Special Topics in Geo-Business Data Analysis Week 3 Covering Topic 6 Spatial Interpolation.

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Presentation transcript:

Special Topics in Geo-Business Data Analysis Week 3 Covering Topic 6 Spatial Interpolation

Point Density Analysis (Berry) Point Density analysis identifies the number of customers with a specified distance of each grid location Roving Window (count)

Identifying Unusually High Density (Berry) Pockets of unusually high customer density are identified as more than one standard deviation above the mean

Identifying Customer Territories (Berry) Clustering on the latitude and longitude coordinates of point locations identify customer territories

Map View vs. Data View (Berry) Mapped data are characterized by their geographic distribution (maps on the left) and their numeric distribution (descriptive statistics and histogram on the right) Geographic Distribution Numeric Distribution

Estimating the Geographic Distribution (Berry) The spatial distribution implied by a set of discrete sample points can be estimated by iterative smoothing of the point values

Spatial Autocorrelation (Variogram) (Berry) A variogram plot depicts the relationship between distance and measurement similarity (spatial autocorrelation) “…nearby things are more alike than distant things”

Spatial Interpolation Mechanics (Berry) Spatial interpolation involves fitting a continuous surface to sample points Roving Window (average)

Inverse Distance Weighted Technique (Berry) Inverse distance weighted interpolation weight-averages sample values within a roving window

Example Calculations (IDW) (Berry) Example Calculations for Inverse Distance Squared Interpolation X X

Title (Berry) A wizard interface guides a user through the necessary steps for interpolating sample data MapCalc Spatial Interpolation Wizard

Comparing Geographic Distributions (IDW vs. Avg) (Berry) Spatial comparison of the project area average and the IDW interpolated surface

Comparison Statistics (IDW vs. Avg) (Berry) Statistics summarizing the difference between the IDW surface and the Average …big difference— more than 75 % of the project area is more than +/- 10 units different

Comparing Geographic Distributions (IDW vs Krig) (Berry) Spatial comparison of IDW and Krig interpolated surfaces

Evaluating Interpolation Performance (Berry) A residual analysis table identifies the relative performance of average, IDW and Krig estimates

Mapping Spatial Dependency (Berry) Spatial dependency in continuously mapped data involves summarizing the data values within a “roving window” that is moved throughout a map …compares the difference in values between the adjacent neighbors (doughnut hole) and distant neighbors (doughnut), assigns the spatial dependency index to the center cell location then moves to next location