Spatio temporal analysis of flows in cdc 2013 data

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Gennady Andrienko, Natalia Andrienko
Fraunhofer IAIS, Germany
Topic: “Spatio-temporal analysis of flows in CDC 2013 data”

Published in: Technology, Economy & Finance
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Spatio temporal analysis of flows in cdc 2013 data

  1. 1. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISSpatio-temporal analysis of flowsin CDC 2013 dataGennady AndrienkoNatalia Andrienkohttp://geoanalytics.net
  2. 2. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISData processing procedures1. Initial processing in Database• Eliminating duplicates (same ID and time stamp)• Eliminating stationary points (speed<2km/h)• Dividing into days (by 3AM)• Further dividing by 30min stops and 1km gaps• Eliminating trajectories consisting of less than 5 points, shorter than 5minutes, within 100m bounding rectangle2. Further processing attempts in main memory• Removing segments with speed > 75km/h• Removing segments with high tortuosity (>2 over 1min), sinuosity (>5over 1min) or being within 100m radius over 10-15 minutes3. Still, the data are far from being perfect• Wrong hardware / software / settings?
  3. 3. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISData quality• Jumping around stops;• Systematically wrong positions
  4. 4. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISSummarization and aggregation of trajectories• Density-driven Voronoi polygons, r=100m: 14,033 polygons country-wide• Correctly reflect the street network
  5. 5. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISFlows between adjacent polygons• 14,033 polygons => 26,094 directed connections• 5,723 used by at least 5 different trajectories• Attribute “N different trajectories” compensates for “hairball” structures @stops
  6. 6. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISHourly time series of flows: transformation and clustering• Only connections used byat least 5 trajectories1. Hourly time series2. Smoothing by 3 hourswindows3. Mean-normalization ofeach time series4. Clustering by k-Meanswith different K
  7. 7. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISMajor clusters
  8. 8. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISCluster 5
  9. 9. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISCluster 3
  10. 10. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISCluster 1
  11. 11. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISCluster 2
  12. 12. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISCluster 4
  13. 13. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISConclusions• Different roads have different temporal signatures• Especially bridges• Too few trajectories per person / per road segment for more sophisticatedanalysis• Data quality issues
  14. 14. © Fraunhofer-Institut für IntelligenteAnalyse- und Informationssysteme IAISWhat we can do:• Analysis of flows and their temporal dynamicsTimesLocationsMoversSpatial eventsSpatial event data Spatial time seriesMovement data Local time seriesSpatial distributionsTrajectoriesDetails:Visual Analytics of Movement: an Overview ofMethods, Tools, and ProceduresInformation Visualization, 12(1), pp.3-24, 2013andVisual Analytics of MovementSpringer-Verlag 2013ISBN 978-3-642-37582-8Due: July 5, 2013

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