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Big Data for Local Context
The document discusses the significance of big data and geospatial standards for understanding and predicting human mobility, marketing, risk analysis, and crime patterns. It highlights the role of the Open Geospatial Consortium (OGC) in developing geospatial standards and facilitating data interoperability for various applications including transportation, retail analytics, and indoor location services. Additionally, it covers advancements in technologies such as the Internet of Things (IoT) and how they are integrated with geospatial information to enhance context discovery and data flow.
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Big Data for Local Context
- 1. ® Big Data forLocal Context George Percivall OGC Chief Engineer, CTO Location and Context 2015 3 November 2015 Copyright © 2015 Open Geospatial Consortium
- 2. OGC ® Power of Location • “Location targeting is holy grail for marketers” – Sir Martin Sorrell, CEO WPP at Mobile World Congress • By measuring entropy of individual’s trajectory, we find 93% potential predictability in user mobility – Limits of Predictability in Human Mobility, Science 2010 • 1st law of geography: "Everything is related to everything else, but near things are more related than distant things.” – Waldo Tobler © 2013 Open Geospatial Consortium 2
- 3. OGC ® Commercial 39% Government 27% NGO 8% Research 6% University 20% The Open GeospatialConsortium Copyright © 2015 Open Geospatial Consortium Not-for-profit, international voluntary consensus standards organization; leading development of geospatial standards • Founded in 1994. • 515+ members and growing • 48 standards • Thousands of implementations • Broad user community implementation worldwide • Alliances and collaborative activities with ISO and many other SDO’s Africa 4 Asia Pacific 86 Europe 209 Middle East 34 North America 182 South America 3
- 4. Contexts & Possibilities PRESENT Behaviors & Actuals PAST Predictions& Potentials FUTURE Source: Jon Spinney, Location Intelligence, Pitney Bowes
- 5. 5 Marketing Automaton Source: Jon Spinney,Location Intelligence, Pitney Bowes
- 6. 6 By Analyzing LargeVolumes of Historical Mobile Location Data Married with Place & Demographic Data, We Can Create Audience Segments and New Enriched Lifestyle Profiles for Targeting. Source: Jon Spinney, Location Intelligence, Pitney Bowes
- 7. 7 Risk Analysis Source: Jon Spinney,Location Intelligence, Pitney Bowes
- 8. 8 By Analyzing LargeVolumes of Historical Travel Data coupled with Historical Accident Observations, and Present Observations, We Can Assign Estimated Risk & Associated Insurance Premiums to Auto Plans. Source: Jon Spinney, Location Intelligence, Pitney Bowes
- 9. OGC ® Geospatial Context via“Space Time Boxes” Jeff Jonas, IBM Fellow. Chief Scientist, Context Computing Strata + Hadoop World 2015
- 10. OGC ® Spatial Bins usedfor Local Context Copyright © 2015 Open Geospatial Consortium Canberra region with hexagon grid.
