Full Terms & Conditions of access and use can be found at
http://www.tandfonline.com/action/journalInformation?journalCode=tgis20
Download by: [37.33.62.160] Date: 25 May 2017, At: 03:30
International Journal of Geographical Information
Science
ISSN: 1365-8816 (Print) 1362-3087 (Online) Journal homepage: http://www.tandfonline.com/loi/tgis20
Enhancing spatial accuracy of mobile phone data
using multi-temporal dasymetric interpolation
Olle Järv , Henrikki Tenkanen & Tuuli Toivonen
To cite this article: Olle Järv , Henrikki Tenkanen & Tuuli Toivonen (2017) Enhancing spatial
accuracy of mobile phone data using multi-temporal dasymetric interpolation, International Journal
of Geographical Information Science, 31:8, 1630-1651, DOI: 10.1080/13658816.2017.1287369
To link to this article: http://dx.doi.org/10.1080/13658816.2017.1287369
© 2017 The Author(s). Published by Informa
UK Limited, trading as Taylor & Francis
Group
View supplementary material
Published online: 07 Feb 2017. Submit your article to this journal
Article views: 704 View related articles
View Crossmark data
Enhancing spatial accuracy of mobile phone data using
multi-temporal dasymetric interpolation
Olle Järv , Henrikki Tenkanen and Tuuli Toivonen
Department of Geosciences and Geography, University of Helsinki, Helsinki, Finland
ABSTRACT
Novel digital data sources allow us to attain enhanced knowledge
about locations and mobilities of people in space and time.
Already a fast-growing body of literature demonstrates the applicability
and feasibility of mobile phone-based data in social
sciences for considering mobile devices as proxies for people.
However, the implementation of such data imposes many theoretical
and methodological challenges. One major issue is the
uneven spatial resolution of mobile phone data due to the spatial
configuration of mobile network base stations and its spatial
interpolation. To date, different interpolation techniques are
applied to transform mobile phone data into other spatial divisions.
However, these do not consider the temporality and societal
context that shapes the human presence and mobility in space
and time. The paper aims, first, to contribute to mobile phonebased
research by addressing the need to give more attention to
the spatial interpolation of given data, and further by proposing a
dasymetric interpolation approach to enhance the spatial accuracy
of mobile phone data. Second, it contributes to population modelling
research by combining spatial, temporal and volumetric
dasymetric mapping and integrating it with mobile phone data.
In doing so, the paper presents a generic conceptual framework of
a multi-temporal function-based dasymetric (MFD) interpolation
method for mobile phone data. Empirical results demonstrate how
the proposed interpolation method can improve the spatial accuracy
of both night-time and daytime population distributions
derived from different mobile phone data sets by taking advantage
of ancillary data sources. The proposed interpolation method
can be applied for both location- and person-based research, and
is a fruitful starting point for improving the spatial interpolation
methods for mobile phone data. We share the implementation of
our method in GitHub as open access Python code.
ARTICLE HISTORY
Received 20 July 2016
Accepted 23 January 2017
KEYWORDS
Mobile phone data; call
detail records; spatial
interpolation; dasymetric
modelling; spatio-temporal
data modelling
1. Introduction
To better understand how societies function and to improve social justice and environmental
sustainability, we need to attain enhanced spatio-temporal knowledge about the
locations and mobilities of people (Hägerstrand 1970, Sheller and Urry 2006, Cresswell
CONTACT Olle Järv olle.jarv@helsinki.fi
The supplemental material for this article can be accessed here.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE, 2017
VOL. 31, NO. 8, 1630–1651
https://doi.org/10.1080/13658816.2017.1287369
© 2017 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited.
and Merriman 2011, Kwan 2013). This knowledge has become possible due to the global
adoption of mobile information and communication devices in the age of the Big Data,
while human interactions through mobile devices create digital footprints of people in
space and time (Kitchin 2014). Thus, ubiquitous mobile phones are considered as proxies
for people (Asakura and Hato 2004, González et al. 2008, Ahas et al. 2010).
For example, passively collected time-stamped and location-aware information on
mobile phone use, such as call detail records (CDRs), is rapidly enhancing our understanding
about individual human mobility (Yuan et al. 2012, Järv et al. 2014). Such data
is applied to understand the relation between human mobility and social networks
(Phithakkitnukoon et al. 2012), ethnic differences in human activity spaces (Järv et al.
2015) and for estimating travel times to public facilities (Wesolowski et al. 2015). At the
population level, flourishing literature demonstrates how CDR data is used for investigating
spatial distributions and temporal dynamics of local and national population
(Reades et al. 2009, Kang et al. 2012, Deville et al. 2014), seasonal migration patterns
(Silm and Ahas 2010), spatial structures of cities (Louail et al. 2014), traffic flow (Wang
et al. 2012), disease risks (Wesolowski et al. 2012) and socio-economic levels of the
population (Soto et al. 2011, Blumenstock et al. 2015).
However, investigation of human presence and mobility based on CDR data imposes
theoretical and methodological challenges. For example, one challenging issue is representing
the whole population while not everyone is using mobile phones for different
reasons (e.g. young children, elderly). Socio-economic biases may exist in a sample while
network operators have different segmentation strategies and service costs. Phone
users’ call activity habits and patterns vary in space and time according to individuals’
socio-economic characteristics, preferences, lifestyle, habits and work attributes (Castells
et al. 2007). Also, authority (legislation) and environmental (physical extent of mobile
network) domains may limit where and when mobile phones can be used. These aspects
may influence research findings derived from CDR data while creating spatial, temporal
and socio-economic biases at both the individual and aggregated levels (Yuan et al.
2012, Wesolowski et al. 2013, Järv et al. 2014, Zhao et al. 2016).
One important methodological challenge is the spatial perspective of CDR data since the
spatial accuracy of given data depends on the spatial distribution of mobile network base
stations, which is not equally distributed in space and changes over time. Furthermore,
while more advanced interpolation techniques are applied (e.g. Louail et al. 2014, Pei et al.
2014), they do not consider the environmental and societal contexts that shape the human
presence and mobility in space and time. To obtain more precise spatio-temporal knowledge
about human presence and mobility, we need to develop a spatial interpolation
technique for CDR data that considers the social structures and human practices.
This paper seeks to contribute to mobile phone-based research in the social sciences
by addressing the need to give more attention to the spatial perspective of passively
collected mobile phone data (e.g. CDR data) and by overcoming the uneven spatial
accuracy problem in base station coverage areas. We suggest applying a dasymetric
interpolation approach that is well known in population mapping studies. With this
paper we aim: (1) to put forward a generic conceptual framework for a multi-temporal
function-based dasymetric (MFD) interpolation method for mobile phone data; and (2)
to empirically investigate how and to what extent the proposed method improves a
night-time and daytime population distribution derived from mobile phone data. In
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1631
doing so, this paper also contributes to dasymetric population modelling by combining
spatial, temporal and volumetric mapping techniques while incorporating a functionbased
population allocation with mobile phone data. To our knowledge, this is the first
attempt to comprehensively apply a dasymetric technique for interpolating mobile
phone data.
Next, we provide a brief overview on how the spatial perspective is considered in
passively collected mobile phone-based research. We then introduce a dasymetric
interpolation approach and put forward the conceptual framework of the proposed
dasymetric method and demonstrate the empirical results in the case of Tallinn, Estonia.
Finally, we discuss the method, obtained outcomes and future steps.
2. Background and literature review
2.1. The spatial perspective of passively collected mobile phone data
To a large extent, the spatial accuracy of passively collected mobile phone data, such as
CDR data, is determined by the locations of base stations – the geographical location of
each mobile phone use is allocated to the base station that provided the network signal.
Hence, the spatial accuracy of data corresponds to the coverage areas of a mobile
network base station, which is spatially not fixed and varies according to the density
of base stations (for some exceptions, see, e.g. Calabrese et al. 2013). The simplest way
to examine given data from spatial perspective is to use (aggregated) geographic
locations of base stations (antennae) as point-based units. Latter approach is widely
applied in individual mobility studies (González et al. 2008, Yuan et al. 2012, Järv et al.
2014) and its relation to social phenomena and processes (Phithakkitnukoon et al. 2012,
Demissie et al. 2013, Järv et al. 2015, Wesolowski et al. 2015). However, this type of data
is difficult to integrate and validate with other data sources.
Certainly, CDR data is further assigned to discrete areal polygons using different techniques.
Most commonly, a spatial overlay technique is used to attribute given data to
predefined spatial (e.g. administrative and statistical) units given the locations of a base
station (Ahas et al. 2010, Wesolowski et al. 2012, Trasarti et al. 2015, Williams et al. 2015).
Conventional choropleth mapping allows for integrating and validating the mobile phone
data with other attribute data in a given spatial division. However, choropleth mapping
ignores the fact that: (1) the spatial distribution of base stations does not depend on
predefined spatial units – an administrative unit may have none or many base stations;
and (2) the spatial division of coverage areas and predefined spatial units does not utterly
coincide – a base station located at one predefined spatial unit may provide a mobile
network signal partly (or entirely) to other predefined units and thus represent CDR data
from an area that is larger than where the given base station is actually located.
Another widely applied technique is to interpolate the study area to theoretical
coverage areas (polygons) of a base station using a Voronoi tessellation (Delaunay
triangulation) technique in Euclidean space (e.g. Wang et al. 2012) with some exceptions
(see Iqbal et al. 2014, Jacobs-Crisioni et al. 2014). This way, station-based mobile phone
data can be attributed to spatial units indicating base stations’ coverage areas. The use
of Voronoi tessellation provides more accurate spatial distribution as compared to
previous approaches; however, the resulting spatial division is not compatible with
1632 O. JÄRV ET AL.
other existing spatial divisions of administration and, hence, does not allow data
integration or validation with data in other spatial divisions. Moreover, the two overlay
techniques described above depend on the locations of base stations, and any changes
in the spatial configuration of a mobile network over time may create biases in both
longitudinal and repeated research.
