
area should be funded and encouraged in such a way that significant
progress might be made within about a ten-year period. Development
and engineering implementations of the results of this research
should be promptly supported.””
What was previously regarded as a tool for doing research (research
with GIS), suddenly became a research topic itself (research about GIS).
The research about GIS lead to something that later would become
known as Spatial Theory, Geographic Information Science, or Spatial
Information Theory. Questions were asked like ““How and why do GIS
work?””, ““How can we make them better?”” or ““What are the impediments
to making them better?””
In 1988, the National Science Foundation of the United States approved
the National Center for Geographic Information and Analysis (NCGIA),
a consortium formed by the University of California Santa Barbara, the
University of New York at Buffalo, and the University of Maine at
Orono. Since its inception the NCGIA has started various research
initiatives and provided major contributions to the progress in GIS
research.
GEOGRAPHIC INFORMATION SYSTEMS 17
The European Science Foundation (ESF) initiated the GISDATA
program that ran from 1993 to 1997. This was a European research
program that resulted in a series of specialist meetings and book
publications. In 1998 the Association of Geographic Information
Laboratories in Europe (AGILE) was founded. Its mission is ““to
promote academic teaching and research on GIS at the European level
and to ensure the continuation of the networking activities that have
emerged as a result of the EGIS Conferences and the European Science
Foundation GISDATA Scientific Programme1.”” AGILE organizes
annual conferences that have been held so far at Enschede, The
Netherlands, in 1998, Rome, Italy, in 1999, Helsinki, Finland, in 2000,
Brno, Czech Republic, in 2001, Mallorca, Spain, in 2002, and Lyon,
France, in 2003.
1.2.3 The Ubiquitous GIS and the Road Ahead
The 1990s can be characterized as a period of the breakthrough of
object-orientation in system and database design, the recognition of
geoinformatics as a professional activity, and spatial information theory
as the theoretical basis for GIS. After a precursor under a different
name in 1992, the first Conference on Spatial Information Theory
(COSIT’’93) was held in Elba, Italy, in 1993. This was the first in a
biennial series of COSIT events. COSIT, the Symposia on Spatial Data
Handling (SDH), and the Symposia on Large Spatial Databases2 (SSD)
are today’’s high-level forum for the presentation of research results in
spatial information theory.
With the rise of the World Wide Web (WWW or the Web), new Internet
protocols, such as the hypertext transfer protocol (HTTP), as well as
easy to use interfaces (browsers), tools and languages (HTML, XML,
Java), the network becomes the system. Internet applications and
Internet GIS are the system of the future. The use of GIS is not any
more restricted to mapping, planning and survey organizations. GIS
enters medium and small businesses and new domains such as geo-
marketing.
1.3 Characteristics of Spatial Data
A GIS deals with spatial data or objects (e.g., parcels, rivers, wells, ……),
their attributes and characteristics (e.g., location, area, length, name,
depth, ……) and the relationships between the objects (e.g., a parcel
boundary follows a river, a well is located in a certain parcel, ……). The
objects are stored in the database with geometric primitives (volumes,
areas, lines, and points) and the relationships between them (topology).
Spatial data have the following characteristics:
Table 2: Characteristics of spatial data
Spatial reference (geographic location,
coordinates)
where?
Attributes (non-spatial) what?
Spatial relationships (topology, metric, order) in what relationship?
Temporal component (different concepts of time) when?
1 This mission statement is taken from the AGILE web site that can be fount at
http://www.agile-online.org.
2 As of 2001 the conference is called Symposium on Spatial and Temporal
Databases (SSTD).
18 GEOGRAPHICINFORMATIONSCIENCE(GIS)
1.4 Functional Components of a GIS
According to the definition a GIS always consists of modules for input,
storage, analysis, display and output of spatial data. Figure 2 shows a
diagram of these modules with arrows indicating the flow of data in the
system. For a particular system, each of these modules may provide
more or less functions. However, if one would be completely missing the
system should not be called a geographic information system. The most
important component of GIS software is the functional complex of
functions for spatial analysis. If this functionality is poorly developed,
we cannot call such a system a GIS.
Input
Data
Management
Manipulation
and Analysis
Output
Figure 2: Functional modules of a GIS
The functions for data input are closely related to the disciplines of
surveying engineering, photogrammetry, remote sensing, and the
processes of digitizing, i.e., the conversion of analog data into a digital
representation. Today, digital data on different media or on a computer
network are used increasingly. Table 3 lists the methods and devices
used in the data input process.
Table 3: Data input
Method Devices
manual digitizing coordinate entry via keyboard
digitizing tablet with cursor or mouse
(digital) photogrammetry
automatic digitizing scanner
semi-automatic digitizing line following devices
input of available digital data magnetic tape
CD ROM
networks
Data output is closely related to the disciplines of cartography, printing
and publishing. Table 4 lists different methods and devices used for the
output of spatial data. Cartography and scientific visualization make
use of these methods and devices to produce their products. The
importance of digital products (datasets) is increasing and data
dissemination on digital media or on computer networks becomes
extremely important.
In both processes, data input and data output, the Internet, and
Internet technology have a major share. The World Wide Web plays the
GEOGRAPHIC INFORMATION SYSTEMS 19
role of an easy to use interface to repositories of large datasets. Aspects
of data dissemination, security, copyright, and pricing require special
attention. Spatial information infrastructure deals with these issues.
Table 4: Data output and visualization
Method Devices
Hardcopy printer
plotter (pen plotter, ink-jet printer, thermal
transfer printer, electrostatic plotter)
film writer
Softcopy computer screen (CRT)
Output of digital datasets magnetic tape
CD ROM
network
21
sers of GIS need to know about certain characteristics of
the data they are using for their applications. Many
institutions provide a type of service that helps to select
the right dataset for a particular application. Such institutions
are called clearinghouse.
The need to have a common set of characteristics of data has lead
to standardization efforts in various organizations and at
different levels. The International Organization for
Standardization (ISO) has established a technical committee
(ISO/TC 211) on geographic information/geomatics that deals
(among others) with the standardization of metadata and data
quality.
The standardization body of the European Union, CEN, is active
in developing a series of standards concerning the same issues.
On a national level, organizations like the Federal Geographic
Data Committee (FGDC) of the United States of America have
also developed standards related to metadata and other processes
in spatial data handling. Currently, the ISO and CEN standards
are draft standards or pre-standards, awaiting their final
approval.
CHAPTER
2 Spatial Data
U
22 GEOGRAPHICINFORMATIONSCIENCE(GIS)
This chapter introduces and explains the concept of metadata,
describes the relevance of data quality as one of the metadata
elements, and explains various sources of error that can affect the
fitness for use of a dataset.
2.1 Metadata
Users looking for datasets for a particular application need to know
about certain characteristics of these data in order to make the right
selection. This type of information is called metadata.
Metadata are data about the content, quality, condition, and other
characteristics of data. Metadata are organized in sections containing
one or more metadata entities, which in turn are a collection of
similar metadata elements.
A feature type is a class of features with common characteristics (e.g.,
country). With feature, we mean a representation of a real world
phenomenon (e.g., Austria). A feature attribute type is a
characteristic relevant for features belonging to a certain feature type
(e.g., area, population,……). A feature attribute is a characteristic of a
specific feature. It has a name from its feature attribute type and a
value from the value domain associated with that feature attribute
type (e.g., area 83,858 km2, population 8,2 million). A logical link
between features is called a feature relationship (e.g., the neighboring
countries of Austria are Germany, Czech Republic, Slovak Republic,
Hungary, Slovenia, Italy, Liechtenstein, and Switzerland).
We can distinguish between eleven sections (packages) that contain the
metadata elements. The following is taken from the ISO 19115:2003
Geographic Information - Metadata standard.
Identification Information. This helps to uniquely identify a dataset.
Constraint Information. This contains information concerning the
restrictions placed on data.
Data Quality Information. Contains a general assessment of the quality
of the dataset (see section 2.2 below).
Maintenance Information. Contains information about the scope and
frequency of updating data.
Spatial Representation Information. Contains information concerning
the mechanisms used to represent spatial information in the dataset.
Reference System Information. This is a description of the spatial and
temporal reference used in the dataset.
Content Information. This section contains definitions and descriptions
of the feature types together with their feature functions, attributes,
and relationships occurring in the dataset.
Portrayal Catalogue Information. This section contains information
identifying the portrayal catalogue used.
Distribution information. Contains information about the distributor of
the dataset and about options for obtaining the dataset.
SPATIAL DATA 23
Metadata Extension Information. Contains information about the userspecified
extensions.
Application Schema Information. Contains information about the
application schema used to build a dataset.
2.2 Data Quality
It is very important to know about the quality of data used in a GIS
application. Often this is expressed as fitness for use.
Data quality is the totality of characteristics of a product that bear on
its ability to satisfy stated and implied needs (ISO 8402). Data
quality is documented by the data quality elements described here,
and tested through data quality measures that result in data quality
results.
The following is taken from the ISO 19113:2002 Geographic
Information –– Quality Principles, and ISO 19114:2003 Geographic
Information –– Quality Evaluation Procedures standards.
The standard distinguishes between data quality elements
(quantitative) and data quality overview elements (qualitative). The
data quality elements are described as follows:
Completeness describes the presence and absence of features, their
attributes and their relationships. Data quality measures are measures
of difference between the specified items and the actual items in a
dataset.
Logical consistency describes the degree to which data is stored in
accordance with the structure of a dataset or the degree to which the
correct encoding of feature attributes into a dataset are in accordance
with a dataset’’s production specification. A data quality measure for
logical consistency is the degree of consistency expressed, for instance,
as the percentage or number of violations.
Positional accuracy describes the accuracy of the position of features in
accordance with a dataset’’s product specification. An error statistic
(standard deviation, % confidence) is used as a data quality measure.
Temporal accuracy describes the accuracy of the temporal features and
feature attributes in accordance with a dataset’’s product specification.
Data quality measures are error statistics (standard deviation, %
confidence), percentage, or conformance.
Thematic accuracy describes the accuracy of the classifications of the
features and feature attributes of a dataset in accordance with a
dataset’’s product specification. Data quality measures common for
thematic accuracy are error statistics (standard deviation, %
confidence), conformance indicators, or a (misclassification) matrix.
Data quality overview elements that give a qualitative description of
the quality of a dataset are:
Purpose describes the rationale for creating the dataset and contains
information about its intended use.
Usage describes the application(s) for which the dataset has been used
by producers or users.
24 GEOGRAPHICINFORMATIONSCIENCE(GIS)
Lineage describes the history of the dataset and the processes that were
involved in its production (life cycle).
For both quantitative and qualitative quality elements, the standard
allows to define additional user-defined quality elements.
2.3 Sources of Error
All geographic data contain some degree of error. During every
processing step in spatial data handling there are many possibilities to
influence the quality of data or products. Often these sources of error
are not easy to detect, or they are hidden by deficiencies of instruments,
software or personnel.
These errors are then propagated through repeated processing steps
and can sometimes render a product unsuitable for a particular purpose
that it was originally meant for. Table 5 lists the most important
sources of error in the different processing steps.
Table 5: Processing steps and sources of error (after ARONOFF, 1989)
Processing Step Error Source
data capture errors with field data capture
errors in maps that are used as data sources
errors in remotely sensed data
data entry inaccuracies introduced during digitizing by the
operators or the hardware
inaccuracies inherent in the objects (e.g., fuzzy
boundaries digitized as clear cut lines)
data storage insufficient numerical accuracy
insufficient spatial accuracy
data manipulation inappropriate class boundaries
error propagation through computer arithmetic
slivers introduced by overlay processes
data output inaccuracy of output devices
instability of output media
use of results insufficient technical knowledge
wrong use of results
25
hen we want to represent phenomena of the real world
in a GIS we need to design a model that describes
these phenomena in the best way for our particular
purposes. This process is called spatial modeling. We store the
data in a GIS database (spatial database).
In the design process of a database, we go through several
modeling steps to produce database schemas from high level
software and hardware independent conceptual and logical
schemas to the implementation with particular database
software.
This chapter introduces the concepts of real world phenomena and
their representations in spatial databases. Concepts of space and
time relevant for spatial data play an important role in spatial
modeling. Several approaches to spatial data modeling and
models of time used in GIS are discussed.
CHAPTER
3 Data Models and
Databases
W
26 GEOGRAPHICINFORMATIONSCIENCE(GIS)
3.1 Introduction to Information Processing
We live in a constantly changing world. What we perceive with our
senses are processes, states and events that happen or exist in the real
world. They may be natural or man-made, and are called real world
phenomena. In our brains, we process the input from our senses, which
leads to mental models, learning, cognition, and knowledge. Everything
that happens in the real world and that we process through our senses
leads to models of reality that we create for ourselves. Usually, we
agree (tacitly) on common principles and interpretations derived from
experience, research or learning that enable us to reach a consensus (at
least largely) on the perception of real world phenomena.
It is evident that for many phenomena such an agreement is obvious
because of the same educational, cultural or societal context, or simply
by common sense. For instance, there is hardly any dispute about the
perception of a tree, water body or mountain, although we give them
different names according to our languages. The essence, however, is
well understood.
We will not discuss here the fundamental philosophical questions of
““What can we know?”” or ““How real is reality?”” They have been the
subjects of philosophical contemplation since the dawn of humankind,
and the answers largely depend on the philosophical or religious
orientation of the persons involved. What we state here, however, is
that in our minds we create models of the real world. The assumption
(based on experience and common sense) is that within a given context
the individual mental models coincide to such extent that we perceive
the same reality.
3.1.1 Real World Phenomena and Their Abstractions
In the real world, we distinguish between natural and man-made
phenomena. Natural phenomena exist independently from human
actions and are subject only to the laws of nature. Examples are the
landscape (topography), the weather, or natural processes that shape
and influence them. Man-made phenomena are objects that have come
into existence by human activities through construction or building
processes.
Based on these phenomena we develop high-level abstract models for
particular purposes and applications. Features (abstractions of
phenomena) populate the models that are usually organized in layers.
Examples of such models are a cadastre, topography, soil, hydrography,
land cover, or land use.
The fact that these models are abstractions of the real world can be
illustrated by the example of a cadastre. Let us assume that a cadastre
is a legal and organizational framework for the handling of land. It is a
very important, clearly described and understood concept. Yet, we do
not see a cadastre when we look around us. What we see are real world
phenomena such as buildings, roads, fences around pieces of land, and
people. A cadastre abstracts from certain phenomena and their
relationships to create something new that is real in a given context.
Layers are an ordering principle for real world phenomena. Again,
when we look around us, we do not see the world in layers. Yet, we are
used to organize phenomena of the real world in such a way that we
DATA MODELS AND DATABASES 27
classify them according to a perceived purpose or characteristic into
subsets (layers) that allow us to deal with them efficiently.
3.1.2 Spatial Data and Information
In order to conceptualize mental models of the real world, we need to
categorize the phenomena we observe. These phenomena exist in space
and time and have therefore a spatial (geometric) and a temporal
extent. They possess certain thematic characteristics that allow us not
only to refer to them in terms of spatiotemporal, but also according to
thematic information. The thematic information of real world
phenomena is the basis for the definition of layers.
Humans perceive signals through their senses, process them and
extract information that leads to knowledge and wisdom. Here, we
focus on spatial information, i.e., information concerning phenomena
with a spatiotemporal extent. It is obvious that thematic information is
an integral part of spatial phenomena.
Data are representations of information in a computer. Spatial data
refer to spatial information that we store in a computer for processing
and analysis.
The following example illustrates the principle. Assume that you stand
on top of a hill and are looking at the landscape surrounding you. What
you see are meadows, fields, trees, and roads. The meadows are green
with grass, the fields are yellow, the trees are green, and the road is
covered with brownish-black asphalt. Your brain has processed the
optical signals that you receive through your eyes and your mind
recognizes the phenomena observed. You also have assigned attributes
to them such as color, (relative) size and you might remember that five
years ago the road was not paved, and what is a field now has been a
forest.
Since you want to build a land cover database of this area, you need to
store a representation of the phenomena in a computer database. To
achieve this you need to define features, collect (spatial and attribute)
data about them and enter them into a database. Geographic
information system (GIS) software will be used for processing and
analysis.
3.2 Concepts of Space and Time
Space and time are two closely related concepts that have been the
subject of philosophical and scientific consideration since the dawn of
mankind. The space that humans live in is the three-dimensional
(Euclidean) space as a frame of reference for our senses of touch and
sight. Of all possible spaces, this is the only space that is illustrative
and that we perceive as being real.
Time is a measure for change in our immediate experience. Usually we
assume time to be of a continuous linear nature extending from the
past, through the present into the future.
Space and time (at least as we perceive them) are so well known and
appear to be given beyond any doubt that we hardly ever contemplate
their structure and characteristics in our everyday lives. When we deal
with information systems that process and manipulate spatiotemporal
28 GEOGRAPHICINFORMATIONSCIENCE(GIS)
features, we need a clear and well-understood model of space and time.
The following sections describe how concepts of space and time
developed in Western philosophy and physics. We discuss them
according to three epochs: (i) pre-Newtonian concepts, (ii) the
Newtonian and classical concepts, and (iii) contemporary concepts of
space and time.
3.2.1 Pre-Newtonian Concepts of Space and Time
The concepts of space and time of this epoch are mainly dominated by
the ideas of Greek philosophers about the logical conditions for things to
change and the structure of the world in which change occurs.
HERACLITUS (around 500 BC) of Ephesos (western Turkey) studied the
problem of change, i.e., how can the identity of things be preserved
when they change. He stated that everything flows, nothing remains,
and the only thing that really exists is change (processes). ““Everything
flows”” and ““We cannot step into the same river twice”” are attributed to
him. Identity of opposites, linked by processes, is his postulate.
