Abstract
Monitoring of real estate growth is essential with the increasing demand for housing and working space in cities. In this study, a new methodological framework is proposed to map the area under real estate using geospatial techniques. In this framework, the built-up area and open land at successive stages of development are used to map the area under real estate. Three machine learning algorithms were used, namely random forest (RF), support vector machine (SVM), and feedforward neural networks (FFNN), to classify the land use and land cover (LULC) map of Delhi NCR during 1990–2018, which is the basic input for real estate mapping. The results of the study show that optimized RF performed better than SVM and FFNN in LULC classification. The real estate land increased by 279% in Delhi NCR during 1990–2018. The area under real estate increased by 33%, 47%, 29%, 21%, and 22% during 1990–1996, 1996–2003, 2003–2008, 2008–2014, and 2014–2018, respectively. Among the cities surrounding Delhi, Gurgaon, Rohtak, Noida, and Faridabad have witnessed maximum real estate growth. The approach used in this study could be used for real estate mapping in other cities across the world.
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The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.
References
Aayog NITI (2023) Real estate. https://niti.gov.in/sites/default/files/2023-08/10th_vol2.pdf. Accessed 2 Mar 2023
Abebe MS, Derebew KT, Gemeda DO (2019) Exploiting temporal-spatial patterns of informal settlements using GIS and remote sensing technique: a case study of Jimma city, Southwestern Ethiopia. Environ Syst Res 8(1):1–11. https://doi.org/10.1186/s40068-019-0133-5
Adabre MA, Chan AP (2021) Modeling the impact of barriers on sustainable housing in developing countries. J Urban Plan Dev 147(1):5020032. https://doi.org/10.1061/(ASCE)UP.1943-5444.0000639
Ahmad MN, Shao Z, Javed A (2023) Modelling land use/land cover (LULC) change dynamics, future prospects, and its environmental impacts based on geospatial data models and remote sensing data. Environ Sci Pollut Res 30(12):32985–33001
Al-Ahmadi K, See L, Heppenstall A, Hogg J (2009) Calibration of a fuzzy cellular automata model of urban dynamics in Saudi Arabia. Ecol Complex 6(2):80–101. https://doi.org/10.1016/j.ecocom.2008.09.004
Aladwan Z, Ahamad MSS (2019) Hedonic pricing model for real property valuation via GIS—A review. Civ Environ Eng Reports. https://doi.org/10.2478/ceer-2019-0022
Alam MJ (2018) Rapid urbanization and changing land values in mega cities: implications for housing development projects in Dhaka, Bangladesh. Bandung 5(1):1–19. https://doi.org/10.1186/s40728-018-0046-0
Alqurashi AF (2021) Quantification of urban patterns and processes through space and time using remote sensing data: A comparative study between three Saudi Arabian cities. Sustainability 13(22):12615. https://doi.org/10.3390/su132212615
Amini S, Saber M, Rabiei-Dastjerdi H, Homayouni S (2022) Urban land use and land cover change analysis using random forest classification of landsat time series. Remote Sens 14(11):2654. https://doi.org/10.3390/rs14112654
Anarock (2021) Residential Market Viewpoints Pan India. https://api.anarock.com/uploads/research/Q2%202021_PAN%20India%20Residential%20Market%20Viewpoints.pdf. Accessed 25 Dec 2021
Appiah OJ, Kpienbaareh D, Adomako JAA (2021) Built-up area expansion in the Wa Municipality of Ghana: implications for vegetation cover loss and fragmentation. African Geogr Rev. https://doi.org/10.1080/19376812.2021.1919532