The latter weaknesses can be overcome by applying a straightforward areal weighting
spatial interpolation technique; the spatial division of coverage areas and its attributes
are simply interpolated by areal intersection to desired spatial units such as
statistical grids (Candia et al. 2008, Louail et al. 2014) or administrative areas (Soto
et al. 2011, Deville et al. 2014). Areal weighting is also applied the other way round;
predefined spatial units and its attributes are interpolated into coverage areas of base
stations (Kang et al. 2012, Doyle et al. 2014). Indeed, using statistical grids or administrative
areas enables data integration and validation with other data sources in any
spatial division. However, this method assumes that mobile phone data as a proxy for
people is equally distributed on a plane surface without considering temporality, environmental
context or societal structures that shape human presence and mobility in
space and time.
Several attempts are made to apply more sophisticated spatial interpolation techniques
to enhance the spatial accuracy of mobile phone data within a coverage area of a
base station. Ratti et al. (2006) applied a neighbour linear interpolation method based on
centroids of a Voronoi polygon to represent the probable population in a raster surface.
Csáji et al. (2013) refined the spatial locations of people within each Voronoi polygon
based on the probability density calculation from the maximum likelihood estimation
and the signal decay of a base station. Similarly, Pei et al. (2014) applied a probabilistic
distribution approach to refine the spatial accuracy of people within each Voronoi
polygon to grid cells using the inverse distance weighting. Nevertheless, the given
sophisticated spatial interpolation techniques still assume space as a plane surface
and people in space as a function of the distance from a base station. To the authors’
knowledge, only in the supporting information in Deville et al. (2014) was there a
preliminary attempt made to apply the dasymetric technique by ancillary land-use
data to interpolating CDR data.
2.2. Dasymetric interpolation approach and the integration of time
In population research, several spatial interpolation methods are used to go beyond
aggregated and predefined census tract and administrative units for finer-scale spatial
resolution (Eicher and Brewer 2001, Mennis 2003, Langford 2006). Given the feasibility
and reliability of widely used dasymetric mapping, the dasymetric interpolation technique
is one of the best approaches for refining the spatial resolution of population data
(Wu et al. 2005, Mennis and Hultgren 2006). The dasymetric technique for refining
population distribution is successfully applied, for example, for studying tourism (Vaz
and Campos 2013), accessibility (Langford et al. 2008, Shannon 2014), risk exposures and
disaster management (Linard and Tatem 2012, Smith et al. 2014).
In general, the dasymetric spatial interpolation technique transforms population data
from one set of spatial units (source zones) into another spatial division (target zones)
using additional ancillary data sources – data that can be related directly or indirectly to
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1633
the spatial presence of people for assisting the interpolation. Physical environmental
(land use, land cover and zonal data) information is widely used to extract a populated
area from a non-populated area and further conduct spatial weighting based on
selected attributes (Dobson et al. 2000, Ruther et al. 2015). Additional ancillary variables
considered in dasymetric mapping include the national mailing information (Langford
et al. 2008), addresses of businesses (Greger 2015), road network proximity and traffic
counts (Smith et al. 2014), points-of-interests (Bakillah et al. 2014), night-imagery
(Dobson et al. 2000), detailed cadastral maps (Maantay et al. 2007) and building functions
with vertical and volumetric information (Aubrecht et al. 2009, Greger 2015, Biljecki
et al. 2016).
In recent years, dasymetric interpolation techniques have witnessed fast development
given new data sources, improved statistical assessment for estimating accuracy, and
advancing multiple areal interpolation with relational data sources, 3D modelling and
spatio-temporal interpolation (see, e.g. Nagle et al. 2014, Ruther et al. 2015, Biljecki et al.
2016, Mennis 2016). Interestingly, the concept of the dasymetric interpolation technique
is well suited for integrating the temporal perspective (hourly, weekday and seasonal
rhythms) for population mapping (e.g. Martin et al. 2015, Mennis 2016). Temporally
sensitive multidimensional dasymetric interpolation modelling of a population based on
census data has already been elaborated upon since 2000 (Dobson et al. 2000, Aubrecht
et al. 2009). Since then, different conceptual frameworks have been proposed for
dynamic population distribution modelling while also distinguishing population subgroups,
such as local residents, visitors and transit (Bhaduri et al. 2007, Aubrecht et al.
2014, Martin et al. 2015, Mennis 2016). In these studies, the spatial distribution of the
population is interpolated as a function of time, while the spatial layer is related to timedependent
ancillary data sources about human presence and activities (e.g. time use
survey, activity travel diary).
However, these advanced dasymetric interpolation methods do not consider the
verticality of a built environment, and vice versa, spatially advanced building population
mapping methods do not consider temporality (Ural et al. 2011, Biljecki et al. 2016),
except a study by Greger (2015). In urbanised societies, the vertical dimension and
volumetric data about a built environment are two of the essential environmental
attributes that determine the spatial distribution of people (Ahola et al. 2007, Biljecki
et al. 2016).
Thus, we propose a conceptual framework of the MFD interpolation for mobile phone
data that also takes into account the verticality of a built environment, similar to Greger
(2015). This enables obtaining more accurate spatio-temporal information about human
presence and mobility derived from mobile phone data such as CDR, which increases
the applicability and reliability of given data.
3. The conceptual framework of an MFD interpolation for mobile phone
data
Since mobile phone data as a proxy for people is predominantly estimated by coverage
areas of a base station (or antennae) as source zones, we propose a generic MFD
interpolation method that first disaggregates the distribution of mobile phone data
within each coverage area, and then aggregates mobile phone data to the desired
1634 O. JÄRV ET AL.
spatial division as target zones using different spatially and temporally sensitive ancillary
data sources (Figure 1). At minimum, three ancillary data sources are needed – a spatial
layer with land-cover data, volume (height) of built environment and time-dependent
human activity data.
In general, the MFD interpolation method for mobile phone data has five modelling
steps (Figure 2): (I) the preparation of a physical surface layer; (II) the spatial disaggregation
of a physical surface layer by both source zones (i.e. coverage areas of a base
Figure 1. The representation of MFD interpolation method – point-based mobile phone data at base
station level (a) – is usually assigned to theoretical coverage areas of a base station as source zones
(b) and interpolated to target zones using simple areal weighting method (c). The proposed MFD
method disaggregates mobile phone data within each source zone using ancillary data (d) to
transform it to desired target zones (e).
Figure 2. The conceptual framework for the proposed MFD interpolation method for refining mobile
phone data as a proxy for people in five modelling steps.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1635
station) and target zone layers; (III) the integration of time-dependent human activity
data with a disaggregated physical surface layer; (IV) the integration of mobile phone
data with a disaggregated physical surface layer; and (V) the spatial aggregation of
disaggregated surface layer to desired target zones as the output of the MFD method. In
the MFD method, any desired spatial division can be used as target zones according to
any research need – statistical grid cells (as in this case study), census tracts, administrative
units, transport analysis zones or any other spatial division. Next, each modelling
step is described in the following subsections.
3.1. The preparation of physical surface layer
First, the physical surface layer is prepared, where (1) the vertical dimension is
incorporated and (2) a functional attribute is assigned, which can be linked to
human activity data. The vertical dimension is essential since the verticality of a
built environment determines the population density whereas the best indicator is
the total floor area of a building (e.g. Biljecki et al. 2016). The method is flexible
regarding the vertical dimension, and input data can be either ready-made data or
estimated (from building footprints and heights) for the buildings’ total floor area
(m2
) or volume (m3
).
The activity function attribute of each spatial unit enables linking the estimated
human presence by activity type in time with the physical surface layer. The method
is flexible regarding the detail level of activity function classification as long as it is
possible to link with time usage of people by classified human activity data (Eurostat
2009, UNSD 2016) and corresponds to available input data and research needs. We
propose to apply six activity function types – residential, work and school, retail and
service, transport, restricted area and other areas, since these types have distinguished
temporal usage patterns.
The required physical surface layer may be already available in case of detail cadastral
data, including the vertical dimension or 3D models on a built environment (Biljecki et al.
2016). However, there are several ways to prepare a given layer, for example, by
integrating land-use and/or land-cover data with building information; building footprints
may be derived from cadastral maps, satellite imagery or volunteered geographic
information databases such as OpenStreetMap (OSM) or GoogleMaps. Building height
can be derived from areal imagery and remote sensing such as LIDAR measurements
(Alahmadi et al. 2013). While information about the vertical dimension at building level
is not available, more rough estimates may be applied such as the average height of a
city block or a neighbourhood.
Building functions related to human activity types in the spatial layer can be further
enhanced with additional crowdsourced data such as OSM and online address (business)
registers (Greger 2015), and even assign several functionalities to a building if such data
is available. Finally, each spatial unit in the physical surface layer includes three attributes:
activity function type a; surface unit type (building; non-building) u; and a vertical
dimension (number of floors; height).
1636 O. JÄRV ET AL.
3.2. The spatial disaggregation by source and target zones
In the second step of the MFD method, two spatial layers are applied in addition to the
physical surface layer – the spatial division of mobile phone data as source zones and
the spatial division of desired spatial units as target zones (Figure 1). The spatial division
of mobile phone data is usually represented by the theoretical coverage areas of a base
station using a Voronoi tessellation method. The desired spatial division of target zones
is applied according to a research need.
