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CSIS workshop on Research Agenda for Spatial Analysis Position paper

CSIS workshop on Research Agenda for Spatial Analysis Position paper. By Atsu Okabe. The real space is complex, but …. Spatial analysts. Through the glasses of spatial analysts Assumption 1. Through the glasses of spatial analysts Assumption 2.

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CSIS workshop on Research Agenda for Spatial Analysis Position paper

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  1. CSIS workshop onResearch Agenda for Spatial AnalysisPosition paper By Atsu Okabe

  2. The real space is complex, but … Spatial analysts

  3. Through the glasses of spatial analystsAssumption 1

  4. Through the glasses of spatial analystsAssumption 2

  5. In spatial pointprocesses,the homogeneous assumption means …. Uniform density

  6. Through the glasses of spatial analystsAssumption 3

  7. Through the glasses of spatial analystsAssumption 4 ∞ e.g. Poisson point processes

  8. Summing up, In most spatial point pattern analysis, Assumption 1: 2-Dimensional Assumption 2: Homogeneous Assumption 3: Euclidean distance Assumption 4: Unbounded The space characterized by these assumptions= “ideal” space Useful for developing pure theories

  9. Advantages Analytical derivation is tractable

  10. Advantages No boundary problem! boundary problem http://www.whitecliffscountry.org.uk/gallery/cliffs1.asp

  11. Actual example Insects on the White desert, Egypt http://www.molon.de/galleries/Egypt_Jan01/WhiteDesert/imagehtm/image12.htm

  12. Actual example “Scattered village” on Tonami plain, Japan http://www.sphere.ad.jp/togen/photo-n.html

  13. Houses on the Tonami plain studied by Matsui

  14. When it comes to spatial analysis in an urbanized area, …

  15. The real city is 3D 3D

  16. The real city consists of many kinds of features heterogeneous

  17. We cannot go through buildings!

  18. The real urban space is bounded by railways, …. bounded

  19. The “ideal” space is far from the real space! Real space “Ideal” space The objective is to fill this gap

  20. Convenience stores in Shibuya constrained by the street network!

  21. Dangerous to ignore the street network

  22. Random? NO!?

  23. Random? YES!!

  24. Misleading Non-random on a plane Random on a network

  25. Too unrealistic! To represent the realspace by the “ideal” space

  26. Alternatively, Represent the real space by network space Assumption 1

  27. Network space is appropriate for traffic accidents http://www.sanantonio.gov/sapd/TrFatalityMap.htm

  28. Robbery and Car Jacking http://www.new-orleans.la.us/cnoweb/nopd/maps/4week/4wkrob.html

  29. Pipe corrosion http://www.fugroairborne.com/CaseStudies/pipe_line.jpg

  30. Network space Network space is appropriate to deal with traffic accidents robbery and car jacking pipe corrosion traffic lights etc. because these events occur on a network.

  31. Banks, stores and many kinds of facilities are not on streets! http://www.do-map.net/

  32. How to use facilities? facilities home sidewalks roads gate Entrance Street Street railways Through networks

  33. Facilities are represented by access points on a network camera shop house Street Street Access point Access point

  34. An example: banks in Shibuya Banks are represented by access points (entrances) on a street network

  35. Assumption 1 Assumption 2 The distance between two points on a network is measured by the shortest-path distance.

  36. Euclidean distance vs shortest path distance Koshizuka and Kobayashi

  37. Ordinary Voronoi diagram vsManhattan Voronoi diagram

  38. One-way

  39. Assumption 1 Heterogeneous A network space is heterogeneous in the sense that it is not isotropic.

  40. Assumption 3: probabilistically homogeneous Sounds unrealistic but NOT!

  41. Density function on a network f(x) Probabilistically homogeneous = uniform distribution

  42. Density function on a network Traffic density NOX density Housing density Population density etc.

  43. Housing density function

  44. Population density function

  45. Probabilistically homogeneous assumption is unrealistic The distribution of stores are affected by the population density. The population distribution is not uniform

  46. Uniform network transformation Any p-heterogeneous network can be transformed into a p-homogeneous network!

  47. y f(x) x Probability integral transformation Uniform distribution Density function on a link: non-uniform distribution

  48. Assumption 4: Bounded

  49. Plane: hard Network: easier Boundary treatment

  50. How to deal with features in 3D space?

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