Skip to main content

Spatial Data Science

  • Chapter
  • First Online:
Machine Learning for Data Science Handbook

Abstract

Spatial data science is a multi-disciplinary field that applies scientific methods to acquire, store, and manage spatial data, as well as to retrieve previously unknown, but potentially useful and non-trivial knowledge and insights from the data. Spatial data science is important for societal applications in public health, public safety, agriculture, environmental science, climate, etc. The challenges of spatial data science are brought about by its interdisciplinary nature and the unique properties of spatial data, such as spatial autocorrelation and spatial heterogeneity. In this section, we discuss spatial data science in its life cycle: data acquisition, data storage, data mining, result validation, and domain interpretation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 219.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

References

  1. GIS At DOT (2017). https://www.transportation.gov/gis

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

    Article  MATH  Google Scholar 

  3. Aggarwal, C.C.: Outlier analysis. In: Data mining, pp. 237–263. Springer (2015)

    Google Scholar 

  4. Agrawal, R., Srikant, R., others: Fast algorithms for mining association rules. In: Proc. 20th int. conf. very large data bases, VLDB, vol. 1215, pp. 487–499 (1994)

    Google Scholar 

  5. Aji, A., Wang, F., Vo, H., Lee, R., Liu, Q., Zhang, X., Saltz, J.: Hadoop GIS: a high performance spatial data warehousing system over MapReduce. Proceedings of the VLDB Endowment 6(11), 1009–1020 (2013)

    Article  Google Scholar 

  6. Anselin, L.: Local indicators of spatial association—LISA. Geographical analysis 27(2), 93–115 (1995)

    Article  Google Scholar 

  7. Anselin, L.: The Moran scatterplot as an ESDA tool to assess local instability in spatial association. In: Spatial Analytical, pp. 111–126. Routledge (2019)

    Google Scholar 

  8. Atluri, G., Karpatne, A., Kumar, V.: Spatio-Temporal Data Mining: A Survey of Problems and Methods. ACM Comput. Surv. 51(4), 83:1–83:41 (2018). https://doi.org/10.1145/3161602

  9. Barua, S., Sander, J.: Mining statistically significant co-location and segregation patterns. IEEE Transactions on Knowledge and Data Engineering 26(5), 1185–1199 (2013)

    Article  Google Scholar 

  10. Beckmann, N., Kriegel, H.P., Schneider, R., Seeger, B.: The r*-tree: an efficient and robust access method for points and rectangles. In: ACM SIGMOD Record, vol. 19, pp. 322–331. ACM (1990)

    Google Scholar 

  11. Brunsdon, C., Fotheringham, S., Charlton, M.: Geographically weighted regression. Journal of the Royal Statistical Society: Series D (The Statistician) 47(3), 431–443 (1998)

    Google Scholar 

  12. Cai, J., Liu, Q., Deng, M., Tang, J., He, Z.: Adaptive detection of statistically significant regional spatial co-location patterns. Computers, Environment and Urban Systems 68, 53–63 (2018). https://doi.org/10.1016/j.compenvurbsys.2017.10.003

    Article  Google Scholar 

  13. Caldwell, P.M., Bretherton, C.S., Zelinka, M.D., Klein, S.A., Santer, B.D., Sanderson, B.M.: Statistical significance of climate sensitivity predictors obtained by data mining. Geophysical Research Letters 41(5), 1803–1808 (2014). https://doi.org/10.1002/2014GL059205

    Article  Google Scholar 

  14. Campbell, J.B., Wynne, R.H.: Introduction to Remote Sensing, Fifth Edition, 5th edition edn. The Guilford Press, New York (2011)

    Google Scholar 

  15. Celik, M., Kang, J.M., Shekhar, S.: Zonal co-location pattern discovery with dynamic parameters. In: Data Mining, 2007. ICDM 2007. Seventh IEEE International Conference on, pp. 433–438. IEEE (2007)

    Google Scholar 

  16. Cheng, Z., Caverlee, J., Lee, K.: You Are Where You Tweet: A Content-based Approach to Geo-locating Twitter Users. In: Proceedings of the 19th ACM International Conference on Information and Knowledge Management, CIKM ’10, pp. 759–768. ACM, New York, NY, USA (2010). https://doi.org/10.1145/1871437.1871535. Event-place: Toronto, ON, Canada

