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

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.

KEYWORDS:

• Coupled natural–human system
• population dynamics
• coastal hazards
• resilience
• Mississippi Delta

Acknowledgment

This article is supported by two research grants from the U.S. National Science Foundation: one under the Dynamics of Coupled Natural Human Systems (CNH) Program [Award No.121211], and the other under the Coastal Science, Engineering and Education for Sustainability (Coastal SEES) Program [Award No. 1427389]. However, any opinions, findings, and conclusions or recommendations expressed in this article are those of the authors and do not necessarily reflect the views of the funding agencies.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Science Foundation [1212112, 1427389].