References
- Aldstadt, J. and Getis, A., 2006. Using AMOEBA to create a spatial weights matrix and identify spatial clusters. Geographical Analysis, 38 (4), 327–343, October. 0016-7363, 1538-4632. doi:https://doi.org/10.1111/j.1538-4632.2006.00689.x.
- Anselin, L., 1995. Local indicators of spatial association-LISA. Geographical Analysis, 27 (2), 93–115. doi:https://doi.org/10.1111/j.1538-4632.1995.tb00338.x.
- Arbia, G., Espa, G., and Quah, D., 2008. A class of spatial econometric methods in the empirical analysis of clusters of firms in the space. Empirical Economics, 34 (1), 81–103. doi:https://doi.org/10.1007/s00181-007-0154-1.
- Besag, J. and Newell, J., 1991. The detection of clusters in rare diseases. Journal of the Royal Statistical Society. Series A (Statistics in Society), 154 (1), 143–155. doi:https://doi.org/10.2307/2982708.
- Chodrow, P.S., 2017. Structure and information in spatial segregation. Proceedings of the National Academy of Sciences, 114 (44), 11591–11596. doi:https://doi.org/10.1073/pnas.1708201114.
- Czamanski, S. and Ablas, L., 1979. Identification of industrial clusters and complexes: a comparison of methods and findings. Urban Studies, 16 (1), 61–80. doi:https://doi.org/10.1080/713702464.
- Dean, N., et al., 2018. Frontiers in residential segregation: understanding neighbourhood boundaries and their impacts. Tijdschrift Voor Economische En Sociale Geografie, in print, 1467-9663, 110 (3), 289–302. doi:https://doi.org/10.1111/tesg.12307.
- Delmelle, E., et al., 2013. Trajectories of multidimensional neighbourhood quality of life change. Urban Studies, 50 (5), 923–941. doi:https://doi.org/10.1177/0042098012458003.
- Dong, G., et al., 2018. Inferring neighbourhood quality with property transaction records by using a locally adaptive spatial multi-level model. Computers, Environment and Urban Systems, 73, 118–125.
- Drukker, M., et al., 2003. Children’s health-related quality of life, neighbourhood socio-economic deprivation and social capital. A contextual analysis. Social Science & Medicine, 57 (5), 825–841, September. doi:https://doi.org/10.1016/S0277-9536(02)00453-7.
- Duque, J.C., Anselin, L., and Rey, S.J., 2012. The max-p-regions problem. Journal of Regional Science, 52 (3), 397–419. doi:https://doi.org/10.1111/j.1467-9787.2011.00743.x.
- Duque, J.C., Church, R.L., and Middleton, R.S., 2011. The p-Regions problem. Geographical Analysis, 43 (1), 104–126. doi:https://doi.org/10.1111/j.1538-4632.2010.00810.x.
- Duque, J.C., Ramos, R., and Surinach, J., 2007. Supervised regionalization methods: a survey. International Regional Science Review, 30 (3), 195. doi:https://doi.org/10.1177/0160017607301605.
- Ester, M., et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise, 226–231. AAAI Press.
- Galster, G., 2001. On the nature of neighbourhood. Urban Studies, 38 (12), 2111. doi:https://doi.org/10.1080/00420980120087072.
- Getis, A. and Ord, J.K., 1996. Local spatial statistics: an overview. Spatial Analysis: Modelling in a GIS Environment, 261–277.
- Griffith, D.A., 2000. Eigenfunction properties and approximations of selected incidence matrices employed in spatial anlaysis. Linear Algebra and Its Applications, 321 (1–3), 95–112. doi:https://doi.org/10.1016/S0024-3795(00)00031-8.
- Griffith, D.A., 2013. Spatial autocorrelation and spatial filtering: gaining understanding through theory and scientific visualization. Berlin, Germany: Springer Science & Business Media.
- Grubesic, T.H., Wei, R., and Murray, A.T., 2014. Spatial clustering overview and comparison: accuracy, sensitivity, and computational expense. Annals of the Association of American Geographers, 104 (6), 1134–1156, November. 0004-5608, 1467-8306. doi:https://doi.org/10.1080/00045608.2014.958389.
