Multi-perspective analysis of D4D fine resolution data Movers - - PowerPoint PPT Presentation

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Multi-perspective analysis of D4D fine resolution data Movers - - PowerPoint PPT Presentation

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Multi-perspective analysis of D4D fine resolution data Movers Gennady & Natalia Andrienko, Georg Fuchs Trajectories M (T S) Fraunhofer Institute IAIS Spatial events Sankt Augustin Spatio-temporal positions E (T S) Germany

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1 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

Multi-perspective analysis of D4D fine resolution data

Gennady & Natalia Andrienko, Georg Fuchs Fraunhofer Institute IAIS Sankt Augustin Germany http://geoanalytics.net/and

Trajectories M(TS)

Movers

Spatio-temporal positions E(TS)

Spatial events

Presence dynamics S(TP(ME))

Space (locations)

Spatial situations T(SP(ME))

Time (time units)

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2 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

MULTIPLE CONCEPTUAL VIEWS

ON MOVEMENT DATA

Spatial time series

Trajectories Events

Integration Extraction Aggregation Extraction Aggregation Extraction

Times Locations Movers Spatial events Spatial event data Spatial time series Movement data Local time series Spatial distributions Trajectories

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3 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

EVALUATING THE DATA PROPERTIES

1.

Days with missing data

2.

Change of IDs every two weeks

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4 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

ASSESSING DAILY AGGREGATES FOR ANTENNAS

  • Overall pattern: some days with missing data
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5 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

ASSESSING DAILY AGGREGATES FOR ANTENNAS

  • Space-time pattern
  • Systematically missing data

for selected regions and time periods week day of week

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6 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

ANALYZING HOURLY AGGREGATES FOR ANTENNAS

  • Yamoussoukro and San Pedro; raw counts (left) Vs. normalized (right)

hour day of week

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7 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

CLUSTERING ANTENNAS BY SIMILAR

HOURLY TEMPORAL PROFILES

1: High in evening: residential districts, regular employment 2: Uniform calling activity: mix of residential and business 4: business districts 3,6,7: residential with partly employed population

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8 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

PEAK EVENT DETECTION AT ANTENNA LEVEL

BASED ON HOURLY TIME SERIES

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9 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

ANALYSIS OF PEAKS:

SIMULTANEOUS EVENTS IN 4 CITIES, UNUSUAL PROFILES

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10 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

FLOWS BETWEEN REGIONS THAT CORRESPOND TO PEAKS IN PEOPLE

PRESENCE

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11 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

ANALYSIS OF FLOWS:

DENSITY-DRIVEN VORONOI TESSELLATION WITH R=100KM

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12 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

SEMANTIC ANALYSIS OF PERSONAL PLACES

  • One individual: home, work, social activities
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13 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

SEMANTIC ANALYSIS OF PERSONAL PLACES

  • Work places and trajectories of

several persons in Abidjan

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14 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

CONCLUSIONS

  • We considered the data from multiple perspectives, including
  • locations of varying resolution
  • time intervals of different length and hierarchical organization
  • trajectories
  • We detected a number of interesting patterns that could facilitate a variety of

applications:

  • Reconstructing demographic information (to replace expensive and difficult to
  • rganize census studies)
  • Reconstructing patterns of mobility (to enhance transportation studies)
  • Identifying places of important activities (for improving land use and

infrastructure)

  • Identifying events (for improving safety and security)
  • Detecting social networks (for marketing purposes)
  • Data quality disables more sophisticated analyses
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15 G.Andrienko, N.Andrienko, G.Fuchs http://geoanalytics.net/and D4D challenge, Boston MA, 1st of May, 2013

MONOGRAPH

Springer, June 2013 ISBN 978-3-642-37582-8 397 p. 200 illus., 178 in colour

  • Publisher’s leaflets with discount codes are available