Spatial Data Science. Soumya K Ghosh

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1 Workshop on Data Science and Machine Learning (DSML 17) ISI Kolkata, March 28-31, 2017 Spatial Data Science Soumya K Ghosh Professor Department of Computer Science and Engineering Indian Institute of Technology, Kharagpur skg@cse.iitkgp.ernet.in

2 Spatial Data Science? Spatial Data Science (SDS) represents an overarching umbrella for studying theories, methods, and applications of spatial analysis/modeling/mining; and spatial data handling, management, and visualization. SDS research are gaining enormous interest as dataintensive, large-scale, and/or multi-scale problems that involve the use and development of GIS and spatial analysis/modeling are becoming ubiquitous in scientific discovery and decision-making in many fields. Spatial Database Spatial Informatics GIS Spatial Data Science

3 Data Management, Accessibility, Analysis, Mining, Prediction, Learning Spatial Data Management/ Analyis

4 Trend Multi-User (Enterprise) Societal Projects Groups/Teams

5 Spatial Data Infrastructure (SDI) Infrastructure implies that there should be some sort of coordination for policy formulation and implementation The SDI provides a basis for spatial data discovery, evaluation, and application for users and providers within all levels of Government, the Commercial sector, the non-profit sector, Academia and by Citizens in general. --The SDI Cookbook

6 Typical SDI

7 Need for Processing Services

8 Towards Spatial Cloud Typical Scenario

9 SDS Research Directions Spatial Webservices/ Orchestration/ Crowd Sourcing Prediction using Interpolation Spatial Big Data Analytics Geospatial Informatics Spatiotemporal Analysis Spatial Prediction using Deep Learning Human Movement Analysis Using GPS Footprints Prediction using Mathematical Morphology

10 Areas of Research Spatial Webservices/ Orchestration/ Crowd Sourcing Prediction using Interpolation Spatial Big Data Analytics Geospatial Informatics Spatiotemporal Analysis Spatial Prediction using Deep Learning Human Movement Analysis Using GPS Footprints Prediction using Mathematical Morphology

11 Areas of Research Spatial Webservices/ Orchestration/ Crowd Sourcing Prediction using Interpolation Spatial Big Data Analytics Geospatial Informatics Spatiotemporal Analysis Spatial Prediction using Deep Learning Human Movement Analysis Using GPS Footprints Prediction using Mathematical Morphology

12 Spatial Interpolation Satellite Image Missing Data Known Samples Missing Value Gap-filled Image Predicted Actual Spatial Prediction

13 Semantic Interpolation Same Terrain but Different Interpretation Semantic Knowledge? Semantic Knowledge for Spatial Interpolation Land-cover Ontology Point Lat Long LC A X1 Y1 B X2 Y2 Point Lat Long LC A X1 Y1 Building Soumya B K Ghosh, X2 CSE, Y2 IIT Kharagpur Lake

14 SemK: Empirical Validation Predicted Surface Imagery (by Several Prediction Methods) Actual Lmean NN IDW OK SemK Actual SemK

15 Spatio-temporal Data Analysis Data Driven Approach Identifying interesting, useful, non-trivial information/patterns In large spatial or spatio-temporal datasets

16 Spatio-temporal Prediction of Time Series data A variable at one location may be influenced by the variables from the neighborhood locations as well Spatial pattern as well as influence of these variables varies from one location to another, depending on the topographical features Example: Spatial variability at river watershed

17 Monidipa Das, Soumya K. Ghosh, Pramesh. Gupta, V. M. Chowdary, Ravoori Nagaraja, and V. K. Dadhwal. FORWARD: A Model for FOrecasting Reservoir WAteR Dynamics using Spatial Bayesian Network (SpaBN). Transactions on Knowledge and Data Engineering (TKDE), IEEE, DOI: /TKDE , (2017)

