PERFECTING THE DENOMINATOR: DEVELOPING A CADASTRAL-BASED EXPERT DASYMETRIC SYSTEM (CEDS) IN NEW YORK CITY

J.A. Maantay, A.R. Maroko

City University of New York, Lehman College

maantay@aol.com

 

This paper discusses the importance of determining an accurate depiction of population distribution for urban areas in order to develop an improved denominator, allowing for more correct rates in GIS analyses involving public health, crime, and urban environmental planning. Rather than using data aggregated by arbitrary administrative boundaries such as census tracts, accuracy is improved by the use of dasymetric mapping, an areal interpolation method using ancillary information to delineate areas of homogeneous values. Specifically, a new methodology called the Cadastral-based Expert Dasymetric System (CEDS) was designed and implemented in order to provide vital population data at the tax-lot level, a geographic unit roughly 350-times smaller than the census tract in New York City. This model is particularly suitable for urban areas, using specific cadastral data, land use filters, modeling by expert system routines, and validation against various census enumeration units and other data. Previous and traditional disaggregation techniques are compared with CEDS to assess efficacy.

The CEDS method differs from these existing disaggregation methods in two major ways. Firstly, the ancillary data used is very detailed cadastral data, more appropriate to estimating population distribution in hyper-heterogeneous urban areas in a continuous (non-binary) way. Secondly, the CEDS method also uses an expert system to determine which of several formulae to use, calculating which method fits the data best. In this way, each source record within the area of interest can be customized as to method of disaggregation, which when validated, yield more accurate results.

The CEDS dasymetric mapping technique is presented through a case study of asthma hospitalizations in New York City, and shows the impact that a more accurate estimation of population distribution has on a current environmental justice and health disparities research project, and its potential for other GIS applications.