Visualization Techniques for Geospatial Data IDV 2015/2016

Transcription

1 Interactive Data Visualization 07 Visualization Techniques for Geospatial Data IDV 2015/2016

2 Notice n Author t João Moura Pires (jmp@fct.unl.pt) n This material can be freely used for personal or academic purposes without any previous authorization from the author, provided that this notice is kept with. n For commercial purposes the use of any part of this material requires the previous authorisation from the author. 2

3 Bibliography n Many examples are extracted and adapted from t Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward, Georges Grinstein, Daniel Keim, 2015 t Visualization Analysis & Design, Tamara Munzner,

4 Table of Contents n Visualizing Spatial Data n Visualization of Point Data n Visualization of Line Data n Visualization of Area Data n Other Issues in Geospatial Data Visualization.. 4

5 Interactive Data Visualization Visualizing Spatial Data 5

6 Geospatial data n Geospatial data is different from other kinds of data in that spatial data describes objects or phenomena with a specific location in the real world. n Often, spatial data sets are discrete samples of a continuous phenomenon. n Because of its special characteristics, the basic visualization strategy for spatial data is straightforward; we map the spatial attributes directly to the two physical screen dimensions, resulting in map visualizations. n Maps are the world reduced to points, lines, and areas. The visualization parameters, including size, shape, value, texture, color, orientation, and shape, show additional information about the objects under consideration. 6

7 Geospatial phenomena data n Spatial phenomena according to their spatial dimension: n point phenomena n line phenomena: have length, but essentially no width n area phenomena: have both length and width n surface phenomena: have length, width, and height n Maps can be subdivided into map types based on properties of the data (qualitative versus quantitative; discrete versus continuous) and the properties of the graphical variables (points, lines, surface, volumes). 7

8 Map Projections n Map projections are concerned with mapping the positions on the globe (sphere) to positions on the screen (flat surface). n Π : (λ, φ) (x, y) Degrees of longitude (λ) in [ 180, 180], where negative values stand for western degrees and positive values for eastern degrees. The degrees of latitude (φ) are defined similarly on the interval [ 90, 90], where negative values are used for southern degrees and positive values for northern degrees. 8

9 Map Projections n Lisbon is located at N W Rossio 9

10 Map Projections n Lisbon is located at N W Rossio 10

11 Latitude and Longitude 11

12 Map Projections n A conformal projection retains the local angles on each point of a map correctly, which means that they also locally preserve shapes. The area, however, is not preserved. n Equivalent or equal area if a specific area on one part of the map covers exactly the same surface on the globe, as another part of the map with the same area. Area-accurate projections result in a distortion of form and angles. n Equidistant if it preserves the distance from some standard point or line. n Gnomonic projections allow all great circles to be displayed as straight 12

13 Map Projections n Gnomonic projections allow all great circles to be displayed as straight lines. They preserve the shortest route between two points. n Azimuthal projections preserve the direction from a central point. Usually these projections also have radial symmetry in the scales, e.g., distances from the central point are independent of the angle and consequently, circles with the central point as center result in circles that have the central point on the map as their center. n In a retroazimuthal projection, the direction from a point S to a fixed location L corresponds to the direction on the map from S to L. 13

14 Map Projections: to what kind of surface n Most cylinder projections preserve local angles and are therefore conformal projections. The degrees of longitude and latitude are usually orthogonal to each other 14

15 Map Projections: to what kind of surface n Plane projections are azimuthal projections that map the surface of the sphere to a plane that is tangent to the sphere, with the tangent point corresponding to the center point of the projection. Some plane projections are true perspective projections. 15

16 Map Projections: to what kind of surface n Cone projections map the surface of the sphere to a cone that is tangent to the sphere. Degrees of latitude are represented as circles around the projection center, degrees of longitude as straight lines emanating from this center. Cone projections may be designed in a way that preserves the distance from the center of the cone. 16

17 Map Projections: Equirectangular Cylindrical Projections x = λ, y = φ. n It maps meridians to equally spaced vertical straight lines and circles of latitude to evenly spread horizontal straight lines. The projection does not have any of the desirable map properties and is neither conformal nor equal area. It has little use in navigation, but finds its main usage in thematic mapping. 17

