Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data. (10th July 2019)
- Record Type:
- Journal Article
- Title:
- Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data. (10th July 2019)
- Main Title:
- Bird's‐Eye ‐ Large‐Scale Visual Analytics of City Dynamics using Social Location Data
- Authors:
- Krueger, Robert
Han, Qi
Ivanov, Nikolay
Mahtal, Sanae
Thom, Dennis
Pfister, Hanspeter
Ertl, Thomas - Abstract:
- Abstract: The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large‐scale and up‐to‐date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up‐to‐date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal‐and‐task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back‐end and an interactive visual analytics interface. The monitoring component uses intelligent pre‐aggregation to allow dynamic queries in near real‐time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advancedAbstract: The analysis of behavioral city dynamics, such as temporal patterns of visited places and citizens' mobility routines, is an essential task for urban and transportation planning. Social media applications such as Foursquare and Twitter provide access to large‐scale and up‐to‐date dynamic movement data that not only help to understand the social life and pulse of a city but also to maintain and improve urban infrastructure. However, the fast growth rate of this data poses challenges for conventional methods to provide up‐to‐date, flexible analysis. Therefore, planning authorities barely consider it. We present a system and design study to leverage social media data that assist urban and transportation planners to achieve better monitoring and analysis of city dynamics such as visited places and mobility patterns in large metropolitan areas. We conducted a goal‐and‐task analysis with urban planning experts. To address these goals, we designed a system with a scalable data monitoring back‐end and an interactive visual analytics interface. The monitoring component uses intelligent pre‐aggregation to allow dynamic queries in near real‐time. The visual analytics interface leverages unsupervised learning to reveal clusters, routines, and unusual behavior in massive data, allowing to understand patterns in time and space. We evaluated our approach based on a qualitative user study with urban planning experts which demonstrates that intuitive integration of advanced analytical tools with visual interfaces is pivotal in making behavioral city dynamics accessible to practitioners. Our interviews also revealed areas for future research. … (more)
- Is Part Of:
- Computer graphics forum. Volume 38:Number 3(2019)
- Journal:
- Computer graphics forum
- Issue:
- Volume 38:Number 3(2019)
- Issue Display:
- Volume 38, Issue 3 (2019)
- Year:
- 2019
- Volume:
- 38
- Issue:
- 3
- Issue Sort Value:
- 2019-0038-0003-0000
- Page Start:
- 595
- Page End:
- 607
- Publication Date:
- 2019-07-10
- Subjects:
- CCS Concepts -- Human‐centered computing → Geographic visualization -- Information Search and Retrieval → Information Filtering
Computer graphics -- Periodicals
006.605 - Journal URLs:
- http://onlinelibrary.wiley.com/doi/10.1111/j.1467-8659.1982.tb00001.x/abstract ↗
http://onlinelibrary.wiley.com/ ↗
http://www.blackwell-synergy.com/servlet/useragent?func=showIssues&code=cgf ↗ - DOI:
- 10.1111/cgf.13713 ↗
- Languages:
- English
- ISSNs:
- 0167-7055
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 3393.982000
British Library DSC - BLDSS-3PM
British Library STI - ELD Digital store - Ingest File:
- 11378.xml