A cadastral-based disaggregated data model for census representation
ISBN 978-85-88783-11-9
Authors
1Strode, G.; 2Mesev, V.
1FLORIDA STATE UNIVERSITY Email: gstrode@admin.fsu.edu
2FLORIDA STATE UNIVERSITY Email: vmesev@fsu.edu
Abstract
Population data are important determinants when allocating scarce public funds. Cartographically, population data are typically constrained by rigid census-collecting zonal representation, despite their widespread availability. This research introduces a data model that alleviates the zonal inflexibility of the choropleth mapping by applying a dasymetric methodology to a cadastral base layer, aggregated into multiple gridded surfaces at various scales. The novel part of this research is that the gridded surfaces are not raster but vector surfaces using the United States National Grid (USNG) standardized format. Vector surfaces can appear identical to raster, but offer an additional benefit of a fully functional relational spatial database. Such a database structure can organize large amounts of data, support attribute queries, spatial queries, and combinations thereof. The USNG is a standardized format and the grid cell identifier implicitly carries scalable spatial location intelligence while generalizing the actual location. The USNG grid system has been adopted by emergency response agencies such as the US Federal Emergency Management Agency (FEMA) and by US state emergency groups such as the Florida Division of Emergency Management (FDEM). The USNG standardized format facilitates data sharing and interoperability between disparate agencies while simultaneously protecting sensitive data. This research describes how the cadastral-based data model is constructed and briefly explains its ability to support detailed visualizations, complex attribute and spatial queries, and private data transfer for multiple socioeconomic characteristics at multiple scales.
Keywords
census; dasymetric; surface model; population data