Academic journal article Cityscape

Increasing the Accuracy of Urban Population Analysis with Dasymetric Mapping

Academic journal article Cityscape

Increasing the Accuracy of Urban Population Analysis with Dasymetric Mapping

Article excerpt

(ProQuest: ... denotes formulae omitted.)

Introduction

Many types of urban policy analyses rely on accurate and precise spatial population data. Of particular note are analyses of exposure and accessibility where one must assess the population in proximity to, or overlapping with, some geographic feature. Examples of such analyses include the estimation of population exposed to natural and technological hazards, such as flooding or air pollution. Other relevant research applications concern access to amenities and resources, such as recreation facilities, health centers, nutritious food, or employment opportunities.

Although the U.S. Census Bureau provides high-resolution demographic data for the United States, certain variables may be available only over coarser spatial units, such as census tracts. Other population-related datasets, such as disease incidence data, may be limited to distribution at a coarse spatial resolution for purposes of privacy protection. In many developing nations, population data at a fine resolution are not available at all, because many countries do not have the resources to invest in census infrastructure. In addition, in all these cases, population data are likely to be available aggregated to spatial units that are derived by convenience of enumeration or are a reflection of administrative or political jurisdiction boundaries and, consequently, are unlikely to capture the nature of the actual population distribution. Thus, the development of small-area estimates for urban population data remains a challenge in both developed and developing nations.

Dasymetric mapping is a technique for estimating population in small areas in situations where one has access to population data aggregated only at a relatively coarser scale (Mennis, 2009). It uses ancillary data, an additional dataset related to the distribution of population but distinct from it, to disaggregate population data from one set of spatial units to another set of smaller spatial units. The formal principles of dasymetric mapping were initially developed for a Russian mapping project in the early 20th century (cf. Petrov, 2012) and were introduced to English-speaking audiences in a series of articles appearing in the 1920s and 1930s, most notably in an article by Wright (1936). The dasymetric mapping technique, however, was little known outside cartographic circles until the widespread availability of Geographic Information System (GIS) software and digital data products that could serve as ancillary data, such as those derived from remotely sensed imagery, spurred the growth of dasymetric mapping algorithms and applications beginning in the 1990s through the present.

Dasymetric mapping more recently has been employed for a wide variety of applications that benefit from high spatial resolution population data, including environmental justice (Mennis, 2002), public health (Maantay, Maroko, and Porter-Morgan, 2008), crime (Poulsen and Kennedy, 2004), and historical population estimation (Gregory and Ell, 2005). It has also been used to create national-level, high-resolution population datasets (Bhaduri et al., 2007).

The purpose of the present article is to describe dasymetric mapping, its theoretical basis, and its implementation using GIS software. As an illustration of dasymetric mapping and its application to urban analysis, an example is presented for Philadelphia, Pennsylvania, where tract-level population data are disaggregated to sub-tract-level spatial units. These data are then used for an analysis of population residing in close proximity to facilities releasing hazardous pollutants to the atmosphere. The tract and the dasymetric data are then compared with an analogous analysis using census block-level data for accuracy assessment.

The Dasymetric Mapping Technique

Dasymetric mapping can be considered a form of areal interpolation, the transformation of data from one set of spatial units to another set of spatial units; for example, the assignment of population originally encoded in U. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed

Oops!

An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.