Identifying regions based on flexible user-defined constraints

References

• Anselin, L., 2006. How (not) to lie with spatial statistics. American Journal of Preventive Medicine, 30 (2S), S3–S6.
   PubMedGoogle Scholar
• Bittner, T. and Smith, B., 2003. A theory of granular partitions. In: Duckham, Matt, Michael F. Goodchild, and Michael Worboys, eds. Foundations of Geographic Information Science. New York: Taylor & Francis, 117–149.
   Google Scholar
• Casati, R. and Varzi, A.C., 1996. The structure of spatial localization. Philosophical Studies, 82 (2), 205–239.
   Web of Science ®Google Scholar
• Duque, J.C., Anselin, L., and Rey, S.J., 2012. The max-p-regions problem. Journal of Regional Science, 52 (3), 397–419.
   Web of Science ®Google Scholar
• Duque, J.C., Betancourt, A., and Marin, F., 2013a. An algorithmic approach for simulating realistic irregular lattices. Technical report, Documentos de trabajo: Economía y Finanzas. Medellin, Colombia: Centro de Investigaciones Económicias y Financieras, 13–20.
   Google Scholar
• Duque, J.C., Church, R.L., and Middleton, R.S., 2011. The p-regions problem. Geographical Analysis, 43 (1), 104–126.
   Web of Science ®Google Scholar
• Duque, J.C., Ye, X., and Folch, D.C., 2013b . spMorph: an exploratory space-time analysis tool for describing processes of spatial redistribution. Papers in Regional Science (in review).
   Google Scholar
• Duque, J.C., Ramos, R., and Suriñach, J., 2007. Supervised regionalization methods: a survey. International Regional Science Review, 30 (3), 195–220.
   Web of Science ®Google Scholar
• Fischer, M.M., 1980. Regional taxonomy: a comparison of some hierarchic and non-hierarchic strategies. Regional Science and Urban Economics, 10 (4), 503–537.
   Web of Science ®Google Scholar
• Galster, G., 2001. On the nature of neighbourhood. Urban studies, 38 (12), 2111–2124.
   Web of Science ®Google Scholar
• Glover, F., 1990. Tabu search: a tutorial. Interfaces, 20 (4), 74–94.
   Web of Science ®Google Scholar
• Gordon, A.D., 1996. A survey of constrained classification. Computational Statistics & Data Analysis, 21 (1), 17–29.
   Web of Science ®Google Scholar
• Greene, W., 2003. Econometric analysis. Upper Saddle River, NJ: Prentice Hall.
   Google Scholar
• Guo, D., 2008. Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22 (7), 801–823.
   Web of Science ®Google Scholar
• Kaufman, L. and Rousseeuw, P.J., 1990. Finding groups in data: an introduction to cluster analysis. New York: Wiley.
   Google Scholar
• Keane, M., 1975. The size of the region-building problem. Environment and Planning A, 7 (5), 575–577.
   Web of Science ®Google Scholar
• Kutner, M.H., et al., 2005. Applied linear statistical models. New York: McGraw-Hill.
   Google Scholar
• Li, W., Goodchild, M.F., and Church, R., 2013. An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems. International Journal of Geographical Information Science, 27 (6), 1227–1250.
   Web of Science ®Google Scholar
• Logan, J.R., et al., 2011. Identifying and bounding ethnic neighborhoods. Urban Geography, 32 (3), 334–359.
   PubMed Web of Science ®Google Scholar
• Martin, D., 2002. Geography for the 2001 census in England and Wales. Population Trends, 108, 7–15.
   PubMedGoogle Scholar
• Milligan, G.W. and Cooper, M.C., 1985. An examination of procedures for determining the number of clusters in a data set. Psychometrika, 50 (2), 159–179.
   Web of Science ®Google Scholar
• Montello, D.R., 2003. Regions in geography: process and content. In: M. Duckham, M.F. Goodchild and M.F. Worboys, eds. Foundations of geographic information science. New York: Taylor & Francis, 173–189.
   Google Scholar
• Murtagh, F., 1985. A survey of algorithms for contiguity-constrained clustering and related problems. The Computer Journal, 28 (1), 82–88.
   Web of Science ®Google Scholar
• Murtagh, F., 1992. Contiguity-constrained clustering for image analysis. Pattern Recognition Letters, 13 (9), 677–683.
   Web of Science ®Google Scholar
• Openshaw, S., 1977. A geographical solution to scale and aggregation problems in region-building, partitioning and spatial modelling. Transactions of the Institute of British Geographers, 2 (4), 459–472.
   Google Scholar
• Openshaw, S., 1983. Multivariate analysis of census data: the classification of areas. In: D.W. Rhind, ed. A census users handbook. London: Methuen 243–263.
   Google Scholar
• Openshaw, S. and Rao, L., 1995. Algorithms for reengineering 1991 Census geography. Environment and Planning A, 27, 425–446.
   PubMed Web of Science ®Google Scholar
• Openshaw, S., Cullingford, D., and Gillard, A., 1980. A critique of the national classifications of OPCS/PRAG. The Town Planning Review, 51 (4), 421–439.
   Web of Science ®Google Scholar
• Park, R.E., 1925. Can neighborhood work have a scientific basis?. In: R.E. Park, E.W. Burgess and R.D. McKenzie, eds. The city: suggestions for investigation of human behavior in the urban environment. Chicago, IL.: The University of Chicago Press.
   Google Scholar
• Rey, S.J. and Anselin, L., 2007. PySAL: A Python library of spatial analytical methods. The Review of Regional Studies, 37 (1), 5–27.
   Google Scholar
• Ricca, F. and Simeone, B., 2008. Local search algorithms for political districting. European Journal of Operational Research, 189 (3), 1409–1426.
   Web of Science ®Google Scholar
• Smith, B. and Mark, D.M., 2003. Do mountains exist? Towards an ontology of landforms. Environment and Planning B, 30 (3), 411–427.
   Web of Science ®Google Scholar
• Spielman, S.E. and Logan, J.R., 2013. Using high-resolution population data to identify neighborhoods and establish their boundaries. Annals of the Association of American Geographers, 103 (1), 67–84.
   PubMed Web of Science ®Google Scholar
• Tversky, A., 1977. Features of similarity. Psychological Review, 84 (4), 327–352.
   Web of Science ®Google Scholar
• Yan, M. and Ye, K., 2007. Determining the number of clusters using the weighted gap statistic. Biometrics, 63 (4), 1031–1037.
   PubMed Web of Science ®Google Scholar