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

International Journal of Geographical Information Science

Volumn 32, Issue 4, 2018, Pages 685-710

  Li, Kenan ^a   Lam, Nina S N ^a  

^a LOUISIANA STATE UNIVERSITY   (United States)

Author keywords

coastal hazards; Coupled natural human system; Mississippi Delta; population dynamics; resilience

Indexed keywords

ACCURACY ASSESSMENT; AREAL INTERPOLATION; COASTAL ZONE MANAGEMENT; DISASTER MANAGEMENT; ENVIRONMENTAL FACTOR; GENETIC ALGORITHM; MONTE CARLO ANALYSIS; POPULATION DYNAMICS; SOCIOECONOMIC STATUS; SPATIAL ANALYSIS; SPATIAL DATA; VULNERABILITY;

LOUISIANA; MISSISSIPPI DELTA; UNITED STATES;

EID: 85035089397     PISSN: 13658816     EISSN: 13623087     Source Type: Journal    
DOI: 10.1080/13658816.2017.1407415     Document Type: Article

References (80)

1. Batty, M., Couclelis, H., and Eichen, M., 1997. Urban systems as cellular automata. Environment and Planning B: Planning and Design, 24, 159–164. doi:10.1068/b240159
2. Batty, M., Xie, Y., and Sun, Z.L., 1994. Modeling urban dynamics through GIS-based cellular automata. Computers, Environment and Urban Systems, 23, 205–233. doi:10.1016/S0198-9715(99)00015-0
3. Benenson, I., and Torrens, P.M., 2004. Geosimulation: automata-based modelling of urban phenomena. West Sussex, UK: Wiley.
4. Blum, M.D., and Roberts, H.H., 2009. Drowning of the Mississippi Delta due to insufficient sediment supply and global sea-level rise. Nature Geoscience Letters, 2 (7), 488–490. doi:10.1038/ngeo553
5. Boccara, N., and Cheong, K., 1992. Automata network SIR models for the spread of infectious diseases in populations of moving individuals. Journal of Physics A: Mathematical and General, 25, 2447–2461. doi:10.1088/0305-4470/25/9/018
6. Boccara, N., and Cheong, K., 1993. Critical behaviour of a probabilistic automata network SIS model for the spread of an infectious disease in a population of moving individuals. Journal of Physics A: Mathematical and General, 26, 3707–3717. doi:10.1088/0305-4470/26/15/020
7. Bolin, B., et al., 2000. Core questions of science and technology for sustainability.Available from: http://sustainabilityscience.org/content.html?contentid=776/ [Accessed 2 Nov 2014].
8. Bonabeau, E., 2002. Agent-based modeling: methods and techniques for simulating human systems. Proceedings of the National Academy of Sciences, 99, 7280–7287. doi:10.1073/pnas.082080899
9. Brown, D.G., and Xie, Y., 2006. Spatial agent-based modelling. International Journal of Geographical Information Science, 20 (9), 941–943. doi:10.1080/13658810600830491
10. Bruch, E., and Mare, R., 2012. Methodological issues in the analysis of residential preferences, residential mobility and neighborhood change. Sociological Methodology, 42 (1), 103–154. doi:10.1177/0081175012444105
11. Cai, H., et al., 2016. Assessing community resilience to coastal hazards in the Lower Mississippi River Basin. Water, 8 (2), 46. doi:10.3390/w8020046
12. Caruso, G., et al., 2007. Spatial configurations in a peri-urban city. A cellular automata based microeconomic model. Regional Science and Urban Economics, 37, 542–567. doi:10.1016/j.regsciurbeco.2007.01.005
13. Cavailhès, J., et al., 2004. Where Alonso meets Sierpinski: an urban economic model of a fractal metropolitan area. Environment and Planning A, 36, 1471–1498. doi:10.1068/a36126
14. Chen, Y., et al., 2012. Defining agents’ behaviour based on urban economic theory to simulate complex urban residential dynamics. International Journal of Geographical Information Science, 26, 1155–1172. doi:10.1080/13658816.2011.626780
15. Clarke, K.C., and Gaydos, L., 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. doi:10.1080/136588198241617
16. Clarke, K.C., Hoppen, S., and Gaydos, L.A., 1997. A self-modifying cellular automaton model of historical urbanization in the San Francisco Bay area. Environment and Planning B: Planning and Design, 24, 247–261. doi:10.1068/b240247