At about the same time, PARMENIDES of Elea (southern Italy) developed
a completely opposite philosophy of the non-existence of the void. He
postulates (through deductive reasoning) that change does not exist and
that the real world (the real being) is plenum (a solid complete compact
being), immutable, without change and eternal. A void (or empty space,
i.e., something non-existent) does not exist. What we perceive as
change is a delusion of our senses. PARMENIDES’’ ideas were further
developed and ““proven”” by his student ZENO.
One of ZENO’’s famous ““proofs”” that change and movement cannot exist
is known as the race between Achilles and the tortoise. The tortoise
gets a head-start and begins the race at point B, whereas Achilles starts
from point A. When Achilles reaches point B, the tortoise already has
moved on to point C. When Achilles reaches point C, the tortoise has
moved on to another point, and so on. The lead of the tortoise gets
smaller and smaller, ad infinitum. We get an infinite number of
(smaller and smaller) leads.
The argument is now: In order to reach the tortoise, Achilles must catch
up an infinite number of (finite in length) leads. It is impossible to run
this infinite number of short distances, because Achilles would have to
run infinitely far (or forever). Therefore, it is impossible for Achilles to
catch the tortoise. Since we can easily catch a tortoise when we would
run against it, we end up with a paradox. This proves that the
assumption that movement and change are real leads to contradictions.
Therefore, movement and change are impossible.
DEMOCRITUS (460––370 BC) did not accept the non-existence of change
as postulated by PARMENIDES. Space is an absolute and empty entity
existing independently from the atoms that fill the space. Atoms are
indivisible real things; they are immutable and eternal, and have
different size and weight. There is no empty space within atoms. An
atom is a plenum. Objects are formed as a collection of atoms. The
importance of the Atomist theory is evident in today’’s modern particle
physics.
The Greek mathematics was strongly dominated by the Pythagorean
number theoretic approach. It was essentially arithmetic based on
(philosophical) properties of numbers, counting and the ratios between
numbers. The discovery of irrational numbers such as 2 (the length
of the diagonal in the unit square) shook the foundation of Greek
DATA MODELS AND DATABASES 29
mathematics that was based on counting in natural units. The need for
a truly geometrical description of the world became apparent.
The great philosopher PLATO (427––347 BC) and one member of his
school, EUCLID (at 300 BC), laid the foundation for a new geometric
modeling of the real world. This geometric model of matter is based on
right triangles as atoms and solids built from these triangles. Matter
consists of four elements: earth, air, fire, and water. Each element is
made of particles, i.e., solids (see Figure 3) that in turn are made from
triangles. Transformations between the elements fire, air and water
are possible through geometric transformations. Earth cannot be
transformed.
Tetrahedron
(fire)
Cube
(earth)
Octahedron
(air)
Icosahedron
(water)
Figure 3: Platonic solids as building blocks of matter.
In his Elements, EUCLID developed a mathematical theory of geometry
that remained valid until the late 19th century. Euclidean geometry
was considered a true description of our physical world until it was
discovered that many consistent geometric systems are possible, some of
them non-Euclidean, and that geometry is not a description of the world
but yet another formal mathematical system with no necessary
reference to real world phenomena.
3.2.2 Classical Concepts of Space and Time
The time between the classical Greek period and the rise of modern
science was dominated by the philosophy and teaching of ARISTOTLE
(384––322 BC). According to his ideas, empty space is impossible, and
time is the measure of motion with regard to what is earlier and later.
Space is defined as the limit of the surrounding body towards what is
surrounded. Space is a place of all things that occupy a place in space.
Following from this approach space can be conceptualized in two
possible ways:
• Absolute space. Space as a set of places. It is an absolute real
entity, the container of all things. Its structure is fixed and
invariable. Generally, this is considered the space as described by
the Euclidean geometry.
• Relative space. Space is a system of relations. It is the set of all
material things, and relations are abstracted from them. Space is a
property of things or things have spatial properties.
The rise of modern science took shape in the works of Nikolaus
COPERNICUS (heliocentric system: the sun seen as the center of our
planetary system), Johannes KEPLER (mathematical foundation of the
heliocentric system), and Galileo GALILEI (foundations of mechanics) in
the 16th and 17th centuries.
Isaac NEWTON (1643 –– 1727 AD) was a brilliant scientist (dynamic
theory) and philosopher. In his philosophy, he was an outspoken
proponent of the concept of absolute space although it strictly
30 GEOGRAPHICINFORMATIONSCIENCE(GIS)
contradicts his dynamics theory. The concept of absolute space
remained dominant until the late 19th century.
Gottfried Wilhelm LEIBNITZ (1646––1716) on the other hand sustained
the concept of relative space. For him, space is a system of relationships
between things. It is interesting to note that both NEWTON and
LEIBNITZ are the founders of mathematical calculus.
One of the greatest philosophers, Immanuel KANT (1724––1804), claimed
that space and time are not empirical physical objects or events. They
are merely a priori true intuitions, not developed by experience, but
used by us to relate and order observations of the real world. Space and
time have empirical reality (they are absolute and a priori given) and
transcendental idealism (they belong to our conceptions of things but
are not part of the things). We cannot know anything about the things
as such. In this regard, KANT can be regarded as a proponent of
absolute space, yet in a far more elaborate and sophisticated way than
the previous philosophical approaches.
3.2.3 Contemporary Concepts of Space and Time
The development of modern physics (field theories, theory of relativity,
quantum theory) and mathematics (non-Euclidean geometries) lead to
the conclusion that traditional Euclidean geometry (describing the
three-dimensional space of our perception) is only an approximation to
the real nature of the world.
The field theories (Michael FARADAY and James Clerk MAXWELL) lead
to the assumption that space is not empty, but is filled with energy.
Therefore, a material existence of space is strongly supported by these
theories.
As a consequence of the special and general theory of relativity by
Albert EINSTEIN (1879––1955), space and time cannot anymore be
considered as two separate entities. We speak of space-time, which is
considered a four-dimensional curved space that can only be described
by non-Euclidean geometry. Quantum mechanics states the principle of
uncertainty and the discrete character of matter and energy. It has
become evident that the space of our perception is not necessarily
identical with the microscopic (sub-atomic) space or the space of cosmic
dimensions.
3.2.4 Concepts of Space and Time in Spatial Information Systems
Spatial information is always related to geographic space, i.e., largescale
space. This is the space beyond the human body, space that
represents the surrounding geographic world. Within such space, we
constantly move around, we navigate in it, and we conceptualize it in
different ways. Geographic space is the space of topographic, cadastral,
and other features of the geographic world. Geographic information
system technology is used to manipulate objects in geographic space,
and to acquire knowledge from spatial facts.
Geographic space is distinct from small-scale space, or tabletop space.
In other words, objects that are smaller than us, objects that can be
moved around on a tabletop, belong to small-scale space and are not
subject of our interest.
The human understanding of space, influenced by language and
cultural background, plays an important role in how we design and use
tools for the processing of spatial data.
DATA MODELS AND DATABASES 31
In the same way as spatial information is always related to geographic
space, it relates to geographic time, the time whose effects we observe in
the changing geographic world around us. We are less interested in
pure philosophical or physical considerations about time or space-time,
but more in the observable spatiotemporal effects that can be described,
measured and stored in information systems.
3.3 The Real World and Its Models
As mentioned in the previous sections we always create models of the
real world in our minds. When we want to acquire, store, analyze,
visualize, and exchange information about the real world, we use other
media and means than just interpersonal communication. We need
representations of our mind models, i.e., models of the real world that
can be used to acquire, store, analyze and transfer information about
real world data.
The most common of these models are——in historic sequence——maps and
databases. Both have distinct characteristics, advantages and
disadvantages. Whereas maps usually have been used to picture real
world phenomena, databases can be used to represent real and virtual
worlds.
Real worlds are subsets of the reality that we perceive. Virtual worlds
are computer generated ‘‘realities’’ that exist only as potentialities with
no counterpart in the real world. Yet, we can visualize them, navigate
through them and perceive them as ‘‘real’’ (therefore the term virtual
reality). There is no difference between real and virtual world models
with regard to their representation. The only difference is that the
former is a model of something that exists in the real world and the
latter is a model of something that exists only in a virtual (physically
non-existent) world.
3.3.1 Maps
The best known (conventional) models of the real world are maps. Maps
have been used for thousands of years to represent information about
the real world. Today, they are usually drawn on paper or other
permanent material and function as data storage and visualization
medium. Their conception and design has developed into an art and
science with a high degree of scientific sophistication and artistic
craftsmanship. Maps have proven to be extremely useful models of
reality for many applications in various domains.
Yet, maps are two-dimensional (flat) and static. It is not easy to
visualize three-dimensional dynamic features without considerable
abstractions in the spatial and temporal domain. We distinguish
between topographic and thematic maps. Other cartographic products
that are not maps are often used to represent three-dimensional and
dynamic phenomena. Such products are, for instance, block diagrams,
animations, and panoramic views.
A disadvantage of maps is that they are restricted to two-dimensional
static representations, and that they always are displayed in a given
scale. The map scale determines the spatial resolution of the graphic
feature representation. The smaller the scale, the less detail a map can
show. The accuracy of the primary data, on the other hand, puts limits
to the scale in which a map sensibly can be drawn. The selection of a
proper map scale is one of the first and most important steps in a map
conception.
32 GEOGRAPHICINFORMATIONSCIENCE(GIS)
A map is always a graphic representation at a certain level of detail,
which is determined by the scale. The process to derive less detailed
representations from a detailed one is called map generalization (or
cartographic generalization). Map sheets have physical boundaries, and
features spanning two map sheets have to be cut into pieces.
Cartography as the science and art of map making functions as an
interpreter translating real world phenomena (primary data) into
correct, clear and understandable representations for our use. Maps
also become a data source for other maps.
With the advent of computer systems, analogue cartography became
digital cartography. It is important to note that whenever we speak
about cartography today, we implicitly assume digital cartography. The
use of computers in map making is an integral part of modern
cartography. The role of the map changed accordingly. Increasingly,
maps lose their role as data storage. This role is taken over by
databases. What remains is the visualization function of maps.
When we look back into the history of digital cartography and
geographic information systems, we see that originally maps were
considered the main data source for GIS databases. To transfer the
contents of a map into a computer database was the major goal. We
observe this also in the scientific literature of that time. The terms map
data model and map data structure were widely used. It was not clearly
understood that we actually want to store representations of real world
phenomena (primary data) and not map data (secondary data). In
short, to store map data into a database instead of primary data means
to create the model of a model.
3.3.2 Databases
Spatial databases store representations of spatial phenomena in the
real world to be used in a geographic information system. They are also
called GIS databases. In the design of a database, we distinguish
between three different levels of definition. A language that we use to
define the database is called a data model; each level typically has its
own data model. The data model used at the level closest to the endusers——and
relatively far from any computer implementation——is called
a conceptual data model. In our context, it is used for spatial data
modeling. The intention is to define which concepts of interest exist in
the application domain for which a database is being designed, and
what their relations are. Such a definition identifies the types of things
relevant for a particular application, for example, a cadastral
administration, a landslide hazard analysis system, a geologic
hypermap toolbox. A commonly used conceptual data model is the
entity-relationship (ER) model; it uses primitives like entity type to
describe independently existing entities, relationship type to define
relationships between entities, and attributes to describe characteristic
values of entities and relationships. The complete database definition is
called the (conceptual) database schema. It can be likened to a story,
written in the language that is the data model. Other, more
implementation-oriented, data models will be discussed in Section 3.4.1.
The assumption for the design of a spatial database schema is that
spatial phenomena exist in a two- or three-dimensional Euclidean
space. All phenomena have various relationships among each other and
possess spatial (geometric), thematic and temporal attributes (they exist
in space and time). Phenomena are classified into thematic layers
depending on the purpose of the database. This is usually described by
DATA MODELS AND DATABASES 33
a qualification of the database as, for example, cadastral, topographic,
land use, or soil database.
The representations of spatial phenomena (spatial features) are stored
in a scale less and seamless manner. Scale less means that all
coordinates are world coordinates given in units that are normally used
to reference features in the real world (geographic coordinates as
latitude and longitude, or metric units in meters). From there,
calculations can be easily performed and any (useful) scale can be
chosen for visualization.
It must be noted, however, that scale plays a role when data are
captured from maps as data source. Here, the scale of the source map
determines the accuracy of the feature coordinates in the database.
Likewise, the accuracy of measurements in field surveys determines the
quality of the data. If the coordinates are given in units other than
geographic coordinates, information concerning the spatial reference
system should also be present.
A seamless database does not show map sheet boundaries or other
partitions of the geographic space other than imposed by the spatial
features themselves.
The advantages of databases as real world models are that they are
scale-less, potentially three-dimensional, dynamic, and seamless. It is
easy to query a database, and to combine data from different layers
(spatial join or overlay). Spatiotemporal databases consider not only the
spatial and thematic but also the temporal extent of the features they
represent. Various spatial, temporal and spatiotemporal data models
have been developed. They will be discussed in Section 3.4.4.
The attentive reader will have noted our threefold use of the word
‘‘model’’. This, perhaps, may be confusing. Except as a verb, where is
means ‘‘to describe’’ or ‘‘to represent’’, it is also used as noun. A ‘‘real
world model’’ is a representation of a number of phenomena in the real
world, usually to enable some type of administration, computation
and/or simulation. It is the result of the activity of ‘‘modeling’’. A ‘‘data
model’’, on the other hand, is a database language used as a tool in
database design, in an activity called ‘‘data modeling’’. The result of that
activity is not a data model, but a database schema, an abstract
definition of the contents of the (future) database. A database schema
can be viewed as a special kind of real world model, but it is abstract
because it identifies only types of things in that real world, and not the
things themselves. In a cadastral setting, for instance, it would identify
Parcel, Owner, Title as three entity types, without identifying any actual
parcels, owners or titles. This would be done in the database itself.
Therefore, we might say that a database schema is an occurrenceindependent
real world model.
3.3.3 Space and Time in Real World Models
In modern physics, it is common to speak of space-time to express the
close connection that exists between space and time according to the
special and general theory of relativity. Here, we do not consider the
physical characteristics of space and time, but the (simplified) ways of
representing spatial phenomena in a GIS database.
In general, modeling can be described as creating a structure
preserving mapping (morphism) from a domain to a co-domain. In
34 GEOGRAPHICINFORMATIONSCIENCE(GIS)
our case, the domain is the real world, and the co-domain is a real
world model. Such a mapping normally creates a ‘‘smaller’’ (i.e.,
abstracted, generalized) image of the original. Structure preserving
means that the elements of the co-domain (spatial features) behave in
the same (however simplified or abstracted) way as the elements of
the domain (spatial phenomena). Figure 4 illustrates the principle of
spatial modeling.
MorphismSpatial phenomena
Domain
(Real World)
Spatial features
Co-domain
(Real World Model)
Figure 4: Spatial modeling is a structure preserving mapping from the real
world to a spatial model.
As mentioned above, we consider space to be the three-dimensional
Euclidean space of our common sense. All phenomena exist in this
space and undergo changes, which we perceive as the passing of time.
In this sense, time is modeled indirectly as changes in (spatial or
thematic) attributes of the features.
We call a spatial data model that also considers time a spatiotemporal
data model. Sometimes such a model is addressed as four-dimensional,
giving the impression that time is the fourth dimension in addition to
the three spatial dimensions. It is, however, better to call it
spatiotemporal instead of four-dimensional. First, time is not of the
same type as the spatial dimensions; it has a distinct different quality.
Secondly, the term ‘‘four-dimensional’’ only makes sense when we always
would consider three spatial dimensions. In most of the cases, however,
the spatial data model only considers two dimensions.
3.4 Real World Models and Their Representation
Spatial data are computer representations of spatial features. A
modeling language for a GIS database is a spatial data model. It is
used in the design of spatial databases. A spatial database holds a
digital representation of the real world, sometimes called a digital
landscape model (DLM).
A DLM is the basis for spatial analysis and manipulation of spatial
data. The feature space for a spatial database is a geometric space in
which we model features at various levels of detail. A good data model
should allow for multiple representations of spatial features. The
transformation of a representation from a detailed to a less detailed
version is called model generalization. This is different from
cartographic generalization where a graphic representation (digital or
analogue) at a smaller scale is derived from a large-scale dataset or map
under certain merely graphic constraints.
DATA MODELS AND DATABASES 35
From the database (DLM), we derive graphic representations in digital
or analog form (cartographic model) that can result in a cartographic
product. Figure 5 shows the different aspects of data modeling for
database and cartographic design. Database creation is a two-step
process: first, the database is designed by defining a database schema
that identifies types but no occurrences. Secondly, the database is filled
with real data, thereby creating a digital landscape model.
We are particularly interested in properties of the geometric space that
remain invariant under certain transformations. This is important to
guarantee a consistent representation of the features in a database.
These properties are related to topology, which will be discussed later in
this chapter.
Some features possess a degree of uncertainty in either their thematic
or spatial extent. For example, soil types do not have crisp boundaries.
Linguistic expressions of vagueness or uncertainty such as ““moderate
slope”” or ““close by Enschede”” are often a better analysis approach than
clear cut class boundaries. We have to devise proper means to take care
of these uncertainties in our spatial data models. Fuzzy logic, a branch
of modern mathematics, provides the tools.
DLM
DCM
Database design
Cartographic design
(generalisation)
Map production
technology
external → conceptual →
logical → physical
Spatial
analysis
Visualisation
Map (analogue model)
Real world
Figure 5: Data modeling from the real world to a database, and from there to
digital cartographic models and analogue products for visualization.
