Visualisation of Spatial Data

Transcription

1 Visualisation of Spatial Data VU Visual Data Science Johanna Schmidt WS 2018/19

2 2 Visual Data Science Introduction to Visualisation Basics of Information Visualisation Charts and Techniques Introduction to Visual Data Science Usage of Charts Things to consider when designing visualizations

3 3 Visual Data Science Next 3 lectures: Specific visualisation topics Spatial data Multivariate data & graphs/networks Machine learning Guest lecture by Thomas Mühlbacher (VRVis) Last 2 lectures: Overview of current visual data science applications/libraries Scientific evaluation of tools

4 4 Visual Data Science Lab Part?

5 5 Spatial Data Spatio -> Space Visualisation for data with a spatial reference [1]

6 6 Spatial Data Broad range of different data types Spatial statistical attributes Point-based data Connections Trajectory data Simulations [3] [1] [2] [4]

7 7 Challenges Spatial reference Large datasets [5]

8 8 Spatial Data Broad range of different data types Spatial statistical attributes Point-based data Connections Trajectory data Simulations [3] [1] [2] [4]

9 9 Spatial Data Broad range of different data types Spatial statistical attributes Point-based data Connections Trajectory data Simulations [3] [1] [2] [4]

10 10 Statistical Attributes Numerical statistical values related to a spatial reference Income per country Poverty level per country Mean house prices per city Crime rate per city area Mean land usage of an area

11 11 Statistical Attributes Choropleth Maps [6]

12 12 Statistical Attributes Choropleth Maps [6]

13 13 Statistical Attributes Choropleth Maps Usage of relative values [7]

14 14 Statistical Attributes Choropleth Maps Choosing the right visual mapping vs. [8]

15 15 Statistical Attributes Choropleth Maps Choosing the right visual mapping [8]

16 16 Statistical Attributes Choropleth Maps Choosing the right color map [7]

17 17 Statistical Attributes Choropleth Maps Choosing the right color map [7]

18 18 Statistical Attributes Choropleth Maps Diverging or sequential color maps [7]

19 19 Statistical Attributes Choropleth Maps Number of colors [9]

20 20 Statistical Attributes Choropleth Maps Number of colors [7]

21 21 Statistical Attributes Choropleth Maps Spatial resolution [7]

22 22 Statistical Attributes Choropleth Maps Spatial resolution [7]

23 23 Statistical Attributes Circle Maps [10]

24 24 Statistical Attributes Tile Grid Maps [11]

25 25 Statistical Attributes Tile Grid Maps [11]

26 26 Statistical Attributes Cartograms Contigous [11]

27 27 Statistical Attributes Cartograms Contigous [12]

28 28 Statistical Attributes Cartograms Non-Contigous [11]

29 29 Statistical Attributes Tile Grid Maps [13]

30 30 Spatial Data Broad range of different data types Spatial statistical attributes Point-based data Connections Trajectory data Simulations [3] [1] [2] [4]

31 31 Point-based Data Example for collective spatial events: pictures being taken Possible visualisation: Growth ring maps Ring grows, the more pictures were taken at a certain location Color shows time [14]

32 32 Spatial Data Broad range of different data types Spatial statistical attributes Point-based data Connections Trajectory data Simulations [3] [1] [2] [4]

33 33 Connections OD-relations O: Origin D: Destination

34 34 Connections OD-relations O: Origin D: Destination [15]

35 35 Connections OD-relations O: Origin D: Destination [5]

36 36 Connections OD-relations Edge Bundling [16]

37 37 Connections OD-relations Edge Bundling

38 38 Connections OD-relations OD maps [17]

39 39 Spatial Data Broad range of different data types Spatial statistical attributes Point-based data Connections Trajectory data Simulations [3] [1] [2] [4]

40 40 Spatio-Temporal Data Spatio -> Space Temporal -> Time Visualisation for data with a spatial and a temporal reference [1]

41 41 Moving Objects Movers = Objects that change spatial position over time Movement = Change of the spatial position of an object τ: T S, [t 1, t 2 ] for a given time interval t 1, t 2 the mapping function τ defines a sequence of spatial positions S, which are defined at certain timestamps T

