Exploring the spatial distribution of community resources focusing on municipalities: A multilevel analysis of the greater Los Angeles area

ABSTRACT

Community resources that meet the basic daily needs of residents have been primarily examined at the neighborhood level while the municipal-level contexts have been overlooked. Therefore, focusing on 180 municipalities in the Los Angeles–Long Beach Combined Statistical Area (CSA), this study explores whether municipal boundaries are influential in the spatial distribution of community resources—related to food, education, healthcare, recreation, and social service and civic organizations—and investigates the multilevel determinants of this distribution. Using the K-means clustering method, this study classifies municipalities into three groups by their socioeconomic, fiscal, and physical features. Then, by employing the analysis of covariance (ANCOVA) and multilevel logistic regression, this study finds an unequal distribution of community resources related to the municipal-level socioeconomic status (SES) when the community resources of adjacent municipalities are considered. The results of this study suggest that more contextualized policies based on municipal SES may address the spatial inequality of community resources.

KEYWORDS:

• Community resources
• municipal-level socioeconomic hierarchy
• urban spatial structure

Acknowledgments

I gratefully acknowledge Victoria Basolo, the editors, and anonymous referees for their valuable comments and suggestions.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Correction Statement

This article has been corrected with minor changes. These changes do not impact the academic content of the article.

Notes

1. The city of Vernon and the city of Industry are excluded because the areas within their boundaries are mostly for industrial use and not for residential use.

2. While reflecting the primary business activity of establishments from the private and public sectors, the NAICS codes do not classify between them. Therefore, community resources measured in this study could be either for-profit, nonprofit, or government organizations.

3. There were 153 census-designated places (CDP)—the statistical equivalent of incorporated places without their governments—as of 2010. The density of resources of CDPs is also considered when the variable of composite community resources is calculated, because a municipality is assumed to be an independent entity with the power of planning community facilities, thereby influencing surrounding areas.

4. A cluster solution that has more than five clusters (e.g., six or above) is not optimal for this study, since it generates a cluster that contains very few of the municipalities.

5. As a sensitivity analysis, municipalities whose median income and percentage of non-Hispanic whites are above the top quintile (80%) are reassigned as privileged municipalities, and ANCOVA and multilevel logistic regressions are repeated. The results of both the municipal- and multi-level analyses are mostly identical.

Additional information

Notes on contributors

Jongho Won

Jongho Won is a Postdoctoral Researcher at Brown University’s Spatial Structures in the Social Sciences (S4) and the Population Studies and Training Center (PSTC). He specializes in urban and spatial inequality, affordable housing policies, neighborhood change, and built environments. He has been published in Urban Affairs Review and Housing Policy Debate.