This work is licensed under the Creative Commons Attribution 4.0 International License.
Alharthi B., El-Damaty T.A., 2022. Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System. Advances in Remote Sensing 11(1): 1–15. DOI 10.4236/ars.2022.111001.AlharthiB.El-DamatyT.A.2022Study the Urban Expansion of Taif City Using Remote Sensing and GIS Techniques for Decision Support System11111510.4236/ars.2022.111001Open DOISearch in Google Scholar
Amici V., Rocchini D., Filibeck G., Bacaro G., Santi E., Geri F., Landi S., Scopola A., Chiarucci A., 2015. Landscape structure effects on forest plant diversity at local scale: exploring the role of spatial extent. Ecological Complexity 21: 44–52. DOI 10.1016/j.ecocom.2014.12.004.AmiciV.RocchiniD.FilibeckG.BacaroG.SantiE.GeriF.LandiS.ScopolaA.ChiarucciA.2015Landscape structure effects on forest plant diversity at local scale: exploring the role of spatial extent21445210.1016/j.ecocom.2014.12.004Open DOISearch in Google Scholar
Anas A., Arnott R., Small K.A., 1998. Urban Spatial Structure. Journal of Economic Literature 36(3): 1426–1464.AnasA.ArnottR.SmallK.A.1998Urban Spatial Structure36314261464Search in Google Scholar
Angel S., Sheppard S.C., Civco D.L., 2005. The dynamics of global urban expansion. The World Bank, Washington, DC.AngelS.SheppardS.C.CivcoD.L.2005The World BankWashington, DCSearch in Google Scholar
Aprillia Y., Pigawati B., 2018. Urban Sprawl Typology in Semarang City. Forum Geografi 32(2): 131–145. DOI 10.23917/forgeo.v31i2.6369.AprilliaY.PigawatiB.2018Urban Sprawl Typology in Semarang City32213114510.23917/forgeo.v31i2.6369Open DOISearch in Google Scholar
Bhatta B., Saraswati S., Bandyopadhyay D., 2010. Quantifying the degree-of-freedom, degree-of-sprawl, and degree-of-goodness of urban growth from remote sensing data. Applied Geography 30(1): 96–111. DOI 10.1016/j.apgeog.2009.08.001.BhattaB.SaraswatiS.BandyopadhyayD.2010Quantifying the degree-of-freedom, degree-of-sprawl, and degree-of-goodness of urban growth from remote sensing data3019611110.1016/j.apgeog.2009.08.001Open DOISearch in Google Scholar
Bhattacharjee S., 2019. Measuring Urban Growth of Silchar Town Using Shannon Entropy Estimation. International Journal of Scientific Research and Reviews 8(1): 2016–2022.BhattacharjeeS.2019Measuring Urban Growth of Silchar Town Using Shannon Entropy Estimation8120162022Search in Google Scholar
Brown S.R., 1995. Measuring the dimension of self-affine fractals: example of rough surfaces, In: Barton C.C., La Pointe P.R. (eds), Fractals in the Earth Sciences. Springer, Boston: 77–78. DOI 10.1007/978-1-4899-1397-5_4.BrownS.R.1995Measuring the dimension of self-affine fractals: example of rough surfacesIn:BartonC.C.La PointeP.R.(eds),SpringerBoston777810.1007/978-1-4899-1397-5_4Open DOISearch in Google Scholar
Chen Y., 2013. Fractal analytical approach of urban form based on spatial correlation function. Chaos, Solitions & Fractals 49: 47–60. DOI 10.1016/j.chaos.2013.02.006.ChenY.2013Fractal analytical approach of urban form based on spatial correlation function49476010.1016/j.chaos.2013.02.006Open DOISearch in Google Scholar
Chen Y., Wang J., Feng J., 2017. Understanding Fractal Dimension of Urban Form through Spatial Entropy. Entropy 19(11): 1–18. DOI 10.3390/e19110600.ChenY.WangJ.FengJ.2017Understanding Fractal Dimension of Urban Form through Spatial Entropy191111810.3390/e19110600Open DOISearch in Google Scholar