First, a geometric union technique is applied for these three spatial layers to disaggregate
the physical surface layer into subunits in order to designate each subunit to a
unique source zone j and target zone z. Second, the area for each disaggregated subunit
polygon is calculated, which enables calculating an estimated total floor area FA for
each spatial subunit s by multiplying the area with the vertical dimension regarding the
number of floors or height, whereas in both cases the default value is set to 1 for nonbuilding
units. This allows for a calculation of a relative floor area RFA for each subunit s
within a base station j given the total sum of estimated total floor area FA of all subunits
within a base station j as follows:
RFA ¼
FAs
P
FAs 2 j
(1)
At this point, each disaggregated subunit in the physical surface layer has three additional
attributes: source zone ID, target zone ID and a relative floor area RFA within a
coverage area of a base station to which a given unit belongs.
3.3. The integration of time-dependent human activity data
In the third step of the MFD method, the disaggregated physical surface layer is linked
with time-dependent human activity data based on the activity function type of a spatial
subunit. The link allows for a more accurate interpolation of mobile phone data between
disaggregated spatial subunits within each base station coverage area, while taking into
account the probabilities of human activity types.
In general, the probability of people to conduct certain activities at certain spatial
subunits at a certain time can be acquired from a time-use survey (Eurostat 2009, UNSD
2016) or activity travel diary data (see Schönfelder and Axhausen 2010). Given data
sources allow for an extraction of the average distribution of people by activity type in
time that can be associated with spatial subunits based on the activity location type. Any
classification of human activity types can be applied, as long as the activity classification
is congruent with activity function types of a spatial subunit, such as associating home
activities with residential areas. Ideally, the data source is from the case study area.
However, the activity classification can represent a more general (e.g. national) level or
even from a neighbouring country or elsewhere, as long as the classification represents
human activity patterns similar enough for the case study area given the similar social
and environmental contexts.
The temporal resolution of human activity data in the MFD method depends on
available data sources and research needs. Thus, the basis of the method is the
distribution of people by activity type at given time intervals, for example, hourly
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1637
intervals. The estimated distribution of people by activity type (e.g. being at home) in a
given time unit can be further refined if input data contains more fine-grained information
about weekly (working day vs. weekend) and seasonal (e.g. summer vs. winter;
holiday weeks and months) variations in mobility and activity patterns of people.
In this modelling step, an estimated human presence EHP in time unit t is calculated
for each disaggregated spatial subunit s within a base station j given its RFA and timedependent
probability for human presence, which is a combination of a daily hour factor
H, a weekday factor W and a seasonal (monthly) factor M, according to a human activity
function type a and spatial unit type u (land or building). The total sum of EHP of
disaggregated spatial subunits within each coverage area of a base station j in time unit
t is 1. EHP is calculated as follows:
EHPt ¼ ðHauWauMauÞ Â RFAsj (2)
MFD method can be used to calculate EHP for the whole population, whereas it can also
be calculated separately for each subgroup of a population (e.g. youth, working-age
people, and elderly; local vs. non-locals) if the given division is available for both human
activity data and mobile phone data. Another possibility is to calculate EHP separately
for local residents and non-residents (e.g. transit, visitors and tourists). This would
provide a more accurate refinement of population distribution within a base station.
It is possible to derive the hourly distribution of people by activity type from time-use
survey data. Activity travel diaries can provide weekday and seasonal differences in the
probable human presence by activity type (Schönfelder and Axhausen 2010). Seasonal
differences can also be derived from longitudinal time-activity studies where personal
time use in indoors and outdoors (e.g. Hussein et al. 2012) can be linked to spatial unit
type (building, land) as an indication for seasonal influence. This combines hourly,
weekday and seasonal factors for calculating the distribution of people by activity
type as a function of time.
3.4. The integration of mobile phone data
In the fourth step of the MFD method, mobile phone data is linked to spatial subunits in the
physical spatial layer by base station j (source zones) and disaggregated into each spatial
subunits according to its EHP by given time unit t (e.g. hourly intervals as in this study).
While mobile phone data is considered as a proxy for population mapping, it is recommended
to normalise mobile phone data to represent the relative population distribution
within the study area. Thus, the total sum of the relative share of mobile phone data RMP in
a study area S in time unit t is always 1. RMP for each base station j is calculated from all
mobile phone data MP conducted within a study area S in given time unit t as follows:
RMP ¼
MPtj
P
MPtj 2 S
(3)
Next, normalised mobile phone data of each base station j in time unit t is interpolated
into disaggregated spatial subunits s within a coverage area of a given base station j
regarding EHP coefficients of each spatial subunit s. A relative observed population ROP
for each disaggregated subunit s for given time unit t is calculated based on an
1638 O. JÄRV ET AL.
estimated human presence EHPt and a relative share of mobile phone data RMPt in the
given time unit t as follows:
ROPt ¼ EHPt  RMPt (4)
3.5. The spatial aggregation to desired target zones
In the final step, disaggregated spatial subunits in the physical surface layer are dissolved
by desired target zones z (each subunit is already assigned to a unique target
zone in the second step of the method). Thus, the share of relative observed population
ROPt of each subunit s within each target zone z in time t is summarised to ZROP as
follows:
ZROPt ¼
X
s2z
ROPt (5)
As an outcome, the distribution of a relative observed population derived from
mobile phone data, which was originally assigned to the coverage areas of a base
station, is now more realistically transformed to the desired spatial division of target
zones. In the next section, an empirical implementation of the proposed MFD method is
described.
4. Implementation of the proposed MFD method
We selected Tallinn, the capital city of Estonia, as our case study area to empirically test
the proposed MFD method given the availability of mobile phone data. Tallinn is a
medium-sized European city and the biggest city in Estonia, located within a total area
of 145 km2
and with over 400,000 inhabitants (Figure 3). Based on the conceptual
framework of the proposed MFD interpolation method for mobile phone data, we
developed an open access tool for the Python programming language, which is freely
available on GitHub (http://doi.org/10.5281/zenodo.252612).
We investigated mobile phone data from a 1-month study period in March 2015,
linked to the 290 base stations that provide the coverage area for a mobile network
signal for the case study area. Three different random mobile phone data sets at the
base station level are applied: (1) raw CDR data from working days (Monday–Friday)
at night (2 AM–6 AM); (2) raw CDR data during working days at daytime (4 PM–5
PM); and (3) the most probable home locations of mobile phone users derived from
CDR data using the anchor point method (Ahas et al. 2010). Comprehensive details
about applied mobile phone data sets are provided in Section S1 in the
Supplemental material.
In addition to mobile phone data (source zones), we applied five different ancillary
data sets derived from six data sources (Table 1) for implementing the proposed MFD
method for mobile phone data for Tallinn, Estonia. We applied two target zone layers to
investigate the method for 500 m (0.25 km2
, n =664) and 100 m (0.01 km2
, n =16,280)
statistical grid cell layers from Statistics Estonia.
Since ready-made data is not available, we prepared the physical surface layer using
four ancillary data sets: (1) land-cover data to classify land-cover parcels by six activity
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1639
function types (residential, work, retail and service, other, transport, restricted); (2)
building footprint data to integrate buildings with the main usage function (residential
and public, non-residential); (3) normalised Digital Surface Model (nDSM) data from
LIDAR to calculate building heights for estimating the number of floors; and (4) the
Open Street Map database to provide additional information to extract buildings with
public, service and retail as the main functions. An overlay method was applied to create
a physical surface layer where each disaggregated spatial subunit in the physical surface
layer has three attributes: the number of floors, activity function type and spatial unit
type of either building or land.
In the second step, a geometric union between the physical surface layer, the Voronoi
polygon layer representing theoretical coverage areas and official statistical grid layer for
target zones is applied. We received two different disaggregated physical surface layers
since we are investigating the results of MFD method at two target zone levels. In the
third step, we applied a national time-use survey data and calculated the average hourly
distribution of people by activity type to estimate the human presence for spatial units
Figure 3. The population registry data representing a night-time population distribution in Tallinn
for 500 m (a) and 100 m spatial resolution (b) in 2015.
Table 1. The list of ancillary data sets used in the case study.
Data set Attributes Source Date
Land-cover data Land-cover parcels with classification (polygon) The Estonian National Topographic
Database (ENTD), the Land Board
of Estonia (LBE)
2015
Building data 1. Building footprints (polygon) with binary usage
classification (residential or public; other)
The ENTD, the LBE 2015
2. Building heights based on LIDAR measurements
with 1 m accuracy (raster)
The nDSM data, the LBE 2013
3. Building functionality based on crowdsourcing
input on building usage (point) for refining
building
usage classification
The OpenStreetMap Database (OSM) 2015
Human activity
data
The average hourly distribution of people by
activity type based on cross-sectional time-use
survey study
The Estonian Time Use Survey,
Statistics Estonia
2010
Population
register data
Residential population data at building level,
which is aggregated to both target zones
(100 m and 500 m grid cells)
The Population registry data, Tallinn
municipality
2015
1640 O. JÄRV ET AL.
in the physical surface layer. We also applied a seasonal factor to refine the estimated
human presence for each disaggregated spatial subunit. In the fourth step, we applied
all three mobile phone data sets and calculated the relative observed population
according to MFD. Finally, we created six spatial output layers in the two target zones
for three data sets. A detailed description of the implementation of the MFD method in
Tallinn is provided in Section S2 in Supplemental material.
