Spatial Data Mining-Applications

Hemant Kumar Jerath,B.Tech.

MS Project Student

Mangalore University

Advisors: Dr. B.K Mohan & Dr.(Mrs.).P. Venkatachalam

CSRE, IIT Bombay

Beyond Conventional GIS Analysis: Spatial Analysis, Geospatial Data Mining, and

Knowledge Discovery

Conventional GIS Operations

Retrieve Spatial and Attribute Data

Measure

Descriptive Statistics

Classification

Overlay

Buffer

Network Analyses

Where do minorities live in New Jersey?

Which census tracts have percent minority > 25%

Conventional GIS Operations: Attribute Query

Conventional GIS Operations: Attribute Query

SQL

Structured (Standard) Query Language

Formal language for interacting with relational databases

SELECT <fields>

FROM <tables>

WHERE <condition>

SELECT %Min

FROM CensusTracts

WHERE %Min > 0.25

Tougher GIS Operations: Finding Spatial Relationships in Very Large, Spatio-Temporal, Multi-Dimensional Data Sets

Some Tougher Questions…

1. Are hazardous facilities clustered in certain areas? If so, what are the socioeconomic characteristics associated with these clusters?

2. What is the influence of degree of urbanization, proximity to transportation, and industrial land use on the relationship between locations of hazardous facilities and the racial and other socioeconomic characteristics of the communities that host these facilities?

Exploratory Spatial Data Analysis (ESDA)

The sophisticated quantitative analysis of spatial and spatio-temporal data, typically involving interactive and dynamic interfaces.

Related terms:

•Spatial Statistics

•Spatial Analysis

A process to discover hidden facts and useful information contained in databases.

Related terms:

•Machine Learning

•Pattern Recognition

Geospatial Data Mining (DM) and Knowledge Discovery (KD)

Some ESDA, DM, and KD Operations

Finding Spatial and Multi-Dimensional Clusters

Summarizing Variables by Other Variables

Finding Associations Among Attributes

Predicting Values

Feature Extraction

GeneralizationDr.Hans,SMU

• 500 weather probes– Monthly mean temp and precipitation

• Weather pattern of season 1990

• 18,000 tuples– Attribute Induction

• Non-spatial(merging of tuples based on non-spatial concept hierarchies)

• Spatial(merging of spatial objects based on the concept hierarchies-spatial region merging and/or spatial approximation)

Spatial Associative Rules

• Finding large towns and nearby objects like mines, country boundary, water(sea, lake, or river) and major highways.– Generalisation of the above spatial objects

Spatial Clustering

• A request to all expensive houses in an area.

• Finding down the relationships of the clusters with other spatial objects like roads, different land-use area

Clustering<house type,price,size>

Distribution

Spatial Data Mining: A Database ApproachMartin Ester, Hans-Peter Kriegel, Jorg Sander

• Step I: Discover centers based on some non-spatial attribute[clustering-descriptive mining]

• Step II: determine the (theoretical) trend of some non-spatial attribute.

• Step III: discover the deviation of the theoretical trends

• Step IV: explain the deviation by the spatial object, e.g. may be presence of some infrastructure.

Spatial Classification

• Non-spatial attribute e.g. no. of salespersons in a store

• Spatially related attribute with non-spatial values, like population living within 1km from store

• Spatial predicates, like – Distance_less_than_10km(X,a)

• Spatial function, like driving_distance(X,beach)

Decision Tree

Description of classified objects

Description of census block group

Buffers are definedFor Trade Area

Classification is developed using ID3 algorithm

High_profit=N

High_profit=Y

Geo-spatial Data Mining and KDD using Decision Tree Algorithm-A case study of soil data sets

Jianting Zang, Diansheng Guo, Qing Wan

Association Rule Mining on RSI using P-TreesQin Ding, Qiang Ding, William Perrizo

• Identification of high and low crop yield

• Insect and weed infestations

• Nutrient requirement

• Flood damage assess

ILP, SPADA(Spatial Pattern Discovery Algorithm)Donalto Malerba, Francesca A Lisi

• Find associations between– Reference objects (Towns)

– Task Relevant Objects ( road network, hydrography and administration layers)

• Stockport Census data– Socio-Economic phenomenon

– Census data (80 tables, 120 attributes)