- 11. OGC ® Discrete Global GridSystems Source: Matt Purss, Geoscience Australia National Nested Grid SCENZ-Grid Earth System Spatial Grid Snyder Grid
- 12. OGC ® OGC Discrete GlobalGrid Systems (DGGS) • Common criteria and terms for spatial grids • Facilitate interoperability and data fusion • Support marketplaces of spatial data • Public comment period to begin next week http://www.opengeospatial.org/projects/groups/dggsswg
- 13. OGC ® Big Data SpatialAnalytics - GeoWave Copyright © 2015 Open Geospatial Consortium Geographic objects and operators in Apache Accumulo Advanced support for OGC spatial types: 3D and temporal GeoServer plugin for OGC Web Services http://ngageoint.github.io/geowave/
- 14. OGC ® Detecting proximity andcolocation in time Copyright © 2013 Open Geospatial Consortium time Spatial plane 1 prism = 1 leaf + 1 sweep (&attribute) End leaf of tracks id=1 Id=2 11:11:20.835 11:11:26.215 11:11:28.021 11:11:30.127 (C) (B) (D) (A) OGC Moving Features Standard implements ISO 19141
- 15. OGC ® OGC Moving FeaturesEncoding Standard • "Moving features" data describes such things as vehicles, pedestrians, airplanes and ships. – This is Big Data – high volume, high velocity. • CSV and XML encodings of ISO 19141 Copyright © 2015 Open Geospatial Consortium
- 16. OGC ® Transportation Survey • GPStracks encoded in OGC Moving Features • Sharing data by local governments, transit companies, etc. • Reduce traffic congestion using transportation surveys. Copyright © 2015 Open Geospatial Consortium Transportation survey 1234 3456 2345 4567 1234 3456 12hr total 24hr total 123 234 3456 4567 3456 7890 Traffic DemandsTraffic Congestions Smart phones People in the city Tracks measured by GPS (encoded by Moving Features)
- 17. OGC ® Crime Pattern • Predicting regions morevulnerable to crime based on Routine Activity Theory Copyright © 2015 Open Geospatial Consortium
- 18. OGC ® Layout design Pedestrian-tracking tounderstand movements in facilities, e.g., shopping malls and transit terminals Copyright © 2015 Open Geospatial Consortium
- 19. OGC ® Indoor Geo-Portal Indoor mCommerce Emergency Control Servicesfor handicapped persons Cruise Ship Hospital Indoor LBS Indoor Security Indoor Robot Indoor Location Application © 2015 Open Geospatial Consortium
- 20. OGC ® Retail Location Analytics Copyright© 2015 Open Geospatial Consortium Source: HP Viewpoint paper - Retail location analytics As an example, Figure 1 shows a department store that has been set up in a grid. Each square in the grid can be labeled with attributes that are significant to the retailer. By tracking the frequency of visits and time spent in a particular department, the interests of nalytics Figure 1. Location grid
- 21. OGC ® National Retail Federation In-StoreUse Cases • Marketing – drive traffic to store – In-store targeting • Customer Service – Locate a product in store – Shopping List - multi-channel – Request store associate • Operations – Store setup – Stocking shelves Copyright © 2015 Open Geospatial Consortium Copyright 2013 NRF. All rights reserved.Page 11 Verbatim reproduction and distribution of this document is permitted in any medium, provided this notice is preserved. Interest. This gives the flexibility to report the information in the most relevant format. 2.2Recommended Coordinate System These different systems are in contention, so the recommendation presented in this best practices paper defines one common coordinate system, and one consistent origin system. Given that the ARTS Location Standard’s objective is to locate objects within a store as a whole, the recommended coordinate system has to apply across all ARTS standards. The floor-plan coordinate system is the recommendation as modified herein. The origin is located at the bottom back left position of the object. This is the same origin used within planograms and the ARTS Video Analytics Standard. This presents an interesting solution to a sloping shelf. The position of the origin of the shelf doesn’t change, since it is anchored at the back. If the bottom front left position was used, then when a shelf is sloped it would change the location of the origin, which would complicate the transformation algorithms. The following table highlights how existing system would be affected by adopting this standard: Floor Plan System Planogram System Origin Bottom Back Left of Fixture Bottom Back Left of Fixture Coordinate System Left-Hand Coordinate System Left-Hand Coordinate System X axis Left to Right Left to Right Y axis Front to Back Front to Back Z axis Bottom to Top Bottom to Top Copyright 2013 NRF. All rights reserved.Page 11 Verbatim reproduction and distribution of this document is permitted in any medium, provided this notice is preserved. The origin is located at the bottom back left position of the object. This is the same origin used within planograms and the ARTS Video Analytics Standard. This