TY - GEN
T1 - Simulating urban patterns of life
T2 - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
AU - Kim, Joon Seok
AU - Kavak, Hamdi
AU - Manzoor, Umar
AU - Crooks, Andrew
AU - Pfoser, Dieter
AU - Wenk, Carola
AU - Züfle, Andreas
N1 - Publisher Copyright:
© 2019 Copyright held by the owner/author(s).
PY - 2019/11/5
Y1 - 2019/11/5
N2 - Data generators have been heavily used in creating massive trajectory datasets to address common challenges of real-world datasets, including privacy, cost of data collection, and data quality. However, such generators often overlook social and physiological characteristics of individuals and as such their results are often limited to simple movement patterns. To address these shortcomings, we propose an agent-based simulation framework that facilitates the development of behavioral models in which agents correspond to individuals that act based on personal preferences, goals, and needs within a realistic geographical environment. Researchers can use a drag-and-drop interface to design and control their own world including the geospatial and social (i.e. geo-social) properties. The framework is capable of generating and streaming very large data that captures the basic patterns of life in urban areas. Streaming data from the simulation can be accessed in real time through a dedicated API.
AB - Data generators have been heavily used in creating massive trajectory datasets to address common challenges of real-world datasets, including privacy, cost of data collection, and data quality. However, such generators often overlook social and physiological characteristics of individuals and as such their results are often limited to simple movement patterns. To address these shortcomings, we propose an agent-based simulation framework that facilitates the development of behavioral models in which agents correspond to individuals that act based on personal preferences, goals, and needs within a realistic geographical environment. Researchers can use a drag-and-drop interface to design and control their own world including the geospatial and social (i.e. geo-social) properties. The framework is capable of generating and streaming very large data that captures the basic patterns of life in urban areas. Streaming data from the simulation can be accessed in real time through a dedicated API.
KW - Agent-based simulation
KW - Data generator
KW - Human behavior
KW - Spatial network
KW - trajectory data
UR - http://www.scopus.com/inward/record.url?scp=85077003680&partnerID=8YFLogxK
U2 - 10.1145/3347146.3359106
DO - 10.1145/3347146.3359106
M3 - Conference contribution
AN - SCOPUS:85077003680
T3 - GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems
SP - 576
EP - 579
BT - 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, ACM SIGSPATIAL GIS 2019
A2 - Banaei-Kashani, Farnoush
A2 - Trajcevski, Goce
A2 - Guting, Ralf Hartmut
A2 - Kulik, Lars
A2 - Newsam, Shawn
PB - Association for Computing Machinery
Y2 - 5 November 2019 through 8 November 2019
ER -