Skip to main content

Advertisement

Log in

Polycentric urban growth and identification of urban hot spots in Faridabad, the million-plus metropolitan city of Haryana, India: a zonal assessment using spatial metrics and GIS

  • Published:
Environment, Development and Sustainability Aims and scope Submit manuscript

Abstract

Polycentric urban structure is the recent trend of new-age sustainable cities in India. Limited research is reported focused on quantitative analysis of polycentric structure at the intracity level. In the present research, a detailed database has been generated using multitemporal satellite imageries. Zone-wise quantitative analysis, supported by the database, exhibits clear evidence of transit-oriented polycentric urban growth around Old Faridabad, NIT and Ballabhgarh. Linear urban expansion close to highways, railways, metro ways and urban extension as Greater Faridabad has led to the vertical expansion of the city. Highly variable urban density has been observed in the southern and south-western zones. Shannon’s entropy has identified even distribution of built-up around Old Faridabad, whereas clustering of high urban density around NIT and Ballabhgarh. Getis-Ord Gi* statistics have identified a consistent increase of urban hot spot areas from 21.92% (2007), 26% (2012) to 27.48% (2017). The present study suggests planners and policymakers to improve the connectivity between the upcoming urban centre Greater Faridabad, three existing urban centres and other under-developed districts of Haryana to achieve balanced development. Such a step would contribute to the sustainable solutions for various issues over the hot spot around the current urban centres and achieve inclusive urban growth in Faridabad. Moreover, current findings contribute to building a model framework for analysing urban growth patterns even for other satellite cities of India.

Graphic abstract

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10

Similar content being viewed by others

References

  • Alidadi, M., & Dadashpoor, H. (2018). Beyond monocentricity: Examining the spatial distribution of employment in Tehran metropolitan region. International Journal of Urban Sciences, 22, 38–58. https://doi.org/10.1080/12265934.2017.1329024

    Article  Google Scholar 

  • Alqurashi, A. F., & Kumar, L. (2014). Land use and land cover change detection in the Saudi Arabian Desert Cities of Makkah and Al-Taif using Satellite Data. Advanced Remote Sensing, 3, 106–119. https://doi.org/10.4236/ars.2014.33009

    Article  Google Scholar 

  • Al-sharif, A. A. A., Pradhan, B., Shafri, H. Z. M., & Mansor, S. (2014). Quantitative analysis of urban sprawl in Tripoli using Pearson’s Chi-Square statistics and urban expansion intensity index. IOP Conference Series: Earth and Environmental Science, 20, 012006. https://doi.org/10.1088/1755-1315/20/1/012006

    Article  Google Scholar 

  • Angel, S., Jason Parent, Daniel L. Civco, & A.M.B. (2011). Making Room for a Planet of Cities, in: Policy Focus Report/Code PF027. Lincoln Institute of Land Policy. https://doi.org/10.4337/9781849808057.00023

  • Bailey, N., & Turok, I. (2001). Central Scotland as a Polycentric Urban Region: Useful planning concept or Chimera? Urban Studies, 38, 697–715. https://doi.org/10.1080/00420980120035295

    Article  Google Scholar 

  • Bharath, H. A., Chandan, M. C., Vinay, S., & Ramachandra, T. V. (2018). Modelling urban dynamics in rapidly urbanising Indian cities. Egyptian Journal of Remote Sensing and Space Science, 21, 201–210. https://doi.org/10.1016/j.ejrs.2017.08.002

    Article  Google Scholar 

  • Bhatta, B. (2009). Analysis of urban growth pattern using remote sensing and GIS: A case study of Kolkata, India. International Journal of Remote Sensing, 30, 4733–4746. https://doi.org/10.1080/01431160802651967

    Article  Google Scholar 

  • Boori, M. S., Netzband, M., Choudhary, K., & Voženílek, V. (2015). Monitoring and modeling of urban sprawl through remote sensing and GIS in Kuala Lumpur,Malaysia. Ecological Processing, 4, 15. https://doi.org/10.1186/s13717-015-0040-2

