Spatial and Spatiotemporal Big Data Science

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.

[1]  Brandon Bennett,et al.  Representing and Reasoning about Changing Spatial Extensions of Geographic Features , 2013, COSIT.

[2]  Chang-Tien Lu,et al.  Spatial Weighted Outlier Detection , 2006, SDM.

[3]  Xing Xie,et al.  Learning travel recommendations from user-generated GPS traces , 2011, TIST.

[4]  Weili Wu,et al.  Spatial contextual classification and prediction models for mining geospatial data , 2002, IEEE Trans. Multim..

[5]  Sanjay Ranka,et al.  A Model-Agnostic Framework for Fast Spatial Anomaly Detection , 2010, TKDD.

[6]  Jiawei Han,et al.  Discovery of Spatial Association Rules in Geographic Information Databases , 1995, SSD.

[7]  Jae-Gil Lee,et al.  Trajectory clustering: a partition-and-group framework , 2007, SIGMOD '07.

[8]  Chris Brunsdon,et al.  Geographically Weighted Regression: The Analysis of Spatially Varying Relationships , 2002 .

[9]  Hui Xiong,et al.  Discovering colocation patterns from spatial data sets: a general approach , 2004, IEEE Transactions on Knowledge and Data Engineering.

[10]  Yukio Kosugi,et al.  Urban change detection related to earthquakes using an adaptive nonlinear mapping of high-resolution images , 2004, IEEE Geoscience and Remote Sensing Letters.

[11]  Joseph M. Hellerstein,et al.  GraphLab: A New Framework For Parallel Machine Learning , 2010, UAI.

[12]  Vipin Kumar,et al.  Summarization – compressing data into an informative representation , 2006, Knowledge and Information Systems.

[13]  Chang-Tien Lu,et al.  On detecting spatial categorical outliers , 2014, GeoInformatica.

[14]  Wei Ding,et al.  Discovery of Geospatial Discriminating Patterns from Remote Sensing Datasets , 2009, SDM.

[15]  Karthik Ganesan Pillai,et al.  A filter-and-refine approach to mine spatiotemporal co-occurrences , 2013, SIGSPATIAL/GIS.

[16]  Shashi Shekhar,et al.  Discovering Non-compliant Window Co-Occurrence Patterns: A Summary of Results , 2015, SSTD.

[17]  Yan Huang,et al.  Finding Sequential Patterns from a Massive Number of Spatio-Temporal Events , 2006, SDM.

[18]  Hans-Peter Kriegel,et al.  Spatial Data Mining: A Database Approach , 1997, SSD.

[19]  Jakob J. van Zyl,et al.  Change detection techniques for ERS-1 SAR data , 1993, IEEE Trans. Geosci. Remote. Sens..

[20]  Hans-Peter Kriegel,et al.  Local outlier detection reconsidered: a generalized view on locality with applications to spatial, video, and network outlier detection , 2012, Data Mining and Knowledge Discovery.

[21]  Tomasz Imielinski,et al.  Mining association rules between sets of items in large databases , 1993, SIGMOD Conference.

[22]  Shashi Shekhar,et al.  Discovering Flow Anomalies: A SWEET Approach , 2008, 2008 Eighth IEEE International Conference on Data Mining.

[23]  Srinivasan Parthasarathy,et al.  Decision Tree Classification of Spatial Data Patterns from Videokeratography using Zernicke Polynomials , 2003, SDM.

[24]  Shashi Shekhar,et al.  A K-Main Routes Approach to Spatial Network Activity Summarization , 2010, IEEE Transactions on Knowledge and Data Engineering.

[25]  Arindam Banerjee,et al.  Climate Multi-model Regression Using Spatial Smoothing , 2013, SDM.

[26]  Markus Schneider,et al.  Spatio-Temporal Predicates , 2002, IEEE Trans. Knowl. Data Eng..

[27]  Graham J. Wills,et al.  Dynamic Graphics for Exploring Spatial Data with Application to Locating Global and Local Anomalies , 1991 .

[28]  W. F. Athas,et al.  Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico. , 1998, American journal of public health.

[29]  Shashi Shekhar,et al.  A Spatio-Temporally Opportunistic Approach to Best-Start-Time Lagrangian Shortest Path , 2015, SSTD.

[30]  Aart J. C. Bik,et al.  Pregel: a system for large-scale graph processing , 2010, SIGMOD Conference.

