Synthesized Population Databases: A US Geospatial Database for Agent-Based Models

Abstract

Agent-based models simulate large-scale social systems. They assign behaviors and activities to "agents" (individuals) within the population being modeled and then allow the agents to interact with the environment and each other in complex simulations. Agent-based models are frequently used to simulate infectious disease outbreaks, among other uses.RTI used and extended an iterative proportional fitting method to generate a synthesized, geospatially explicit, human agent database that represents the US population in the 50 states and the District of Columbia in the year 2000. Each agent is assigned to a household; other agents make up the household occupants.For this database, RTI developed the methods for generating synthesized households and personsassigning agents to schools and workplaces so that complex interactions among agents as they go about their daily activities can be taken into accountgenerating synthesized human agents who occupy group quarters (military bases, college dormitories, prisons, nursing homes).In this report, we describe both the methods used to generate the synthesized population database and the final data structure and data content of the database. This information will provide researchers with the information they need to use the database in developing agent-based models.Portions of the synthesized agent database are available to any user upon request. RTI will extract a portion (a county, region, or state) of the database for users who wish to use this database in their own agent-based models.

Figure 1

An example of how the synthetic agent database may be used in disease modeling

Figure 2

The integrated spatial and tabular model of the synthesized population database

Figure 3

Public Use Microdata Areas for Mecklenburg County, N.C.

Figure 4

Generalized flow chart of the synthetic population data processing

Figure 5

Frequency distribution of the percentage difference in household population between the synthesized data set and the Census data for Durham County, N.C.

Figure 6

Frequency distribution of the percentage difference in median household income between the synthesized data set and the Census data for Durham County, N.C.

Figure 7

Flow chart of the process of assigning school-age children to schools

Figure 8

Allocation of high-school-age synthesized agents to Kings County, Wash., high schools

Figure 9

Allocation of 12th-grade synthesized agents to a sample private school

Figure 10

Flow chart of the process of assigning workers to workplaces

Figure 11

Flow chart of the process of creating group quarters locations and assigning residents