    Article  Google Scholar 

  • Chen, G., Hay, G. J., Carvalho, L. M. T., & Wulder, M. A. (2012). Object-based change detection. International Journal of Remote Sensing, 33, 4434–4457. https://doi.org/10.1080/01431161.2011.648285

    Article  Google Scholar 

  • Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment, 37, 35–46. https://doi.org/10.1016/0034-4257(91)90048-B

    Article  Google Scholar 

  • Dadashpoor, H., & Alidadi, M. (2017). Towards decentralization : Spatial changes of employment and population in Tehran Metropolitan Region, Iran. Applied Geography, 85, 51–61. https://doi.org/10.1016/j.apgeog.2017.05.004

    Article  Google Scholar 

  • Dadashpoor, H., & Nateghi, M. (2017). Simulating spatial pattern of urban growth using GIS-based SLEUTH model: A case study of eastern corridor of Tehran metropolitan region, Iran. Environment, Development and Sustainability, 19, 527–547. https://doi.org/10.1007/s10668-015-9744-9

    Article  Google Scholar 

  • Dadashpoor, H., & Salarian, F. (2020). Urban sprawl on natural lands: Analyzing and predicting the trend of land use changes and sprawl in Mazandaran city region, Iran. Environment, Development and Sustainability , 22, 593–614. https://doi.org/10.1007/s10668-018-0211-2

    Article  Google Scholar 

  • Dadashpoor, H., Azizi, P., & Moghadasi, M. (2019). Analyzing spatial patterns, driving forces and predicting future growth scenarios for supporting sustainable urban growth: Evidence from Tabriz metropolitan area, Iran. Sustainable Cities and Society, 47, 101502. https://doi.org/10.1016/j.scs.2019.101502

    Article  Google Scholar 

  • Dadras, M., Shafri, H. Z. M., Ahmad, N., Pradhan, B., & Safarpour, S. (2015). Spatio-temporal analysis of urban growth from remote sensing data in Bandar Abbas city, Iran. The Egyptian Journal of Remote Sensing and Space Sciences, 18, 35–52. https://doi.org/10.1016/j.ejrs.2015.03.005

    Article  Google Scholar 

  • Dutta, D., Rahman, A., Paul, S. K., & Kundu, A. (2021). Impervious surface growth and its inter-relationship with vegetation cover and land surface temperature in peri-urban areas of Delhi. Urban Climate, 37, 100799. https://doi.org/10.1016/j.uclim.2021.100799

    Article  Google Scholar 

  • Dutta, D., Rahman, A., & Paul, S.K. (2019). Changing pattern of urban landscape and its effect on land surface temperature in and around Delhi.

  • ESRI. (1999). The ESRI guide to GIS analysis: Volume1, Geographic patterns & Relationships. ESRI Press.

    Google Scholar 

  • ESRI. (2009). The ESRI Guide to GIS analysis, Volume 2: Spartial measurements and statistics. ESRI Press: Redlands, CA, USA.

  • Fernández-Maldonado, A. M., Romein, A., Verkoren, O., & Pessoa, P. P. R. (2014). Polycentric Structures in Latin American Metropolitan Areas: Identifying Employment Sub-centres. Regional Studies, 48, 1954–1971. https://doi.org/10.1080/00343404.2013.786827

    Article  Google Scholar 

  • Follmann, A., Hartmann, G., & Dannenberg, P. (2018). Multi-temporal transect analysis of peri-urban developments in Faridabad, India. Journal of Maps, 14, 17–25. https://doi.org/10.1080/17445647.2018.1424656

    Article  Google Scholar 

  • Getis, A., & Ord, J. K. (1992). The analysis of spatial association by use of distance statistics. Geographical Analysis, 24, 189–206. https://doi.org/10.1111/j.1538-4632.1992.tb00261.x

    Article  Google Scholar 

  • Ghosh, S., N., K.V., Kumar, S., & Midya, K. (2021). Seasonal contrast of land surface temperature in Faridabad. https://doi.org/10.4018/978-1-7998-2249-3.ch008

  • Ghosh, S., & Ghosh, S. (2017). Evaluating Patterns of Urban Growth in Faridabad. Sub- Region of NCR, India Using Remote Sensing, GIS and Entropy Approach, in: Indian Cartographer, 37, 258–261.