[31]  Weili Wu,et al.  Modeling Spatial Dependencies for Mining Geospatial Data , 2001, SDM.

[32]  Anthony K. H. Tung,et al.  Spatial clustering methods in data mining : A survey , 2001 .

[33]  Yoram Yakimovsky,et al.  Boundary and Object Detection in Real World Images , 1974, JACM.

[34]  David H. Douglas,et al.  ALGORITHMS FOR THE REDUCTION OF THE NUMBER OF POINTS REQUIRED TO REPRESENT A DIGITIZED LINE OR ITS CARICATURE , 1973 .

[35]  A. Gelfand,et al.  Handbook of spatial statistics , 2010 .

[36]  Leen-Kiat Soh,et al.  Spatio-temporal polygonal clustering with space and time as first-class citizens , 2013, GeoInformatica.

[37]  Farnoush Banaei Kashani,et al.  Spatiotemporal summarization of traffic data streams , 2010, IWGS '10.

[38]  Shashi Shekhar,et al.  Time-Aggregated Graphs for Modeling Spatio-temporal Networks , 2006, J. Data Semant..

[39]  Ahmed Eldawy,et al.  SpatialHadoop: A MapReduce framework for spatial data , 2015, 2015 IEEE 31st International Conference on Data Engineering.

[40]  Shashi Shekhar,et al.  Spatiotemporal change footprint pattern discovery: an inter‐disciplinary survey , 2014, WIREs Data Mining Knowl. Discov..

[41]  Shashi Shekhar,et al.  Spatio-temporal Network Databases and Routing Algorithms: A Summary of Results , 2007, SSTD.

[42]  Tieniu Tan,et al.  Comparison of Similarity Measures for Trajectory Clustering in Outdoor Surveillance Scenes , 2006, 18th International Conference on Pattern Recognition (ICPR'06).

[43]  J. Ratcliffe Crime Mapping: Spatial and Temporal Challenges , 2010 .

[44]  Jae-Gil Lee,et al.  Mining Discriminative Patterns for Classifying Trajectories on Road Networks , 2011, IEEE Transactions on Knowledge and Data Engineering.

[45]  Chi-Yin Chow,et al.  iGSLR: personalized geo-social location recommendation: a kernel density estimation approach , 2013, SIGSPATIAL/GIS.

[46]  Sarit Kraus,et al.  Scalable Classification in Large Scale Spatiotemporal Domains Applied to Voltage-Sensitive Dye Imaging , 2009, 2009 Ninth IEEE International Conference on Data Mining.

[47]  Hans-Peter Kriegel,et al.  A Density-Based Algorithm for Discovering Clusters in Large Spatial Databases with Noise , 1996, KDD.

[48]  Shashi Shekhar,et al.  A Critical-Time-Point Approach to All-Departure-Time Lagrangian Shortest Paths , 2015, IEEE Transactions on Knowledge and Data Engineering.

[49]  Phaedon C. Kyriakidis,et al.  Geostatistical Space–Time Models: A Review , 1999 .

[50]  Nathaniel Troutman,et al.  Enhanced spatiotemporal relational probability trees and forests , 2012, Data Mining and Knowledge Discovery.

[51]  Robert THOMA,et al.  Motion compensating interpolation considering covered and uncovered background , 1989, Signal Process. Image Commun..

[52]  Atsuyuki Okabe,et al.  The SANET Toolbox: New Methods for Network Spatial Analysis , 2006, Trans. GIS.

[53]  Sujing Wang,et al.  A polygon-based clustering and analysis framework for mining spatial datasets , 2014, GeoInformatica.

[54]  Antonio Iodice,et al.  A Novel Approach for Disaster Monitoring: Fractal Models and Tools , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[55]  Jing Yuan,et al.  A framework of traveling companion discovery on trajectory data streams , 2013, ACM Trans. Intell. Syst. Technol..

[56]  Jean-Marie Nicolas,et al.  Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[57]  Shashi Shekhar,et al.  A neighborhood graph based approach to regional co-location pattern discovery: a summary of results , 2011, GIS.

[58]  Badrinath Roysam,et al.  Image change detection algorithms: a systematic survey , 2005, IEEE Transactions on Image Processing.

[59]  Geoffrey J. Hay,et al.  Object-based change detection , 2012 .