To compare the outcome of the MFD method, we created another six spatial output
layers where population distribution derived from mobile phone data is interpolated to
target zones using the simple areal weighting (AW) interpolation method. We applied
population register data at the building level that is aggregated to both target zone
layers as the best available baseline to empirically demonstrate how the proposed MFD
method improves a night-time (sleeping) population distribution compared to the AW
method. To evaluate the accuracy and validity of the proposed MFD method against AW
method for refining population distribution derived from mobile phone data, we performed
three statistical analysis: (1) linear regression for comparing correlation coefficients
and standard error of the estimate (Maantay et al. 2007); (2) the mean absolute
error; and (3) the coefficient of variation (CV) based on root mean square error of each
target zone normalised by the baseline target zone value (Mennis 2016).
5. Results
5.1. Population distribution by activity function type
In general, the proposed MFD interpolation method refines a night-time population
distribution derived from mobile phone data by activity function type significantly
better than areal weighting (AW) when compared to reference data (Figure 4). For the
case of a night-time population distribution, some 94% of people are at home according
to a national time-use survey. In comparison, the proposed MFD method relocates 86%
Figure 4. The population distribution by activity function type by MFD and AW interpolation
methods regarding three mobile phone data sets in comparison to population register and national
time-use survey data.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1641
of a night-time population derived from night-time call activities to residential areas,
whereas with AW the given share is only 22%. In the case of a sleeping population
distribution at night, we can consider that 100% of population register data indicates a
sleeping population in residential areas. In comparison, we applied home anchor points
of mobile phone users derived from call activities as an indicator for the sleeping
population. The MFD method relocates 100% of home anchor points to residential
areas, whereas the given share is only 24% in the case of the AW method.
During the day (as an example of 4 PM to 5 PM), population distribution by activity
function type between the two interpolation approaches is less obvious, although
population division with the MFD method coincides more with a national time-use
survey.
5.2. Night-time population distribution in target zones
A visual comparison between two interpolation methods in interpolating the spatial
distribution of a night-time population derived from home anchor points (Figure 5) and
call activities conducted between 2 AM and 6 AM (Figure S3 in Supplemental material)
reveals distinct spatial differences regarding both target zones; the MFD method
Figure 5. The distribution of home anchor points derived from call activities as an indication for a
night-time population by a simple AW and the proposed MFD interpolation methods with both
target zone layers.
1642 O. JÄRV ET AL.
relocates the population to residential areas compared to the AW method, which
distributes people equally in space.
The comparison of population register data as a baseline distribution for a night-time
population distribution and population distribution in the case of home anchor points
(Figure 6) and call activities (Figure S4 in Supplemental material) demonstrates the
differences with the baseline distribution between two interpolation approaches, especially
in case of 100 m grid cells. In general, the population distribution using the MFD
method has fewer differences with population register data, whereas the influence of
the method is more significant in areas where larger coverage areas of a base station
occur.
Interestingly, the given difference maps reveal an inherent weakness of mobile phone
data. There is a tendency to systematically overestimate the population in the city centre
(blue colour) where human communication is significantly more active. However, the
aim of the proposed MFD method is not to resolve the given bias, but to refine mobile
phone data (e.g. population) within the coverage areas of a base station. Thus, the
proposed method relocates the population into more populated target zones and
increases the number of target zones with no population as compared to a simple AW
method (Figure S5 and Figure S6 in Supplemental material).
Figure 6. Difference (percentage points) in a night-time population distribution between population
register data as the baseline distribution and home anchor points by MFD and AW interpolation
methods with both target zone layers. Target zones in red colour indicate underestimation and blue
colour overestimation of population, indicated by home anchor points.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1643
5.3. Evaluating MFD method for interpolating population distribution
Table 2 summarises the statistical comparison of the MFD method and simple AW
method for refining mobile phone-based population distribution in comparison to
population register data as a baseline distribution for a night-time population.
Regardless of the evaluation measure, the MFD method outperforms the AW method;
the former has higher correlation coefficients with a lower standard error (Figure S7 and
Figure S8 in Supplemental material), lower mean absolute error and coefficient of
variation of RMSE, especially for the 500 m × 500 m target zone resolution. The MFD
method improves population distribution more for home anchor points since it corresponds
better with population register data, as both indicate a sleeping population. The
finding supports the visual evidence presented in the previous subsections suggesting
that incorporating spatio-temporally dependent ancillary data in interpolating mobile
phone data by coverage areas of base station clearly improves the refinement of human
presence to target zones as compared to a simple areal weighting interpolation method.
5.4. Daytime population distribution in target zones
A visual comparison between two interpolation methods in interpolating the spatial
distribution of a daytime population derived from call activities conducted between 4
PM and 5 PM (Figure 7) reveals fewer distinct spatial differences regarding both target
zones. During a given time period, call activities are clearly concentrated to the city
centre, where a dense base station network already exists, and the proposed MFD
method has a less significant effect on refining population distribution as compared to
a night-time population.
Regardless of target zone resolution, MFD method has a less significant effect in relocating
a daytime population to target zones within the study area (Figure 8) given the strong
correlation in population distribution between two interpolation approaches (Figure S9 in
Supplemental material). On a smaller scale, however, some significant population relocations
occur at grid level. In the case of 500 m target zones, for example, in almost 50% of all
target zones, the MFD method relocates ±0.01–0.05% points of the relative observed
population (Figure S10 in Supplemental material). This indicates ±40–200 relocated people
Table 2. Summary of evaluating the results of both interpolation methods against population
register data regarding two target zone levels and two mobile phone data sets to indicate a
night-time population.
Target
zone
level Evaluation method
Call activities
2 AM to 6 AM
Home anchor
points
MFD AW MFD AW
500 m grid cells (n = 664) Linear regression Correlation coefficient 0.674 0.511 0.832 0.686
Std. Error 0.189 0.220 0.142 0.186
The mean absolute error (MAE) 0.087 0.122 0.072 0.107
Coefficient of variation (CV) based on the RMSE 0.211 0.242 0.142 0.186
100 m grid cells (n = 16280) Linear regression Correlation coefficient 0.472 0.233 0.618 0.347
Std. Error 0.016 0.018 0.014 0.017
The mean absolute error (MAE) 0.005 0.008 0.004 0.008
Coefficient of variation (CV) based on the RMSE 0.019 0.019 0.015 0.017
1644 O. JÄRV ET AL.
in a target zone as compared to the population estimate by the AW method. Furthermore,
almost 10% of all target zones have more than ±200 relocated people in a target zone with
the MFD method.
Figure 7. The distribution of call activities (4 PM to 5 PM) as an indication for a daytime population in given
time period by simple AW and the proposed MFD interpolation methods with both target zone layers.
Figure 8. Difference (percentage points) in the distribution of call activities (4 PM – 5 PM) as an
indication for a daytime population between the proposed MFD and simple AW interpolation
methods with both target zone layers. Target zones in red colour indicate higher population
distribution with the proposed MFD interpolation method as compared to simple AW. Grid cells
with blue colour indicate the opposite.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1645
6. Discussion and conclusions
The implementation of passively collected mobile phone data such as CDR data to
investigate human presence and mobility is mushrooming in social sciences. However,
the given data has theoretical and methodological challenges that one needs to
acknowledge. This paper gives attention to one of the important challenges that has
not been widely discussed to date – the uneven spatial resolution of CDR data due to
the uneven spatial configuration of mobile network base stations, and its spatial interpolation.
We introduced a dasymetric interpolation approach as one promising way
to overcome this. Thus, we proposed a generic conceptual framework of the multitemporal
function-based dasymetric (MFD) interpolation to enhance the spatial resolution
of mobile phone data, while taking into account the socio-spatial structure (land
use, building volume) and time-dependent human activity data.
The empirical findings demonstrate that the applied MFD interpolation method
transforms CDR data as an indication of population distribution to desired target
zones (statistical grid cells) more accurately than a simple areal weighting method.
The finding was confirmed by evaluating the results of both interpolation methods
against population register data as a baseline for a night-time population. In general, the
results were consistent with call activity and home anchor point data sets and two
different target zone levels with 100 and 500 m resolution. The MFD method had less of
an effect for a daytime population interpolation compared to the areal weighting
method at the city level; however, it refines the population between target zones
significantly on a smaller scale, which is essential, for example, in planning and risk
assessment at the micro level.
As the results clearly show, the proposed interpolation method refines the population
distribution estimates within a base station coverage area. Yet, it does not resolve the
inherent bias of mobile phone data – the fact that it is influenced by the variance of
human interactions with mobile devices in space and time, whereas one spatial outcome
is the tendency to overestimate the city centre, given its higher probability for human
interaction. However, the proposed MFD method can reduce the given biases to some
extent when introducing population subgroups to the method, which is similar to
Martin et al. (2015).
In general, the method can be improved by providing more accurate ancillary data
about the socio-spatial structure and human activities or by incorporating additional
data sources. For instance, traffic count data refines an estimated human presence for
different sections of a road network. Also, more accurate knowledge about coverage
areas of a base station would improve the spatial interpolation of CDR data since
commonly (also in this case study), a straightforward Voronoi tessellation for theoretical
coverage areas of a base station is applied, which does not coincide with the actual
coverage areas. Certainly, more detailed research about the sensitive scale issue of EHP
at different spatial resolutions and the comprehensive validation of accuracy estimates
of the method need to be conducted in future.
We did not demonstrate how the proposed MFD method can be applied to examine
societal issues. However, the method is generally applicable in the broad field of social
sciences since the method: (1) can transform mobile phone data to any desired spatial
division of target zones (e.g. grids and census tracts) given the research need; (2) enables
1646 O. JÄRV ET AL.
reliable data comparison, integration and validation of mobile phone data with other data
sources; (3) enables reliable mobile phone data interpolation for longitudinal and repeated
research; and (4) can improve a systematic estimate of population distribution in time and
space from a small scale (e.g. neighbourhood level) to a country level (e.g. global level),
depending on mobile phone and ancillary data availability. Thus, the method can provide
dynamic population distribution mapping to assess object-oriented risks (Smith et al. 2014)
or to model more realistic spatio-temporal accessibility to services (Tenkanen et al. 2016).
