You can see the commute distance lines between work location and home
location. The current dataset is limited to work location Census blocks which
contain 1,000 to 15,000 employees. The data is filtered to only
include employees who travel at least 24,000 meters (roughly 15
miles). The commute distance is also filtered on the high end to
remove telecommuters. The high end limit for a commute distance is set to
100,000 meters or 62 miles. All distances are calculated as the most
direct path between the two points. Travel distances therefore may be
greater depending on the available highway routes between locations.
The map is pre-set to display the commute lines for all employees within the dataset who are between the ages of 14 - 29 years.
You can pan and zoom in the usual ways. The controls at the top right let you show and hide different map layers: Age Ranges, Earnings Level, and Industry Type. The Show option allows for turning on and off the background map and location labels.
Residence to workplace employment commute data have a broad interest group including urban and regional planners, social science and transportation researchers, businesses, and emergency management and assessment.
The colors show the 3 distance ranges of commute to work lengths. The
ranges are divided almost evenly across all 3 layer categories. Distance is derived from straight line
calculation between the workplace and residence locations.
Goods Producing is comprised of jobs in the following NAICS sectors:
The data is sourced completely from the Census Bureau, specifically the LEHD Origin-Destination Employment Statistics (LODES).
The LODES data is processed and assimilated into geospatial files using Python. The geospatial data is transitioned into vector tiles using Tippecanoe. The tiles are stored on Mapbox. Leaflet is the main driver of the map utilizing Leaflet Vectorgrid to display the tile sets stored on Mapbox.
Mark Cruse created this map as final project for the Master of Science in Digital Mapping at the University of
Kentucky. You can learn more about this project by accessing Mark's github repository.
Learn more about the New Maps Plus program at the University of Kentucky.