42 42 Trajectories Movers are characterized by their trajectories Trajectories Points sampled in space (usually GPS) Interpolated curve Optional additional attributes (e.g., speed) [18]

43 43 Trajectories Challenges

44 44 Trajectories Challenges Noise Source: Sensor data

45 45 Trajectories Challenges Noise Solution: Filtering Mean/Median Filter Kalman Filter Particle Filter [19]

46 [20] 46 Trajectories Challenges Map-Matching Aligns trajecory with a road network (represented by a graph) Can be done geometrically (acc. to shape), topologically (acc. to connectivity of road network), or probabilistic (esp. to deal with low sampling rates)

47 47 Trajectories [21] Challenges Segmentation Divide trajectory into meaningful segments Can be done based on time intervals, turning points (where trajectory changes direction), shape (key points), or semantics (e.g., stays)

48 48 Trajectories Visualisation

49 49 Trajectories Space-Time Cube Well-known technique Uses three dimensions X/Z: map data Y: time Shows spatial extent as well as temporal patterns [22]

50 50 Trajectories Space-Time Cube [23]

51 51 Trajectories Space-Time Cube [24]

52 52 Trajectories Visualisation Challenges

53 53 Trajectories Visual Clutter [25]

54 54 Trajectories Visual Clutter Aggregation [25]

55 55 Trajectories [25] Visual Clutter Aggregation

56 56 Trajectories Visual Clutter Edge Bundling [26]

57 57 Trajectory Parameters Stop detection Stops (or stays) defined as ponts in space, where people stayed for a while When calculating stops, trajectory T can be divided into series of timestamped spation points p n : T = p 1 t 1 p 2 t 2 t n p n Stops have to be calculated based on trajectory points

58 58 Stop Detection Stay Point 1: very unlikely Stay Point 2: Positions shifting around [27]

59 59 Stop Detection Simple Approach [Li et al.: Mining user similarity based on location history, 2008]: Check if distance between an anchor point (p 5 ) and the successors is larger than a treshold (e.g., 100m). Find the last successor that fulfulls this criteria (p 8 ) Check if the timespan between p 5 and p 8 is above a threshold If so, the points p5, p6, p7 and p8 are considered to represent a stop [27]

60 60 Stop Detection Vehicles [Yan Z.: Towards semantic trajectory data analysis, 2009]: A point is considered a stop if v p 0.4 v avg (v p velovity at point p, v avg.. average velocity of vehicle) v p 0.3 v rc (v rc.. average velocity of nearest road crossing) v p 0.3 v rs (v rs.. average velocity of current road segment)

61 61 Stop Detection Visualisation of stops [28]

62 62 Stop Detection Visualisation of stops [28]

63 63 Stop Detection Stop Semantics vs. [29] [27]

64 64 Conclusion Overview of techniques for visualising spatial data Challenges for data analysis and visualisation Pitfalls for choropleth maps

65 65 References [1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] Alsino Skowronnek: Beyond choropleth maps: A review of techniques to visualize quantitative areal geodata. INFOVIS READING GROUP WS 2015/16 [12] John Paull and Benjamin D. Hennig: Atlas of Organics: Four maps of the world of organic agriculture, Article online, May 2016 [13] [14] Bak et al.: Algorithmic and Visual Analysis of Spatiotemporal Stops in Movement Data, 2012 [15] [16]

66 66 References [17] Wood et al.: Visualisation of origins, destinations and flows with OD maps. In The Cartographic Journal, 47(2), 2010 [18] [19] Zheng Yu: Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology 6(3), 2015 [20] [21] [22] [23] [24] Martin Nöllenburg: Geographic Visualization. In Human-Centered Visualization Environments: GI-Dagstuhl Research Seminar, March 5-8, 2006 [25] [26] Scheepens et al.: Interactive Visualization of Multivariate Trajectory Data with Density Maps, 2011 [27] Zheng Yu: Trajectory Data Mining: An Overview. ACM Transactions on Intelligent Systems and Technology 6(3), 2015 [28] Bak et al.: Algorithmic and Visual Analysis of Spatiotemporal Stops in Movement Data, 2012 [29]