We only applied the proposed interpolation method for population modelling at an
aggregated level; however, it can also be used for person-based research for refining
personal mobility patterns and activity spaces at the individual level. The method can be
developed to refine the probable location of an individual within a base station depending
on the individual’s spatio-temporal activities derived from one’s mobile phone
usage. For example, individual meaningful locations (e.g. home and work) derived
from one’s CDR data at the base station level (Ahas et al. 2010) allow the model to
calculate personal probabilities for a given individual to be associated with a certain
type of spatial subunit within certain coverage areas in a given time. Certainly, one must
acknowledge and preserve the privacy of phone users regarding the use of mobile
phone data (see, e.g. Yin et al. 2015).
We conclude that the proposed multi-temporal, function-based dasymetric interpolation
method offers a promising approach to increase the usability and reliability of
passively collected mobile phone data. Our empirical findings provide a solid base to
improve the spatial interpolation methods for mobile phone-based research. Furthermore,
this study contributes to developing dasymetric population modelling by incorporating
mobile phone data. To facilitate the future development of the proposed approach, we
share the implementation of our method in GitHub as open access Python code.
Acknowledgements
We thank Prof. Rein Ahas at University of Tartu and Positium LBS for kindly providing us mobile
phone data, Dr. Anto Aasa for helping us to receive land-cover and building data, and Ivari
Rannama at Tallinn municipality for providing population registry data. This research received
financial support from the Helsinki Metropolitan Region Urban Research Program and Kone
Foundation. H.T. thanks the DENVI doctoral program at University of Helsinki for support. We
would like to thank also the valuable and constructive comments of the four anonymous referees,
which helped us in improving the content and clarity of this paper.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This research received financial support from the Helsinki Metropolitan Region Urban Research
Program (MetropAccess project) and Kone Foundation. H.T. thanks the DENVI doctoral program at
University of Helsinki for support.
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1647
ORCID
Olle Järv http://orcid.org/0000-0003-3446-1545
Henrikki Tenkanen http://orcid.org/0000-0002-0918-4710
Tuuli Toivonen http://orcid.org/0000-002-6625-4922
References
Ahas, R., et al., 2010. Using mobile positioning data to model locations meaningful to users of
mobile phones. Journal of Urban Technology, 17 (1), 3–27. doi:10.1080/10630731003597306
Ahola, T., et al., 2007. A spatio-temporal population model to support risk assessment and damage
analysis for decision-making. International Journal of Geographical Information Science, 21 (8),
935–953. doi:10.1080/13658810701349078
Alahmadi, M., Atkinson, P., and Martin, D., 2013. Estimating the spatial distribution of the population
of Riyadh, Saudi Arabia using remotely sensed built land cover and height data. Computers,
Environment and Urban Systems, 41, 167–176. doi:10.1016/j.compenvurbsys.2013.06.002
Asakura, Y. and Hato, E., 2004. Tracking survey for individual travel behaviour using mobile
communication instruments. Transportation Research Part C: Emerging Technologies, 12 (3–4),
273–291. doi:10.1016/j.trc.2004.07.010
Aubrecht, C., et al., 2009. Integrating earth observation and GIScience for high resolution spatial
and functional modeling of urban land use. Computers, Environment and Urban Systems, 33 (1),
15–25. doi:10.1016/j.compenvurbsys.2008.09.007
Aubrecht, C., Steinnocher, K., and Huber, H., 2014. DynaPop–population distribution dynamics as
basis for social impact evaluation in crisis management. In: Proceedings of the 11th International
ISCRAM Conference. University Park, PA: The Pennsylvania State University, 314–318.
Bakillah, M., et al., 2014. Fine-resolution population mapping using OpenStreetMap points-ofinterest.
International Journal of Geographical Information Science, 28 (9), 1940–1963.
doi:10.1080/13658816.2014.909045
Bhaduri, B., et al., 2007. LandScan USA: a high-resolution geospatial and temporal modeling
approach for population distribution and dynamics. GeoJournal, 69 (1–2), 103–117.
doi:10.1007/s10708-007-9105-9
Biljecki, F., et al., 2016. Population estimation using a 3D city model: a multi-scale country-wide
study in the Netherlands. PLoS One, 11 (6), e0156808. doi:10.1371/journal.pone.0156808
Blumenstock, J., Cadamuro, G., and On, R., 2015. Predicting poverty and wealth from mobile phone
metadata. Science, 350 (6264), 1073–1076. doi:10.1126/science.aac4420
Calabrese, F., et al., 2013. Understanding individual mobility patterns from urban sensing data: a
mobile phone trace example. Transportation Research Part C: Emerging Technologies, 26, 301–
313. doi:10.1016/j.trc.2012.09.009
Candia, J., et al., 2008. Uncovering individual and collective human dynamics from mobile phone
records. Journal of Physics A: Mathematical and Theoretical, 41 (22), 224015. doi:10.1088/1751-
8113/41/22/224015
Castells, M., et al., 2007. Mobile communication and society: a global perspective: a project of the
annenberg research network on international communication. Cambridge, MA: MIT Press.
Cresswell, T. and Merriman, P., 2011. Geographies of mobilities: practices, spaces, subjects. Farnham:
Ashgate.
Csáji, B.C., et al., 2013. Exploring the mobility of mobile phone users. Physica A: Statistical
Mechanics and Its Applications, 392 (6), 1459–1473. doi:10.1016/j.physa.2012.11.040
Demissie, M.G., de Almeida Correia, G.H., and Bento, C., 2013. Exploring cellular network handover
information for urban mobility analysis. Journal of Transport Geography, 31 (0), 164–170.
doi:10.1016/j.jtrangeo.2013.06.016
Deville, P., et al., 2014. Dynamic population mapping using mobile phone data. Proceedings of the
National Academy of Sciences, 111 (45), 15888–15893. doi:10.1073/pnas.1408439111
1648 O. JÄRV ET AL.
Dobson, J.E., et al., 2000. LandScan: a global population database for estimating populations at
risk. Photogrammetric Engineering and Remote Sensing, 66 (7), 849–857.
Doyle, J., et al., 2014. Population mobility dynamics estimated from mobile telephony data. Journal
of Urban Technology, 21 (2), 109–132. doi:10.1080/10630732.2014.888904
Eicher, C.L. and Brewer, C.A., 2001. Dasymetric mapping and areal interpolation: implementation
and evaluation. Cartography and Geographic Information Science, 28 (2), 125–138. doi:10.1559/
152304001782173727
Eurostat, 2009. Harmonised European time use surveys: 2008 guidelines. Luxembourg: European
Communities.
González, M.C., Hidalgo, C.A., and Barabási, A.-L., 2008. Understanding individual human mobility
patterns. Nature, 453 (7196), 779–782. doi:10.1038/nature06958
Greger, K., 2015. Spatio-temporal building population estimation for highly urbanized areas using
GIS: spatio-temporal building population estimation. Transactions in GIS, 19 (1), 129–150.
doi:10.1111/tgis.2015.19.issue-1
Hägerstrand, T., 1970. What about people in regional science? Papers of the Regional Science
Association, 24 (1), 6–21. doi:10.1007/BF01936872
Hussein, T., Paasonen, P., and Kulmala, M., 2012. Activity pattern of a selected group of school
occupants and their family members in Helsinki — Finland. Science of the Total Environment,
425, 289–292. doi:10.1016/j.scitotenv.2012.03.002
Iqbal, M.S., et al., 2014. Development of origin–destination matrices using mobile phone call
data. Transportation Research Part C: Emerging Technologies, 40, 63–74. doi:10.1016/j.
trc.2014.01.002
Jacobs-Crisioni, C., et al., 2014. Evaluating the impact of land-use density and mix on spatiotemporal
urban activity patterns: an exploratory study using mobile phone data. Environment and
Planning A, 46 (11), 2769–2785. doi:10.1068/a130309p
Järv, O., Ahas, R., and Witlox, F., 2014. Understanding monthly variability in human activity spaces:
a twelve-month study using mobile phone call detail records. Transportation Research Part C:
Emerging Technologies, 38 (0), 122–135. doi:10.1016/j.trc.2013.11.003
Järv, O., et al., 2015. Ethnic differences in activity spaces as a characteristic of segregation: a study
based on mobile phone usage in Tallinn, Estonia. Urban Studies, 52 (14), 2680–2698.
doi:10.1177/0042098014550459
Kang, C., et al., 2012. Towards estimating urban population distributions from mobile call data.
Journal of Urban Technology, 19 (4), 3–21. doi:10.1080/10630732.2012.715479
Kitchin, R., 2014. The data revolution: big data, open data, data infrastructures and their consequences.
London: Sage Publications Ltd. doi:10.4135/9781473909472
Kwan, M.-P., 2013. Beyond space (As We Knew It): toward temporally integrated geographies of
segregation, health, and accessibility. Annals of the Association of American Geographers, 103 (5),
1078–1086. doi:10.1080/00045608.2013.792177
Langford, M., 2006. Obtaining population estimates in non-census reporting zones: an evaluation
of the 3-class dasymetric method. Computers, Environment and Urban Systems, 30 (2), 161–180.
doi:10.1016/j.compenvurbsys.2004.07.001
Langford, M., et al., 2008. Urban population distribution models and service accessibility estimation.
