Understanding the distribution of activities of urban dwellers using the Space Time Cube

Irma Kveladze, Menno-Jan Kraak, Rein Ahas

Research output: Contribution to conference without publisher/journalConference abstract for conferenceResearchpeer-review

Abstract

Urban geographers study the development of cities, and seek to understand the fac-tors that influence human movements over space and time. New communication tech-nologies are significantly impacting these studies, especially in field of data collec-tion. The use case presented here is based on the active mobile positioning data col-lected in the Tallinn metropolitan area (figure 1). The movement trajectories of subur-ban dwellers were gathered over eight days with the aim to get insight in their urban space consumption. Among the questions of interest to the urban geographer were several with a typical temporal nature: ‘Is there a difference in distribution of activi-ties between weekdays and weekends?’ and ‘Are there differences during the day?’
To answer these questions a visual problem solving approach was followed where different graphic representations of the data were used. The choice of the maps and diagrams is based on the questions to be answered, for instance a map for the domi-nant where-questions, and the Space Time Cube (STC) for the dominant when-questions. All graphics were integrated in a single multiple coordinated view envi-ronment which allows one to see the impact of interaction immediately in all views.
The STC was selected because it can reveal temporal trends in the data. It was originally introduced during the nineteen seventies to visually describe the movement behavior of individuals. The STC includes several elements for the representation of the track data gathered: space-time paths, stations and prisms. Additional attribute and annotated information can be added.
Resent technological achievements both in software and data collection have re-newed interests in the STC. Literature reveals a wide diversity of applications, many based on large amounts of data, with the risk of over-plotting. However, despite its increase usage, not much is known about its effectiveness and efficiency in these new data rich circumstances. Only little usability research has been conducted, and this was mainly oriented on the comparison of STC with 2D maps.
The cooperation between Twente and Tartu was stimulated by the opportunities of the STC in supporting solving temporal questions. The Tartu side offered data and questions, while the Twente side worked on visualization solutions with the STC. The visualization solution is based on a better design of the STC content in combina-tion with a visual workflow based on Shneiderman’s visual information seeking man-tra. The utilization of both design and strategies is based on intensive discussions with domain experts (Tartu partners) and based on the user centered design paradigm. An extensive evaluation process is foreseen.
Fig. 1 gives an overview of the eight days of data. The map emphasized the spatial distribution and the STC clearly shows the temporal distribu-tion.
Fig. 2 shows a detailed analysis of the path of a single individual. The map highlights the selected path. The STC displays the path of the person with mornings in white, afternoons in light gray, evenings in dark gray, and nights in black. The inset map shows the location where the person spent most of her time.
Original languageEnglish
Publication date15 Aug 2012
Publication statusPublished - 15 Aug 2012

Fingerprint

distribution
visualization
positioning
metropolitan area
diagram
trajectory
communication
spatial distribution
software
urban study
trend
consumption
city
attribute
evaluation
analysis
station
co-operation
comparison

Bibliographical note

FRDinMOVE : Workshop on Future Research Directions in MOVEment. COST-MOVE meeting at Delft University of Technology (TU), Delft, TheNetherlands

