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Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies

Author

Listed:
  • Charlotte Shade

    (Villanova University, Department of Geography and the Environment, Villanova, PA 19085, USA)

  • Peleg Kremer

    (Villanova University, Department of Geography and the Environment, Villanova, PA 19085, USA)

Abstract

Urbanization is a rapid global trend, leading to consequences such as urban heat islands and local flooding. Imminent climate change is predicted to intensify these consequences, forcing cities to rethink common infrastructure practices. One popular method of adaptation is green infrastructure implementation, which has been found to reduce local temperatures and alleviate excess runoff when installed effectively. As cities continue to change and adapt, land use/landcover modeling becomes an important tool for city officials in planning future land usage. This study uses a combination of cellular automata, machine learning, and Markov chain analysis to predict high resolution land use/landcover changes in Philadelphia, PA, USA for the year 2036. The 2036 landcover model assumes full implementation of Philadelphia’s green infrastructure program and past temporal trends of urbanization. The methodology used to create the 2036 model was validated by creating an intermediate prediction of a 2015 landcover that was then compared to an existing 2015 landcover. The accuracy of the validation was determined using Kappa statistics and disagreement scores. The 2036 model successfully met Philadelphia’s green infrastructure goals. A variety of landscape metrics demonstrated an overall decrease in fragmentation throughout the landscape due to increases in urban landcover.

Suggested Citation

  • Charlotte Shade & Peleg Kremer, 2019. "Predicting Land Use Changes in Philadelphia Following Green Infrastructure Policies," Land, MDPI, vol. 8(2), pages 1-19, February.
  • Handle: RePEc:gam:jlands:v:8:y:2019:i:2:p:28-:d:202864
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    Citations

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    Cited by:

    1. Mads Christensen & Jamal Jokar Arsanjani, 2020. "Stimulating Implementation of Sustainable Development Goals and Conservation Action: Predicting Future Land Use/Cover Change in Virunga National Park, Congo," Sustainability, MDPI, vol. 12(4), pages 1-28, February.
    2. Diana Dushkova & Annegret Haase & Manuel Wolff & Dagmar Haase, 2021. "Editorial for Special Issue “Nature-Based Solutions (NBS) in Cities and Their Interactions with Urban Land, Ecosystems, Built Environments and People: Debating Societal Implications”," Land, MDPI, vol. 10(9), pages 1-7, September.
    3. Rifat, Shaikh Abdullah Al & Liu, Weibo, 2022. "Predicting future urban growth scenarios and potential urban flood exposure using Artificial Neural Network-Markov Chain model in Miami Metropolitan Area," Land Use Policy, Elsevier, vol. 114(C).
    4. Nan Wang & Peijuan Zhu & Guohua Zhou & Xudong Xing & Yong Zhang, 2022. "Multi-Scenario Simulation of Land Use and Landscape Ecological Risk Response Based on Planning Control," IJERPH, MDPI, vol. 19(21), pages 1-29, November.
    5. Bernard Fosu Frimpong & Frank Molkenthin, 2021. "Tracking Urban Expansion Using Random Forests for the Classification of Landsat Imagery (1986–2015) and Predicting Urban/Built-Up Areas for 2025: A Study of the Kumasi Metropolis, Ghana," Land, MDPI, vol. 10(1), pages 1-21, January.
    6. Motuma Shiferaw Regasa & Michael Nones, 2022. "Past and Future Land Use/Land Cover Changes in the Ethiopian Fincha Sub-Basin," Land, MDPI, vol. 11(8), pages 1-20, August.
    7. J. Ronald Eastman & Jiena He, 2020. "A Regression-Based Procedure for Markov Transition Probability Estimation in Land Change Modeling," Land, MDPI, vol. 9(11), pages 1-12, October.

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