Congalton R.G., 1991. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment 37(1): 35–46.CongaltonR.G.1991A review of assessing the accuracy of classifications of remotely sensed data3713546Search in Google Scholar
De Oliveira M.A.B., Brandi A.C., dos Santos C.A., Botelho P.H.H., Cortez J.L.L. de Godoy M.F., Braile D.M., 2014. Comparison of fractal dimension and Shannon entropy in myocytes from rats treated with histidine-tryptophanglutamate and histidine-tryptophan cetoglutarate. Revista Brasileira de Cirurgia Cardiovascular 29(2): 156–62. DOI 10.5935/1678-9741.20140052.De OliveiraM.A.B.BrandiA.C.dos SantosC.A.BotelhoP.H.H.CortezJ.L.L.de GodoyM.F.BraileD.M.2014Comparison of fractal dimension and Shannon entropy in myocytes from rats treated with histidine-tryptophanglutamate and histidine-tryptophan cetoglutarate2921566210.5935/1678-9741.20140052Open DOISearch in Google Scholar
Deka J., Tripathi O.P., Khan M.L., 2010. Urban growth trend analysis using Shannon Entropy approach – A case study in North-East India. International Journal of Geomatics and Geosciences 2(4): 1072–1078.DekaJ.TripathiO.P.KhanM.L.2010Urban growth trend analysis using Shannon Entropy approach – A case study in North-East India2410721078Search in Google Scholar
DRC, 2017. Monograph of the Batna Region. Direction régionale du commerce, N02: 1–35.DRC2017Direction régionale du commerce, N02:135Search in Google Scholar
El-Raey M., Nasr S., El-Hattab M., Frihy, O., 1995. Change detection of Rosetta promontory over the last forty years. International Journal of Remote Sensing 16: 825–834.El-RaeyM.NasrS.El-HattabM.FrihyO.1995Change detection of Rosetta promontory over the last forty years16825834Search in Google Scholar
Fan Y., Zhu X., He Z., Zhang S., Geo J., Chen F., Peng X., Li J., 2017. Urban expansion assessment in Huaihe river basin, China from 1998 to 2013 using remote sensing data. Journal of Sensors ID 9281201: 1–10. DOI 10.1155/2017/9281201.FanY.ZhuX.HeZ.ZhangS.GeoJ.ChenF.PengX.LiJ.2017Urban expansion assessment in Huaihe river basin, China from 1998 to 2013 using remote sensing dataID 9281201:11010.1155/2017/9281201Open DOISearch in Google Scholar
Ge S., Nan J., Yang L., Bin H., 2018. Analysis of the dynamic urban expansion based on multi-sourced data from 1998 to 2013: a case study of Jiangsu province. Sustainability 10(10) 3467: 1–18. DOI 10.3390/su10103467.GeS.NanJ.YangL.BinH.2018Analysis of the dynamic urban expansion based on multi-sourced data from 1998 to 2013: a case study of Jiangsu province10103467:11810.3390/su10103467Open DOISearch in Google Scholar
Geri F., Amici V., Rocchini D., 2011. Spatially-based accuracy assessment of forestation prediction in a complex Mediterranean landscape. Applied Geography, 31(3): 881–890. DOI 10.1016/j.apgeog.2011.01.019.GeriF.AmiciV.RocchiniD.2011Spatially-based accuracy assessment of forestation prediction in a complex Mediterranean landscape31388189010.1016/j.apgeog.2011.01.019Open DOISearch in Google Scholar
