Census Data

Spatial patterns analysis provides insights into monitoring conditions, tracks changes through time, and compares features over space and time. The purpose of this analysis is to develop a thematic map and report any observed spatial patterns in the U.S. Census data with a focus on the total estimate of available 1- bedroom apartments with a cash rent of $300-499. The result of the analysis will inform a decision on the possible locations to look for affordable housing by a group of students looking for renter-occupier housing units and housing leases in Buncombe county, NC State. 

Methods: I downloaded the renter costs data from the US Census website using the Advanced Search link. I selected three filters: Year = 2018, Topic = Renter Costs, and Geography=All Block Groups within Buncombe County, North Carolina. After unzipping the file, I review the data downloaded and identified the field of interest based on the description provided in the metadata. I saved the table (CSV file) as an excel workbook (xlsx file). I used the Excel to Table tool to add the tabular data to the project as a standalone table. I used the Add Field tool to add a new field with text as the field type. Then, I used the Calculate Field tool to define a query to slice and select only the last 12 digits of the GEOID column. This is to match the unique Census Block Group IDs of the required shapefile. Next, I downloaded the census block-group shapefile for this tabular data. After unzipping the file, I added it to the map. I created a query definition for this layer on the map to select only the block-group features within Buncombe County from the NC State Block-Group shapefile. I used the Export Feature tool to export the selected block-group polygon features. I used the Project tool to change the projection of the exported Buncombe County Block-Group feature class to the NAD 1983 StatePlane North Carolina FIPS 3200 Coordinate System with units in US Feet. Also, I changed the projection of the map to that of the projected Buncombe County Block-Group feature class. By selecting the projected block-group feature from the content pane, I joined it to the tabular census data (standalone table) using their respective unique identifier fields (GEOID) using the Add Join tool. Lastly, I defined a query to select only block-group polygons that have at least one (1) housing unit with $300-499 cash rent for 1-bedroom apartments. I changed the symbology to graduated colors. I used the Hot Spot Analysis tool (Getis-Ord Gi*) to examine the spatial pattern (identify clusters and the location of clusters) of the selected housing units with the renter cost of interest. The workflow diagram of this analysis is presented in Figure 1.