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Research Articles

An adaptive detection of multilevel co-location patterns based on natural neighborhoods

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Pages 556-581 | Received 16 Dec 2019, Accepted 24 May 2020, Published online: 16 Jun 2020

References

  • Barua, S. and Sander, J., 2014. Mining statistically significant co-location and segregation patterns. IEEE Transactions on Knowledge and Data Engineering, 26 (5), 1185–1199. doi:10.1109/TKDE.2013.88.
  • Bembenikr, R. and Rybiński, H., 2009. FARICS: a method of mining spatial association rules and collocations using clustering and Delaunay diagrams. Journal of Intelligent Information Systems, 33 (1), 41–64. doi:10.1007/s10844-008-0076-1.
  • Boucher, D.H., James, S., and Keeler, K.H., 1982. The ecology of mutualism. Annual Review of Ecology and Systematics, 13 (1), 315–347. doi:10.1146/annurev.es.13.110182.001531.
  • Cai, J.N., et al., 2018. Adaptive detection of statistically significant regional spatial co-location patterns. Computers, Environment and Urban Systems, 68, 53–63. doi:10.1016/j.compenvurbsys.2017.10.003.
  • Cai, J.N., et al., 2019. Nonparametric significance test for discovery of network-constrained spatial co-location patterns. Geographical Analysis, 51 (1), 3–22. doi:10.1111/gean.12155.
  • Celik, M., Kang, J.M., and Shekhar, S., 2007. Zonal co-location pattern discovery with dynamic parameters. In: Proceedings of the 7th IEEE International Conference on Data Mining, 28–31, October, Omaha, NE, IEEE.
  • Clark, P. and Evans, F., 1954. Distance to nearest neighbor as a measure of spatial relationships in populations. Ecology, 35 (4), 445–453. doi:10.2307/1931034.
  • Cormen, T.H., et al., 2009. Introduction to algorithms. 3rd edn. London: The MIT Press.
  • Deng, M., et al., 2017a. Multi-level method for discovery of regional co-location patterns. International Journal of Geographical Information Science, 31 (9), 1846–1870. doi:10.1080/13658816.2017.1334890.
  • Deng, M., et al., 2017b. Multi-scale approach to mining significant spatial co-location patterns. Transactions in GIS, 21 (5), 1023–1039. doi:10.1111/tgis.12261.
  • Ding, W., et al., 2011. A framework for regional association rule mining and scoping in spatial datasets. Geoinformatica, 15 (1), 1–28. doi:10.1007/s10707-010-0111-6.
  • Edelsbrunner, H., Kirkpatrick, D., and Seidel, R., 1983. On the shape of a set of points in the plane. IEEE Transactions on Information Theory, 29 (4), 551–559. doi:10.1109/TIT.1983.1056714.
  • Estivill-Castro, V. and Lee, I., 2002. Multi-level clustering and its visualization for exploratory spatial analysis. GeoInformatica, 6 (2), 123–152. doi:10.1023/A:1015279009755.
  • Fortin, M.J. and Dale, M.R., 2005. Spatial analysis: a guide for ecologists. Cambridge: Cambridge University Press.
  • Goreaud, F. and Pélissier, R., 2003. Avoiding misinterpretation of biotic interactions with the intertype K12-function: population independence vs. random labelling hypotheses. Journal of Vegetation Science, 14 (5), 681–692.
  • Guo, D. and Mennis, J., 2009. Spatial data mining and geographic knowledge discovery—An introduction. Computers, Environment and Urban Systems, 34 (2), 175. doi:10.1016/j.compenvurbsys.2009.11.001.
  • Guo, D.S. and Wang, H., 2011. Automatic region building for spatial analysis. Transactions in GIS, 15 (s1), 29–45. doi:10.1111/j.1467-9671.2011.01269.x.
  • Huang, Y., Shekhar, S., and Xiong, H., 2004. Discovering colocation patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering, 16 (12), 1472–1485. doi:10.1109/TKDE.2004.90.
  • Hubalek, Z., 1982. Coefficients of association and similarity, based on binary (presence-absence) data: an evaluation. Biological Reviews, 57 (4), 669–689. doi:10.1111/j.1469-185X.1982.tb00376.x.
  • İnkaya, T., Kayalıgil, S., and Özdemirel, N., 2015. An adaptive neighbourhood construction algorithm based on density and connectivity. Pattern Recognition Letters, 52, 17–24. doi:10.1016/j.patrec.2014.09.007
  • Keddy, P.A., 2010. Wetland ecology: principles and conservation. UK: Cambridge University Press.
  • Li, Y. and Shekhar, S., 2018. Local co-location pattern detection: a summary of results. In: Proceedings of the 10th International Conference on Geographic Information Science (GIScience 2018), Article No. 10. Melbourne, Australia, 1–15.
  • Mohan, P., et al., 2011. A neighborhood graph based approach to regional co-location pattern discovery: a summary of results. In: Proceedings of the 19th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, November 1-4, Chicago, IL. New York: ACM, 122–132.
