Skip to main content

Advertisement

Log in

Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model

  • Research Article
  • Published:
Journal of the Indian Society of Remote Sensing Aims and scope Submit manuscript

Abstract

The main objective of this paper is to analyze urban sprawl in the metropolitan city of Tripoli, Libya. Logistic regression model is used in modeling urban expansion patterns, and in investigating the relationship between urban sprawl and various driving forces. The 11 factors that influence urban sprawl occurrence used in this research are the distances to main active economic centers, to a central business district, to the nearest urbanized area, to educational area, to roads, and to urbanized areas; easting and northing coordinates; slope; restricted area; and population density. These factors were extracted from various existing maps and remotely sensed data. Subsequently, logistic regression coefficient of each factor is computed in the calibration phase using data from 1984 to 2002. Additionally, data from 2002 to 2010 were used in the validation. The validation of the logistic regression model was conducted using the relative operating characteristic (ROC) method. The validation result indicated 0.86 accuracy rate. Finally, the urban sprawl probability map was generated to estimate six scenarios of urban patterns for 2020 and 2025. The results indicated that the logistic regression model is effective in explaining urban expansion driving factors, their behaviors, and urban pattern formation. The logistic regression model has limitations in temporal dynamic analysis used in urban analysis studies. Thus, an integration of the logistic regression model with estimation and allocation techniques can be used to estimate and to locate urban land demands for a deeper understanding of future urban patterns.

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

Similar content being viewed by others

References

  • Al-shalabi, M., Billa, L., Pradhan, B., Mansor, S., & Al-Sharif, A. A. A. (2012). Modelling urban growth evolution and land-use changes using GIS based cellular automata and SLEUTH models: the case of Sana’a metropolitan city, Yemen. Environmental Earth Sciences. doi:10.1007/s12665-012-2137-6.

  • Al-shalabi, M., Pradhan, B., Billa, L., Mansor, S., & Althuwaynee, O. F. (2013). Manifestation of remote sensing data in modeling urban sprawl using the SLEUTH model and brute force calibration: a case study of Sana’a City, Yemen. Journal of the Indian Society of Remote Sensing. doi:10.1007/s12524-012-0215-6.

  • Angotti, T. (1993). Metropolis 2000: Planning, poverty and politics. London: Routledge.

    Google Scholar 

  • Bui, D. T., Lofman, O., Revhaug, I., & Dick, O. (2011). Landslide susceptibility analysis in the Hoa Binh province of Vietnam using statistical index and logistic regression. Natural Hazards, 59, 1413–1444.

    Article  Google Scholar 

  • Campbell, C. E., Allen, J., & Lu, K. S. (2007). Modeling growth and predicting future developed land in the upstate of South Carolina. Report submitted to the Saluda-Reedy Watershed Consortium. Strom Thurmond Institute, Clemson University, Clemson, South Carolina.

  • Cheng, J., Ottens, H., Masser, I., & Turkstra, J. (2003). Understanding urban growth: a conceptual model. International Journal of Urban Sciences, 7, 83–101.

    Article  Google Scholar 

  • Clarke, K. C., & Gaydos, L. J. (1998). Loose-coupling a cellular automaton model and GIS: long-term urban growth prediction for San Francisco and Washington/Baltimore. International Journal of Geographical Information Science, 12, 699–714.

    Article  Google Scholar 

  • Devkota, K. C., Regmi, A. D., Pourghasemi, H. R., Yoshida, K., Pradhan, B., Ryu, I. C., Dhital, M. R., & Althuwaynee, O. F. (2013). Landslide susceptibility mapping using certainty factor, index of entropy and logistic regression models and their comparison at a landslide prone area in Nepal Himalaya. Natural Hazards, 65(1), 135–165. doi:10.1007/s11069-012-0347-6.

    Google Scholar 

  • Dietzel, C., & Clarke, K. (2006). The effect of disaggregating land use categories in cellular automata during model calibration and forecasting. Computers, Environment and Urban Systems, 30, 78–101.

    Article  Google Scholar 

  • Eyoh, A., Olayinka, D. N., Nwilo, P., Okwuashi, O., Isong, M., & Udoudo, D. (2012). Modelling and predicting future urban expansion of lagos, nigeria from remote sensing data using logistic regression and GIS. International Journal of Applied Science and Technology, 2, 116–124.

