Skip to main content

Advertisement

SpringerLink
Log in

Logistic Regression Model of Built-Up Land Based on Grid-Digitized Data Structure: A Case Study of Krabi, Thailand

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

Abstract

In order to measure and detect land-use changes, it is necessary to know the pace at which land-use changes from one type of land to another. However, due to limited resources, researchers are having difficulty doing statistical analyses on polygonal data structures. The digital data structure lends itself to statistical analysis using general-purpose software, whereas land-use change was assessed solely by counting grid cells. The polygonal data were converted to digital data using the grid-digitized approach. This study compares land-use changes in Thailand's Krabi province in 2000–2009, and 2009–2018. Thematic maps and the bubble plot were used to depict land-use change across Krabi province, with a digitized grid (100 by 100 m) encompassing the entire region. A logistic regression model was used to examine the probability of built-up land. According to the findings, total built-up land was 5164 hectares (1.1% of all areas) in 2000, 9881 hectares (2.1% of all areas) in 2009, and 18,832 hectares in 2018. (4% of all areas). Built-up land rose by 91.3% and 90.6%, respectively, between 2009 and 2018. Each location's land-use was related to the probability of built-up land. Rubber plantation and agricultural land provided the majority of the built-up land. Furthermore, areas closer to metropolitan centers saw a higher percentage rise in built-up land than rural areas. Receiver operating characteristic curves and F-scores indicated that the models were accurate enough.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

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

  • Aguayo, M. I., Wiegand, T., Azócar, G. D., Wiegand, K., & Vega, C. E. (2007). Revealing the driving forces of mid-cities urban growth patterns using spatial modeling: A case study of Los Ángeles, Chile. Ecology and Society, 12(1), 13.

  • Alsharif, A. A., & Pradhan, B. (2014). Urban sprawl analysis of Tripoli Metropolitan city (Libya) using remote sensing data and multivariate logistic regression model. Journal of the Indian Society of Remote Sensing, 42(1), 149–163.

    Article  Google Scholar 

  • Baker, W. L. (1989). A review of models of landscape change. Landscape Ecology, 2(2), 111–133.

    Article  Google Scholar 

  • Bhatta, B. (2010). Analysis of urban growth and sprawl from remote sensing data. New York: Springer Science & Business Media.

    Book  Google Scholar 

  • Burrough, P. A., McDonnell, R., McDonnell, R. A., & Lloyd, C. D. (2015). Principles of geographical information systems. Oxford University Press.

    Google Scholar 

  • Buya, S., Tongkumchum, P., & Owusu, B. E. (2020). Modelling of land-use change in Thailand using binary logistic regression and multinomial logistic regression. Arabian Journal of Geosciences, 13, 437.

    Article  Google Scholar 

  • Chand, R., Raju, S. S., & Pandey, L. M. (2010). Effect of global recession on Indian agriculture. Indian Journal of Agricultural Economics, 65(3), 488–496.

  • Chuangchang, P., & Tongkumchum, P. (2014). Modeling land development along highway 4 in Southern Thailand. Songklanakarin Journal of Science & Technology, 36(6), 719–725.

  • Chuangchang, P., Sangkhaduang, T., & Tongkumchum, P. (2016a). Modelling developed land in Phuket Province of Thailand: 2000–2009. Pertanika Journal of Social Sciences & Humanities, 24(2), 795–810.

  • Chuangchang, P., Thinnukool, O., & Tongkumchum, P. (2016b). Modelling urban growth over time using grid-digitised method with variance inflation factors applied to spatial correlation. Arabian Journal of Geosciences, 9(5), 342.

    Article  Google Scholar 

  • Drummond, M. A., & Loveland, T. R. (2010). Land-use pressure and a transition to forest-cover loss in the eastern United States. BioScience, 60(4), 286–298.

    Article  Google Scholar 

  • Epstein, J., Payne, K., & Kramer, E. (2002). Techniques for mapping suburban sprawl. Photogrammetric Engineering and Remote Sensing, 68(9), 913–918.

    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, 2(5), 116–124.

