ACS Data Conversion From Census Geographies To Neighborhood & Community Boundaries
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Abstract
American Community Survey (ACS) data from one geography can be converted into the appropriate boundaries for communities and neighborhoods using a weighting approach. The American Community Study (ACS) is an annual study that collects data on the social, economic, housing, and demographic characteristics of the United States and its population. The Census Bureau develops its geographic divisions, which are used by other regional, state, and federal entities, but it's necessary to recognize geographical connections when using Census Bureau data effectively. While ACS data is accessible for census geographies like tracts and block groups, it is not available for blocks. Additionally, certain towns and neighborhoods need particular details about their geographic borders, which may not always coincide with the boundaries established by the Census Bureau. To transform ACS data into the appropriate geographical boundaries for the community or neighborhood, a weighting approach is employed to change them from one geography to another. With the help of R programming, we can distribute ACS data from census areas to neighborhood and community districts and we can also access complex spatial data. The ACS data is converted to reallocate data at the neighborhood and community levels using the "tidy verse," sf" and "dplyr" R packages. After the data has been reallocated, the "ggplot2" and "sf" functions may provide geographic visualizations displaying how it was distributed throughout the neighborhoods. Geographical data, which is spatial data recorded into a file format, is contained in GIS mapping files. The U.S. Census Bureau's geographic spatial data is represented by TIGER, which stands for Topologically Integrated Geographic Encoding and Referencing System. A weighting method distributes data from block groups across neighborhoods. This involves determining the block level (population or housing units)-the weighted proportion of the block group data that should be allocated to each neighborhood and then sending the data to the neighborhood that is closest. The conversion process makes it possible to examine socioeconomic and demographic trends in specific communities and neighborhoods to a greater extent. The results of the study demonstrate the significance of accurate and appropriate data for community and neighborhood growth and development activities.
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Introduction -- Review of literature -- Methodology -- Results -- Discussion -- Conclusion
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M.S. (Master of Science)