18 Map Projections: Lambert cylindrical projection n Is an equal area projection that is easy to compute and provides nice world maps. 18

19 Map Projections: Mercator projection n is a cylindrical map projection. n It became the standard map projection for nautical purposes because of its ability to represent lines of constant course. 19

20 Map Projections: Mercator projection n is a cylindrical map projection. n It became the standard map projection for nautical purposes because of its ability to represent lines of constant course. Google Maps uses a close variant of the Mercator projection, and therefore cannot accurately show areas around the poles. 20

21 Visual Variables for Spatial Data n n n n n n n size: size of individual symbols, width of lines, or size of symbols in areas; shape: shape of individual symbols or pattern symbols in lines and areas; brightness: brightness of symbols, lines, or areas; color: color of symbols, lines, or areas; orientation: orientation of individual symbols or patterns in lines and areas; spacing (texture): spacing of patterns in symbols, lines, or areas; perspective height: perspective three-dimensional view of the phenomena with the data value mapped to the perspective height of points, lines, or areas; n arrangement: arrangement of patterns within the individual symbols (for point phenomena), patterns of dots and dashes (for line phenomena), or regular versus random distribution of symbols (for area phenomena). 21

22 Visual Variables for Spatial Data 22

23 Issues for spatial data mapping n Note that in spatial data mapping, the chosen class separation, normalization, and spatial aggregation may have a severe impact on the resulting visualization: Different class separation with a significant impact on the generated map 23

24 Issues for spatial data mapping n Note that in spatial data mapping, the chosen class separation, normalization, and spatial aggregation may have a severe impact on the resulting visualization: Absolute versus relative mapping. On the right numbers are displayed relative to the population numbers 24

25 Issues for spatial data mapping n Note that in spatial data mapping, the chosen class separation, normalization, and spatial aggregation may have a severe impact on the resulting visualization: London cholera example with different area aggregations, resulting in quite different maps 25

26 Interactive Data Visualization Visualization of Point Data 26

27 Visualization of Point Data n Point data are discrete in nature, but they may describe a continuous phenomenon. 27

28 Visualization of Point Data n Point data are discrete in nature, but they may describe a continuous phenomenon. n Discrete data are presumed to occur at distinct locations, while continuous data are defined at all locations n Smooth data refers to data that change in a gradual fashion, while abrupt data change suddenly. 28

29 Dot Maps n Point phenomena can be visualized by placing a symbol or pixel at the location where that phenomenon occurs. n A quantitative parameter may be mapped to the size or the color of the symbol or pixel. t Symbols: circles are the most widely used symbol in dot maps, but squares, bars, or any other symbol can be used as well t Size: calculating the correct size of the symbols does not necessarily mean that the symbols will be perceived correctly. The perceived size of the symbols depends on their local neighborhood (e.g., the Ebbinghaus illusion) t Color: take into account the problems of color 29

30 Dot Maps n Point phenomena can be visualized by placing a symbol or pixel at the location where that phenomenon occurs. n A quantitative parameter may be mapped to the size or the color of the symbol or pixel. t Symbols: circles are the most widely used symbol in dot maps, but squares, bars, or any other symbol can be used as well t Size: calculating the correct size of the symbols does not necessarily mean that the symbols will be perceived correctly. The perceived size of the symbols depends on their local neighborhood (e.g., the Ebbinghaus illusion) t Color: take into account the problems of color 29

31 Dot Maps n When large data sets are drawn on a map, the problem of overlap or overplotting of data points arises in highly populated areas, while low-population areas are virtually empty 30

32 Dot Maps n When large data sets are drawn on a map, the problem of overlap or overplotting of data points arises in highly populated areas, while low-population areas are virtually empty n Spatial data are highly non-uniformly distributed in real-world data sets. Credit card payments, telephone calls, health statistics, environmental records, crime data, and census demographics,, etc.. n Approaches for coping with dense spatial data t t 2.5D visualization showing data points aggregated up to map regions Individual data points as bars, according to their statistical value on a map 31

