Shaping urbanization to achieve sustainable communities resilient to floods

Its been a while since we have written about cellular automata models but recently Mona Hemmati, Hussam Mahmoud, Bruce Ellingwood and myself have a new paper entitled "Shaping urbanization to achieve sustainable communities resilient to floods" published in Environmental Research Letters. Our motivation for the paper is that flood risk is increasing in urban communities due to climate change and socioeconomic development (e.g., urban growth). For example, urbanization can change the nature of flood hazard, as it add more impervious surfaces and increases runoff. 

In order to better understand the relationship between the interactions of urbanization and flood risk and to assist with flood risk management in this paper we build a spatial urban growth model (i.e., a cellular automata model) for simulating city expansion over time, using the City of Boulder, Colorado as a testbed. This urban growth model is integrated with a flood hazard model (i.e., HAZUS-MH) to assess the effect of urbanization on future flood risk. We capture the feedback between different urbanization scenarios, influenced by various land-use policies and socioeconomic incentives (e.g., no policy, current policy and risk-informed planning policies), and flood risk in a urbanizing city (e.g., City of Boulder). This framework is intended to assist city planners and stakeholders in examining tradeoffs between costs and benefits of future land development, considering uncertainties in flood hazards and the performance of built environment in order to in achieve more sustainable and resilient cities.   If this sounds of interest, below we provide the abstract to the paper, along with some figures that accompany the paper (e.g., the framework itself, different development plan scenarios, growth projections, and the economic consequences of these scenarios). Finally at the bottom of the post the full reference is given and a link to the paper itself. 

Abstract: Flood risk is increasing in urban communities due to climate change and socioeconomic development. Socioeconomic development is a major cause of urban expansion in flood-prone regions, as it places more physical, economic, and social infrastructure at risk. Moreover, in light of the 2030 Agenda for Sustainable Development by the United Nations, it has become an international imperative to move toward sustainable cities. Current approaches to quantify this risk use scenario-based methods involving arbitrary projections of city growth. These methods seldom incorporate geographical, social, and economic factors associated with urbanization and cannot mimic city growth under various urban development plans. In this paper, we introduce a framework for understanding the interactions between urbanization and flood risk as an essential ingredient for flood risk management. This framework integrates an urban growth model with a hazard model to explore flood risk under various urban development scenarios. We then investigate the effectiveness of coupling nonstructural flood mitigation measures - in terms of urban planning policies and socioeconomic incentives - with urban growth processes to achieve sustainable and resilient communities. Using this framework, we can not only simulate urban expansion dynamics through time and its effect on flood risk but also model the growth of a region under various urban planning policies and assess the effectiveness of these measures in reducing flood risk. Our analysis reveals that while current urban development plans may put more people and assets at flood risk, the nonstructural strategies considered in this study mitigated the consequences of floods. Such a framework could 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.

Key Words: Adaptation policies; Flood risk; Resilience; Urbanization; Urban planning.

 

(a) Four modules comprising the sustainable development framework. (b) Configuration of the city for base-year 2020. (c) No-Policy scenario: urbanization happens in adjacent lands to the current city boundary. (d) Current Policy scenario: urbanization happens based on the current development plans applied by the city. Analysis shows more buildings inside the floodplains for (d) compared to (c).  (e) Policy I-Risk-informed planning for urbanization by creating land-use and zoning restrictions: no development is allowed in susceptible areas to floods. (f) Policy II - Risk-informed planning for urbanization by creating socioeconomic incentives: new entertainment facilities and schools are built on less vulnerable areas to encourage resident moving to safer locations. Analysis shows fewer buildings inside the floodplains for (e) compared to (f).

The development plan for each scenarios: (a) No Policy: Development is permitted in Area IV and happens based on growth potentials. (b) Current Policy: Development is allowed in Area II (c) Policy I: Development is allowed in Area II and not allowed in Area III. In this scenario if the demand is not satisfied with the considered permitted areas, development can happen in Area IV as well. (d) Policy II: development can happen in Area IV but the local government will build schools and entertainment centers in the specified region to promote development in less susceptible areas.

