Using geographic information systems to define and map commuting patterns as inputs to agent-based models

By David Chrest, William Wheaton

By understanding the movement patterns of people, mathematical modelers can develop models that can better analyze and predict the spread of infectious diseases. People can come into close contact in their workplaces. This report describes methods to develop georeferenced commuting patterns that can be used to characterize the work-related movement of US populations and help agent-based modelers predict workplace contacts that result in disease transmission. We used a census data product called "Census Spatial Tabulation: Census Track of Work by Census Tract of Residence (STP64)" as the data source to develop commuting pattern data for agent-based synthesized populations databases and to develop map products to visualize commuting patterns in the United States. The three primary maps we developed show inbound, outbound, and net change levels of inbound versus outbound commuters by census tract for the year 2000. Net change counts of commuters are visualized as elevations. The results can be used to quantify and assign commuting patterns of synthesized populations among different census tracts.


Chrest, D., & Wheaton, W. (2009). Using geographic information systems to define and map commuting patterns as inputs to agent-based models. (RTI Press Publication No. MR-0012-0906). Research Triangle Park, NC: RTI Press.

© 2019 RTI International. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.


David ChrestDavid P. Chrest, BS, is a geographic information systems analyst at RTI International.

William WheatonWilliam D. Wheaton, MA, is a senior research geographer and director of RTI International’s Geospatial Science and Technology program.

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