Earlier this month I was invited to give a talk as part the Criminal Investigations and Network Analysis Center (CINA) Distinguished Speaker Series. As readers of the blog might expect, I chose to talk about how open data (e.g. OpenStreetMap, Twitter) can be utilized in agent-based models to study a variety of applications (many of which can be found over on my research page). The talk itself was entitled: "Utilizing Agent-based Models and Open Data to Examine the Movement of People and Information: A Gallery of Applications." Below you can read the brief abstract of the paper and if this peaks your interest, CINA recorded my talk and highlighted (short) version is given below (while the full talk can be found at: https://youtu.be/iIvSnE-IBZI).
Abstract:
Today we are awash with many new forms of open data (e.g. crowdsourced, social media), but we are still challenged with how individuals make decisions and how this leads to more aggregate patterns emerging. One way to explore how individuals make decisions, or are impacted by information and their resulting consequences, is via agent-based modeling. Agent-based modeling allows for simulating heterogenous actors and their decision-making processes within complex systems. Through a series of example applications ranging from the small-scale movement of pedestrians over seconds, to that of the movement of people over borders over hours and days, I will demonstrate how open data can be leveraged within the agent-based building process. Specifically, the examples will show that by focusing on individuals, or groups of individuals and the networks that connect them, more aggregate patterns emerge from the bottom up.
Cities are complex systems which are constantly changing because of the interactions between the
people and their environment. Such systems often go through several life cycles which are shaped by
various processes. These may include urban growth, sprawl, shrinkage, and gentrification. These
processes affect the urban land markets which in turn affect the formation of a city through feedback
loops. Through models we can explore such dynamics, populations, and the environments in which
people inhabit. The model proposed in this paper intends to simulate the aforementioned dynamics
to capture the effect of agents’ choices and actions on the city structure. Specifically, this model
explores the effect of gentrification on population density and housing values. The proposed model is
significant in its integration of ideas from complex systems theory which is operationalized within an
agent-based model stylized on urban theories to study gentrification as a cause of increased in land
values. The model is stylized on urban theories and results from the model show that the agents move
to and reside in properties within their income range, neighboring agents that have similar economic
status. The model also shows the role of gentrification by capturing both the supply and demand
aspects of this process in the displacement and immobilization of agents with lower incomes. This
is one of the first models that combines several processes to explore the life cycle of a city through
agent-based modeling.
Model graphical user interface at default settings.
Gentrification by demand in the 10th neighborhood of the inner-city.
Turning to our second paper which was presented as a poster, entitled "Modeling Social Networks in an Agent-Based Model of a Nuclear Weapon of Mass Destruction Event" we discussed our continuing work on disasters. Specifically our project on how people might react in an event of Nuclear Weapon of Mass Destruction (NWMD) in New York City when one integrates social networks into an agent-based model. In the paper we discuss preliminary results which demonstrate how we can integrate household social networks explicitly into a spatially explicit model. Furthermore we demonstrate and benchmark agent commuting patterns for the New York City Commuter Region with a sample population (as we show in one of the movies below) along with demonstrating agents initial reactions post NWMD detonation.
Abstract:
Connections between human beings often influence where people go and
how they behave, yet their representation as social networks are rarely
modeled as a factor of human behavior in agent-based models. Social
networks are increasingly being used to study human behavior in
disasters, and empirical work has shown that human beings prioritize the
safety of themselves and loved ones (i.e., households) before helping
neighbors and coworkers. In this poster, we briefly present our
agent-based model being used to characterize the New York City area
population’s reaction to a Nuclear Weapon of Mass Destruction (NWMD)
event. The model methodology demonstrates how social networks can be
integrated into an agent-based model and act as a basis for
decision-making during a disaster. Preliminary simulations show how
agents potentially respond to a NWMD event with measurable changes in
location and network formations over space and time.
Keywords:
Agent-Based Model, Human Behavior, Social Networks, Emergency, Disaster Response, Nuclear Weapon of Mass Destruction.
References
Bagheri-Jebelli, N., Crooks, A.T. and Kennedy, W.G. (2019), Capturing the Effects of Gentrification on Property Values: An Agent-Based Modeling Approach, The 2019 Computational Social Science Society of Americas Conference, Santa Fe, NM. (pdf) Burger, A. G., Kennedy, W.G., Crooks, A.T., Jiang, N. and Guillen-Piazza, D. (2019), Modeling Social Networks in an Agent-Based Model of a Nuclear Weapon of Mass Destruction Event, The 2019 Computational Social Science Society of Americas Conference, Santa Fe, NM. (paper pdf) (poster pdf)
