STICC: a multivariate spatial clustering method for repeated geographic pattern discovery with consideration of spatial contiguity

References

• Akaike, H., 1973. Block toeplitz matrix inversion. SIAM Journal on Applied Mathematics, 24 (2), 234–241.
   Web of Science ®Google Scholar
• Aldstadt, J., 2010. Spatial clustering. In: Handbook of applied spatial analysis. Berlin: Springer, 279–300.
   Google Scholar
• Anderberg, M.R., 1973. Cluster analysis for applications. Vol. 19. New York: Academic Press.
   Google Scholar
• Ankerst, M., et al., 1999. Optics: Ordering points to identify the clustering structure. ACM SIGMOD Record, 28 (2), 49–60.
   Google Scholar
• Aragam, B., and Zhou, Q., 2015. Concave penalized estimation of sparse gaussian Bayesian networks. Journal of Machine Learning Research, 16 (1), 2273–2328.
   Google Scholar
• Assunção, R.M., et al., 2006. Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science, 20 (7), 797–811.
   Web of Science ®Google Scholar
• Aydin, O., et al., 2021. A quantitative comparison of regionalization methods. International Journal of Geographical Information Science, 35 (11), 2287–2315.
   Web of Science ®Google Scholar
• Aydin, O., et al., 2018. Skater-con: unsupervised regionalization via stochastic tree partitioning within a consensus framework using random spanning trees. In: Proceedings of the 2nd ACM SIGSPATIAL international workshop on AI for geographic knowledge discovery, 33–42. New York, NY: ACM.
   Google Scholar
• Bação, F., Lobo, V., and Painho, M., 2005. Self-organizing maps as substitutes for k-means clustering. In: International conference on computational science. Berlin, Heidelberg: Springer, 476–483.
   Google Scholar
• Boyd, S., et al., 2010. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends® in Machine Learning, 3 (1), 1–122.
   Google Scholar
• Candes, E.J., and Tao, T., 2005. Decoding by linear programming. IEEE Transactions on Information Theory, 51 (12), 4203–4215.
   Web of Science ®Google Scholar
• Chen, Y., et al., 2018. Hispatialcluster: a novel high-performance software tool for clustering massive spatial points. Transactions in GIS, 22 (5), 1275–1298.
   Web of Science ®Google Scholar
• Cliff, A.D., and Ord, J.K., 1973. Spatial autocorrelation. London: Pion.
   Google Scholar
• Crucitti, P., Latora, V., and Porta, S., 2006. Centrality measures in spatial networks of urban streets. Physical Review. E, Statistical, Nonlinear, and Soft Matter Physics, 73 (3 Pt 2), 036125.
   PubMed Web of Science ®Google Scholar
• Dacey, M.F., 1965. A review on measures of contiguity for two and k-color maps. Technical report. Evanston, IL: Northwestern University.
   Google Scholar
• Deng, M., et al., 2011. An adaptive spatial clustering algorithm based on delaunay triangulation. Computers, Environment and Urban Systems, 35 (4), 320–332.
   Web of Science ®Google Scholar
• Deng, M., et al., 2019. A density-based approach for detecting network-constrained clusters in spatial point events. International Journal of Geographical Information Science, 33 (3), 466–488.
   Web of Science ®Google Scholar
• Donoho, D.L., 2006. Compressed sensing. IEEE Transactions on Information Theory, 52 (4), 1289–1306.
   Web of Science ®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., Ramos, R., and Suriñach, J., 2007. Supervised regionalization methods: A survey. International Regional Science Review, 30 (3), 195–220.
   Web of Science ®Google Scholar
• Esri 2021. How spatially constrained multivariate clustering works. Available from: https://pro.arcgis.com/en/pro-app/latest/tool-reference/spatial-statistics/how-spatially-constrained-multivariate-clustering-works.htm [Accessed 24 June 2021].
   Google Scholar
• Ester, M., et al., 1996. A density-based algorithm for discovering clusters in large spatial databases with noise. International Conference on Knowledge Discovery and Data Mining, 96, 226–231.
