Creating neighborhood typologies of GIS-based data in the absence of neighborhood-based sampling: a factor and cluster analytic strategy.
This article describes an innovative means of identifying a neighborhood typology that can be used for analyses of individual-level data that were not obtained through neighborhood-based sampling. A two-step approach was employed. First, exploratory factor analysis was used to reduce the number of neighborhood indicators to five clear factors of neighborhood characteristics. Second, a cluster analytic procedure was used to identify neighborhood types based on the five factors. These analyses resulted in a parsimonious solution of five distinct neighborhood clusters, or types, that constituted a manageable number of categories that could be used for future analyses of individuals grouped within neighborhood types. This method is a promising way to conduct neighborhood impact analyses that maximize the ability of researchers to characterize neighborhoods accurately (without sampling at the neighborhood level) while retaining the ability to conduct analyses of participants grouped within types of neighborhoods.
Gershoff
Elizabeth T
ET
University of Michigan School of Social Work, 1080 S. University, Ann Arbor, MI 48109, USA. liztg@umich.edu
Pedersen
Sara
S
Lawrence Aber
J
J
D000293:Adolescent; D019352:Censuses; D002648:Child; D016000:Cluster Analysis; D003710:Demography; D005163:Factor Analysis, Statistical; D005260:Female; D040362:Geographic Information Systems; D006801:Humans; D008297:Male; D009519:New York City; D012111:Residence Characteristics; D012574:Schools; D017062:Small-Area Analysis; D012959:Socioeconomic Factors; D014754:Violence / Q000517:prevention & control