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Computational Geometry-Based Kinematic Morphology for Urban Growth

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Abstract

The present study presents a novel approach for studying the dynamic boundary evolution of an urban area. It uses the satellite remote sensing-derived urban growth maps of the study area. The derived urban maps are used to extract the boundaries of study areas for three time intervals, 1999, 2009 and 2019. Using the centroid of urban boundary of 1999, the urban boundaries are discretized into 36 parts at 10 degree interval. Thus, each line emanating from the centroid towards the boundaries thus intersects the boundaries at three points indicating the position of boundary at three durations. Since there are three points, a second order polynomial equation is constructed and differentiated with time to obtain the growth rates of 36 points. The growth rates are then related to various potential driver variables using a linear regression analysis. The results show that the urban boundary expansion is predominant in areas closer the roads and airports, having milder slopes and towards North and East directions of Mumbai Metropolitan Region. However, the growth is occurring away from the urban centres and suburban railway stations because of lack of spaces for further development in close proximity of these areas. The present study thus solves the inverse evolution problem and can be helpful for the planners for effective planning of the resources. It uses the geometric kinematics to study the boundary growth rates and then relates it with potential driver variables using a linear regression model.

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Availability of Data and Materials

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

References

  1. Abhishek N, Jenamani M, Mahanty B (2017) Urban growth in Indian cities: are the driving forces really changing? Habitat Int 69:48–57

    Article  Google Scholar 

  2. Ahmad F, Goparaju L, Qayum A (2017) Lulc analysis of urban spaces using Markov chain predictive model at Ranchi in India. Spatial Inform Res 25(3):351–359

    Article  Google Scholar 

  3. Angel S, Blei AM, Civco DL, Parent J (2016) Atlas of urban expansion. Lincoln Institute of Land Policy, Cambridge

    Google Scholar 

  4. Arsanjani JJ, Helbich M, Kainz W, Boloorani AD (2013) Integration of logistic regression, markov chain and cellular automata models to simulate urban expansion. Int J Appl Earth Observ Geoinform 21:265–275

    Article  Google Scholar 

  5. Batty M, Longley P (1994) Fractal cities: a geometry of form and functions. Academic press, San Diego

    Google Scholar 

  6. Berberoğlu S, Akın A, Clarke KC (2016) Cellular automata modeling approaches to forecast urban growth for Adana, Turkey: a comparative approach. Landsc Urban Plan 153:11–27

    Article  Google Scholar 

  7. Bhatta B (2010) Analysis of urban growth and sprawl from remote sensing data. Springer Science & Business Media

  8. Bocquier P (2005) World Urbanization Prospects: an alternative to the UN model of projection compatible with the mobility transition theory. Demogr Res 12(9):197–236

    Article  Google Scholar 

  9. Bosch M, Jaligot R, Chenal J (2020) Spatiotemporal patterns of urbanization in three swiss urban agglomerations: insights from landscape metrics, growth modes and fractal analysis. Landsc Ecol 35:879–891

    Article  Google Scholar 

  10. Congalton RG, Green K (2019) Assessing the accuracy of remotely sensed data: principles and practices. CRC Press

    Book  Google Scholar 

  11. Dewa DD, Buchori I, Sejati AW, Liu Y (2022) Shannon entropy-based urban spatial fragmentation to ensure sustainable development of the urban coastal city: a case study of semarang, indonesia. Remote Sens Appl Soc Environ 28:100839

    Google Scholar 

  12. Gazis DC, Herman R, Potts RB (1959) Car-following theory of steady-state traffic flow. Oper Res 7(4):499–505

    Article  MathSciNet  Google Scholar 

  13. Huang H-J, Xia T, Tian Q, Liu T-L, Wang C, Li D (2020) Transportation issues in developing china’s urban agglomerations. Transport Policy 85:A1–A22

    Article  Google Scholar 

  14. Hyandye C, Martz LW (2017) A markovian and cellular automata land-use change predictive model of the usangu catchment. Int J Remote Sens 38(1):64–81

    Article  Google Scholar 

  15. Islam K, Rahman MF, Jashimuddin M (2018) Modeling land use change using cellular automata and artificial neural network: the case of Chunati wildlife sanctuary, Bangladesh. Ecol Indicat 88:439–453

    Article  Google Scholar 

  16. Kachroo P (2018) Pedestrian dynamics: mathematical theory and evacuation control

  17. Kumar P, Dobriyal M, Kale A, Pandey A (2021) Temporal dynamics change of land use/land cover in Jhansi district of Uttar Pradesh over past 20 years using Landsat tm, etm+ and oli sensors. Remote Sens Appl Soc Environ 23:100579