Computers, Environment and Urban Systems, 32 (1), 66–80. doi:10.1016/j.
compenvurbsys.2007.06.001
Linard, C. and Tatem, A.J., 2012. Large-scale spatial population databases in infectious disease
research. International Journal of Health Geographics, 11 (1), 1. doi:10.1186/1476-072X-11-7
Louail, T., et al., 2014. From mobile phone data to the spatial structure of cities. Scientific Reports, 4,
5276. doi:10.1038/srep05276
Maantay, J.A., Maroko, A.R., and Herrmann, C., 2007. Mapping population distribution in the urban
environment: the Cadastral-based Expert Dasymetric System (CEDS). Cartography and
Geographic Information Science, 34 (2), 77–102. doi:10.1559/152304007781002190
Martin, D., Cockings, S., and Leung, S., 2015. Developing a flexible framework for spatiotemporal
population modeling. Annals of the Association of American Geographers, 105 (4), 754–772.
doi:10.1080/00045608.2015.1022089
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1649
Mennis, J., 2003. Generating surface models of population using dasymetric mapping. The
Professional Geographer, 55 (1), 31–42.
Mennis, J., 2016. Dasymetric spatiotemporal interpolation. The Professional Geographer, 68 (1), 92–
102. doi:10.1080/00330124.2015.1033669
Mennis, J. and Hultgren, T., 2006. Intelligent dasymetric mapping and its application to areal
interpolation. Cartography and Geographic Information Science, 33 (3), 179–194. doi:10.1559/
152304006779077309
Nagle, N.N., et al., 2014. Dasymetric modeling and uncertainty. Annals of the Association of
American Geographers, 104 (1), 80–95. doi:10.1080/00045608.2013.843439
Pei, T., et al., 2014. A new insight into land use classification based on aggregated mobile phone
data. International Journal of Geographical Information Science, 28 (9), 1988–2007. doi:10.1080/
13658816.2014.913794
Phithakkitnukoon, S., Smoreda, Z., and Olivier, P., 2012. Socio-geography of human mobility: a
study using longitudinal mobile phone data. PLoS ONE, 7 (6), e39253. doi:10.1371/journal.
pone.0039253
Ratti, C., et al., 2006. Mobile landscapes: using location data from cell phones for urban analysis.
Environment and Planning B: Planning and Design, 33 (5), 727–748. doi:10.1068/b32047
Reades, J., Calabrese, F., and Ratti, C., 2009. Eigenplaces: analysing cities using the space–time
structure of the mobile phone network. Environment and Planning B: Planning and Design, 36
(5), 824–836. doi:10.1068/b34133t
Ruther, M., Leyk, S., and Buttenfield, B.P., 2015. Comparing the effects of an NLCD-derived
dasymetric refinement on estimation accuracies for multiple areal interpolation methods.
Giscience & Remote Sensing, 52 (2), 158–178. doi:10.1080/15481603.2015.1018856
Schönfelder, S. and Axhausen, K.W., 2010. Urban rhythms and travel behaviour : spatial and
temporal phenomena of daily travel. Farnham: Ashgate.
Shannon, J., 2014. What does SNAP benefit usage tell us about food access in low-income
neighborhoods? Social Science & Medicine, 107, 89–99. doi:10.1016/j.socscimed.2014.02.021
Sheller, M. and Urry, J., 2006. The new mobilities paradigm. Environment and Planning A, 38 (2),
207–226. doi:10.1068/a37268
Silm, S. and Ahas, R., 2010. The seasonal variability of population in Estonian municipalities.
Environment and Planning A, 42 (10), 2527–2546. doi:10.1068/a43139
Smith, A., Martin, D., and Cockings, S., 2014. Spatio-temporal population modelling for enhanced
assessment of urban exposure to flood risk. Applied Spatial Analysis and Policy, 9 (2), 145–163.
doi:10.1007/s12061-014-9110-6
Soto, V., et al., 2011. Prediction of socioeconomic levels using cell phone records. In: J.A. Konstan,
et al. eds. User Modeling, Adaption and Personalization. UMAP 2011. Lecture Notes in Computer
Science, vol 6787. Berlin: Springer, 377–388.
Tenkanen, H., et al., 2016. Health research needs more comprehensive accessibility measures:
integrating time and transport modes from open data. International Journal of Health
Geographics, 15 (1), 1–12. doi:10.1186/s12942-016-0052-x
Trasarti, R., et al., 2015. Discovering urban and country dynamics from mobile phone data with
spatial correlation patterns. Telecommunications Policy, 39 (3–4), 347–362. doi:10.1016/j.
telpol.2013.12.002
UNSD, 2016. United Nations statistics division time use data portal [online]. Available from: unstats.
un.org/unsd/gender/timeuse/index.html [Accessed 10 Oct 2016].
Ural, S., Hussain, E., and Shan, J., 2011. Building population mapping with aerial imagery and GIS
data. International Journal of Applied Earth Observation and Geoinformation, 13 (6), 841–852.
doi:10.1016/j.jag.2011.06.004
Vaz, E. and Campos, A.C., 2013. A multi-dasymetric mapping approach for tourism. Journal of
Spatial and Organizational Dynamics, 1 (4), 265–277.
Wang, P., et al., 2012. Understanding road usage patterns in urban areas. Scientific Reports, 2, 1001.
Wesolowski, A., et al., 2013. The impact of biases in mobile phone ownership on estimates of
human mobility. Journal of the Royal Society Interface, 10 (81), 20120986. doi:10.1098/
rsif.2012.0986
1650 O. JÄRV ET AL.
Wesolowski, A., et al., 2012. Quantifying the impact of human mobility on malaria. Science, 338
(6104), 267–270. doi:10.1126/science.1223467
Wesolowski, A., et al., 2015. Quantifying the impact of accessibility on preventive healthcare in
sub-Saharan Africa using mobile phone data. Epidemiology, 26 (2), 223–228. doi:10.1097/
EDE.0000000000000239
Williams, N.E., et al., 2015. Measures of human mobility using mobile phone records enhanced with
GIS data. PLoS ONE, 10 (7), e0133630. doi:10.1371/journal.pone.0133630
Wu, S., Qiu, X., and Wang, L., 2005. Population estimation methods in GIS and remote sensing: a
review. GIScience & Remote Sensing, 42 (1), 80–96. doi:10.2747/1548-1603.42.1.80
Yin, L., et al., 2015. Re-identification risk versus data utility for aggregated mobility research using
mobile phone location data. PLoS One, 10 (10), e0140589. doi:10.1371/journal.pone.0140589
Yuan, Y., Raubal, M., and Liu, Y., 2012. Correlating mobile phone usage and travel behavior – a case
study of Harbin, China. Special Issue: Geoinformatics 2010, 36 (2), 118–130.
Zhao, Z., et al., 2016. Understanding the bias of call detail records in human mobility research.
International Journal of Geographical Information Science, 30 (9), 1738–1762. doi:10.1080/
13658816.2015.1137298
INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE 1651
Supplemental Information for
Enhancing spatial accuracy of mobile phone data
using multi-temporal dasymetric interpolation
CONTENTS
S1. Description of Mobile Phone Data 1
S2. Implementation of MFD method in Tallinn, Estonia 2
S3. Nighttime population distribution 7
S4. Daytime population distribution 10
References 10
S1. Description of Mobile Phone Data
In general, mobile devices are widely used in Estonia. Some 95% of Estonia’s 1.3 million inhabitants used mobile
phones in 2008, and the mobile phone subscribers (SIM cards) penetration rate was over 150% in 2014.
The call detail record (CDR) data used in this study is collected by the largest mobile network operator in
Estonia with a market share of 40–50% of mobile subscribers in Estonia. CDR data is further processed by a spinoff
company Positium LBS, who provided the datasets for this study. The data is recorded in accordance with
Estonian legislation for billing purposes by the operator, and not for the purposes of this study. This study is in
accordance with European Union legislation on the use of personal data (European Commission 2002). The
database contains records of all outgoing call activities initiated by a mobile phone owner. Each CDR includes the
unique identification (ID) number of the phone (randomly generated by the operator for every mobile phone), the
exact time (HH:MM:SS) and date (YYYY.MM.DD) of call activity, and geographical coordinates of the mobile
network antenna that provided the network signal. The precision of the spatial accuracy of the CDR corresponds
to the coverage area of the network antenna. The precision of the temporal accuracy of the CDR depends on call
activity of people.
We study only mobile phone data that is linked to those 290 base stations that provide the coverage area
of mobile network signal for the city of Tallinn (Järv et al. 2014; p. 127). Since the actual coverage areas of a base
station are unknown, Voronoi tessellation is applied to calculate theoretical coverage areas (polygons) for each
base station. The average spatial extent of given theoretical coverage areas is 1.1 km2
, however, coverage areas
are not spatially fixed (Figure S1). The spatial extent of a theoretical coverage area at a minimum is 0.01 km2
and
maximum is 48.4 km2
, whereas a lower quartile value is 0.13 km2
, a median 0.36 km2
and an upper quartile 0.88
km2
. The spatial extent of coverage areas is directly related to demand of mobile phone usage. The smallest
coverage areas are located in densely populated city (sub)centres and larger in more sparsely populated areas.
Figure S1. All studied 290 theoretical coverage areas of a base station in Tallinn and the ranking of coverage areas by
their spatial extent in a logarithmic scale.