    Google Scholar 

  • Goel, N. (2011). Dynamic planning and development of Peri Urban areas: A case of Faridabad city. nstitute of Town Planners, India, 8, 15–20.

    Google Scholar 

  • GoH, 2017. VISION 2030.

  • Goswami, M., Nautiyal, S., & Manasi, S. (2020). Drivers and consequences of biophysical landscape change in a peri-urban–rural interface of Guwahati, Assam. Environment, Development and Sustainability 22, 791–811. https://doi.org/10.1007/s10668-018-0220-1

    Article  Google Scholar 

  • Guo, Z., Wang, S. D., Cheng, M. M., & Shu, Y. (2012). Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Procedia Environmental Sciences, 13, 935–942. https://doi.org/10.1016/j.proenv.2012.01.087

    Article  Google Scholar 

  • UN Habitat, 2020. The new urban agenda. UN-Habitat, p. 194.

  • He, Q., Zeng, C., Xie, P., Tan, S., & Wu, J. (2019). Comparison of urban growth patterns and changes between three urban agglomerations in China and three metropolises in the USA from 1995 to 2015. Sustainable Cities and Society, 50, 101649. https://doi.org/10.1016/j.scs.2019.101649

    Article  Google Scholar 

  • Jain, M., Siedentop, S., Taubenböck, H., & Namperumal, S. (2013). From Suburbanization to Counterurbanization? Investigating Urban Dynamics in the National Capital Region Delhi, India. Environment and Urbanization ASIA, 4, 247–266. https://doi.org/10.1177/0975425313510765

    Article  Google Scholar 

  • Jain, M., Taubenböck, H., & Namperumal, S. (2011). Seamless urbanisation and knotted city growth: Delhi Metropolitan Region. REAL CORP 2011 Proceedings/Tagungsband 853–862.

  • JNNURM (2006). City Development Plan 2006–2012.

  • Kasraian, D., Maat, K., & van Wee, B. (2019). The impact of urban proximity, transport accessibility and policy on urban growth: A longitudinal analysis over five decades. Environment and Planning B: Urban Analytics and City Science, 46, 1000–1017. https://doi.org/10.1177/2399808317740355

    Article  Google Scholar 

  • Kawamura, M., Jayamanna, S., & Tsujiko, Y. (1996). Relation Between Social and Environmental Conditions in Colombo. Sri Lanka and the Urban Index Estimated by Satellite Remote Sensing Data. In International Society of Photogrammetry and Remote Sensing (Ed.), Resource and Environmental Monitoring. pp. 321–326.

  • Kim, Y., Newman, G., & Güneralp, B. (2020). A review of driving factors, scenarios, and topics in urban land change models. Land. https://doi.org/10.3390/LAND9080246

    Article  Google Scholar 

  • Kuffer, M., Pfeffer, K., & Sliuzas, R. (2016). Slums from space-15 years of slum mapping using remote sensing. Remote Sensing. https://doi.org/10.3390/rs8060455

    Article  Google Scholar 

  • Kushwaha, S., & Nithiyanandam, Y. (2019). The study of heat Island and its relation with urbanisation in Gurugram, Delhi NCR for the Period of 19990 to 2018 XLII, 10–11.