Ghosh P., Mukhopadhyay A., Chanda A., Mondal P., Akhand A., Mukherjee S., Nayak S.K., Ghosh S., Mitra D., Ghosh T., Hazra S., 2017. Application of cellular automata and Markov-chain model in geospatial environmental modelling – A review. Remote Sensing Applications: Society and Environment 5: 64–77. DOI 10.1016/j.rsase.2017.01.005.GhoshP.MukhopadhyayA.ChandaA.MondalP.AkhandA.MukherjeeS.NayakS.K.GhoshS.MitraD.GhoshT.HazraS.2017Application of cellular automata and Markov-chain model in geospatial environmental modelling – A review5647710.1016/j.rsase.2017.01.005Open DOISearch in Google Scholar
Gyeltshen S., Tran T.V., Khunta W., Kannaujiya S., 2022. Assessing Spatiotemporal Built-up Dynamics in Chiang Mai City, Thailand using Entropy approach. Research Square: 1–22. DOI 10.21203/rs.3.rs-1179652/v1.GyeltshenS.TranT.V.KhuntaW.KannaujiyaS.2022Assessing Spatiotemporal Built-up Dynamics in Chiang Mai City, Thailand using Entropy approach12210.21203/rs.3.rs-1179652/v1Open DOISearch in Google Scholar
Halimi M., Sedighifar Z., Mohammadi C., 2017. Analyzing spatiotemporal land use/cover dynamic using remote sensing imagery and GIS techniques case: Kan basin of Iran. GeoJournal 83: 1067–1077. DOI 10.1007/s10708-017-9819-2.HalimiM.SedighifarZ.MohammadiC.2017Analyzing spatiotemporal land use/cover dynamic using remote sensing imagery and GIS techniques case: Kan basin of Iran831067107710.1007/s10708-017-9819-2Open DOISearch in Google Scholar
Hamad R., 2019. A remote sensing and GISbased analysis of urban sprawl in Soran District, Iraqi Kurdistan. SN Applied Sciences 2, 24. DOI 10.1007/s42452-019-1806-4.HamadR.2019A remote sensing and GISbased analysis of urban sprawl in Soran District, Iraqi Kurdistan22410.1007/s42452-019-1806-4Open DOISearch in Google Scholar
Hotar V., Salac P., 2014. Surface evaluation by estimation of fractal dimension and statistical tools. Scientific World Journal 2014, ID 435935: 1–10. DOI 10.1155/2014/435935.HotarV.SalacP.2014Surface evaluation by estimation of fractal dimension and statistical tools2014, ID 435935:11010.1155/2014/435935Open DOISearch in Google Scholar
Hu S., Tong L., Frazier A.E., Liu Y., 2015. Urban boundary extraction and sprawl analysis using Landsat images: a case study in Wuhan, China. Habitat International 47: 183–195. DOI 10.1016/j.habitatint.2015.01.017.HuS.TongL.FrazierA.E.LiuY.2015Urban boundary extraction and sprawl analysis using Landsat images: a case study in Wuhan, China4718319510.1016/j.habitatint.2015.01.017Open DOISearch in Google Scholar
Jain S., Siddiqui A., Tiwari P.S., Shashi M., 2016. Urban growth assessment using CA Markov model: a case study of Dehradun city. 9th International Geographic Union, Delhi: 1–9.JainS.SiddiquiA.TiwariP.S.ShashiM.2016Urban growth assessment using CA Markov model: a case study of Dehradun city19Search in Google Scholar
Jat M.K., Garg P.K., Khare D., 2008. Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India). International Journal of Remote Sensing 29(2): 543–567. DOI 10.1080/01431160701280983.JatM.K.GargP.K.KhareD.2008Modelling of urban growth using spatial analysis techniques: a case study of Ajmer city (India)29254356710.1080/01431160701280983Open DOISearch in Google Scholar
Jensen J.R.E., 1983. Urban/suburban land-use analysis. American Society of Photogrammetry 2: 1571–1666.JensenJ.R.E.1983Urban/suburban land-use analysis215711666Search in Google Scholar
