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Spatial Data Mining

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Spatial Data Mining Introduction Spatial data mining is the process of discovering interesting, useful, non-trivial patterns from large spatial datasets E.g. co ... – PowerPoint PPT presentation

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Title: Spatial Data Mining


1
Spatial Data Mining
2
Introduction
  • Spatial data mining is the process of
    discovering interesting, useful, non-trivial
    patterns from large spatial datasets
  • E.g. co-location patterns of water pumps and
    cholera
  • Determining hotspots unusual locations
  • Spatial Data Mining Tasks
  • Classification/Prediction
  • Co-location Mining
  • Clustering
  • Recap of special properties of Spatial Data
  • Spatial autocorrelation
  • Spatial heterogeneity
  • Implicit Spatial Relations

3
Spatial Relations
  • Spatial databases do not store spatial relations
    explicitly
  • Additional functionality required to compute them
  • Three types of spatial relations specified by the
    OGC reference model
  • Distance relations
  • Euclidean distance between two spatial features
  • Direction relations
  • Ordering of spatial features in space
  • Topological relations
  • Characterise the type of intersection between
    spatial features

4
Distance relations
  • If dist is a distance function and c is some real
    number
  • dist(A,B)gtc,
  • dist(A,B)ltc and
  • dist(A,B)c

A
B
A
B
B
A
5
Direction relations
  • If directions of B and C are required with
    respect to A
  • Define a representative point, rep(A)
  • rep(A) defines the origin of a virtual coordinate
    system
  • The quadrants and half planes define the
    direction relations
  • B can have two values northeast, east
  • Exact direction relation is northeast

C north A
C
B northeast A
B
A
rep(A)
6
Topological Relations
  • Topological relations describe how geometries
    intersect spatially
  • Simple geometry types
  • Point, 0-dimension
  • Line, 1-dimension
  • Polygon, 2-dimension
  • Each geometry represented in terms of
  • boundary (B) geometry of the lower dimension
  • interior (I) points of the geometry when
    boundary is removed
  • exterior (E) points not in the interior or
    boundary
  • Examples for simple geometries
  • For a point, I point, B and EPoints not
    in I and B
  • For a line, Ipoints except boundary points,
    Btwo end points and EPoints not in I and B
  • For a polygon, Ipoints within the boundary,
    Bthe boundary and Epoints not in I and B

7
DE-9IM
  • Topological relations are defined using any one
    of the following models
  • 4IM, four intersection model (only B and E
    considered)
  • 9IM, nine intersection models (B, I, and E)
  • DE-9IM, dimensionally extended 9 intersection
    model
  • DE-9IM is an OGC complaint model
  • Dim is the dimension function

8
Example
  • Consider two polygons
  • A - POLYGON ((10 10, 15 0, 25 0, 30 10, 25 20, 15
    20, 10 10))
  • B - POLYGON ((20 10, 30 0, 40 10, 30 20, 20 10))

9
9-Intersection Matrix of example geometries
10
DE-9IM for the example geometries
11
Relationships using DE-9IM
  • Different geometries may give rise to different
    numbers in the DE-9IM
  • For a specific type of relationship we are only
    interested in certain values in certain positions
  • That is, we are interested in patterns in the
    matrix than actual values
  • Actual values are replaced by wild cards
  • T value is "true" - non empty - any dimension gt
    0
  • F value is "false" - empty - dimension lt 0
  • Don't care what the value is
  • 0 value is exactly zero
  • 1 value is exactly one
  • 2 value is exactly two

12
Topological Relations
  • x.Disjoint(y)
  • FFFF
  • x.Touches(y)
  • FT Area/Area, Line/Line, Line/Area,
    Point/Area
  • FT Not Point/Point
  • FT
  • x.Crosses(y)
  • TT Point/Line, Point/Area, Line/Area
  • 0 Line/Line
  • x.Within(y)
  • TFF
  • x.Overlaps(y)
  • TTT Point/Point, Area/Area
  • 1TT Line/Line
  • DE-9IM string for example geometries was
    212101212 (from earlier slide)
  • A crosses B
  • A overlaps B

13
Approaches to Spatial Data Mining
  • Materialize spatial features and use Weka
  • Required features are added as additional
    attributes to the main feature
  • To create a flat file of data
  • Use special data mining techniques that take
    spatial dependency into account

14
Materializing features- Example
15
Materializing features- Example (2)
16
Spatial Data Mining Architecture
  • Retrieve data belonging to multiple themes
  • Preprocess spatial data to materialize spatial
    features
  • Select the required features
  • Use the methods to compute spatial relations to
    create a flat file of data
  • Use Weka like tool to perform data mining

Weka
Flat File
Feature Selection OGC complaint methods to
compute relations
Multiple Themes
OGC Complaint Spatial DBMS
17
Spatial Clustering
  • Also called spatial segmentation
  • Input
  • a table of area names and their corresponding
    attributes such as population density, number of
    adult illiterates etc.
  • Information about the neighbourhood relationships
    among the areas
  • A list of categories/classes of the attributes
  • Output
  • Grouped (segmented) areas where each group has
    areas with similar attribute values
  • Census Website has plenty of examples
  • http//www.statistics.gov.uk/census2001/censusmaps
    /index.html

18
Similarity with image segmentation
  • Spatial segmentation is performed in image
    processing
  • Identify regions (areas) of an image that have
    similar colour (or other image attributes).
  • Many image segmentation techniques are available
  • E.g. region-growing technique

19
Region Growing Technique
  • There are many flavours of this technique
  • One of them is described below
  • Assign seed areas to each of the segments
    (classes of the attribute)
  • Add neighbouring areas to these segments if the
    incoming areas have similar values of attributes
  • Repeat the above step until all the regions are
    allocated to one of the segments
  • Functionality to compute spatial relations
    (neighbours) assumed

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1
1
1
1
1
2
2
2
2
2
2
2
20
Summary
  • Spatial data storage available as extensions of
    RDBMS
  • Visualization of Spatial data available in GIS
  • Spatial Data Mining requires functionality to
    compute spatial relations
  • OGC specifications provide the standards for all
    the above resources
  • MYSQL provides data spatial data storage
  • But only partially provides the functionality for
    computing relations
  • Several OpenSource systems provide all the above
    resources for spatial data
  • OpenJump, GeoTools
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