33 Dot Maps n Approaches for coping with dense spatial data Geo-Spatial Data Viewer: From Familiar Land-covering to Arbitrary Distorted Geo-Spatial Quadtree Maps Daniel A. Keim, Christian Panse, Jorn Schneidewind, Mike Sips 32

34 Dot Maps n Approaches for coping with dense spatial data Geo-Spatial Data Viewer: From Familiar Land-covering to Arbitrary Distorted Geo-Spatial Quadtree Maps Daniel A. Keim, Christian Panse, Jorn Schneidewind, Mike Sips 33

35 Dot Maps n Approaches for coping with dense spatial data Geo-Spatial Data Viewer: From Familiar Landcovering to Arbitrary Distorted Geo-Spatial Quadtree Maps Daniel A. Keim, Christian Panse, Jorn Schneidewind, Mike Sips 34

36 Visualization of Point Data: PixelMaps n The idea is to reposition pixels that would otherwise overlap Daniel A. Keim, Christian Panse, Mike Sips, and Stephen C. North. Pixel Based Visual Mining of Geo-Spatial Data. Computers and Graphics 28:3 (2004), Available online ( 2008/6967). 35

37 Interactive Data Visualization Visualization of Line Data 36

38 Visualization of Line Data n The basic idea for visualizing spatial data describing linear phenomena is to represent them as line segments between pairs of endpoints specified by longitude and latitude. n A standard mapping of line data allows data parameters to be mapped to line width, line pattern, line color, and line labeling. n In addition, data properties of the starting and ending points, as well as intersection points, may also be mapped to the visual parameters of the nodes, such as size, shape, color, and labeling. n Lines do not need to be straight, but may be polylines or splines 37

39 Visualization of Line Data n Network Maps 38

40 Visualization of Line Data n Network Maps Visualization study of inbound traffic measured in billions of bytes on the NSFNET T1 backbone for September

41 Visualization of Line Data n Flow Maps and Edge Bundling 40

42 Visualization of Line Data n Flow Maps and Edge Bundling Spatial Clustering in SOLAP Systems to Enhance Map Visualization, Ricardo Silva, João Moura-Pires, Maribel Yasmina Santos 41

43 Visualization of Line Data n Flow Maps and Edge Bundling 42

44 Visualization of Line Data n Geo-espacial lines Mapping London's smells: smellscapes show which streets stink 43

45 Interactive Data Visualization Visualization of Area Data 44

46 Visualization of Area Data: Thematic maps n Choropleth maps: values of an attribute or statistical variable are encoded as colored or shaded regions on the map t Assume that the mapped attribute is uniformly distributed in the regions U.S. election results of the 2008 Obama versus McCain presidential election. 45

47 Visualization of Area Data: Thematic maps n Choropleth maps: values of an attribute or statistical variable are encoded as colored or shaded regions on the map t Assume that the mapped attribute is uniformly distributed in the regions n A problem of choropleth maps is that the most interesting values are often concentrated in densely populated areas with small and barely visible polygons, and less interesting values are spread out over sparsely populated areas with large and visually dominating polygons. 46

48 Visualization of Area Data: Thematic maps n Choropleth maps: values of an attribute or statistical variable are encoded as colored or shaded regions on the map t Assume that the mapped attribute is uniformly distributed in the regions Portuguese population 47

49 Visualization of Area Data: Thematic maps Choropleth map of the percent of population over 65 years old By using a diverging color scheme that can be dynamically manipulated, we can begin to see clusters of localities with low levels of elderly residents (areas circled in yellow near Lisbon and Porto in the center map) By moving the color scheme divergence point to a higher percentage of elderly residents (right), we can more clearly see the clusters of elderly residents that were faintly visible in the original map 48

50 Visualization of Area Data: Thematic maps Deteção e Caracterização Geo-Espacial das Zonas de Acumulação de Acidentes Rodoviários, Luís Filipe Cruz Ramos 49

51 Visualization of Area Data: Thematic maps 50

54 Visualization of Area Data: Thematic maps n If the attribute has a different distribution than the partitioning into regions, other techniques, such as dasymetric maps, are used. The variable to be shown forms areas independent of the original regions. To do this, ancillary information is acquired, which means the cartographer steps statistical data according to extra information collected within the boundary 53

