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

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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

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Outline • Geospatial DataAccessibility • 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

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Data Geospatial-Accessibility • Methodologiesemployed to enable and access the data’s geospatial content – generate the geography then access the geography 1. 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 Crawler 2. 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(?)

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Accessing through SpatialHierarchy 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 May Block 1 Block 1 Block 1 Block 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

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Accessing through SpatialHierarchy 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

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Predictive Geospatial DataAccess Modelling • i2maps (Dr. Alexei Pozdnoukhov, NCG) • Real Time Weather Prediction

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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)

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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

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Flows of callsform communities North-South divide: typical destinations of calls from cells “ “

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Dynamics of links:communities (Fergal Walsh, NCG)

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Dynamics of links: community tracking in time • Time/Space Clustering of Mobile Communications Network Cells (Fergal Walsh, NCG)

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First steps: Twitterat NCG Preliminary 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 saved Natural language processing: some experience in NER task with NLTK, SENNA packages More work needed to get messages semantics and relate topics to activities.

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First steps: Twitterat NCG (Dr. Christian Kaiser, NCG)

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Road Side features– Fences / walls/ barriers (Dr. Conor McElhinney, NCG)

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Road Side features– Fences / walls/ barriers Wall (Dr. Conor McElhinney, NCG)

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Road Side features– Fences / walls/ barriers Crash barrier (Dr. Conor McElhinney, NCG)

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Road Side features– Fences / walls/ barriers Fence

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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

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MMS GeoSpatial DataFramework • Fully Interactive Browser Implementation for Geo-Referenced Environment modelling data • Access, Processing and Visualisation

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To Wrap-Up • MMSWork 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 Forward Research 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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