While our book "Agent-based Modelling and Geographical Information Systems" has a lot of details and examples about how to use GIS data within NetLogo (see https://github.com/abmgis/abmgis), as I was preparing for my course this semester entitled "Spatial Simulation" I thought I would develop a more detailed tutorial on how to use vector data to build a segregation model in NetLogo. The model itself is inspired by Schelling's model of segregation, but unlike the regular cell versions which are commonly used as examples here the cells are polygons which are based on census boundaries of Washington DC. The movie below gives a sense of what it looks like when completed.
We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture the innate needs of a human-like population and explore how such needs shape social constructs such as friendship and wealth. Urban Life is a spatially explicit model to explore how urban form impacts agents’ daily patterns of life. By meeting up at places agents form social networks, which in turn affect the places the agents visit. In our model, location and co-location affect all levels of decision making as agents prefer to visit nearby places. Co-location is necessary (but not sufficient) to connect agents in the social network. The Urban Life model was used in the Ground Truth program as a virtual world testbed to produce data in a setting in which the underlying ground truth was explicitly known. Data was provided to research teams to test and validate Human Domain research methods to an extent previously impossible. This paper summarizes our Urban Life model’s design and simulation along with a description of how it was used to test the ability of Human Domain research teams to predict future states and to prescribe changes to the simulation to achieve desired outcomes in our simulated world.
Our generated maps colored based on different aggregation levels.
A screenshot of the graphical user interface from a representative model run. Top-Left: The spatial network and agents. Bottom left: Simulation parameters that can be specified prior to simulation start. Top-middle: the social network. Bottom-middle: Summary statistics of the simulation during tun-time such as friendship. Right: Profiles of recreational sites.
Screenshot of the epidemic simulator depicting the French Quarter, New Orleans, LA, USA.
Full Reference:
Züfle, A., Wenk, C., Pfoser, D., Crooks, A.T., Kavak, H., Kim, J-S. and Jin, H. (2021), Urban Life: A Model of People and Places, Computational and Mathematical Organization Theory. Available at https://doi.org/10.1007/s10588-021-09348-7 (pdf)
With more and more people living in urban areas and the current COVID pandemic, human mobility within cities has changed. With this change there is a a growing debate about what it would take to make cities more accessible. For example, what would it take for the inhabitants of cities be able to access most of their daily essentials (e.g., shopping, work, education, entertainment) within 15 minutes, commuting from their own doorstep either via walking, cycling, or other modes of transportation (e.g., bus, rail)?
With progressively increased people living in cities, and lately
the global COVID-19 outbreak, human mobility within cities
has changed. Coinciding with this change, is the recent uptake
of the ‘15-Minute City’ idea in urban planning around the world.
One of the hallmarks of this idea is to create a high quality of
life within a city via an acceptable travel distance (i.e., 15
minutes). However, a definitive benchmark for defining a ‘15-
Minute City’ has yet to be agreed upon due to the
heterogeneous character of urban morphologies worldwide.
To shed light on this issue, we develop an agent-based model
named ‘D-FMCities’ utilizing realistic street networks and
points-of-interest, in this instance the borough of Queens in
New York City as a test case. Through our modeling we grow
diverse communities from the bottom up and estimate the size
of such local communities to delineate 15-minute cities. Our
findings suggest that the model could be helpful to detect the
flexibility of defining the extent of a ‘15-minute city’ and
consequently support uncovering the underlying factors that
may affect its various definitions and diverse sizes throughout
the world.
Keywords: 15-minute city, Agent-based modelling, Local
communities, Street networks, Point-of-interests,
COVID-19.
Model Demo:
Full Reference:
Chen, Q and Crooks, A.T. (2021). Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities. In GeoSim '21: Proceedings of the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, November 2, 2021, Beijing, China, pp 29-37. (PDF)
The talk was a synthesis of two publications (Crooks et al. 2019 and Heppenstall et al. 2021) along with some things we are currently working on. For those who are interested the conference has released not only our talk but also the other keynotes.
Building upon a previous post where Mona Hemmati, Hussam Mahmoud, Bruce Ellingwood and myself presented a cellular automata model that was developed to understand the relationship between urbanization and flood risk, we have a new paper entitled "Unraveling the complexity of human behavior and urbanization on community vulnerability to floods"published in Scientific Reports. This paper not only adds to this understanding of how urbanization can lead to greater flooding vulnerability but also considers human behavior with respect to urbanization and flooding in the form of adding agents who buy and sell properties which is linked to an urban growth model. If this sounds of interest below we provide the abstract to the paper, some figures from the paper, including growth projections for the City of Boulder and the full reference to the paper.
Abstract:
Floods are
among the costliest natural hazards and their consequences are expected
to increase further in the future due to urbanization in flood-prone
areas. It is essential that policymakers understand the factors
governing the dynamics of urbanization to adopt proper disaster risk
reduction techniques. Peoples’ relocation preferences and their
perception of flood risk (collectively called human behavior) are among
the most important factors that influence urbanization in flood-prone
areas. Current studies focusing on flood risk assessment do not consider
the effect of human behavior on urbanization and how it may change the
nature of the risk. Moreover, flood mitigation policies are implemented
without considering the role of human behavior and how the community
will cope with measures such as buyout, land acquisition, and relocation
that are often adopted to minimize development in flood-prone regions.
Therefore, such policies may either be resisted by the community or
result in severe socioeconomic consequences. In this study, we present a
new Agent-Based Model (ABM) to investigate the complex interaction
between human behavior and urbanization and its role in creating future
communities vulnerable to flood events. We identify critical factors in
the decisions of households to locate or relocate and adopt policies
compatible with human behavior. The results show that when people are
informed about the flood risk and proper incentives are provided, the
demand for housing within 500-year floodplain may be reduced as much as
15% by 2040 for the case study considered. On the contrary, if people
are not informed of the risk, 29% of the housing choices will reside in
floodplains. The analyses also demonstrate that neighborhood
quality—influenced by accessibility to highways, education facilities,
the city center, water bodies, and green spaces, respectively—is the
most influential factor in peoples’ decisions on where to locate. These
results provide new insights that may be used to assist city planners
and stakeholders in examining tradeoffs between costs and benefits of
future land development in achieving sustainable and resilient cities.
Conceptual model of the proposed behavioral urban growth model.
Growth projections for the City of Boulder, considering developer’s: (a) Normal Behavior, (b) Risk-informed Behavior, (c) Policy I, and (d) Policy II
Full Reference
Hemmati, M., Hussam N., Ellingwood B.R. and Crooks, A.T. (2021), Unraveling the Complexity of Human Behavior and Urbanization on Community Vulnerability to Floods, Scientific Reports, 11, 20085. Available at: https://doi.org/10.1038/s41598-021-99587-0 (pdf)
