GIS PROGRESS BRIEFTianyuan Liu

Jan 22 2016

Outline

■ Cluster Presentation (for Annotation Purpose)

■ Probability surfaces

■ Spatial weighted overlay (distance to TRO + density)

CLUSTER PRESENTATION

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

Aggregate Points

Distance=50m

Aggregate Points

Distance=30m

Aggregate Points

Distance=10m

Intersection with building footprintsUsing distance=10m as an example

Intersection with building footprintsFootprint data

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

#>500

#>10000

Building selection

#>500

Building selection

#>1000

Building selection

#>10000

PROBABILITY SURFACE

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

EBK

Estimating the total counts based on the

integrated points

Kernel density

SPATIAL WEIGHT OVERLAY

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

Raster cells with highest weights

TRO EXPOSURE EVALUATION

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

Total Count of Points Falling in the Buffers

69126

2782

0

500

1000

1500

2000

2500

3000

0_30 30_50 50_100

buffer

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

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

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

TRO 88 & 87