   Google Scholar
• Estivill-Castro, V., and Lee, I., 2002. Argument free clustering for large spatial point-data sets via boundary extraction from delaunay diagram. Computers, Environment and Urban Systems, 26 (4), 315–334.
   Google Scholar
• Friedman, J., Hastie, T., and Tibshirani, R., 2008. Sparse inverse covariance estimation with the graphical Lasso. Biostatistics (Oxford, England), 9 (3), 432–441.
   PubMed Web of Science ®Google Scholar
• Fujino, A., Isozaki, H., and Suzuki, J., 2008. Multi-label text categorization with model combination based on f1-score maximization. In: Proceedings of the third international joint conference on natural language processing: volume-II. Hyderabad, India: IJCNLP.
   Google Scholar
• Gao, S., Janowicz, K., and Couclelis, H., 2017. Extracting urban functional regions from points of interest and human activities on location-based social networks. Transactions in GIS, 21 (3), 446–467.
   Web of Science ®Google Scholar
• Goodchild, M., 2001. Issues in spatially explicit modeling. In: Agent-based models of land-use and land-cover change, October 4–7, 13–17. Irvine, CA: NCGIA.
   Google Scholar
• Goodchild, M.F., 2004. Giscience, geography, form, and process. Annals of the Association of American Geographers, 94 (4), 709–714.
   Web of Science ®Google Scholar
• Grekousis, G., 2021. Local fuzzy geographically weighted clustering: a new method for geodemographic segmentation. International Journal of Geographical Information Science, 35 (1), 152–174.
   Web of Science ®Google Scholar
• Guha, S., Rastogi, R., and Shim, K., 1998. Cure: an efficient clustering algorithm for large databases. ACM SIGMOD Record, 27 (2), 73–84.
   Google Scholar
• Hallac, D., et al., 2017. Toeplitz inverse covariance-based clustering of multivariate time series data. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 215–223. New York, NY: ACM.
   Google Scholar
• Hastie, T., Tibshirani, R., and Wainwright, M., 2015. Statistical learning with sparsity: the Lasso and generalizations. Boca Raton, FL: Chapman and Hall/CRC.
   Google Scholar
• Henriques, R., Bação, F., and Lobo, V., 2009. Geosom suite: a tool for spatial clustering. In: International conference on computational science and its applications. Springer, 453–466.
   Google Scholar
• Hinneburg, A., and Keim, D.A., 1998. An efficient approach to clustering in large multimedia databases with noise. KDD, 98, 58–65.
   Google Scholar
• Hsieh, C.-J., et al., 2014. QUIC: quadratic approximation for sparse inverse covariance estimation. Journal of Machine Learning Research, 15 (83), 2911–2947.
   Google Scholar
• Hu, Y., et al., 2015. Extracting and understanding urban areas of interest using geotagged photos. Computers, Environment and Urban Systems, 54, 240–254.
   Web of Science ®Google Scholar
• Janowicz, K., et al., 2020. GeoAI: spatially explicit artificial intelligence techniques for geographic knowledge discovery and beyond. International Journal of Geographical Information Science, 34 (4), 625–636.
   Web of Science ®Google Scholar
• Kang, Y., et al., 2019. Extracting human emotions at different places based on facial expressions and spatial clustering analysis. Transactions in GIS, 23 (3), 450–480.
   Web of Science ®Google Scholar
• Kodinariya, T.M., and Makwana, P.R., 2013. Review on determining number of cluster in k-means clustering. International Journal, 1 (6), 90–95.
   Google Scholar
• Koller, D., and Friedman, N., 2009. Probabilistic graphical models: principles and techniques. Cambridge, MA: MIT Press.
   Google Scholar
• Liqiang, Z., et al., 2013. A spatial cognition-based urban building clustering approach and its applications. International Journal of Geographical Information Science, 27 (4), 721–740.
   Web of Science ®Google Scholar
• Liu, K., et al., 2020. Investigating urban metro stations as cognitive places in cities using points of interest. Cities, 97, 102561.
   Web of Science ®Google Scholar
• Liu, Q., et al., 2012. A density-based spatial clustering algorithm considering both spatial proximity and attribute similarity. Computers & Geosciences, 46, 296–309.
