Park service population analysis using Geographic Information system methods: A case study in Bexar County, Texas

dc.contributor.advisorXie, Hongjie
dc.contributor.authorBi, Yunbo
dc.contributor.committeeMemberRudnicki, Ryan
dc.contributor.committeeMemberSparks, Corey
dc.contributor.committeeMemberYan, Alice
dc.date.accessioned2024-02-09T19:29:44Z
dc.date.available2024-02-09T19:29:44Z
dc.date.issued2012
dc.descriptionThis item is available only to currently enrolled UTSA students, faculty or staff. To download, navigate to Log In in the top right-hand corner of this screen, then select Log in with my UTSA ID.
dc.description.abstractThis study gives particular attention to spatial inequity in terms of park service. The methodology presented is a step by step approach using Geographic Information System and Spatial Cluster Analysis that are easy to adopt by any public authority. A case study of the spatial distribution of parks service in Bexar County, Texas is presented here. In addition to the traditional methods such as Buffer method and Thiessen Polygon method, this study uses Park Congestion Index and Growth Index as two indicators to locate potential future park sites. The Park Congestion Index is calculated based on the park service population using 2010 census block group population data, and the unit of Park Congestion Index is in park acres per 1,000 residents. Park Growth Index is calculated using annual park service population change rate between 2000 to 2010. To compare the results calculated by zip code, census tract, census block group, and census block level population data of 2010, Pearson's Correlation Coefficient is used to determine correlation between any two levels. Zip code level result is obviously different from the other three levels with relatively low correlation coefficients (r <0.70). Tract, block group, and block level results are highly correlated with each other (r >0.98). Spatial autocorrelation tests are performed on the reciprocal of Park Congestion Index based on two separate methods: Getis-Ord Gi* statistic and Moran's I statistic. Only hot spots (spatial clusters with high values) is observed on the output map of Getis-Ord Gi* statistic. The area of hot spots in the output map of Local Moran's I statistic, though smaller, is very similar to the areas of Getis-Ord Gi* hot spots. Significant cold spots (spatial cluster with low values) are also observed using the Local Moran's I statistic. Raster Analysis is chosen to identify Thiessen Polygon areas with both high growth and high congestion. To avoid overlapping construction, buffer areas with walkable distance (400 m) around the perimeter of existing parks are removed from the Thiessen Polygon areas with both high growth and high congestion to locate future locations of park. Current land use is overlapped with those areas to determine which locations would be the best choice. The results indicate there are some Thiessen Polygons located in the west forest areas, where the park congestion and annual population change rate are both high. These are the candidate future park sites that public decision makers need to focus on.
dc.description.departmentGeosciences
dc.format.extent67 pages
dc.format.mimetypeapplication/pdf
dc.identifier.urihttps://hdl.handle.net/20.500.12588/2991
dc.languageen
dc.subjectGIS
dc.subjectHot Spot Analysis
dc.subjectPark Service
dc.subject.classificationGeographic information science and geodesy
dc.subject.classificationSocial sciences education
dc.subject.classificationSociology
dc.titlePark service population analysis using Geographic Information system methods: A case study in Bexar County, Texas
dc.typeThesis
dc.type.dcmiText
dcterms.accessRightspq_closed
thesis.degree.departmentGeosciences
thesis.degree.grantorUniversity of Texas at San Antonio
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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