The theory of fuzzy sets is based on the principle of a membership
function that determines for every element to which degree it belongs to
a set. The membership grade is a real number between zero (no
membership) and one (complete membership). With a proper
membership function (user-defined and depending on the application),
we can model vague thematic and spatial attributes of spatial features.
Spatial analysis can be performed on fuzzy sets in a similar way as on
crisp sets.
Spatial data exist not only in space but also in time. They carry
temporal characteristics that data models must be able to handle.
Several spatiotemporal data models have been proposed. Some of them
will be discussed below (see section 3.4.4).
36 GEOGRAPHICINFORMATIONSCIENCE(GIS)
3.4.1 Database Design
In designing a database, we have to pass through several design phases.
The result of every phase is a schema of the respective level. A schema
is a representation of the database as described with a particular data
model. This approach is called the ANSI/SPARC architecture; it
comprises four levels.
A database is generally thought to serve multiple users or user groups.
They may have different perceptions of the data stored. At this level,
each user (group) is supported with its own external view of the
database. We may define an external view as a personalized conceptual
database schema. There will be as many external views as there are
users and user groups. Mainly domain experts do spatial modeling at
this level.
All external views are merged into a single conceptual schema of the
database. This is usually done with high-level semantic data models
such as the entity-relationship model (ER model), the extended ER
model (EER), or object-oriented data models. The basic constructs of
the E-R model are entity types (e.g., country), attribute types (e.g.,
population) and relationship types (e.g., neighbor of). Instances of the
types populate the database, e.g., ‘‘The Netherlands’’, ‘‘15 million’’, and
‘‘Germany, Belgium’’ are instances of the entity type ‘‘country’’, the
attribute type ‘‘population’’, and the relationship type ‘‘neighbor of’’,
respectively. A conceptual schema is implementation-independent and
not related to any particular database management software. It
provides an answer to the question what phenomena are represented in
the database.
The conceptual schema is transformed into a logical schema using one
of the logical data models. Currently, the most popular one is the
relational data model, which is based on relational algebra. Most
commercial database implementations provide support for this model.
It is easy to understand because it is based on relations——sets of
records——that have a straightforward implementation as tables. The
logical schema is meant to provide the definition of a redundancy-free
dataset. Each fact should be stored only once in the database.
A physical schema is the result of the implementation of the logical
schema with particular database management software. Table 6
summarizes the ANSI/SPARC architecture.
Table 6: Data models and schemas in database design (the ANSI/SPARC
architecture).
Schema Models used to derive the schema
External views Depending on different user perspectives, a subset of the
real world is defined and described (spatial modeling).
Conceptual schema A synthesis of external views to create a conceptual
schema making use of semantic data modeling
techniques such as the entity-relationship approach.
Logical schema Transformation of the conceptual schema into a logical
schema using the relational model. Emphasis is on
redundancy removal.
Physical schema This is the mapping of the logical schema into data
structures and algorithms. It is normally not accessible
or visible to the user. Its emphasis is on processing
speed.
DATA MODELS AND DATABASES 37
3.4.2 Topology and Spatial Relationships
Topology is the branch of mathematics that deals with properties of
spaces that remain invariant (i.e., do not change) under certain
transformations. A simple example will illustrate what we mean.
Assume you have some geometric figures that are drawn on a sheet of
rubber (Figure 6). Now, take the sheet and pull on its edges, but do not
tear or break it. The figures will change in shape and size. Some
properties, however, do not change.
E is still inside D, the neighborhood relationships between A, B, C, D,
and E remain, and the boundary lines have the same start and end
points. The areas are still bounded by the same boundary lines, only
their shapes and the length of the perimeters have changed.
These relationships are invariant under a continuous transformation.
The properties are called topological properties, and the transformation
is called a topological mapping (or homeomorphism). The result is
topologically equivalent (or homeomorphic) to the original dataset.
A
B
C
ED
A
B
C
ED
2
11
2
33 44
55
66
77
Figure 6: Rubber sheet transformation.
The mathematical properties of the geometric space used for spatial
data can be described as follows:
• The space is a three-dimensional Euclidean space where for every
point we can determine its three-dimensional coordinates as a triple
(x, y, z) of real numbers. In this space, we can define points, lines,
polygons, and volumes as geometric primitives of the respective
dimension. A point is a zero-dimensional, a line a one-dimensional, a
polygon a two-dimensional, and a volume is a three-dimensional
primitive.
• The space is a metric space, i.e., we can always compute the distance
between two points according to a given distance function (metric).
• The space is a topological space, i.e., for every point there exists a
neighborhood that is topologically equivalent (homeomorphic) to an
interval (one-dimensional), disk (two-dimensional) or ball (threedimensional),
depending on the dimension of the space in use. These
neighborhoods allow us to define open sets that under certain
conditions define a topology on the space.3
• We can structure space by simple topological sub-spaces that are
easy to handle and can be used as building blocks for spatial data
(Figure 7). They are called simplices and are the simplest geometric
3 A formal introduction to topological spaces is beyond the scope of this text. We
refer the reader to the literature mentioned in the bibliography.
38 GEOGRAPHICINFORMATIONSCIENCE(GIS)
shapes of a respective dimension: point (0-simplex), line segment (1simplex),
triangle (2-simplex), and tetrahedron (3-simplex). It is
common to use their homeomorphic images (or cells) in spatial data
models. Cells are nodes (0-cells), arcs (1-cells), polygons (2-cells), and
volumes (3-cells). A collection of cells (or cell complex) is the
topological equivalent of a collection of spatial features (Figure 8).
• Interior and boundary are properties of spatial features that remain
invariant under topological mappings. This means, that under any
topological mapping, the interior and the boundary of a feature
remains unbroken and intact.
0-simplex
1-simplex
2-simplex
3-simplex
Simplicial complex
Figure 7: Simplexes and simplicial complex. Features are approximated by a
set of points, line segments, triangles, and tetrahedrons.
In many applications, we will use a two-dimensional space only, i.e., the
usual Euclidean plane. In fact, data models for three-dimensional
spatial data are more difficult than two-dimensional data models.
The consistency of a spatial dataset is important for any processing and
analysis that might be applied. The data model must provide means to
define and enforce topological consistency in a database. Topology
provides us with a set of rules that can easily be applied to any twodimensional
spatial dataset.
If we can show that the following rules are satisfied for every element in
the dataset, we know that it is a topologically consistent twodimensional
configuration.
1. Every arc must be bounded by two nodes (start node and end node).
2. For every arc, there exist two polygons (left polygon and right
polygon).
3. Every polygon has a closed boundary consisting of an alternating
sequence of nodes and arcs.
4. Around every node, there exists an alternating closed sequence of
arcs and polygons.
5. Arcs do not intersect except at nodes.
DATA MODELS AND DATABASES 39
0-cell
1-cell
2-cell
3-cell
Cell complex
Figure 8: Cells and a cell complex. Spatial features are represented as
topological images of cells.
In mathematical terms, these rules guarantee that the configuration is
a two-dimensional manifold where every point has a neighborhood
equivalent to an open disk. These rules are valid only for a twodimensional
cell complex. They cannot be applied without additions
and modifications to higher dimensions.
Whereas relationships between simplices or cells define consistency
constraints for spatial data, we can use the topological properties of
interior and boundary to define relationships between spatial features.
Since the properties of interior and boundary do not change under
topological mappings, we can investigate their possible relations
between spatial features.4
Let us assume two spatial regions A and B. Both have their respective
boundary and interior. When we consider all possible combinations of
intersections between the boundaries and the interiors of A and B, we
know that these will not change under any topological transformation.
From these intersection patterns, we can derive eight mutual spatial
relationships between two regions. If, for instance, the boundary of A
intersects the boundary of B, and the interiors of A and B do not
intersect, we say that A and B meet. Figure 9 shows all possible eight
spatial relationships: disjoint, meet, equal, inside, covered by, contains,
covers, and overlap. These relationships can be used, for instance, in
queries against a spatial database.
Networks, like road or river networks, are best modeled as a onedimensional
subset (or skeleton) of a cell complex. The topological
relationships are then reduced to incidence relationships between edges
(arcs) and nodes. Such a structure is also called a graph. Graph theory,
although closely related to topology, has developed as an independent
mathematical discipline.
4 We restrict ourselves here to relationships between spatial regions (twodimensional
features without holes).
40 GEOGRAPHICINFORMATIONSCIENCE(GIS)
A special type of graph frequently used in GIS is a planar graph. It is
completely embedded in the plane such that no two edges intersect
except at nodes.5
disjoint
meet
equal
inside
covered by
contains
covers
overlap
Figure 9: Spatial relationships between two regions derived from the
topological invariants of boundary and interior.
3.4.3 Spatial Data Models
Among spatial data models, we can distinguish two major types, fieldand
object-based models. Field-based models consider spatial
phenomena to be of a continuous nature where in every point in space a
value of the field can be determined. Examples of such phenomena are
temperature, barometric pressure, or elevation. Object-based models
consider space to be populated by well distinguishable, discrete,
bounded objects with the space between objects potentially being empty.
Examples are a cadastre with clearly identifiable objects like parcels
and buildings.
Field versus object can be viewed——in general terms——as a
manifestation of the philosophical conception of plenum versus atomic
space (see Section 3.2.1), or as in modern physics, wave versus particle
(see Section 3.2.3). In GIS, fields are usually implemented with a
tessellation approach, objects with a (topological) vector approach. The
following sections briefly illustrate the two different model types.
3.4.3.1 Field-based Models
The underlying space for a field-based model is usually taken as the
two- or three-dimensional Euclidean space. A field is a computable
function from a geometrically bounded set of positions (in 2D or 3D) to
some attribute domain. Computable means that for every position
within the geometric bounds a value can be determined by either
measurement or by computation. A field-based model consists of a
finite collection of such fields (Figure 10).
Fields can be discrete, continuous and differentiable. Discrete fields
represent features with boundaries; continuous fields are used for
features where the underlying function is continuous, such as for
5 Graphs are the logical choice for networks. It should be noted, however, that some
(historic) two-dimensional data modeling approaches have been based on planar
graphs. Here, the 2-cells (polygons) bounded by 1-cells (edges) and 0-cells (nodes)
are counted as ““faces”” of the graph.
DATA MODELS AND DATABASES 41
temperature, barometric pressure or elevation. If the field function is
differentiable, we can even compute the slope at every position.
Figure 10: Two data layers in a field-based model.
It is important to note that our definition of field is completely
independent of implementation considerations. This means that we do
have not considered a data structure to represent them. It is, however,
obvious that some data structures seem to be the logical choice for
certain types of field.
Though geometrically bounded, the domain of a field is still an infinite
set of positions. Computers cannot easily represent field values for all
these positions, so we must accept an approximation. The standard way
to obtain this is to finitely represent the geometrically bounded space
through a subdivision into a regular or irregular tessellation, consisting
either of square (cubic) or triangular (tetrahedral) parts. These
individual parts are called locations; points often approximate them.
Our finite approximation of positions into locations leads so some forms
of interpolation that must be dealt with. The field value at a location
can be interpreted as one for the complete tessellation cell, in which
case the field is discrete, not continuous or even differentiable. Some
convention is needed to state which value prevails on cell boundaries;
with square cells, this convention often says that lower and left
boundaries belong to the cell.
Another option is to interpret the field value at a location as
representative only for some position within the cell. Again that
position is fixed by convention, and may be the cell centroid or, for
instance, its left lower corner. Field values for positions other than
these must be computed through some form of interpolation function,
which will use one or more nearby field values to compute the value at
the requested position. This allows to represent continuous, even
differentiable, functions.
To represent spatial features using a field-based approach we have to
perform the following steps:
• Define or use a suitable model for the underlying space (tessellation).
• Find suitable domains for the attributes.
• Sample the phenomena at the locations of the tessellation to
construct the spatial field functions.
• Perform analysis, i.e., compute with the spatial field functions.
In Chapter 5, we will discuss some more details regarding tessellations,
also in cases where the space is irregularly divided.
42 GEOGRAPHICINFORMATIONSCIENCE(GIS)
3.4.3.2 Object-based Models
Object-based models decompose the underlying space into identifiable,
describable objects that have spatial, thematic, and temporal attributes,
as well as relationships among each other (Figure 11). The space
outside the objects is empty. Examples of objects are buildings, cities,
towns, districts or countries; attributes are, for instance, the number of
floors, population, boundary or area.
In a GIS database implementation, objects are represented as a
structured collection of geometric primitives (points, lines, polygons,
and volumes) under geometric, thematic and topological constraints.
Object-based models are discrete models. Operations in the model
always refer to the manipulation of individual objects or sets of objects.
Manipulations concern the spatial, thematic, topological or temporal
domain. Accordingly, they are realized through geometric, attribute
manipulation or topological operations.
Figure 11: Layers in an object based model.
Topology plays a major role in object-based models. It is the ““language””
that allows us to specify and enforce consistency constraints for spatial
databases.
The majority of object-based models are two-dimensional. Recently,
three-dimensional data models have been proposed. Their topology is
more difficult to handle than for the standard two-dimensional cases.
3.4.4 Spatiotemporal Data Models
Beside geometric, thematic and topological properties, spatial data
possess also temporal characteristics. It is, for instance, interesting to
know who were the owners of a land parcel in 1980, or how did land use
of a given piece of land change over the last 20 years.
Spatiotemporal data models are data models that can also handle
temporal information in spatial data. Several models have been
proposed. The most important ones will be discussed briefly.
Before we describe the major characteristics of the spatiotemporal data
models, we need a framework to describe the nature of time itself. Time
can be characterized according to the following properties:
Time density. Time can be discrete or continuous, Discrete time is
composed of discrete elements (seconds, minutes, hours, days, months,
or years). In continuous time, for two points in time, there is always
another point in between. We can also structure time by events (points
DATA MODELS AND DATABASES 43
in time) or states (time intervals). When we represent states by
intervals bounded by nodes (events) we can derive temporal
relationships between events and states such as ““before””, ““overlap””,
““after””, etc.
Dimensionality of time. Valid time (or world time) is the time when an
event really happened. Transaction time (or database time) is the time
when the event was recorded in the database.
Time order. Time can be linear, extending from the past to the present,
and into the future. We know also branching time (possible scenarios
from a certain point in time onwards) and cyclic time (repeating cycles
such as seasons or days of a week).
Measures of time. A chronon is the shortest non-decomposable unit of
time that is supported by a database (e.g., a millisecond). The life span
of an object is measured by a (finite) number of chronons. Granularity
is the precision of a time value in a database (e.g., year, month, day,
second, etc.). Different applications require different granularity. In
cadastral applications, time granularity can be a day; in geology
mapping, time granularity is more likely in the order of thousands of
years.
Time reference. Time can be represented as absolute (fixed time) or
relative (implied time). Absolute time marks points on the timeline
where events happen (e.g., ““6 July 1999 at 11:15 p.m.””). Relative time is
indicated relative to other points in time (e.g., ““yesterday””, ““last year””,
““tomorrow,”” which are all relative to ““now””, or ““two weeks later,”” which
may be relative to an arbitrary point in time.).
In spatiotemporal data models, we consider changes of spatial and
thematic attributes over time. In data analysis, we can keep the spatial
domain fixed and look only at the attribute changes over time for a
given location in space. We would, for instance, be interested how land
cover changed for a given location or how the land use changed for a
given land parcel over time, provided its boundary did not change.
On the other hand, we can keep the attribute domain fixed and consider
the spatial changes over time for a given thematic attribute. In this
case, we could be interested to see which locations were covered by
forest over a given period.
Finally, we can assume both the spatial and attribute domain variable
and consider how an object changed over time. This actually leads to
notions of object motion, and these are a subject of current research,
with two of the applications being traffic control and mobile telephony.
But many more applications are on the horizon: think of wildlife
tracking, disease control, and weather forecasting. Here, the problem of
object identity becomes apparent. When does a change or movement
cause an object to disappear and become a new one?
In the following, we describe the major characteristics of some popular
spatiotemporal models.
3.4.4.1 Space-Time Cube Model
This model is based on a two-dimensional space (spanned by the x- and
y-axis) whose features are traced through time (along the z-axis)
thereby creating a space-time cube. The traces of objects through time
create a worm-like trajectory in the space-time cube. This model
potentially allows absolute, continuous, linear, branching and cyclic
time. It supports only valid time. The attribute domain is kept fixed
and the spatial domain variable.
44 GEOGRAPHICINFORMATIONSCIENCE(GIS)
3.4.4.2 Snapshot Model
In the snapshot model, layers of the same theme are time-stamped. For
every point in time that we would like to consider, we have to store a
layer and assign the time to it as an attribute. We do not have any
information about the events that caused different states between
layers. This model is based on a linear, absolute, discrete time. It
supports only valid time and multiple granularity. The spatial domain
is fixed (field-based) and the attribute domain is variable.
3.4.4.3 Space-Time Composite Model
The space-time composite model starts with a two-dimensional situation
(plane or layer) at a given start time. Every change of features that
happens later is projected onto the initial plane and intersected with the
existing features, thereby creating an incrementally built polygon mesh.
Every polygon in this mesh has its attribute history stored with it. The
space-time composite model is based on linear, discrete, relative time.
It supports both valid and transaction time, and multiple granularity.
It keeps the attribute domain fixed and the spatial domain variable.
3.4.4.4 Event-Based Model
In an event-based model, we start with an initial state and record
events (changes) along the time line. Whenever a change occurs, an
entry is recorded. This is a time-based model. The spatial and thematic
attribute domains are secondary. The model is based on discrete,
linear, relative time, supports only valid time and multiple granularity.
