Population estimation at the pixel level : Developing the expectation maximization technique
- Authors: Harvey, Jack
- Date: 2003
- Type: Text , Book chapter
- Relation: Remotely sensed cities Chapter 8 p. 181-205
- Full Text: false
- Description: 2003000369
Estimating census district populations from satellite imagery : Some approaches and limitations
- Authors: Harvey, Jack
- Date: 2002
- Type: Text , Journal article
- Relation: International Journal of Remote Sensing Vol. 23, no. 10 (2002), p. 2071-2095
- Full Text: false
- Reviewed:
- Description: Small-area population densities and counts were estimated for Australian census collection districts (CDs), using Landsat TM imagery. A number of mathematical and statistical refinements to previously reported methods were explored. The robustness of these techniques as a practical methodology for population estimation was investigated and evaluated using a primary image for model development and training, and a second image for validation. Correlations of up to 0.92 in the training set and up to 0.86 in the validation set were obtained between census and remote sensing estimates of CD population density, with median proportional errors of 17.4% and 18.4%, respectively. Total urban populations were estimated with errors of + 1% and - 3%, respectively. These results indicate a moderate level of accuracy and a substantial degree of robustness. Accuracy was greatest in suburban areas of intermediate population density. There was a general tendency towards attenuation in all models tested, with high densities being under-estimated and low densities being over-estimated. It is concluded that the level of accuracy obtainable with this methodology is limited by heterogeneity within the individual CDs, particularly large rural CDs, and that further improvements are in principle unlikely using the aggregated approach. An alternative statistical approach is foreshadowed.
- Description: 2003000104
Population estimation models based on individual TM pixels
- Authors: Harvey, Jack
- Date: 2002
- Type: Text , Journal article
- Relation: Photogrammetric Engineering and Remote Sensing Vol. 68, no. 11 (2002), p. 1181-1192
- Full Text: false
- Reviewed:
- Description: 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).
- Description: 2003000103
Estimation of population using satellite imagery
- Authors: Harvey, Jack
- Date: 1999
- Type: Text , Thesis , PhD
- Full Text:
- Description: The basic aims of this research were twofold; to extend and refine statistical image analysis methodologies for directly estimating small area populations and population densities from Landsat TM images and to validate procedures developed and to explore their robustness to geographical and seasonal differences within Australia, and hence to explore the potential of this methodology to provide a genuine operational alternative to existing methods of population estimation."
- Description: Doctor of Philosophy