Jimoh R., Afonja Y., Albert Ch., Amoo N., 2018. Spatio-temporal urban expansion analysis in a growing city of Oyo Town, Oyo state, Nigeria using remote sensing and geographic information system (GIS) tools. International Journal of Environment and Geoinformatics 5(2): 104–113. DOI 10.30897/ijegeo.354627.JimohR.AfonjaY.AlbertCh.AmooN.2018Spatio-temporal urban expansion analysis in a growing city of Oyo Town, Oyo state, Nigeria using remote sensing and geographic information system (GIS) tools5210411310.30897/ijegeo.354627Open DOISearch in Google Scholar
Joshi P.K., Lele N., Agarwal S.P., 2006. Entropy as an indicator of fragmented landscape. Current Science 91(3): 276–278.JoshiP.K.LeleN.AgarwalS.P.2006Entropy as an indicator of fragmented landscape913276278Search in Google Scholar
Kaya H.S., Bölen F., 2011. Kentsel dokudaki değişimin fraktal geometri yöntemiyle incelenmesi. İTÜ Dergisi/A Mimarlık 10(1): 39–50.KayaH.S.BölenF.2011Kentsel dokudaki değişimin fraktal geometri yöntemiyle incelenmesi1013950Search in Google Scholar
Landis J.R., Koch G.G., 1977. The measurement of observer agreement for categorical data. Biometrics 33(1): 159–174.LandisJ.R.KochG.G.1977The measurement of observer agreement for categorical data331159174Search in Google Scholar
Liu H., Lin X., Xie T., 2014. Urban sprawl and its evolution trend of fuzhou city, China. BioTechnology An Indian Journal 10(22): 13923–13934.LiuH.LinX.XieT.2014Urban sprawl and its evolution trend of fuzhou city, China10221392313934Search in Google Scholar
Liu X., Li X., Shi X., Wu S., Liu T., 2008. Simulating complex urban development using kernel-based non-linear cellular automata. Ecological Modelling 211(1–2): 169–181. DOI 10.1016/j.ecolmodel.2007.08.024.LiuX.LiX.ShiX.WuS.LiuT.2008Simulating complex urban development using kernel-based non-linear cellular automata2111–216918110.1016/j.ecolmodel.2007.08.024Open DOISearch in Google Scholar
Ma Y., Xu R., 2010. Remote sensing monitoring and driving force analysis of urban expansion in Guangzhou city, China. Habitat International 34(2): 228–235. DOI 10.1016/j.habitatint.2009.09.007.MaY.XuR.2010Remote sensing monitoring and driving force analysis of urban expansion in Guangzhou city, China34222823510.1016/j.habitatint.2009.09.007Open DOISearch in Google Scholar
Makhamreha Z., Almanasyeha N., 2011. Analyzing the state and pattern of urban growth and city planning in Amman using satellite images and GIS. European Journal of Social Sciences 24(2): 225–264.MakhamrehaZ.AlmanasyehaN.2011Analyzing the state and pattern of urban growth and city planning in Amman using satellite images and GIS242225264Search in Google Scholar
Martin L.R.G., 1986. Change Detection in the Urban Fringe Employing Landsat Satellite Imagery. Plan Canada 26(7): 182–190.MartinL.R.G.1986Change Detection in the Urban Fringe Employing Landsat Satellite Imagery267182190Search in Google Scholar
Morency C., Chapleau R., 2003. Fractal geometry for the characterisation of urban-related states: Greater Montreal Case. Harmonic and Fractal Image Analysis – HarFA e-Journal: 30–34. Online: www.fch.vut.cz/lectures/imagesci/download_ejournal/09_C.Morency.pdf (accessed on 17 April 2018).MorencyC.ChapleauR.2003Fractal geometry for the characterisation of urban-related states: Greater Montreal Case3034Online: www.fch.vut.cz/lectures/imagesci/download_ejournal/09_C.Morency.pdf (accessed on 17 April 2018).Search in Google Scholar