17. Collins, S.L., et al., 2011. An integrated conceptual framework for long-term social-ecological research. Frontiers in Ecology and the Environment, 9 (6), 351–357. doi:10.1890/100068
18. Conte, R., 2002. Agent-based modeling for understanding social intelligence. Proceedings of the National Academy of Sciences of the United States of America PNAS, 99 (Supplement 3), 7189–7190. doi:10.1073/pnas.072078999
19. CPRA, Coastal Protection and Restoration Authority of Louisiana, 2012. Louisiana’s comprehensive master plan for a sustainable coast. Baton Rouge, LA: CPRA, State of Louisiana.
20. Cromley, R.G., Hanink, D.M., and Bentley, G.C., 2012. A quantile regression approach to areal interpolation. Annals of the Association of American Geographers, 102 (4), 763–777. doi:10.1080/00045608.2011.627054
21. Dökmeci, V., and Berköz, L., 2000. Residential-location preferences according to demographic characteristics in Istanbul. Landscape and Urban Planning, 48 (1–2), 45–55. doi:10.1016/S0169-2046(99)00080-8
22. Doubilet, P., et al., 1985. Probabilistic sensitivity analysis using Monte Carlo simulation: a practical approach. Medical Decision Making, 5, 157–177. doi:10.1177/0272989X8500500205
23. Fontaine, C.M., and Rounsevell, M.D.A., 2009. An agent-based approach to model future residential pressure on a regional landscape. Landscape Ecology, 24 (9), 1237–1254. doi:10.1007/s10980-009-9378-0
24. Forrester, J.W, 1961. Industrial dynamics. Pegasus Communications, MA: the M.I.T. press.
25. Forrester, J.W., 1969. Urban dynamics. Cambridge, MA: the M.I.T. press.
26. Fujita, M., and Thisse, J.F., 2002. Economics of agglomeration: cities, industrial location and regional growth. Cambridge: Cambridge University Press.
27. Goodchild, M.F., and Lam, N.S.N., 1980. Areal interpolation: a variant of the traditional spatial problem. Geo-Processing, 1, 297–312.
28. Hellweger, F., 2008. Spatially explicit individual-based modeling using a fixed super-individual density. Computers & Geosciences, 34, 144–152. doi:10.1016/j.cageo.2006.12.006
29. Hoerl, A., and Kennard, R., 1988. Ridge regression. In encyclopedia of statistical sciences. Vol. 8. New York: Wiley, 129–136.
30. Irwin, E.G., and Bockstael, N.E., 2002. Interacting agents, spatial externalities and the evolution of residential land use patterns. Journal of Economic Geography, 2, 31–54. doi:10.1093/jeg/2.1.31
31. Jennings, N., 2000. On agent-based software engineering. Artificial Intelligence, 117, 277–296. doi:10.1016/S0004-3702(99)00107-1
32. Kates, R.W., 2011. From the unity of nature to sustainability science: ideas and practice. CID Working Paper (218). Cambridge, MA: Center for International Development, Harvard University. Available from: http://www.hks.harvard.edu/centers/cid/publications/faculty-workingpapers/cid-working paperno.-218 [Accessed 15 Nov 2011].
33. Krugman, P., 1991. Increasing returns and economic geography. Journal of Political Economy, 99 (3), 483–499. doi:10.1086/261763
34. Lam, N.S.N., 1980. Methods and Problems of Areal Interpolation. PhD Dissertation. University of Western Ontario, London, Ontario, Canada.
35. Lam, N.S.N., 1983. Spatial interpolation methods: a review. The American Cartographer, 10 (2), 129–150. doi:10.1559/152304083783914958
36. Lam, N.S.N., et al., 2009a. An estimate of population impacted by climate change along the U.S. coast. Journal of Coastal Research, Special Issue 56, 1522–1526.
37. Lam, N.S.N., et al., 2009b. Business return in New Orleans: decision making amid post-Katrina uncertainty. Public Library of Science (Plos ONE), 4 (8), 6765.
38. Lam, N.S.N., 2012. Geospatial methods for reducing uncertainties in environmental health risk assessment: challenges and opportunities. Annals of the Association of American Geographers, 102 (5), 942–950. doi:10.1080/00045608.2012.674900
39. Lam, N.S.N., et al., 2012. Predictors of business return in New Orleans after Hurricane Katrina. PLoS One, 7 (10), e47935: 1–8. doi:10.1371/journal.pone.0047935
