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Spatial and Spatiotemporal Big Data Science

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Spatial Big Data Science

Abstract

This chapter provides an overview of spatial and spatiotemporal big data science. This chapter starts with the unique characteristics of spatial and spatiotemporal data, and their statistical properties. Then, this chapter reviews recent computational techniques and tools in spatial and spatiotemporal data science, focusing on several major pattern families, including spatial and spatiotemporal outliers, spatial and spatiotemporal association and tele-connection, spatial and spatiotemporal prediction, partitioning and summarization, as well as hotspot and change detection.

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References

  1. S. Shekhar, Z. Jiang, R.Y. Ali, E. Eftelioglu, X. Tang, V.M.V. Gunturi, X. Zhou, Spatiotemporal data mining: a computational perspective. ISPRS Int. J. Geo-Inf. 4(4), 2306 (2015)

    Article  Google Scholar 

  2. K. Koperski, J. Adhikary, J. Han, Spatial data mining: progress and challenges survey paper, in Proceedings of ACM SIGMOD Workshop on Research Issues on Data Mining and Knowledge Discovery, Montreal, Canada (Citeseer, 1996), pp. 1–10

    Google Scholar 

  3. M. Ester, H.-P. Kriegel, J. Sander, Spatial data mining: a database approach, in Proceedings of Fifth Symposium on Rules in Geographic Information Databases (1997)

    Google Scholar 

  4. S. Shekhar, M.R. Evans, J.M. Kang, P. Mohan, Identifying patterns in spatial information: a survey of methods. Wiley Interdis. Rev. Data Min. Knowl. Disc. 1(3), 193–214 (2011)

    Article  Google Scholar 

  5. H.J. Miller, J. Han, Geographic Data Mining and Knowledge Discovery (Taylor & Francis Inc., Bristol, 2001)

    Book  Google Scholar 

  6. H.J. Miller, J. Han, in Geographic Data Mining and Knowledge Discovery (CRC Press, 2009)

    Google Scholar 

  7. S. Shekhar, P. Zhang, Y. Huang, R.R. Vatsavai, Trends in spatial data mining, in Data Mining: Next Generation Challenges and Future Directions (2003), pp. 357–380

    Google Scholar 

  8. S. Kisilevich, F. Mansmann, M. Nanni, S. Rinzivillo, in Spatio-Temporal Clustering (Springer, Berlin, 2010)

    Google Scholar 

  9. C.C. Aggarwal, in Outlier Analysis (Springer Science & Business Media, 2013)

    Google Scholar 

  10. X. Zhou, S. Shekhar, R.Y. Ali, Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey. Wiley Interdis. Rev. Data Min. Knowl. Disc. 4(1), 1–23 (2014)

    Article  Google Scholar 

  11. A. Karpatne, Z. Jiang, R.R. Vatsavai, S. Shekhar, V. Kumar, Monitoring land-cover changes: A machine-learning perspective. IEEE Geosci. Rem. Sens. Mag. 4(2), 8–21 (2016)

    Google Scholar 

  12. S. Shekhar, S. Chawla, in Spatial Databases: A Tour (Prentice Hall, Englewood-Cliffs, 2003)

    Google Scholar 

  13. M. Worboys, M. Duckham, in GIS: A Computing Perspective, 2nd edn. (CRC, 2004). ISBN: 978-0415283755

    Google Scholar 

  14. Z. Li, J. Chen, E. Baltsavias, in Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book, vol 7 (CRC Press, 2008)

    Google Scholar 

  15. M. Yuan, Temporal gis and spatio-temporal modeling, in Proceedings of Third International Conference Workshop on Integrating GIS and Environment Modeling, Santa Fe, NM (1996)

    Google Scholar 

  16. J.F. Allen, Towards a general theory of action and time. Artif. Intell. 23(2), 123–154 (1984)

    Article  MATH  Google Scholar 

  17. B. George, S. Kim, S. Shekhar, Spatio-temporal network databases and routing algorithms: a summary of results, in Proceedings of International Symposium on Spatial and Temporal Databases (SSTD’07) (Boston, 2007)

    Google Scholar 

  18. B. George, S. Shekhar, Time aggregated graphs: a model for spatio-temporal network, in Proceedings of the Workshops (CoMoGIS) at the 25th International Conference on Conceptual Modeling (ER2006) (Tucson, AZ, USA, 2006)

    Google Scholar 

  19. A.E. Gelfand, P. Diggle, P. Guttorp, M. Fuentes, in Handbook of Spatial Statistics (CRC Press, 2010)

    Google Scholar 

  20. C.E. Campelo, B. Bennett, in Representing and Reasoning About Changing Spatial Extensions of Geographic Features (Springer, Berlin, 2013)