Mundhe N.N., Jaybhaye R.G., 2015. Measuring urban growth of Pune city using Shannon Entropy approach. The Journal of Geography and Geology. Photon 119: 290–302.MundheN.N.JaybhayeR.G.2015Measuring urban growth of Pune city using Shannon Entropy approach119290302Search in Google Scholar
Nasehi S., Namin A.I., Salehi E., 2018. Simulation of land cover changes in urban area using CA-MARKOV model (case study: zone 2 in Tehran, Iran). Modeling Earth Systems and Environment 5(1): 193–202. DOI 10.1007/s40808-018-0527-9.NasehiS.NaminA.I.SalehiE.2018Simulation of land cover changes in urban area using CA-MARKOV model (case study: zone 2 in Tehran, Iran)5119320210.1007/s40808-018-0527-9Open DOISearch in Google Scholar
Nazarnia N., Hardinga C., Jaegera J.A.G., 2019. How suitable is Entropy as a measure of urban sprawl? Landscape and Urban Planning 184: 32–43. DOI 10.1016/j.landurbplan.2018.09.025.NazarniaN.HardingaC.JaegeraJ.A.G.2019How suitable is Entropy as a measure of urban sprawl?184324310.1016/j.landurbplan.2018.09.025Open DOISearch in Google Scholar
Nelson A.C., 1999. Comparing states with and without growth management analysis based on indicators with policy implications. Land Use Policy 16(2): 121–127. DOI 10.1016/S0264-8377(99)00009-5.NelsonA.C.1999Comparing states with and without growth management analysis based on indicators with policy implications16212112710.1016/S0264-8377(99)00009-5Open DOISearch in Google Scholar
Nouri J., Gharagozlou A., Arjmandi R., Faryadi S., Adl M., 2014. Predicting urban land use changes using a CA-Markov model. Arabian Journal for Science and Engineering 39: 5565–5573. DOI 10.1007/s13369-014-1119-2.NouriJ.GharagozlouA.ArjmandiR.FaryadiS.AdlM.2014Predicting urban land use changes using a CA-Markov model395565557310.1007/s13369-014-1119-2Open DOISearch in Google Scholar
Ozturk D., 2017. Assessment of urban sprawl using Shannon's Entropy and fractal analysis: a case study of Atakum, Ilkadim and Canik (Samsun, Turkey). Journal of Environmental Engineering and Landscape Management 25(3): 264–276. DOI 10.3846/16486897.2016.1233881.OzturkD.2017Assessment of urban sprawl using Shannon's Entropy and fractal analysis: a case study of Atakum, Ilkadim and Canik (Samsun, Turkey)25326427610.3846/16486897.2016.1233881Open DOISearch in Google Scholar
Parker D.C., Manson S.M., Janssen M.A., Hoffman M.J., Deadman P., 2003. Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review. Annals of the Association of American Geographers, 93(2): 314–337. DOI 10.1111/1467-8306.9302004.ParkerD.C.MansonS.M.JanssenM.A.HoffmanM.J.DeadmanP.2003Multi-Agent Systems for the Simulation of Land-Use and Land-Cover Change: A Review93231433710.1111/1467-8306.9302004Open DOISearch in Google Scholar
PDAU [Plan directeur d’aménagement et d’urbanisme], 2012. Plan directeur d’aménagement et d’urbanisme. Online: https://www.mhuv.gov.dz/fr/pdau/ (accessed October 23, 2022).PDAU [Plan directeur d’aménagement et d’urbanisme]2012Online: https://www.mhuv.gov.dz/fr/pdau/ (accessed October 23, 2022).Search in Google Scholar
Rastogi K., Jain G.V., 2018. Urban sprawl analysis using Shannon's entropy and fractal analysis: A case study on Tiruchirappalli city, India. In: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”, 20–23 November 2018, Dehradun, India. DOI 10.5194/isprs-archives-XLII-5-761-2018.RastogiK.JainG.V.2018In:The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XLII-5, 2018 ISPRS TC V Mid-term Symposium “Geospatial Technology – Pixel to People”20–23 November 2018Dehradun, India10.5194/isprs-archives-XLII-5-761-2018Open DOISearch in Google Scholar
