Jeremy Mennis
Temple University

Many types of urban policy analyses, particularly those relating to exposure to hazards or accessibility to resources, rely on accurate and precise spatial population data, although such data are not always available. Dasymetric mapping is a technique for disaggregating population data from one set of source spatial units to a finer resolution set of target spatial units through the use of an ancillary dataset, typically land use, zoning, or similar nominal datasets related to population distribution. Dasymetric mapping operates by employing weights that capture both the relative areas of the target spatial units and the relative population densities of the different nominal ancillary classes, and it is typically implemented in Geographic Information System, or GIS, software. An example application demonstrates the efficacy of the dasymetric approach by comparing census tract-level and dasymetric data in an assessment of the population living in proximity to hazardous air pollutant releases in Philadelphia, Pennsylvania, using block-level data as a validation dataset.