55 Visualization of Area Data: Thematic maps n A third important type of map is an isarithmic map, which shows the contours of some continuous phenomena: isometric maps, if the contours are determined from real data points such as temperatures measured at a specific location. An isarithmic map showing the number of pictures taken on Mainau Island, using a heatmap, where the colors range from black to red to yellow, with yellow representing the most photographs 54

56 Visualization of Area Data: Thematic maps An isarithmic map showing the the average anual temperature 55

57 Visualization of Area Data: Thematic maps n A third important type of map is an isarithmic map, which shows the contours of some continuous phenomena: isopleth maps, if the data are measured for a certain region (such as a county) and, for example, the centroid is considered as the data point. Source 56

58 Visualization of Area Data: Thematic maps n A third important type of map is an isarithmic map, which shows the contours of some continuous phenomena. n isometric maps, if the contours are determined from real data points such as temperatures measured at a specific location. n isopleth maps, if the data are measured for a certain region (such as a county) and, for example, the centroid is considered as the data point. n One of the main tasks in generating isarithmic maps is the interpolation of the data points to obtain smooth contours, which is done, for example, by triangulation, or inverse distance mapping. 57

59 Visualization of Area Data: Cartograms n A problem of choropleth maps is that the most interesting values are often concentrated in densely populated areas with small and barely visible polygons, and less interesting values are spread out over sparsely populated areas with large and visually dominating polygons. n Cartograms are generalizations of ordinary thematic maps that avoid the problems of choropleth maps by distorting the geography according to the displayed statistical value: n The size of regions is scaled to reflect a statistical variable, leading to unique distortions of the map geometry. 58

60 Visualization of Area Data: Cartograms 59

61 Visualization of Area Data: Cartograms n Noncontinuous cartograms can exactly satisfy area and shape constraints, but don t preserve the input map s topology. Because the scaled polygons are drawn inside the original regions, the loss of topology doesn t cause perceptual problems. n More critical is that the polygon s original size restricts its final size. 60

62 Visualization of Area Data: Cartograms n Noncontiguous cartograms, scale all polygons to their target sizes, perfectly satisfying the area objectives. They provide perfect area adjustment, with good shape preservation. However, they lose the map s global shape and topology, which can make perceiving the generated visualization as a map difficult. 61

63 Visualization of Area Data: Cartograms n Circular cartograms, completely ignore the input polygon s shape, representing each as a circle in the output. Circular cartograms have some of the same problems as noncontiguous cartograms. 62

64 Visualization of Area Data: Cartograms n Continuous cartograms retain a map s topology perfectly, but they relax the given area and shape constraints. In general, cartograms can t fully satisfy shape or area objectives, so cartogram generation involves a complex optimization problem in searching for a good compromise between shape and area preservation 63

65 Visualization of Area Data: Cartograms n A U.S. state population cartogram with the presidential election results of The area of the states in the cartogram corresponds to the population, and the color (shaded and not shaded areas) corresponds to the percentage of the vote. A bipolar color map depicts which candidate has won each state. Figure Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward, Georges Grinstein, Daniel Keim,

66 Visualization of Area Data: Cartograms 65

67 Interactive Data Visualization Other Issues in Geospatial Data Visualization 66

68 Other Issues in Geospatial Data Visualization n Map generalization is the process of selecting and abstracting information on a map. Generalization is used when a small-scale map is derived from a large-scale map containing detailed information. t map generalizations are application- and task-dependent, e.g., good map generalizations emphasize the map elements that are most important for the task at hand, while still representing the geography in the most accurate and recognizable way: simplify points, simplify lines, simplify polygons, etc.. t Map labeling deals with placing textual or figurative labels close to points, lines, and polygons 67

69 Other Issues in Geospatial Data Visualization 68

72 Interactive Data Visualization Further Reading and Summary 71

73 Further Reading n Pag from Interactive Data Visualization: Foundations, Techniques, and Applications, Matthew O. Ward, Georges Grinstein, Daniel Keim, 2015 Further Reading and Summary 72