- Thank you for the Introduction!- My name is Joao Peixoto, I'm a PhD Student at University of Minho, Portugal.- My PhD work is about Urban Mobility.
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As Yu said: The current Geographic Information Systems are structured to represent the spatial component of data but lack good support for the temporal component
- And we have another question: the huge size of datasets collected make complex the representation and visualization of the urban mobility
One approach to deal with these questions is using the Temporal snapshots of space occupation.But, because is a snapshot it’s not the most effective for the analysis of pattern changes.
Another approach is using Trajectories. But if the time interval between source and destination are large, we may lose intermediate movements.In the other hand, if we want represent the connection between source and destination, we may affect the Time component.
According to all these questions, our initial work is focused in the creation of a framework for the representation of spatio-temporal data.
- One requirement for this framework is the integration of different type of mobility data, acquired from different sensors- And must be sufficient flexible to deal with different scenarios of mobility.
So… let's see our framework and concepts that we defined.First… it all starts with Raw Data… from different sensors
- With these Raw Data we create the Observations Observations are a formal description of the position of the artefact… spatial and temporal position. the observations say: where and when the artefact are observed.
The arrows shows the transformation processes between conceptsThe open circle represent the process that we implemented in this paper
Based on the Observations we extracted the Places.The Place is a set of aggregated Locations. In the Observations we don’t have Places, but Locations. Symbolical locations or geometrical locations according to the sensor that we use.And the detection of the Places are important for the next concepts.
-Because we only can detect a Stay, based on the Places.-the concept Stay describe a time interval between observations when the artefact is in the same Place.
-because the Observations have information about the Location of the artefact (for example: a GPS trace)… we can describe another concept called Elementary Movement to represent this kind of mobility.- The elementary movement is important to describe movements with short time interval between Observations. For this reason, one Elementary Movements occurs when we have a change of Locations in time.
-But, if the time interval is longer and we cannot say with certain that the artefact did exactly the movement that we observed, we are in the presence of a Space Leap.
The same happens when we artefact is in the same Place, but the time interval is longer and for this reason we cannot say that the artefact didn't move meanwhile.These two last concepts are important because it’s normal that we cannot follow exactly all the movements of the artefact....
The last concept is Trajectory… this concept is a list of Elementary Movements… that represent with great precision the real movement of the artefact.
- To validate our framework, concepts and transformation processes we made an implementation based in clustering algorithm
Our focus in this paper has only validate the firsts three concepts and the processes used to derive Places from Observations, and Stays from Observations and Places
We collect data using an android Smartphone applicationCollects three types of data over the day
Examples of the threedifferent Raw Data: GPS, WIFI and GSM
- Adequation of these three Raw Data to the Observation ConceptWe can see the two forms of representation of the Location: Geometrical and Symbolic
-In our clustering algorithm the probability that two observations having been taken at the same place is calculated according to 6 different Probabilities Functions- Two examples of these Probabilities Functions: for GPS and Wi-Fi
Place described by three componentsA candidate place is assumed to be a real relevant one if the total accumulated time spent at that place is longer than a minimum amount of time (e.g. two minutes).For a single person during one month we detect 13 relevant places, with more than 2 minutes of total staying time. A stay occurred when the time elapsed between consecutive observations in a place do not exceed a given threshold (Tmax)
The place in red is the most relevant one, with a total staying time of four hundred hours (in one month)
- Because we want understand the mobility of groups of citizens, the privacy question may not be a problem. We only show aggregated data.
Dealing with multiple source spatio-temporal data in urban dynamics analysis Joao Peixoto, Adriano Moreira - University of Minho
ICCSA 2012 Dealing with multiple source spatio- temporal data in urban dynamics analysis João Peixoto and Adriano Moreira, Mobile and Ubiquitous Systems Group
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…
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
MOTIVATION The dynamics associated with the mobility in urban areasalways has two components, Time and Space, creating new challenges
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)
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 visualization rather than solutions
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)
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)
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
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
BASIC CONCEPTS It all starts with the Raw Data collected by a multitude of sensors RAW DATA
BASIC CONCEPTSThe observation of an artefact in a specific point of a spatio- temporal space (Id_Observation, Artefact, Location, Timestamp) OBSERVATION RAW DATA
BASIC CONCEPTSTransformation process between Raw Data and Observation OBSERVATION RAW DATA
BASIC CONCEPTS Based on Observations we extract the Places OBSERVATION PLACE RAW DATA
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
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
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
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
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
MAPPING DATA INTO THE FRAMEWORKGoal: validate the concepts of our proposed framework for the representation of spatio-temporal data
MAPPING DATA INTO THE FRAMEWORK Our focus in this paper is only on three concepts: Observation, Place and Stay
MAPPING DATA INTO THE FRAMEWORKAndroid Smartphone Application that collects data from three different types: GPS, Wi-Fi and GSM.
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
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
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
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)
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
THANK YOU ! firstname.lastname@example.org email@example.com 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.