<p> As developing countries experience substantial urban growth and expansion, remotely sensed based estimates of population and demographic characteristics can provide researchers and humanitarian aid workers timely and spatially explicit information for planning and development. In this exploratory analysis, high spatial resolution satellite imagery, in combination with fine resolution census data, is used to determine the degree to which spatial features are able to identify spatial patterns of demographic variables in Accra, Ghana. Traditionally when using satellite imagery, spectral characteristics are used on a per-pixel basis to produce land cover classifications; however, in this study, a new methodology is presented that quantifies spatial characteristics of built-up areas, and directly relates them to census-derived variables. Spatial features are image metrics that analyze groups of pixels in order to describe the geometry, orientation, and patterns of objects in an image. By using spatial features, city infrastructure variations, such as roads and buildings, can be quantified and related to census-derived variables, such as living standards, housing conditions, employment and education. To test the associations between spatial patterns and demographic variables, five spatial features (line support regions, PanTex, histograms of oriented gradients, local binary patterns, and Fourier transform) were quantified and extracted from the imagery, and then correlated to census-derived variables. Findings demonstrate that, while spectral information (such as the normalized difference vegetation index) reveals many strong correlations with population density, housing density, and living standards, spatial features provide comparable correlation coefficients with density and housing characteristics. The results from this study suggest that there are relationships between spatial features derived from satellite imagery and socioeconomic characteristics of the people of Accra, Ghana.</p>
| Identifer | oai:union.ndltd.org:PROQUEST/oai:pqdtoai.proquest.com:1589680 |
| Date | 16 June 2015 |
| Creators | Sandborn, Avery |
| Publisher | The George Washington University |
| Source Sets | ProQuest.com |
| Language | English |
| Detected Language | English |
| Type | thesis |
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