Wednesday, November 06, 2019

Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework

At the ACM SIGSPATIAL'19 conference, Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Dieter Pfoser, Carola Wenk, Andreas Züfle and myself have a paper entitled "Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework." The general idea behind the paper is that while trajectory data is being used to capture human mobility in many applications (e.g. traffic prediction, ride-sharing applications), the use of real-world trajectory data raises serious concerns with respect to the privacy of users who contribute such information. 

To overcome privacy concerns we have created a geo-social data generator by utilizing agent-based modeling. The notion behind this generator is to allow users to develop and customize the logic of agent behaviors for different applications domains (e.g. commuting around a city). Once the basic model is created, the simulation can then be run and  geo-social data is generated which can then be used as a substitute to real-world trajectory data to study human mobility. If you wish to find out more about this paper, below is the abstract to the paper, along with some figures of the framework architecture and a link to the paper. Further supplementary materials including a demo video (which is also below) and sample data can be found at: http://sigspatial19demo.joonseok.org.

Abstract:
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. 
Keywords: Agent-based simulation, trajectory data, data generator, spatial network, human behavior.
Causality in human behavior

Architecture of framework

Layout of model builder and sample model




Full Reference:
Kim, J-S., Kavak, H., Manzoor, U., Crooks, A.T., Pfoser, D., Wenk C. and Z├╝fle, A (2019), Simulating Urban Patterns of Life: A Geo-Social Data Generation Framework, in Banaei-Kashani, F., Trajcevski, G., Güting, R.H., Kulik, L. and Newsam, S. (eds.), Proceedings of the 27th International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2019), Chicago, IL. (pdf)

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