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

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

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 ?

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.

Structure of spatial data

Class or Category Object

Identifier Attribute

Geometric attribute Non-geometric attribute

Relationship Spatial relationship Temporal relationship

Object ・・・

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・・・

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.

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”.

Concept model of spatial data

Set of objects in “mountain” class

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

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

Example of vector data

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.

Topology data

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

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

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)

Spatial inference

An example of topology data

Nodeshibuya＝ Node1003, 　 Nodeharajyku＝ Node1004,Nodeyoyogi＝ Node1005, 　 Nodeshinjuku＝ Node1006

Linkshibuya→harajuku＝ Link505＝（ Node1003, 　Node1004）Linkharajuku→yoyogi＝ Link506＝（ Node1004, 　 Node100

5）Linkyoyogi→shinjuku＝ Link507＝（ Node1005, 　 Node100

6）

Networkyamanote_line＝ Network303

　　＝｛ Link505, Link506, Link507｝

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.

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

R0 : set of mesh=1, R1 : set of mesh=2R2 : set of mesh=4, R3 : set of mesh=9

R4 : although set of mesh = 4， regions in HDD are separated.

Spatial index, Spatial query how to make mesh - 1

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

Spatial index, Spatial query how to make mesh - 2

An example of quad-tree division

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

Spatial index, Spatial query how to make mesh - 3

An example of K-d tree division

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.

Spatial index, Spatial query how to make mesh - 4

An example of R-tree division and its hierarchical structure