3.4.4.5 Spatiotemporal Object Model
This model is based on spatiotemporal objects (ST-objects) that are a
complex of ST-atoms (STA). Both objects and atoms have a spatial and
a temporal extent. The model is based on discrete, absolute, linear
time, and supports valid and transaction time as well as multiple
granularity.
3.5 Summary
Geographical information systems process spatial information. The
information is derived from spatial data in a database. To sensibly
work with these systems, we need models of spatial information as a
framework for database design. These models address the spatial,
thematic and temporal dimensions of real world phenomena. An
understanding of the principle concepts of space and time rooted in
philosophy, physics and mathematics is a necessary prerequisite to
develop and use spatial data models.
We know two major approaches to spatial data modeling, the analogue
map approach, and spatial databases. Today, the function of maps as
data storage (map as a database) is increasingly taken over by spatial
databases. In databases, we store representations of relevant
phenomena in the real world. These representations are abstractions
according to selected spatial data models. We know two fundamental
approaches to spatial data modeling, field-based and object-based
models. Both have their merits, advantages and disadvantages for
particular applications.
Consistency is an important requirement for every model. Topology
provides us with the mathematical tools to define and enforce
DATA MODELS AND DATABASES 45
consistency constraints for spatial databases, and to derive a formal
framework for spatial relationships among spatial objects.
Spatial data not only possess spatial and thematic attributes, but
extend also into the temporal domain. A model of time for spatial
information is an important ingredient for any spatial data model, thus
leading to what is called spatiotemporal data models.
47
n important characterization of spatial data structures is
derived from the way we look at two- or three-dimensional
space, and how the phenomena that we are interested in
are organized in that space. The primitives that are used to
represent features in the object view comprise point, line, area,
and volume items. It is often called the vector-based approach.
In the field view, the part of space relevant to us is divided up
completely into regular or irregular tessellations——usually
squares, triangles, or cubes——that define a two- or threedimensional
raster or grid. The more common name for the field
view approach is the raster-based approach.
Below, we will pay most attention to the vector-based approaches,
and we will discuss point data structures, line data structures,
and area data structures.
CHAPTER
4 Spatial Data Structures
A
48 GEOGRAPHICINFORMATIONSCIENCE(GIS)
4.1 Point Data
A good way to study a specific class of data structures is to address the
questions when and how they are used. For point data we can
distinguish the following four common types of query:
• Point query: Is point (x, y) in the database?
• Range query: Find all points in a given range (e.g., in a rectangle).
• Boolean query: Logical composition of the above types (AND, OR,
NOT).
• Neighbor query: Find the n nearest neighbors of a given point.
• Distance scan: Enumerate points by increasing distance from a given
point.
The point data structures presented in the following sections are
illustrated using a sample dataset taken from the textbook of SAMET
(1990a). The dataset comprises locations of eight North American
cities. For the sake of simplicity, we have chosen simplified values for
their coordinates. The reader is referred to Table 7 for the coordinate
values of these cities.
Table 7: Sample point dataset (after SAMET 1990a)
Name x y
Chicago 35 40
Mobile 50 10
Toronto 60 75
Buffalo 80 65
Denver 5 45
Omaha 25 35
Atlanta 85 15
Miami 90 5
4.1.1 Cell Method
The cell method divides space into equally sized cells (squares). The
data structure is a two-dimensional array with one entry per cell. Each
cell contains the head of a linked list, and the items in this list are the
data points lying within the cell. The cell method is sometimes also
known as the fixed grid method. Figure 12 is a cell method
representation for the data of Table 7. Each cell is considered to be
open on its upper and right boundaries and closed on its lower and left
boundaries. Therefore, Buffalo is in the cell centered at (90,70). This is
a rather common convention for other data structures also.
A spatial index structure stores the entities in a collection of buckets.
Typically, each bucket is associated with a single page in secondary
memory, but more than one bucket may be associated with the same
page. In some approaches, the bucket size may vary and a bucket may
have more than one page associated with it. With each bucket is
associated a bucket region, a part of space that contains all entities in
its bucket. It is computationally easiest to let bucket regions be
rectangles. For point data, buckets are best disjoint and together they
should partition the relevant part of space.6 Point data organized with
the cell method can be stored through point bucket techniques.
6 With area data, bucket regions are sometimes allowed to overlap.
SPATIAL DATA STRUCTURES 49
(5,45)
Denver (35,40)
Chicago
(25,35)
Omaha
(50,10)
Mobile
(60,75)
Toronto
(80,65)
Buffalo
(85,15)
Atlanta
(90,5)
Miami
(0,0) (100,0)
(100,100)(0,100)
x
y
Figure 12: Grid representation for data in Table 7 (after SAMET 1990a)
4.1.2 Point Quadtrees
A point quadtree is a quaternary search tree that can be used to store
point data. It is assumed that the coordinate pair of each point is
unique, as otherwise overflow lists have to be maintained. A node in a
point quadtree holds the data for the point, and provides four more
pointers to other nodes. These pointers refer to a point in the NW, NE,
SW, and SE quadrant, respectively, if such a point exists. Otherwise,
the pointer is nil obviously.
This organization means that the point quadtree’’s shape depends on the
order in which points are added to it. With reference to Figure 12, if the
cities on our map are added in the order Chicago, Mobile, Toronto,
Buffalo, Denver, Omaha, Atlanta, Miami, the resulting point quadtree
would be as in Figure 13.
An important characteristic of navigation through a point quadtree is
that at each node, if it is not the one that is searched for, the pointer to
follow is determined by two conditions. The first is the relative position
of the searched x-coordinate with respect to the x-coordinate of the
current node; the other is the comparison between the y-coordinates of
the searched and current nodes. In other words, point quadtrees
require two-dimensional navigation.
Chicago
MobileToronto OmahaDenver
Buffalo Atlanta Miami
NW NE SW SE
Figure 13: Point Quadtree obtained from insertion indicated in the text
50 GEOGRAPHICINFORMATIONSCIENCE(GIS)
4.1.3 K-D Trees
Navigation through a point quadtree can sometimes be cumbersome.
For instance, when we search for a node in the tree, we have to compare
both x- and y-coordinates at each subtree. There are a few other
shortcomings of point quadtrees.
Some of these disadvantages are overcome by the k-d tree. A k-d tree in
principle is a binary tree with the added requirement that at each
following depth in the tree another coordinate (or other attribute) is
used for navigation. In the two-dimensional case, this means that to
navigate through the tree, at the root and each subsequent even depth,
we use the x-coordinate value, while at the odd depths we use the ycoordinate
value. This lets itself easily be generalized to arbitrary
dimension k.
In the word k-d tree, k stands for the dimension of the space being
represented, i.e., for our sample dataset we talk about a 2-d tree, since
these points are represented in the plane.
Figure 14 shows the k-d tree for the data points in Table 7 when they
are inserted in the order Chicago, Mobile, Toronto, Buffalo, Denver,
Omaha, Atlanta, and Miami. Here is what happens during insertion:
1. Chicago becomes the root node (obviously).
2. Since Mobile’’s x-coordinate is bigger than Chicago’’s it goes to the
right subtree.
3. Since Toronto’’s x -coordinate is bigger than Chicago’’s, and its ycoordinate
is bigger than Mobile’’s it goes to Mobile’’s right subtree.
4. Since Buffalo’’s x-coordinate is bigger than Chicago’’s, its ycoordinate
is bigger than Mobile’’s, and its x-coordinate is bigger
than Toronto’’s, it goes to Toronto’’s right subtree.
5. Since Denver’’s x-coordinate is smaller than Chicago’’s, it goes to
Chicago’’s left subtree, etc.
Like the point quadtree, the order of insertion determines the shape of
the resulting tree.
Finally, we should remark that there exist alternative k-d trees that do
not store real points in nodes internal to the tree, but rather only in leaf
nodes. This is done by generating artificial internal nodes that help
balancing the tree to some extent.
(0,0) (100,0)
(100,100)(0,100)
x
y
(35,40)
Chicago
(50,10)
Mobile
(60,75)
Toronto
(85,15)
Atlanta
(25,35)
Omaha
(5,45)
Denver
(80,65)
Buffalo
(90,5)
Miami
Chicago
Denver Mobile
Omaha Miami Toronto
Buffalo
Atlanta
Figure 14: A k-d tree for the data in Table 7 (after SAMET 1990a)
SPATIAL DATA STRUCTURES 51
4.1.4 PR Quadtrees
A PR quadtree (which stands for Point-Region quadtree) is based on a
hierarchical subdivision of space into quadrants much like the standard
region quadtree (for which see section 4.3.2). Like in region quadtrees,
nodes are either empty, full, or intermediate. An empty node means
there are no points in the quadrant of the node. A full node contains the
data of a single node. An intermediate node provides a further
subdivision into smaller quadrants. Figure 15 shows the PR quadtree of
the data in Table 7. Every leaf node is either empty or contains a data
point and its coordinates.
As with the cell method, we assume that the quadrants are on their
lower and left boundaries, and open on their upper and right
boundaries. The shape of the PR quadtree is independent of the order
in which the data points are inserted. This is an important distinction
from the point quadtree and the k-d tree.
(5,45)
Denver
(35,40)
Chicago
(25,35)
Omaha
(50,10)
Mobile
(60,75)
Toronto
(80,65)
Buffalo
(85,15)
Atlanta
(90,5)
Miami
(0,0) (100,0)
(100,100)(0,100)
x
y
Toronto Buffalo Denver
Chicago Omaha
Mobile
Atlanta Miami
NW
NE SW
SE
Figure 15: A PR quadtree of the data points in Table 7 (after SAMET 1990a)
4.1.5 Grid Files
The grid file is a 2-D or 3-D variation of the cell method that relaxes the
requirement that cells are square and equal size. In grid files, cells
(here called grid blocks) are still required to be rectangular, but that is
all that is required. The purpose of grid files is to always retrieve
records with at most two disk accesses, and to have efficient range
query performance. A grid directory consists of grid blocks, and all
points in such a block are stored in the same bucket. Several grid
blocks may share the same bucket, but only if their union is again
rectangular. This requirement ensures the range query performance.
The grid directory consists of two parts: a dynamic, two-dimensional
array containing one entry for each grid block, and two one-dimensional
arrays called linear scales. The values in the two-dimensional array are
pointers to the buckets containing the data; the linear scales define a
partition of the domains of the x-coordinate and the y-coordinate. These
linear scales thus determine the grid block (cell) boundaries. The grid
directory may be kept on disk, the linear scales are always kept in main
memory. Figure 16 shows the grid file partition and the linear scales
for the data of Table 7. The bucket capacity is assumed to be two, there
are two different attributes (namely x and y ). In this case, the grid
directory consists of a two-dimensional array with nine grid blocks and
six buckets labeled A to F, and two one-dimensional arrays for the
linear scales. The dashed lines between the grid blocks indicate the
52 GEOGRAPHICINFORMATIONSCIENCE(GIS)
sharing of buckets for these blocks. Every point in a grid file can be
retrieved with two disk accesses: one disk access for the grid block,
which will provide us with the bucket address, and one to obtain the
bucket itself.
(5,45)
Denver
(35,40)
Chicago
(25,35)
Omaha
(50,10)
Mobile
(60,75)
Toronto
(80,65)
Buffalo
(85,15)
Atlanta
(90,5)
Miami
(0,0) (100,0)
(100,100)(0,100)
x
y
A
B
C
D
E
F
B
D
E
x: 0 | 45 | 70 | 100
y: 0 | 42 | 70 | 100
y = 70
y = 42
x = 45 x = 70
Figure 16: Grid file partition and linear scales for the data of Table 7 (after
SAMET 1990a)
4.2 Line Data
Lines are the second important class of spatial data. They usually
appear in two different forms:
• Straight line segments. These are used to represent features like
boundaries, rivers, and roads. They are frequently the result of a
digitizing process.
• Parameterized mathematical descriptions. These are frequently
used for line data in surveying or engineering applications.
For the representation of line segments, many data structures are
known. They range from simple coordinate lists to sophisticated
hierarchical data structures based on quadtrees.
4.2.1 Arc/Node Structure
The arc/node structure is based on the topological principle of
representing a graph as a set of nodes and a set of edges (or arcs).
Every line segment has a start and end node. The internal vertices of
the segments are stored as a list of coordinates defining the trajectory
from the start to the end node. This list is usually implemented as a
two- or three-dimensional array.
If no other topology information is represented, we obtain a simple
‘‘spaghetti’’ model of coordinate lists. This model can be used for
producing images, but it is less useful for more advanced spatial
analysis requiring more topologic knowledge. If more topology is
needed, the topological relationships can be encoded in tables. The
reader may be familiar with ARC/INFO’’s topology tables, which are a
SPATIAL DATA STRUCTURES 53
good illustration. Figure 17 shows a network represented by an
arc/node structure.
A
B
C
a b
c
d
1
2
3
4 5
6
arc from to left right
1 d c C A
2 d b A B
3 d a B C
4 a c O C
5 c b O A
6 a b B O
Figure 17: An arc/node structure
4.2.2 Chain Codes
A chain code describes a line with a sequence of unit vectors (i.e., one
pixel wide) in the principal directions. A code 0123 would thus describe
the boundary of a single pixel. However, it is also possible to use more
than the four principal directions to allow a better approximation in
case of non-rectilinear data. Figure 18 shows the codes used, and
illustrates a sample line. The chain code representation of this line is
122132
464212 where n
c means code c repeated n times. Chain codes
are essentially raster data techniques.
0
1
2
3
4
5
6
7
Figure 18: Chain code and sample line
4.3 Area Data
Area data can be represented on a vector basis and on a raster basis. A
vector-based typically uses a boundary model, i.e., an arc/node structure
for polygon boundaries and additional topological relationships for the
polygons. Raster-based representation can be organized as a set of
pixels with common pixel values. In this case, a region quadtree is a
suitable hierarchical data structure. Such structures are not only used
for the representation of data but also as a spatial index.
A spatial index is an extra data structure used to get faster access to
features at a particular spatial location (point or range). It does not
store those features, but provides quick navigation to them. Frequently,
regions are indexed using their minimum bounding rectangles. A
spatial index is built using these rectangles as address placeholders of
the features. Hierarchical data structures like quadtrees or R-trees are
used in this process. Spatial indices speed up the querying but slow
down the updating, as the index will have to be updated also.
54 GEOGRAPHICINFORMATIONSCIENCE(GIS)
4.3.1 Boundary Model
The boundary model is based on the arc/node structure for polygon
boundaries and additional topological relationship information stored in
tables. Polygons are defined by their bounding arcs. For every arc the
start node, end node, and the polygon to the left and to the right are
stored. Figure 19 shows a sample set of polygons and the corresponding
arc table. This approach is sometimes also called the topological data
model or geo-relational model.
A
B
C
a b
c
d
1
2
3
4 5
6
arc from to left right
1 d c C A
2 d b A B
3 d a B C
4 a c O C
5 c b O A
6 a b B O
Figure 19: Sample polygons and their topological data model. The outside
region (background or world polygon) is denoted as O
4.3.2 Region Quadtrees
The region quadtree is the best known type of quadtree. It is used for
two-dimensional binary regions in raster form (a pixel array consisting
of only 0’’s and 1’’s). The idea is to successively subdivide a square area
into four equal-sized quadrants. Quadrants are further subdivided as
long as they do not contain only 0’’s or only 1’’s. A quadrant with only 0’’s
is marked as ‘‘empty’’, and a quadrant with only 1’’s as ‘‘full’’. The region
quadtree can be characterized as a variable resolution data structure.7
NW
NE SW
SE
1
2 3 4 5 6
7 8 9 10
11 12 13 14
15 16 17 18
19
1
2 3
4 5
6
7 8
9 10
11 12
13 14
15 16
17 18
19
0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0
0 0 0 0 1 1 1 1
0 0 0 0 1 1 1 1
0 0 0 1 1 1 1 1
0 0 1 1 1 1 1 1
0 0 1 1 1 0 0 0
0 0 1 1 1 1 0 0
(a) (b) (c)
(d)
Figure 20: Sample (a) region, (b) its binary array, (c) its maximal blocks, and
(d) the corresponding quadtree (after SAMET 1990a)
7A data structure related to the region quadtree is the pyramid (or image pyramid).
Given a nn
22 × image array )(nA , a pyramid is a sequence of arrays )}({ iA such
that )1( −iA is a version of )(iA at half the scale of )(iA , and )0(A is a single
pixel. Contrary to the region quadtree the pyramid is a multiresolution data
structure.
SPATIAL DATA STRUCTURES 55
This means that a ‘‘full’’ node in the tree stands for a completely covered
quadrant, but we need to know the level of the node in the tree to decide
what the size of the quadrant is. Figure 20 shows an example of a
region, its binary array, its maximal blocks and the corresponding
quadtree. For instance, nodes 8 and 14 both represent full quadrants,
but quadrant 8 is four times smaller than quadrant 14.
To facilitate the implementation of a quadtree, an indexing method is
applied making use of locational codes corresponding to the quadrants:
0 (NW), 1 (NE), 2 (SW), 3 (SE). This encoding is applied throughout all
subquadrants in a consistent manner. It provides a linear
representation of the quadtree leaf nodes (linear quadtree). Figure 21
shows the locational codes corresponding to the quadtree in Figure 20.
000
100 110
120 130
200
210 211
212 213
220 230
300 310
320321
322 323
330
Figure 21: Locational codes corresponding to the quadtree in Figure 20 (after
SAMET 1990b)
4.4 Volume Data
Volume data structures are needed for the representation of threedimensional
features. In analogy with two-dimensional representations
of regions, data structures for volume data can be designed.