- Harris, R., Johnston, R., and Burgess, S., 2007. Neighborhoods, ethnicity and school choice: developing a statistical framework for geodemographic analysis. Population Research and Policy Review, 26 (5), 553–579. 1573-7829. doi:https://doi.org/10.1007/s11113-007-9042-9.
- Harris, R., Sleight, P., and Webber, R., 2005. Geodemographics, GIS and neighbourhood targeting. Vol. 7. Hoboken, NJ: John Wiley and Sons.
- Hastie, T.J., Tibshirani, R.J., and Friedman, J.H., 2009. The elements of statistical learning: data mining, inference, and prediction. New York: Springer Series in Statistics, Springer. ISBN 978-0-387-84857-0.
- Hubert, L.J., et al., 1985. Measuring association between spatially defined variables: an alternative procedure. Geographical Analysis, 17 (1), 36–46. doi:https://doi.org/10.1111/j.1538-4632.1985.tb00825.x.
- Isard, W., 1956. Regional science, the concept of region, and regional structure. Papers in Regional Science, 2 (1), 13–26. doi:https://doi.org/10.1111/j.1435-5597.1956.tb01542.x.
- Jacquez, G.M., Kaufmann, A., and Goovaerts, P., 2008. Boundaries, links and clusters: a new paradigm in spatial analysis? Environmental and Ecological Statistics, 15 (4), 403–419, December. 1352-8505, 1573-3009. doi:https://doi.org/10.1007/s10651-007-0066-4.
- Kim, K., Chun, Y., and Kim, H., 2017. P-functional clusters location problem for detecting spatial clusters with covering approach. Geographical Analysis, 49 (1), 101–121. 1538-4632. doi:https://doi.org/10.1111/gean.12109.
- Knaap, E., et al., 2019. The dynamics of urban neighborhoods: a survey of approaches for modeling socio-spatial structure. Geography Compass, Under Review. doi:https://doi.org/10.31235/osf.io/3frcz
- Kulldorff, M. and Nagarwalla, N., 1995. Spatial disease clusters: detection and inferenc. Statistics in Medicine, 14 (8), 799–810. doi:https://doi.org/10.1002/sim.4780140809.
- Li, W., Church, R.L., and Goodchild, M.F., 2014. The p-compact-regions problem. Geographical Analysis, 46 (3), 250–273. doi:https://doi.org/10.1111/gean.12038.
- McInnes, L., Healy, J., and Astels, S., 2017. Hdbscan: hierarchical density based clustering. The Journal of Open Source Software, 2 (11), 205. doi:https://doi.org/10.21105/joss.00205.
- Murray, A.T., Grubesic, T.H., and Wei, R., 2014. Spatially significant cluster detection. Spatial Statistics, 10, 103–116. doi:https://doi.org/10.1016/j.spasta.2014.03.001
- Neill, D.B., et al., 2005. Detecting significant multidimensional spatial clusters. Advances in Neural Information Processing Systems, 17, 969–976.
- Ng, A.Y., Jordan, M.I., and Weiss, Y., 2002. On spectral clustering: analysis and an algorithm. Proceedings of the 14th International Conference on Neural Information Processing Systems: Natural and Synthetic, Vancouver, British Columbia, Canada. 849–856.
- Oehrlein, J. and Haunert, J.-H., 2017. A cutting-plane method for contiguity- constrained spatial aggregation. Journal of Spatial Information Science, (15), December. 1948-660X. doi:https://doi.org/10.5311/JOSIS.2017.15.379.
- Pavlis, M., Dolega, L., and Singleton, A., 2018. A modified DBSCAN clustering method to estimate retail center extent: clustering retail agglomerations. Geographical Analysis, 50 (2), 141–161, April. 00167363. doi:https://doi.org/10.1111/gean.12138.
- Pedregosa, F., et al., 2011. Scikit-learn: machine learning in python. Journal of Machine Learning Research, 12, 2825–2830.