[60]  Ray A. Jarvis,et al.  Clustering Using a Similarity Measure Based on Shared Near Neighbors , 1973, IEEE Transactions on Computers.

[61]  Shashi Shekhar,et al.  Capacity-Constrained Network-Voronoi Diagram , 2015, IEEE Trans. Knowl. Data Eng..

[62]  Shashi Shekhar,et al.  Mixed-Drove Spatiotemporal Co-Occurrence Pattern Mining , 2008, IEEE Transactions on Knowledge and Data Engineering.

[63]  Jiawei Han,et al.  Geographic Data Mining and Knowledge Discovery , 2001 .

[64]  Vipin Kumar,et al.  Testing the significance of spatio-temporal teleconnection patterns , 2012, KDD.

[65]  Chang-Tien Lu,et al.  On Detecting Spatial Outliers , 2008, GeoInformatica.

[66]  Hui Xiong,et al.  A Framework for Discovering Co-Location Patterns in Data Sets with Extended Spatial Objects , 2004, SDM.

[67]  Marijke F. Augusteijn,et al.  Fusion of image classifications using Bayesian techniques with Markov random fields , 1999 .

[68]  T. Tango,et al.  A Space–Time Scan Statistic for Detecting Emerging Outbreaks , 2011, Biometrics.

[69]  Jeremy Mennis,et al.  Cubic Map Algebra Functions for Spatio-Temporal Analysis , 2005 .

[70]  Shashi Shekhar,et al.  A Joinless Approach for Mining Spatial Colocation Patterns , 2006, IEEE Transactions on Knowledge and Data Engineering.

[71]  Mohamed F. Mokbel,et al.  Location-based and preference-aware recommendation using sparse geo-social networking data , 2012, SIGSPATIAL/GIS.

[72]  Cecilia Mascolo,et al.  Mining User Mobility Features for Next Place Prediction in Location-Based Services , 2012, 2012 IEEE 12th International Conference on Data Mining.

[73]  Jinyang Chen,et al.  Clustering of trajectories based on Hausdorff distance , 2011, 2011 International Conference on Electronics, Communications and Control (ICECC).

[74]  Noel A. C. Cressie,et al.  Statistics for Spatial Data: Cressie/Statistics , 1993 .

[75]  Shashi Shekhar,et al.  Mixed-Drove Spatio-Temporal Co-occurrence Pattern Mining : A Summary of Results , 2006 .

[76]  S. Shekhar,et al.  Discovering Co-location Patterns from Spatial Datasets : A General Approach , 2004 .

[77]  Shashi Shekhar,et al.  Ring-Shaped Hotspot Detection: A Summary of Results , 2014, 2014 IEEE International Conference on Data Mining.

[78]  Patrick Bogaert,et al.  Forest change detection by statistical object-based method , 2006 .

[79]  Andrew W. Moore,et al.  A Fast Multi-Resolution Method for Detection of Significant Spatial Disease Clusters , 2003, NIPS.

[80]  Vipin Kumar,et al.  Chameleon: Hierarchical Clustering Using Dynamic Modeling , 1999, Computer.

[81]  Yan Huang,et al.  A Framework for Mining Sequential Patterns from Spatio-Temporal Event Data Sets , 2008, IEEE Transactions on Knowledge and Data Engineering.

[82]  L. Anselin Local Indicators of Spatial Association—LISA , 2010 .

[83]  VARUN CHANDOLA,et al.  Anomaly detection: A survey , 2009, CSUR.

[84]  Zhe Jiang,et al.  Monitoring Land-Cover Changes: A Machine-Learning Perspective , 2016, IEEE Geoscience and Remote Sensing Magazine.

[85]  Song Wang,et al.  Regional Co-locations of Arbitrary Shapes , 2013, SSTD.

[86]  M. Kulldorff A spatial scan statistic , 1997 .

[87]  Slava Kisilevich,et al.  Spatio-temporal clustering , 2010, Data Mining and Knowledge Discovery Handbook.

[88]  Shashi Shekhar,et al.  Identifying patterns in spatial information: A survey of methods , 2011, WIREs Data Mining Knowl. Discov..

[89]  Stan Openshaw,et al.  Modifiable Areal Unit Problem , 2008, Encyclopedia of GIS.

[90]  Carol A. Gotway,et al.  Statistical Methods for Spatial Data Analysis , 2004 .