A straightforward extension of the region quadtree is the octree where,
instead of two-dimensional pixels, three-dimensional volume elements
(or voxels) are included. Space is successively divided into octants until
one octant contains only voxels of one kind. According to the
decomposition of space into octants a tree with eight children per node
should be used.
The extension of the boundary model to three dimensions leads to a
topological description of solid objects by their bounding faces (nodes,
arcs, polygons) and the topological relationships among them. Tabular
techniques for topological relationships are far from trivial.
4.5 Classification of Spatial Data Structures
Data structures can be classified according to the following criteria:
• Data structures that are mainly used for representation in main
memory (or in-core applications) and those that are used for efficient
access to disk data (bucket methods). The total volume of the data is
the discriminating factor here.
• Hierarchical and non-hierarchical data structures: hierarchical data
structures are based on a recursive (regular or irregular)
decomposition of space (e.g., squares, or triangles). Hierarchically
organized data are usually relatively easy to search through, and
insert into by using recursive algorithms.
56 GEOGRAPHICINFORMATIONSCIENCE(GIS)
• Data types can be based on vector and raster techniques. The
determining factor quite often is the procedure followed for data
acquisition. The need for spatial analysis techniques is another
important factor.
Table 8 gives an overview of the data structures presented here
according to their distinguishing criteria.
Table 8: Classification of data structures
Data Structure In-core Bucket Hierar-
chical
Non-
Hierar-
chical
Vector Raster
cell method
k-d tree
point quadtree
PR quadtree
grid file
arc/node
structure
chain code
boundary model
region quadtree
octree
57
he most distinguishing part of a GIS are its functions for
spatial analysis, i.e., to process given data in order to
derive new information. Spatial queries and process
models play an important role in satisfying user needs. The
combination of a database, GIS software, rules, and a reasoning
mechanism (implemented as a so-called inference engine) leads to
what is known as spatial decision support systems (SDSS).
This chapter introduces the mentioned concepts and lists the most
common analytical functions that a GIS should support. Here, we
do not discuss remote sensing and image processing, although
today both GIS and remote sensing are integrated to a large
extent and support spatial data handling in a complementary
fashion.
CHAPTER
5 Spatial Analysis
T
58 GEOGRAPHICINFORMATIONSCIENCE(GIS)
5.1 Data Organization and Spatial Queries
A GIS differs from other information systems by its ability for spatial
analysis. In a GIS data are stored in layers (or themes), e.g., hydrology,
transportation, land use. Usually, several themes are part of a project.8
The functions of a GIS for spatial analysis use the spatial and nonspatial
attributes of the data in a spatial database to answer questions
about the real world.
Models play an important role. They can be described verbally, as a
mathematical function or as a set of spatial relationships stored in the
form of maps or a spatial database. In the context of spatial analysis
models mainly refer to process models, i.e., they are a representation of
a natural or man-made process. The kind of questions, answers and the
respective GIS functions are listed in Table 9.
Table 9: Types of queries
Questions Answers GIS functions
What is……? Display of data as maps,
reports and tables, e.g.,
What are the name and
the address of the owner
of that parcel of land?
Storage and query
functions
What pattern……? A pattern in the data,
e.g., all parcels with an
area greater than
2000 2
m
Query functions with
constraints
What …… if……? A prediction about the
data at a certain time or
at a certain location
Modeling functions
The following presents the most important query and analysis functions
of a GIS (after ARONOFF 1989). They are grouped into three major
classes according to common characteristics.
5.2 Maintenance and Analysis of the Spatial Data
Representatives of this class mainly operate on the spatial attributes of
GIS data, and provide a user with functions as described below.
Format transformations functions convert between data formats of
different systems or representations, e.g., reading a DXF file into a GIS.
Geometric transformations assist in setting up an original for
digitizing. They transform device coordinates (coordinates from
digitizing tablets or screen coordinates) into world coordinates
(geographic coordinates, meters, etc.).
Map projections provide means to map spatial geographic coordinates
onto a flat surface (map) and vice-versa.
Edge matching is the process of joining two or more map sheets. At the
map sheet boundaries feature representations have to matched to each
other.
8 In ARC/INFO, for instance, a workspace corresponds roughly to a project, and
coverage to a layer.
SPATIAL ANALYSIS 59
Editing of graphic elements. Features that have been digitized need to
be edited in order to remove and correct errors, and to prepare a clean
dataset for topology building.
Coordinate thinning is a process that often is applied to remove
redundant coordinates from line representations. This is related to the
cartographic generalization function of (line) simplification
(generalization).
5.3 Maintenance and Analysis of the Attribute Data
These functions operate on the non-spatial attributes of the data. They
correspond mainly to functions of conventional database systems and
contain attribute editing and attribute query functions.
5.4 Integrated Analysis of Spatial and Attribute Data
Functions of this class operate on both spatial and non-spatial
attributes of data, and can be grouped into the following subclasses.
5.4.1 Retrieval, classification, and measurement
Retrieval functions allow the selective search and manipulation of data
without the need to create new entities.
Classification allows to assign certain features to classes according to
their attributes or attribute ranges (definition of data patterns).
Generalization9 is a function that joins sub-classes with common
characteristics to a higher level (generalized) class10.
Measurement functions allow to measure distances, length, or areas.
5.4.2 Overlay Operations
Overlay operations belong to the most frequently used functions in a
GIS application. They allow to combine two different layers and apply
the set theoretic operations of intersection, union, difference, and
complement.
5.4.3 Neighborhood Operations
Neighborhood functions operate on the neighboring features of a given
feature or set of features.
Search functions allow the retrieval of features that fall within a given
search window (rectangle, circle, or polygon).
Line-in-polygon and point-in-polygon functions determine whether a
given linear or point feature is located within a given polygon, or they
report the polygon(s) that a given point or line are contained in.
9 The DISSOLVE function in ARC/INFO belongs to this type.
10 The term generalization has different meanings in different contexts. In
geography often the term aggregation is used to indicate a process that we called
generalization. In cartography generalization means either the process of producing
a graphic representation of smaller scale from a larger scale original (cartographic
generalization), or the process of deriving a smaller resolution representation from a
more detailed representation within a database (model generalization). Finally, in
computer science generalization is one abstraction mechanism in object orientation.
60 GEOGRAPHICINFORMATIONSCIENCE(GIS)
Topographic functions compute the slope or aspect from a given digital
representation of the terrain (digital terrain model or DTM).
Interpolation functions predict unknown values using the known
values at neighboring locations.
Contour generation functions calculate contours as a set of lines that
connect points with the same attribute value. Examples are points with
the same elevation (contours), depth (bathymetric contours), barometric
pressure (isobars), or temperature (isothermal lines).
5.4.4 Connectivity Operations
The characteristic of these operations is that they accumulate values as
they traverse a feature or a set of features.
Contiguity measures evaluate characteristics of spatial units that are
contiguous (are connected with unbroken adjacency). An example
would be the search for a contiguous piece of forest of a certain area and
shape.
Proximity functions. The best known example of a proximity function
is the buffer zone generation (or buffering).
Network analysis is used to computer the shortest path (in terms of
distance or travel time) between two points in a network (routing), or all
the points that can be reached within a given distance or duration from
a center (allocation).
Visibility functions are used to compute the points that are visible from
a given location (viewshed modeling or viewshed mapping) from a
digital terrain model.
61
very GIS needs an infrastructure that enables it to
function. Such an infrastructure can be regarded as a
technical and organizational infrastructure. The technical
infrastructure mainly deals with the hardware and network
architectures. The organizational infrastructure addresses issues
of procedures and policies to make GIS work.
Information processing systems are computer systems with
appropriate hardware for the processing, storage and transfer of
data, as well as software components for the management of the
hardware, peripheral devices and data. This chapter discusses
the components of an information processing system with special
consideration for spatial data handling and information
processing.
First, a brief introduction to computer hardware and software is
given with a description of the peripheral devices used for the
capture and display of spatial data. Software components,
computer networks, the Internet, and the World Wide Web are
discussed.
CHAPTER
6 Technical Infrastructure
E
62 GEOGRAPHICINFORMATIONSCIENCE(GIS)
6.1 Introduction
Geo-information infrastructures only function effectively when reliable
and efficient computing and communication technologies are in place.
These technologies must, in particular, support communication among
computer systems and networks. Increasingly large databases have to
be linked for the transfer of data or to provide a basis for
interoperability of heterogeneous software and hardware systems.
Many of these technologies have resulted from military research and
applications. The Internet is perhaps the best known example.
Together with the World Wide Web they currently form the most widely
used vehicles for geo-information infrastructures.
6.2 Basic Hardware And Software
6.2.1 Computer Hardware
Computer hardware is a necessary component of an information
processing system. Electronic computers exist only since about 60
years. Within this short period, we saw a rapid development that we
had never seen before in modern technology. When we compare the
size, performance and price of a computer in the early 1980’’s with the
systems available today, we see orders of magnitude in performance
increase and an equally steep decrease in size and price.
A computer is a general-purpose machine that can be programmed to
manipulate symbols. Computers can perform calculations precisely,
reliably and at high speed on large volumes of data. When we look at
the architecture of a general-purpose computer, we can distinguish the
following three components: a central processing unit (CPU), memory
(storage), and peripherals (see Figure 22).
The top half is the central processing unit and its directly accessible
parts. The bottom half indicates various parts that may have been
added, such as external memory and other devices as keyboard, mouse,
printer, and modem. In between the two are interfaces that enable the
communication; they consist of hardware cards and cabling, and
conventions about what communication signals mean.
The central processing unit (CPU) is the part of a computer that
controls all other parts. It consists of the control unit, the arithmetic
and logic unit (ALU), and memory (registers, cache and RAM).
The control unit fetches instructions from memory, decodes them, and
produces signals to control other parts of the computer. The arithmetic
and logic unit performs arithmetic and logic calculations (addition,
multiplication, and logical operations like AND and OR). A separate
floating-point unit (FPU) often performs floating-point——i.e., arithmetic
with real numbers——instructions.
The CPU uses different types of memory. Registers are a small set of
fast memory locations that are used by the ALU (or FPU), while a cache
is fast intermediate memory that holds data that have been recently
accessed, thereby speeding up subsequent access to the same data.
Random access memory (RAM) is the main memory that holds the data
and programs to be executed. The RAM size of modern personal
computers is minimally 128 Mbytes (megabytes; 1Mbyte = 210 = 1024
kilobytes, where a kilobyte is 1024 bytes) or more. Systems for spatial
TECHNICAL INFRASTRUCTURE 63
data processing typically have 512 Mbytes (sometimes even more) as
minimum requirement.
The performance of a processor is determined by cycles per second (or
clock rate); in each cycle, a computer performs its most basic operations.
Each instruction (like adding two numbers) takes a small number of
basic operations. The clock rate is given as a frequency in megahertz
(MHz; 1 MHz = 106 = 1,000,000 Hz).11 For instance, the first PC from
the beginning of the 1980’’s had a clock rate of 4.77 MHz (nearly 5
million cycles per second). Today’’s processors, several generations later,
perform at a rate of about 1 GHz and higher.
Control unit ALU
Main
Memory
CPU
Communication
dev ices
Storage dev ices
I/O
dev ices
modem, network disk, tape
monitor, printer,
mouse
interf aces
Figure 22: General computer architecture
The data are stored permanently on storage devices, such as disks,
tapes and other magnetic or optical media. The most common devices
are disks——for PC’’s usually with a capacity of 40 Gbytes (gigabytes; 1
Gbyte = 210 Mbytes) and more. Portable disks such as floppy disks (with
a capacity of 1.4 Mbytes) or other types (with a capacity of 250 Mbytes
up to 2 Gbyte) allow easy transportation of relatively large datasets.
Compact disks (CD’’s) are optical media that can be read and written
using laser technology. They typically can store up to 600 Mbytes of
data. Digital versatile disks (DVD) become more and more also the
medium for data storage.
Communication devices link computers to networks (such as the
Internet) and allow them to retrieve or exchange data over long
distances without the need for physical storage media. Modems are
used to establish connections over (analogue or digital) telephone lines;
network cards link computers to high-speed networks (Intranet and
Internet).
6.2.2 Peripheral Devices
Peripheral devices are connected to a computer and serve the task of
data input and output. For a general-purpose computer we distinguish
11 The Hertz is the unit of counting per second. A seconds arm on a clock ticks at
1 Hz, the minutes arm at
60
1
Hz.
64 GEOGRAPHICINFORMATIONSCIENCE(GIS)
between the following standard input and output devices: keyboard,
mouse, monitor, and printer. For spatial data, additional devices for
input and output are being used: digitizer, scanner, and plotter.
Peripheral devices can be classified according to their purpose with
regard to data flow into input and output devices. Output devices can
further be distinguished into softcopy and hardcopy devices. Softcopy
devices (like the computer monitor) display a non-permanent image that
disappears when the screen is being erased or the power is turned off.
Hardcopy devices (e.g., printers) produce a permanent display on a
physical medium like paper or film.
The CPU controls peripheral devices. They are linked to the CPU
through interfaces. These are controller cards for disk and tape drives,
graphic and video cards for display devices, network cards and modems
for communication devices, and general-purpose interface cards for
serial and parallel communication with for instance a printer, plotter,
scanner, mouse or digitizer.
Keyboard. A keyboard is used to enter text (characters and numbers)
and to control the operating system of the computer. The keyboard
layout differs among natural languages and many users need some time
to adjust to a keyboard when they use computer systems different from
the one used in their home country. Beside keys for characters of the
alphabet and punctuation marks (!, ;, ?, etc.), every keyboard contains
also function and control keys.
Mouse. Another common input device is the mouse. This is a pointing
device with one, two or three buttons used to identify objects on the
monitor screen. A mouse is not only used to control objects but also to
enter graphic primitives (lines, shapes, and symbols) into a software
application. The movement of the mouse over a flat surface——usually on
a mouse pad——is echoed by a corresponding movement of a cursor——
usually an arrow, cross hair, or other symbol——on the screen.
Monitor. The computer monitor is a graphic output device. Today’’s
systems all use color graphics monitors with high resolution, based on
cathode ray tube (CRT) or liquid crystal display (LCD) technology. LCD
displays are flat-panel displays, and we distinguish between active and
passive matrix displays. Active-matrix displays (like the Thin-Film
Transistor or TFT display) produce a brighter and sharper image than
passive-matrix displays. They are, however, more expensive. The
graphic image on the monitor screen is represented by a raster image
where every raster cell has a color code assigned. For instance, to be
able to display 256 (= 28) different colors per raster cell (picture element
or pixel) we need 1 byte of storage space per pixel.
The resolution of a monitor is measured in the number of dots that fill
the monitor screen in horizontal and vertical direction. Typical
resolutions are 640 (horizontal) by 480 (vertical), 800 by 600, 1,024 by
768, and 1,280 by 1,024. The more dots are used, the higher the
resolution of the screen, and the sharper the image.
The chosen resolution and the available display memory of the
computer system determine the number of colors that can
simultaneously be displayed on the screen. With a given amount of
display memory, the number of simultaneously displayable colors is
indirectly proportional to the resolution, i.e., the higher the resolution
the less colors can be supported. Typical numbers of colors range from
256 (hundreds) through 65,536 (thousands) up to 16,777,216 (millions).
TECHNICAL INFRASTRUCTURE 65
To allow dynamic images on the screen, the raster image has to be redrawn
(refreshed) at least 24 times per second (24 Hz) to avoid
flickering and adverse graphic effects. Common refresh rates are 60 Hz,
75 Hz, and 85 Hz or more.
A typical setting for a computer screen is a resolution of 1,024 × 768
with 65,536 simultaneously displayable colors and a refresh rate of 75
Hertz. This requires a minimum display memory size of 1,024 × 768 ×
16 bits equaling 1.5 Mbytes.
Digitizer. Graphic input devices frequently used for spatial data are the
digitizer (or digitizing tablet) and scanners. A digitizer is a manual
graphic input device that allows capturing coordinates from given
source material mounted on the surface of a tablet. A pointing device
(called cursor) is moved over the source material to capture coordinates.
This cursor is equipped with crosshairs for positioning and buttons that
usually have (software-defined) functions assigned to them. Whenever
a button is pressed, a coordinate pair (in device coordinates as
centimeters, millimeters, or inches) is captured and sent to the
computer. The application program reads the coordinates and performs
proper actions. This is called point-mode digitizing. It is also possible
that the digitizer sends a continuous stream of coordinates once a
button on the cursor is pressed or the cursor is moved a certain
distance. We call this stream-mode digitizing.
The resolution of a digitizer is measured in the smallest interval of
cursor movement that can be recorded. For most devices, this is 0.001
inch (or 0.0254 mm). The overall achievable positioning accuracy in
manual digitizing is about 0.1 to 0.15 millimeters.
Scanner. Scanners are used for automatic digitizing. A laser beam
sweeps (scans) the surface of the source document and the reflectance of
light is recorded as a raster image. The result of a scan process is
always a raster image with black-and-white (monochrome) or grayscale
pixels according to color components (color scanner). We distinguish
between flatbed scanners and drum scanners. On a flatbed scanner, the
source material is placed on a flat surface and the scan head is moved
over this surface to record the image. These scanners can handle source
materials up to A3 format (420 by 297 mm). Drum scanners can
accommodate larger source documents that are mounted on a rotating
drum.
The resolution of a scanner is measured in dots per inch (dpi). Office
scanners typically have resolutions between 300 and 800 dpi, i.e., about
12 to 31 dots per millimeter. Cartographic scanners for high-resolution
data capture work at resolutions of 2,000 dpi (or 79 dots per millimeter).
Printer and Plotter. Modern printers serve two purposes. They are
used to produce text documents and graphic representations (plots) as
well. Printers make use of different technologies to produce output on
the print medium. The most common are laser and ink-jet technologies.