40. Lam, N.S.N., et al., 2016. Measuring community resilience to coastal hazards along the Northern Gulf of Mexico. Natural Hazards Review. doi:10.1061/(ASCE)NH.1527-6996.0000193
41. Lam, N.S.N., and Quattrochi, D.A., 1992. On the issues of scale, resolution, fractal analysis in the Mapping Sciences. The Professional Geographer, 44 (1), 88–98. doi:10.1111/j.0033-0124.1992.00088.x
42. Lam, N.S.N., 2009. Spatial interpolation. In: R., Kitchin and N., Thrift, eds. International encyclopedia of human geography. Vol. 10. Oxford: Elsevier, 369–376.
43. LeSage, J.P., et al., 2011a. New Orleans business recovery in the aftermath of Hurricane Katrina. Journal of Royal Statistics Society A, 174 (4), 1007–1027. doi:10.1111/j.1467-985X.2011.00712.x
44. LeSage, J.P., et al., 2011b. Space-time modeling of natural disaster impacts. Journal of Economic and Social Measurement, 36, 169–191.
45. LeSage, J.P., et al., 2011c. Do what the neighbors do: reopening businesses after Hurricane Katrina. Significance, 8 (4), 160–163. doi:10.1111/j.1740-9713.2011.00520.x
46. Li, X., and Liu, X.P., 2007. Defining agents’ behaviors to simulate complex residential development using multi-criteria evaluation. Journal of Environmental Management, 85 (4), 1063–1075. doi:10.1016/j.jenvman.2006.11.006
47. Li, X., and Liu, X.P., 2008. Embedding sustainable development strategies in agent-based models for use as a planning tool. International Journal of Geographical Information Science, 22 (1), 21–45. doi:10.1080/13658810701228686
48. Ligtenberg, A., Bregt, A.K., and Lammeren, R.V., 2001. Multi-actor-based land use modelling: spatial planning using agents. Landscape and Urban Planning, 56, 21–33. doi:10.1016/S0169-2046(01)00162-1
49. Lindberg, E., et al., 1992. Residential location preferences across the life-span. Journal of Environmental Psychology, 12, 187–198. doi:10.1016/S0272-4944(05)80070-2
50. Liu, J.G., et al., 2007a. Complexity of coupled human and natural systems. Science, 317 (5844), 1513–1516. doi:10.1126/science.1144004
51. Liu, J.G., et al., 2007b. Coupled human and natural systems. AMBIO: A Journal of the Human Environment, 36 (8), 639–649. doi:10.1579/0044-7447(2007)36[639:CHANS]2.0.CO;2
52. Malanson, G.P., Zeng, Y., and Walsh, S.J., 2006a. Complexity at advancing ecotones and frontiers. Environment and Planning A, 38, 619–632. doi:10.1068/a37340
53. Malanson, G.P., Zeng, Y., and Walsh, S.J., 2006b. Landscape frontiers, geography frontiers: lessons to be learned. The Professional Geographer, 58, 383–396. doi:10.1111/j.1467-9272.2006.00576.x
54. McFadden, D., (1978). Choice of residential location. Cowles Foundation Discussion Paper 477.
55. Munroe, D.K., 2007. Exploring the determinants of spatial pattern in residential land markets: amenities and disamenities in Charlotte, NC, USA. Environment and Planning B: Planning and Design, 34, 336–354. doi:10.1068/b32065
56. Niedomysl, T., 2008. Residential preferences for interregional migration in Sweden: demographic, socioeconomic, and geographical determinants. Environment and Planning A, 40, 1109–1131. doi:10.1068/a39177
57. Nijkamp, P., Van Wissen, L., and Rima, A., 1993. A household life cycle model for residential relocation behaviour. Socioeconomic Planning Sciences, 27, 35–53. doi:10.1016/0038-0121(93)90027-G
58. Parker, D.C., and Filatova, T., 2008. A conceptual design for a bilateral agent-based land market with heterogeneous economic agents. Computers, Environment and Urban Systems, 32, 454–463. doi:10.1016/j.compenvurbsys.2008.09.012
59. Plyer, A., 3 May 2013. Facts for Features: hurricane Katrina Recovery. GNOCDC. Greater New Orleans Community Data Center, 30 Jan. 2013. Web. Available from: http://www.gnocdc.org/Factsforfeatures/HurricaneKatrinaRecovery/.
60. Pontius Jr, R.G., et al., 2008b. Comparing the input, output, and validation maps for several models of land change. The Annals of Regional Science, 42 (1), 11–37. doi:10.1007/s00168-007-0138-2
