Thursday, December 02, 2021

Urban life: A model of people and places

We have just wrapped up project that created a simple agent-based simulation of urban life as part of DARPA's Ground Truth Program. To this end we have just published a  new paper entitled "Urban life: a model of people and places" published in Computational and Mathematical Organization Theory, with Andreas Züfle, Carola Wenk, Dieter Pfoser, Joon-Seok Kim, Hamdi Kavak, Umar Manzoor, Hyunjee Jin  and myself. In the paper we provide an overview of the model and how it was used to test and validate human domain research. For interested readers, below you can find the abstract  to the paper along with some images that will give you a sense of our simulation model (which for interested readers was created with MASON and its GIS extension (GeoMason). While at the bottom of the post you can find the full reference and a link to the paper. 

 Abstract

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)

Tuesday, November 16, 2021

Delineating a ‘15-Minute City’: An Agent-based Modeling Approach

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)?

To explore this notion of a 15 minute city, at the GeoSim'21: the 4th ACM SIGSPATIAL International Workshop on GeoSpatial Simulation, Qingqing Chen  and myself had a paper entitled "Delineating a ‘15-Minute City’: An Agent-based Modeling Approach to Estimate the Size of Local Communities." While below we provide the abstract to the paper, being an online workshop, the talks were recorded so if you don't want to read the paper, you can watch Qingqing introduce the paper and see an example model run below. If this is of interest, at the bottom of the post we provide a link to the paper, while the actual model along with data needed to run the model can be found at https://tinyurl.com/15minsCity.  

Abstract:

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)

 

 

Tuesday, November 09, 2021

GIS and ABM: Past, Present and Future

The other day, Alison Heppenstall and myself were invited to give a keynote at the 2021 International Conference on Geospatial Information Sciences. Its not hard to guess what we chose to be the title of our talk: "GIS and Agent-Based-Modelling: Past, Present and Future."

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.


Sunday, October 31, 2021

Unraveling the complexity of human behavior and urbanization on community vulnerability to floods

Building upon a previous post where , ,

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

Thursday, September 30, 2021

An Integrated Framework of Global Sensitivity Analysis and Calibration for Spatially Explicit ABMs

In the past we have written about the challenges of validation and to some extent the calibration of agent-based models but never really went into much detail about the calibration process. To this end, Jeon-Young Kang, Alexander Michels, Jared Aldstadt, Shaowen Wang and myself recently had a paper published in Transactions in GIS entitled "An Integrated Framework of Global Sensitivity Analysis and Calibration for Spatially Explicit Agent-Based Models." In the paper we have present an integrated framework for global sensitivity analysis and calibration (GSA-CAL), and then apply the framework to a spatially explicit agent-based model of influenza transmission as a case study in the city of Miami, FL. If this sounds of interest, below you can read the abstract to the paper, see some of the figures from the paper including the general workflow and some of the results. At the bottom of the post you can find the full citation and a link to the paper.

Abstract

Calibration of agent-based models (ABMs) is a major challenge due to the complex nature of the systems being modeled, the heterogeneous nature of geographical regions, the varying effects of model inputs on the outputs, and computational intensity. Nevertheless, ABMs need to be carefully tuned to achieve the desirable goal of simulating spatiotemporal phenomena of interest, and a well-calibrated model is expected to achieve an improved understanding of the phenomena. To address some of the above challenges, this article proposes an integrated framework of global sensitivity analysis (GSA) and calibration, called GSA-CAL. Specifically, variance-based GSA is applied to identify input parameters with less influence on differences between simulated outputs and observations. By dropping these less influential input parameters in the calibration process, this research reduces the computational intensity of calibration. Since GSA requires many simulation runs, due to ABMs' stochasticity, we leverage the high-performance computing power provided by the advanced cyberinfrastructure. A spatially explicit ABM of influenza transmission is used as the case study to demonstrate the utility of the framework. Leveraging GSA, we were able to exclude less influential parameters in the model calibration process and demonstrate the importance of revising local settings for an epidemic pattern in an outbreak.


Workflow of global sensitivity analysis and calibration

Study area

Daily activity-based contact network construction

Results from calibration distance-based mobility (DBM): (a) simulated results from an initial model; (b) simulated results from the calibrated model; and (c) sum of RMSE
 

Full Reference:

Kang, J-Y., Michels, A., Crooks, A.T., Aldstradt, J. and Wang, S. (2021), An Integrated Framework of Global Sensitivity Analysis and Calibration for Spatially Explicit Agent-Based Models, Transactions in GIS. https://doi.org/10.1111/tgis.12837 (pdf)