  • Fan, J., Upadhye, S., & Worster, A. (2006). Understanding receiver operating characteristic (ROC) curves. Canadian Journal of Emergency Medicine, 8(1), 19–20.

    Article  Google Scholar 

  • Farley, K. A., Ojeda-Revah, L., Atkinson, E. E., & Eaton-González, B. R. (2012). Changes in land use, land tenure, and landscape fragmentation in the Tijuana River Watershed following reform of the ejido sector. Land Use Policy, 29(1), 187–197.

    Article  Google Scholar 

  • Firman, T. (1997). Land conversion and urban development in the northern region of West Java Indonesia. Urban Studies, 34(7), 1027–1046.

    Article  Google Scholar 

  • Geoghegan, J., Villar, S. C., Klepeis, P., Mendoza, P. M., Ogneva-Himmelberger, Y., Chowdhury, R. R., Turner Ii, B. L., & Vance, C. (2001). Modeling tropical deforestation in the southern Yucatan peninsular region: Comparing survey and satellite data. Agriculture, Ecosystems & Environment, 85(1–3), 25–46.

    Article  Google Scholar 

  • Hanley, J. A., & McNeil, B. J. (1982). The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology, 143(1), 29–36.

    Article  Google Scholar 

  • Hewitt, R., & Escobar, F. (2011). The territorial dynamics of fast-growing regions: Unsustainable land use change and future policy challenges in Madrid Spain. Applied Geography, 31(2), 650–667.

    Article  Google Scholar 

  • Hosmer Jr, D. W., Lemeshow, S., & Sturdivant, R. X. (2013). Applied logistic regression (Vol. 398). John Wiley & Sons.

  • Hu, Z., & Lo, C. P. (2007). Modeling urban growth in Atlanta using logistic regression. Computers, Environment and Urban Systems, 31(6), 667–688.

    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(1–3), 7–24.

    Article  Google Scholar 

  • Jiang, B., & Yao, X. (Eds.). (2010). Geospatial analysis and modelling of urban structure and dynamics (Vol. 99). Springer Science & Business Media.

  • Klajnšek, G., & Žalik, B. (2005). Merging polygons with uncertain boundaries. Computers & Geosciences, 31(3), 353–359.

    Article  Google Scholar 

  • Kok, K., & Veldkamp, A. (2001). Evaluating impact of spatial scales on land use pattern analysis in Central America. Agriculture, Ecosystems & Environment, 85(1–3), 205–221.

    Article  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(4), 261–269.

    Article  Google Scholar 

  • Lee, K. (2003). Should land and capital be taxed at a uniform rate? Canadian Journal of Economics/revue Canadienne D’économique, 36(2), 350–372.

    Article  Google Scholar 

  • Li, X., & Yeh, A. G. O. (2002). Neural-network-based cellular automata for simulating multiple land use changes using GIS. International Journal of Geographical Information Science, 16(4), 323–343.

    Article  Google Scholar 

  • Liang, K. Y., & Zeger, S. L. (1993). Regression analysis for correlated data. Annual Review of Public Health, 14(1), 43–68.

    Article  Google Scholar 

  • McNeil, N., & Chooprateep, S. (2014). Modeling sea surface temperatures of the North Atlantic Ocean. Theoretical and Applied Climatology, 116(1–2), 11–17.

    Article  Google Scholar 

  • Pontius, R. G., & Neeti, N. (2010). Uncertainty in the difference between maps of future land change scenarios. Sustainability Science, 5(1), 39.

    Article  Google Scholar 

  • Pontius, R. G., & Pacheco, P. (2004). Calibration and validation of a model of forest disturbance in the Western Ghats, India 1920–1990. GeoJournal, 61(4), 325–334.

    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(1–3), 239–248.

    Article  Google Scholar 

  • Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv: 2010.16061

  • R Development Core Team. (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing.

    Google Scholar 

  • Rao, J. N. K., & Scott, A. J. (1992). A simple method for the analysis of clustered binary data. Biometrics, 48(2), 577–585.

  • Rebelo, E. M. (2009). Land economic rent computation for urban planning and fiscal purposes. Land Use Policy, 26(3), 521–534.