Laser printers transfer toner to paper (or transparency) that has been
charged using a laser beam. The output can be monochrome or in color.
The output format is A4 or A3. Laser printers work at resolutions of
300, 600, and 1,200 dpi.
Ink-jet printers transfer (liquid or solid) ink onto the output medium.
They support different media (paper, film or transparencies) and
formats (A4 up to A0). Their resolution varies between 300 and 2400
dpi. Laser printers and ink-jet printers produce a matrix of dots (a
raster image) on the output medium.
66 GEOGRAPHICINFORMATIONSCIENCE(GIS)
Plotters are devices that are solely used for the output of graphics. Pen
plotters are seldom used nowadays. They are devices that draw the
graphics with a pen (or several color pens) on the output medium,
producing (colored) lines. This technology is rather slow and lacks the
capability to produce solid area fills. Today, pen plotters are hardly
ever used.
An even less used technology is electrostatic plotters. These devices
produce the output on specially coated paper that is electrostatically
charged and subsequently moved through a toner liquid.
For high quality graphic production, film writers are used whose output
is a film (or color separates on film) that can be used for printing. The
resolution of film writers is in the range of 2,000 to 2,500 dpi. Table 10
summarizes the characteristics of the various peripheral devices.
Table 10: Peripheral input and output devices overview
Device Resolution Comments
pen plotter 0.001 inch no solid color fill
ink jet plotter 300 to 2,400 dpi
electrostatic plotter 400 dpi
laser printer 300 to 1,200 dpi DTP quality
film writer 2,500 dpi cartographic quality
monitor 640 × 480 dots VGA (Video Graphics Array)
800 × 600 dots SVGA (Super Video Graphics Array)
1,024 × 768 dots XVGA (eXtended Video Graphics
Array)
1,280 × 1,024 dots
digitizer 0.001 inch overall achievable accuracy is 0.1 to
0.15 mm
scanner 300 to 2,400 dpi office scanner
2,400 dpi cartographic scanner
6.2.3 Software
With software, we mean computer programs that the computer
hardware can execute to serve various purposes. A computer program
is a list of instructions that we want the computer to carry out. It is
called software because in contrast to hardware, we can easily change
it——it is non-physical. We can distinguish the following types of
software:
• Operating systems
• Programming languages
• Application programs
The task and purpose of an operating system (OS) is to control and
manage the hardware components and system resources of a computer
and to serve as an interface between the hardware and the users.
Currently, the most popular operating systems are Microsoft Windows
(Windows 98, Windows NT, Windows 2000, and Windows XP) and Unix
(in various dialects such as Linux). Today, most operating systems
manage multiple users. A user communicates with the OS either
through a graphic user interface or by typing commands. Other, less
used, operating systems are DOS (Disk Operating System, the
predecessor of Windows) and systems that are mainly used for large
multipurpose mainframe computers.
Application programs are written in a programming language, a
language that allows to express instructions for the computer. We know
TECHNICAL INFRASTRUCTURE 67
many languages and their dialects. The best-known and widely used
are Basic and C, and object-oriented versions like C++. Java is used for
Internet applications mostly, but it is in fact also a general purpose
programming language. Often an operating system can be customized
to serve particular user needs. Today, visual programming languages
such as Visual Basic or Visual C are widely used.
Application programs perform certain tasks that are needed by users.
Text processors, spreadsheets, presentation and graphics applications,
database software, and programs written by end-users belong to this
group. The software used for spatial data handling (image processing
software, geographic information systems) is all application programs
as well. Many of these have already been developed, and this means
that many users do not have to be capable of programming.
6.2.4 Computer Networks
Networks allow computers to transfer data, exchange and execute
programs across computers at different locations, and to acquire
information made available by other users and content providers from
various sources. The Internet is the most widely used computer
network in the world. Millions of computers are linked to it. Data and
programs are transported over the network according to defined
protocols such as TCP/IP (transmission control protocol/internet
protocol), FTP (file transfer protocol), and HTTP (hypertext transfer
protocol).
The World Wide Web (WWW or the Web) is one of the most popular
applications running on the Internet. Users communicate with the
WWW through browsers (such as Netscape Navigator or Microsoft
Internet Explorer).
In a network, computers have different roles. Often, one or more
computers (the servers) serve many others with programs or data. The
others are clients of the servers. This concept is called client-server
architecture (Figure 23). It is an approach frequently used in modern
computer networks. A computer network connects clients and servers.
Individual computers are connected to the network either through
permanent network links or through dial-up lines (using modems).
Server 3
Client Client
Client
Internet
Server 2
Server 1
Figure 23: Client/Server concept in computer networks
68 GEOGRAPHICINFORMATIONSCIENCE(GIS)
6.2.5 Trends
The development of computer hardware proceeds at an enormously fast
speed. Almost every six months, a faster, more powerful processor
generation replaces the previous one. Software develops somewhat
slower and often cannot fully use the possibilities offered by the
hardware. Generally, we can observe the following trends:
• Miniaturization and improved portability of computer systems
• Increase in storage capacity and storage requirement
• Open systems and interoperability
Computers get smaller and at the same time, their performance
increases. The power that we have available in today’’s portable
notebook computers is a multiple of the performance that the first PC
had when it was introduced in the early 1980’’s. In fact, current PC
systems have orders of magnitude more memory and storage than the
so-called minicomputers of 20 years ago. Moreover, they fit on an office
desk. However, software providers produce application programs and
operating systems that consume more and more memory. To efficiently
run a computer with Windows XP and some general-purpose office
applications, a PC should be minimally equipped with 256 Mbytes of
main memory and 40 or more Gbytes of disk storage.
At the same time, computers have become increasingly portable.
Notebook and handheld computers are now commonplace in business
and personal use. Mobile phones are frequently used to communicate
with computers and the Internet (through the wireless application
protocol or WAP). The communication between portable computers and
networks is still rather slow when they are connected via a mobile
phone. The transmission rate currently supported by mobile
communication providers is only 9,600 bits per second (bps). Digital
telephone links (ISDN) support 64,000 bps up to 128,000 bps, and highspeed
computer networks have a capacity of several million bps. In
private households broadband Internet connections are used frequently
(ADSL or Asynchronous Digital Subscriber Line).
In the future high-speed mobile communication will be available with
the new standard UMTS (Universal Mobile Telecommunications
System) that will allow up to 2 Mbps (Mbits/sec).
Open systems use standard architectures and protocols for networking.
Interoperability is the ability of hardware and software of multiple
computers from different vendors to communicate with each other. An
interoperable database would for instance allow heterogeneous
databases to appear as a single homogenous database to a user.
6.3 System architecture
There are several ways to organize computers so that they can perform
their tasks properly. Computer systems may be arranged as individual
stand-alone machines, or cooperate in computer networks. Today,
computer networks are an indispensable component of any geoinformation
infrastructure. Special attention is given to the clientserver
architecture, which is of great importance not only in spatial
data handling but also in general data processing.
Two important underlying principles of system architectures are open
systems architectures and interoperability. Open systems cooperate
with each other using standards for access, processing, and transfer of
TECHNICAL INFRASTRUCTURE 69
data. They are not hindered by system-specific structures that impede
cooperation. Interoperability is the ability of software and hardware on
multiple computers from multiple vendors to communicate.
Interoperable databases play an important role in geo-information
infrastructures.
Individual computers have been used since the beginning of electronic
data processing. They are usually dedicated to specific tasks or
applications. However, they are not linked to anything else, and data
can only be transferred this can only happen by physically transporting
files on magnetic or optical media such as tapes, disks or CDs (Figure
24).
Advantages of stand-alone computers are that we know who is precisely
responsible for them and that they are ‘‘isolated’’ from each other, which
makes unwanted transfers difficult. However, the isolation is also a
drawback that causes overhead, redundancy, and frequent duplication.
This can be overcome by cooperative computers in various kinds of
networks or distributed systems.
A client-server architecture is a common form of distributed system
where software is split between client and server tasks. In a distributed
system, a user does not see which computer is performing which
functions. It essentially behaves as a virtual single processor. The
characteristic feature of a distributed system is that it is a software
system running on top of a computer network.
Application 1 Application 2 Application 3
Figure 24: Stand-alone computers without cooperation
A client is a process or computer that requests the services of another
computer or process (server) using some rules (protocol). The server
processes the request and sends the result back to the client. The
exchange of information is done through message exchange according to
the protocol. A server program may run continuously, waiting for
requests to arrive, or it may be invoked by some higher level program.
Server programs are also called daemons.
A server computer provides services to client machines. A common
example is a file server, which is a computer with a local disk servicing
requests from remote clients (workstations or PCs) to read and write
files on that disk. File servers often use the Network File System (NFS)
protocol for workstations or Novell Netware for personal computers. In
a client-server setup, we usually find many clients using a few servers.
Clients and servers may even run different operating systems, and
servers can be clients of other servers. Figure 25 illustrates the clientserver
architecture.
70 GEOGRAPHICINFORMATIONSCIENCE(GIS)
Server Client
Client
Client
Network
Network
Network
Figure 25. Client-server architecture
6.4 Computer networks
The exchange of data and information among computers in a geoinformation
infrastructure is a necessity. A computer network is an
interconnected collection of autonomous computers. By autonomous we
mean that there is no hierarchical relationship between the computers
in a network. If one computer (master) is able to start or stop the others
(slaves) we would call it a master-slave architecture rather than a
computer network. There are various ways to connect computers in a
network. The most common methods are wires, fiber optics,
microwaves, and communication satellites.
The communication units that are sent between computers in a network
are called messages. They can be transmitted one piece at a time from
a sending to a receiving computer. If this is the case, we speak about
message switching. Often it is necessary to split the message into
several data packets to avoid transmission problems. If a network
applies this technique we speak about packet switching. One of the
principles of packet switching is that individual packets may travel
distinct routes to their destination. The speed of transmission is
measured in bits per second (or bps). The orders of magnitude used
throughout section 3.1 are k (kilo or 103), M (mega or 106) and G (giga or
109). Today, transmission speeds of several hundred megabits per
second are common.
6.4.1 Transmission media
The messages transmitted in a computer network are made up of bit
streams. They are sent from one computer to another using different
media. We can distinguish between guided media, like cables and fiber
optics, and unguided media, such as radio or microwaves, and lasers.
Each of these media has its own characteristics in terms of bandwidth,
costs, installation, and maintenance. In the following we take a closer
look at some of them.
6.4.1.1 Twisted pair
The most common medium is the twisted pair which consists of two
insulated copper wires twisted together to reduce electrical interference
TECHNICAL INFRASTRUCTURE 71
from other wires nearby. Each of the wires is about 1 mm thick. The
twisted pair is most commonly found in telephone systems and can
reach a bandwidth of several megabits per second at short distances.
These cables can run several kilometers without amplification, and are
usually bundled together. They are often referred to as UTP (or
Unshielded Twisted Pair).
6.4.1.2 Coaxial cable
The coaxial cable is a common medium used for computer and cable
television networks. It is better shielded than the twisted pair and
allows for higher transmission speeds (up to one or two gigabits per
second).
Protective plastic covering Braided outer conductor
Insulating material
Copper core
Figure 26: Coaxial cable
These cables consist of a stiff copper wire surrounded by insulating
material. This is surrounded by a cylindrical conductor in the form of a
braided mesh, which in turn is covered by a protective plastic sheath
(Figure 26). Coaxial cables are distinguished into baseband coaxial
cables for digital transmission and broadband coaxial cables for
analogue cable television.
6.4.1.3 Fiber optics
Fiber cables are an optical transmission system. Such a system consists
of three components: a light source, the transmission medium, and a
detector. The light source is either an LED (light emitting diode) or a
semiconductor laser. The transmission medium is an ultra-thin fiber of
glass; the detector is a photodiode, which generates an electrical pulse
when hit by light. Due to total reflection, light emitted at a source can
travel within the fiber for many kilometers with virtually no loss. A
fiber cable is similar to a coaxial cable. It has a glass core about the
diameter of a human hair, surrounded by a glass cladding which serves
to keep the light in the core. An outer plastic jacket covers the cladding
and the core (Figure 27). As with twisted pairs, optical fibre cables are
usually bundled together, protected by an outer sheath.
Jacket (plastic) Cladding (glass)
Core (glass)
Figure 27: Fiber cable
72 GEOGRAPHICINFORMATIONSCIENCE(GIS)
With current technology, data transmission through fiber cables reaches
a practical bandwidth of one gigabit per second, although theoretically a
much higher transmission rate is possible. Fiber optics is used in local
as well as long-haul transmissions. Apart from providing a larger
bandwidth, fiber cables have other advantages over copper wires. They
provide data transmission over longer distances without repeaters, they
cannot be affected by power surges or electromagnetic inference, they
are thin, and do not leak light. Moreover, fiber cables are more difficult
to tap than copper wires.
6.4.2 Wireless transmission
Electromagnetic waves are used for wireless transmissions. Their
advantage is that once broadcast by a transmitting antenna, they travel
with the speed of light and can be received by a receiver some distance
away. Depending on the range used within the electromagnetic
spectrum, we speak of radiowaves, microwaves, infrared-, visible light,
ultraviolet light, X-rays, and gamma rays. Only radio- and microwaves
as well as infrared and visible light are in fact used for transmitting
information. The other parts of the spectrum are dangerous to living
things.
The advantage of wireless media is that cables do not have to be
installed and one transmitter can, in principle, reach many receivers.
Depending on the wavelength used, a repeater must be installed at
certain distances. For the frequently used microwave links, a repeater
is required about every 50 km. This medium is used mainly for data
and voice transmission in the telecommunication business. Infrared
transmission is well known for its use in remote control of video and
audio equipment within a room. It is also used for linking printers to
notebook computers. The nature of infrared requires, however, that the
transmitter is in sight of the receiver and that the distance between
them is not too large. In the visible light range, lasers are used as a
transmission medium. Communication satellites receive a microwave
signal, amplify it and rebroadcast it back to the surface of the Earth.
Wireless transmission was a booming market in the 1990s and became
even more popular today for wireless local area networks. Notebook
and hand-held computers are linked through modems to the systems in
the office or base by using telephone lines. These lines can be analogue
or digital. A bandwidth of 2 Mbps is typically achievable.
A modem connection via a standard analogue telephone line can reach
28.8 kbps, and with compression and error correction features built in, a
slightly higher effective data rate can be achieved. An alternative to the
analogue telephone network is the Integrated Services Digital Network
(ISDN). Its goal is to provide voice and data transfer simultaneously on
a digital network. ISDN comes in two versions, narrowband ISDN (NISDN)
and broadband ISDN (B-ISDN). The former provides only 64
kbps, whereas the latter is designed for a bandwidth of 155 Mbps. NISDN
is virtually useless for modern high speed networks, whereas BISDN
based on Asynchronous Transfer Mode (ATM) technology meets
their requirements. The slower N-ISDN can, nevertheless, be very
useful for the home user who wishes to download web pages at a higher
speed than possible with a 28.8 kbps analogue modem. In recent years,
Asynchronous Digital Subscriber Lines (ADSL) are beginning to replace
ISDN connections for Internet access. ADSL offers a download speed of
768 kbps and an upload speed of 128 kbps.
TECHNICAL INFRASTRUCTURE 73
6.4.3 Network hardware
We can classify computer networks according to the transmission
technology they use, the scale at which they operate, and the network
topology, i.e., the shape of the graph of pairwise connections between
computers.
There are two types of transmission technology: (1) broadcast networks,
and (2) point-to-point networks. In broadcast networks short messages,
called packets, are sent from one computer to all other computers.
These packets contain address information identifying the computer for
which they are intended. Only this computer will process the packets,
all others will ignore them. This technique is called broadcasting. If a
subset of computers in the network is addressed, this is called
multicasting. In a point-to-point network, each computer is linked to
one or more computers and communication is between pairs of
machines. If, for instance, a message is to be sent from computer A to
computer B, it may travel through intermediate machines before it
reaches the destination computer. Routing algorithms searching the
optimal path through the network are of particular importance. Larger
networks are mainly point-to-point, whereas smaller, geographically
limited networks use broadcasting.
Another way of classifying networks is according to their scale.
Networks of up to a size of 1 km are known as Local Area Networks
(LANs), while Metropolitan Area Networks (MANs) roughly span the
area of a city (up to 10 km). Larger areas such as countries or
continents are networked with Wide Area Networks (WANs), and at a
global scale they are called internetworks. The Internet is an obvious
example of the latter.
The structure of nodes and links between them is called the network
topology. Figure 28 shows the most common network topologies: star,
ring, tree, complete graph, and bus.
Star Ring Tree
Complete graph Bus
Figure 28: Network topologies
6.4.3.1 Local area networks (LANs)
Local area networks usually link computers within a building or on a
campus. They are mainly used to connect PCs, workstations, and other
devices such as printers. LANs are broadcast networks, using a single
cable to which all computers and devices are linked (bus or ring
topology). In a bus topology, one machine is allowed to send at a time,
the others have to wait. If two or more machines want to transmit
74 GEOGRAPHICINFORMATIONSCIENCE(GIS)
simultaneously, an arbitration mechanism has to resolve the conflict.
This mechanism can be centralized or distributed. A typical example is
the standard IEEE 802.3 or Ethernet. It uses a decentralized
arbitration mechanism and operates at a bandwidth of 10 to 100 Mbps.
Another type of topology for a LAN is the ring. The IEEE 802.5 (or IBM
token ring) operates at bandwidths of 4 and 16 Mbps.
