Uncontrolled rapid urban growth due to built-up area expansion over the last few decades has created critical problems to urban planners. Therefore, it is worth to identify spatial distribution pattern of built-up area in a town. Remote Sensing (RS) and Geographical Information System (GIS) provide a great potential for urban data collection & spatial analysis and not much academic literature exists on how to best utilize these technologies for urban studies. Traditional approaches to data collection and analysis no longer adequate to provide updated information for urban planners. This book, therefore, introduces alternative techniques for data collection, updating, detection of built-up area change and unauthorised constructions, identifying urban spatial distribution pattern considering built-up area density. New GIS spatial analysis techniques such as weighted mean centre and spatial autocorrelation tools introduced. These analysis techniques should help to shed light on the field of urban planning, and should be especially useful to students and professionals in urban planning or anyone else who may be considering utilizing RS and GIS for urban spatial structure analysis.
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Uncontrolled rapid urban growth due to built-up area expansion over the last few decades has created critical problems to urban planners. Therefore, it is worth to identify spatial distribution pattern of built-up area in a town. Remote Sensing (RS) and Geographical Information System (GIS) provide a great potential for urban data collection & spatial analysis and not much academic literature exists on how to best utilize these technologies for urban studies. Traditional approaches to data collection and analysis no longer adequate to provide updated information for urban planners. This book, therefore, introduces alternative techniques for data collection, updating, detection of built-up area change and unauthorised constructions, identifying urban spatial distribution pattern considering built-up area density. New GIS spatial analysis techniques such as weighted mean centre and spatial autocorrelation tools introduced. These analysis techniques should help to shed light on the field of urban planning, and should be especially useful to students and professionals in urban planning or anyone else who may be considering utilizing RS and GIS for urban spatial structure analysis.
K.A.M.Perera, MBCS,CITP(U.K.), PGDip. (Geoinformatics): Studied RS at IIRS, India & AIT, Geoinformatics at Univ. of Colombo, Sri Lanka. Database Administrator at Urban Development Authority, Sri Lanka.Lasantha Manawadu, BA, MSc: Studied Geography at Univ. of Colombo and GIS at AIT, Thailand. Senior Lecturer, Dept. of Geography, Univ. of Colombo.
"Sobre este título" puede pertenecer a otra edición de este libro.
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Taschenbuch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -Uncontrolled rapid urban growth due to built-up area expansion over the last few decades has created critical problems to urban planners. Therefore, it is worth to identify spatial distribution pattern of built-up area in a town. Remote Sensing (RS) and Geographical Information System (GIS) provide a great potential for urban data collection & spatial analysis and not much academic literature exists on how to best utilize these technologies for urban studies. Traditional approaches to data collection and analysis no longer adequate to provide updated information for urban planners. This book, therefore, introduces alternative techniques for data collection, updating, detection of built-up area change and unauthorised constructions, identifying urban spatial distribution pattern considering built-up area density. New GIS spatial analysis techniques such as weighted mean centre and spatial autocorrelation tools introduced. These analysis techniques should help to shed light on the field of urban planning, and should be especially useful to students and professionals in urban planning or anyone else who may be considering utilizing RS and GIS for urban spatial structure analysis. 88 pp. Englisch. Nº de ref. del artículo: 9783846511367
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