  17. Costa, M.A., Assunção, R.M., Kulldorff, M.: Constrained spanning tree algorithms for irregularly-shaped spatial clustering. Computational Statistics & Data Analysis 56(6), 1771–1783 (2012). https://doi.org/10.1016/j.csda.2011.11.001

    Article  MathSciNet  Google Scholar 

  18. Cressie, N.: Statistics for Spatial Data. John Wiley & Sons (2015)

    Google Scholar 

  19. Daley, D.: GOP Racial Gerrymandering Mastermind Participated in Redistricting in More States Than Previously Known, Files Reveal (2019). https://theintercept.com/2019/09/23/gerrymandering-gop-west-virginia-florida-alabama/

  20. Deng, M., Cai, J., Liu, Q., He, Z., Tang, J.: Multi-level method for discovery of regional co-location patterns. International Journal of Geographical Information Science 31(9), 1846–1870 (2017). https://doi.org/10.1080/13658816.2017.1334890

    Article  Google Scholar 

  21. Dixon, P.M.: Ripley’s K Function. In: Encyclopedia of Environmetrics. John Wiley & Sons, Ltd (2006). https://doi.org/10.1002/9780470057339.var046

  22. Eck, J., Chainey, S., Cameron, J., Wilson, R.: Mapping crime: Understanding hotspots (2005). http://discovery.ucl.ac.uk/11291/1/11291.pdf

  23. Eftelioglu, E., Li, Y., Tang, X., Shekhar, S., Kang, J.M., Farah, C.: Mining Network Hotspots with Holes: A Summary of Results. In: Geographic Information Science, Lecture Notes in Computer Science, pp. 51–67. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45738-3_4

  24. Eftelioglu, E., Shekhar, S., Kang, J.M., Farah, C.C.: Ring-Shaped Hotspot Detection. IEEE Transactions on Knowledge and Data Engineering 28(12), 3367–3381 (2016). https://doi.org/10.1109/TKDE.2016.2607202

    Article  Google Scholar 

  25. Eick, C.F., Parmar, R., Ding, W., Stepinski, T.F., Nicot, J.P.: Finding Regional Co-location Patterns for Sets of Continuous Variables in Spatial Datasets. In: Proceedings of the 16th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, GIS ’08, pp. 30:1–30:10. ACM, New York, NY, USA (2008). https://doi.org/10.1145/1463434.1463472

  26. Eldawy, A., Mokbel, M.F.: SpatialHadoop: A MapReduce framework for spatial data. In: 2015 IEEE 31st International Conference on Data Engineering, pp. 1352–1363 (2015). https://doi.org/10.1109/ICDE.2015.7113382

  27. Elmasri, R., Navathe, S.B.: Fundamentals of Database Systems, 7 edn. Pearson, Hoboken, NJ (2015)

    Google Scholar 

  28. ESRI: GIS Tools for Hadoop by Esri. http://esri.github.io/gis-tools-for-hadoop/

  29. Ester, M., Kriegel, H.P., Sander, J., Xu, X., others: A density-based algorithm for discovering clusters in large spatial databases with noise. In: Kdd, vol. 96, pp. 226–231 (1996)

    Google Scholar 

  30. Finkel, R.A., Bentley, J.L.: Quad trees a data structure for retrieval on composite keys. Acta informatica 4(1), 1–9 (1974)

    Article  MATH  Google Scholar 

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

    Google Scholar 

  32. Guha, S., Rastogi, R., Shim, K.: Rock: A robust clustering algorithm for categorical attributes. Information systems 25(5), 345–366 (2000)

    Article  Google Scholar 

  33. Guttman, A.: R-trees: A dynamic index structure for spatial searching, vol. 14. ACM (1984)

    Google Scholar 

  34. Hilbert, D.: Über die stetige abbildung einer linie auf ein flächenstück. In: Dritter Band: Analysis· Grundlagen der Mathematik· Physik Verschiedenes, pp. 1–2. Springer (1935)

    Google Scholar 

  35. Huang, Y., Pei, J., Xiong, H.: Mining Co-Location Patterns with Rare Events from Spatial Data Sets. GeoInformatica 10(3), 239–260 (2006). https://doi.org/10.1007/s10707-006-9827-8

