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Modelling Spatial Patterns of Urban Growth in Pune Metropolitan Region, India

  • Bhartendu Pandey
  • P. K. Joshi
  • T. P. Singh
  • A. Joshi
Chapter

Abstract

Explaining urban growth patterns is a fundamental need to understand the recent rapid urbanization globally. This study identifies geographic features explaining the spatial patterns of urban land expansion (ULE) in the rapidly urbanizing Pune metropolitan region (India). ULE maps were derived from Landsat Thematic Mapper and Operational Land Imager images using support vector machine (SVM) classification. Relation between geographic features and spatial patterns of ULE was analyzed using statistical modelling including ordinary least squares (OLS) regression, spatial lag model (SLM), spatial error model (SEM), and geographically weighted regression (GWR). SEM specification best modeled ULE patterns. High density of existing urban areas is identified to negatively affect ULE, suggesting dominant dispersed urban growth. In addition, proximity to special economic zones and transportation infrastructure explains multicentric growth in the region. GWR model was identified inappropriate due to the presence of high local collinearity. Models accounting for spatial dependencies are recommended while studying ULE patterns.

Keywords

Urban growth Spatial lag model Spatial error model Geographically weighted regression Special economic zone 

References

  1. Al-sharif AAA, Pradhan B (2015) A novel approach for predicting the spatial patterns of urban expansion by combining the chi-squared automatic integration detection decision tree, Markov chain and cellular automata models in GIS. Geocarto Int 30:858–881CrossRefGoogle Scholar
  2. Angel S, Sheppard S, Civco DL, Buckley R, Chabaeva A, Gitlin L, Kraley A, Parent J, Perlin M. (2005 The dynamics of global urban expansion [Internet]. [place unknown]: Citeseer; [cited 2016 Jul 26]. Available from: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.309.2715&rep=rep1&type=pdf
  3. Anselin L (1988) Spatial econometrics: methods and models. Dordrecht. Kluwer Academic Publishers, BostonCrossRefGoogle Scholar
  4. Anselin L, Griffith DA (1988) Do spatial effects really matter in regression analysis? Pap Reg Sci 65:11–34CrossRefGoogle Scholar
  5. Census of India (2011 Census of India, 2011. India Provisional Popul Totals Pap. 1Google Scholar
  6. Cheng J, Masser I (2003) Urban growth pattern modeling: a case study of Wuhan city, PR China. Landsc Urban Plan 62:199–217CrossRefGoogle Scholar
  7. Clark TN, Lloyd R, Wong KK, Jain P (2002) Amenities drive urban growth. J Urban Aff 24:493–515CrossRefGoogle Scholar
  8. Concepción ED, Moretti M, Altermatt F, Nobis MP, Obrist MK (2015) Impacts of urbanisation on biodiversity: the role of species mobility, degree of specialisation and spatial scale. Oikos 124:1571–1582CrossRefGoogle Scholar
  9. Dimitriadou E, Hornik K, Leisch F, Meyer D, Weingessel A, Leisch MF (2006) The e1071 package. Misc Funct Dep Stat E1071 TU Wien [Internet]. [cited 2016 Jul 26]. Available from: http://ftp.auckland.ac.nz/software/CRAN/doc/packages/e1071.pdf
  10. Farooq S, Ahmad S (2008) Urban sprawl development around Aligarh city: a study aided by satellite remote sensing and GIS. J Indian Soc Remote Sens 36:77–88CrossRefGoogle Scholar
  11. Fazal S (2001) The need for preserving farmland: a case study from a predominantly agrarian economy (India). Landsc Urban Plan 55:1–13CrossRefGoogle Scholar
