spatio temporal analysis of flows in cdc 2013 data

© Fraunhofer-Institut für Intelligente

Analyse- und Informationssysteme IAIS

Spatio-temporal analysis of flows

in CDC 2013 data

Gennady Andrienko

Natalia Andrienko

http://geoanalytics.net

http://geoanalytics.net/



Data processing procedures

1. 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 5

minutes, within 100m bounding rectangle

2. Further processing attempts in main memory

• Removing segments with speed > 75km/h

• Removing segments with high tortuosity (>2 over 1min), sinuosity (>5

over 1min) or being within 100m radius over 10-15 minutes

3. Still, the data are far from being perfect

• Wrong hardware / software / settings?



Data quality

• Jumping around stops;

• Systematically wrong positions



Summarization and aggregation of trajectories

• Density-driven Voronoi polygons, r=100m: 14,033 polygons country-wide

• Correctly reflect the street network



Flows 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



Hourly time series of flows: transformation and clustering

• Only connections used by

at least 5 trajectories

1. Hourly time series

2. Smoothing by 3 hours

windows

3. Mean-normalization of

each time series

4. Clustering by k-Means

with different K



Major clusters



Cluster 5



Cluster 3



Cluster 1



Cluster 2



Cluster 4



Conclusions

• Different roads have different temporal signatures

• Especially bridges

• Too few trajectories per person / per road segment for more sophisticated

analysis

• Data quality issues



What we can do:

• Analysis of flows and their temporal dynamics

Times

Locations

Movers

Spatial events

Spatial event data Spatial time series

Movement data Local time series

Spatial distributions

Trajectories

Details:

Visual Analytics of Movement: an Overview of

Methods, Tools, and Procedures

Information Visualization, 12(1), pp.3-24, 2013

and

Visual Analytics of Movement

Springer-Verlag 2013

ISBN 978-3-642-37582-8

Due: July 5, 2013

spatio temporal analysis of flows in cdc 2013 data

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