Population estimation models based on individual TM pixels

There is a fundamental spatial mismatch in the data available for modeling human population from satellite imagery. Spectral reflectances are available for each pixel of an image, but ground reference population data are available only for larger zones. The general response has been to build models for the average population density of the zones, utilizing spatially aggregated spectral data. This approach has limitations, both for the modeling process and for the utilization of the resulting spatially aggregated population estimates. A pixel-based alternative is described. Pixels of a Landsat TM image were classified as residential or non-residential using standard techniques. Initial reference populations were assigned by uniformly distributing the population of each zone across its residential pixels. An expectation-maximization (EM) algorithm was used to iteratively regress pixel population on spectral indicators and re-estimate pixel populations. Predictive validity was tested by applying the fitted regression equation to a second image. The pixel-based model produced population estimates of comparable accuracy to those resulting from a much more complex zone-based modeling procedure. The pixel-based procedure was also more robust and more amenable to refinement, particularly at the extremes of population density. The relative error in the estimated total urban population of both primary and secondary study areas was less than 1 percent. Median relative error in the population of individual zones was 16 percent in the primary study area (14 percent for urban zones) and 21 percent in the secondary study area (17 percent for urban zones)