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Collaborating to Create Healthier Communities.

Amanda W Scarbrough1, Jack Hill2, Jesus Rodriguez3

  • 11 College of Health Sciences, Sam Houston State University, Huntsville, TX, USA.

International Quarterly of Community Health Education
|December 31, 2016
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Summary

Geospatial analysis revealed payday loan clusters in low-income areas of Montgomery County, Texas. This data-driven approach empowered community leaders to enact positive changes for improved well-being.

Keywords:
communitiesgeographical information systemsgrass roots

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Area of Science:

  • Public Health
  • Community Health
  • Geospatial Analysis

Background:

  • Assessing public health and community assets/gaps in Montgomery County, Texas.
  • Utilizing quantitative geospatial data and geographical information systems (GIS).

Purpose of the Study:

  • Compare local health issues with state-wide data using maps.
  • Demonstrate the impact of spatial analysis on a specific community issue.

Main Methods:

  • Integrated quantitative geospatial data and demographic information.
  • Applied geographical information systems (GIS) for spatial analysis.
  • Created an asset map identifying payday loan service concentrations.

Main Results:

  • Identified dense clusters of payday loan services in areas with high poverty rates.
  • Visual comparison highlighted significant health issues within Montgomery County.
  • Asset mapping revealed previously unrecognized concentrations of payday lenders.

Conclusions:

  • Spatial analysis and asset mapping effectively identified community needs.
  • Data visualization empowered community leaders to address payday loan risks.
  • Grassroots efforts, informed by data, led to positive community changes.