6.4.3.2 Metropolitan area networks (MANs)
Many commercial activities are expected to take place in metropolitan
areas making use of computer network technology. Increasingly, cable
companies open their networks for other services such as Internet
access, telecommunications, and emergency services. As in LANs, the
transmission mode in MANs is broadcast or multicast. For MANs an
international topology standard has been adopted as IEEE 802.6 under
the name of Distributed Queue Dual Bus (or DBDQ), consisting of two
buses with unidirectional communication.
6.4.3.3 Wide area networks (WANs)
Wide area networks span a large geographical area and contain a
collection of machines called hosts or end systems. The hosts usually
belong to a LAN in which there is a special computer, called a router,
that connects the LAN to a communication subnet which, in turn,
connects to other LANs (Figure 29). The routers are also called packet
switching nodes. Routers that are not directly connected, communicate
indirectly via other routers through a store-and-forward mechanism.
The subnets are typically point-to-point networks. Common topologies
for WANs are star, ring, tree, or complete graph. Irregular topologies,
often reflecting the historic growth of the network, are also common.
Another possibility for WANs are satellite systems. These are broadcast
systems in which many ground stations can listen to the satellite
transmission, and they are usually part of a bigger network.
LAN LAN
LANLAN
Subnet
Router
Host
Figure 29: Wide area network
6.4.3.4 Internetworks
Several heterogeneous networks connected through gateways are called
an internetwork. In analogy to WANs, where a router connects LANs,
in an internetwork the gateways connect WANs. The Internet is
perhaps the best known, but not the only example. It is a global
internetwork whose origin dates back to a project by the American
Department of Defense when the Defense Advanced Research Projects
Agency (or DARPA) launched the ARPANET in 1969. By the early
1980s, the ARPANET had grown steadily to include hundreds of host
TECHNICAL INFRASTRUCTURE 75
computers and several of today’’s key protocols were stable and had
already been adopted.
When the supercomputer network of the American National Science
Foundation NSFNET was connected to the ARPANET the growth
became exponential. Many other networks joined such as BITNET,
HEPNET, and EARN. By 1990, the ARPANET was shut down and
dismantled. Nowadays there are millions of computers connected to the
Internet and high-speed links connect continents running from 64 kbps
to 2 Mbps.
The Internet has many applications that are indispensable in today’’s
information infrastructures: electronic mail, news, remote login, file
transfer, and the World Wide Web. The application with the strongest
impact on society is the WWW. Since its invention in the early 1990s
and the introduction of browsers (such as Mosaic, Netscape Navigator,
and Internet Explorer) websites have been created at an enormous rate.
Almost every company, organization, government, and university, but
also private individuals, have their own home page. And their number
is increasing.
A computer is said to be linked to the Internet when it has an IP
address, runs the TCP/IP protocol stack, and can send IP packets to all
other machines on the Internet. The next section provides a more
detailed explanation of the terms used here.
6.4.4 Network software
When computers communicate with each other they must ‘‘speak’’ the
same language, and implement correct procedures to establish and
maintain a connection. Computer networks and network software are
complex. They are therefore organized as a series of layers or levels.
Each layer offers certain services to higher layers. Interfaces between
layers define which primitive operations a layer offers to a higher layer.
Layer n on one computer (host) communicates with layer n on another
computer according to certain conventions and rules. These rules are
called protocol. The respective layers communicating with each other
are called peers. The peers do not communicate directly. Information is
passed to lower layers through the interfaces to the physical medium
(below layer 1) where the actual communication takes place. Figure 30
illustrates this concept.
A network architecture is a set of layers and protocols. Details of
implementation and specifications for interfaces are not part of a
network architecture. They may actually vary among systems. A set of
protocols for a certain system, one for each level, is called a protocol
stack.
Physical transmission medium
Layer 1
Layer 2
Layer 3
Layer 1/2 interface
Layer 2/3 interface
Layer 1
Layer 2
Layer 3
Layer 1 protocol
Layer 2 protocol
Layer 3 protocol
Host 1 Host 2
Layer 2/3 interface
Layer 1/2 interface
Figure 30: Layers, protocols and interfaces
76 GEOGRAPHICINFORMATIONSCIENCE(GIS)
6.4.4.1 Layer Services
Communication between computers means sending messages from one
machine to another. If travel in only one direction is allowed we speak
of simplex communication. A transfer in both directions, but not
simultaneously, is called half-duplex communication, and if there are no
restrictions, we have full-duplex communication.
With so many computers involved, it is extremely important to have an
addressing mechanism that allows unique identification of senders and
receivers. Rules must be defined for how data can travel between
layers. Other services refer to error-detection and error-correction,
disassembling and reassembling long messages, avoiding data overflow
due to fast transmitters and slow receivers, and routing of messages.
There are two models of significance for modern computer networks: the
ISO/OSI and the TCP/IP reference models. The first is an international
standard, yet of little practical importance; the latter is less of a model
but it is widely used in the Internet. The next section discusses both
models in detail.
6.4.4.2 The OSI Reference Model
The Open Systems Interconnection (OSI) reference model is a standard
approved by the International Standards Organization (ISO). It was
developed in the early 1980s and comprises two major components: an
abstract model of networking (the seven-layer model) and a set of
protocols. The model divides a networking system into layers. Every
layer has a certain functionality and communicates with the layer
directly under it through an interface. Layers communicate with their
corresponding peers in other systems. The ISO/OSI model has a stack
of seven layers on top of the physical medium (Figure 31): physical
layer, data link layer, network layer, transport layer, session layer,
presentation layer, and application layer.
This model is important in that it is a means to explain and describe
network architectures. Its protocols are of minor importance. They
have been overtaken by the TCP/IP protocols that play an important
role in the Internet.
Physical
Data link
Network
Transport
Session
Presentation
Application
Physical
Data link
Network
Transport
Session
Presentation
Application
Transmission network
Figure 31: ISO/OSI reference model
Physical layer. The physical layer is mainly concerned with the
mechanical and electrical interfaces on top of the physical medium that
guarantee that bits are transmitted correctly over a communication
channel. For instance, this layer defines the type of cable connectors,
size of cable, or the voltage and duration to represent 0 and 1 bits.
TECHNICAL INFRASTRUCTURE 77
Data link layer. The data link layer describes the logical organization of
data bits that are transmitted through a channel such that an error-free
transfer is achieved. This is done by the sender breaking the bit stream
into data frames (up to 10 kByte in length) and process
acknowledgement frames that are sent back by the receiver. Correction
of damaged, duplicate, or lost frames is another task of the data link
layer. It must also exercise buffer management, which means
alleviating transmission speed differences between sender and receiver.
Network layer. Whereas the data link layer is only responsible for
moving frames from one end of a cable to the other, the network link
layer must take care of the proper operation of the communication
subnet by routing packets from the source to the destination. Routing
can become an important issue when accounting and billing are
involved. Another important task is to resolve naming conflicts
(addressing schemes may be different among heterogeneous networks)
and differences in protocols.
Transport layer. The transport layer describes the nature and quality
of the data delivery. Its major task is to accept data from the session
layer (above it), split it into pieces, pass them through to the lower
layers, and ensure the correct arrival at the remote end. This layer is
the first with a true end-to-end communication, i.e., the transport layer
on the source machine conceptually communicates with the transport
layer on the destination machine, without a concept of intermediate
nodes. Another function of the transport layer is flow control, to
regulate the flow of information, and to establish and delete connections
across the network.
Session layer. The session layer’’s prime task is to accommodate
sessions between users on different computers. Sessions might be
ordinary data transport, remote login, or dialogue control. Other
functions are token management and synchronization. The session
layer provides tokens that regulate who is allowed to perform certain
actions. Only the holder of a token is allowed to perform a critical
operation, thereby avoiding that two machines attempt such an
operation at the same time. Synchronization establishes checkpoints
that help in the recovery of network failures. When a large data set is
being transferred from one machine to the other, it is sometimes
interrupted by network failures. If this happens, only the data that
were lost after the last checkpoint have to be re-transmitted.
Presentation layer. The presentation layer provides support for some
common functions that have to know the syntax and semantics of the
data. Character encoding for text (such as ASCII), dates, or floating
point numbers are done here.
Application layer. The application layer is the level that real users see
when they work in a computer network. Several application protocols
are supported that directly target the end user. These protocols support
file transfer between different operating systems and take care of
problems that may arise due to incompatible file naming conventions
(imagine, for instance, the different file naming conventions in DOS and
UNIX). Other functions supported are electronic mail, remote job entry,
or directory lookup. It must be noted, however, that most of the
protocols we use on the Internet are not OSI protocols, but TCP/IP
model protocols, which are explained in the next section.
78 GEOGRAPHICINFORMATIONSCIENCE(GIS)
6.4.4.3 The TCP/IP Reference Model
When the Internet evolved from the old ARPANET, and other networks
were added, it soon became necessary to design a framework to link
these heterogeneous networks. The TCP/IP reference model, named
after its two major protocols, the Transmission Control Protocol (TCP)
and the Internet Protocol (IP), was defined in 1974. Figure 32 shows a
comparison between the ISO/OSI and the TCP/IP reference model
layers.
Physical
Data link
Network
Transport
Session
Presentation
Application
Host-to-network
(PPP, SLIP)
Internet (IP)
Transport (TCP, UDP)
Application
(FTP, HTTP,...)
Transmission network
Figure 32: ISO/OSI and TCP/IP layers
We see that the TCP/IP reference model has fewer layers than the
ISO/OSI model. In particular, the network access layer (host-tonetwork)
is vaguely defined. The model only requires some protocols to
take care of a correct connection to the network and to guarantee that
the data packets from the Internet layer are transmitted correctly. One
of these protocols that is widely used to connect home computers
through telephone lines to host computers of Internet providers is the
Point-to-Point Protocol (PPP).
Internet layer. The Internet layer supports the Internet Protocol (IP) as
the most basic means of transportation of data packets. IP is a protocol
that treats each packet independently. It does not even check whether
the packets reach their destination and takes no corrective action if they
do not arrive. IP is basically an unreliable protocol because it contains
no error detection or correction. It provides the following services:
• Addressing. The sending and receiving hosts are identified by
32-bit IP addresses. These addresses are used by intermediate
routers to channel the packets through the network. Domain
names are alphanumeric strings identifying Internet hosts.
Examples of domain names are www.itc.nl or hp18.itc.nl. In this
example, itc is a subdomain of the top-level domain nl. There are
two types of top-level domains: generic and countries. Generic
domains are com (commercial), edu (educational institutions),
gov (US government), org (non-profit organizations), int
(international organizations), mil (US armed forces), and net
(network providers). Country domains use the ISO 3166 country
codes. Generic domains are generally used in the United States,
country domains elsewhere. Domain names are converted into
IP addresses, a 32-bit number used to identify an Internet host
through the Domain Name Service (DNS). IP addresses are
written in a ‘‘dotted quad’’ notation, e.g., 127.18.53.10.
TECHNICAL INFRASTRUCTURE 79
• Fragmentation. The IP splits data packets into small pieces,
thus allowing larger packets to travel on networks that can only
handle smaller packets. Fragmentation and defragmentation is
done transparently.
• Packet timeouts. Every IP packet contains a counter that is
decreased whenever it passes a router. This prohibits packets
travelling in circles forever.
• Type of service. Every IP packet can indicate that it requires a
particular type of service, such as a high transmission speed
more than reliability (for voice transfer) or high reliability more
than speed (for file transfer).
Transport layer. The transport layer allows two corresponding peers in
a network to communicate. As in the ISO/OSI model, end-to-end
protocols have been defined for the TCP/IP model. The two protocols
used are the Transmission Control Protocol (TCP) and the User
Datagram Protocol (UDP).
The TCP is a reliable connection-oriented protocol that allows two peer
entities to communicate. It makes up for the deficiencies of the IP. The
UDP, however, is used for the types of communication where speed is of
higher priority than accurate delivery. This protocol is therefore often
used for transmitting speech or video.
Application layer. There are no session and presentation layers in the
TCP/IP model. In the application layer we find user-level protocols that
allow remote login, file transfer, electronic mail, and more. The
following is a list of the most important of these protocols:
• TELNET. This is a simple terminal-oriented remote login
service. It was and still is popular with text-oriented terminal
emulations on remote machines.
• FTP. The File Transfer Protocol is one of the oldest Internet
protocols and is still in widespread use. It is based on the TCP
and is used for the transfer of text and binary data files.
Normally, users must identify themselves with a username and
password on the host machine when they want to transfer data.
Anonymous FTP is a variant of FTP that allows users to access
host machines without having a personal account on that
particular computer.
• SMTP is the Simple Mail Transfer Protocol that allows the
sending of electronic mail.
• DNS is the Domain Name Service that maps domain names to
their corresponding IP addresses.
• NFS, the Network File System, is a service that allows TCP/IP
clients to share files among each other.
• NNTP is the Network News Transfer Protocol that is used to
handle newsgroups. Electronic mail normally transfers
messages from one sender to one or more receivers. News
messages could potentially reach every Internet host and user.
• HTTP (or Hypertext Transfer Protocol) is the youngest of the
Internet protocols. It allows communication of multimedia and
hypermedia documents that are made available mainly through
the services of the WWW. These documents are mostly written
in HTML (Hypertext Markup Language).
80 GEOGRAPHICINFORMATIONSCIENCE(GIS)
6.4.4.4 Comparison between the OSI and TCP/IP Reference Models
Both reference models have much in common: the concept of a stack of
independent protocols and similar layer functionality. TCP/IP,
however, is older and was designed with an already operational network
(ARPANET) in mind. The ISO/OSI model is younger and was developed
as a reference model with the aim of defining (not necessarily building)
a network architecture. The ISO/OSI model is therefore a better vehicle
to explain and describe network architectures, although it is of less
practical importance. The TCP/IP model reflects a pragmatic approach
and can teach us a lot about practical network design.
The OSI model makes a clear conceptual distinction between services,
interfaces and protocols. The protocols of the TCP/IP model are not well
hidden and they are less clear in distinguishing different concepts.
6.5 The World Wide Web
One of the recent Internet developments which has had an enormous
impact on the market as well as on society is the World Wide Web
(WWW), or as it is generally called, the Web. It was started at the
European centre for nuclear research, CERN, out of the need to
exchange documents of various kinds among scientists spread over
many countries. A first text-based prototype was demonstrated in late
1991, and the first graphical interface, called Mosaic, was released in
1993. Such interfaces became known as web browsers. Today, two
browsers dominate the market, Netscape Communicator from Netscape
Communications Corporation, and Microsoft’’s Internet Explorer. The
World Wide Web Consortium was established in 1994 to further the
development of the Web. Its home page can be found at
http://www.w3.org.
The Web is a client-server system; on the client side it presents itself to
the user as a vast collection of web pages. These pages contain
hyperlinks to other pages. Besides text, different media may be used to
present the contents of a page, such as sound, video, or images. We
then speak of hypermedia. To view different types of media, the
browsers need external viewers, or helper applications. Frequently
these helper applications are built into the browser as a plug-in.
Machines accessing the Web through a browser must be directly
connected to the Internet, or at least able to establish a PPP connection
with an Internet provider. This is necessary because browsers establish
TCP connections with Web servers to access pages. Sites that provide
pages on the Web run a server process that listens to requests from
clients (browsers). The hypertext transfer protocol (see above) handles
these requests.
Web pages are addressed through a Uniform Resource Locator (URL).
For instance, the home page of the University of Vienna has the URL
http://www.univie.ac.at/home.htm. A URL consists of three parts: the
name of the protocol (http), the host name where the page resides
(www.itc.nl), and the name of the hypertext document (home.htm). The
following is what happens when a web page is displayed in a browser
window:
1. A user clicks a hyperlink or enters a URL.
2. The browser determines the URL (when a hyperlink was
clicked).
TECHNICAL INFRASTRUCTURE 81
3. The browser asks the domain name service (DNS) for the IP
address of the host computer.
4. DNS resolves the name and returns the IP address.
5. The browser establishes a TCP connection to the host.
6. The browser requests the document.
7. The host server sends the requested file.
8. The TCP connection is closed.
9. The browser displays all the text of the document.
10. The browser fetches and displays all images in the document.
Web pages are written in HTML (HyperText Markup Language) or
Java. HTML is useful for writing static web pages. Java, developed by
Sun Microsystems, is an object-oriented programming language that
allows a provider to write applets. A hyperlink can point to an applet
that is downloaded and run by the browser locally. Applets provide
interactivity and flexibility over static HTML pages. However, by
executing ‘‘foreign’’ code on a local machine, they also pose a potential
security threat.
83
IS need an infrastructure that enables them to function.
Whereas the technical infrastructure mainly deals with
the hardware and network architectures. The
organizational infrastructure addresses issues of procedures and
policies to make GIS work.
The need to exchange data among different systems leads to the
definition of standards for the transfer of spatial data. The
purpose of these transfer standards is to move the contents of one
GIS database to a different GIS database with a minimal loss of
structure and information. Since the early 1980’’s, many efforts
have been made to develop such standards on a local, national
and international level. Today, we have operational transfer
standards that support the dissemination of spatial data.
In a transfer process data is always physically moved from one
system to the other. A completely different approach to data
sharing is interoperability of GIS. Here, GIS software accesses
data on different systems (connected through a computer
network) through standardized interfaces. Data does not need to
be physically transferred and converted. The Open GIS
Consortium is the leading organization.
CHAPTER
7 Organizational
Infrastructure
G
84 GEOGRAPHICINFORMATIONSCIENCE(GIS)
7.1 Database Technology
Geo-information infrastructures are based on large amounts of spatial
data distributed over many agencies. It is important to provide an
organization that supports reliable and efficient sharing of the data
among many users. A simple collection of data files or a file directory is
not sufficient for this purpose. The solution is database systems, one of
the enabling technologies for geo-information infrastructures. A
database system comprises a database and a database management
system. The database (DB) is an organized collection of stored data,
and a database management system (DBMS) is software for building
and maintaining a database.