[91]  Shashi Shekhar,et al.  Identifying K Primary Corridors from urban bicycle GPS trajectories on a road network , 2016, Inf. Syst..

[92]  Shashi Shekhar,et al.  Book chapter in data mining: Next generation chal-lenges and future directions , 2003 .

[93]  Anthony J. T. Lee,et al.  Mining frequent trajectory patterns in spatial-temporal databases , 2009, Inf. Sci..

[94]  Jay M. Ver Hoef,et al.  Applications of spatial statistical network models to stream data , 2014 .

[95]  John R. Jensen,et al.  Object‐based change detection using correlation image analysis and image segmentation , 2008 .

[96]  Christoph F. Eick,et al.  A framework for regional association rule mining and scoping in spatial datasets , 2011, GeoInformatica.

[97]  Zhe Jiang,et al.  Grid-Based Colocation Mining Algorithms on GPU for Big Spatial Event Data: A Summary of Results , 2017, SSTD.

[98]  W. Tobler A Computer Movie Simulating Urban Growth in the Detroit Region , 1970 .

[99]  Shashi Shekhar,et al.  Spatiotemporal Data Mining: A Computational Perspective , 2015, ISPRS Int. J. Geo Inf..

[100]  Martin Ester,et al.  A multi-relational approach to spatial classification , 2009, KDD.

[101]  Jörg Sander,et al.  Mining Statistically Significant Co-location and Segregation Patterns , 2014, IEEE Transactions on Knowledge and Data Engineering.

[102]  Noel A Cressie,et al.  Statistics for Spatial Data, Revised Edition. , 1994 .

[103]  Florian Verhein Mining Complex Spatio-Temporal Sequence Patterns , 2009, SDM.

[104]  Michael F. Worboys,et al.  GIS : a computing perspective , 2004 .

[105]  Yasuhiko Morimoto,et al.  Mining frequent neighboring class sets in spatial databases , 2001, KDD '01.

[106]  Jun Chen,et al.  Advances in Photogrammetry, Remote Sensing and Spatial Information Sciences: 2008 ISPRS Congress Book , 2008 .

[107]  Noel A Cressie,et al.  Statistics for Spatial Data. , 1992 .

[108]  Shashi Shekhar,et al.  Cascading Spatio-Temporal Pattern Discovery , 2012, IEEE Transactions on Knowledge and Data Engineering.

[109]  Hao Wang,et al.  Location recommendation in location-based social networks using user check-in data , 2013, SIGSPATIAL/GIS.

[110]  Marc Souris,et al.  Exploring spatial patterns and hotspots of diarrhea in Chiang Mai, Thailand , 2009, International journal of health geographics.

[111]  Chang-Tien Lu,et al.  Detecting spatial outliers with multiple attributes , 2003, Proceedings. 15th IEEE International Conference on Tools with Artificial Intelligence.

[112]  Shashi Shekhar,et al.  Lagrangian Xgraphs: A Logical Data-Model for Spatio-Temporal Network Data: A Summary , 2014, ER Workshops.

[113]  Atsuyuki Okabe,et al.  Statistical Analysis of the Distribution of Points on a Network , 2010 .

[114]  Joel H. Saltz,et al.  Hadoop-GIS: A High Performance Spatial Data Warehousing System over MapReduce , 2013, Proc. VLDB Endow..

[115]  Til Aach,et al.  Bayesian algorithms for adaptive change detection in image sequences using Markov random fields , 1995, Signal Process. Image Commun..

[116]  Til Aach,et al.  Statistical model-based change detection in moving video , 1993, Signal Process..

[117]  Zhe Zhu,et al.  What's Your Next Move: User Activity Prediction in Location-based Social Networks , 2013, SDM.

[118]  John R. Jensen,et al.  A change detection model based on neighborhood correlation image analysis and decision tree classification , 2005 .

[119]  Shashi Shekhar,et al.  Cascading Spatio-temporal Pattern Discovery: A Summary of Results , 2010, SDM.

[120]  Karsten Steinhaeuser,et al.  Data Mining for Climate Change and Impacts , 2008, 2008 IEEE International Conference on Data Mining Workshops.

[121]  Michelangelo Ceci,et al.  Spatial Associative Classification at Different Levels of Granularity: A Probabilistic Approach , 2004, PKDD.

[122]  Shashi Shekhar,et al.  A Critical-Time-Point Approach to All-Start-Time Lagrangian Shortest Paths: A Summary of Results , 2011, SSTD.

