Spatial Data Mining

hari agung

What is Spatial Data?

Used in/for: GIS - Geographic Information Systems Meteorology Astronomy Environmental studies, etc.

• The data related to objects that occupy space– traffic, bird habitats, global

climate, logistics, ... • Object types:

– Points, Lines, Polygons,etc.

Why do we need Data Mining?• Large number of records(cases) (108-1012

bytes)– One thousand (103) bytes = 1 kilobyte (KB)– One million (106) bytes = 1 megabyte (MB)– One billion (109) bytes = 1 gigabyte (GB)– One trillion (1012) bytes = 1 terabyte (TB)

• High dimensional data (variables)– 10-104 attributes

• Only a small portion, typically 5% to 10%, of the collected data is ever analyzed

• We are drowning in data, but starving for knowledge!

Spatial Data Mining• Spatial Patterns

– Spatial outliers– Location prediction– Associations, co-locations– Hotspots, Clustering, trends, …

• Primary Tasks– Mining Spatial Association Rules– Spatial Classification and Prediction – Spatial Data Clustering Analysis– Spatial Outlier Analysis

Spatial Classification

• Use spatial information at different (coarse/fine) levels (different indexing trees) for data focusing

• Determine relevant spatial or non-spatial features

• Perform normal supervised learning algorithms– e.g., Decision trees,

Spatial Clustering

• Use tree structures to index spatial data

• DBSCAN: R-tree

• CLIQUE: Grid or Quad tree

• Clustering with spatial constraints (obstacles need to adjust notion of distance)

Spatial Association Rules• Spatial objects are of major interest, not

transactions

• A B– A, B can be either spatial or non-spatial (3

combinations)– What is the fourth combination?

• Association rules can be found w.r.t. the 3 types

Spatial Data Mining Results

• Understanding spatial data, discovering relationships between spatial and nonspatial data, construction of spatial knowledge bases, etc.

• In various forms– The description of the general weather patterns in a set

of geographic regions is a spatial characteristic rule.– The comparison of two weather patterns in two

geographic regions is a spatial discriminant rule.– A rule like “most cities in Canada are close to the

Canada-US border” is a spatial association rule• near(x,coast) ^ southeast(x, USA) ) hurricane(x), (70%)

– Others: spatial clusters,…

Basic Concepts (1)• Spatial data mining follows along the same

functions in data mining, with the end objective to find patterns in geography, meteorology, etc.

• The main difference (Spatial autocorrelation)– the neighbors of a spatial object may have an influence

on it and therefore have to be considered as well

• Spatial attributes– Topological

• adjacency or inclusion information

– Geometric• position (longitude/latitude), area, perimeter, boundary polygon

Basic Concepts (2)• Spatial neighborhood

– Topological relation• “intersect”, “overlap”, “disjoint”,

…

– distance relation• “close_to”, “far_away”,…

– direction/orientation relation• “left_of”, “west_of”,…

• Global model might be inconsistent with regional models

Global Model

Local Model

Applications• 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

Example: What Kind of Houses Are Highly Valued?—Associative Classification

• Data– ERA-15 using a T106L31 model (from 1978 to 1994) with 1.125◦

resolution– Terabytes– Comprises data from approx. 20 variables (such as

temperature,humidity, pressure, etc.) at 30 pressure levels of a 360x360 nodes grid

6

SOM Application for DataMining

Downscaling Weather Forecasts

AdaptiveCompetitive

Learning

Sub-grid details scape from numerical models

Dept. of Applied Mathematics

Universidad de Cantabria

Santander, Spain

And now discussion