A Comparison of Small-Area Population Estimation Techniques Using Built-Area and Height Data, Riyadh, Saudi Arabia


Little-area population estimation is vital for several applications. This paper explores the usefulness of Landsat $bf ETM + $ information, remotely sensed height data, census population, and dwelling unit knowledge to produce small-area population estimates. Riyadh, Saudi Arabia was selected as a appropriate space to test a collection of methods for population downscaling. 2 broad approaches were applied: one) statistical modeling and a couple of) areal interpolation. With regard to statistical modeling, regression through the origin was used to model the relationship between density of dwelling units and engineered area proportion at the block level and therefore the coefficients were used to downscale the density of dwelling units to the parcel level. Areal interpolation with ancillary knowledge (dasymetric mapping) used the block and parcel levels as the source and target zones, respectively. The population distribution was then estimated primarily based on the average population per dwelling unit. Eight models were developed and tested. A standard regression model, using solely designed space as a covariate, was used as a benchmark and compared with the more sophisticated models. Remotely sensed height data were used to: 1) create number of floors; two) classify the built space into different categories; and 3) increase the user’s accuracy of the engineered area. It was found that remotely sensed height information were helpful to elucidate the variation within the dependent variable across the chosen study space. Dasymetric mapping was applied so as to provide a comparison, while acknowledging that the tactic uses population knowledge not offered in the regression approach.

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