  12. Fotheringham AS, Wong DW (1991) The modifiable areal unit problem in multivariate statistical analysis. Environ Plan A 23:1025–1044CrossRefGoogle Scholar
  13. Fotheringham AS, Brunsdon C, Charlton M (2003) Geographically weighted regression: the analysis of spatially varying relationships [Internet]. [place unknown]: John Wiley & Sons; [cited 2016 July 26]. Available from: https://books.google.com/books?hl=en&lr=&id=9DZgV1vXOuMC&oi=fnd&pg=PR7&dq=Geographically+weighted+regression:+Wiley+New+York+book&ots=64FJNgo9KG&sig=rvfajcybZupWNRMGTen6intGITc
  14. Ganguly K, Kumar R, Reddy KM, Rao PJ, Saxena MR, Shankar GR (2016) Optimization of spatial statistical approaches to identify land use/land cover change hot spots of Pune region of Maharashtra using remote sensing and GIS techniques. Geocarto Int 0:1–20Google Scholar
  15. Gollini I, Lu B, Charlton M, Brunsdon C, Harris P (2013) GWmodel: an R package for exploring spatial heterogeneity using geographically weighted models. ArXiv Prepr ArXiv13060413 [Internet]. [cited 2016 July 26]. Available from: http://arxiv.org/abs/1306.0413
  16. Hu Z, Lo CP (2007) Modeling urban growth in Atlanta using logistic regression. Comput Environ Urban Syst 31:667–688CrossRefGoogle Scholar
  17. Jantz CA, Goetz SJ, Shelley MK (2004) Using the SLEUTH urban growth model to simulate the impacts of future policy scenarios on urban land use in the Baltimore-Washington metropolitan area. Environ Plan B Plan Des 31:251–271CrossRefGoogle Scholar
  18. Jat MK, Garg PK, Khare D (2008) Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. Int J Appl Earth Obs Geoinf 10:26–43CrossRefGoogle Scholar
  19. Kantakumar LN, Kumar S, Schneider K (2016) Spatiotemporal urban expansion in Pune metropolis, India using remote sensing. Habitat Int 51:11–22CrossRefGoogle Scholar
  20. Kowe P, Pedzisai E, Gumindoga W, Rwasoka DT (2015) An analysis of changes in the urban landscape composition and configuration in the Sancaktepe District of Istanbul Metropolitan City, Turkey using landscape metrics and satellite data. Geocarto Int 30:506–519CrossRefGoogle Scholar
  21. Lafazani P, Lagarias A (2016) Applying multiple and logistic regression models to investigate periurban processes in Thessaloniki, Greece. Geocarto Int 31:927–942CrossRefGoogle Scholar
  22. Li X, Zhou W, Ouyang Z (2013) Forty years of urban expansion in Beijing: what is the relative importance of physical, socioeconomic, and neighborhood factors? Appl Geogr 38:1–10CrossRefGoogle Scholar
  23. Linard C, Tatem AJ, Gilbert M (2013) Modelling spatial patterns of urban growth in Africa. Appl Geogr 44:23–32CrossRefGoogle Scholar
  24. Lu B, Harris P, Charlton M, Brunsdon C (2014) The GWmodel R package: further topics for exploring spatial heterogeneity using geographically weighted models. Geo-Spat Inf Sci 17:85–101CrossRefGoogle Scholar
  25. Luo J, Wei YD (2009) Modeling spatial variations of urban growth patterns in Chinese cities: the case of Nanjing. Landsc Urban Plan 91:51–64CrossRefGoogle Scholar
  26. Luo J, Yu D, Xin M (2008) Modeling urban growth using GIS and remote sensing. GISci Remote Sens 45:426–442CrossRefGoogle Scholar
  27. Moghadam HS, Helbich M (2013) Spatiotemporal urbanization processes in the megacity of Mumbai, India: a Markov chains-cellular automata urban growth model. Appl Geogr 40:140–149CrossRefGoogle Scholar
  28. Mondal B, Das DN, Dolui G (2015) Modeling spatial variation of explanatory factors of urban expansion of Kolkata: a geographically weighted regression approach. Model Earth Syst Environ 1:29CrossRefGoogle Scholar