Growth projections for the year 2040, based on: (a) No-Policy scenario, (b) Current Policy scenario, (c) Policy I, and (d) Policy II.

Economic consequences, in 1,000 $US, to buildings and contents. Social consequences, in terms of displaced people and households for each pre-defined growth scenarios under different flooding return periods.

Full Reference: 

Hemmati, M., Hussam N., Ellingwood B.R. and Crooks, A.T. (2021), Shaping Urbanization to Achieve Sustainable Communities Resilient to Floods, Environmental Research Letters, 16, 094033 https://doi.org/10.1088/1748-9326/ac1e3c. (pdf)

Organizing Theories for Disasters into a Complex Adaptive System Framework

In past posts we have discussed or demonstrated how computational social science (CSS) (i.e. the study of social science through computational methods) and complexity theory can be utilized explore disasters or diseases but this has not really been  formalized.  To this end, Annetta Burger, William Kennedy and myself have a new review paper in Urban Science entitled "Organizing Theories for Disasters into a Complex Adaptive System Framework." In the paper we review over a century of disaster research and demonstrate the properties and dynamics of complex adaptive systems in such studies and argue how complexity theory is integral to understanding human behavior in disasters by addressing the interactions across systems (i.e., physical, social, and individual systems). We discuss the characteristics of a complex adaptive system (e.g., heterogeneity, webs of connections, relationships and interactions, and adaptations arising from individual actions, decisions, and learning) and how such characteristics can be applied to disaster research and explore implications for future disaster research with an eye on sustainable and resilient cities. If this sounds of interest, and you want to find out more, below we provide the abstract to the paper and a  link to the the paper itself.

Abstract: Increasingly urbanized populations and climate change have shifted the focus of decision1makers from economic growth to the sustainability and resilience of urban infrastructure and communities, especially when communities face multiple hazards and need to recover from recurring disasters. Understanding human behavior and its interactions with built-environments in disasters requires disciplinary crossover to explain its complexity, therefore we apply the lens of complex adaptive systems (CAS) to review disaster studies across disciplines. Disasters can be understood to consist of three interacting systems: 1) the physical system, consisting of geological, ecological, and human-built systems; 2) the social system, consisting of informal and formal human collective behavior; and 3) the individual actor system. Exploration of human behavior in these systems shows that CAS properties of heterogeneity, interacting subsystems, emergence, adaptation, and learning are integral, not just to cities, but to disaster studies and connecting them in the CAS framework provides us with a new lens to study disasters across disciplines. This paper explores the theories and models used in disaster studies, provides a framework to study and explain disasters, and discusses how complex adaptive systems can support theory-building in disaster science for promoting more sustainable and resilient cities.

Keywords: Cities; Complex Adaptive Systems; Computational Social Science, Disasters; Human Behavior.

Framework for Understanding the Intersecting Complex Adaptive Systems of Disaster.

Full Reference:

Burger, A., Kennedy, W.G. and Crooks A.T. (2021), Organizing Theories for Disasters into a Complex Adaptive System Framework, Urban Science, 5(3), 61; https://doi.org/10.3390/urbansci5030061 (pdf)

 

An Agent-based Model of Interactional Theory of Delinquency

While agent-based modeling is growing within many areas (e.g., geography, ecology) one area that has not seen many applications is that of social work. For example how can we explore what may cause an increase or a decrease in delinquency and recidivism within a given population? To this end, JoAnn Lee and myself recently had a paper published in the  Journal of Artificial Societies and Social Simulation entitled "Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-based Model of Interactional Theory of Delinquency." In the paper we explore how one can test the interactional theory of delinquency via and agent-based model and as such provides a means of increasing our understanding of delinquency.

If this sounds of interest, below we provide the abstract to the paper and some of the figures (including the graphical user interface of the model, the conceptual model of interactions and how the model actually works. At the bottom of the post you can find the full citation of the paper and a link to it. The model itself was created in NetLogo and a detailed Overview, Design concepts, and Details plus Decision (ODD + D) protocol document is available at: https://bit.ly/YaTASERPS. We provide this documentation in order to provide more details about the model and aid others in replicating the results presented in the paper along with extending the model if so desired. 

