Improving Population Mapping and Exposure Assessment: Three-Dimensional Dasymetric Disaggregation in New York City and São Paulo, Brazil

Abstract

Dasymetric mapping is a process of disaggregating spatial data from a coarser to a finer unit of analysis, using additional (or “ancillary”) data to refine the locations of population and achieve greater accuracy. Disaggregating population data reported by census tracts or other administrative or political geographic units can provide a more realistic depiction of actual population distribution and location. This is particularly important in assessing environmental exposures and impacts. Additionally, because exposures can occur in three dimensions (e.g., air pollution is a three-dimensional phenomenon), modeling residential population in three dimensions might produce more reliable estimates of exposure. Population exposure estimates are improved through dasymetric disaggregation and 3D extrusion, using a combination of cadastral data (residential area by property tax lot), building footprint data, and building height data. Population in census units is dasymetrically disaggregated into individual buildings using residential area derived from property tax lots and then extruded vertically based on building height. This 3D dasymetric mapping technique is presented through a New York City–based case study, and contrasted with a case study of São Paulo, Brazil, to demonstrate the possibilities of using this technique in different settings of data availability.