    Google Scholar 

  • Firman, T. (1997). Land conversion and urban development in the northern region of West Java, Indonesia. Urban Studies, 34, 1027–1046.

    Article  Google Scholar 

  • Gillham, O. (2002). The limitless city: A primer on the urban sprawl debate (pp. 328). Washington, DC, USA: Island Press.

  • Helbich, M., & Leitner, M. (2010). Postsuburban spatial evolution of Vienna's urban fringe: evidence from point process modeling. Urban Geography, 31, 1100–1117.

    Article  Google Scholar 

  • Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31, 667–688.

    Article  Google Scholar 

  • Huang, B., Zhang, L., & Wu, B. (2009). Spatiotemporal analysis of rural–urban land conversion. International Journal of Geographical Information Science, 23, 379–398.

    Article  Google Scholar 

  • Irwin, E. G., & Geoghegan, J. (2001). Theory, data, methods: developing spatially explicit economic models of land use change. Agriculture, Ecosystems & Environment, 85, 7–24.

    Article  Google Scholar 

  • Jat, M. K., Garg, P. K., & Khare, D. (2008). Monitoring and modelling of urban sprawl using remote sensing and GIS techniques. International Journal of Applied Earth Observation and Geoinformation, 10, 26–43.

    Article  Google Scholar 

  • Jiang, B., & Yao, X. (2010). Geospatial analysis and modelling of urban structure and dynamics (Vol. 99, p. 440). Springer: Netherlands.

  • Jokar Arsanjani, J. (2011). Dynamic land use/cover change modelling: Geosimulation and multiagent-based modelling (hardback)(series: springer theses) (XVII, p. 139), Springer: Berlin Heidelberg.

  • Jokar Arsanjani, J., Helbich, M., Kainz, W., & Darvishi Boloorani, A. (2013). Integration of logistic regression, Markov chain and cellular automata models to simulate urban expansion. International Journal of Applied Earth Observation and Geoinformation, 21, 265–275.

    Article  Google Scholar 

  • Kleinbaum, D. G., & Klein, M. (2010). Logistic regression: A self-learning text. New York: Springer.

    Book  Google Scholar 

  • Knox, P. L. (1993). The restless urban landscape. Englewood Cliffs: Prentice Hall.

    Google Scholar 

  • Lambin, E. F., & Geist, H. J. (2006). Land-use and land-cover change: Local processes and global impacts. Berlin Heidelberg: Springer.

    Book  Google Scholar 

  • Lambin, E. F., Turner, B. L., Geist, H. J., Agbola, S. B., Angelsen, A., Bruce, J. W., et al. (2001). The causes of land-use and land-cover change: moving beyond the myths. Global Environmental Change, 11, 261–269.

    Article  Google Scholar 

  • Lin, Y.-P., Chu, H.-J., Wu, C.-F., & Verburg, P. H. (2010). Predictive ability of logistic regression, auto-logistic regression and neural network models in empirical land-use change modeling – a case study. International Journal of Geographical Information Science, 25, 65–87.

    Article  Google Scholar 

  • López, E., Bocco, G., Mendoza, M., & Duhau, E. (2001). Predicting land-cover and land-use change in the urban fringe: a case in Morelia city, Mexico. Landscape and Urban Planning, 55, 271–285.

    Article  Google Scholar 

  • Mahiny, A. S., & Turner, B. J. (2003). Modeling past vegetation change through remote sensing and GIS: a comparison of neural networks and logistic regression methods. In Proceedings of the 7th international conference on geocomputation. University of Southampton, UK.

  • Masser, I. (2001). Managing our urban future: the role of remote sensing and geographic information systems. Habitat International, 25, 503–512.

    Article  Google Scholar 

  • Menard, S. (2004). Six approaches to calculating standardized logistic regression coefficients. The American Statistician, 58, 218–223.

    Article  Google Scholar 

  • Overmars, K. P., & Verburg, P. H. (2005). Analysis of land use drivers at the watershed and household level: linking two paradigms at the Philippine forest fringe. International Journal of Geographical Information Science, 19, 125–152.

    Article  Google Scholar 

  • Peng, C.-Y. J., Lee, K. L., & Ingersoll, G. M. (2002). An introduction to logistic regression analysis and reporting. The Journal of Educational Research, 96, 3–14.