    Article  Google Scholar 

  36. Huang, Y., Shekhar, S., Xiong, H.: Discovering colocation patterns from spatial data sets: a general approach. IEEE Transactions on Knowledge and Data Engineering 16(12), 1472–1485 (2004)

    Article  Google Scholar 

  37. Huang, Y., Xiong, H., Shekhar, S., Pei, J.: Mining Confident Co-location Rules Without a Support Threshold. In: Proceedings of the 2003 ACM Symposium on Applied Computing, SAC ’03, pp. 497–501. ACM, New York, NY, USA (2003). https://doi.org/10.1145/952532.952630

  38. Im, J., Jensen, J., Tullis, J.: Object-based change detection using correlation image analysis and image segmentation. International Journal of Remote Sensing 29(2), 399–423 (2008)

    Article  Google Scholar 

  39. International Federation of Surveyors: FIG Definition of the Functions of the Surveyor (2004). http://www.fig.net/about/general/definition/index.asp

  40. Jia, X., Willard, J., Karpatne, A., Read, J., Zwart, J., Steinbach, M., Kumar, V.: Physics guided RNNs for modeling dynamical systems: A case study in simulating lake temperature profiles. In: Proceedings of the 2019 SIAM International Conference on Data Mining, pp. 558–566. SIAM (2019)

    Google Scholar 

  41. Jiang, Z., Sainju, A.M., Li, Y., Shekhar, S., Knight, J.: Spatial ensemble learning for heterogeneous geographic data with class ambiguity. ACM Transactions on Intelligent Systems and Technology (TIST) 10(4), 43 (2019)

    Google Scholar 

  42. Jiang, Z., Shekhar, S., Zhou, X., Knight, J., Corcoran, J.: Focal-Test-Based Spatial Decision Tree Learning. IEEE Transactions on Knowledge and Data Engineering 27(6), 1547–1559 (2015). https://doi.org/10.1109/TKDE.2014.2373383

    Article  Google Scholar 

  43. Joshi, N., Baumann, M., Ehammer, A., Fensholt, R., Grogan, K., Hostert, P., Jepsen, M.R., Kuemmerle, T., Meyfroidt, P., Mitchard, E.T.A., Reiche, J., Ryan, C.M., Waske, B.: A Review of the Application of Optical and Radar Remote Sensing Data Fusion to Land Use Mapping and Monitoring. Remote Sensing 8(1), 70 (2016). https://doi.org/10.3390/rs8010070

  44. Kaufman, L., Rousseeuw, P.J.: Finding groups in data: an introduction to cluster analysis, vol. 344. John Wiley & Sons (2009)

    Google Scholar 

  45. Kulldorff, M.: A spatial scan statistic. Communications in Statistics—Theory and Methods 26(6), 1481–1496 (1997). https://doi.org/10.1080/03610929708831995

    Article  MathSciNet  MATH  Google Scholar 

  46. Lens, M.C., Meltzer, R.: Is Crime Bad for Business? Crime and Commercial Property Values in New York City. Journal of Regional Science 56(3), 442–470 (2016). https://doi.org/10.1111/jors.12254

    Google Scholar 

  47. Li, W., Du, Q.: A survey on representation-based classification and detection in hyperspectral remote sensing imagery. Pattern Recognition Letters 83, 115–123 (2016)

    Article  Google Scholar 

  48. Li, Y., Kotwal, P., Wang, P., Shekhar, S., Northrop, W.: Trajectory-aware Lowest-cost Path Selection: A Summary of Results. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD ’19, pp. 61–69. ACM, Vienna, Austria (2019). https://doi.org/10.1145/3340964.3340971

  49. Li, Y., Shekhar, S.: Local Co-location Pattern Detection: A Summary of Results. In: S. Winter, A. Griffin, M. Sester (eds.) 10th International Conference on Geographic Information Science (GIScience 2018), Leibniz International Proceedings in Informatics (LIPIcs), vol. 114, pp. 10:1–10:15. Schloss Dagstuhl–Leibniz-Zentrum fuer Informatik, Dagstuhl, Germany (2018). https://doi.org/10.4230/LIPIcs.GISCIENCE.2018.10