  • Lan, F., Da, H., Wen, H., & Wang, Y. (2019). Spatial structure evolution of urban agglomerations and its driving factors in mainland China: From the monocentric to the polycentric dimension. Sustain., 11. https://doi.org/10.3390/su11030610

  • Li, X., & Shao, G. (2014). Object-based land-cover mapping with high resolution Aerial photography at a County Scale in Midwestern USA. Remote Sensing, 6, 11372–11390. https://doi.org/10.3390/rs61111372

    Article  Google Scholar 

  • Li, C., Zhao, J., & Xu, Y. (2017). Examining spatiotemporally varying effects of urban expansion and the underlying driving factors. Sustainable Cities and Society, 28, 307–320. https://doi.org/10.1016/j.scs.2016.10.005

    Article  Google Scholar 

  • Liu, Z., & Liu, S. (2018). Polycentric development and the role of Urban Polycentric Planning in China’s Mega Cities: An examination of Beijing’s Metropolitan Area. Sustainability, 10, 1588. https://doi.org/10.3390/su10051588

    Article  Google Scholar 

  • Liu, D., & Xia, F. (2010). Assessing object-based classification: Advantages and limitations. Remote Sens. Lett., 1, 187–194. https://doi.org/10.1080/01431161003743173

    Article  Google Scholar 

  • Liu, X., Derudder, B., & Wu, K. (2016). Measuring Polycentric Urban Development in China: An intercity transportation network perspective. Regional Studies, 50, 1302–1315. https://doi.org/10.1080/00343404.2015.1004535

    Article  Google Scholar 

  • Liu, X., Derudder, B., & Wang, M. (2017). Polycentric Urban Development in China: A Multi-Scale Analysis. Landscape and Urban Planning. https://doi.org/10.1177/2399808317690155

  • Liu, K., Murayama, Y., & Ichinose, T. (2020). Using a new approach for revealing the spatiotemporal patterns of functional urban polycentricity: A case study in the Tokyo metropolitan area. Sustainable Cities and Society, 59, 102176. https://doi.org/10.1016/j.scs.2020.102176

    Article  Google Scholar 

  • Mandal, J., Ghosh, N., & Mukhopadhyay, A. (2019). Urban growth dynamics and changing land-use land-cover of Megacity Kolkata and its environs. The Journal of the Indian Society of Remote Sensing, 47, 1707–1725. https://doi.org/10.1007/s12524-019-01020-7

    Article  Google Scholar 

  • Mendiratta, P., & Gedam, S. (2018). Assessment of urban growth dynamics in Mumbai Metropolitan Region, India using object-based image analysis for medium-resolution data. Applied Geography, 98, 110–120. https://doi.org/10.1016/j.apgeog.2018.05.017

    Article  Google Scholar 

  • Munshi, T., Brussel, M., Zuidgeest, M., & Van Maarseveen, M. (2018). Development of employment Sub-centres in the City of Ahmedabad, India. Environment and Urbanization ASIA, 9, 37–51. https://doi.org/10.1177/0975425317748521

    Article  Google Scholar 

  • Nathalia, D., Kumar, K. E. M., Kishore, N., & Krishnan, G. (2017). Environmental change detection using Geo-Spatial Techniques in Aravalli hills and Environs (Faridabad District, Haryana). The International Journal of Applied Environmental Sciences, 12, 865–875.

    Google Scholar 

  • National Institute of Urban Affairs, (2020). Transit Oriented Development For Indian Smart Cities [WWW Document]. URL https://niua.org/tod/todfisc/book.php?book=1&section=2#supersection-1-

  • Nkeki, F. N. (2016). Spatio-temporal analysis of land use transition and urban growth characterization in Benin metropolitan region, Nigeria. Remote Sensing Applications: Society and Environment, 4, 119–137. https://doi.org/10.1016/j.rsase.2016.08.002

    Article  Google Scholar 

  • NRSC (2012). National Land Use Land Cover Mapping using Multi-temporal Satellite Data Technical Manual (2nd Cycle) NRSC. Hyderabad.

