Mapping Density, clustering and spatial distributions in China

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These are the slides for my final project for a vector analysis course that I took.

Data & Analytics

Mapping Density, clustering,
and spatial distributions in
China
By: Miles Lilly

Some of my geoprocessing

Building Density rasters
● To build the density rasters I used two different feature classes
○ I used a roads feature class that I obtained via open street map
○ I used a point feature class of road junctions that I obtained via beijingcitylab.com
● I used both methods of for computing density raster on both feature classes
○ Simple Density
○ Kernel Density

Spatial Distributions
● In this part of the project I look at the spatial distribution of road junctions
● I compute the mean and median for this point feature class as well as the
standard distance and the directional distribution

Cluster analysis
I used three type of cluster analysis on a census township feature class

High-high low low clustering

Running Spatial AutoCorrelation(Morgan’s I)
According to The ESRI guide to analysis volume 2, “Morgan’s calculates the
difference between the target feature and the mean of all features, and the
difference between each neighbor and the mean. It then compares these
difference for the target”(pg. 118)
This statistic measures the degree of autocorrelation. This is process by we use to
determine the relation between any arbitrary feature and its neighbors.

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Mapping Density, clustering and spatial distributions in China

1. Mapping Density, clustering, and spatial distributions in China By: Miles Lilly

2. Some of my geoprocessing

3. Building Density rasters ● To build the density rasters I used two different feature classes ○ I used a roads feature class that I obtained via open street map ○ I used a point feature class of road junctions that I obtained via beijingcitylab.com ● I used both methods of for computing density raster on both feature classes ○ Simple Density ○ Kernel Density

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10. Spatial Distributions ● In this part of the project I look at the spatial distribution of road junctions ● I compute the mean and median for this point feature class as well as the standard distance and the directional distribution

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13. Cluster analysis I used three type of cluster analysis on a census township feature class

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15. High-high low low clustering

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17. Running Spatial AutoCorrelation(Morgan’s I) According to The ESRI guide to analysis volume 2, “Morgan’s calculates the difference between the target feature and the mean of all features, and the difference between each neighbor and the mean. It then compares these difference for the target”(pg. 118) This statistic measures the degree of autocorrelation. This is process by we use to determine the relation between any arbitrary feature and its neighbors.