ICCSA 2012       Dealing with multiple source spatio-   temporal data in urban dynamics analysis João Peixoto and Adriano ...
MOTIVATIONThe mobility of citizens in an urban area is the source of various         problems: traffic congestion, environ...
MOTIVATION     It is important to understand the mobility behaviour ofindividuals in space, understand space itself, and u...
MOTIVATION The dynamics associated with the mobility in urban areasalways has two components, Time and Space, creating new...
MOTIVATIONThe current Geographic Information Systems are structured torepresent the spatial component of data but lack goo...
MOTIVATIONDetect the presence and mobility of people in urban spaces              requires the collection of data
MOTIVATIONThe huge size of datasets being collected these days is creatingmore challenges to representation and visualizat...
RELATED WORK       Temporal snapshots of space occupationDue the dynamics of the urban space, this approach may not be    ...
RELATED WORK           Trajectories with source-destination• Large interval between samples  we lose intermediate movemen...
BASIC CONCEPTS                         TRAJECTORY                    Our initial goal                          TIME LEAP  ...
BASIC CONCEPTS                           TRAJECTORY                     Our initial goal                            TIME L...
BASIC CONCEPTS It all starts with the Raw Data collected by a multitude of                          sensors               ...
BASIC CONCEPTSThe observation of an artefact in a specific point of a spatio-                        temporal space       ...
BASIC CONCEPTSTransformation process between Raw Data and Observation                 OBSERVATION                         ...
BASIC CONCEPTS      Based on Observations we extract the Places                 OBSERVATION         PLACE                 ...
BASIC CONCEPTS Time interval between the first and last observation of an                  artefact in the same place   (I...
BASIC CONCEPTS     A Change of Location of an artefact occurred over time(Id_Movement, Artefact, Location_Start, Location_...
BASIC CONCEPTS                                 SPACE LEAPA Change of Location of an artefact occurred over a long time    ...
BASIC CONCEPTS                                TIME LEAP                               SPACE LEAPLong time period between t...
BASIC CONCEPTS                               TRAJECTORY                                TIME LEAP                          ...
MAPPING DATA INTO THE FRAMEWORKGoal: validate the concepts of our proposed framework for the           representation of s...
MAPPING DATA INTO THE FRAMEWORK    Our focus in this paper is only on three concepts:              Observation, Place and ...
MAPPING DATA INTO THE FRAMEWORKAndroid Smartphone Application that collects data from three            different types: GP...
MAPPING DATA INTO THE FRAMEWORK                                  Raw DataTimestamp             Latitude   Longitude   Alti...
MAPPING DATA INTO THE FRAMEWORK                        ObservationsTimestamp                   Location                  O...
MAPPING DATA INTO THE FRAMEWORK                      Place Learning                   Psameplace(oi, oj)  Prob. function  ...
MAPPING DATA INTO THE FRAMEWORK                        Results                     Results - Places  – Place is described ...
MAPPING DATA INTO THE FRAMEWORK                  Results
CONCLUSIONS AND FUTURE WORK• The proposed concepts and framework are  appropriate to represent the three types of records ...
CONCLUSIONS AND FUTURE WORK• Process massive datasets   – Space occupied at the level of storage   – Aggregate a large num...
THANK YOU !                 joao.peixoto@algoritmi.uminho.pt                adriano.moreira@algoritmi.uminho.pt           ...
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Dealing with multiple source spatio-temporal data in urban dynamics analysis Joao Peixoto, Adriano Moreira - University of Minho

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Dealing with multiple source spatio-temporal data in urban dynamics analysis Joao Peixoto, Adriano Moreira - University of Minho

