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Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
Geo-referenced human-activity-data; access, processing and knowledge extraction
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Geo-referenced human-activity-data; access, processing and knowledge extraction

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Talk given by Paul Lewis

Talk given by Paul Lewis

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  • 1. Geo-Referenced Human-Activity-Data; Access, Processing and Knowledge Extraction Paul Lewis (paul.lewis@nuim.ie) Dr. Conor McElhinney, Dr. Alexei Pozdnoukhov, Dr. Christian Kaiser, Fergal Walsh Tuesday 31st May 2011 University of Bremen
  • 2. Outline• Geospatial Data Accessibility • Modelling Challenges • Spatial Hierarchy Model • Access Process Examples• Processing Paradigms and Knowledge Extraction • Knowledge Extraction Decision Processes • Spatial Workflow Patterns • Temporal Dynamics in Communities • Taking on the Tweets • Feature extraction informs Risk Assessment Knowledge• Data and Knowledge Visualisation • Web Integration • Urban Model Data Extraction • Web geospatial knowledge extraction visualisation• Wrap-Up
  • 3. Data Geospatial-Accessibility• Methodologies employed to enable and access the data’s geospatial content – generate the geography then access the geography1. Raw Data Access - Function of Data Source Complexity • LiDAR/Imagery - 2D or 3D • Web-Content Air Quality Sensor Weather Measurements VGI Feed (e.g. Twitter) Surveillance Camera • Push SMS Web Page XML Video • Polling • Streaming Push Polling Stream Stream Data Data Receiver Stream Handler Stream Handler Crawler2. Creating the Geospatial Content • MMS – GeoSpatial content is Inherent at very high resolution • Geocrowd - Semantics need to be well understood in non explicit context, twitter location(?)
  • 4. Accessing through Spatial Hierarchy Models• Spatial Hierarchy Modelling • MMS context uses a spatial extent modelling approach • Geocrowd will define this process on a content type access model Constrained Workflow LiDAR folder Survey 10 Apr Survey 5 Dec Survey 2 MayBlock 1 Block 1 Block 1Block 2 Block 2 Block 2 .......Block 3 Block 3 Block 3 . . . .Block N . Block N . Block N . . .MetaData: Geo Bounds, date, MetaData: Geo Bounds, date, MetaData: Geo Bounds, date,processing done processing done processing done
  • 5. Accessing through Spatial Hierarchy Models• Optimising Data Accessibility in a circular data generation model • Intelligent Query Access now enabled for • Temporal • Spatial • Attributes • etc…… Acquire Query Store Data Model Spatially Visualisation Model
  • 6. Accessing through Spatial Hierarchy Models• Optimising Data Accessibility in a circular data generation model • Intelligent Query Access now enabled for • Temporal • Spatial • Attributes • etc…… Acquire Query Store Data Model Spatially Visualisation Model
  • 7. Accessing through Spatial Hierarchy Models• Optimising Data Accessibility in a circular data generation model • Intelligent Query Access now enabled for • Temporal • Spatial • Attributes • etc…… Acquire Query Store Data Model Spatially Visualisation Model
  • 8. Accessing through Spatial Hierarchy Models• Optimising Data Accessibility in a circular data generation model • Intelligent Query Access now enabled for • Temporal • Spatial • Attributes • etc…… Acquire Query Store Data Model Spatially Visualisation Model
  • 9. Predictive Geospatial Data Access Modelling• i2maps (Dr. Alexei Pozdnoukhov, NCG) • Real Time Weather Prediction
  • 10. Geospatial Data Processing• Knowledge Extraction informs Decision Support Processes• What does this mean in a processing context? • A paradigm that is?? • Centralised - Distributed • Spatial, Temporal……. • MMS Context is Constrained to static data Survey Processing • Temporal at best and partially, but (un)intentionally, Spatial. • Not collected independent of decision expectations• High Level Decisions • Alternative model approaches • Geocrowd (Dictionary of Models)
  • 11. Geospatial Data Processing• Where we could go with this at a physical level • CLOUD • Distributed processing, parallelism, scalability, flexibility• Parallelism • SDBMS access takes 1 sec. • But processing takes 60 sec. • Scalability • Processing scales to data model updating – Weather, Twitter • Storage scales model to data acquisition – Lidar/Imagery• This enables a Spatially-lead Workflow model at a knowledge level • Allows for fast information extraction • Allows for future knowledge extraction
  • 12. Flows of calls form communitiesNorth-South divide: typical destinations of calls from cells “ “
  • 13. Dynamics of links: communities (Fergal Walsh, NCG)
  • 14. Dynamics of links: community tracking in time• Time/Space Clustering of Mobile Communications Network Cells (Fergal Walsh, NCG)
  • 15. First steps: Twitter at NCGPreliminary work on the content-rich data streams: • Real-time Twitter feed is monitored • Geo-referencing is done by tweet, user, or location • Activity levels processed and visualised with heat maps • Tags and messages are savedNatural language processing:some experience in NER task with NLTK, SENNA packages More work needed to get messages semantics and relate topics to activities.
  • 16. First steps: Twitter at NCG (Dr. Christian Kaiser, NCG)
  • 17. Road Side features –Fences / walls/ barriers (Dr. Conor McElhinney, NCG)
  • 18. Road Side features –Fences / walls/ barriers (Dr. Conor McElhinney, NCG)
  • 19. Road Side features –Fences / walls/ barriers (Dr. Conor McElhinney, NCG)
  • 20. Road Side features –Fences / walls/ barriers Wall (Dr. Conor McElhinney, NCG)
  • 21. Road Side features –Fences / walls/ barriers Crash barrier (Dr. Conor McElhinney, NCG)
  • 22. Road Side features –Fences / walls/ barriers Fence
  • 23. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 24. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 25. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 26. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 27. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 28. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 29. Visualisation and Interaction• Where things are at… • Desktop tools for visualisation are well defined, developed and implemented• Where things are going… • Browser support boundaries constantly being expanded • WebGL for 3D visualisation • Is this the future? • i2maps thinks so and will continue to implement this paradigm
  • 30. MMS GeoSpatial Data Framework• Fully Interactive Browser Implementation for Geo-Referenced Environment modelling data • Access, Processing and Visualisation
  • 31. To Wrap-Up• MMS Work completed in 1.5 years • With 1.5 people years• i2maps is a long-term open source project. Next releases: July 31st, for OSGeo LiveDVD and a FOSS4G workshop at Denver, on September 13th. Going ForwardResearch problems we’d like to solve within Geocrowd at NCG: 1) Relate activity levels to content-rich data sources to enhance interpretability 2) Make it computationally efficient and scalable (Internet-scale)

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