Keywords

  • Movement analysis

Cite this

@conference{f3d3525820384f2bb1d8b027a4e4794d,
title = "Understanding the distribution of activities of urban dwellers using the Space Time Cube",
abstract = "Urban geographers study the development of cities, and seek to understand the fac-tors that influence human movements over space and time. New communication tech-nologies are significantly impacting these studies, especially in field of data collec-tion. The use case presented here is based on the active mobile positioning data col-lected in the Tallinn metropolitan area (figure 1). The movement trajectories of subur-ban dwellers were gathered over eight days with the aim to get insight in their urban space consumption. Among the questions of interest to the urban geographer were several with a typical temporal nature: ‘Is there a difference in distribution of activi-ties between weekdays and weekends?’ and ‘Are there differences during the day?’To answer these questions a visual problem solving approach was followed where different graphic representations of the data were used. The choice of the maps and diagrams is based on the questions to be answered, for instance a map for the domi-nant where-questions, and the Space Time Cube (STC) for the dominant when-questions. All graphics were integrated in a single multiple coordinated view envi-ronment which allows one to see the impact of interaction immediately in all views.The STC was selected because it can reveal temporal trends in the data. It was originally introduced during the nineteen seventies to visually describe the movement behavior of individuals. The STC includes several elements for the representation of the track data gathered: space-time paths, stations and prisms. Additional attribute and annotated information can be added.Resent technological achievements both in software and data collection have re-newed interests in the STC. Literature reveals a wide diversity of applications, many based on large amounts of data, with the risk of over-plotting. However, despite its increase usage, not much is known about its effectiveness and efficiency in these new data rich circumstances. Only little usability research has been conducted, and this was mainly oriented on the comparison of STC with 2D maps. The cooperation between Twente and Tartu was stimulated by the opportunities of the STC in supporting solving temporal questions. The Tartu side offered data and questions, while the Twente side worked on visualization solutions with the STC. The visualization solution is based on a better design of the STC content in combina-tion with a visual workflow based on Shneiderman’s visual information seeking man-tra. The utilization of both design and strategies is based on intensive discussions with domain experts (Tartu partners) and based on the user centered design paradigm. An extensive evaluation process is foreseen.Fig. 1 gives an overview of the eight days of data. The map emphasized the spatial distribution and the STC clearly shows the temporal distribu-tion. Fig. 2 shows a detailed analysis of the path of a single individual. The map highlights the selected path. The STC displays the path of the person with mornings in white, afternoons in light gray, evenings in dark gray, and nights in black. The inset map shows the location where the person spent most of her time.",
keywords = "Movement analysis",
author = "Irma Kveladze and Menno-Jan Kraak and Rein Ahas",
note = "FRDinMOVE : Workshop on Future Research Directions in MOVEment. COST-MOVE meeting at Delft University of Technology (TU), Delft, TheNetherlands",
year = "2012",
month = "8",
day = "15",
language = "English",

}

Understanding the distribution of activities of urban dwellers using the Space Time Cube. / Kveladze, Irma; Kraak, Menno-Jan; Ahas, Rein .

2012.

Research output: Contribution to conference without publisher/journalConference abstract for conferenceResearchpeer-review

TY - ABST

T1 - Understanding the distribution of activities of urban dwellers using the Space Time Cube

AU - Kveladze, Irma

AU - Kraak, Menno-Jan

AU - Ahas, Rein

N1 - FRDinMOVE : Workshop on Future Research Directions in MOVEment. COST-MOVE meeting at Delft University of Technology (TU), Delft, TheNetherlands

