A spatial dynamic model of population changes in a vulnerable coastal environment

Kenan Li,N. Lam

Published 2018 in International Journal of Geographical Information Science

ABSTRACT

ABSTRACT This study developed a spatial dynamic model to examine the coupled natural–human responses in the form of changes in population and associated developed land area in the Lower Mississippi River Basin region. The goal was to identify key socioeconomic factors (utility) and environmental factors (hazard damages, elevation, and subsidence rate) that affected population changes, as well as to examine how population changes affected the local utility and the local environment reciprocally. We first applied areal interpolation techniques with the volume-preserving property to transform all the data at Year 2000 into a unified 3 km by 3 km cellular space. We then built an Elastic Net model to extract 12 variables from a set of 33 for the spatial dynamic model. Afterward, we calibrated the neighborhood effects with a genetic algorithm and use the spatial dynamic model to simulate population and developed land area in 2010. Furthermore, we took a Monte Carlo approach for analyzing the uncertainty of the model outcome. Our accuracy assessment shows that the model on average slightly overpredicts the number of population and the developed land percentage at 2010, as indicated by the low values of mean absolute deviation (MAD) due to quantity. On the other hand, the MADs due to allocation are larger than the MADs due to quantity, with most outliers found in the New Orleans region where population and urban development declined significantly during 2000–2010 after Hurricane Katrina. The proposed model sheds light on the complex relationships between coastal hazards and human responses and provides useful insights to strategic development for coastal sustainability.

PUBLICATION RECORD

  • Publication year

    2018

  • Venue

    International Journal of Geographical Information Science

  • Publication date

    2018-04-03

  • Fields of study

    Geography, Computer Science, Environmental Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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REFERENCES

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