The advantages of using a database system are that the DBMS provides
the following features: persistency, efficient storage management,
recovery, concurrency control, ad hoc queries, and data security. A
persistent storage means that data exist independently of the execution
of a program, unlike data in program variables that do not survive the
execution of the program. Transactions are operations that move a
database from one consistent state to another consistent state. If, for
whatever reason, a transaction cannot be successfully completed, the
database is returned to the consistent state before the transaction
(recovery). To avoid inconsistencies caused by concurrent read and
write operations to the database by many users accessing the database
at the same time a concurrency control mechanism is employed by the
DBMS. This gives every user the impression that he/she is the only one
accessing the database.
Not all queries can be known beforehand. A DBMS must provide means
for formulating and satisfying ad hoc queries without having to write
application programs. Query languages provide this kind of function.
Furthermore, a DBMS must allow for multiple users with different
rights for accessing the database (data security).
Konzeptionelle Sicht
Externe Sicht 1 Externe Sicht 2
Interne Sicht
Konzeptionelles Schema
Externes Schema 1 Externes Schema 2
Internes Schema
Nutzer
Speicher
Figure 33: Database architecture
ORGANIZATIONAL INFRASTRUCTURE 85
7.1.1 Database architectures
A database architecture is divided into three levels: internal,
conceptual, and external level (Figure 33). This architecture was
proposed by the ANSI/SPARC Study Group on Data Base Management
Systems in 1978, and has been widely adopted. The external level is
closest to the users; the internal level is closest to the physical storage.
Individual users may have different external views on the database.
There is only one conceptual view and one internal view. The views are
defined by schemas using a data definition language (DDL). The data
manipulation language (DML) is used to describe database objects’’
processing.
7.1.2 Data models
Databases store models of a perceived reality. In the design process of a
database, several data models are being used to describe subsets of the
real world at different levels of abstraction. A data model describes the
contents, structure, and meaning of data. They can be classified into
conceptual-, logical-, and physical data models (Figure 34). A database
designer starts with an abstract subset of the real world (external model
or application model) which is subsequently mapped into a conceptual
model. The best known conceptual data model is the entity-relationship
model. This, in turn, is mapped into a logical data model, which can be
implemented with a commercial database management system. The
most popular is the relational data model, which was introduced in
1970. It uses a simple data structure, the relation. Relations are
tables, a concept that is easily understood by human common sense.
Wirklichkeit
Externes Modell
(Anwendungsmodell)
Konzeptionelles
Modell
Logisches Modell
Internes Modell
Konzeptionelles Schema
Logisches Schema
Internes Schema
DBMS unabhängig
DBMS spezifisch
Figure 34: Models in database design
The database language that supports the relational data model is SQL
(or Structured Query Language). It became an ANSI standard as SQL1
in 1986 (ANSI X3.135) and an ISO standard in 1987. A revised and
extended version of the standard was approved as SQL2 (also known as
SQL92) in 1992, and SQL3 is nearing its completion. SQL is both a
data definition and data manipulation language and is supported by all
major relational DBMS vendors.
86 GEOGRAPHICINFORMATIONSCIENCE(GIS)
7.1.3 Distributed databases
In a geo-information infrastructure, many databases are distributed
over numerous organizations and agencies. A distributed database
consists of a number of sites interconnected through a communication
network. Every site runs an autonomous database management
system. Local applications run only on a local database, global
applications span some or all the sites of the distributed database.
A distributed database looks like a local database to the user.
Important concepts of distributed databases are distribution
transparency, fragmentation, and replication. Distribution
transparency means that a user does not see where the data in the
database are physically located. Even if the same data are stored in
duplicate at different locations for reasons of efficiency (replication) or
different columns of a table are physically located at different local
databases (fragmentation), the user always perceives a central
database.
Whereas traditional database design involves conceptual-, logical-, and
physical design, for distributed databases we also have distribution
design. There are two possible approaches to the design of distributed
databases: top down and bottom up. The top-down design is applied to
new databases that are designed from scratch (Figure 35).
Conceptual schema
Fragmentation
schema
Allocation schema
Conceptual design
Logical schema 1
Physical schema 1
Logical schema 2
Physical schema 2
Fragmentation design
Allocation design
Local logical design
Local physical design
Figure 35: Top-down distributed database design
Very often, however, individual databases already exist, and have been
used as such for a long time. When the need arises to integrate these
databases for a higher level application, a special kind of distribution
architecture comes into place which is called federated databases. The
federated database design approach is presented in Figure 36.
7.2 Clearinghouse
The United States Federal Geographic Data Committee (FGDC) defines
a clearinghouse as a ‘‘system of software and institutions to facilitate the
discovery, evaluation, and downloading of digital geospatial data.’’ Such
a clearinghouse usually consists of a number of servers on the Internet
that contain information about available digital spatial data known as
metadata.
ORGANIZATIONAL INFRASTRUCTURE 87
External schema
Federated schema
Export schema Export schema Export schemaExport schema
Local schema A
Component
database A
Component
schema
Local schema B
Component
database B
Component
schema
Schema definition
Schema translation
Schema integration
Figure 36: Federated database design
A clearinghouse is an example of a client-server architecture. The
server machines hold the metadata, and the clients request information
about the availability of spatial data by visiting the server nodes,
usually through a Web browser. In the ideal case not only the metadata
but also a link to the data itself is provided by the server. There is no
hierarchical relationship among the servers. They act as peers within
the clearinghouse activities. Servers are usually installed at providers,
such as national mapping agencies, at different organizational levels. A
typical clearinghouse architecture consists of many interoperating
metadata servers.
Providers of a clearinghouse must have access to a machine directly
connected to the Internet. This server holds the metadata and must run
server software. One protocol selected to provide search interoperability
among different servers is the ISO 10163-1995 (or ANSI Z39.50-1995)
search and retrieve protocol. The Z39.50 was initially developed for the
library community. It contains client and server software that
establishes a connection, relays a query, returns the query result, and
presents retrieved documents in various formats. Figure 37 shows the
clearinghouse concept.
Metadata
Server 3
Client Client
Client
Internet
Metadata
Server 2Metadata
Server 1
Figure 37: Clearinghouse concept
88 GEOGRAPHICINFORMATIONSCIENCE(GIS)
7.3 Spatial Data Transfer
When we transfer data from one system to an other, we might
encounter various problems: transfer media are not compatible, physical
and logical file formats are different, we might not have any information
concerning the quality of the dataset. Moreover, do we need translators
from and into every format that we might have to deal with. In the
worst case, we need for n different systems
2
)1( −nn
translators. Using
a standard reduces this number to n2 , because we only need one
translator ——between the system and the standard—— for each system12.
Figure 38 illustrates this situation.
SDTS
)( 2
nO )(nO
Figure 38: No standard versus a transfer standard
The solution to these problems is to exchange spatial data in a
standardized way thereby keeping as much as possible of the structure
and relationships among the features in the dataset.
7.3.1 Spatial Data Transfer Process
A data transfer standard defines a data model for the transfer as well as
a translation mechanism from a GIS to the exchange format and into
the exchange mechanism. In transferring data from system X to system
Y we need a translator that converts the contents of the database in
system X into the model of the spatial data transfer standard (SDTS)13.
The components of the model are represented by modules that are
converted into a computer readable format. A standard often used is
the ISO 8211 Data Descriptive File. On the receiving side, system Y
needs a translator that converts the data from the transfer standard
into the database. In the ideal case, after the transfer is completed, no
further processing is needed. The data are ready to use.
12 We need n translators to translate into the standard and n to translate from the
standard, which makes it n2 .
13 We use here SDTS as abbreviation for the term spatial data transfer standard.
This should not be confused with the FIPS 173 SDTS, the federal spatial data
transfer standard of the United States of America.
ORGANIZATIONAL INFRASTRUCTURE 89
System
X
T
r
a
n
s
l
a
t
o
r
X
Support
Software
Encode
Support
Software
Decode
T
r
a
n
s
l
a
t
o
r
Y
System
Y
Source Target
SDTS
Figure 39 Spatial data transfer process
7.3.2 Examples of Spatial Data Transfer Standards
The following table gives an overview of some exchange standards of
different countries and organizations:
Table 11: Examples of spatial data transfer standards
NATO DIGEST (Digital Geographic Exchange Standard) of the
Digital Geographic Information Working Group (DGIWG);
members are Belgium, Canada, Denmark, France, Germany,
Italy, The Netherlands, Norway, Spain, United Kingdom,
USA
United Kingdom NTF (National Transfer Format)
USA SDTS (Spatial Data Transfer Standard)
France EDIGéO
These standards are accepted as federal or national standards based on
international ISO standards.
Industry standards are for instance DLG (digital line graph of the
USGS), DXF (AutoCAD Format). Industry standards are frequently
used standards that were introduced by a company or organization but
which are not accepted as national or federal standards.
91
patial information infrastructures (also called geoinformation
infrastructures) are being built in many
countries. They comprise the setting up of an
organizational and technological framework to share and
disseminate geo-spatial data. One crucial part in this endeavor is
the introduction of GIS technology into an organization.
This chapter describes the problems encountered and procedures
to be followed when an organization goes GIS.
CHAPTER
8 GIS Technology in
Organizations
S
92 GEOGRAPHICINFORMATIONSCIENCE(GIS)
8.1 Prerequisites and Problems
In setting up spatial information infrastructures issues of organization,
policy, hardware and software procurement, networking, training and
education have to be addressed. The implementation of GIS software is
one of the important steps in this process.
The implementation of a GIS requires a thorough analysis of the
structure, workflow and equipment needed of the organization where
the system will be installed. Experience shows that certain problems
occur frequently in implementing a GIS. The following points should
always be considered:
• A GIS is not only hard- and software, but requires also personnel
and organization.
• Many customers have big problems in describing the functioning,
workflow, and required products of their own organization.
• Hard- and software is often procured without a previous analysis
and only then, it is considered what could be done with the
system.
• Very often, it is overlooked that a system without data is useless.
Data capture is up to ten times more expensive than the
equipment.
• An ““objective”” selection of systems is very often impeded by policy
decisions.
• Trained personnel are very important for running the system.
There are many different tasks, e.g., a digitizing operator needs
not know anything about databases, but has to know the
digitizing sub-system of the GIS very well.
8.2 Implementation Strategy
The successful implementation of a GIS can be described as a six-step
process (after ARONOFF 1989).
Awareness. The advantages of GIS are recognized and potential
users are identified.
Developing system requirements. A project for the introduction of
GIS is defined and systematic work is started, often by installing a
““modernization committee”” (information gathering, user
requirements, visit to trade fairs, product demonstrations).
System evaluation. Based on the system requirements potential
systems are selected and subjected to a benchmark test. Sometimes it
is also useful to conduct a pilot study.
System justification and development of an implementation
plan. After system evaluation a decision has to be made in favor or
against the use of GIS technology. This can be supported by a cost-
GIS TECHNOLOGY IN ORGANIZATIONS 93
benefit analysis. If a decision is made for GIS then an
implementation plan must be designed.
System acquisition and start-up. System acquisition and
installation, training, building of the database
Operational system. The database is ready, work procedures are
automated and standardized, training and education of staff,
maintenance
95
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Worboys M.F., 1995, GIS: A Computing Perspective. Taylor & Francis.
97
absolute space. See space
ALU. See arithmetic and logic unit
arc/node structure, 52
arithmetic and logic unit, 62
atom, 28
atomist, 28
attribute, 42
Auto Carto, 15
benchmark test, 92
boundary, 38
boundary model, 54, 55
bucket methods, 55
buffering, 60
Burrough, Peter, 16, 95
cartographic generalization. See
generalization
cell, 38
cell method, 48
central processing unit, 62
chain code, 53
chronon, 43
client, 67
co-domain, 33
communication devices, 63
completeness. See data quality
complex
cell, 38, 39
simplicial, 38
computer architecture, 63
computer network, 67
computer science, 59
consistency, 38
contiguity, 60
contour, 60
Corbett, James, 16, 95
COSIT, 17
CPU. See central processing unit
Dangermond, Jack, 16, 95
Data, 27
data handling, ix, 15, 21, 24, 57
data model, 32
spatiotemporal, 42
topological, 54
data quality, 23, See metadata
completeness, 23
lineage, 24
logical consistency, 23
positional accuracy, 23
purpose, 23
temporal accuracy, 23
thematic accuracy, 23
data structures, ix, 47, 48, 52, 53, 55, 56
spatial, 16
database, 17, 25, 48, 57, 58, 59, 83, 88,
93
relational, 16
DIGEST, 89
digital landscape model, 34
digitizer, 65
cursor, 65
digitizing, 18, 24, 52, 58, 92
digitizing tablet. See digitizer
DLG, 89
DLM. See digital landscape model
domain, 33
DTM. See digital terrain model
DXF, 89
dynamic theory, 29
edge matching, 58
EDIGEO, 89
elements, 29
empirical reality, 30
Euclidean geometry, 29
Euclidean space, 37
event-based model, 44
feature, 22
feature attribute, 22
feature attribute type, 22
feature relationship, 22
feature type, 22
Field-based models, 40
film writer, 66
floating-point unit, 62
FPU. See floating point unit
FTP, 67
Index
98 GEOGRAPHICINFORMATIONSCIENCE(GIS)
fuzzy logic, 35
generalization, 32, 34, 59
geographic information system, 12, 13,
18
geographic space. See space
geographic time. See time
geoinformatics, ix, 13, 15, 17
geo-information infrastructure. See
spatial information infrastructure
geo-information system. See geographic
information system
geomatics, 13
geometric primitives, 17
geo-relational model, 54
GIS. See geographic information
system
granularity, 43
graph, 39
planar, 40
graph theory, 39
grid directory, 51
grid file, 51
hardcopy, 19
hardware, 62
homeomorphism, 37
HTML, 17
HTTP, 17, 67
identity of opposites, 28
information, ix, 12, 17, 18, 19, 21, 22,
23, 52, 54, 57, 58, 83, 88, 91, 92
information infrastructure. See
infrastructure
infrastructure, 15, 19
interior, 38
Internet, 17, 18
interoperability, 83
ISO 8211, 88
Java, 17, 67
k-d tree, 50, 51, 56
keyboard, 64
land information system. See
geographic information system
layers, 26, 58
lineage. See data quality
linear quadtree, 55
linear scales, 51
locations, 41
logical consistency. See data quality
map, 31
mathematics, 15
Maxwell, James Clerk, 30
memory, 62
metadata, 22
application schema information, 23
data constraint information, 22
data quality, 22
distribution information, 22
extension information, 23
feature catalog information, 22
identification information, 22
maintenance information, 22
portrayal catalogue information, 22
reference system information, 22
spatial representation information, 22
modem, 63
monitor, 64
morphism, 33
Morton order, 15
mouse, 64
multi-purpose cadastre. See geographic
information system
NCGIA, 16
network analysis, 60
NTF, 89
object, 42
object-based models, 40
octree, 55
operating system, 66
overlay, 59
peripheral devices, 63
phenomena, 26
man-made, 26
natural, 26
Platonic solids, 29
plotter
electrostatic plotter, 66
pen plotter, 66
point quadtree, 49
point-mode digitizing. See digitizer
PR quadtree, 51
printer
ink-jet printer, 65
laser printer, 65
proximity, 60
purpose. See data quality
pyramid, 54
quadtree, 15, 49, 50, 51, 53, 54, 55, 56
real world, 22, 25, 26, 27, 28, 29, 30, 31,
32, 33, 34, 35, 36, 58
reasoning, 57
region quadtree, 54
register, 62
relationship, 42
relative space. See space, See space
resolution, 64, 65
Scaleless, 33
scanner, 65
INDEX 99
schema
conceptual, 36
logical, 36
physical, 36
SDH, 17
SDSS. See spatial decision support
system
SDTS, 88, 89
seamless, 33
server, 67
simplex, 37
SIT. See spatial information theory
skeleton, 39
snapshot model, 44
softcopy, 19
software, 66
space, 14, 27
absolute, 29
metric, 37
relative, 29
topological, 37
space-time composite model, 44
space-time cube model, 43
spatial data. See data
spatial decision support system, 15
spatial information infrastructure, 92
spatial information system. See
geographic information system
spatial information theory, 14, 16, 17
spatial phenomena. See phenomena
spatial reasoning, 14
spatiotemporal data model. See data
model
spatiotemporal object model, 44
SSD, 17
storage devices, 63
stream-mode digitizing. See digitizer
SVGA, 66
tabletop space. See space
TCP/IP, 67
temporal accuracy. See data quality
TFT. See thin-film transistor
thematic accuracy. See data quality
theme. See layer
thin-film transistor, 64
time, 27
absolute, 43
branching, 43
continuous, 42
cyclic, 43
database, 43
discrete, 42
fixed, 43
implied, 43
linear, 43
relative, 43
transaction, 43
world, 43
Tomlinson, Roger, 15
topological consistency. See consistency
topological mapping. See
homeomorphism
topology, 15, 17, 35, 37, 52, 59
transcendental idealism, 30
UMTS. See Universal Mobile
Telecommunications System
uncertainty, 35
Universal Mobile Telecommunications
System, 68
usage. See data quality
VGA, 66
view, 36
visibility, 60
void, 28
voxel, 55
WAP. See Wireless Application Protocol
Wireless Application Protocol, 68
workflow, 92
World Wide Web, 17, 18
WWW. See World Wide Web
XML, 17
XVGA, 66
Zeno, 28