Arshad S, Ahmad SR, Abbas S, Asharf A, Siddiqui NA, ul Islam Z (2022) Quantifying the contribution of diminishing green spaces and urban sprawl to urban heat island effect in a rapidly urbanizing metropolitan city of Pakistan. Land Use Policy 113:105874. https://doi.org/10.1016/j.landusepol.2021.105874
Baba KA, Lal D, Bello A (2019) Application of remote sensing and GIS techniques in urban planning, development and management.(A case study of allahabad district, India). Int J Sci Eng Res 10(6):1127–1134
Baig MF, Mustafa MRU, Baig I, Takaijudin HB, Zeshan MT (2022) Assessment of land use land cover changes and future predictions using CA-ANN simulation for selangor, Malaysia. Water 14(3):402
Balaji L, Muthukannan M (2021) Investigation into valuation of land using remote sensing and GIS in Madurai, Tamil Nadu, India. Eur J Remote Sens 54:167–175. https://doi.org/10.1080/22797254.2020.1772118
Balarabe AT, Jordanov I (2021) LULC image classification with convolutional neural network. In: 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS. IEEE, pp 5985–5988 https://doi.org/10.1109/IGARSS47720.2021.9555015
Barcella BLS, Melazzo ES (2020) Urban expansion and real estate dynamics: comparing real estate agent land strategies in medium-sized cities. Soc Nat 32:100–115. https://doi.org/10.14393/SN-v32-2020-42908
Basu U (2019) Migration corridors: a study on Murshidabad, west Bengal. Econ Aff 64(3):663–672. https://doi.org/10.30954/0424-2513.3.2019.23
Batra L (2009) A review of urbanisation and urban policy in post-independent India. Centre for the Study of Law and Governance, New Delhi
Bill R, Blankenbach J, Breunig M, Haunert JH, Heipke C, Herle S, Werner M (2022) Geospatial information research: state of the art, case studies and future perspectives. PFG J Photogramm Remote Sens Geoinform Sci 90(4):349–389. https://doi.org/10.1007/s41064-022-00217-9
Black HC (1990) Black’s law dictionary: definitions of the terms and phrases of American. Minnesota
Bobáľová H, Benová A, Kožuch M (2021) Hierarchical object-based mapping of urban land cover using sentinel‑2 data: a case study of six cities in central europe. PFG J Photogramm Remote Sens Geoinform Sci 89:15–31. https://doi.org/10.1007/s41064-020-00135-8
Bose PS (2014) Living the way the world does: global Indians in the remaking of Kolkata. Ann Assoc Am Geogr 104(2):391–400
Breiman L (2001) Random forests. Mach Learn 45(1):5–32
Business Today (2021) The top players in the Indian real estate market. https://www.businesstoday.in/industry/infra/story/the-top-players-in-the-indian-real-estate-market-146963-2016-08-02
Census of India (1951) Census reports—1951. Part II-C-economic tables (rural & urban population). Centre for Studies in Social Sciences, Calcutta
Chadchan J, Shankar R (2012) An analysis of urban growth trends in the post-economic reforms period in India. Int J Sustain Built Environ 1(1):36–49. https://doi.org/10.1016/j.ijsbe.2012.05.001
Chaudhuri G, Mainali KP, Mishra NB (2022) Analyzing the dynamics of urbanization in Delhi National Capital Region in India using satellite image time-series analysis. Environ Plan B Urban Anal City Sci. https://doi.org/10.1177/23998083211007
Chen Y, Liu X, Chen Y (2019) Land cover classification using high-resolution images and a feedforward neural network. IEEE J Sel Top Appl Earth Observations Remote Sensing 12(3):965–974
Clapp JM, Cohen JP, Lindenthal T (2021) Are estimates of rapid growth in urban land values an artifact of the land residual model? J Real Estate Finan Econ. https://doi.org/10.1007/s11146-021-09834-4
Clark A, Phinn S, Scarth P (2023) Optimised U‑net for land use-land cover classification using aerial photography. PFG J Photogramm Remote Sens Geoinform Sci. https://doi.org/10.1007/s41064-023-00233-3