  • Pasupuleti, N. S., Sharma, A., & Lathwal, S. (2016). Sustainable smart solutions for City of Faridabad—a case study addressing Urban Infrastructure Problems. International Journal of Civil Engineering Research, 7, 33–40.

    Google Scholar 

  • Patowary, S., & Sarma, A. K. (2018). Model-based analysis of urban settlement process in eco-sensitive area of developing country: A study with special reference to hills of an Indian city. Environment, Development and Sustainability, 20, 1777–1795. https://doi.org/10.1007/s10668-017-9965-1

    Article  Google Scholar 

  • Philippe, M. T., & Karume, K. (2019). Assessing forest cover change and deforestation hot-spots in the North Kivu Province, DR-Congo using remote sensing and GIS. American Journal of Geographic Information System, 8, 39–54. https://doi.org/10.5923/j.ajgis.20190802.01

    Article  Google Scholar 

  • Pramanik, S., & Punia, M. (2020). Land use/land cover change and surface urban heat island intensity: Source–sink landscape-based study in Delhi, India. Environment, Development and Sustainability, 22, 7331–7356. https://doi.org/10.1007/s10668-019-00515-0

    Article  Google Scholar 

  • Pramanik, M. M. A., & Stathakis, D. (2016). Forecasting urban sprawl in Dhaka city of Bangladesh. Environment and Planning B Planning and Design, 43, 756–771. https://doi.org/10.1177/0265813515595406

    Article  Google Scholar 

  • PwC and CII, 2015. Making Haryana smart.

  • Rai, B., & Nair, S. S. (2013). Change detection of Barkhal Lake in Faridabad District of Haryana Using Geo-Informatic Techniques. Int. J. Remote International Journal of Remote Sensing and Geoscience, 2, 38–41.

    Google Scholar 

  • Rai, S. C., & Saha, A. K. (2015). Impact of urban sprawl on groundwater quality: A case study of Faridabad city, National Capital Region of Delhi. Arabian Journal of Geosciences , 8, 8039–8045. https://doi.org/10.1007/s12517-015-1811-x

    Article  CAS  Google Scholar 

  • Riggan, N. D. J., & Weih, R. C. J. (2009). A comparison of Pixel-based versus object-based land use/land cover classification methodologies. Journal of the Arkansas Academy of Science, 63, 145–152.

    Google Scholar 

  • 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. Science of the Total Environment , 628–629, 1557–1566. https://doi.org/10.1016/j.scitotenv.2018.02.170

    Article  CAS  Google Scholar 

  • Sangwan, H., & Mahima, M. (2019). Growth of Urban Population in Haryana: A Spatio-Temporal analysis. International Journal of Research and Analytical Reviews, 6, 752–756.

    Google Scholar 

  • Sankhe, S., Vittal, I., Dobbs, R., Mohan, A., Gulati, A., Ablett, J., Gupta, S., Kim, A., Paul, S., Sanghvi, A., Sethy, G., & McKinsey. (2010). India’s urban awakening: Building inclusive cities, sustaining economic growth. McKinsey Glob. Inst.

  • Sarkar, A., & Chouhan, P. (2020). Modeling spatial determinants of urban expansion of Siliguri a metropolitan city of India using logistic regression. Modeling Earth Systems and Environment, 6, 2317–2331. https://doi.org/10.1007/s40808-020-00815-9

    Article  Google Scholar 

  • Sathish Kumar, D., Arya, D. S., & Vojinovic, Z. (2013). Modeling of urban growth dynamics and its impact on surface runoff characteristics. Computers, Environment and Urban Systems, 41, 124–135. https://doi.org/10.1016/j.compenvurbsys.2013.05.004

    Article  Google Scholar 

  • Schwarz, N. (2010). Urban form revisited—Selecting indicators for characterising European cities. Landscape and Urban Planning, 96, 29–47. https://doi.org/10.1016/j.landurbplan.2010.01.007

