2. Spatial Database
2
Stores a large amount of space-related data
Maps
Remote Sensing
Medical Imaging
VLSI chip layout
Have Topological and distance information
Require spatial indexing, data access, reasoning ,geometric
computation and knowledge representation techniques
3. Spatial Data Mining
3
Extraction of knowledge, spatial relationships from
spatial databases
Can be used for understanding spatial data and spatial
relationships
Applications:
GIS, Geomarketing, Remote Sensing, Image database
exploration, medical imaging, Navigation
Challenges
Complexity of spatial data types and access methods
Large amounts of data
4. Cont.
4
Non-spatial Information
Same as data in traditional data mining
Numerical, categorical, ordinal, boolean, etc
e.g., city name, city population
Spatial Information
Spatial attribute: geographically referenced
Neighborhood and extent
Location, e.g., longitude, latitude, elevation
Spatial data representations
Raster: gridded space
Vector: point, line, polygon
Graph: node, edge, path
7. Statistical techniques
7
Popular approach to analyze spatial data
Assumes independence among spatial data
Can be performed only by experts
Do not work well with symbolic values
8. Spatial Data Warehousing
8
Spatial data warehouse: Integrated, subject-oriented, time-variant,
and nonvolatile spatial data repository.
It consists of both spatial and non spatial in support of spatial data mining
and spatial-data-related decision-making processes.
Spatial data cube: multidimensional spatial database
Both dimensions and measures may contain spatial components.
Challenging issues:
Spatial data integration: a big issue
Structure-specific formats (raster- vs. vector-based, OO vs. relational models,
different storage and indexing, etc.)
Vendor-specific formats (ESRI, MapInfo, Intergraph, IDRISI, etc.)
Realization of Fast and flexible OLAP in spatial data warehouses.
9. Dimensions and Measures in Spatial
Data Warehouse
9
Dimensions
non-spatial
e.g. “25-30 degrees” generalizes to“hot” (both are strings)
spatial-to-non spatial
e.g. Seattle generalizes to description “Pacific Northwest” (as a string)
spatial-to-spatial
e.g. Seattle generalizes to Pacific Northwest (as a spatial region)
Measures
numerical (e.g. monthly revenue of a region)
distributive (e.g. count, sum)
algebraic (e.g. average)
holistic (e.g. median, rank)
spatial
collection of spatial pointers (e.g. pointers to all regions with temperature of
25-30 degrees in July)
10. Example: British Columbia Weather
Pattern Analysis
10
Input
A map with about 3,000 weather probes scattered in B.C.
Recording daily data for temperature, precipitation, wind velocity, etc. for a designated
small area and transmitting signal to a provincial weather station.
Data warehouse using star schema
Output
A map that reveals patterns: merged (similar) regions
Goals
Interactive analysis (drill-down, slice, dice, pivot, roll-up)
Fast response time
Minimizing storage space used
Challenge
A merged region may contain hundreds of “primitive” regions (polygons)
11. Star Schema of the BC Weather
Warehouse
Spatial data warehouse
Dimensions
region_name
time
temperature
precipitation
Measurements
region_map
area
count
11Fact tableDimension table
12. 12
Can we precompute all of the possible spatial merges
and store them in the corresponding cuboid cells of a
spatial data cube?
Probably not.
It requires multi-megabytes of storage.
On-line computation is slow and expensive.
14. Methods for Computing Spatial Data
Cubes
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On-line aggregation: collect and store pointers to spatial
objects in a spatial data cube
expensive and slow, need efficient aggregation techniques
Precompute and store all the possible combinations
huge space overhead
Precompute and store rough approximations in a spatial data
cube
accuracy trade-off, MBR
Selective computation: only materialize those which will be
accessed frequently
a reasonable choice
15. Mining Spatial Association and
Co-location Patterns
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Spatial association rule: A ⇒ B [s%, c%]
A and B are sets of spatial or non-spatial predicates
Topological relations: intersects, overlaps, disjoint, etc.
Spatial orientations: left_of, west_of, under, etc.
Distance information: close_to, within_distance, etc.
s% is the support and c% is the confidence of the rule
Examples
is_a(x, “School”) ^ Close_to(x, “Sports_Center”) → close_to(x, “Park”)
[7%, 85%]
16. Progressive Refinement
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Progressive Refinement:
spatial association mining needs to evaluate multiple spatial relationships
among a large no. of spatial object – expensive.
Hierarchy of spatial relationship:
First search for rough relationship and then refine it
Superset coverage property – all the potential answers should be perserved
(i.e.false-positive test).
Two-step mining of spatial association:
Step 1: Rough spatial computation (as a filter)
Using MBR for rough estimation
Step2: Detailed spatial algorithm (as refinement)
Apply only to those objects which have passed the rough spatial association test
(no less than min_support)
17. Spatial co-locations
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Just what one really wants to explore.
Based on the property of spatial autocorrelation, interesting
features likely coexist in closely located regions.
Efficient methods - Apriori , progressive refinement,etc.
19. Spatial Cluster Analysis
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• Mining clusters—k-means, k-medoids, hierarchical, density-based,
etc.
• Analysis of distinct features of the clusters
20. Spatial Classification
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Analyze spatial objects to derive classification schemes, such
as decision trees, in relevance to certain spatial properties
(district, highway, river, etc.)
Classifying medium-size families according to income, region, and infant mortality
rates
Mining for volcanoes on Venus
Employ methods such as:
Decision-tree classification, Naïve-Bayesian classifier + boosting, neural network,
genetic programming, etc.
21. Spatial Trend Analysis
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Function
Detect changes and trends along a spatial dimension
Study the trend of non-spatial or spatial data changing with space
Application examples
Observe the trend of changes of the climate or vegetation with
increasing distance from an ocean
Crime rate or unemployment rate change with regard to city geo-
distribution.
Traffic flows in highways and in cities.
24. Other Applications
24
Spatial data mining is used in
NASA Earth Observing System (EOS): Earth science data
National Inst. of Justice: crime mapping
Census Bureau, Dept. of Commerce: census data
Dept. of Transportation (DOT): traffic data
National Inst. of Health(NIH): cancer clusters
Commerce, e.g. Retail Analysis
