Department of Geography Assessing the Cartographic Performance of Real-Time Quadtree-based Generalisation of Point Data Pia Bereuter & Robert Weibel Department of Geography, University of Zurich (UZH) {pia.bereuter | robert.weibel}@geo.uzh.ch 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 1
Department of Geography Motivation: Web & mobile maps today Too Clustered Too Too generalised cluttered Source: http://www.openstreetmap.org http://www.myswitzerland.com http://www.google.com/publicdata http://map.search.ch/ 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 2
Department of Geography Objective Provide a set of algorithms for on-the-fly generalisation of foreground point data • points of interest (POIs) • point collections (animal observations, Flickr points etc.) Proposed approach, contributions - exploit properties of quadtrees as an auxiliary data structure for generalisation - develop the major generalisation operators on point sets: selection, simplification, aggregation and displacement - real-time performance - Assessing the cartographic performance of the algorithms 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 3
Department of Geography Content 1. Background 2. Quadtree-based algorithms for point data generalisation 3. Assessing the cartographic performance 4. Conclusions & outlook 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 4
Department of Geography Background
Department of Geography Real-time point data generalisation Focus today is on real-time generalisation algorithms and the assessment of their cartographic performance. Existing algorithms: Pre- Simple computation & algorithms hierarchical using heuristics data structures to achieve real- time performance Efficiency Flexibility Initial solution for real-time aggregation based on the quadtree data structure: Burghardt et al. (2004) and Edwardes et al. (2005) 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 6
Department of Geography Quadtree-based algorithms for point data generalisation
Department of Geography Useful properties of quadtree for point data generalisation Property Use Spatial Index Speed up spatial search Hierarchy Progressive subdivision = scale progression Quad coverage Estimates of densities and distribution Topology Quad neighbourhood Key idea: The quadtree data structure lends itself to a number of simple, yet efficient and effective algorithms. See: Bereuter P and Weibel R, 2013. Real-time generalization of point data in mobile and web mapping using quadtrees. Cartography and Geographic Information Science , 40 (4), 1–11. 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 8
Department of Geography Method: Three main steps 1 2 3 Apply Create generalisation quadtree algorithm Storage (optional) Target LOD Target LOD 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 9
Department of Geography Object Directed Operators – Point Reduction • Selection: Based on attribute values; no geometric criteria used. • Simplification: Centrality based – use geometric criteria to select subset of points — as in line simplification. • Aggregation: Various principles: midpoint, cluster- based, collocation 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 10
Department of Geography Object directed operators – Displacement Direction Capacity Total 1 C 1 3 3 3 4 B 2 4,3 7 A M A 1 1 1 C B B C 3 2 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 11
Department of Geography Storage Level 0 Quadnode 1 Occupied leaf 2 Empty leaf Stored generalization 3 result 4 Algorithm search range Storage structure for generalisation results for interactive zooming 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 12
Department of Geography Assessing the cartographic perfomance
Department of Geography Implementation Java Processing http://www.processing.org Open source programming language and IDE (1991 - now) 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 14
Department of Geography Data Swiss Lichens of the Nationales Daten- und Informationszentrum der Schweizer Flechten – WSL, Birmensdorf. Lichen Species & Collection attribute Species name, Genus, Ecotype, observation date Locality Coordinate (randomised < 1km max), XY precision, Habitat, Substrate Population characteristics Size and vitality Ecology and conservation Red list status, Conservational priority, Ecological indicator (Eutrophication, Humidity, Continentality, Light, Temperatur 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 15
Department of Geography Performance Small data set Medium data set Large data set ca. 290 points ca. 2800 points ca. 86000 points Quadtree 0.8 ms 8 ms 460 ms creation Average computation time to move between two different zoom levels Selection 0.13 ms 3 ms 150 ms Median Selection 0.06 ms 2 ms 110 ms Aggregation 0.05 ms 1.6 ms 90 ms Displacement 0.98 ms 13 ms 800 ms 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013800 ms 16
Department of Geography Data Reduction What is the data reduction rate in relation to the zoom level? SwissLichens Dataset: Displacement Selection, Simplification, Aggregation Random Point Pattern: Displacement Selection, Simplification, Aggregation Regular Point Pattern: All generalisation operators Clustered Point Pattern: 20 clusters, stdev. 0.1 Displacement Selection, Simplification, Aggregation Clustered Point Pattern: 10 clusters, stdev. 0.05 Displacement Selection, Simplification, Aggregation Radical law 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 17
Department of Geography Data Reduction What is the data reduction rate in relation to the zoom level? Radical law with various base zoom levels Radical law with base zoom level 15 SwissLichens Dataset: Displacement Selection, Simplification, Aggregation Area of positive difference Selection, Simplification, Aggregation Displacement Area of negative difference Selection, Simplification, Aggregation Displacement 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 18
Department of Geography Conflict reduction How many overlaps between points symbols are resolved? Quadtree selection operator 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 19
Department of Geography Conflict reduction How many overlaps between points symbols are resolved? Quadtree selection operator - including horizontal and vertical neighbour check 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 20
Department of Geography Conflict reduction How many overlaps between points symbols are resolved? Quadtree selection operator - including horizontal and vertical neighbour check - including diagonal neighbour check 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 21
Department of Geography Conflict reduction How many overlaps between points symbols are resolved? Quadtree selection operator - including horizontal and vertical neighbour check - including diagonal neighbour check - Debug view 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 22
Department of Geography Conflict reduction How many overlaps between points symbols are resolved? Selection ~ 200ms with no conflict reduction Selection ~ 450ms with horizontal & vertical conflict reduction Selection ~ 700ms with diagonal conflict reduction 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 23
Department of Geography Data Enhancement How are important point attributes retained? Map zoom level 10 Red list status (IUCN) Least concern Near threatened Endangered species Critically endangered 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 24
Department of Geography Data Enhancement How are important point attributes retained? Map zoom level 9 Red list status (IUCN) Least concern Near threatened Endangered species Critically endangered 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 25
Department of Geography Data Enhancement How are important point attributes retained? Map zoom level 8 Red list status (IUCN) Least concern Near threatened Endangered species Critically endangered 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 26
Department of Geography Data Enhancement Global selection (global hotspots) versus local selection (retaining local context) Map zoom level 8 Map zoom level 8 Global selection (Radical Law) Local selection (Quadtree) zoom level 8 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 27
Department of Geography Displacement Measures Map zoom level 11 How is displacement achieved? Centrality-based simplification 647 points 25 Aug 2013 ICA Workshop on Generalisation and Map Production, Dresden, 2013 28
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