LEADER 04990nam 2201273z- 450 001 9910557577103321 005 20231214132928.0 035 $a(CKB)5400000000043878 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/77082 035 $a(EXLCZ)995400000000043878 100 $a20202201d2021 |y 0 101 0 $aeng 135 $aurmn|---annan 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aSpatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries 210 $aBasel, Switzerland$cMDPI - Multidisciplinary Digital Publishing Institute$d2021 215 $a1 electronic resource (304 p.) 311 $a3-0365-2541-6 311 $a3-0365-2540-8 330 $aOver the last two decades, many researchers have focused on developing countries' urbanization patterns and processes. In this context, the scarcity of spatial data has been an obstacle to studying urbanization quantitatively, especially in Asian and African cities. The use of remote sensing data and geographical information systems (GIS) techniques can overcome the above limitations. Data on land use and land cover, land surface temperature, population density, and energy consumption can be extracted based on remote sensing at various spatial and temporal resolutions. GIS techniques can be used to analyze urbanization patterns and predict future patterns. Thus, the link between urbanization and sustainable urban development has increasingly become a principal issue in designing and developing sustainable cities at the local, regional, and global levels. This volume shows the spatiotemporal analysis of urbanization using GIS and remote sensing in developing countries, with a special emphasis on future urban sustainability in Asia and Africa. Capturing the spatial-temporal variation of urbanization patterns will help introduce proper sustainable urban planning in developing countries, especially for Asian and African cities. 606 $aResearch & information: general$2bicssc 606 $aGeography$2bicssc 610 $aLST 610 $aurban-rural gradient 610 $asub-Saharan region 610 $aAddis Ababa 610 $aEthiopia 610 $acellular automata 610 $aspatial layout 610 $atransportation infrastructure 610 $aLUCC 610 $aspatial patterns 610 $aspatial differences 610 $aDMSP-OLS 610 $aChina 610 $aIndia 610 $alandscape pattern 610 $aindustrial rural area 610 $arural landscape 610 $alandscape ecology 610 $asouthern Jiangsu 610 $aland use and cover 610 $aland surface temperature 610 $abuilt-up land 610 $aagricultural land 610 $agradient analysis 610 $aNuwara Eliya 610 $aSri Lanka 610 $aurban public space 610 $aenvironment 610 $acheck-in data 610 $asocial media platform 610 $apoint of interest 610 $aurbanization 610 $aGIS 610 $aurban development zones 610 $aurban sustainability 610 $aregression analysis 610 $aGWR 610 $afragmentation 610 $anon-agricultural conversion of rural land 610 $aurban green space 610 $aRSEI 610 $aremote sensing 610 $aecological status 610 $adynamic motoring 610 $aPingtan Island 610 $aurban land expansion 610 $aspatial pattern 610 $adriving forces 610 $aPearl River Delta 610 $aurban agglomeration 610 $aurban heat island 610 $aimpervious surface area 610 $abiophysical composition index 610 $acoastal city 610 $aXiamen 610 $asurface urban heat island 610 $aMODIS 610 $aland cover 610 $ahabitat quality 610 $aspatiotemporal analysis 610 $aYangtze River Delta Urban Agglomeration 610 $aurban planning 610 $aLULC change 610 $atransition matrix 610 $asystematic transition 610 $aBlantyre city 610 $alife quality index (LQI) 610 $aKandy city 610 $aAHP 610 $aMCDM 610 $aCOVID-19 pandemic 610 $aenvironmental quality 610 $aPM10 concentration 615 7$aResearch & information: general 615 7$aGeography 700 $aMurayama$b Yuji$4edt$01303463 702 $aSimwanda$b Matamyo$4edt 702 $aRanagalage$b Manjula$4edt 702 $aMurayama$b Yuji$4oth 702 $aSimwanda$b Matamyo$4oth 702 $aRanagalage$b Manjula$4oth 906 $aBOOK 912 $a9910557577103321 996 $aSpatio-Temporal Analysis of Urbanization Using GIS and Remote Sensing in Developing Countries$93027076 997 $aUNINA