    Google Scholar 

  21. P. Tan, M. Steinbach, V. Kumar, et al., in Introduction to Data Mining (Pearson Addison Wesley Boston, 2006)

    Google Scholar 

  22. P. Bolstad, in GIS Fundamentals: A First Text on GIS (Eider Press, 2002)

    Google Scholar 

  23. A.R. Ganguly, K. Steinhaeuser, Data mining for climate change and impacts, in ICDM Workshops (2008), pp. 385–394

    Google Scholar 

  24. M. Erwig, M. Schneider, F. Hagen, Spatio-temporal predicates. IEEE Trans. Knowl. Data Eng. 14, 881–901 (2002)

    Article  Google Scholar 

  25. J. Chen, R. Wang, L. Liu, J. Song, Clustering of trajectories based on hausdorff distance, in 2011 International Conference on Electronics, Communications and Control (ICECC) (IEEE, 2011), pp. 1940–1944

    Google Scholar 

  26. Z. Zhang, K. Huang, T. Tan, Comparison of similarity measures for trajectory clustering in outdoor surveillance scenes, in 18th International Conference on Pattern Recognition, 2006. ICPR 2006, vol. 3 (IEEE, 2006), pp. 1135–1138

    Google Scholar 

  27. P. Zhang, Y. Huang, S. Shekhar, V. Kumar, Correlation analysis of spatial time series datasets: a filter-and-refine approach, in Advances in Knowledge Discovery and Data Mining (Springer, Berlin, 2003), pp. 532–544

    Google Scholar 

  28. J. Kawale, S. Chatterjee, D. Ormsby, K. Steinhaeuser, S. Liess, V. Kumar, Testing the significance of spatio-temporal teleconnection patterns, in KDD (2012), pp. 642–650

    Google Scholar 

  29. M. Celik, S. Shekhar, J.P. Rogers, J.A. Shine, J.S. Yoo, Mixed-drove spatio-temporal co-occurrence pattern mining: a summary of results, in ICDM ’06: Proceedings of the Sixth International Conference on Data Mining (IEEE Computer Society, Washington, DC, USA, 2006), pp. 119–128

    Google Scholar 

  30. K.G. Pillai, R.A. Angryk, B. Aydin, A filter-and-refine approach to mine spatiotemporal co-occurrences, in SIGSPATIAL/GIS (2013), pp. 104–113

    Google Scholar 

  31. P. Mohan, S. Shekhar, J.A. Shine, J.P. Rogers, Cascading spatio-temporal pattern discovery. IEEE Trans. Knowl. Data Eng. 24(11), 1977–1992 (2012)

    Article  Google Scholar 

  32. P. Mohan, S. Shekhar, J.A. Shine, J.P. Rogers, Cascading spatio-temporal pattern discovery: a summary of results in SDM (2010), pp. 327–338

    Google Scholar 

  33. Y. Huang, L. Zhang, P. Zhang, A framework for mining sequential patterns from spatio-temporal event data sets. IEEE Trans. Knowl. Data Eng. 20(4), 433–448 (2008)

    Article  Google Scholar 

  34. Y. Huang, L. Zhang, P. Zhang, Finding sequential patterns from a massive number of spatio-temporal events, in SDM (2006), pp. 634–638

    Google Scholar 

  35. J. Mennis, R. Viger, C.D. Tomlin, Cubic map algebra functions for spatio-temporal analysis. Cartography Geogr. Inf. Sci. 32(1), 17–32 (2005)

    Article  Google Scholar 

  36. D.G. Brown, R. Riolo, D.T. Robinson, M. North, W. Rand, Spatial process and data models: toward integration of agent-based models and gis. J. Geogr. Syst. 7(1), 25–47 (2005)

    Article  Google Scholar 

  37. J. Quinlan, in C4.5: Programs for Machine Learning (Morgan Kaufmann Publishers, 1993)

    Google Scholar 

  38. V. Varnett, T. Lewis, in Outliers in Statistical Data (Wiley, New York, 1994)

    Google Scholar 

  39. T. Agarwal, R. Imielinski, A. Swami, Mining association rules between sets of items in large databases, in Proceedings of the ACM SIGMOD Conference on Management of Data (Washington, D.C., 1993)

    Google Scholar 

  40. R. Agrawal, R. Srikant, Fast algorithms for mining association rules, in Proceedings of Very Large Databases (1994)

    Google Scholar 

  41. A. Jain, R. Dubes, in Algorithms for Clustering Data (Prentice Hall, 1988)

    Google Scholar 

  42. S. Banerjee, B. Carlin, A. Gelfand, in Hierarchical Modeling and Analysis for Spatial Data (Chapman & Hall, 2004)

    Google Scholar 

  43. O. Schabenberger, C. Gotway, in Statistical Methods for Spatial Data Analysis (Chapman and Hall, 2005)

    Google Scholar 

  44. N.A.C. Cressie, in Statistics for Spatial Data (Wiley, New York, 1993)

    Google Scholar 

  45. S. Banerjee, B.P. Carlin, A.E. Gelfrand, in Hierarchical Modeling and Analysis for Spatial Data (CRC Press, 2003)

    Google Scholar 

  46. A. Fotheringham, C. Brunsdon, M. Charlton, in Geographically Weighted Regression: The Analysis of Spatially Varying Relationships (Wiley, New York, 2002)

    Google Scholar 

  47. C.E. Warrender, M.F. Augusteijn, Fusion of image classifications using Bayesian techniques with Markov rand fields. Int. J. Remote Sens. 20(10), 1987–2002 (1999)