    Google Scholar 

  18. Maithani S, Arora M, Jain R (2010) An artificial neural network based approach for urban growth zonation in Dehradun city, India. Geocarto Int 25(8):663–681

    Article  Google Scholar 

  19. Makse HA, Havlin S, Stanley HE (1995) Modelling urban growth patterns. Nature 377(6550):608–612

    Article  Google Scholar 

  20. Moghadam HS, Helbich M (2013) Spatiotemporal urbanization processes in the megacity of Mumbai, India: a markov chains-cellular automata urban growth model. Appl Geogr 40:140–149

    Article  Google Scholar 

  21. Prigogine I, Herman R (1971) Kinetic theory of vehicular traffic. Elsevier Science Ltd

  22. Programme UNHS (2020) Unpacking the value of sustainable urbanization. In: World Cities Report 2020, United Nations pp. 43–74

  23. Ramachandra T, Aithal BH, Sanna DD (2012) Insights to urban dynamics through landscape spatial pattern analysis. Int J Appl Earth Observ Geoinform 18:329–343

    Article  Google Scholar 

  24. Rastogi K, Jain G (2018) Urban sprawl analysis using Shannon’s entropy and fractal analysis: a case study on Tiruchirappalli city, India. Int Arch Photogram Remote Sens Spatial Inform Sci 42:20–23

    Google Scholar 

  25. 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

    Google Scholar 

  26. Shu B, Zhang H, Li Y, Qu Y, Chen L (2014) Spatiotemporal variation analysis of driving forces of urban land spatial expansion using logistic regression: a case study of port towns in Taicang city, China. Habitat Int 43:181–190

    Article  Google Scholar 

  27. Siddiqui A, Siddiqui A, Maithani S, Jha A, Kumar P, Srivastav S (2018) Urban growth dynamics of an Indian metropolitan using ca Markov and logistic regression. Egypt J Remote Sens Space Sci 21(3):229–236

    Google Scholar 

  28. Sun Y, Zhao S (2018) Spatiotemporal dynamics of urban expansion in 13 cities across the Jing-Jin-Ji urban agglomeration from 1978 to 2015. Ecol Indicat 87:302–313

    Article  Google Scholar 

  29. Tripathy P, Kumar A (2019) Monitoring and modelling spatio-temporal urban growth of Delhi using cellular automata and geoinformatics. Cities 90:52–63

    Article  Google Scholar 

  30. Wang J, Maduako IN (2018) Spatio-temporal urban growth dynamics of Lagos metropolitan region of Nigeria based on hybrid methods for Lulc modeling and prediction. Eur J Remote Sens 51(1):251–265

    Article  Google Scholar 

  31. Weber C (2003) Interaction model application for urban planning. Landscape Urban Plan 63(1):49–60

    Article  Google Scholar 

  32. Weber C, Puissant A (2003) Urbanization pressure and modeling of urban growth: example of the Tunis metropolitan area. Remote Sens Environ 86(3):341–352

    Article  Google Scholar 

  33. Yeh A G-O, Li X (2001) Measurement and monitoring of urban sprawl in a rapidly growing region using entropy. Photogram Eng Remote Sens

  34. Yılmaz M, Terzi F (2021) Measuring the patterns of urban spatial growth of coastal cities in developing countries by geospatial metrics. Land Use Policy 107:105487

    Article  Google Scholar 

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Acknowledgements

Prof. Pushkin Kachroo is thankful to IIT Delhi and IIT Bombay for the visiting professor positions.

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Author notes

  1. Pushkin Kachroo, Samarth Y. Bhatia and Gopal R. Patil contributed equally to this work.

Authors and Affiliations

  1. Department of Electrical and Computer Engineering, University of Nevada Las Vegas, 4505 S. Maryland Pkwy Street, Las Vegas, NV, 89154-4026, USA

    Pushkin Kachroo

  2. Department of Civil Engineering, Indian Institute of Technology Bombay, Powai, 400076, Mumbai, India

    Samarth Y. Bhatia & Gopal R. Patil

Authors
  1. Pushkin Kachroo
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  2. Samarth Y. Bhatia
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  3. Gopal R. Patil
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Contributions

Pushkin Kachroo wrote the paper and developed a new technique based on the previous work done by Samarth Y. Bhatia and Gopal R. Patil on this problem. Samarth Y. Bhatia did the numerical analysis on GIS and helped write and revise the manuscript.

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Correspondence to Pushkin Kachroo.

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Kachroo, P., Bhatia, S.Y. & Patil, G.R. Computational Geometry-Based Kinematic Morphology for Urban Growth. Transp. in Dev. Econ. 10, 11 (2024). https://doi.org/10.1007/s40890-024-00204-2

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