    Article  Google Scholar 

  • Poelmans, L., & Van Rompaey, A. (2010). Complexity and performance of urban expansion models. Computers, Environment and Urban Systems, 34, 17–27.

    Article  Google Scholar 

  • Pontius, R. G., Jr., & Schneider, L. C. (2001). Land-cover change model validation by an ROC method for the Ipswich watershed, Massachusetts, USA. Agriculture, Ecosystems & Environment, 85, 239–248.

    Article  Google Scholar 

  • Pradhan, B. (2011). Manifestation of an advanced fuzzy logic model coupled with Geo-information techniques to landslide susceptibility mapping and their comparison with logistic regression modelling. Environmental and Ecological Statistics, 18(3), 471–493. doi:10.1007/s10651-010-0147-7.

  • Pradhan, B. (2010a). Remote sensing and GIS-based landslide hazard analysis and cross-validation using multivariate logistic regression model on three test areas in Malaysia. Advances in Space Research, 45(10), 1244–1256. doi:10.1016/j.asr.2010.01.006.

  • Pradhan, B. (2010b). Landslide susceptibility mapping of a catchment area using frequency ratio, fuzzy logic and multivariate logistic regression approaches. Journal of the Indian Society of Remote Sensing, 38(2), 301–320. doi:10.1007/s12524-010-0020-z.

  • Pradhan, B. (2009). Flood susceptible mapping and risk area estimation using logistic regression, GIS and remote sensing. Journal of Spatial Hydrology, 9(2), 1–18.

    Google Scholar 

  • Serneels, S., & Lambin, E. F. (2001). Proximate causes of land-use change in Narok District, Kenya: a spatial statistical model. Agriculture, Ecosystems & Environment, 85, 65–81.

    Article  Google Scholar 

  • Sudhira, H. S., Ramachandra, T. V., & Jagadish, K. S. (2004). Urban sprawl: metrics, dynamics and modelling using GIS. International Journal of Applied Earth Observation and Geoinformation, 5, 29–39.

    Article  Google Scholar 

  • Sweet, S. A., & Grace-Martin, K. (1999). Data analysis with SPSS (Vol. 1, p. 204): Allyn & Bacon.

  • Tv, R., Aithal, B. H., & Sanna, D. D. (2012). Insights to urban dynamics through landscape spatial pattern analysis. International Journal of Applied Earth Observation and Geoinformation, 18, 329–343.

    Article  Google Scholar 

  • Veldkamp, A., & Lambin, E. F. (2001). Predicting land-use change. Agriculture, Ecosystems & Environment, 85, 1–6.

    Article  Google Scholar 

  • Verburg, P., & Overmars, K. (2007). Dynamic simulation of land-use change trajectories with the clue–s model. Modelling Land-Use Change, 321–335.

  • Verburg, P. H., Kok, K., Pontius, R. G., & Veldkamp, A. (2006). Modeling land-use and land-cover change. Land-Use and Land-Cover Change, 117–135.

  • Wang, J., & Mountrakis, G. (2011). Developing a multi-network urbanization model: a case study of urban growth in Denver, Colorado. International Journal of Geographical Information Science, 25, 229–253.

    Article  Google Scholar 

  • Yang, X., & Lo, C. (2003). Modelling urban growth and landscape changes in the Atlanta metropolitan area. International Journal of Geographical Information Science, 17, 463–488.

    Article  Google Scholar 

  • Youssef, A. M., Pradhan, B., & Tarabees, E. (2011). Integrated evaluation of urban development suitability based on remote sensing and GIS techniques: contribution from the analytic hierarchy. Arabian Journal of Geosciences, 4(3–4). doi:10.1007/s12517-009-0118-1.

  • Zhao, Y., & Murayama, Y. (2011). Urban dynamics analysis using spatial metrics geosimulation. Spatial Analysis and Modeling in Geographical Transformation Process, 153–167.

Download references

Acknowledgments

The first author greatly acknowledges Libyan government for providing data and financial support for this work.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Biswajeet Pradhan.

About this article

Cite this article

Alsharif, A.A.A., Pradhan, B. Urban Sprawl Analysis of Tripoli Metropolitan City (Libya) Using Remote Sensing Data and Multivariate Logistic Regression Model. J Indian Soc Remote Sens 42, 149–163 (2014). https://doi.org/10.1007/s12524-013-0299-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-013-0299-7

Keywords

Navigation