    Article  Google Scholar 

  48. N. Cressie, Statistics for Spatial Data, Revised edn. (Wiley, New York, 1993)

    MATH  Google Scholar 

  49. L. Anselin, Local indicators of spatial association-lisa. Geograp. Anal. 27(2), 93–155 (1995)

    Article  Google Scholar 

  50. S. Openshaw, in The Modifiable Areal Unit Problem, (OCLC, 1983), ISBN: 0860941345

    Google Scholar 

  51. B.D. Ripley, Modelling spatial patterns, inJournal of the Royal Statistical Society. Series B (Methodological) (1977), pp. 172–212

    Google Scholar 

  52. E. Marcon, F. Puech, et al., Generalizing Ripley’s k function to inhomogeneous populations. Technical report (Mimeo, 2003)

    Google Scholar 

  53. M. Kulldorff, A spatial scan statistic. Commun. Stat. Theor. Methods 26(6), 1481–1496 (1997)

    Article  MathSciNet  MATH  Google Scholar 

  54. S.N. Chiu, D. Stoyan, W.S. Kendall, J. Mecke, in Stochastic Geometry and Its Applications (Wiley, 2013)

    Google Scholar 

  55. X. Guyon, in Random Fields on a Network: Modeling, Statistics, and Applications (Springer Science & Business Media, 1995)

    Google Scholar 

  56. A. Okabe, H. Yomono, M. Kitamura, Statistical analysis of the distribution of points on a network. Geograph. Anal. 27, 152–175 (1995)

    Google Scholar 

  57. A. Okabe, K. Sugihara, in Spatial Analysis Along Networks: Statistical and Computational Methods (Wiley, New York, 2012)

    Google Scholar 

  58. A. Okabe, K. Okunuki, S. Shiode, The sanet toolbox: new methods for network spatial analysis. Trans. GIS 10(4), 535–550 (2006)

    Article  Google Scholar 

  59. N. Cressie, C.K. Wikle, in Statistics for Spatio-Temporal Data (Wiley, New York, 2011)

    Google Scholar 

  60. R.H. Shumway, D.S. Stoffer, in Time Series Analysis and Its Applications: With R Examples (Springer Science & Business Media, 2010)

    Google Scholar 

  61. P.C. Kyriakidis, A.G. Journel, Geostatistical space-time models: a review. Math. Geol. 31(6), 651–684 (1999)

    Article  MathSciNet  MATH  Google Scholar 

  62. N.A.C. Cressie, in Statistics for Spatial Data (Wiley, New York, 1993), ISBN: 978-0471002550

    Google Scholar 

  63. V. Barnett, T. Lewis, in Outliers in Statistical Data, 3rd edn. (Wiley, New York, 1994)

    Google Scholar 

  64. V. Chandola, A. Banerjee, V. Kumar, Anomaly detection: a survey. ACM Comput. Surv. 41(3), 15:1–15:58 (2009)

    Google Scholar 

  65. S. Shekhar, C. Lu, P. Zhang, A unified approach to detecting spatial outliers. GeoInformatica 7(2), 139–166 (2003)

    Google Scholar 

  66. J. Haslett, R. Bradley, P. Craig, A. Unwin, G. Wills, Dynamic graphics for exploring spatial data with application to locating global and local anomalies, in American Statistician (1991), pp. 234–242

    Google Scholar 

  67. A. Luc, Exploratory spatial data analysis and geographic information systems, in New Tools for Spatial Analysis, ed. by M. Painho (1994), pp. 45–54

    Google Scholar 

  68. D. Chen, C.-T. Lu, Y. Kou, F. Chen, On detecting spatial outliers. GeoInformatica 12(4), 455–475 (2008)

    Article  Google Scholar 

  69. C.-T. Lu, D. Chen, Y. Kou, Detecting spatial outliers with multiple attributes, in ICTAI ’03: Proceedings of the 15th IEEE International Conference on Tools with Artificial Intelligence (IEEE Computer Society, Washington, DC, USA, 2003), p. 122

    Google Scholar 

  70. Y. Kou, C.-T. Lu, D. Chen, Spatial weighted outlier detection, in SDM (2006), pp. 614–618

    Google Scholar 

  71. X. Liu, F. Chen, C.-T. Lu, On detecting spatial categorical outliers. GeoInformatica 18(3), 501–536 (2014)

    Article  Google Scholar 

  72. E. Schubert, A. Zimek, H.-P. Kriegel, Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection. Data Min. Knowl. Discov. 28(1), 190–237 (2014)

    Article  MathSciNet  MATH  Google Scholar 

  73. M. Wu, C. Jermaine, S. Ranka, X. Song, J. Gums, A model-agnostic framework for fast spatial anomaly detection. TKDD 4(4), 20 (2010)

    Article  Google Scholar 

  74. A.M. Sainju, Z. Jiang. Grid-based co-location mining algorithms on GPU for big spatial event data: a summary of results, in Proceedings of International Symposium on Spatial and Temporal Databases (SSTD), (2017 to appear)

    Google Scholar 

  75. J.M. Kang, S. Shekhar, C. Wennen, P. Novak, Discovering flow anomalies: a SWEET approach, in International Conference on Data Mining (2008)