- Rey, S.J. and Anselin, L., 2007. PySAL: a python library of spatial analytical methods. The Review of Regional Studies, 37 (1), 5–27.
- Rey, S.J. and Mattheis, D.J., 2000. Identifying regional industrial clusters in California: volume I conceptual design. San Diego State University Technical Report.
- Rogerson, P. and Yamada, I., 2009. Statistical detection and surveillance of geographic clusters. Boca Raton, FL: Chapman & Hall/CRC.
- Shelton, T. and Poorthuis, A., 2019. The nature of neighborhoods: using big data to rethink the geographies of Atlanta’s neighborhood planning unit system. Annals of the American Association of Geographers, 109 (5), 1341–1361, September. 2469-4452, 2469-4460. doi:https://doi.org/10.1080/24694452.2019.1571895.
- Shi, J. and Malik, J., 2000. Normalized cuts and image segmentation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22 (8), 888–905.
- Singleton, A. and Longley, P.A., 2009. Creating open source geodemographics: refining a national classification of census output areas for applications in higher education. Papers in Regional Science, 88 (3), 643–666, August. doi:https://doi.org/10.1111/j.1435-5957.2008.00197.x.
- Singleton, A. and Spielman, S.E., 2014. The past, present, and future of geodemographic research in the United States and United Kingdom. The Professional Geographer, 66 (4), 558–567. doi:https://doi.org/10.1080/00330124.2013.848764.
- 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. doi:https://doi.org/10.1080/00045608.2012.685049.
- Turnbull, B.W., et al., 1990. Monitoring clusters of disease: application to Leukemia incidence in upstate New York. American Journal of Epidemiology, 132 (1), 136–143. doi:https://doi.org/10.1093/oxfordjournals.aje.a115775.
- van der Walt, S., Colbert, S.C., and Varoquaux, G., 2011. The NumPy array: a structure for efficient numerical computation. Computing in Science & Engineering, 13 (2), 22–30, March. 1521-9615. doi:https://doi.org/10.1109/MCSE.2011.37.
- Von Luxburg, U., 2007. A tutorial on spectral clustering. Statistics and Computing, 17 (4), 395–416. doi:https://doi.org/10.1007/s11222-007-9033-z.
- Wang, X. and Davidson, I., 2010. Flexible constrained spectral clustering. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. Washington, DC: ACM, 563–572.
- Wang, X., Qian, B., and Davidson, I., 2014. On constrained spectral clustering and its applications. Data Mining and Knowledge Discovery, 28 (1), 1–30, January. 1384-5810, 1573-756X. doi:https://doi.org/10.1007/s10618-012-0291-9.
- White, S. and Smyth, P., 2005. A spectral clustering approach to finding communities in graphs. In: Proceedings of the 2005 SIAM International Conference on Data Mining. Newport Beach, CA: SIAM, 274–285.
- Wolf, L.J., Knaap, E., and Rey, S., 2019. Geosilhouettes: geographical measures of cluster fit. Environment and Planning B: Urban Analytics and City Science, 48 (3), 521–539.
- Wu, X. and Murray, A.T., 2008. A new approach to quantifying spatial contiguity using graph theory and spatial interaction. International Journal of Geographical Information Science, 22 (4), 387–407, April. 1365-8816, 1362-3087. doi:https://doi.org/10.1080/13658810701405615.
- Yuan, S., et al., 2015. Constrained spectral clustering for regionalization: exploring the trade-off between spatial contiguity and landscape homogeneity. In: Data Science and Advanced Analytics (DSAA), 2015. 36678 2015. IEEE International Conference On. IEEE, Paris, France. 1–10.
- Zhu, D., et al., 2020. Understanding place characteristics in geographic contexts through graph convolutional neural networks. Annals of the American Association of Geographers, 110 (2), 408–420, March. 2469-4452. doi:https://doi.org/10.1080/24694452.2019.1694403.
- Zwiers, M., Kleinhans, R., and Van Ham, M., 2016. The path-dependency of low income neighbourhood trajectories: an approach for analysing neighbourhood change. Applied Spatial Analysis Policy, 25, 1–18.