presents an interesting solution to a sloping shelf. The position of the origin of the shelf doesn’t change, since it is anchored at the back. If the bottom front left position was used, then when a shelf is sloped it would change the location of the origin, which would complicate the transformation algorithms. The following table highlights how existing system would be affected by adopting this standard: Floor Plan System Planogram System Origin Bottom Back Left of Fixture Bottom Back Left of Fixture Coordinate System Left-Hand Coordinate System Left-Hand Coordinate System X axis Left to Right Left to Right Y axis Front to Back Front to Back Z axis Bottom to Top Bottom to Top Graphics source: ARTS Coordinate System & Insertion Points Best Practices Version 1.0 ARTS Coordinate S Points Best Practices Version 1.0 November 29, 2013 – Last Call Working Draft Chairman: Dennis Blankenship V Phuc Do T Members: Graeme Shaw (Team Captain) O Curtis Philbrook J Amit Chetal C Amnon Ribak I Andy Mattice A Arun Hampapur I Bart McGlothin C Marc MacDonald E Doug Wick D In-store coordinate Reference Systems
- 22. OGC ® CityGML and IndoorGML 1stlayer: Topographic space model – building structure – geometric-topological model – network for route planning 2nd layer: Sensor space model – Radio/Beacon footprints – coverage of sensor areas – transition between sensor areas • Builds on existing International standards CityGML and IFC © 2015 Open Geospatial Consortium 22
- 23. } The ILApromotes the deployment of indoor location based services in mobile environment } ILA and OGC working together on standards based architecture
- 24. OGC ® Context Discovery acrossIoT Data Flow Copyright © 2015 Open Geospatial Consortium IEEE ACCESS Data Archive User Smart Things Sensors Smart Wearable Visualisation / Presentation / Recommendation Cloud Intermediary Communication and Processing Devices =Context Discovery Real Time Processing Fig. 6. Data Flow in IoT Solutions in High-level. Context can be discovers in different stages / phases in the data flow. A typical IoT solution may some part of the data flow architecture depending on the their intended functionalities. First we introduce the name of the IoT solution in the column (1) in Table II. We also provide the web page link of the each product / solution. It is important to note that, these products does not have any related academic publication. Therefore, we believe that web page links are the most reliable collects and processes context information such as locatio temperature, light, relative humidity and biometric pressu in order to enhance the visibility and transparency of t supply chain. SenseAware uses both hardware and softwa components in their sensor-based logistic solution. such d Perera, C.; Liu, C.H.; Jayawardena, S.; Min Chen, "A Survey on Internet of Things From Industrial Market Perspective,” Access, IEEE , vol.2, no., pp.1660-1679, 2014 doi: 10.1109/ACCESS.2015.2389854
- 25. OGC ® OGC SensorThings APIfor IoT • Builds on OGC Sensor Web Enablement (SWE) standards that are operational around the world • Builds on Web protocols; easy-to-use RESTful style • OGC candidate standard for open access to IoT devices Copyright © 2015 Open Geospatial Consortium Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Emergence of the Internet of Things Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm!Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm!Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Graphic from Dr. Steve Liang Associate Professor / AITF-Microsoft Industry Chair" OGC SensorThings SWG Chair Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! Temperature: 29 C ! Humidity: 29%! Windspeed: 11 km/h! CO: 0.23 ppm! NO: 0.22 ppm! (Graphic source: Steve Liang, University of Calgary)
- 26. OGC ® Region-Centric Geospatial Information Feature-Centric Geospatial Information Human-Centric Geospatial Information Device-Centric Geospatial Information 1980s 1990s 2000s 2010s Source of slide: Steve Liang, Univ. Calgary and chairman of OGC Sensor Web 4 IoT Progression of geospatial information © 2013 Open Geospatial Consortium, Inc. 26 èTowards Micro-geography
- 27. OGC ® Scale of 1to 1 by Lewis Carroll, 1893 Copyright © 2015 Open Geospatial Consortium “And then came the grandest idea of all! We actually made a map of the country, on the scale of a mile to the mile!” “Have you used it much?” I enquired. “It has never been spread out, yet. The farmers object: they said it would cover the whole country, and shut out the sunlight! So we now use the country itself, as its own map, and I assure you it does nearly as well.”
- 28. OGC ® Join the OGC OpenGeospatial Consortium www.opengeospatial.org OGC Standards - freely available www.opengeospatial.org/standards OGC on YouTube http://www.youtube.com/user/ogcvideo George Percivall gpercivall@opengeospatial.org Copyright © 2015 Open Geospatial Consortium