    Google Scholar 

  76. Y. Huang, S. Shekhar, H. Xiong, Discovering co-location patterns from spatial datasets: a general approach. IEEE Trans. Knowl. Data Eng. (TKDE) 16(12), 1472–1485 (2004)

    Article  Google Scholar 

  77. M. Celik, S. Shekhar, J.P. Rogers, J.A. Shine, Mixed-drove spatiotemporal co-occurrence pattern mining. IEEE Trans. Knowl. Data Eng. 20(10), 1322–1335 (2008)

    Article  Google Scholar 

  78. Y. Chou, in Exploring Spatial Analysis in Geographic Information System (Onward Press, 1997)

    Google Scholar 

  79. K. Koperski, J. Han, Discovery of Spatial Association Rules in Geographic Information Databases, in Proceedings of Fourth International Symposium on Large Spatial Databases (Maine, 1995), pp. 47–66

    Google Scholar 

  80. Y. Morimoto, Mining frequent neighboring class sets in spatial databases, in ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (2001)

    Google Scholar 

  81. H. Xiong, S. Shekhar, Y. Huang, V. Kumar, X. Ma, J.S. Yoo, A framework for discovering co-location patterns in data sets with extended spatial objects, in SDM (2004), pp. 78–89

    Google Scholar 

  82. Y. Huang, J. Pei, H. Xiong, Mining co-location patterns with rare events from spatial data sets. GeoInformatica 10(3), 239–260 (2006)

    Article  Google Scholar 

  83. S. Wang, Y. Huang, X.S. Wang, Regional co-locations of arbitrary shapes, in SSTD (2013), pp. 19–37

    Google Scholar 

  84. W. Ding, C.F. Eick, X. Yuan, J. Wang, J.-P. Nicot, A framework for regional association rule mining and scoping in spatial datasets. GeoInformatica 15(1), 1–28 (2011)

    Article  Google Scholar 

  85. P. Mohan, S. Shekhar, J.A. Shine, J.P. Rogers, Z. Jiang, N. Wayant, A neighborhood graph based approach to regional co-location pattern discovery: a summary of results, in GIS (2011), pp. 122–132

    Google Scholar 

  86. S. Barua, J. Sander, Mining statistically significant co-location and segregation patterns. IEEE Trans. Knowl. Data Eng. 26(5), 1185–1199 (2014)

    Article  Google Scholar 

  87. J.S. Yoo, S. Shekhar, A joinless approach for mining spatial colocation patterns. IEEE Trans. Knowl. Data Eng. (TKDE) 18(10), 1323–1337 (2006)

    Google Scholar 

  88. H. Cao, N. Mamoulis, D.W. Cheung, Discovery of collocation episodes in spatiotemporal data, in ICDM (2006), pp. 823–827

    Google Scholar 

  89. H. Cao, N. Mamoulis, D.W. Cheung, Mining frequent spatio-temporal sequential patterns, in ICDM (2005), pp. 82–89

    Google Scholar 

  90. F. Verhein, Mining complex spatio-temporal sequence patterns, in SDM (2009), pp. 605–616

    Google Scholar 

  91. L.A. Tang, Y. Zheng, J. Yuan, J. Han, A. Leung, W.-C. Peng, T.F.L. Porta, A framework of traveling companion discovery on trajectory data streams. ACM TIST 5(1), 3 (2013)

    Google Scholar 

  92. W.R. Tobler, A computer movie simulating urban growth in the detroit region. Econ. Geograph. 46, 234–240 (1970)

    Article  Google Scholar 

  93. I. Vainer, S. Kraus, G. Kaminka, H. Slovin, Scalable classification in large scale spatiotemporal domains applied to voltage-sensitive dye imaging, in Ninth IEEE International Conference on Data Mining, 2009. ICDM ’09 (2009), pp. 543–551

    Google Scholar 

  94. M. Ceci, A. Appice, D. Malerba, Spatial associative classification at different levels of granularity: a probabilistic approach, in PKDD (2004), pp. 99–111

    Google Scholar 

  95. W. Ding, T.F. Stepinski, J. Salazar, Discovery of geospatial discriminating patterns from remote sensing datasets, in SDM (SIAM, 2009), pp. 425–436

    Google Scholar 

  96. R. Frank, M. Ester, A.J. Knobbe, A multi-relational approach to spatial classification, in KDD (2009), pp. 309–318

    Google Scholar 

  97. M.D. Twa, S. Parthasarathy, T.W. Raasch, M. Bullimore, Decision tree classification of spatial data patterns from videokeratography using zernicke polynomials, in SDM (2003), pp. 3–12

    Google Scholar 

  98. J. Li, A.D. Heap, A review of comparative studies of spatial interpolation methods in environmental sciences: performance and impact factors. Ecol. Inf. 6(3), 228–241 (2011)

    Article  Google Scholar 

  99. S. Bhattacharjee, P. Mitra, S.K. Ghosh, Spatial interpolation to predict missing attributes in GIS using semantic kriging. IEEE Trans. Geosci. Remote Sens. 52(8), 4771–4780 (2014)