61. Pontius Jr, R.G., and Millones, M., 2011. Death to Kappa: birth of quantity disagreement and allocation disagreement for accuracy assessment. International Journal of Remote Sensing, 32 (15), 4407–4429. doi:10.1080/01431161.2011.552923
62. Pontius Jr, R.G., Thontteh, O., and Chen, H., 2008a. Components of information for multiple resolution comparison between maps that share a real variable. Environmental and Ecological Statistics, 15 (2), 111–142. doi:10.1007/s10651-007-0043-y
63. Qiang, Y., and Lam, N.S.N., 2015. Modeling land use and land cover changes in a vulnerable coastal region using artificial neural networks and cellular automata. Environmental Monitoring and Assessment, 187. doi:10.1007/s10661-015-4298-8
64. Qiang, Y., and Lam, N.S.N., 2016. The impact of Hurricane Katrina on urban growth in Louisiana: an analysis using data mining and simulation approaches. International Journal of Geographical Information Science, 30, 1832–1852. doi:10.1080/13658816.2016.1144886
65. Scheffer, M., et al., 1995. Super-individuals a simple solution for modelling large populations on an individual basis. Ecological Modelling, 80, 161–170. doi:10.1016/0304-3800(94)00055-M
66. Schultz, J., and Elliott, J.R., 2012. Natural disasters and local demographic change in the United States. Population Environment, 34 (3), 293–312. doi:10.1007/s11111-012-0171-7
67. Shi, W., and Pang, M.Y., 2000. Development of Voronoi-based cellular automata -an integrated dynamic model for GeographicalInformation Systems. International Journal Geographic Information Sciences, 14, 455–474. doi:10.1080/13658810050057597
68. Shu, Y., Lam, N.S., and Reams, M., 2010. A new method for estimating carbon dioxide emissions from transportation at fine spatial scales. Environmental Research Letters, 5 (4), 044008. doi:10.1088/1748-9326/5/4/044008
69. Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. Journal R Statistic Social B, 58, 267–288.
70. Torrens, P.M., 2001. Can geocomputation save urban simulation? Throw some agents into the mixture, simmer, and wait. University College of London, Centre for Advanced Spatial Analysis, London, UK: Working Paper series 32.
71. Twilley, R.R., et al., 2016. Co-evolution of wetland landscapes, flooding and human settlement in the Mississippi River Delta Plain. Sustainability Science, 11, 711–731. doi:10.1007/s11625-016-0374-4
72. Vaz, E., et al., 2012. A multi-scenario forecast of urban change: a study on urban growth in the Algarve. Landscape and Urban Planning, 104, 201–211. doi:10.1016/j.landurbplan.2011.10.007
73. Whittingham, M.J., et al., 2006. Why do we still use stepwise modelling in ecology and behaviour? Journal of Animal Ecology, 75 (5), 1182–1189. doi:10.1111/j.1365-2656.2006.01141.x
74. Wright, J.A., and Alajmi, A.F., 2005. The robustness of genetic algorithms in solving unconstrained building optimization problems. In: 9th International IBPSA Building Simulation Conference, 15-18 August 2005. Montréal, Canada.
75. Wu, F., and Webster, C.J., 2000. Simulating artificial cities in a GIS environment: urban growth under alternative regulation regimes. International Journal of Geographical Information Science, 14, 625–648. doi:10.1080/136588100424945
76. Xie, Y.C., 1997. A generalized model for cellular urban dynamics. Geographical Analysis, 28, 350–373. doi:10.1111/gean.1996.28.issue-4
77. Yin, L., and Muller, B., 2007. Residential location and the biophysical environment: exurban development agents in a heterogeneous landscape. Environment and Planning B: Planning and Design, 34, 279–295. doi:10.1068/b31182
78. Zhou, C.H., Sun, Z.H., and Xie, Y.C., 1999. The research of geographical cellular automata. Beijing, China: Science Press, 1–163.
79. Zou, H., and Hastie, T., 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67 (2), 301–320. doi:10.1111/rssb.2005.67.issue-2
80. Zou, L., et al., 2016. Evaluating land subsidence rates and their implications for land loss in the Lower Mississippi River Basin. Water, 8 (1). doi:10.3390/w8010010.15p