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- 1. Modeling Count-based Raster Data Using R with ArcGIS Desktop Jeremy Heffner HunchLab Product Manager jheffner@azavea.com
- 2. We have events that occur in space (i.e. crimes)
- 3. ? ? ? But why do they occur where they do? Do events correlate with geographic features?Can we predict the rate of events at particular locations?
- 4. Let’s create a raster covering formed of square cells
- 5. And bring in features of the geography that may explain the pattern
- 6. For some geographic features we may use a proximity measure of spatial influence
- 7. For some geographic features we may use a proximity measure of spatial influence
- 8. For some geographic features we may use a proximity measure of spatial influence
- 9. For other geographic features we may look at the concentration of the features (density)
- 10. For each raster cell we have values for these explanatory variables
- 11. So can’t we use ArcGIS’s built-in regression models?
- 12. They all assume a normal distribution for the response variable } y = b0 + b1x1 + b2x2 + …
- 13. Our cells have 0 or more events and are not a normal distribution
- 14. Poisson ProcessThis is a process which counts independentevents happening in a given interval (time,space).Poisson DistributionThis process leads to a Poisson distribution ofcounts. Source: WikipediaGeneralized Linear Model y = exp(b0 + b1x1 + b2x2 + …)A GLM can represent this distribution in aregression model.
- 15. Our counts fit aPoisson distribution much better
- 16. We can process our geographic data sets in ArcGIS and then export the cells to R for modeling Raster Calculate Export to Convert to Build Model Processing Predictions ASCII CSV (R) (ArcGIS) (ArcGIS)
- 17. Here is sample output from fitting a Poisson model in R:
- 18. We can take the fitted coefficients from R and plug theminto the equation within ArcGIS using the ‘raster calculator’ y = exp(b0 + b1x1 + b2x2 + …)
- 19. Here’s an example of the outputwhich explains the expectationof shootings based uponthe location ofdrug arrests andbus stops.
- 20. This example is derived from a collaborative project between Azavea and the Rutgers Center on Public Security For more information: Jeremy Heffner HunchLab Product Manager jheffner@azavea.com

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