Census Data

I used ArcGIS Pro 3.0.2 to solve this problem. The main geoprocessing tools that I used include “Add Join” and “Hot Spot Analysis”. Our team agreed to download median household income data for Wake County, NC, for the year 2020. The exact title of the tabular dataset is “B19013 | MEDIAN HOUSEHOLD INCOME IN THE PAST 12 MONTHS (IN 2020 INFLATION-ADJUSTED DOLLARS)”. The dataset consists of the 2020 median household income at each census tract in Wake County. The field we were using was “B19013_001E”, which represents “Estimate!!Median household income in the past 12 months (in 2020 inflation-adjusted dollars)”.

I first processed the tabular data by saving the csv file in excel format and only keeping one header row. I then imported the shape file of census tracts in North Carolina and tabular data of median household income for each census tract in wake county. Then I projected the shape file and the map to NAD 1983 (2011) StatePlane North Carolina FIPS 3200 (US Feet). I prepared the tabular data by creating a new field of GEO_ID. I used Calculate Field and Python 3 expression type: !GEO_ID![-11:] to extract the 11-digit GEO_ID, which would be a match with the GEO_ID from the census tract layer. I then used “Add join” to the census tract layer from the tabular data using the common field of GEO_ID. Finally, I used graduated color to symbolize the joined layer of census tracts in Wake County. I used the “Hot Spot Analysis” tool with a fixed distance band of 15840 feet to explore any local spatial patterns of median household income in Wake County. Then I was able to identify areas where high-income census tracts are clustering as well as areas where low-income census tracts are clustering.

Being able to download specific census data is a very useful skill. Lots of research involves the use of census data. In my own thesis, I will analyze the median household income in Orange, Durham, and Wake Counties. I will use spatial join between census data and bird diversity data to explore the luxury effect (income positively correlates with biodiversity). A possible scenario for using census data could be exploring the number of people who are in poverty in Wake County census tracts so that the government can prioritize the establishment of food banks in clustering high-poverty census tracts.

Problem description: As a GIS specialist, I am asked to perform Local Spatial Pattern analysis on the poverty level in census tracts of Wake County.

Data needed: Tabular data of poverty level in census tracts of Wake County and shapefiles of census tracts of Wake County.

Analysis procedures: I would use Add Join to integrate both data through the common field of census tract ID. Then, I would use Hot Spot Analysis to identify the clustering of high-poverty census tracts. Finally, I will make a map that shows the hot and cold spots of the clustering pattern to illustrate areas that need new food banks.