    Article  Google Scholar 

  • Sen, A., & Yadav, A. (2017). Re-imagining post-industrial Cities: Exploring Newer Identities in Faridabad. Haryana. Sustain. Smart Cities India, 85–108. https://doi.org/10.1007/978-3-319-47145-7_23

  • Shannon, C. E. (1948). A mathematical theory of communication. Bell System Technical Journal, 27, 379–423. https://doi.org/10.1002/j.1538-7305.1948.tb00917.x

    Article  Google Scholar 

  • Sharma, R., & Joshi, P. K. (2013). Monitoring Urban Landscape Dynamics Over Delhi (India) Using Remote Sensing (1998–2011) Inputs. The Journal of the Indian Society of Remote Sensing, 41, 641–650. https://doi.org/10.1007/s12524-012-0248-x

    Article  Google Scholar 

  • Sharma, R., & Joshi, P. K. (2016). Mapping environmental impacts of rapid urbanization in the National Capital Region of India using remote sensing inputs. Urban Climate, 15, 70–82. https://doi.org/10.1016/j.uclim.2016.01.004

    Article  Google Scholar 

  • Singh, T., & Bhatia, A. K. (2013). Ground Water Information Booklet Faridabad District, Haryana. Central Ground Water Board, Ministry of Water Resources, Government of India, North Western Region Chandigarh.

  • Somvanshi, S. S., Bhalla, O., Kunwar, P., Singh, M., & Singh, P. (2020). Monitoring spatial LULC changes and its growth prediction based on statistical models and earth observation datasets of Gautam Budh Nagar, Uttar Pradesh, India. Environment, Development and Sustainability, 22, 1073–1091. https://doi.org/10.1007/s10668-018-0234-8

    Article  Google Scholar 

  • Taubenböck, H., Wegmann, M., Roth, A., Mehl, H., & Dech, S. (2009). Urbanization in India—spatiotemporal analysis using remote sensing data. Computers, Environment and Urban Systems, 33, 179–188. https://doi.org/10.1016/j.compenvurbsys.2008.09.003

    Article  Google Scholar 

  • Taubenböck, H., Wiesner, M., Felbier, A., Marconcini, M., Esch, T., & Dech, S. (2014). New dimensions of urban landscapes: The spatio-temporal evolution from a polynuclei area to a mega-region based on remote sensing data. Applied Geography, 47, 137–153. https://doi.org/10.1016/j.apgeog.2013.12.002

    Article  Google Scholar 

  • Taubenböck, H., Kraff, N. J., & Wurm, M. (2018). The morphology of the arrival city—A global categorization based on literature surveys and remotely sensed data. Applied Geography, 92, 150–167. https://doi.org/10.1016/j.apgeog.2018.02.002

    Article  Google Scholar 

  • Taubenböck, H., Weigand, M., Esch, T., Staab, J., Wurm, M., Mast, J., & Dech, S. (2019). A new ranking of the world’s largest cities—Do administrative units obscure morphological realities? Remote Sensing of Environment, 232, 111353. https://doi.org/10.1016/j.rse.2019.111353

    Article  Google Scholar 

  • Taylor, P., & Bhatta, B. (2009). International Journal of Digital Earth Modelling of urban growth boundary using geoinformatics, pp 37–41. https://doi.org/10.1080/17538940902971383

  • Teotia, M.K., & Kumar, R. (2015). The State of Cities in North-Western India : A Case of Selected JNNURM Cities (Study Focus City: Faridabad). CRRID,Chandigarh.