Robbany I.F., Gharghi A., Traub K.P., 2019. Land Use Change Detection and Urban Sprawl Monitoring in Metropolitan Area of Jakarta (Jabodetabek) from 2001 to 2015. KnE Engineering 4(3): 257–268. DOI 10.18502/keg.v4i3.5862.RobbanyI.F.GharghiA.TraubK.P.2019Land Use Change Detection and Urban Sprawl Monitoring in Metropolitan Area of Jakarta (Jabodetabek) from 2001 to 20154325726810.18502/keg.v4i3.5862Open DOISearch in Google Scholar
Ruwashdi M.F., Khakani E.T., 2022. Simulating and predicting of urban expansion in Al Najaf city utilizing a Ca-Markov model. AIP Conference Proceedings 2398, 020058. DOI 10.1063/5.0094462.RuwashdiM.F.KhakaniE.T.2022Simulating and predicting of urban expansion in Al Najaf city utilizing a Ca-Markov model239802005810.1063/5.0094462Open DOISearch in Google Scholar
Serdaroğlu Sağ N., 2021. Assessment of urban development pattern and urban sprawl using Shannon's entropy: A case study of Konya (Turkey). Journal of Human Sciences 18(2): 252–265. DOI 10.14687/jhs.v18i2.6158.Serdaroğlu SağN.2021Assessment of urban development pattern and urban sprawl using Shannon's entropy: A case study of Konya (Turkey)18225226510.14687/jhs.v18i2.6158Open DOISearch in Google Scholar
Shen G., 2002. Fractal dimension and fractal growth of urbanized areas. International Journal of Geographical Information Science 16(5): 419–437. DOI 10.1080/13658810210137013.ShenG.2002Fractal dimension and fractal growth of urbanized areas16541943710.1080/13658810210137013Open DOISearch in Google Scholar
Sridhar M.B., Sathyanathan R., Subramani R., Sudalaimathu K., 2020. Urban sprawl analysis using remote sensing data and its impact on surface water bodies: case study of Surat, India. IOP Conference Series: Materials Science and Engineering 912 062070. DOI 10.1088/1757-899X/912/6/062070.SridharM.B.SathyanathanR.SubramaniR.SudalaimathuK.2020Urban sprawl analysis using remote sensing data and its impact on surface water bodies: case study of Surat, India912 062070.10.1088/1757-899X/912/6/062070Open DOISearch in Google Scholar
Sudhira H.S., Ramachandra T.V., Jagadish K.S., 2004. Urban sprawl: metrics, dynamics and modeling using GIS. International Journal of Applied Earth Observation 5(1): 29–39. DOI 10.1016/j.jag.2003.08.002.SudhiraH.S.RamachandraT.V.JagadishK.S.2004Urban sprawl: metrics, dynamics and modeling using GIS51293910.1016/j.jag.2003.08.002Open DOISearch in Google Scholar
Sun H., Forsythe W., Waters N., 2007. Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada. Networks and Spatial Economics 7(4): 353–376. DOI 10.1007/s11067-007-9030-y.SunH.ForsytheW.WatersN.2007Modeling urban land use change and urban sprawl: Calgary, Alberta, Canada7435337610.1007/s11067-007-9030-yOpen DOISearch in Google Scholar
Tannier C., Pumain D., 2005. Fractals in urban geography: a theoretical outline and an empirical example. Cybergeo 307: 1–22. DOI 10.4000/cybergeo.3275.TannierC.PumainD.2005Fractals in urban geography: a theoretical outline and an empirical example30712210.4000/cybergeo.3275Open DOISearch in Google Scholar