    Article  Google Scholar 

  100. A.K. Bhowmik, P. Cabral, Statistical evaluation of spatial interpolation methods for small-sampled region: a case study of temperature change phenomenon in bangladesh, in Computational Science and Its Applications-ICCSA 2011 (Springer, Berlin, 2011), pp. 44–59

    Google Scholar 

  101. S. Li, in A Markov Random Field Modeling (Computer Vision Publisher, Springer, 1995)

    Google Scholar 

  102. S. Shekhar, P.R. Schrater, R.R. Vatsavai, W. Wu, S. Chawla, Spatial Contextual Classification and Prediction Models for Mining Geospatial Data. IEEE Trans. Multimedia 4(2), 174–188 (2002)

    Google Scholar 

  103. C.-H. Lee, R. Greiner, O.R. Zaïane, Efficient spatial classification using decoupled conditional random fields, in PKDD (2006), pp. 272–283

    Google Scholar 

  104. L. Anselin, Spatial Econometrics: Methods and Models (Kluwer, Dordrecht, 1988)

    Book  MATH  Google Scholar 

  105. S. Chawla, S. Shekhar, W.-L. Wu, U. Ozesmi, Modeling spatial dependencies for mining geospatial data. ACM SIGMOD Workshop Res. Issues Data Min. Knowl. Disc. 70–77, 2000 (2000)

    Google Scholar 

  106. S. Chawla, S. Shekhar, W. Wu, U. Ozesmi, Modeling spatial dependencies for mining geospatial data, in 1st SIAM International Conference on Data Mining (2001)

    Google Scholar 

  107. A. Liu, G. Jun, J. Ghosh, Spatially cost-sensitive active learning, in SDM (SIAM, 2009), pp. 814–825

    Google Scholar 

  108. K. Subbian, A. Banerjee, Climate multi-model regression using spatial smoothing, in SDM (2013), pp. 324–332

    Google Scholar 

  109. A. McGovern, N. Troutman, R.A. Brown, J.K. Williams, J. Abernethy, Enhanced spatiotemporal relational probability trees and forests. Data Min. Knowl. Discov. 26(2), 398–433 (2013)

    Article  MathSciNet  Google Scholar 

  110. J.-G. Lee, J. Han, X. Li, H. Cheng, Mining discriminative patterns for classifying trajectories on road networks. IEEE Trans. Knowl. Data Eng. 23(5), 713–726 (2011)

    Article  Google Scholar 

  111. A. Noulas, S. Scellato, N. Lathia, C. Mascolo, Mining user mobility features for next place prediction in location-based services, in ICDM (2012), pp. 1038–1043

    Google Scholar 

  112. J.J.-C. Ying, W.-C. Lee, V.S. Tseng, Mining geographic-temporal-semantic patterns in trajectories for location prediction. ACM TIST 5(1), 2 (2013)

    Google Scholar 

  113. H. Cheng, J. Ye, Z. Zhu, What’s your next move: User activity prediction in location-based social networks, in SDM (2013), pp. 171–179

    Google Scholar 

  114. J.-D. Zhang, C.-Y. Chow, iGSLR: personalized geo-social location recommendation: a kernel density estimation approach, in SIGSPATIAL/GIS (2013), pp. 324–333

    Google Scholar 

  115. B. Liu, Y. Fu, Z. Yao, H. Xiong, Learning geographical preferences for point-of-interest recommendation, in KDD (2013), pp. 1043–1051

    Google Scholar 

  116. Y. Zheng, X. Xie, Learning travel recommendations from user-generated GPS traces. ACM TIST 2(1), 2 (2011)

    Google Scholar 

  117. H. Wang, M. Terrovitis, N. Mamoulis, Location recommendation in location-based social networks using user check-in data, in SIGSPATIAL/GIS (2013), pp. 364–373

    Google Scholar 

  118. J. Bao, Y. Zheng, M.F. Mokbel, Location-based and preference-aware recommendation using sparse geo-social networking data, in SIGSPATIAL/GIS (2012), pp. 199–208

    Google Scholar 

  119. J. Han, M. Kamber, A.K.H. Tung, Spatial Clustering Methods in Data Mining: A Survey, in Geographic Data Mining and Knowledge Discovery (Taylor and Francis, 2001)

    Google Scholar 

  120. G. Karypis, E.-H. Han, V. Kumar, Chameleon: hierarchical clustering using dynamic modeling. IEEE Comput. 32(8), 68–75 (1999)

    Article  Google Scholar 

  121. M. Ester, H.-P. Kriegel, J. Sander, X. Xu, A density-based algorithm for discovering clusters in large spatial databases with noise. KDD 96, 226–231 (1996)

    Google Scholar 

  122. R.A. Jarvis, E.A. Patrick, Clustering using a similarity measure based on shared near neighbors. IEEE Trans. Comput. 100(11), 1025–1034 (1973)

    Article  Google Scholar 

  123. M. Worboys, in GIS: A Computing Perspective (Taylor and Francis, 1995)

    Google Scholar 

  124. D. Joshi, A. Samal, L.-K. Soh, A dissimilarity function for clustering geospatial polygons, in Proceedings of the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (ACM, 2009), pp. 384–387