Abstract: Risk assessments are designed to measure cumulative risk and promotive factors for delinquency and recidivism, and are used by criminal and juvenile justice systems to inform sanctions and interventions. Yet, these risk assessments tend to focus on individual risk and often fail to capture each individual’s environmental risk. This paper presents an agent-based model (ABM) which explores the interaction of individual and environmental risk on the youth. The ABM is based on an interactional theory of delinquency and moves beyond more traditional statistical approaches used to study delinquency that tend to rely on point-in-time measures, and to focus on exploring the dynamics and processes that evolve from interactions between agents (i.e., youths) and their environments. Our ABM simulates a youth’s day, where they spend time in schools, their neighborhoods, and families. The youth has proclivities for engaging in prosocial or antisocial behaviors, and their environments have likelihoods of presenting prosocial or antisocial opportunities. Results from systematically adjusting family, school, and neighborhood risk and promotive levels suggest that environmental risk and promotive factors play a role in shaping youth outcomes. As such the model shows promise for increasing our understanding of delinquency. 

Keywords: Agent-based Modeling, Antisocial Behaviors, Delinquency, Risk Factors, Youth, Social Work.

Graphical user interface of the model at model initialization. The model environment (right) shows youths (grey) at home (blue) and their neighborhood (green) and their school (brown).

Conceptual model of interactions.

Flow diagram of the overall model logic during the simulation.

 Full reference:

Lee, J. and Crooks A.T. (2021), Youth and their Artificial Social Environmental Risk and Promotive Scores (Ya-TASERPS): An Agent-Based Model of Interactional Theory of Delinquency, Journal of Artificial Societies and Social Simulation. 24 (4) 2. Available at: https://www.jasss.org/24/4/2.html (pdf)

 

Postdoctoral Associate Position Available

Come work with me!

The Department of Geography at the University at Buffalo (UB) invites applicants for a postdoctoral researcher position with broad interests in urban analytics with a particular emphasis on modeling and simulation. 

The postdoctoral researcher will be expected develop systems to collect and store social media data and use such data to inform and develop a portfolio of innovative agent-based models utilizing machine learning methods to study urban areas. The ideal candidate will have experience with developing agent-based models, social network analysis, GIS and machine learning.  

Key responsibilities:

• Collecting, storing and analyzing social media data for a variety of urban topics.
• Developing geographically explicit agent-based models.
• Presenting material to broader community (i.e., publications conference presentations).

Required knowledge skills and abilities: 

• PhD. in geography, computer science or a related field (or expect to have obtained their PhD. by the time the post commences).  
• Ability to work independently and in an interdisciplinary research team.
• Excellent written communication skills demonstrated by prior publications.
• Strong programming skills in Python or Java.

Preferred Qualifications

• Familiarity with GIS, machine learning and social media data.
• Experience with developing agent-based models.

This position is affiliated with the Department of Geography at the University at Buffalo (UB), a large R1 Research University on the shores of Lake Erie. 

Outstanding Benefits Package

Working at UB comes with benefits that exceed salary alone. There are personal rewards including comprehensive health and retirement plan options. We also focus on creating and sustaining a healthy mix of work, personal and academic pursuit – all in an effort to support your work-life effectiveness. Visit our benefits website to learn about our benefit packages. 

About UB

The University at Buffalo is SUNY’s most comprehensive public research university, and an outstanding place to work. UB amplifies ambition for faculty and staff by offering endless possibilities to achieve more. Here, people from all backgrounds and cultures challenge and inspire each other to discover, learn and succeed. Dedicated staff and engaged faculty collaborate to further knowledge and understanding, and develop tenacious graduates who are valued for their talents and their impact on global society. Visit our website to learn more about the University at Buffalo.

As an Equal Opportunity / Affirmative Action employer, the Research Foundation will not discriminate in its employment practices due to an applicant’s race, color, religion, sex, sexual orientation, gender identity, national origin and veteran or disability status.

 

Further Details and How to Apply: https://www.ubjobs.buffalo.edu/postings/29858