This document summarizes various spatial analysis methods for evaluating tobacco retailer outlet (TRO) exposure, including: 1) using cluster analysis and spatial weighting to identify areas of high smoking activity and buildings associated with clusters; 2) creating probability surfaces of smoking activity through empirical Bayesian kriging and kernel density estimation; and 3) evaluating TRO exposure by calculating the number of smoking activity points within buffer zones of varying distances from each TRO to rank the most exposed TROs.

1. GIS PROGRESS BRIEF
Tianyuan Liu
Jan 22 2016

2. Outline
■ Cluster Presentation (for Annotation Purpose)
■ Probability surfaces
■ Spatial weighted overlay (distance toTRO + density)

3. CLUSTER
PRESENTATION

4. Aggregate Points
■ Generate polygons to enclose points that shows clustered patterns
– Tool: CartographyTools/Generalization/Aggregate Points
– Simplify the presentation of clusters
■ Use the polygons to intersect with existing building footprints
– Potentially identify the buildings where the person spends long time
■ Caveats:
– Oversimplify the cluster
■ Cluster of 3 points or more

5. Aggregate
Points
Distance=50m

6. Aggregate
Points
Distance=30m

7. Aggregate
Points
Distance=10m

8. Intersection
with building
footprints
Using distance=10m as an example

9. Intersection
with building
footprints
Footprint data

10. Map Partition
■ Generate grids based on the number of features
– # of points>500
– # of points>1,000
– # of points>10,000
■ Shape size of the grids and intensity of cluster is negatively related
– Smaller grids indicates more intense cluster
■ Select the shape with smallest size and intersect with building footprint

11. #>500

12. #>10000

13. Building
selection
#>500

14. Building
selection
#>1000

15. Building
selection
#>10000

16. PROBABILITY
SURFACE

17. Intra-polation
■ Empirical Bayesian Kriging
– Integrate the proximal points together
– Collect the points=create z-value for calculation
– Predict the total number of points in the raster cell
■ Kernel Density (cont’d)
– Original points layer
– Kernel density + reclassify
– The raster cell need further specification

18. EBK
Estimating the total counts based on the
integrated points

19. Kernel density

20. SPATIALWEIGHT
OVERLAY

21. SpatialWeight Overlay (testing)
■ Using multiple factors to calculate the weights of each raster cell
– Euclidean distance toTROs 1(furthest) -5(nearest)
– Point density (potentially smoking events) 1(most sparse)-6(most clustered)
– Other factors
■ Caveats
– The weights need to be adjusted based on the importance of the factors

22. Raster cells
with highest
weights

23. TRO EXPOSURE
EVALUATION

24. Total count of numbers within the
geometric buffer
■ UsingTRO buffers to intersect the original data points
– 30m buffer
– 30-50m buffer ring
– 50-100m buffer ring
■ Rank theTROs by the total number of points fall in the three buffer (ring)
■ Most exposedTROs and the distribution of activity points within the buffer

25. Total Count of Points Falling in the
Buffers
69
126
2782
0
500
1000
1500
2000
2500
3000
0_30 30_50 50_100
buffer

26. 2466
134 91 52 41 22 20 17 13 13
0
500
1000
1500
2000
2500
3000
88 188 18 87 28 29 111 241 38 214
sum

27. Distribution of the # of points
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
88 188 18 87 28 29 111 241 38 214
% of points falling in the three buffer zones
0_30buffer 30_50buffer 50_100buffer
2% 3%
95%
%
0_30buffer 30_50buffer 50_100buffer

28. Most exposedTROs
TRO_ID 0_30buffer 30_50buffer 50_100buffer final name
88 7 17 2442 bull market
188 1 0 133 Walmart
18 5 55 31 Sunshine BP
87 4 3 45 Fast Food Mart
28 3 17 21 Han Dee Hugos 76
29 3 0 19 Carolina Food Mart
111 0 0 20 Academy Quick Stop
241 11 5 1 0
38 9 1 3 Stop 1 food mart
214 8 0 5 Walgreens

29. TRO 88 & 87