We investigated the proposed multi-temporal function-based dasymetric (MFD) method for mobile phone data
with three mobile phone data sets selected from a random sample of 50% from the original databases in March
2015 provided by Positium LBS. Two data sets are extracted from a one-month raw CDR database consisting of
a 5.3 million call activities made during working days (Mon.–Fri.) by 58,470 subscribers in the study area. From
the latter we select one data set including CDR data from 2 AM to 6 AM to represent a nighttime population and
second data set includes CDR data from 4 PM to 5 PM as an example to represent a daytime population. The third
data set consists of the most probable home locations of given phone users at a base station level. These data were
derived from raw CDR data using the anchor point model (Ahas et al. 2010). For each individual in the sample,
this model finds the two most frequently used mobile network base stations where mobile phone usage occurs
during a month. These base stations are called as anchor points of one’s daily life. Furthermore, by taking into
account (1) the average time of day and its standard deviations of (outgoing) CAs conducted in each activity
location and (2) the spatial relationships between neighbouring activity locations, the model distinguishes homeand
work-time locations. In total, the anchor point model extracted 56,778 the most probable home locations that
are distributed between selected 290 base stations in the study area.
Certainly, individual mobile phone usage varies significantly in temporal and spatial terms as well as in
relation to the overall amount of phone usage. To minimise differences between phone users in terms of total
amount of CDRs, we calculated a weighted physical presence for each individual in each base station for each
hour of the given time frame based on the number of CDRs generated in each base station from all CDRs conducted
during a given hour. Hence, we can better reflect the physical presence of phone users and their time spent between
the coverage areas of a base station depending on one’s call activity frequency in each base station. For example,
to some extent, this solves the problem of the amplification of CDRs conducted by very active phone users who
are immobile and located in the city centre (office workers) or who are very mobile throughout the city (e.g. taxi
drivers, couriers).
S2. Implementation of MFD method in Tallinn, Estonia
S2.1. The preparation of physical surface layer
To prepare a physical surface layer in the multi-temporal function-based dasymetric (MFD) method for the spatial
disaggregation of the study area, first, from The Estonian National Topographic Database (ENTD) a land cover
layer is created by aggregating land cover parcels based on six activity function classes, as indicated in Table S1.
This enables linking the land cover layer with the classification of building usage and human activity types,
similarly to a study by Greger (2015).
Table S1. The classification of activity function type and linkage to land parcels by land cover and buildings by
functionality, and the association with human activity types.
Activity
function type
Land parcels
by land cover type
Building units
by functionality type
Human activity type
Residential residential area residential, accommodation, prison
at home or
accommodation
Work industrial and business area
industrial, office, public institutions predominantly not
for in situ services (e.g. police department, hospital, town
hall), educational
at work or school
Retail &
Service
retail and service (e.g. gas station, cleaner service, local
bank branch)
shopping and using
services
Other
open area (e.g. park, square),
forest, cemetery
public institutions for in situ services (e.g. libraries, sport
facilities), leisure and entertainment, eatery, churches
other (leisure) activities
Transport
transport networks
(e.g. road, railway, parking)
travelling, on the move
Restricted
water, wetland, arable land,
restricted area (e.g. wasteland)
not accessible, no activity
Second, we prepared the building layer by refining building usage type and estimating total floor area. First, all
buildings with a footprint area over 20 m2 are selected from the up-to-date building footprint layer provided by
the ENTD. These building units initially have the binary classification of a residential (or public) building and
non-residential building. To further refine building classification according to six land cover classes (Table S1),
we applied the OSM database to obtain more detailed information about activities and functionalities of buildings.
Classification is done semi-automatically since only one building usage type for each building is assigned to
indicate the main usage. Hence, from residential buildings assigned by the ENTD all public buildings were
distinguished and further assigned with either work/educational or leisure-related usage type. The main usage type
for accommodation buildings is considered a residential usage type since customers use it as a temporary home,
although it has an important work-related usage type as well. Other (i.e. non-residential building) buildings
assigned by the ENTD are considered to have a work-related building usage type. Additionally, the OSM data
reveals buildings that have public, retail and service as the main functional type. We acknowledge that it is rather
arbitrary to assign one usage type for each building and not to consider multifunctional buildings, however, we
believe this ancillary data to be sufficient for highlighting how the dasymetric interpolation approach increases the
spatial accuracy of mobile phone data.
Third, we included a vertical dimension for buildings. Since we do not have ready-made available data
about the total floor area for each building, we calculated the estimated values based on the building footprint area
and building height. We prefer to apply estimates for total floor area (m2) of buildings, which provides a more
accurate population estimation than rough building volumes (m3), based on a study by Biljecki et al. (2016). The
average height for each building is calculated based on LIDAR measurement points at 1 m accuracy obtained from
the normalised Digital Surface Model, which intersects building footprint polygons (see Alahmadi et al. 2013). To
further estimate the number of floors, the average building height is divided by the floor height set for 3.5 m for
residential and 4.5 m for other buildings. The given floor height estimations were set by dividing the measured
building height with the actual number of floors based on a random sample of buildings. We further excluded
buildings less than 2 m high from the building layer as unsuitable to be inhabited by people. Applied floor heights
are rather arbitrary; however, we believe this ancillary data to be sufficient for the case study to highlight how the
dasymetric interpolation approach increases the spatial accuracy of mobile phone data.
Fourth, an overlay method was applied to calculate the geometric union of a land cover layer with a
building layer, whereas in the case of building polygons, only building attributes remain. For subunits with the
surface unit type of land the default value for the number of floors is set to 1. Now, each disaggregated spatial
subunit in the physical surface layer has three attributes: the number of floors, activity function type, and spatial
unit type of either building or land.
S2.2. The spatial disaggregation by source and target zones
In the second step, a geometric union with the physical surface layer, the layer representing 290 Voronoi polygons
as theoretical coverage areas of a base station (source zones) and the official statistical grid cell layer (target zones)
is applied to designate a unique base station ID and a unique official statistical grid cell ID for each spatial unit in
the physical surface layer. While we apply two statistical grid cell layers with 100 m x 100 m and 500 m x 500 m
resolutions, we obtain two physical surface layers with different disaggregation levels. Next, the area for each
disaggregated subunit polygon is calculated, which enables a calculation of the estimated total floor FA for each
spatial subunit by multiplying area with the number of floors. This allows for a calculation of a relative floor area
RFA for each subunit within a base station from the sum of FA within the given base station, as illustrated in Table
S2. For example, base station ID 10 covers an area of 70 km2
with a total sum of FA 160 km2
. While a subunit ID
10101 has FA 50 km2
, then it has RFA 0.29 (50/170=0.29) within the base station ID 10. For comparison, in the
case of a simple areal weighting method, all subunits would have RFA 0.14 (10/70=0.14).
Table S2. An illustration of calculating a relative floor area (RFA) for each subunit within a base station from the estimated
total floor area of the base station (FA).
Subunit
ID
Base Station ID
(source zone)
Grid cell ID
(target zone)
Activity
function type
Spatial unit
type
Area
[km2
]
Number
of floors
FA
[km2
]
RFA
[%]
10101 10 10 Residential building 10 5 50 0.29
10201 10 20 Residential land 10 1 10 0.06
10202 10 20 Work building 10 3 30 0.18
10203 10 20 Work land 10 1 10 0.06
10301 10 30 Retail&servic
e
building 10 5 50 0.29
10302 10 30 Restricted land 10 1 10 0.06
10303 10 30 Transport land 10 1 10 0.06
Total 70 170 1
S2.3. The integration of time-dependent human activity data
In the third step of the MFD method, the physical surface layer is linked with data on human activity in time. For
Tallinn, we applied the time usage of people by activity type on working days (Mon.–Fri.) provided by Statistics
Estonia (Figure S2). The data is based on the Estonian Time Use Survey in 2010 that was conducted using the
harmonised methodology of Eurostat time use surveys (Eurostat 2009). The average hourly distribution of people
by activity type is calculated based on the average time usage of people by activity type for each hour of a day at
an aggregated level. In this case study, subpopulation groups are not considered as proposed by the MFD
interpolation method, but is calculated for the entire population.
Figure S2. The average hourly distribution of people by human activity type classification on an average working day in
Estonia. Source: The Estonian Time Use Survey data in 2010 from Statistics Estonia.
Temporally sensitive human activity information is incorporated to disaggregated spatial subunits in the physical
surface layer by linking human activity type with activity function type of subunits (Table S1). To calculate an
estimated human presence EHP for each disaggregated spatial subunit for every hour of the day, both a daily hour
factor H and a seasonal factor M for estimating human activity is used (Table S3). First, the average hourly
distribution of people by activity type is considered as a proxy for H for each human activity type. For example,
both subunits of ID 10101 and 10201 have an activity location type of a residential building, and hence, for the
time period between 4 PM and 5 PM its H has the coefficient of 0.42, since at given time period, 42% of Estonians
are at home, on average. For spatial subunits with restricted activity function type we consider them inaccessible
for people and assume no human presence at any time.
Table S3. An illustration of calculating an estimated human presence (EHP) for each subunit within a theoretical coverage
area (Voronoi polygon) of the base station ID 10 in the case of the time period between 4 PM and 5 PM.