  50. Li, Y., Shekhar, S., Wang, P., Northrop, W.: Physics-guided Energy-efficient Path Selection: A Summary of Results. In: Proceedings of the 26th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’18, pp. 99–108. ACM, Seattle, WA, USA (2018). https://doi.org/10.1145/3274895.3274933

  51. Lin, Y., Chiang, Y.Y., Franklin, M., Eckel, S.P., Ambite, J.L.: Building autocorrelation-aware representations for fine-scale spatiotemporal prediction. In: 2020 IEEE International Conference on Data Mining (ICDM), pp. 352–361. IEEE (2020)

    Google Scholar 

  52. Mac Aodha, O., Cole, E., Perona, P.: Presence-only geographical priors for fine-grained image classification. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 9596–9606 (2019)

    Google Scholar 

  53. Marcus, G., Davis, E.: Eight (No, Nine!) Problems With Big Data. The New York Times (2014). http://www.nytimes.com/2014/04/07/opinion/eight-no-nine-problems-with-big-data.html

  54. Mohan, P., Shekhar, S., Shine, J.A., Rogers, J.P., Jiang, Z., Wayant, N.: 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, GIS ’11, pp. 122–132. ACM, New York, NY, USA (2011). https://doi.org/10.1145/2093973.2093991

  55. Morton, G.M.: A computer oriented geodetic data base and a new technique in file sequencing (1966)

    Google Scholar 

  56. National Cancer Institute: GIS at the National Cancer Institute. https://gis.cancer.gov/gis-nci/gis_nci.html

  57. National Geospatial-Intelligence Agency: About NGA. https://www.nga.mil/About/Pages/Default.aspx

  58. Neill, D.B.: Expectation-based scan statistics for monitoring spatial time series data. International Journal of Forecasting 25(3), 498–517 (2009)

    Article  Google Scholar 

  59. Neill, D.B., Moore, A.W.: Rapid Detection of Significant Spatial Clusters. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04, pp. 256–265. ACM, New York, NY, USA (2004). https://doi.org/10.1145/1014052.1014082

  60. Open Geospatial Consortium: OpenGIS Implementation Specification for Geographic information—Simple feature access—Part 2: SQL option. http://portal.opengeospatial.org/files/?artifact_id=25354

  61. Open Geospatial Consortium: OGC Standards and Supporting Documents (2019). http://www.opengeospatial.org/standards/

  62. Ploner, A.: The use of the variogram cloud in geostatistical modelling. Environmetrics: The official journal of the International Environmetrics Society 10(4), 413–437 (1999)

    Article  Google Scholar 

  63. Qian, F., Chiew, K., He, Q., Huang, H.: Mining regional co-location patterns with kNNG. Journal of Intelligent Information Systems 42(3), 485–505 (2014). https://doi.org/10.1007/s10844-013-0280-5

    Article  Google Scholar 

  64. Qian, F., He, Q., He, J.: Mining Spatial Co-location Patterns with Dynamic Neighborhood Constraint. In: Machine Learning and Knowledge Discovery in Databases, Lecture Notes in Computer Science, pp. 238–253. Springer, Berlin, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04174-7_16

  65. Sellis, T., Roussopoulos, N., Faloutsos, C.: The r+-tree: A dynamic index for multi-dimensional objects. Tech. rep. (1987)

    Google Scholar 

  66. Shekhar, S., Chawla, S.: Spatial Databases: A Tour, 1 edition edn. Prentice Hall, Upper Saddle River, N.J (2003)

    Google Scholar 

  67. Shekhar, S., Evans, M.R., Kang, J.M., Mohan, P.: Identifying patterns in spatial information: A survey of methods. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 1(3), 193–214 (2011). https://doi.org/10.1002/widm.25

    Google Scholar 

  68. Shekhar, S., Jiang, Z., Ali, R., Eftelioglu, E., Tang, X., Gunturi, V., Zhou, X.: Spatiotemporal data mining: A computational perspective. ISPRS International Journal of Geo-Information 4(4), 2306–2338 (2015)

    Article  Google Scholar 

  69. Shekhar, S., Schrater, P.R., Vatsavai, R.R., Wu, W., Chawla, S.: Spatial contextual classification and prediction models for mining geospatial data. IEEE Transactions on Multimedia 4(2), 174–188 (2002)

