Title: Spatial Data Mining in Geo-Business

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Title Spatial Data Mining in Geo-Business
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Overview
Paper available online at www.innovativegis.com/ba
sis/present/GeoTec08/

• Twisting the Perspective of Map Surfaces
  describes the character of spatial distributions
  through the generation of a customer density
  surface
• Linking Numeric and Geographic Distributions
   investigates the link between numeric and
  geographic distributions of mapped data
• Interpolating Spatial Distributions  discusses
  the basic concepts underlying spatial
  interpolation
• Interpreting Interpolation Results  describes
  the use of residual analysis for evaluating
  spatial interpolation performance
• Characterizing Data Groups  describes the use of
  data distance to derive similarity among the
  data patterns in a set of map layers
• Identifying Data Zones  describes the use of
  level-slicing for classifying locations with a
  specified data pattern (data zones)
• Mapping Data Clusters  describes the use of
  clustering to identify inherent groupings of
  similar data patterns
• Mapping the Future  describes the use of linear
  regression to develop prediction equations
  relating dependent and independent map variables
• Mapping Potential Sales  describes an extensive
  geo-business application that combines retail
  competition analysis and product sales prediction

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Density Surface Analysis
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Identifying Pockets of High Density
Unusually High Mean 1 Standard Deviation
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Grid-based Analysis Frame (Keystone Concept)
GeoCoding plots customers address on the
streets map
appends Lat, Lon, Column, Row location to
customer records
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Surface Modeling (Spatial Interpolation)
maps the variance by using geographic position
to help explain the differences in the sample
values.
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IDW Interpolation (Inverse Distanced Weighted)
3) Weight-average values in the window based on
distance to grid location (1/Distance)2
Value closer has more influence
2) Calculate distance from location to data
points Pythagorean Theorem 11distance
22.80 14distance 26.08 15distance
6.32 16distance 14.14
5) Move window to next grid location and repeat
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Average vs. IDW Interpolated Surface
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IDW vs. Krig Interpolated Surfaces
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Assessing Relationships Among Maps
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Geographic Space ?? Data Space
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Assessing Map Similarity
Data Distance determines similarity among data
patterns
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Identifying Data Patterns of Interest
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Level-Slicing Classifier (two variables)
Data Space
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Level-Slicing Classifier (three variables)
common data zones can be mapped by identifying
specific levels of each mapped variable then
adding the binary maps
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Spatial Data Clustering
data clusters are identified as groups of
neighboring data points in Data Space, and then
mapped as corresponding grid cells in
Geographic Space
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Spatial Regression (prediction equation)
relationship between Loan Concentration and
independent variables housing Density, Value and
Age
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Competition Analysis (Spatial Analysis Steps)
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Predictive Modeling (Spatial Statistics Steps)
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Map Analysis Framework
While discrete sets of points, lines and polygons
have served our mapping demands for over 8,000
years and keep us from getting lost
the expression of mapped data as continuous
spatial distributions (surfaces) provides a new
foothold for the contextual and numerical
analysis of mapped data Thinking with Maps
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References
Paper available online at www.innovativegis.com/ba
sis/present/GeoTec08/

• Twisting the Perspective of Map Surfaces
  describes the character of spatial distributions
  through the generation of a customer density
  surface
• Linking Numeric and Geographic Distributions
   investigates the link between numeric and
  geographic distributions of mapped data
• Interpolating Spatial Distributions  discusses
  the basic concepts underlying spatial
  interpolation
• Interpreting Interpolation Results  describes
  the use of residual analysis for evaluating
  spatial interpolation performance
• Characterizing Data Groups  describes the use of
  data distance to derive similarity among the
  data patterns in a set of map layers
• Identifying Data Zones  describes the use of
  level-slicing for classifying locations with a
  specified data pattern (data zones)
• Mapping Data Clusters  describes the use of
  clustering to identify inherent groupings of
  similar data patterns
• Mapping the Future  describes the use of linear
  regression to develop prediction equations
  relating dependent and independent map variables
• Mapping Potential Sales  describes an extensive
  geo-business application that combines retail
  competition analysis and product sales prediction

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www.innovativegis.com/basis/present/GeoTec08/ to
download this PowerPoint slide set
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Spatial Data Mining in Geo-Business
Weighted Average Calculations for Inverse
Distance Weighting (IDW) Spatial Interpolation
Technique
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Evaluating Interpolation Performance