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Kazuhiko HAMAMOTO Dept. of Information Media Technology,

Spatiotemporal Information Processing No.6 Data expression and Application to GIS (Geographic Information System). Kazuhiko HAMAMOTO Dept. of Information Media Technology, School of Information and Telecommunication Eng., Tokai University, Japan. Today’s Contents.

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Kazuhiko HAMAMOTO Dept. of Information Media Technology,

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  1. Spatiotemporal Information ProcessingNo.6Data expression and Application to GIS (Geographic Information System) Kazuhiko HAMAMOTO Dept. of Information Media Technology, School of Information and Telecommunication Eng., Tokai University, Japan

  2. Today’s Contents • Point of view for spatial information • Structure of spatial information • Basic techniques of spatial information processing • Level classification • Spatial inference • Spatial index • Spatial query

  3. Point of view for spatial information - 1 • Its expression is not “algorithmism” but “dataism”. • Spatial information includes not only “visible information” but “semantic information”. • “There is a bread shop along right side of the road which is 50m far from next cross road.” • How to express “semantic information” in spatial information ?

  4. Point of view for spatial information - 2 • How to express “semantic information” in spatial information ? • Spatial information is expressed as “spatial structure of knowledge of the real world” and input it to a computer • Conventional image data is a non-structured data, so the role or property is different. The same expression cannot be used.

  5. Structure of spatial data • Class or Category • Object • Identifier • Attribute • Geometric attribute • Non-geometric attribute • Relationship • Spatial relationship • Temporal relationship • Object • ・・・

  6. 空間データの構築 • クラス(class)またはカテゴリ(category)：分類名 • オブジェクト(object)：実体 • 識別子(identifier) • 属性(attribute) • 幾何属性(geometric attribute) • 非幾何属性(non-geometric attribute) • 関連(relationship) • 空間的関連(spatial relationship) • 時間的関連(temporal relationship) • オブジェクト • 識別子 • ・・・

  7. Attribute • Data set which expresses an object • Geometric attribute • Abstract expression of an object by simple geometrical figure (line, dot, etc.) • Non-geometric attribute • Name, area, texture, etc. of an object “Attribute” does not comprehend all of information. It is a part of information which is extracted under a condition.

  8. Relationship • Relationship among objects • Spatial relationship • For example, “An object is next to an object.” • It is expressed by “vector data”. • Temporal relationship • For example, “this object was made earlier than that one”.

  9. Concept model of spatial data Set of objects in “mountain” class

  10. Vector data - 1 • How to express “relationship” • Geometrical figure data constructed by “dot” data • dot • coordinates • line • a set of dot. • start point and end point are different

  11. Vector data - 2 • Geometrical figure data constructed by “dot” data • region • a closed line, whose start point and end point are the same. • surface • data about 2D region or the third data for (x,y) coordinates • solid • 3D geometrical data • 2 data or more data for (x,y) coordinates

  12. Example of vector data

  13. Another vector data : Topology • connection, connotation, etc. • “A road is connected to a road.” • “There is a region on the right side of a road.” • “There is a road along the region.” • “There is a building in the region.” • It is easy to restructure and recognize spatial information • Intelligent spatial information processing • The shortest path problem, etc.

  14. Topology data

  15. Raster data • It has a value on a grid at regular intervals. • For example, image (texture), land heights data, etc. • But “contour” ,which expresses the same height is vector data

  16. Basic techniques of spatial information processing • Level classification • Information is classified to objects. • This process enables to : • Spatial inference • The spatial relationship is obtained by topology • Spatial index • Spatial query • Rapid retrieval from huge amount of data

  17. Spatial inference • To obtain spatial relationship • “be next to”, “be connected to” and “can go there in 5minutes”, etc. • Topology data is needed • Spatial relationship cannot be expressed by only position data • Basic components of topology data • Node (vertex) • Link (edge)

  18. Spatial inference • An example of topology data Nodeshibuya＝Node1003, Nodeharajyku＝Node1004, Nodeyoyogi＝Node1005, Nodeshinjuku＝Node1006 Linkshibuya→harajuku＝Link505＝（Node1003,Node1004） Linkharajuku→yoyogi＝Link506＝（Node1004,Node1005） Linkyoyogi→shinjuku＝Link507＝（Node1005,Node1006） Networkyamanote_line＝Network303 　　＝｛Link505, Link506, Link507｝

  19. Spatial inference • Addition of “region” by network and arrival time • Inference of “Shinjuku sta. is the third sta. beyond Shibuya sta.” or “The shortest path is ・・・” is possible.

  20. Spatial index, Spatial query • Indexing spatial data by mesh • The mesh has hierarchical framework. • The standard mesh in Japan • 1st mesh : 80km, 2nd mesh : 10km, 3rd mesh : 1km • Retrieve a target included in directed area • Obtain sets of mesh which covers the directed area • Judge whether a target in the sets of mesh is included in directed area or not • Retrieval time depends on size of the mesh

  21. Spatial index, Spatial query how to make mesh - 1 R0 : set of mesh=1, R1 : set of mesh=2 R2 : set of mesh=4, R3 : set of mesh=9 R4 : although set of mesh = 4，regions in HDD are separated.

  22. Spatial index, Spatial query how to make mesh - 2 • Spatial query by “quad-tree” • The area is divided into 4 parts. • The division process is repeated. • Finally, each part has the same number of spatial data, which is less than a constant. • Simple algorithm because division interval is constant. • If distribution of spatial data varies widely, the depth of retrieval is different by dense or sparse. • Retrieval is late in a dense region

  23. An example of quad-tree division

  24. Spatial index, Spatial query how to make mesh - 3 • Spatial query by “K-d tree” • The boundary of division is changed according to amount of data • The depth of retrieval is constant. That means retrieval speed is constant • If the data is not renewed, it is effective

  25. An example of K-d tree division

  26. Spatial index, Spatial query how to make mesh - 4 • Spatial query by “R-tree” • Spatial data is connoted by the smallest rectangular area. The rectangular area is a representative of the spatial data. • Adjacent set of the representative area is structured. • If data is renewed, the depth of tree can be kept regularly.

  27. An example of R-tree division and its hierarchical structure

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