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Identifying Clusters if the Fort Worth Fire Department Incidents and Dallas, TX Household Incomes
Problem and Objective
Exercise 1: The fire chief would like me to create a map of incident clusters from January of 2015. This map needs to display the relationship of these clusters to the median household income in the different groupings from the 2010 Census.
Exercise 2: Dallas County would like me to create a map to display the hot and cold spot clusters in their median household income data collected from the 2010 Census. They would like to be able to easily see the lows and the highs so they know where to target job creation programs and charity-collection efforts.
Analysis Procedures
To find these solutions, I used the ArcGIS Pro software. Specifically, I used the Cluster and Outlier Analysis (Anselin Local Moran’s I) and the Hot Spot Analysis (Getis Ord-Gi*) spatial analysis tools. For both problems I used US Census data to display the median household incomes based on the groupings from the 2010 Census. For the first problem, this data was from Fort Worth County. And in the second problem it was from Dallas County. I was also given data from the Fort Worth fire department on the incidents from January 2015.
To complete the first task for the Fort Worth Fire Department, I started by uploading the data that they provided me and the US Census data on median household incomes in the county. I then ran the Cluster and Outlier Analysis (Anselin Local Moran’s I) spatial analysis tool with the incident data to create a new layer that displays the different cluster types. This tool was ran with an inverse distance for spatial relationships. I then changed the symbology of the median household income layer to be an intuitive gradient from low to high income. Finally, I used this data to create a map so the relationship between incidents from January 2015 and median household income could be seen.
Looking at the same data, I ran the Cluster and Outlier Analysis (Anselin Local Moran's I) spatial analysis tool again. This time I changed the parameter to be a fixed distance band of 900 feet in order to reduce threshold errors. I changed the symbology again and created a second map from these results.
To complete the second task for Dallas County, I started by uploading the US 2010 Census data on median household incomes in this area. I then ran this data throught the Hot Spot Analysis (Getis Ord-Gi*) tool to create a new layer that displayed the hot and cold spots of the household income. I created a map with the data for Dallas County so they could now make informed decisions on where to target job-creation programs and charity-collection efforts.
Results
The above map depicts the results of the results of the cluster analysis for service calls in January 2015 related to median household income. It was found that most calls correspond to lower income household area.
The above map shows the cluster analysis results of running the Cluster and Outlier Analysis (Anselin Local Moran's I) tool with a parameter of a fixed distance band of 900 feet. This analysis proved that in January 2015, the calls for service mostly came from lower household income areas.
The above map depicts the hot and cold clusters of median household income in the Dallas Cunty. This analysis used the Hot Spot Analysis (Getis Ord-Gi*) tool and data from the 2010 Census.
Application and Reflection
In solving these problems, I learned skills on spatial pattern analysis and identifying clusters that I will be able to take into my future career. Another example of how powerful these skills will be is described below.
Problem Description
I am working for Wake County of North Carolina as a GIS analyst. The county is interested in knowing how 911 calls that require ambulances are related to poverty. I am to perform this analysis and present my results in a map.
Data Needed
For this task, the county need to provide data on where their 911 calls that need an ambulance come from and they would need to specify the time frame that they are interested in. I would also need to use US Census data to display the distribution of households under the poverty line. I would used the US Census Bureau Data download site to find this data.
Analysis Procedure
Once I have all the data and have uploaded it to ArcGIS Pro I would be able to being the analysis. I would use the Cluster and Outlier Analysis (Anselin Local Moran's I) spatial analysis tool on the 911 call data to locate where the calls cluster. I then would be able to use the Hot Spot Analysis tool on the poverty data to display the hot and cold spots for household poverty. From this map, the county would be able to visually identify the relationship between 911 calls and the high and low spots for household poverty in Wake County, NC.
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