   Web of Science ®Google Scholar
• Liu, Q., et al., 2021. SNN_flow: a shared nearest-neighbor-based clustering method for inhomogeneous origin-destination flows. International Journal of Geographical Information Science, 36 (2), 1–27.
   Web of Science ®Google Scholar
• Liu, X., Huang, Q., and Gao, S., 2019. Exploring the uncertainty of activity zone detection using digital footprints with multi-scaled dbscan. International Journal of Geographical Information Science, 33 (6), 1196–1223.
   Web of Science ®Google Scholar
• MacQueen, J., et al., 1967. Some methods for classification and analysis of multivariate observations. Berkeley Symposium on Mathematical Statistics and Probability, 1, 281–297.
   Google Scholar
• Mai, G., et al., 2018. Adcn: An anisotropic density-based clustering algorithm for discovering spatial point patterns with noise. Transactions in GIS, 22 (1), 348–369.
   Web of Science ®Google Scholar
• Miller, H.J., and Han, J. 2009. Geographic data mining and knowledge discovery. New York: CRC Press.
   Google Scholar
• Moayedi, A., Abbaspour, R.A., and Chehreghan, A., 2019. An evaluation of the efficiency of similarity functions in density-based clustering of spatial trajectories. Annals of GIS, 25 (4), 313–327.
   Web of Science ®Google Scholar
• Murray, A.T., and Estivill-Castro, V., 1998. Cluster discovery techniques for exploratory spatial data analysis. International Journal of Geographical Information Science, 12 (5), 431–443.
   Web of Science ®Google Scholar
• Murray, A.T., and Shyy, T.-K., 2000. Integrating attribute and space characteristics in choropleth display and spatial data mining. International Journal of Geographical Information Science, 14 (7), 649–667.
   Web of Science ®Google Scholar
• Ng, I., Ghassami, A., and Zhang, K., 2020. On the role of sparsity and DAG constraints for learning linear DAGs. In: Advances in Neural Information Processing Systems. Vancouver, Canada: NeurIPS.
   Google Scholar
• Nosovskiy, G.V., Liu, D., and Sourina, O., 2008. Automatic clustering and boundary detection algorithm based on adaptive influence function. Pattern Recognition, 41 (9), 2757–2776.
   Web of Science ®Google Scholar
• Novikov, A.V., 2019. Pyclustering: data mining library. Journal of Open Source Software, 4 (36), 1230.
   Google Scholar
• Ogbuabor, G., and Ugwoke, F., 2018. Clustering algorithm for a healthcare dataset using silhouette score value. International Journal of Computer Science and Information Technology, 10 (2), 27–37.
   Google Scholar
• Pei, T., et al., 2015. Density-based clustering for data containing two types of points. International Journal of Geographical Information Science, 29 (2), 175–193.
   Web of Science ®Google Scholar
• Pei, T., et al., 2006. A new approach to the nearest-neighbour method to discover cluster features in overlaid spatial point processes. International Journal of Geographical Information Science, 20 (2), 153–168.
   Web of Science ®Google Scholar
• Perruchet, C., 1983. Constrained agglomerative hierarchical classification. Pattern Recognition, 16 (2), 213–217.
   Web of Science ®Google Scholar
• Rao, J., et al., 2020. Q. LSTM-TrajGAN: a deep learning approach to trajectory privacy protection. In:11th International Conference on Geographic Information Science (GIScience 2021)-Part I. Schloss Dagstuhl-Leibniz-Zentrum für Informatik, Vol. 117, 12:1–12:17.
   Google Scholar
• Rasmussen, C.E., 1999. The infinite Gaussian mixture model. NIPS, 12, 554–560.
   Google Scholar
• Rey, S.J., and Anselin, L., 2010. Pysal: a python library of spatial analytical methods. In: Handbook of applied spatial analysis. Berlin: Springer, 175–193.
   Google Scholar
• Rue, H., and Held, L. 2005. Gaussian Markov random fields: theory and applications (monographs on statistics and applied probability). Boca Raton, FL: Chapman Hall/CRC.
   Google Scholar
• Scheinberg, K., Ma, S., and Goldfarb, D., 2010. Sparse inverse covariance selection via alternating linearization methods. In: Advances in neural information processing systems, 23. Vancouver, Canada: NIPS.