  • Theil, H., & Finizza, A. J. (1971). A note on the measurement of racial integration of schools by means of informational concepts†. Journal of Mathematical Sociology, 1, 187–193. https://doi.org/10.1080/0022250X.1971.9989795

    Article  Google Scholar 

  • Tripathy, P., & Kumar, A. (2019). Monitoring and modelling spatio-temporal urban growth of Delhi using Cellular Automata and geoinformatics. Cities, 90, 52–63. https://doi.org/10.1016/j.cities.2019.01.021

    Article  Google Scholar 

  • Vani, M., & Prasad, P. R. C. (2020). Assessment of spatio-temporal changes in land use and land cover, urban sprawl, and land surface temperature in and around Vijayawada city, India. Environment, Development and Sustainability, 22, 3079–3095. https://doi.org/10.1007/s10668-019-00335-2

    Article  Google Scholar 

  • Veneri, P., & Burgalassi, D. (2012). Questioning polycentric development and its effects. Issues of definition and measurement for the Italian NUTS-2 regions. European Planning Studies, 20, 1017–1037. https://doi.org/10.1080/09654313.2012.673566

    Article  Google Scholar 

  • Vinayak, B., Lee, H. S., & Gedem, S. (2021). Prediction of land use and land cover changes in Mumbai city, India, using remote sensing data and a multilayer perceptron neural network-based Markov Chain model. Sustain., 13, 1–22. https://doi.org/10.3390/su13020471

    Article  Google Scholar 

  • Wadhawan, M., & Ahmad, S. (2010). Changes in Land use Pattern due to Mining in Faridabad (Haryana). In 11th ESRI India User Conference.

  • Wang, W., Li, W., Zhang, C., & Zhang, W. (2018a). Improving Object-Based Land Use/Cover Classification from Medium Resolution Imagery by Markov Chain Geostatistical Post-Classification. Land, 7, 31. https://doi.org/10.3390/land7010031

    Article  Google Scholar 

  • Wang, X., Liu, S., Du, P., Liang, H., Xia, J., & Li, Y. (2018b). Object-Based change detection in urban areas from high spatial resolution images based on multiple features and ensemble learning. Remote Sensimg, 10, 276. https://doi.org/10.3390/rs10020276

    Article  Google Scholar 

  • Wang, Z., Mao, P., Yang, H., Zhao, Y., He, T., & Dawson, R. J. (2018). Measuring the urban land surface temperature variations in Zhengzhou City Using the Landsat-Like Data. Preprints 1–17. https://doi.org/10.20944/preprints201809.0192.v1

  • Xia, C., Zhang, A., Wang, H., Zhang, B., & Zhang, Y. (2019). Land Use Policy Bidirectional urban fl ows in rapidly urbanizing metropolitan areas and their macro and micro impacts on urban growth : A case study of the Yangtze River middle reaches megalopolis, China. Land Use Policy, 82, 158–168. https://doi.org/10.1016/j.landusepol.2018.12.007

    Article  Google Scholar 

  • Yadav, A., & Sen, A. (2015). Re-inventing Newer Urban Identities in Faridabad.

  • Yeh, A. G. O., & Li, X. (2001). Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogrammetric Engineering & Remote Sensing, 67, 83–90.

    Google Scholar 

  • Zhou, N., Hubacek, K., & Roberts, M. (2015). Analysis of spatial patterns of Urban Growth across South Asia Using DMSP-OLS Nighttime Lights data. Applied Geography, 63, 292–303.

    Article  Google Scholar 

Download references

Acknowledgements

The corresponding author acknowledges the Science & Engineering Research Board (SERB), Department of Science & Technology (DST), Government of India, for funding this work through project File no. ECR/2017/000331. The authors thank the editor and anonymous reviewers for their valuable suggestions and comment, which enriched the manuscript.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Swagata Ghosh.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Kumar, S., Ghosh, S. & Singh, S. Polycentric urban growth and identification of urban hot spots in Faridabad, the million-plus metropolitan city of Haryana, India: a zonal assessment using spatial metrics and GIS. Environ Dev Sustain 24, 8246–8286 (2022). https://doi.org/10.1007/s10668-021-01782-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10668-021-01782-6

Keywords

Navigation