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

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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 ... continued below

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viii, 65 p. : ill., maps

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Nepali, Anjeev August 2010.

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This thesis is part of the collection entitled: UNT Theses and Dissertations and was provided by UNT Libraries to Digital Library, a digital repository hosted by the UNT Libraries. It has been viewed 986 times , with 5 in the last month . More information about this thesis can be viewed below.

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  • Nepali, Anjeev

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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.

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viii, 65 p. : ill., maps

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  • August 2010

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  • Jan. 6, 2011, 6:55 a.m.

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  • April 26, 2016, 4:54 p.m.

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Nepali, Anjeev. County Level Population Estimation Using Knowledge-Based Image Classification and Regression Models, thesis, August 2010; Denton, Texas. (digital.library.unt.edu/ark:/67531/metadc30498/: accessed September 26, 2017), University of North Texas Libraries, Digital Library, digital.library.unt.edu; .