    Article  Google Scholar 

  70. Shi, L., Janeja, V.P.: Anomalous Window Discovery for Linear Intersecting Paths. IEEE Transactions on Knowledge and Data Engineering 23(12), 1857–1871 (2011). https://doi.org/10.1109/TKDE.2010.212

    Article  Google Scholar 

  71. Shvachko, K., Kuang, H., Radia, S., Chansler, R., et al.: The Hadoop distributed file system. In: MSST, vol. 10, pp. 1–10 (2010)

    Google Scholar 

  72. Sinha, S., Jeganathan, C., Sharma, L.K., Nathawat, M.S.: A review of radar remote sensing for biomass estimation. International Journal of Environmental Science and Technology 12(5), 1779–1792 (2015). https://doi.org/10.1007/s13762-015-0750-0

    Article  Google Scholar 

  73. Srinivasan, S.: Spatial Regression Models. In: S. Shekhar, H. Xiong, X. Zhou (eds.) Encyclopedia of GIS, pp. 1–6. Springer International Publishing, Cham (2015). https://doi.org/10.1007/978-3-319-23519-6_1294-2

    Google Scholar 

  74. Stewart, A.J., Mosleh, M., Diakonova, M., Arechar, A.A., Rand, D.G., Plotkin, J.B.: Information gerrymandering and undemocratic decisions. Nature 573(7772), 117–121 (2019). https://doi.org/10.1038/s41586-019-1507-6

    Article  Google Scholar 

  75. Tang, X., Eftelioglu, E., Oliver, D., Shekhar, S.: Significant Linear Hotspot Discovery. IEEE Transactions on Big Data 3(2), 140–153 (2017). https://doi.org/10.1109/TBDATA.2016.2631518

    Article  Google Scholar 

  76. Tang, X., Eftelioglu, E., Shekhar, S.: Elliptical Hotspot Detection: A Summary of Results. In: Proceedings of the 4th International ACM SIGSPATIAL Workshop on Analytics for Big Geospatial Data, BigSpatial’15, pp. 15–24. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2835185.2835192

  77. Tang, X., Eftelioglu, E., Shekhar, S.: Detecting Isodistance Hotspots on Spatial Networks: A Summary of Results. In: M. Gertz, M. Renz, X. Zhou, E. Hoel, W.S. Ku, A. Voisard, C. Zhang, H. Chen, L. Tang, Y. Huang, C.T. Lu, S. Ravada (eds.) Advances in Spatial and Temporal Databases, Lecture Notes in Computer Science, pp. 281–299. Springer International Publishing (2017)

    Google Scholar 

  78. Tobler, W.R.: A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography 46(sup1), 234–240 (1970). https://doi.org/10.2307/143141

  79. Toth, C., Jóźków, G.: Remote sensing platforms and sensors: A survey. ISPRS Journal of Photogrammetry and Remote Sensing 115, 22–36 (2016). https://doi.org/10.1016/j.isprsjprs.2015.10.004

    Article  Google Scholar 

  80. Walsh, B.: Google’s Flu Project Shows the Failings of Big Data. https://time.com/23782/google-flu-trends-big-data-problems/

  81. Wang, S., Huang, Y., Wang, X.S.: Regional Co-locations of Arbitrary Shapes. In: Advances in Spatial and Temporal Databases, pp. 19–37. Springer Berlin Heidelberg (2013). https://doi.org/10.1007/978-3-642-40235-7_2

  82. Wong, C., Sorensen, P., Hollywood, J.S.: Evaluation of National Institute of Justice-Funded Geospatial Software Tools (2014). https://www.rand.org/pubs/research_reports/RR418.html

  83. Wu, B., Yu, B., Wu, Q., Yao, S., Zhao, F., Mao, W., Wu, J.: A Graph-Based Approach for 3D Building Model Reconstruction from Airborne LiDAR Point Clouds. Remote Sensing 9(1), 92 (2017). https://doi.org/10.3390/rs9010092

    Article  Google Scholar 

  84. Xie, Y., Eftelioglu, E., Ali, R.Y., Tang, X., Li, Y., Doshi, R., Shekhar, S.: Transdisciplinary Foundations of Geospatial Data Science. ISPRS International Journal of Geo-Information 6(12), 395 (2017)