[123]  Pedro Cabral,et al.  Statistical Evaluation of Spatial Interpolation Methods for Small-Sampled Region: A Case Study of Temperature Change Phenomenon in Bangladesh , 2011, ICCSA.

[124]  Vipin Kumar,et al.  Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.

[125]  Kannan,et al.  ON IMAGE SEGMENTATION TECHNIQUES , 2022 .

[126]  Hui Xiong,et al.  Mining Co-Location Patterns with Rare Events from Spatial Data Sets , 2006, GeoInformatica.

[127]  Min Wang,et al.  Mining Spatial-temporal Clusters from Geo-databases , 2006, ADMA.

[128]  Yan Huang,et al.  Correlation Analysis of Spatial Time Series Datasets: A Filter-and-Refine Approach , 2003, PAKDD.

[129]  Hui Xiong,et al.  Learning geographical preferences for point-of-interest recommendation , 2013, KDD.

[130]  Yin Yang,et al.  Continuous k-Means Monitoring over Moving Objects , 2008, IEEE Transactions on Knowledge and Data Engineering.

[131]  Shashi Shekhar,et al.  A Unified Approach to Detecting Spatial Outliers , 2003, GeoInformatica.

[132]  Florence Puech,et al.  Generalizing Ripley's K function to inhomogeneous populations , 2009 .

[133]  James F. Allen Towards a General Theory of Action and Time , 1984, Artif. Intell..

[134]  Derya Birant,et al.  ST-DBSCAN: An algorithm for clustering spatial-temporal data , 2007, Data Knowl. Eng..

[135]  Chi-Hoon Lee,et al.  Efficient Spatial Classification Using Decoupled Conditional Random Fields , 2006, PKDD.

[136]  G. Karypis,et al.  Multi-Constraint Mesh Partitioning for Contact/Impact Computations , 2003, ACM/IEEE SC 2003 Conference (SC'03).

[137]  Pabitra Mitra,et al.  Spatial Interpolation to Predict Missing Attributes in GIS Using Semantic Kriging , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[138]  Varun Chandola,et al.  Using Time Series Segmentation for Deriving Vegetation Phenology Indices from MODIS NDVI Data , 2010, 2010 IEEE International Conference on Data Mining Workshops.

[139]  L. Anselin Spatial Econometrics: Methods and Models , 1988 .

[140]  T. Warren Liao,et al.  Clustering of time series data - a survey , 2005, Pattern Recognit..

[141]  Martin Kulldorff,et al.  Prospective time periodic geographical disease surveillance using a scan statistic , 2001 .

[142]  Andrew W. Moore,et al.  Detection of emerging space-time clusters , 2005, KDD '05.

[143]  Jin Li,et al.  A review of comparative studies of spatial interpolation methods in environmental sciences: Performance and impact factors , 2011, Ecol. Informatics.

[144]  Beng Chin Ooi,et al.  Continuous Clustering of Moving Objects , 2007, IEEE Transactions on Knowledge and Data Engineering.

[145]  Nikos Mamoulis,et al.  Mining frequent spatio-temporal sequential patterns , 2005, Fifth IEEE International Conference on Data Mining (ICDM'05).

[146]  Leen-Kiat Soh,et al.  A dissimilarity function for clustering geospatial polygons , 2009, GIS.

[147]  Nikos Mamoulis,et al.  Discovery of Collocation Episodes in Spatiotemporal Data , 2006, Sixth International Conference on Data Mining (ICDM'06).

[148]  William Rand,et al.  Spatial process and data models: Toward integration of agent-based models and GIS , 2005, J. Geogr. Syst..

[149]  Goo Jun,et al.  Spatially Cost-Sensitive Active Learning , 2009, SDM.

[150]  Wang-Chien Lee,et al.  Mining geographic-temporal-semantic patterns in trajectories for location prediction , 2013, ACM Trans. Intell. Syst. Technol..

[151]  Sudarshan S. Chawathe,et al.  Organizing Hot-Spot Police Patrol Routes , 2007, 2007 IEEE Intelligence and Security Informatics.

[152]  Ramakrishnan Srikant,et al.  Fast algorithms for mining association rules , 1998, VLDB 1998.

[153]  Shashi Shekhar,et al.  Summarizing trajectories into k-primary corridors: a summary of results , 2012, SIGSPATIAL/GIS.