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


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.

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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). …

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