Subunit
ID
Grid cell ID
(target zone)
Activity
function type
Spatial unit
type
RFA
[%]
H
[%]
RFA
x
H
M
[%]
∑(𝑹𝑭𝑨 𝒙 𝑯) ∈ 𝒍
x
M
EHP
[%]
10101 10 Residential building 0.29 0.42 0.122 0.90 0.132 0.58
10201 20 Residential land 0.06 0.42 0.025 0.10 0.015 0.07
10202 20 Work building 0.18 0.23 0.041 0.90 0.049 0.21
10203 20 Work land 0.06 0.23 0.014 0.10 0.006 0.02
10301 30 Retail&servic
e
building 0.29 0.05 0.015 1.00 0.015 0.07
10302 30 Restricted land 0.06 0.00 0.000 1.00 0.000 0.00
10303 30 Transport land 0.06 0.19 0.011 1.00 0.011 0.05
Total 1 0.228 0.228 1
Second, the seasonal factor M is applied since seasonality influences the division of human presence between
indoors and outdoors. Here, M is applied as the probability coefficient of people to refine locations of people
between disaggregate spatial subunits based on spatial unit type (land; building) within each activity function type
and within each base station. A study by Hussein et al. (2012) found that people spend some 90% of time indoors
and 10% outdoors in Helsinki, Finland during March. We expect these findings to fit well in our case study given
the general similarities of human activity travel behaviour, identical climatological conditions between Helsinki
and Tallinn, and both studies are conducted in the same season of the year. Hence, an assumption is made that if
an activity function type (e.g. at home) includes both spatial unit types (e.g. residential building; residential land),
then M has the coefficient of 0.90 for building subunits and 0.10 for land subunits in spring (March). In this study,
these coefficients are used for three activity function types (residential; work; and other) and for other activity
function types M has the coefficient of 1. For example, subunit ID 10101 and 10201 have both residential activity
function type, while the former has M as 0.90 since it is a building, and the latter 0.1 since it is a land subunit
(Table S4).
For example, both subunits of ID 10101 and 10201 have residential activity function type and are located
within a Voronoi polygon (theoretical coverage area) of base station ID 10. For the time period between 4 PM and
5 PM EHP for subunit ID 10101 is calculated as follows: (((0.29 x 0.42) + 0.025) x 0.9) / 0.228 = 0.58 (Table S4).
In other words, for a given hour of the day, some 58% of all people located within the coverage area of the base
station ID 10 are estimated to be located within the subunit ID 10101.
S2.4. The integration of mobile phone data
In the fourth step in the MFD method, mobile phone data is linked to the physical spatial layer by base station ID.
First, the hourly distribution of mobile phone data as a proxy for people distributed between Voronoi polygons of
a base station in given time is normalised to represent the relative population distribution RMP of the case study
area. Thus, the total sum of RMP by base station coverage area (Voronoi polygon) is always 1. Here, RMP is
calculated for all three mobile phone data sets used in this study (Section S1). For example, if RMP is 0.200 for
the Voronoi polygon of a base station ID 10, some 20% of all mobile phone data in the case study area is located
within a given Voronoi polygon (Table S4). Second, the relative observed population ROP for each subunit is
calculated based on EHP of given subunit within a base station and RMP of a base station to which a given spatial
subunit is assigned. For comparison, ROP is also calculated based on a simple areal weighting (AW) and a vertical
areal weighting (VAW) interpolation method.
Table S4. An example of calculating relative observed population ROP for each subunit within the base station ID 10
regarding simple areal weighting (AW), vertical areal weighting (VAW) and multi-temporal function-based dasymetric
(MFD) interpolation method for the time period 4 PM – 5 PM.
Subunit
ID
Grid cell ID
(target zone)
Activity
function type
Spatial
unit
type
AW
[%]
RFA
[%]
EHP
[%]
RMP
[%]
ROP [%]
MFD
(EHP X RMP)
AW
(AW X RMP)
VAW
(RFA X RMP)
10101 10 Residential building 0.143 0.29 0.58 0.116 0.028 0.058
10201 20 Residential land 0.143 0.06 0.07 0.014 0.029 0.012
10202 20 Work building 0.143 0.18 0.21 0.042 0.028 0.036
10203 20 Work land 0.143 0.06 0.02 0.004 0.029 0.012
10301 30 Retail&servic
e
building 0.143 0.29 0.07 0.014 0.028 0.058
10302 30 Restricted land 0.143 0.06 0.00 0.000 0.029 0.012
10303 30 Transport land 0.143 0.06 0.05 0.010 0.028 0.012
Total 1 1 1 0.200 0.200 0.200 0.200
For example, ROP based on EHP for the subunit ID 10101 is 0.58 x 0.200 = 0.116, which means that some 11.6%
of all people in the case study area derived from mobile phone data is located within given subunit (Table S4). For
comparison, with a vertical areal weighting (VAW) method, ROP for the same subunit would be 0.29 x 0.200 =
0.058 indicating 5.8% of people to be located in given subunit. With a simple areal weighting (AW) method ROP
would be 0.029 since each subunit has an equal area of 10 km2
(10/70 x 0.200=0.029).
S2.5. The spatial aggregation to desired target zones
Finally, each disaggregate subunit now has ROP based on EHP and is assigned to a unique official statistical grid
cell ID, which in this case study is an indication of the target zones layer of the MFD method. By summarising
ROP of all disaggregate spatial subunits by the official statistical grid cell, the method now provides an outcome
of ROP by official grid cells as target zones in this case study. Hence, the MFD method has interpolated relative
observed population derived from mobile phone data with spatial resolution of Voronoi polygons of a base station
(source zones) into official statistical grid cells (target zones).
In the case of the base station ID 10, its 20% of relative observed population from all population is further
interpolated into three predefined grid cells whereas, for example, some 11.6% of the population is estimated to
be located within the grid cell ID 10 (Table S5). For comparison, with a simple areal weighting (AW) interpolation
method, only some 2.8% of the population, and with a vertical areal weighting (VAW) interpolation method, some
5.8% would have been estimated to be located in grid cell ID 10.
For the implementation of the proposed MFD interpolation method for mobile phone data in Tallinn we
developed an open access tool for the Python programming language, which is freely available from GitHub
(http://doi.org/10.5281/zenodo.252612).
Table S5. An example of interpolating relative observed population (ROP) from a theoretical coverage area of a base
station (Voronoi polygon) into predefined grid cells in case of the proposed multi-temporal function-based dasymetric
(MFD), simple areal weighting (AW) and vertical areal weighting (VAW) interpolation method.
Grid cell ID
(target zone)
Base Station ID
(source zone)
ROP [%]
MFD AW VAW
10 10 0.116 0.028 0.058
20 10 0.060 0.086 0.060
30 10 0.024 0.086 0.082
Total 0.200 0.200 0.200
S3. Nighttime population distribution
Figure S3. The distribution of call activities (2 AM – 6 AM) as an indication for a nighttime population by a simple areal
weighting (AW) and the proposed MFD interpolation methods with both target zone layers in Tallinn.
Figure S4. Difference (percentage points) in a nighttime population distribution between population register data as the
baseline distribution and call activities (2 AM – 6 AM) by the proposed MFD and a simple areal weighting (AW)
interpolation methods with both target zone layers. Target zones in red colour indicate underestimation and blue colour,
overestimation of population indicated by call activities.
Figure S5. The distribution of target zones by the difference of a sleeping population in a target zone between population
register data as the baseline distribution and home anchor points by the proposed MFD and a simple areal weighting (AW)
interpolation methods with both target zone layers. Note that differences are indicated in percentage points for 500 m and
in per-millage points for 100 m target zone resolution.
Figure S6. The distribution of target zones by the difference of a nighttime population in a target zone between population
register data as the baseline distribution and call activities (2 AM – 6 AM) by the proposed MFD and a simple areal
weighting (AW) interpolation methods with both target zone layers. Note that differences are indicated in percentage points
for 500 m and in per-millage points for 100 m target zone resolution.
Figure S7. The correlation of a nighttime population distribution between population register data and home anchor points
refined by the proposed MFD and a simple areal weighting (AW) interpolation methods with both target zone layers.
Figure S8. The correlation of a nighttime population distribution between population register data and call activities (2
AM – 6 AM) refined by the proposed MFD and an areal weighting (AW) interpolation methods with two target zone layers.
S4. Daytime population distribution
Figure S9. The correlation of a daytime population distribution between population register data and call activities (4 PM
– 5 PM) refined by the proposed MFD and a simple areal weighting (AW) interpolation methods with both target zone
layers.
Figure S10. The distribution of target zones by the difference of a daytime population derived from call activities (4 PM –
5 PM) in a target zone between the proposed MFD and a simple areal weighting (AW) interpolation methods with both
target zone layers.
References
Ahas, R., Silm, S., Järv, O., Saluveer, E., and Tiru, M., 2010. Using Mobile Positioning Data to Model Locations
Meaningful to Users of Mobile Phones. Journal of Urban Technology, 17 (1), 3–27.
Alahmadi, M., Atkinson, P., and Martin, D., 2013. Estimating the spatial distribution of the population of Riyadh,
Saudi Arabia using remotely sensed built land cover and height data. Computers, Environment and Urban
Systems, 41, 167–176.
Biljecki, F., Arroyo Ohori, K., Ledoux, H., Peters, R., and Stoter, J., 2016. Population Estimation Using a 3D City
Model: A Multi-Scale Country-Wide Study in the Netherlands. PLOS ONE, 11 (6), e0156808.
European Commission, 2002. Directive 2002/58/EC of the European Parliament and of the Council on privacy
and electronic communications. Official Journal of the European Communities, 37–47.
Eurostat, 2009. Harmonised European time use surveys: 2008 guidelines. Luxembourg: European Communities.
Greger, K., 2015. Spatio-Temporal Building Population Estimation for Highly Urbanized Areas Using GIS:
Spatio-Temporal Building Population Estimation. Transactions in GIS, 19 (1), 129–150.
Hussein, T., Paasonen, P., and Kulmala, M., 2012. Activity pattern of a selected group of school occupants and
their family members in Helsinki — Finland. Science of The Total Environment, 425, 289–292.
Järv, O., Ahas, R., and Witlox, F., 2014. Understanding monthly variability in human activity spaces: A twelvemonth
study using mobile phone call detail records. Transportation Research Part C: Emerging
Technologies, 38 (0), 122–135.