    Google Scholar 

  125. S. Wang, C.F. Eick, A polygon-based clustering and analysis framework for mining spatial datasets. GeoInformatica 18(3), 569–594 (2014)

    Article  Google Scholar 

  126. R.M. Haralick, L.G. Shapiro, Image segmentation techniques, in 1985 Technical Symposium East (International Society for Optics and Photonics, 1985), pp. 2–9

    Google Scholar 

  127. K. Yang, A.H. Shekhar, D. Oliver, S. Shekhar, Capacity-constrained network-voronoi diagram. IEEE Trans. Knowl. Data Eng. 27(11), 2919–2932 (2015)

    Article  MATH  Google Scholar 

  128. G. Karypis, Multi-constraint mesh partitioning for contact/impact computations, in Proceedings of the 2003 ACM/IEEE Conference on Supercomputing (ACM, 2003), p. 56

    Google Scholar 

  129. D. Joshi, A. Samal, L.-K. Soh, Spatio-temporal polygonal clustering with space and time as first-class citizens. GeoInformatica 17(2), 387–412 (2013)

    Article  Google Scholar 

  130. D. Birant, A. Kut, St-dbscan: an algorithm for clustering spatial-temporal data. Data Knowl. Eng. 60(1), 208–221 (2007)

    Article  Google Scholar 

  131. M. Wang, A. Wang, A. Li, Mining spatial-temporal clusters from geo-databases, in Advanced Data Mining and Applications (Springer, Berlin, 2006), pp. 263–270

    Google Scholar 

  132. T.W. Liao, Clustering of time series data-a survey. Pattern Recogn. 38(11), 1857–1874 (2005)

    Article  MATH  Google Scholar 

  133. J.-G. Lee, J. Han, K.-Y. Whang, Trajectory clustering: a partition-and-group framework, in Proceedings of the 2007 ACM SIGMOD International Conference on Management of Data (ACM, 2007), pp. 593–604

    Google Scholar 

  134. Z. Zhang, Y. Yang, A.K. Tung, D. Papadias, Continuous k-means monitoring over moving objects. IEEE Trans. Knowl. Data Eng. 20(9), 1205–1216 (2008)

    Article  Google Scholar 

  135. C.S. Jensen, D. Lin, B.C. Ooi, Continuous clustering of moving objects. IEEE Trans. Knowl. Data Eng. 19(9), 1161–1174 (2007)

    Article  Google Scholar 

  136. A.J. Lee, Y.-A. Chen, W.-C. Ip, Mining frequent trajectory patterns in spatial-temporal databases. Inf. Sci. 179(13), 2218–2231 (2009)

    Article  MATH  Google Scholar 

  137. V. Chandola, V. Kumar, Summarization-compressing data into an informative representation. Knowl. Inf. Syst. 12(3), 355–378 (2007)

    Article  Google Scholar 

  138. D. Oliver, S. Shekhar, J.M. Kang, R. Laubscher, V. Carlan, A. Bannur, A k-main routes approach to spatial network activity summarization. IEEE Trans. Knowl. Data Eng. 26(6), 1464–1478 (2014)

    Article  Google Scholar 

  139. B. Pan, U. Demiryurek, F. Banaei-Kashani, C. Shahabi, Spatiotemporal summarization of traffic data streams, in Proceedings of the ACM SIGSPATIAL International Workshop on GeoStreaming (ACM, 2010), pp. 4–10

    Google Scholar 

  140. M.R. Evans, D. Oliver, S. Shekhar, F. Harvey, Summarizing trajectories into k-primary corridors: a summary of results, in Proceedings of the 20th International Conference on Advances in Geographic Information Systems (ACM, 2012), pp. 454–457

    Google Scholar 

  141. Z. Jiang, M. Evans, D. Oliver, S. Shekhar, Identifying K primary corridors from urban bicycle GPS trajectories on a road network. Inf. Syst. (2015) (to appear)

    Google Scholar 

  142. M. Kulldorff, Satscan user guide for version. 9, 4–107 (2011)

    Google Scholar 

  143. N. Levine, in CrimeStat 3.0: A Spatial Statistics Program for the Analysis of Crime Incident Locations (Ned Levine & Associatiates: Houston, TX/National Institute of Justice: Washington, DC, 2004)

    Google Scholar 

  144. E. Eftelioglu, S. Shekhar, D. Oliver, X. Zhou, M.R. Evans, Y. Xie, J.M. Kang, R. Laubscher, C. Farah, Ring-shaped hotspot detection: a summary of results, in 2014 IEEE International Conference on Data Mining, ICDM 2014, Shenzhen, China, December 14–17, 2014 (2014), pp. 815–820

    Google Scholar 

  145. T. Tango, K. Takahashi, K. Kohriyama, A space-time scan statistic for detecting emerging outbreaks. Biometrics 67(1), 106–115 (2011)

    Article  MathSciNet  MATH  Google Scholar 

  146. D.B. Neill, A.W. Moore, A fast multi-resolution method for detection of significant spatial disease clusters, in Advances in Neural Information Processing Systems (2003)

    Google Scholar 

  147. J. Ratcliffe, Crime mapping: spatial and temporal challenges, in Handbook of Quantitative Criminology (Springer, Berlin, 2010), pp. 5–24