Conrad C, Rudloff M, Abdullaev I, Thiel M, Löw F, Lamers JPA (2015) Measuring rural settlement expansion in Uzbekistan using remote sensing to support spatial planning. Appl Geogr 62:29–43. https://doi.org/10.1016/j.apgeog.2015.03.017
Cracknell AP (2018) The development of remote sensing in the last 40 years. Int J Remote Sens 39(23):8387–8427. https://doi.org/10.1080/01431161.2018.1550919
Das P, Sah V, Sharma D, Singh V, Galuppo L (2013) Real estate development process in India. J Real Estate Lit 21(2):271–292. https://doi.org/10.1080/10835547.2013.12090362
Das T, Shahfahad, Naikoo MW, Talukdar S, Parvez A, Rahman A, Mosavi A (2022) Analysing process and probability of built-up expansion using machine learning and fuzzy logic in English Bazar, West Bengal. Remote Sens 14(10):2349. https://doi.org/10.3390/rs14102349
Dhanaraj K, Angadi DP (2021) Urban expansion quantification from remote sensing data for sustainable land-use planning in Mangaluru, India. Remote Sens Appl Soc Environ 23:100602. https://doi.org/10.1016/j.rsase.2021.100602
Dolean BE, Bilașco Ș, Petrea D, Moldovan C, Vescan I, Roșca S, Fodorean I (2020) Evaluation of the built-up area dynamics in the first ring of Cluj-Napoca metropolitan area, Romania by semi-automatic GIS analysis of landsat satellite images. Appl Sci 10(21):7722. https://doi.org/10.3390/app10217722
Dutta D, Rahman A, Paul SK, Kundu A (2020) Estimating urban growth in peri-urban areas and its interrelationships with built-up density using earth observation datasets. Ann Reg Sci 65(1):67–82. https://doi.org/10.1007/s00168-020-00974-8
Erdoğan S, Memduhoğlu A (2019) A spatiotemporal exploratory analysis of real estate sales in Turkey using GIS. J Eur Real Estate Res. https://doi.org/10.1108/JERER-06-2018-0024
Fan P, Chen J, Wu J (2019) Evolving landscapes under institutional change, globalization, and cultural influence in contrasting urban systems. Landsc Urban Plan 187:129–131. https://doi.org/10.1016/j.landurbplan.2019.04.013
Fazal S (2000) Urban expansion and loss of agricultural land—a GIS based study of Saharanpur City, India. environ urban 12(2):133–149. https://doi.org/10.1177/0956247800012002
Galal A, Razzaz O (2001) Reforming land and real estate markets vol 2616. World Bank Publications https://doi.org/10.1596/1813-9450-2616
Gandhi S, Tandel V, Tabarrok A, Ravi S (2021) Too slow for the urban march: Litigations and the real estate market in Mumbai, India. J Urban Econ 123:103330. https://doi.org/10.1016/j.jue.2021.103330
García-Palacios P, Gross N, Gaitán J, Maestre FT (2018) Climate mediates the biodiversity-ecosystem stability relationship globally. Proc Natl Acad Sci USA 115(33):8400–8405. https://doi.org/10.1073/pnas.1800425115
Ghatak M, Mookherjee D (2014) Land acquisition for industrialization and compensation of displaced farmers. J Dev Econ 110:303–312. https://doi.org/10.1016/j.jdeveco.2013.01.001
Gholipour HF, Al-Mulali U, Mohammed AH (2014) Foreign investments in real estate, economic growth and property prices: evidence from OECD countries. J Econ Policy Reform 17(1):33–45. https://doi.org/10.1080/17487870.2013.828613
Gupta M, Gupta R (2017) Demand for rental housing: evidence from slum settlements in Delhi. Urbanisation 2(1):9–27. https://doi.org/10.1177/2455747117700942
Hansen MC, Potapov PV, Moore R, Hancher M, Turubanova SA, Tyukavina A, Townshend J (2013) High-resolution global maps of 21st-century forest cover change. Science 342(6160):850–853. https://doi.org/10.1126/science.1244693
Huang C, Chen Y, Zhang S, Wu J (2018) Detecting, extracting, and monitoring surface water from space using optical sensors: a review. Rev Geophys 56(2):333–360. https://doi.org/10.1029/2018RG000598