18 Spatio-Temporal Change Pattern Analysis Spatio-temporal (ST) change pattern analysis is important for various applications: Urban sprawl analysis, Regional climate change pattern analysis etc. Analyzing ST pattern from geometric perspective provides insights on the temporal change in spatial distribution of ST phenomena Example: Spatio-temporal Change in Climate Zone Distribution in Australia

19 Monidipa Das and Soumya K. Ghosh. Modeling spatio-temporal change pattern using mathematical morphology. In Proceedings of the 3rd IKDD (Indian chapter of ACM SIGKDD) Conference on Data Science (CoDS), page 4. ACM, (2016) Monidipa Das and Soumya K. Ghosh, Spatio-temporal Pattern Analysis for Regional Climate Change using Mathematical Morphology, ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. II-4/W2 (2015)

20 Movement Analysis Using GPS Footprints Mobility data is recorded at a wide scale, main source of which is GPS data Analytics at both individual and societal level can be made Analysing and mining of human mobility pattern can be applied to various use cases: Car pool suggestions Anomaly detection Clustering different type of users Power upgradation of mobile towers, etc. Analysing GPS traces from spatio-temporal context and extract movement patterns for exploring some implicit knowledge Ex. What is the movement pattern of a particular category of users (example - student or professor of an university) without knowing their identity?

21 Trajectory Summarization Applications Raw GPS Traces Mobility Summary framework. Mobility Summary of a User Trajectory Preprocessing Trajectory Similarity Trajectory Clustering Trajectory Summarization Applications of Trajectory Summary Next Path Prediction Anomaly Detection Mobility Data of people collected at a large scale Various sources of data GPS traces, Call Detail Records, Location Based Social Networks Analysis at both individual and community level is possible Other applications of trajectory mining All Trajectories (Test User) includes animal cluster movement prediction, hurricane movement, etc. Mobility Summaries

22 Urban Planning from GPS Footprints Storage of Spatial Points Efficient indexing strategy for spatial point/range query Geo-tagged Data Reverse geo-coding to map nearby landmark information Urban Planning Land Use Mapping Prediction of possible land use of an unknown region Stay Point detection Geo-tagging of points

23 Orchestration Engine for VGI Data Orchestration is a mechanism for aggregation of web services by using business process It is used to chain a set of web services to generate complex information The Volunteered Geographic Information (VGI) is a user generated content which are voluntarily contributed by unskilled people for specific purpose Rule Repository Catalog for Data and Processing Srvice Load Rule Find Data and Processing Service Client interface Business Process Executer Load Map Process Geospatial Data Load Geospatial Data Web Map Services Web Processing Services Web Feature Services

24 Example: Flood Risk Estimation Plot of VGI points based on risk value Volunteers Data from mobile network Flood Risk Estimation On VGI points Curated data From EGIS Orchestration of feature sets to reveal risk zones Geospatial Risk Estimation Model Elevation Risk Zones

25 Spatial Big Data Analytics Spatial Data Sources GPS Enabled Devices (Crowd Sourcing) Satellite Remote Sensing Sensor Networks Digital Cameras Aerial Surveying Variety Volume Applications Climate science Numerical weather prediction Prediction of missing attributes Very Frequent Acquisition Velocity E.g.: Traffic data is recorded within each ~5sub-seconds time-slot Spatial Big Data Analytics: Challenges Spatial Datasets larger than the capacity of current computing systems 3Vs (Volume, Variety and Velocity) Computing intensive

26 Big Data Analytics for Spatial Interpolation Map of land stations where air temperature was measured Source: NOAA National Climatic Data Centre Interpolated surface is created with 0.5 x 0.5 degree resolution Solutions Parallel implementation of the interpolation algorithms Exploiting faster matrix manipulation algorithms (Strassen s) Exploiting Distributed framework like Hadoop MapReduce and Apache Spark

27 Thank You! Soumya K. Ghosh Professor Department of Computer Science and Engineering IIT Kharagpur

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