    Google Scholar 

  148. A. Luc, Local indicators of spatial association: LISA. Geograph. Anal. 27(2), 93–115 (1995)

    Google Scholar 

  149. N. Chaikaew, N.K. Tripathi, M. Souris, International journal of health geographics. Int. J. Health Geograph. 8, 36 (2009)

    Article  Google Scholar 

  150. S.S. Chawathe, Organizing hot-spot police patrol routes, in Intelligence and Security Informatics, 2007 IEEE (IEEE, 2007), pp. 79–86

    Google Scholar 

  151. M. Celik, S. Shekhar, B. George, J.P. Rogers, J.A. Shine, Discovering and quantifying mean streets: a summary of results. Technical Report 025 (University of Minnesota, 07 2007)

    Google Scholar 

  152. S. Shiode, A. Okabe, Network variable clumping method for analyzing point patterns on a network, in Unpublished Paper Presented at the Annual Meeting of the Associations of American Geographers (Philadelphia, Pennsylvania, 2004)

    Google Scholar 

  153. W. Chang, D. Zeng, H. Chen, Prospective spatio-temporal data analysis for security informatics, in Intelligent Transportation Systems, 2005. Proceedings. 2005 IEEE (IEEE, 2005), pp. 1120–1124

    Google Scholar 

  154. D. Neill, A. Moore, M. Sabhnani, K. Daniel, Detection of emerging space-time clusters, in Proceedings of the eleventh ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (ACM, 2005), pp. 218–227

    Google Scholar 

  155. V. Chandola, D. Hui, L. Gu, B. Bhaduri, R. Vatsavai, Using time series segmentation for deriving vegetation phenology indices from MODIS NDVI data, in IEEE International Conference on Data Mining Workshops (Sydney, Australia, 2010), pp. 202–208

    Google Scholar 

  156. M. Worboys, M. Duckham, in GIS: A Computing Perspective, (CRC, 2004), ISBN: 0415283752

    Google Scholar 

  157. F. Bujor, E. Trouvé, L. Valet, J.-M. Nicolas, J.-P. Rudant, Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal sar images. IEEE Trans. Geosci. Remote Sens. 42(10), 2073–2084 (2004)

    Article  Google Scholar 

  158. Y. Kosugi, M. Sakamoto, M. Fukunishi, W. Lu, T. Doihara, S. Kakumoto, Urban change detection related to earthquakes using an adaptive nonlinear mapping of high-resolution images. IEEE Geosci. Remote Sens. Lett. 1(3), 152–156 (2004)

    Article  Google Scholar 

  159. G. Di Martino, A. Iodice, D. Riccio, G. Ruello, A novel approach for disaster monitoring: fractal models and tools. IEEE Trans. Geosci. Remote Sens. 45(6), 1559–1570 (2007)

    Article  Google Scholar 

  160. R. Radke, S. Andra, O. Al-Kofahi, B. Roysam, Image change detection algorithms: a systematic survey. IEEE Trans. Image Process. 14(3), 294–307 (2005)

    Article  MathSciNet  Google Scholar 

  161. R. Thoma, M. Bierling, Motion compensating interpolation considering covered and uncovered background. Sig. Process. Image Commun. 1(2), 191–212 (1989)

    Article  Google Scholar 

  162. T. Aach, A. Kaup, Bayesian algorithms for adaptive change detection in image sequences using markov random fields. Sig. Process. Image Commun. 7(2), 147–160 (1995)

    Article  Google Scholar 

  163. G. Chen, G.J. Hay, L.M. Carvalho, M.A. Wulder, Object-based change detection. Int. J. Remote Sens. 33(14), 4434–4457 (2012)

    Article  Google Scholar 

  164. B. Desclee, P. Bogaert, P. Defourny, Forest change detection by statistical object-based method. Remote Sens. Environ. 102(1), 1–11 (2006)

    Article  Google Scholar 

  165. J. Im, J. Jensen, J. Tullis, Object?based change detection using correlation image analysis and image segmentation. Int. J. Remote Sens. 29(2), 399–423 (2008)

    Article  Google Scholar 

  166. T. Aach, A. Kaup, R. Mester, Statistical model-based change detection in moving video. Sig. Process. 31(2), 165–180 (1993)

    Article  MATH  Google Scholar 

  167. E.J. Rignot, J.J. van Zyl, Change detection techniques for ERS-1 SAR data. IEEE Trans. Geosci. Remote Sens. 31(4), 896–906 (1993)

    Article  Google Scholar 

  168. J. Im, J. Jensen, A change detection model based on neighborhood correlation image analysis and decision tree classification. Remote Sens. Environ. 99(3), 326–340 (2005)

    Article  Google Scholar 

  169. Y. Yakimovsky, Boundary and object detection in real world images. J. ACM (JACM) 23(4), 599–618 (1976)

    Article  MathSciNet  MATH  Google Scholar 

  170. D.H. Douglas, T.K. Peucker, Algorithms for the reduction of the number of points required to represent a digitized line or its caricature. Cartographica Int. J. Geograph. Inf. Geovisualization 10(2), 112–122 (1973)