IBEF (2023) Indian Real Estate Industry. IBEF: India Brand Equity Foundation. https://www.ibef.org/download/1699270877_Real_Estate_August_2023.pdf. Accessed 27 Nov 2023
Jacobus CJ, Harwood B, Chmura LJ (2010) Real estate principles vol 9. Cengage Learning
Jain M, Korzhenevych A (2019) Detection of urban system in India: Urban hierarchy revisited. Landsc Urban Plan 190:103588. https://doi.org/10.1016/j.landurbplan.2019.103588
Jaypee Greens (2021) Wish town Noida. http://www.jaypeegreens.com/noidaoverview.aspx
Joyce KE, Belliss SE, Samsonov SV, McNeill SJ, Glassey PJ (2009) A review of the status of satellite remote sensing and image processing techniques for mapping natural hazards and disasters. Prog Phys Geogr 33(2):183–207. https://doi.org/10.1177/0309133309339563
Kay K (2016) Breaking the bundle of rights: conservation easements and the legal geographies of individuating nature. Environ Plan A Econ Space 48(3):504–522. https://doi.org/10.1177/0308518X15609318
Knight Frank (2019) Indian Real Estate, Residential and Office. https://content.knightfrank.com/research/659/documents/en/india-real-estate-january-june-2019-6498.pdf. Accessed 25 Jan 2021
Knight Frank (2020) Investment in real estate, trends in privet equity investment. https://content.knightfrank.com/research/1997/documents/en/investment-in-real-estate-2020-indian-real-estate-residential-office-7251.pdf
Koch D, Despotovic M, Leiber S, Sakeena M, Döller M, Zeppelzauer M (2019) Real estate image analysis: a literature review. J Real Estate Lit 27(2):269–300. https://doi.org/10.22300/0927-7544.27.2.269
Kodors S, Rausis A, Ratkevics A, Zvirgzds J, Teilans A, Ansone I (2017) Real estate monitoring system based on remote sensing and image recognition technologies. Procedia Comput Sci 104:460–467. https://doi.org/10.1016/j.procs.2017.01.160
Koko AF, Yue W, Abdullahi Abubakar G, Hamed R, Alabsi NAA (2021) Analyzing urban growth and land cover change scenario in Lagos, Nigeria using multi-temporal remote sensing data and GIS to mitigate flooding. Geomatics Nat Hazards Risk 12(1):631–652. https://doi.org/10.1080/19475705.2021.1887940
Krajewska M, Szopińska K, Siemińska E (2021) Value of land properties in the context of planning conditions risk on the example of the suburban zone of a Polish city. Land Use Policy 109:105697. https://doi.org/10.1016/j.landusepol.2021.105697
Kumari B, Tayyab M, Hang HT, Khan MF, Rahman A (2019) Assessment of public open spaces (POS) and landscape quality based on per capita POS index in Delhi, India. SN Appl Sci 1(4):1–13. https://doi.org/10.1007/s42452-019-0372-0
Kumari B, Shahfahad, Tayyab M, Ahmed IA, Baig MRI, Ali MA, Rahman A (2022) Land use/land cover (LU/LC) change dynamics using indices overlay method in Gautam Buddha Nagar District-India. GeoJournal 87:2287–2305
Kundu S, Pal S, Talukdar S, Mandal I (2021) Impact of wetland fragmentation due to damming on the linkages between water richness and ecosystem services. Environ Sci Pollut Res 28(36):50266–50285. https://doi.org/10.1007/s11356-021-14123-x
Kurvinen A, Saari A (2020) Urban housing density and infrastructure costs. Sustainability 12(2):497. https://doi.org/10.3390/su12020497
Lee Y, Brody SD (2018) Examining the impact of land use on flood losses in Seoul, Korea. Land Use Policy 70:500–509. https://doi.org/10.1016/j.landusepol.2017.11.019
Leffers D (2018) Real estate developers’ influence of land use legislation in the Toronto region: an institutionalist investigation of developers, land conflict and property law. Urban Stud 55(14):3059–3075. https://doi.org/10.1177/004209801773642
Li H, Cheng EW (2005) A GIS based site selection system for real estate projects. Constr Innov 5(4):231–241. https://doi.org/10.1108/14714170510815276
