Cluster Analysis

ArcGIS provides a set of statistical cluster analysis tools that allows you to specify each parameter in your analysis. As part of the assignment, I was asked to complete 2 exercises the updated version of Chapter 9: Analyzing Patterns from GIS Tutorial II - Spatial Analysis Workbook by David W. Allen (2016) provided in the theme Assignment block The tasks were to demonstrate understanding of local pattern analysis concepts and demonstrate understanding of methods for analyzing local spatial patterns

Strategies: For this problem, I used ESRI's ArcGIS Pro 2.8 along with tools such as the Hot Spot Analysis (Getis-Ord Gi*) tool and the Cluster and Outlier Analysis (Anselin Local Moran's I) tool For this project, the instructor provided data for the assignment in a folder containing 4 mxd. Files MXD (Map Exchange Document) which is a file format in which the maps created from ArcGIS software can be stored. MXD not only stores maps but also stores the symbology, layout, hyperlinks, toolbars added., I used two of these tools—the Hot Spot Analysis (Getis-Ord Gi*) tool and the Cluster and Outlier Analysis (Anselin Local Moran's I) tool which Given a set of weighted features, identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran’s I statistics. These tools provided with more control over my analysis. and allowed me to refine my analysis so that it better meets my needs.

Method:

Exercise 1: To complete this exercise I performed Outlier Analysis (Anselin Local Moran's I to show hot spots and cold spots based on feature locations and the ranking of the calls. Given a set of weighted features, this tool identifies statistically significant hot spots, cold spots, and spatial outliers using the Anselin Local Moran's I statistic. Followed by overlaying the data on the census data to see if there is some type of relationship between the two datasets.

Exercise 2: To complete this assignment I used Hot Spot Analysis (Getis-Ord Gi*) to identify statistically significant spatial clusters of high values (hot spots) and low values (cold spots). The tool creates an Output Feature Class with a z-score, p-value, and confidence level bin field (Gi_Bin) for each feature in the Input Feature Class in our case in income data for Dallas County. for the Conceptualization of Spatial Relationships parameter, I used Fixed distance band which ensure that each feature has at least one neighbor

Process Diagram

While completing this assignment I was able to fully understand the result the different shades of colors in the layer generated after running either analysis is very easy analyze Both tools provide an easy-to-understand visual for identifying patterns or clumps of similar features, which follows Tobler's first law of geography. I learned how to interpret the results and understand the meaning of Confidence levels and overall as well as learning how to use locations to deduce different outcomes depending on the situation.

New problem statement: A crime sensibilization group in want to focus their effort on the parts of wake county with the most crimes proficient with social media. The company wants to recruit in areas with high social media usage. Using the Optimized Hot Spot Analysis tool, the company can identify areas of statistically significant social media usage.

Data Needed: Data required for this problem would be wake county polygon data with zip codes as attributes and police reports of crimes committed by zip codes from the police department.

Analysis procedure: To solve this problem will perform Outlier Analysis (Anselin Local Moran's I to show hot spots and cold spots based on zip codes. Followed by overlaying the data on the polygon layer to see if there is some type of relationship between the two datasets.