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  Partner: UNT Libraries
 Country: United States
 Degree Discipline: Applied Geography
 Collection: UNT Theses and Dissertations
County Level Population Estimation Using Knowledge-Based Image Classification and Regression Models

County Level Population Estimation Using Knowledge-Based Image Classification and Regression Models

Date: August 2010
Creator: Nepali, Anjeev
Description: This paper presents methods and results of county-level population estimation using Landsat Thematic Mapper (TM) images of Denton County and Collin County in Texas. Landsat TM images acquired in March 2000 were classified into residential and non-residential classes using maximum likelihood classification and knowledge-based classification methods. Accuracy assessment results from the classified image produced using knowledge-based classification and traditional supervised classification (maximum likelihood classification) methods suggest that knowledge-based classification is more effective than traditional supervised classification methods. Furthermore, using randomly selected samples of census block groups, ordinary least squares (OLS) and geographically weighted regression (GWR) models were created for total population estimation. The overall accuracy of the models is over 96% at the county level. The results also suggest that underestimation normally occurs in block groups with high population density, whereas overestimation occurs in block groups with low population density.
Contributing Partner: UNT Libraries