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Title: Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework
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.  more » « less
Award ID(s):
1637541 1637576
NSF-PAR ID:
10187146
Author(s) / Creator(s):
; ; ; ; ; ;
Date Published:
Journal Name:
Proceedings of the 27th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems
Page Range / eLocation ID:
576 to 579
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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