Modeling Urban Population Growth from Remotely Sensed Imagery and TIGER GIS Road Data

Remotely sensed images provide alternative opportunities for estimating population in urban and suburban areas. Satellite imagery allows measurements related to population and the identification of its spatial concentration in an efficient manner. This article reports on a study undertaken to estimate the population growth from 1990 to 2000 in the northern Dallas-Fort Worth Metroplex, an area of known high population growth in that decade. The authors used two different methods; the first is a traditional model based on land-use change detected from remote sensing data; the second is based on measures of new road development derived from GIS data. These methods were applied at both city and census-tract levels and were evaluated against the actual population growth. The authors found that accurate population growth estimates are achieved by both methods. At the census-tract level, these models yielded a comparable result with that obtained from a more complex commercial demographics model. At both city and census-tract levels, models using road development were better than those using land-use change detection. The authors conclude that, in addition to being efficient in cost and time, these models provide direct visualization of the distribution of the actual population growth within cities and census tracts when compared to commercial demographic models. The authors mention that the population growth models used in this study assumed a general uniform population density per unit area or per unit length of road in the study area.

  • Availability:
  • Authors:
    • Qiu, Fang
    • Woller, Kevin L
    • Briggs, Ronald
  • Publication Date: 2003-9


  • English

Media Info

  • Media Type: Print
  • Features: Figures; Photos; References; Tables;
  • Pagination: pp 1031-1042
  • Serial:

Subject/Index Terms

Filing Info

  • Accession Number: 01001864
  • Record Type: Publication
  • Files: TRIS
  • Created Date: Jul 14 2005 10:11AM