  1. 1. ICCSA 2012 Dealing with multiple source spatio- temporal data in urban dynamics analysis João Peixoto and Adriano Moreira, Mobile and Ubiquitous Systems Group
  2. 2. MOTIVATIONThe mobility of citizens in an urban area is the source of various problems: traffic congestion, environmental impacts, inadequacy of public transport, and spreading of diseases…
  3. 3. MOTIVATION It is important to understand the mobility behaviour ofindividuals in space, understand space itself, and understand the use people make of the urban space
  4. 4. MOTIVATION The dynamics associated with the mobility in urban areasalways has two components, Time and Space, creating new challenges
  5. 5. MOTIVATIONThe current Geographic Information Systems are structured torepresent the spatial component of data but lack good support for the temporal component (Yu and Shaw, 2004)
  6. 6. MOTIVATIONDetect the presence and mobility of people in urban spaces requires the collection of data
  7. 7. MOTIVATIONThe huge size of datasets being collected these days is creatingmore challenges to representation and visualization rather than solutions
  8. 8. RELATED WORK Temporal snapshots of space occupationDue the dynamics of the urban space, this approach may not be the most effective for the analysis of pattern changes (Hagen-Zanker and Timmermans 2008) Reades, J., Calabrese, F., Sevtsuk, A., Ratti, C. (2007)
  9. 9. RELATED WORK Trajectories with source-destination• Large interval between samples  we lose intermediate movements• To connect the source to destination we may have to affect the Time component Brockmann and Theis (2008)
  10. 10. BASIC CONCEPTS TRAJECTORY Our initial goal TIME LEAP SPACE LEAP ELEMENTARY MOVEMENT Create a flexible and comprehensive framework for the spatio-temporal representation of movement data STAY OBSERVATION PLACE RAW DATA
  11. 11. BASIC CONCEPTS TRAJECTORY Our initial goal TIME LEAP SPACE LEAP ELEMENTARY MOVEMENT To integrate different types of data from different sensors To apply different scenarios of urban mobility STAY OBSERVATION PLACE RAW DATA
  12. 12. BASIC CONCEPTS It all starts with the Raw Data collected by a multitude of sensors RAW DATA
  13. 13. BASIC CONCEPTSThe observation of an artefact in a specific point of a spatio- temporal space (Id_Observation, Artefact, Location, Timestamp) OBSERVATION RAW DATA
  14. 14. BASIC CONCEPTSTransformation process between Raw Data and Observation OBSERVATION RAW DATA
  15. 15. BASIC CONCEPTS Based on Observations we extract the Places OBSERVATION PLACE RAW DATA
  16. 16. BASIC CONCEPTS Time interval between the first and last observation of an artefact in the same place (Id_Stay, Artefact, Place, Timestamp_Initial, Timestamp_Final) STAY OBSERVATION PLACE RAW DATA
  17. 17. BASIC CONCEPTS A Change of Location of an artefact occurred over time(Id_Movement, Artefact, Location_Start, Location_End, Timestap_Initial, Time stap_Final) ELEMENTARY MOVEMENT STAY OBSERVATION PLACE RAW DATA
  18. 18. BASIC CONCEPTS SPACE LEAPA Change of Location of an artefact occurred over a long time ELEMENTARY MOVEMENT period(Id_SpaceLeap, Artefact, Location _Start, Location _End, Timestap_Initial, STAY Timestap_Final) OBSERVATION PLACE RAW DATA
  19. 19. BASIC CONCEPTS TIME LEAP SPACE LEAPLong time period between two sequential observations of an ELEMENTARY MOVEMENT artefact in the same place STAY (Id_TimeLeap, Artefact, Place, Timestamp_Initial, Timestamp_Final) OBSERVATION PLACE RAW DATA
  20. 20. BASIC CONCEPTS TRAJECTORY TIME LEAP SPACE LEAP ELEMENTARY MOVEMENTTime-ordered list of ElementarySTAY Movements of an artefact over the space OBSERVATION PLACE (Id_Trajectory, Artefact, List of Elementary Movements) RAW DATA
  21. 21. MAPPING DATA INTO THE FRAMEWORKGoal: validate the concepts of our proposed framework for the representation of spatio-temporal data
  22. 22. MAPPING DATA INTO THE FRAMEWORK Our focus in this paper is only on three concepts: Observation, Place and Stay
  23. 23. MAPPING DATA INTO THE FRAMEWORKAndroid Smartphone Application that collects data from three different types: GPS, Wi-Fi and GSM.
  24. 24. MAPPING DATA INTO THE FRAMEWORK Raw DataTimestamp Latitude Longitude Altitude Speed Accuracy Bearing2011/06/29 15:25:07 1,297077 103,7808 93,5 0,75 17,88854 652011/06/29 15:25:18 1,297077 103,7808 108,2 0,75 26,83282 162,42011/06/29 15:25:31 1,297213 103,7806 134,4 1 40 283,8 Timestamp BSSID RSSI SSID 2011/06/29 15:25:08 00:27:0d:07:d6:c0 -90 NUS 2011/06/29 15:25:11 00:27:0d:07:d6:c0 -88 NUS 2011/06/29 15:25:12 00:27:0d:07:d6:c0 -88 NUS Timestamp CID LAC MNC SIGNAL_STRENGTH 2011/06/29 15:25:08 962335 441 3 9 2011/06/29 15:25:10 962335 441 3 8 2011/06/29 15:25:11 962335 441 3 8
  25. 25. MAPPING DATA INTO THE FRAMEWORK ObservationsTimestamp Location Optional Attibutes Position Symbolic Name Sensor_type Latitude Longitude15:25:07 1,297077 103,7808 GPS15:25:08 00:27:0d:07:d6:c0 WIFI15:25:08 962335 GSM15:25:10 962335 GSM15:25:11 00:27:0d:07:d6:c0 WIFI15:25:11 962335 GSM15:25:18 1,297077 103,7808 GPS
  26. 26. MAPPING DATA INTO THE FRAMEWORK Place Learning Psameplace(oi, oj) Prob. function GPS Wi-Fi GSM GPS P1 P2 P3 Wi-Fi P2 P4 P5 GSM P3 P5 P6
  27. 27. MAPPING DATA INTO THE FRAMEWORK Results Results - Places – Place is described by its GPS part, Wi-Fi part, and GSM part – If the total accumulated time spent at that place is longer than a minimum of two minutes  Place – For a single person we detect 13 different Places – If the time elapsed between consecutive observations in a place do not exceed a given threshold (Tmax = 60 seconds)  Stay
  28. 28. MAPPING DATA INTO THE FRAMEWORK Results
  29. 29. CONCLUSIONS AND FUTURE WORK• The proposed concepts and framework are appropriate to represent the three types of records used.• Additional concepts might also need to be defined – Trajectory is only linked with Elementary Movement• Include anothers sensors to validate the concepts (for example: ticketing data used in buses)
  30. 30. CONCLUSIONS AND FUTURE WORK• Process massive datasets – Space occupied at the level of storage – Aggregate a large number of records• Validate the place learning algorithm and try different approaches• Extend the study to groups of citizens – Popular Places – Popular Flows
  31. 31. THANK YOU ! joao.peixoto@algoritmi.uminho.pt adriano.moreira@algoritmi.uminho.pt Mobile and Ubiquitous Systems GroupResearch group supported by FEDER Funds through the COMPETE and National Fundsthrough FCT – o para a Ciência e a Tecnologia under the Project: FCOMP-01-FEDER-0124-022674.

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