Li X, Chen J, Li J, Du Y (2017) Random forest classification of land cover using multi-source remote sensing data: a case study in the Loess Plateau of China. Int J Appl Earth Obs Geoinform 56:107–115. https://doi.org/10.1080/01431161.2020.1731771
LiasesForas (2020) Residential Real Estate Market Report. https://www.liasesforas.com/admin/WhitePaper/44/WhitePaper_2020-02-19_63717718003537.pdf
Liu H, Zhou L, Tang D (2022) Urban expansion simulation coupled with residential location selection and land acquisition bargaining: a case study of Wuhan urban development zone, central China’s Hubei province. Sustainability 15(1):290. https://doi.org/10.3390/su15010290
Lv H, Guan X, Meng Y (2021) Study on economic value of urban land resources based on emergy and econometric theories. Environ Dev Sustain 23(1):1019–1042. https://doi.org/10.1007/s10668-019-00573-4
Mahmood KW (2023) GIS-AHP for selecting the best locations for residential lands—case study: Erbil City, Iraq. GeoJournal 88, 6073–6090. https://doi.org/10.1007/s10708-023-10957-9
Mahtta R, Mahendra A, Seto KC (2019) Building up or spreading out? Typologies of urban growth across 478 cities of 1 million. Environ Res Lett 14(12):124077. https://doi.org/10.1088/1748-9326/ab59b
Manjunatha MC, Basavarajappa HT (2020) Assessment of Land use land cover classification through Geospatial approach: a case study of Mysuru taluk of Karnataka state, India. J Environ Waste Manag 7(1):326–338
Mathur S (2013) Use of land pooling and reconstitution for urban development: experiences from Gujarat, India. Habitat Int 38:199–206. https://doi.org/10.1016/j.habitatint.2012.06.007
Mehra P (2019) The lost decade (2008–18): how India’s growth story devolved into growth without a story. Penguin Random House India Private
Mete MO, Yomralioglu T (2021) Implementation of serverless cloud GIS platform for land valuation. Int J Digit Earth 14(7):836–850. https://doi.org/10.1080/17538947.2021.1889056
Moradi E, Sharifi A (2023) Assessment of forest cover changes using multi-temporal Landsat observation. Environ Dev Sustain 25(2):1351–1360. https://doi.org/10.1007/s10668-021-02097-2
Morris S, Pandey A (2007) Towards reform of land acquisition framework in India. Economic and Political Weekly, 2083–2090. https://www.jstor.org/stable/4419665
Naikoo MW, Rihan M, Ishtiaque M (2020) Analyses of land use land cover (LULC) change and built-up expansion in the suburb of a metropolitan city: Spatio-temporal analysis of Delhi NCR using landsat datasets. J Urban Manag 9(3):347–359. https://doi.org/10.1016/j.jum.2020.05.004
Naikoo MW, Rihan M, Peer AH, Talukdar S, Mallick J, Ishtiaq M, Rahman A (2022) Analysis of peri-urban land use/land cover change and its drivers using geospatial techniques and geographically weighted regression. Environ Sci Pollut Res. https://doi.org/10.1016/j.jenvman.2022.116441
Naikoo MW, Talukdar S, Ishtiaq M, Rahman A (2023) Modelling built-up land expansion probability using the integrated fuzzy logic and coupling coordination degree model. J Environ Manag 325:116441. https://doi.org/10.1016/j.jenvman.2022.116441
NCRPB (2016) Functional Plan for Economic Development of National Capital Region. http://ncrpb.nic.in/pdf_files/functionalplan_economicdevelopment.pdf. Accessed 25 Dec 2021
Newell G, Kamineni R (2007) The significance and performance of real estate markets in India. J Real Estate Portfolio Manag 13(2):161–172. https://doi.org/10.1080/10835547.2007.12089769
NRSC (1995) Natural resource census-land use land cover database. Technical Report—Ver.1. https://bhuvan-app1.nrsc.gov.in/2dresources/thematic/2LULC/lulc1112.pdf. Accessed 1 Jan 2023
Rahman A, Kumar Y, Fazal S, Bhaskaran S (2011) Urbanization and quality of urban environment using remote sensing and GIS techniques in East Delhi-India. J Geogr Inf Syst 3(01):62