  29. Mondal B, Das DN, Bhatta B (2016) Integrating cellular automata and Markov techniques to generate urban development potential surface: a study on Kolkata agglomeration. Geocarto Int 32:1–19Google Scholar
  30. Openshaw S (1983) The modifiable areal unit problem. GeoBooks, NorwichGoogle Scholar
  31. Openshaw S (1984) The modifiable areal unit problem. In: [place unknown]: Geo Abstracts University of East AngliaGoogle Scholar
  32. Pathirana A, Denekew HB, Veerbeek W, Zevenbergen C, Banda AT (2014) Impact of urban growth-driven landuse change on microclimate and extreme precipitation—a sensitivity study. Atmos Res 138:59–72CrossRefGoogle Scholar
  33. Pimpri-Chinchwad Municipal Corporation (2008) Comprehensive mobility plan (CMP) for PCMC. [place unknown]Google Scholar
  34. Pontius RG Jr, Millones M (2011) Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. Int J Remote Sens 32:4407–4429CrossRefGoogle Scholar
  35. Pune Municipal Corporation (2008) Comprehensive mobility plan for Pune city. PuneGoogle Scholar
  36. Ramachandra TV, Setturu B, Aithal BA (2012) Per-urban to urban landscape patterns elucidation through spatial metrics. Int J Eng Res Dev 2(12):58–81Google Scholar
  37. Schneider A, Woodcock CE (2008) Compact, dispersed, fragmented, extensive? A comparison of urban growth in twenty-five global cities using remotely sensed data, pattern metrics and census information. Urban Stud 45:659–692CrossRefGoogle Scholar
  38. Shafizadeh-Moghadam H, Helbich M (2015) Spatiotemporal variability of urban growth factors: a global and local perspective on the megacity of Mumbai. Int J Appl Earth Obs Geoinf 35:187–198CrossRefGoogle Scholar
  39. Sudhira HS, Ramachandra TV, Jagadish KS (2004) Urban sprawl: metrics, dynamics and modelling using GIS. Int J Appl Earth Obs Geoinf 5:29–39CrossRefGoogle Scholar
  40. Taubenböck H, Wegmann M, Roth A, Mehl H, Dech S (2009) Urbanization in India–spatiotemporal analysis using remote sensing data. Comput Environ Urban Syst 33:179–188CrossRefGoogle Scholar
  41. Triantakonstantis D, Stathakis D (2015) Examining urban sprawl in Europe using spatial metrics. Geocarto Int 30:1092–1112CrossRefGoogle Scholar
  42. Wheeler D, Tiefelsdorf M (2005) Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 7:161–187CrossRefGoogle Scholar
  43. Yu D-L (2006) Spatially varying development mechanisms in the Greater Beijing Area: a geographically weighted regression investigation. Ann Reg Sci 40:173–190CrossRefGoogle Scholar
  44. Zeng C, Zhang M, Cui J, He S (2015) Monitoring and modeling urban expansion—a spatially explicit and multi-scale perspective. Cities 43:92–103CrossRefGoogle Scholar
  45. Zhang Q, Seto KC (2011) Mapping urbanization dynamics at regional and global scales using multi-temporal DMSP/OLS nighttime light data. Remote Sens Environ 115:2320–2329CrossRefGoogle Scholar
  46. Zhang Z, Su S, Xiao R, Jiang D, Wu J (2013) Identifying determinants of urban growth from a multi-scale perspective: a case study of the urban agglomeration around Hangzhou Bay, China. Appl Geogr 45:193–202CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Bhartendu Pandey
    • 1
  • P. K. Joshi
    • 2
  • T. P. Singh
    • 3
  • A. Joshi
    • 4
  1. 1.School of Forestry & Environmental StudiesYale UniversityNew HavenUSA
  2. 2.School of Environmental SciencesJawaharlal Nehru UniversityNew DelhiIndia
  3. 3.Symbiosis Institute of Geo-informaticsSymbiosis International University (SIU)PuneIndia
  4. 4.Department of StatisticsKumaun UniversityAlmoraIndia

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