    Google Scholar 

  171. M. Kulldorff, W. Athas, E. Feurer, B. Miller, C. Key, Evaluating cluster alarms: a space-time scan statistic and brain cancer in los alamos, new mexico. Am. J. Public Health 88(9), 1377–1380 (1998)

    Article  Google Scholar 

  172. M. Kulldorff, Prospective time periodic geographical disease surveillance using a scan statistic. J. Roy. Stat. Soc. Ser. A (Stat. Soc.) 164(1), 61–72 (2001)

    Article  MathSciNet  MATH  Google Scholar 

  173. D.J. Isaak, E.E. Peterson, J.M. Ver Hoef, S.J. Wenger, J.A. Falke, C.E. Torgersen, C. Sowder, E.A. Steel, M.-J. Fortin, C.E. Jordan et al., Applications of spatial statistical network models to stream data. Wiley Interdisc. Rev. Water 1(3), 277–294 (2014)

    Google Scholar 

  174. D. Oliver, A. Bannur, J.M. Kang, S. Shekhar, R. Bousselaire, A k-main routes approach to spatial network activity summarization: A summary of results, in 2010 IEEE International Conference on Data Mining Workshops (ICDMW) (IEEE, 2010), pp. 265–272

    Google Scholar 

  175. V.M.V. Gunturi, S. Shekhar, Lagrangian xgraphs: a logical data-model for spatio-temporal network data: A summary, in Advances in Conceptual Modeling - ER 2014 Workshops, ENMO, MoBiD, MReBA, QMMQ, SeCoGIS, WISM, and ER Demos, Atlanta, GA, USA, October 27–29, 2014. Proceedings (2014), pp. 201–211

    Google Scholar 

  176. V.M. Gunturi, E. Nunes, K. Yang, S. Shekhar, A critical-time-point approach to all-start-time lagrangian shortest paths: A summary of results, in Advances in Spatial and Temporal Databases, vol. 6849. Lecture Notes in Computer Science, ed. by D. Pfoser, Y. Tao, K. Mouratidis, M. Nascimento, M. Mokbel, S. Shekhar, Y. Huang (Springer, Berlin, 2011), pp. 74–91

    Google Scholar 

  177. V. Gunturi, S. Shekhar, K. Yang, A critical-time-point approach to all-departure-time lagrangian shortest paths. IEEE Trans. Knowl. Data Eng. 99, 1 (2015)

    Google Scholar 

  178. S. Ramnath, Z. Jiang, H.-H. Wu, V.M. Gunturi, S. Shekhar, A spatio-temporally opportunistic approach to best-start-time lagrangian shortest path, in International Symposium on Spatial and Temporal Databases (Springer, 2015), pp. 274–291

    Google Scholar 

  179. J. Speed, Iot for v2v and the connected car, www.slideshare.net/JoeSpeed/aw-megatrends-2014-joe-speed

  180. R.Y. Ali, V.M. Gunturi, A. Kotz, S. Shekhar, W. Northrop, Discovering non-compliant window co-occurrence patterns: A summary of results, in Accepted in 14th International Symposium on Spatial and Temporal Databases (2015)

    Google Scholar 

  181. ESRI, Breathe Life into Big Data: ArcGIS Tools and Hadoop Analyze Large Data Stores, http://www.esri.com/esriOnews/arcnews/summer13articles/breatheOlifeOintoObigOdata!

  182. ESRI, ESRI: GIS and Mapping Software, http://www.esri.com

  183. A. Aji, F. Wang, H. Vo, R. Lee, Q. Liu, X. Zhang, J. Saltz, Hadoop GIS: a high performance spatial data warehousing system over mapreduce. Proc. VLDB Endow. 6(11), 1009–1020 (2013)

    Article  Google Scholar 

  184. A. Eldawy, M.F. Mokbel, Spatialhadoop: a mapreduce framework for spatial data, in Proceedings of the IEEE International Conference on Data Engineering (ICDE’15) (IEEE, 2015)

    Google Scholar 

  185. C. Avery, Giraph: Large-Scale Graph Processing Infrastructure on Hadoop (Proceedings of the Hadoop Summit, Santa Clara, 2011)

    Google Scholar 

  186. Y. Low, J.E. Gonzalez, A. Kyrola, D. Bickson, C.E. Guestrin, J. Hellerstein, Graphlab: a new framework for parallel machine learning (2014). arXiv:1408.2041

  187. G. Malewicz, M.H. Austern, A.J. Bik, J.C. Dehnert, I. Horn, N. Leiser, G. Czajkowski, Pregel: a system for large-scale graph processing, in Proceedings of the 2010 ACM SIGMOD International Conference on Management of Data (ACM, 2010), pp. 135–146

    Google Scholar 

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  1. Department of Computer Science, University of Alabama, Tuscaloosa, AL, USA

    Zhe Jiang

  2. Department of Computer Science, University of Minnesota, Minneapolis, MN, USA

    Shashi Shekhar

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Jiang, Z., Shekhar, S. (2017). Spatial and Spatiotemporal Big Data Science. In: Spatial Big Data Science. Springer, Cham. https://doi.org/10.1007/978-3-319-60195-3_2

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