JOBS PROXIMITY INDEX Summary The jobs proximity index quantifies the
accessibility of a given residential neighborhood as a function of its distance
to all job locations within a CBSA, with larger employment centers weighted
more heavily. Specifically, a gravity model is used, where the accessibility
(Ai) of a given residential block- group is a summary description of the
distance to all job locations, with the distance from any single job location
positively weighted by the size of employment (job opportunities) at that
location and inversely weighted by the labor supply (competition) to that
location. More formally, the model has the following specification: Where i
indexes a given residential block-group, and j indexes all n block groups
within a CBSA. Distance, d, is measured as “as the crow flies” between
block-groups i and j, with distances less than 1 mile set equal to 1. E
represents the number of jobs in block-group j, and L is the number of workers
in block-group j. The Longitudinal
Employer-Household Dynamics (LEHD) has missing jobs data in all of Puerto Rico
and a concentration of missing records in Massachusetts. Interpretation Values are percentile ranked with values
ranging from 0 to 100. The higher the index value, the better the access to
employment opportunities for residents in a neighborhood. Data Source: Longitudinal Employer-Household Dynamics (LEHD) data, 2017. Related AFFH-T Local Government, PHA and State Tables/Maps: Table 12; Map 8. To learn more about the Jobs Proximity Index visit: https://www.hud.gov/program_offices/fair_housing_equal_opp/affh; https://www.hud.gov/sites/dfiles/FHEO/documents/AFFH-T-Data-Documentation-AFFHT0006-July-2020.pdf Date of Coverage: 07/2020
Creator:
United States. Department of Housing and Urban Development
HUD and the dataset and metadata authors assume no responsibility for the use or misuse of the dataset. No warranty, expressed or implied is made with regard to the accuracy of the spatial accuracy, and no liability is assumed by the U.S. Government in general, the dataset creators or the U.S. Department of Housing and Urban Development specifically, as to the spatial or attribute accuracy of the data.