Social Services Gap Analysis (SSGAPA) for the County of Los Angeles

Publication Date


Committee Chair

Mark P. Kumler, Ph.D.

Committee Members

Karen K. Kemp, Ph.D.


Currently, social services in Los Angeles are being provided by the County of Los Angeles and many independent providers, with little knowledge of the spatial relationships between the providers’ infrastructures and the clients’ locations. A spatial analysis is required to understand and optimize the delivery of these services. The results of this analysis will influence reforms being sought and will provide an opportunity to improve the County’s service delivery system. The Chief Administrative Office (CAO), Service Integration Branch (SIB), Urban Research Unit was tasked with developing a solution to this issue, and Sal Aguilar, University of Redlands Master of Science in Geographic Information System (MS-GIS) candidate, was requested to assist in establishing a methodology for a Social Services Gap Analysis (SSGAPA) for the County of Los Angeles.

The benefits of this analysis will likely extend beyond the immediate beneficiary – Department of Children and Family Services (DCFS) serving children and families, to various other County departments. However, these other departments are not part of the scope of this project.

A proximity spatial analysis is provided by this SSGAPA to determine the locations of services relative to where clients needing these services live. The approach to the analysis was broken down into three areas: 1) a case study in two DCFS administrative service areas; 2) an analysis of the countywide supply of social services; and 3) an analysis of the Los Angeles County Children’s Planning Council (CPC) ScoreCard data indicators of child well-being across five outcome areas adopted by the County. Two models were developed in the SSGAPA: one for the countywide analysis, and the second for the CPC analysis. The countywide analysis indicates three general regions where demand exceeds supply. The CPC analysis reveals a correlation between the CPC ScoreCard “poverty” map and the SSGAPA composite index.

Full text is available at the University of Redlands


Article Location