Cartographic Generalization Process of abstraction which react on change of map purpose or scale Process is influenced by symbolics and character of displayed area Process encompasses Reduction of complexity Emphasis of essential and suppress unimportant Maintenance of relationship among map objects Preservation of aesthetic quality Definitions (ICA'73­McMaster'92­Ruas'95) Selection and simplified representation of detail appropriate scale and/or purpose of map Process of deriving from data source through the application of transformations, where derivation objectives are to reduce data in scope of amount, type and cartographic portrayal with maintenance of consistency and clarity of presentation Process of thematic and geometric resolution reduction Automated Generalization Algorithmisation of transformations Three main types Computer aided generalization ­ cartographic generalization provided by humans with computer based procedures Multiple representation ­ to design adaptable cartographic visualization of geodata Real-time generalization ­ automated map derivation from geodata Roles of Generalization Map production Electronic map design Web cartography Mobile cartography Geodata integration GIS Map Production Classical task (purpose oriented universal maps) Off-line processing Computer aided generalization Reduce time of map compilation Reduce map inconsistency Ability of creation of new special purpose maps Electronic Map Design Adaptive zoom ­ smooth change of map face and legend according to scale Adaptive visualization - change map face and legend according to purpose and/or context Audience Position Time Conditions Multiple representation/real-time Less importance of annotations Web & Mobile Cartography Reduction of amount of transferred data To generate more individual maps Often opposition to map production ­ as less data as is possible Client/server distribution of tasks Multi-scale map-face Homogenize geo-data from various sources Data Integration To update geodatabase from various resources To find identical features in different scales Reverse engineering Harmonize geodata creation Homogenize existing geodata To support geoprocessing based on various resources GIS To save geoprocessing time Iterative geoprocessing Identification of spatial patterns To eliminate random errors Support for cartographic analysis of geodata Visual interface support for GIS To avoid redundancy and save storage space Models of generalization Framework how to generalize From holistic process to procedures sequence From map compilation to geodatabase processing From empiricism to analytics Separation of generalization into two parts Model generalization Visual generalization Ratajski model '67 Quantitative processes Reduction of map content according scale Qualitative processes Transform form representation to more abstract forms Generalization point ­ scale which map capacity is decreased to the level where qualitative change is necessary Bertin model '83 Similar to Ratajski Conceptual generalization New conceptualization of phenomena Structural generalization Simplify phenomenon reperesentation (shape, symbol or distribution) Structural = Quantitative, Conceptual extending qualitative Brassel-Weibel Model '88 Generalization controls Source DB Structure of original data Process library Target DB Target map Process recognition Structure recognition Process execution Data displayProcess modeling Process types & parameters Brassel-Weibel Model '88 Structure recognition ­ through cartometric analysis Identification object or aggregates Their spatial and semantic relations Their importance Generalization controls Purpose Scale difference between source and target Source quality Symbols specifications Process recognition Data modification Parameters setup Process modeling Compile rules and procedures McMaster-Shea model '92 Why to generalize Theoretical issues Reducing complexity Maintaining accuracy, consistency, logical hierarchy Maintaining aesthetic quality Application issues Scale Audience Clarity Computational issues Algorithm cost Maximum data reduction McMaster-Shea model '92 When to generalise Geometric conditions Congestion Coalescence Complication Inconsistency Imperceptibility Controls Operators Algorithms Parameters Measures Density Distribution Length and sinusoity Shape Distance Gestalt McMaster-Shea model '92 How to generalize Simplification Smoothing Aggregation Amalgamation Merging Collapse Refinement Exaggeration Enhancement Displacement Classification Symbolisation Typification (added later ) Operations (source ESRI) Kilpelainen(Sarjakoski) model '94 Model generalization separation Incremental generalization Generalization task is divided into modules which represent separable part of processed geodatabase Generalization operations are divided into increasing conflict (displacement, exaggeration) and non increasing conflict (rest) Content (selection, aggregation,amalgamation and reclassification) and graphic (rest) ones Contemporary approaches rule based constraint based agent based combinatorial optimalisation based continuous optimalisation based amplified intelligence Rule based expert systems legacy If something then action blind end ? generalization is too complex not easy to identify rules rigidity not exactly ! generalized modus ponens reverse engineering non-inventory objects Constraint based approach set of conditions for example : size, min. distance... is impossible to satisfy all optimization synthesis of conditions flexibility of method choice results oriented Agents communicating objects have plans how to reach better status perceive environment collaborate combine local and global view explicitly define map objects and its relations follows incremental model but too complex "hill climbing" (source Weibel '05) Activate Characterize & evaluate collect plans select & trigger best plan characterization & decision worstbetterperfect Pasive no more plans backtrack backtrackno plan left at node Combinatorial Optimization generate trial solutions test against criterion (a) trials are combination of methods cost of constraint violation discrete search space complicated tuning of parameters sometimes leads to deformations Continuous Optimization To find min. or max. of function defined in continuous space Good for continuous and partial deformations Define constraints Formulate constraints like functions of point locations Measure Optimize by standard math. method Generalize according to point locations Least Square Finite Elements Elastic Beams ... Amplified Intelligence Improvement of computer aided generalization Computer offer possible solutions WYSIWYG conflicts display Parameters are transformed in "human readable" form Holistic Strongly depend on user Implementations more computer aided generalization Intergraph first MGE MapGeneralizer DynaGen Geomedia extension (???) McMaster Model various simple functions WYSIWYG parameters tuning Implementations ESRl in last years simplification support big plans in future Laser - Scan is most developed related to AGENT and MAGNET CLARITY environment Various NMA's products CartACom - IGN Change ­ roads and buidings complex generalisation, Univ. of Hannover Implementations Open Source world JTS is heavy used Plug-ins web services ICA working group for algorithms sharing