  • Phillips, P. and Lee, I., 2012. Mining co-distribution patterns for large crime datasets. Expert Systems with Applications, 39 (14), 11556–11563. doi:10.1016/j.eswa.2012.03.071.
  • Qian, F., et al., 2014. Mining regional co-location patterns with kNNG. Journal of Intelligent Information Systems, 42 (3), 485–505. doi:10.1007/s10844-013-0280-5.
  • Ripley, B.D., 1976. The second-order analysis of stationary point processes. Journal of Applied Probability, 13 (2), 255–266. doi:10.2307/3212829.
  • Shi, Y., et al., 2016. Adaptive detection of spatial point event outliers using multilevel constrained Delaunay triangulation. Computers, Environment and Urban Systems, 59, 164–183. doi:10.1016/j.compenvurbsys.2016.06.001.
  • Sundaram, V.M. and Thnagavelu, A., 2015. A Delaunay diagram‐based min–max CP‐tree algorithm for spatial data analysis. Wiley Interdisciplinary Reviews. Data Mining and Knowledge Discovery, 5 (3), 142–154.
  • Tan, P.N., et al., 2006. Introduction to data mining. London: Pearson Education, Inc.
  • Wan, Y. and Zhou, J., 2008. KNFCOM-T: a k-nearest features-based co-location pattern mining algorithm for large spatial data sets by using T-trees. International Journal of Business Intelligence and Data Mining, 3 (4), 375–389. doi:10.1504/IJBIDM.2008.022735.
  • Wang, J.M., Hsu, W., and Lee, M.L., 2005. A framework for mining topological patterns in spatio-temporal databases. In: Proceedings of the 14th ACM International Conference on Information and Knowledge Management, October 31-November 5, Bermen, Germany. ACM: 429–436.
  • Wang, L., et al., 2009. An order-clique-based approach for mining maximal co-locations. Information Sciences, 179 (19), 3370–3382. doi:10.1016/j.ins.2009.05.023.
  • Wang, S., et al., 2013. Regional co-locations of arbitrary shapes. In: M.A. Nascimento, et al., ed.. Advances in spatial and temporal databases. SSTD 2013. Lecture notes in computer science. Vol. 8098. Berlin, Heidelberg: Springer, 19–37.
  • Xiao, X.Y., et al., 2008, Density based co-location pattern discovery. In Proceedings of the16th ACMSIGSPATIAL International Conference on Advances in Geographic Information Systems, November 5-7, Irvine, CA. ACM, 102–114.
  • Yang, P.Z., Wang, L.Z., and Wang, X.X., 2019. A MapReduce approach for spatial co-location pattern mining via ordered-clique-growth. Distributed and Parallel Databases. doi:10.1007/s10619-019-07278-7.
  • Yao, X.J., et al., 2016. A fast space-saving algorithm for maximal co-location pattern mining. Expert Systems with Applications, 63, 310–323. doi:10.1016/j.eswa.2016.07.007.
  • Yao, X.J., et al., 2017. A co-location pattern-mining algorithm with a density-weighted distance thresholding consideration. Information Sciences, 396, 144–161. doi:10.1016/j.ins.2017.02.040.
  • Yao, X.J., et al., 2018. A spatial co-location mining algorithm that includes adaptive proximity improvements and distant instance references. International Journal of Geographical Information Science, 32 (5), 980–1005. doi:10.1080/13658816.2018.1431839.
  • Yoo, J.S., et al., 2019. Parallel co-location mining with MapReduce and NoSQL systems. Knowledge and Information Systems. doi:10.1007/s10115-019-01381-y.
  • Yoo, J.S. and Bow, M., 2012. Mining spatial colocation patterns: a different framework. Data Mining and Knowledge Discovery, 24 (1), 159–194. doi:10.1007/s10618-011-0223-0.
  • Yoo, J.S. and Shekhar, S., 2004. A partial-join approach for mining co-location patterns. In: Proceedings of the 12th Annual ACM International Workshop on Geographic Information Systems, November 12-13, Washington, DC. ACM, 241–249.
  • Yoo, J.S. and Shekhar, S., 2006. A joinless approach for mining spatial colocation patterns. IEEE Transactions on Knowledge and Data Engineering, 18 (10), 1323–1327. doi:10.1109/TKDE.2006.150.
  • Yu, W.H., et al., 2017. Spatial co-location pattern mining of facility points-of-interest improved by network neighborhood and distance decay effects. International Journal of Geographical Information Science, 31 (2), 280–296. doi:10.1080/13658816.2016.1194423.
  • Zhou, M., et al., 2019. A visualization approach for discovering colocation patterns. International Journal of Geographical Information Science, 33 (3), 567–592. doi:10.1080/13658816.2018.1550784.
  • Zhou, Y., et al., 2011. Development of percentile estimation formula for skewed distribution. Acta Physica Sinica, 60 (8), 089201.
  • Zimmer, K.D., Hanson, M.A., and Butler, M.G., 2003. Interspecies relationships, community structure, and factors influencing abundance of submerged macrophytes in prairie wetlands. Wetlands, 23 (4), 717–728. doi:10.1672/0277-5212(2003)023[0717:IRCSAF]2.0.CO;2.

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