Rahman A, Roy SS, Talukdar S (2023) Rethinking progress in approaches and techniques for the Urban environmental studies. In: Advancements in Urban environmental studies: application of Geospatial technology and artificial intelligence in urban studies. Springer, Cham, pp 471–478
Randin CF, Ashcroft MB, Bolliger J, Cavender-Bares J, Coops NC, Dullinger S, Payne D (2020) Monitoring biodiversity in the Anthropocene using remote sensing in species distribution models. Remote Sens Environ 239:111626. https://doi.org/10.1016/j.rse.2019.111626
Rani D (2019) Application of trickle-down theory in economic policies of India: a study of industrial innovation in national capital region. Delhi Bus Rev 20(1):1–10
Rathee G (2014) Trends of land-use change in India. In: Urbanization in Asia. Springer, New Delhi, pp 215–238
Roy B, Kasemi N (2021) Monitoring urban growth dynamics using remote sensing and GIS techniques of Raiganj Urban Agglomeration, India. Egypt J Remote Sens Space Sci 24(2):221–230. https://doi.org/10.1016/j.ejrs.2021.02.001
Rudel TK, O’Neill K, Gottlieb P, McDermott M, Hatfield C (2011) From middle to upper class sprawl? Land use controls and changing patterns of real estate development in northern New Jersey. Ann Assoc Am Geogr 101(3):609–624. https://doi.org/10.1080/00045608.2011.560062
Sahana M, Hong H, Sajjad H (2018) Analyzing urban spatial patterns and trend of urban growth using urban sprawl matrix: a study on Kolkata urban agglomeration, India. Sci Total Environ 628:1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.170
Sarkar R (2019) Urbanization in India before and after the economic reforms: what does the census data reveal? J Asian Afr Stud 54(8):1213–1226. https://doi.org/10.1177/0021909619865581
Sarkhel P, Mukherjee A (2021) Land acquisition, markets and political networks: evidence from the Indian sundarbans. J South asian Dev 16(2):194–219. https://doi.org/10.1177/0973174121102487
Schrotter G, Hürzeler C (2020) The digital twin of the city of Zurich for urban planning. PFG J Photogramm Remote Sens Geoinform Sci 88(1):99–112. https://doi.org/10.1007/s41064-020-00092-2
Searle LG (2016) Landscapes of accumulation. In: Landscapes of accumulation. University of Chicago Press,
Sengupta U, Tipple AG (2007) The performance of public-sector housing in Kolkata, India, in the post-reform milieu. Urban Stud 44(10):2009–2027. https://doi.org/10.1080/00420980701471927
Shah YS, Bhagat SS (2019) Housing policy in India: challenges and reforms. Int J Res Eng Sci Manag 2(12):180–183
Shahfahad NMW, Islam ARMT, Mallick J, Rahman A (2022) Land use/land cover change and its impact on surface urban heat island and urban thermal comfort in a metropolitan city. Urban Clim 41:101052. https://doi.org/10.1016/j.uclim.2021.101052
Shahfahad RM, Naikoo MW, Ali MA, Usmani TM, Rahman A (2021) Urban heat island dynamics in response to land-use/land-cover change in the coastal city of Mumbai. J Indian Soc Remote Sens 49(9):2227–2247. https://doi.org/10.1007/s12524-021-01394-7
Siddiqui K (2017) Globalization, trade liberalisation and the issues of economic diversification in the developing countries. J Bus Econ Policy 4(4):30–43
Simsek D, Sertel E (2018) Spatial analysis of two different urban landscapes using satellite images and landscape metrics. Photogramm Eng Remote Sensing 84(11):711–721. https://doi.org/10.14358/PERS.84.11.711
Solórzano JV, Mas JF, Gao Y, Gallardo-Cruz JA (2021) Land use land cover classification with U‑net: advantages of combining sentinel‑1 and sentinel‑2 imagery. Remote Sens 13(18):3600
