liut_gis_jan22_brief
TRANSCRIPT
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GIS PROGRESS BRIEFTianyuan Liu
Jan 22 2016
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Outline
■ Cluster Presentation (for Annotation Purpose)
■ Probability surfaces
■ Spatial weighted overlay (distance to TRO + density)
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CLUSTER PRESENTATION
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Aggregate Points
■ Generate polygons to enclose points that shows clustered patterns
– Tool: Cartography Tools/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
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Aggregate Points
Distance=50m
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Aggregate Points
Distance=30m
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Aggregate Points
Distance=10m
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Intersection with building footprintsUsing distance=10m as an example
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Intersection with building footprintsFootprint data
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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
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#>500
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#>10000
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Building selection
#>500
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Building selection
#>1000
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Building selection
#>10000
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PROBABILITY SURFACE
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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
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EBK
Estimating the total counts based on the
integrated points
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Kernel density
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SPATIAL WEIGHT OVERLAY
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Spatial Weight Overlay (testing)
■ Using multiple factors to calculate the weights of each raster cell
– Euclidean distance to TROs 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
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Raster cells with highest weights
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TRO EXPOSURE EVALUATION
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Total count of numbers within the geometric buffer
■ Using TRO buffers to intersect the original data points
– 30m buffer
– 30-50m buffer ring
– 50-100m buffer ring
■ Rank the TROs by the total number of points fall in the three buffer (ring)
■ Most exposed TROs and the distribution of activity points within the buffer
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Total Count of Points Falling in the Buffers
69126
2782
0
500
1000
1500
2000
2500
3000
0_30 30_50 50_100
buffer
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2466
134 91 52 41 22 20 17 13 130
500
1000
1500
2000
2500
3000
88 188 18 87 28 29 111 241 38 214
sum
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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
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Most exposed TROs
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
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TRO 88 & 87