PY - 2012/8/15

Y1 - 2012/8/15

N2 - Urban geographers study the development of cities, and seek to understand the fac-tors that influence human movements over space and time. New communication tech-nologies are significantly impacting these studies, especially in field of data collec-tion. The use case presented here is based on the active mobile positioning data col-lected in the Tallinn metropolitan area (figure 1). The movement trajectories of subur-ban dwellers were gathered over eight days with the aim to get insight in their urban space consumption. Among the questions of interest to the urban geographer were several with a typical temporal nature: ‘Is there a difference in distribution of activi-ties between weekdays and weekends?’ and ‘Are there differences during the day?’To answer these questions a visual problem solving approach was followed where different graphic representations of the data were used. The choice of the maps and diagrams is based on the questions to be answered, for instance a map for the domi-nant where-questions, and the Space Time Cube (STC) for the dominant when-questions. All graphics were integrated in a single multiple coordinated view envi-ronment which allows one to see the impact of interaction immediately in all views.The STC was selected because it can reveal temporal trends in the data. It was originally introduced during the nineteen seventies to visually describe the movement behavior of individuals. The STC includes several elements for the representation of the track data gathered: space-time paths, stations and prisms. Additional attribute and annotated information can be added.Resent technological achievements both in software and data collection have re-newed interests in the STC. Literature reveals a wide diversity of applications, many based on large amounts of data, with the risk of over-plotting. However, despite its increase usage, not much is known about its effectiveness and efficiency in these new data rich circumstances. Only little usability research has been conducted, and this was mainly oriented on the comparison of STC with 2D maps. The cooperation between Twente and Tartu was stimulated by the opportunities of the STC in supporting solving temporal questions. The Tartu side offered data and questions, while the Twente side worked on visualization solutions with the STC. The visualization solution is based on a better design of the STC content in combina-tion with a visual workflow based on Shneiderman’s visual information seeking man-tra. The utilization of both design and strategies is based on intensive discussions with domain experts (Tartu partners) and based on the user centered design paradigm. An extensive evaluation process is foreseen.Fig. 1 gives an overview of the eight days of data. The map emphasized the spatial distribution and the STC clearly shows the temporal distribu-tion. Fig. 2 shows a detailed analysis of the path of a single individual. The map highlights the selected path. The STC displays the path of the person with mornings in white, afternoons in light gray, evenings in dark gray, and nights in black. The inset map shows the location where the person spent most of her time.

AB - Urban geographers study the development of cities, and seek to understand the fac-tors that influence human movements over space and time. New communication tech-nologies are significantly impacting these studies, especially in field of data collec-tion. The use case presented here is based on the active mobile positioning data col-lected in the Tallinn metropolitan area (figure 1). The movement trajectories of subur-ban dwellers were gathered over eight days with the aim to get insight in their urban space consumption. Among the questions of interest to the urban geographer were several with a typical temporal nature: ‘Is there a difference in distribution of activi-ties between weekdays and weekends?’ and ‘Are there differences during the day?’To answer these questions a visual problem solving approach was followed where different graphic representations of the data were used. The choice of the maps and diagrams is based on the questions to be answered, for instance a map for the domi-nant where-questions, and the Space Time Cube (STC) for the dominant when-questions. All graphics were integrated in a single multiple coordinated view envi-ronment which allows one to see the impact of interaction immediately in all views.The STC was selected because it can reveal temporal trends in the data. It was originally introduced during the nineteen seventies to visually describe the movement behavior of individuals. The STC includes several elements for the representation of the track data gathered: space-time paths, stations and prisms. Additional attribute and annotated information can be added.Resent technological achievements both in software and data collection have re-newed interests in the STC. Literature reveals a wide diversity of applications, many based on large amounts of data, with the risk of over-plotting. However, despite its increase usage, not much is known about its effectiveness and efficiency in these new data rich circumstances. Only little usability research has been conducted, and this was mainly oriented on the comparison of STC with 2D maps. The cooperation between Twente and Tartu was stimulated by the opportunities of the STC in supporting solving temporal questions. The Tartu side offered data and questions, while the Twente side worked on visualization solutions with the STC. The visualization solution is based on a better design of the STC content in combina-tion with a visual workflow based on Shneiderman’s visual information seeking man-tra. The utilization of both design and strategies is based on intensive discussions with domain experts (Tartu partners) and based on the user centered design paradigm. An extensive evaluation process is foreseen.Fig. 1 gives an overview of the eight days of data. The map emphasized the spatial distribution and the STC clearly shows the temporal distribu-tion. Fig. 2 shows a detailed analysis of the path of a single individual. The map highlights the selected path. The STC displays the path of the person with mornings in white, afternoons in light gray, evenings in dark gray, and nights in black. The inset map shows the location where the person spent most of her time.

KW - Movement analysis

UR - https://research.utwente.nl/en/publications/understanding-the-distribution-of-activities-of-urban-dwellers-us

M3 - Conference abstract for conference

ER -