Terzi F., Kaya H.S., 2008. Analyzing Urban Sprawl Patterns through Fractal Geometry: The Case of Istanbul Metropolitan Area. Centre for Advanced Spatial Analysis Working Papers 144: 1–23. DOI 10.1080/13658810210137013.TerziF.KayaH.S.2008Analyzing Urban Sprawl Patterns through Fractal Geometry: The Case of Istanbul Metropolitan Area14412310.1080/13658810210137013Open DOISearch in Google Scholar
Tewolde M.G., Cabral P., 2011. Urban sprawl analysis and modeling in Asmara, Eritrea. Remote Sensing 3(10): 2148–2165. DOI 10.3390/rs3102148.TewoldeM.G.CabralP.2011Urban sprawl analysis and modeling in Asmara, Eritrea3102148216510.3390/rs3102148Open DOISearch in Google Scholar
Theiler J., 1990. Estimating fractal dimension. Journal of the Optical Society of America A 7(6): 1055–1073. DOI 10.1364/JOSAA.7.001055.TheilerJ.1990Estimating fractal dimension761055107310.1364/JOSAA.7.001055Open DOISearch in Google Scholar
Thomas I., Frankhauser P., 2013. Fractal dimensions of the built-up footprint: buildings versus roads. Fractal evidence from Antwerp (Belgium). Environment and Planning B: Urban Analytics and City Science 40(2): 310–329. DOI 10.1068/b38218.ThomasI.FrankhauserP.2013Fractal dimensions of the built-up footprint: buildings versus roads. Fractal evidence from Antwerp (Belgium)40231032910.1068/b38218Open DOISearch in Google Scholar
Torrens P.M., Alberti M., 2000. Measuring sprawl. Centre for Advanced Spatial Analysis Working Papers 27: 1–34.TorrensP.M.AlbertiM.2000Measuring sprawl27134Search in Google Scholar
Vanum G., Hadgu K.M., 2012. GIS and remote sensing based urban sprawl detection and its implications on sustainable development. International Journal of Management, IT and Engineering 2(9): 452–478.VanumG.HadguK.M.2012GIS and remote sensing based urban sprawl detection and its implications on sustainable development29452478Search in Google Scholar
Wu K., Ye X., Qi Z.F., Zhang H., 2013. Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: The case of fast-growing Hangzhou metropolitan area, China. Cities 31: 276–284. DOI 10.1016/j.cities.2012.08.003.WuK.YeX.QiZ.F.ZhangH.2013Impacts of land use/land cover change and socioeconomic development on regional ecosystem services: The case of fast-growing Hangzhou metropolitan area, China3127628410.1016/j.cities.2012.08.003Open DOISearch in Google Scholar
Xiao J., Shen Y., Ge J., Tateishi R., Tang C., Liang Y., Huang Z., 2006. Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing. Landscape and Urban Planning 75(1–2): 69–80. DOI 10.1016/j.landurbplan.2004.12.005.XiaoJ.ShenY.GeJ.TateishiR.TangC.LiangY.HuangZ.2006Evaluating urban expansion and land use change in Shijiazhuang, China, by using GIS and remote sensing751–2698010.1016/j.landurbplan.2004.12.005Open DOISearch in Google Scholar
Yeh A.G.-O., Li X., 2001. Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogrammetric Engineering and Remote Sensing 67(1): 83–90.YehA.G.-O.LiX.2001Measurement and monitoring of urban sprawl in a rapidly growing region using entropy6718390Search in Google Scholar
Zhao Y., Xie D., Zhang X., Ma S., 2021. Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area. Land 10(6): 633. DOI 10.3390/land10060633pp1-19.ZhaoY.XieD.ZhangX.MaS.2021Integrating Spatial Markov Chains and Geographically Weighted Regression-Based Cellular Automata to Simulate Urban Agglomeration Growth: A Case Study of the Guangdong–Hong Kong–Macao Greater Bay Area10663310.3390/land10060633pp1-19Open DOISearch in Google Scholar