    Article  Google Scholar 

  85. Xie, Y., Gupta, J., Li, Y., Shekhar, S.: Transforming smart cities with spatial computing. In: 2018 IEEE International Smart Cities Conference (ISC2), pp. 1–9. IEEE (2018)

    Google Scholar 

  86. Xie, Y., Shekhar, S.: A Nondeterministic Normalization based Scan Statistic (NN-scan) towards Robust Hotspot Detection: A Summary of Results. In: Proceedings of the 2019 SIAM International Conference on Data Mining, Proceedings, pp. 82–90. Society for Industrial and Applied Mathematics (2019). https://doi.org/10.1137/1.9781611975673.10

  87. Xie, Y., Shekhar, S.: Significant DBSCAN Towards Statistically Robust Clustering. In: Proceedings of the 16th International Symposium on Spatial and Temporal Databases, SSTD ’19, pp. 31–40. ACM, New York, NY, USA (2019). https://doi.org/10.1145/3340964.3340968. Event-place: Vienna, Austria

  88. Xie, Y., Zhou, X., Shekhar, S.: Discovering interesting sub-paths with statistical significance from spatio-temporal datasets. ACM Transactions on Intelligent Systems and Technology (2019)

    Google Scholar 

  89. Xiong, H., Shekhar, S., Huang, Y., Kumar, V., Ma, X., Yoc, J.: A Framework for Discovering Co-location Patterns in Data Sets with Extended Spatial Objects. In: Proceedings of the 2004 SIAM International Conference on Data Mining, Proceedings, pp. 78–89. Society for Industrial and Applied Mathematics (2004)

    Google Scholar 

  90. Yan, H.S., Ceccarelli, M. (eds.): International Symposium on History of Machines and Mechanisms: Proceedings of HMM 2008. History of Mechanism and Machine Science. Springer Netherlands (2009)

    Google Scholar 

  91. Yan, Z., Chakraborty, D., Parent, C., Spaccapietra, S., Aberer, K.: Semantic Trajectories: Mobility Data Computation and Annotation. ACM Trans. Intell. Syst. Technol. 4(3), 49:1–49:38 (2013). https://doi.org/10.1145/2483669.2483682

  92. Yao, X., Mokbel, M.F., Alarabi, L., Eldawy, A., Yang, J., Yun, W., Li, L., Ye, S., Zhu, D.: Spatial coding-based approach for partitioning big spatial data in Hadoop. Computers & Geosciences 106, 60–67 (2017). https://doi.org/10.1016/j.cageo.2017.05.014

    Article  Google Scholar 

  93. Yoo, J.S., Shekhar, S.: A Joinless Approach for Mining Spatial Colocation Patterns. IEEE Transactions on Knowledge and Data Engineering 18(10), 1323–1337 (2006). https://doi.org/10.1109/TKDE.2006.150

    Article  Google Scholar 

  94. Yu, J., Wu, J., Sarwat, M.: GeoSpark: A Cluster Computing Framework for Processing Large-scale Spatial Data. In: Proceedings of the 23rd SIGSPATIAL International Conference on Advances in Geographic Information Systems, SIGSPATIAL ’15, pp. 70:1–70:4. ACM, New York, NY, USA (2015). https://doi.org/10.1145/2820783.2820860

  95. Zheng, Y.: Trajectory Data Mining: An Overview. ACM Trans. Intell. Syst. Technol. 6(3), 29:1–29:41 (2015). https://doi.org/10.1145/2743025

  96. Zhou, X., Shekhar, S., Ali, R.Y.: Spatiotemporal change footprint pattern discovery: an inter-disciplinary survey. Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 4(1), 1–23 (2014). https://doi.org/10.1002/widm.1113

    Google Scholar 

  97. Zhou, X., Shekhar, S., Mohan, P., Liess, S., Snyder, P.K.: Discovering interesting sub-paths in spatiotemporal datasets: A summary of results. In: Proceedings of the 19th ACM SIGSPATIAL international conference on advances in geographic information systems, pp. 44–53. ACM (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shashi Shekhar .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Li, Y., Xie, Y., Shekhar, S. (2023). Spatial Data Science. In: Rokach, L., Maimon, O., Shmueli, E. (eds) Machine Learning for Data Science Handbook. Springer, Cham. https://doi.org/10.1007/978-3-031-24628-9_18

Download citation

Publish with us

Policies and ethics