    Article  Google Scholar 

  • Rubber Authority of Thailand. (2019). Thailand rubber price. Retrieved March 28, 2018, from http://www.rubber.co.th/rubber2012/rubberprice_yr.php

  • Sarkar, S. K., & Midi, H. (2010). Importance of assessing the model adequacy of binary logistic regression. Journal of Applied Sciences, 10(6), 479–486.

    Article  Google Scholar 

  • Takahashi, K., Uchiyama, H., Yanagisawa, S., & Kamae, I. (2006). The logistic regression and ROC analysis of group-based screening for predicting diabetes incidence in four years. Kobe Journal of Medical Sciences, 52(6), 171.

    Google Scholar 

  • Thinnukool, O., Chuangchang, P., & Kongkraphan, K. (2014). Analysing land-use change using grid-digitised method. Songklanakarin Journal of Science and Technology, 36(2), 235–240.

    Google Scholar 

  • Thothong, W., Huon, S., Janeau, J. L., Boonsaner, A., De Rouw, A., & Planchon, O. (2011). Impact of land use change and rainfall on sediment and carbon accumulation in a water reservoir of North Thailand. Agriculture, Ecosystems & Environment, 140(3–4), 521–533.

    Article  Google Scholar 

  • Tongkumchum, P., & McNeil, D. (2009). Confidence intervals using contrasts for regression model. Songklanakarin Journal of Science & Technology, 31(2).

  • Wang, J., Chen, Y., Shao, X., Zhang, Y., & Cao, Y. (2012). Land-use changes and policy dimension driving forces in China: Present, trend and future. Land Use Policy, 29(4), 737–749.

    Article  Google Scholar 

  • Wang, J., & Mountrakis, G. (2011). Developing a multi-network urbanisation model: A case study of urban growth in Denver, Colorado. International Journal of Geographical Information Science, 25(2), 229–253.

    Article  Google Scholar 

  • Whiteside, T. G., Boggs, G. S., & Maier, S. W. (2011). Comparing object-based and pixel-based classifications for mapping savannas. International Journal of Applied Earth Observation and Geoinformation, 13(6), 884–893.

    Article  Google Scholar 

Download references

Acknowledgments

This work was partially supported by a grant from Faculty of Science and Technology, Prince of Sonkla University. We are grateful for Land Development Department, Ministry of Agriculture and Cooperatives for providing the data. Professor Don McNeil is acknowledged for his valuable suggestions.

Author information

Authors and Affiliations

  1. Department of Mathematics and Computer Science, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Mueang, Pattani, 94000, Thailand

    Suhaimee Buya, Phattrawan Tongkumchum & Santhana Chaimontree

  2. Medical Data Center for Research and Innovation, Faculty of Medicine, Prince of Songkla University, Songkhla, 90110, Thailand

    Suhaimee Buya

  3. Department of Sciences, Faculty of Science and Technology, Prince of Songkla University, Pattani Campus, Mueang, Pattani, 94000, Thailand

    Kua Rittiboon

Authors
  1. Suhaimee Buya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Phattrawan Tongkumchum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Kua Rittiboon
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Santhana Chaimontree
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

The study's conceptualization and design were contributed by Suhaimee Buya and Phattrawan Tongkumchum. Suhaimee Buya and Phattrawan Tongkumchum were in responsibility of material preparation, data collection, and analysis. Suhaimee Buya wrote the first draft of the manuscript, and all contributors provided feedback on prior versions. The final manuscript was read and approved by all authors.

Corresponding author

Correspondence to Suhaimee Buya.

Ethics declarations

Conflict of interests

The authors declare that they have no competing interests.

Additional information

Publisher's Note

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

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Buya, S., Tongkumchum, P., Rittiboon, K. et al. Logistic Regression Model of Built-Up Land Based on Grid-Digitized Data Structure: A Case Study of Krabi, Thailand. J Indian Soc Remote Sens 50, 909–922 (2022). https://doi.org/10.1007/s12524-022-01503-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12524-022-01503-0

Keywords

Search

Navigation