   Google Scholar
• Soliman, A., et al., 2017. Social sensing of urban land use based on analysis of twitter users’ mobility patterns. PLoS One, 12 (7), e0181657.
   PubMed Web of Science ®Google Scholar
• Steinley, D., 2004. Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9 (3), 386–396.
   PubMed Web of Science ®Google Scholar
• Sugar, C.A., and James, G.M., 2003. Finding the number of clusters in a dataset: an information-theoretic approach. Journal of the American Statistical Association, 98 (463), 750–763.
   Web of Science ®Google Scholar
• Syakur, M., et al., 2018. Integration k-means clustering method and elbow method for identification of the best customer profile cluster. In: IOP conference series: materials science and engineering, Vol. 336. Surabaya, Indonesia: IOP Publishing, 012017.
   Google Scholar
• Tibshirani, R., 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological), 58 (1), 267–288.
   Web of Science ®Google Scholar
• Tobler, W.R., 1970. A computer movie simulating urban growth in the Detroit region. Economic Geography, 46 (sup1), 234–240.
   Web of Science ®Google Scholar
• Viterbi, A.J., 1967. Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Transactions on Information Theory, 13 (2), 260–269.
   Web of Science ®Google Scholar
• Wang, F., 2020. Why public health needs GIS: a methodological overview. Annals of GIS, 26 (1), 1–12.
   PubMed Web of Science ®Google Scholar
• Webster, R., and Burrough, P., 1972. Computer-based soil mapping of small areas from sample data: I. multivariate classification and ordination. Journal of Soil Science, 23 (2), 210–221.
   Google Scholar
• Wilson, J.P., et al., 2020. A five-star guide for achieving replicability and reproducibility when working with GIS software and algorithms. Annals of the American Association of Geographers, 111 (5), 1311–1317.
   Web of Science ®Google Scholar
• Wolf, L.J., 2021. Spatially–encouraged spectral clustering: a technique for blending map typologies and regionalization. International Journal of Geographical Information Science, 35 (11), 2356–2373.
   Web of Science ®Google Scholar
• Wu, L., et al., 2014. Intra-urban human mobility and activity transition: evidence from social media check-in data. PLoS One, 9 (5), e97010.
   PubMed Web of Science ®Google Scholar
• Xing, H., and Meng, Y., 2018. Integrating landscape metrics and socioeconomic features for urban functional region classification. Computers, Environment and Urban Systems, 72, 134–145.
   Web of Science ®Google Scholar
• Yan, B., et al., 2017. From ITDL to Place2Vec: reasoning about place type similarity and relatedness by learning embeddings from augmented spatial contexts. In: Proceedings of the 25th ACM SIGSPATIAL international conference on advances in geographic information systems, 1–10. New York, NY: ACM.
   Google Scholar
• Yang, D., et al., 2014. Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Transactions on Systems, Man, and Cybernetics: Systems, 45 (1), 129–142.
   Web of Science ®Google Scholar
• Yuan, N.J., et al., 2015. Discovering urban functional zones using latent activity trajectories. IEEE Transactions on Knowledge and Data Engineering, 27 (3), 712–725.
   Web of Science ®Google Scholar
• Zhang, T., Ramakrishnan, R., and Livny, M., 1996. Birch: an efficient data clustering method for very large databases. ACM SIGMOD Record, 25 (2), 103–114.
   Google Scholar
• Zhao, Z., et al., 2016. Understanding the bias of call detail records in human mobility research. International Journal of Geographical Information Science, 30 (9), 1738–1762.
   Web of Science ®Google Scholar
• Zhu, A.-X., et al., 2018. Spatial prediction based on third law of geography. Annals of GIS, 24 (4), 225–240.
   Web of Science ®Google Scholar
• Zhu, R., Janowicz, K., and Mai, G., 2019. Making direction a first-class citizen of tobler’s first law of geography. Transactions in GIS, 23 (3), 398–416.
   Web of Science ®Google Scholar
• Zou, H., 2006. The adaptive Lasso and its oracle properties. Journal of the American Statistical Association, 101 (476), 1418–1429.
   Web of Science ®Google Scholar