Embedded Files
Using Census Data to Determine Who Has the Longest and Shortest Commute Times
Problem:
As the population in the Raleigh-Durham-Chapel Hill metro area continues to grow, so do the commute times for residents. Expanding areas of residential development increases the number of inter-county commuters and, therefore, the amount of time workers spend traveling to and from their jobs. Understanding the emerging trends of increased commute time is important for planning roadway construction projects and mass transit initiatives that will more efficiently and expeditiously move the working population each day.
Analysis Procedures
According to the US Department of Transportation Americans commute an average of 15 miles one way to get to work. I used this value to set a fixed distance and ran the Hot Spot Analysis (Getis-Ord Gi*) tool in ArcGIS Pro on data I obtained from the U.S. Census Bureau’s American Fact Finder Website. The data set, ACS_10_5YR_S0801 comes from the table S0801-Commuting Characteristics by Sex, summarizes various aspects of commuting between sexes at the census tract level. This data includes information on means of transportation, time leaving for work, time traveled and place of work. The field name used in calculating hot spots, HC01_EST_VC55, is the mean travel time to work for both sexes for a given census tract. This data was spatially referenced using TIGER/Line shapefiles of North Carolina census tracts.
Various data sets were examined using the interactive viewer on the US Census data website. Once data was identified for analysis it was downloaded along with the shapefiles for North Carolina census tracts. I added the shapefiles to a new map in ArcGIS Pro and imported the commuter data using the Excel-to-Table tool. In the standalone table, I added a new text field and used the field calculator to copy the census tract numbers into the new field as text. This new column in the table matched a column in the attribute table of the census tract shapefile allowing me to join the commuter data table to the census tract feature class. Select-By-Attributes was then used to select the thirteen counties that make up the Triangle area and surrounding counties. I made a new layer from the selected features and adjusted the symbology of the new layer to show the average commute time for each census tract in the study area. Graduated colors, inspired by the colors of a traffic light, show the shortest commute in dark green ranging to the longest commute shown in deep red. I chose to use the Hot Spot Analysis (Getis-Ord Gi*) tool to find clustering of low values/short commutes and high values/long commute times. To do this a fixed distance band of 15 miles (average American commute) was used with other options remaining set to defaults.
Results
Click images for a larger version
Length of time people spend commuting in the RDU area of North Carolina using census tract level data from the American Community Survey.
Census tract level data showing hot spots of long commutes and cold spots of shorter commutes. Note the short commute in eastern Nash county where people are commuting to Rocky Mount.
Application and Reflection
Problem description:
One of the topics I investigated as a potential topic for this project was to analyze manufacturing information to identify areas of investment. An analysis of this type would provide a great deal of insight if combined with an analysis such as job creation analysis I completed in the Data Cardinality project.
Data needed:
While the 2017 data has only been partially released, the 2012 Economic Census data provides manufacturing data at the county level. This data could be combined with data on demographics and unemployment.
Analysis procedures:
By analyzing fields in the data such as “Total Value of Shipments” and “Total Capital Expenditures” it would be possible to find the locations where there is solid cash flow with high amounts of income and spending on infrastructure. Intersecting these two data sets would find counties with high values in both areas and an analyst would be able to identify locations of high profitability and investment where there is likely to be job growth. Furthermore, such a study would be important in determining what local and state government incentives helped to create these environments of economic benefit.
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