Song W, Deng X (2017) Land-use/land-cover change and ecosystem service provision in China. Sci Total Environ 576:705–719. https://doi.org/10.1016/j.scitotenv.2016.07.078
Sridhar KS (2010) Impact of land use regulations: evidence from India’s cities. Urban Stud 47(7):1541–1569. https://doi.org/10.1177/0042098009353813
Stoica IV, Vîrghileanu M, Zamfir D, Mihai BA, Săvulescu I (2020) Comparative assessment of the built-up area expansion based on Corine land cover and landsat datasets: a case study of a post-socialist city. Remote Sens 12(13):2137. https://doi.org/10.3390/rs12132137
Tan Z, Guan Q, Lin J, Yang L, Luo H, Ma Y, Wang N (2020) The response and simulation of ecosystem services value to land use/land cover in an oasis, Northwest China. Ecol Indic 118:106711. https://doi.org/10.3390/rs12132137
United Nations (2018) The World’s cities in 2018. Department of Economic and Social Affairs, Population Division, World Urbanization Prospects, pp 1–34
Ward PM (2019) Self-help housing. The wiley blackwell encyclopedia of urban and regional studies, pp 1–6 https://doi.org/10.1002/9781118568446.eurs0281
Weslati O, Bouaziz S, Sarbeji MM (2023) Modelling and assessing the spatiotemporal changes to future land use change scenarios using remote sensing and CA-markov model in the mellegue catchment. J Indian Soc Remote Sens 51(1):9–29. https://doi.org/10.1007/s12524-022-01618-4
Yang J, Gong P, Fu R, Zhang M, Chen J, Liang S, Dickinson R (2013) The role of satellite remote sensing in climate change studies. Nature Clim Change 3(10):875–883. https://doi.org/10.1038/nclimate1908
Zeng TQ, Zhou Q (2001) Optimal spatial decision making using GIS: a prototype of a real estate geographical information system (REGIS). Int J Geogr Inf Sci 15(4):307–321. https://doi.org/10.1080/136588101300304034
Zhang Q, Zhang W, Li M, Huang Q, Li F (2012) Land banking: a mechanism for urban sustainable development in China. AMBIO 41(8):904–906. https://doi.org/10.1007/s13280-012-0297-y
Zheng L, Long F, Chang Z, Ye J (2019) Ghost town or city of hope? The spatial spillover effects of high-speed railway stations in China. Transp Policy 81:230–241. https://doi.org/10.1016/j.tranpol.2019.07.005
Acknowledgements
The lead author is grateful to the University Grant Commission (UGC) of India for providing doctoral fellowship during this work. The authors are thankful to the United States Geological Survey (USGS) for providing the Landsat data for free.
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M.W. Naikoo participated in designing the study as well as data collection, statistical analysis, and writing of the initial draft; Shahfahad participated in the field survey, data modeling, land use classification, and collected the background information; S. Talukdar reviewed the manuscript and participated in the literature survey; M. Rihan participated in land use classification and validation; I.A. Ahmed and H. Thi Hang helped in data collection, data modeling and collected the background information; M. Ishtiaq and A. Rahman participated in conceptualization and revision of the manuscript. All the authors have read and approved the manuscript.
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M.W. Naikoo, Shahfahad, S. Talukdar, M. Rihan, I.A. Ahmed, H. Thi Hang, M. Ishtiaq and A. Rahman declare that they have no competing interests.
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Naikoo, M.W., Shahfahad, Talukdar, S. et al. A Geospatial Approach to Mapping and Monitoring Real Estate-Induced Urban Expansion in the National Capital Region of Delhi. PFG 92, 177–200 (2024). https://doi.org/10.1007/s41064-024-00278-y
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DOI: https://doi.org/10.1007/s41064-024-00278-y