Journal Articles by Andrew Crooks
Journal of Simulation, 2026
Global disruptions have shown that shocks to supply chains can quickly ripple through entire econ... more Global disruptions have shown that shocks to supply chains can quickly ripple through entire economies, highlighting the need to identify vulnerabilities and evaluate mitigation strategies to build resilience. In this paper, we propose a simulation methodology, Hybrid Integrated Supply-Chain Simulation (HISS), to identify and mitigate potential disruptions in supply chains. We demonstrate HISS using a generic pharmaceutical supply chain model including sourcing, outsourcing, production, packaging, and distribution processes, created using MASON's hybrid modeling capabilities. We classify disruptions from malicious actors and analyze their timing, impact, and scope. The simulation is further extended to modeling mitigation strategies and assessing their efficacy. Extensive optimization allowed us to identify worst-case disruptions and optimized safety stock strategies reduced impacts by a factor of five, while anomaly detection achieved a high recall of 0.966. The modeling approach proposed in this paper provides a basis for planning tools that support resilience and preparedness of supply chains.
Journal of Open Source Software, 2026
Synthetic networks are commonly used to study the structure and dynamics of social systems, trans... more Synthetic networks are commonly used to study the structure and dynamics of social systems, transportation infrastructure and other complex phenomena. Classical random graph models, such as the Erdős-Rényi, Watts-Strogatz and Barabási-Albert models, generate abstract networks with different structural characteristics: the Erdős-Rényi model connects nodes at random with equal probability, the Watts-Strogatz model rewires a ring lattice to produce small-world networks, and the Barabási-Albert model yields scale-free networks through preferential attachment (Barabási & Albert, 1999; Erdös & Rényi, 1960; Watts & Strogatz, 1998). In their standard form these models ignore the spatial positions of nodes; yet in many empirical settings (e.g., human social networks, commuting patterns or infrastructure networks) proximity strongly influences who connects to whom.
npj Urban Sustain, 2025
This study introduces a new modeling paradigm called gravity-networked spatial interaction zonesb... more This study introduces a new modeling paradigm called gravity-networked spatial interaction zonesbased spatial panel modeling (GSIZs-Spanel). Using Detroit as a case study, this paper investigates urban shrinkage by integrating shrinkage driving factors, their regional interactions, networks of cities, spatial processes, and longitudinal dynamics. Results suggest that high minority population concentration and persistent poverty are the primary factors impacting Detroit's inner-city shrinkage. Demographics, economics, and development practices affect shrinkage in suburbs and surrounding cities. Shrinkage spreads outwards like waves; different juxtapositions of driving factors affect shrinkage resilience; spillover effects are particularly vibrant at 25-50 GSIZs; rightsizing is a rational strategy, but it failed to work alone. Integrating spatial planning of driving factors, land uses, spillover effects, rightsizing strategy, and regional collaboration among federal, regional, and local organizations could moderate urban decline. GSIZs-Spanel, which was developed here, could be applied in any U.S. city or other global city.
Environment and Planning B: Urban Analytics and City Science, 2025
Intelligence (AI) is impacting all aspects of our lives (e.g., Arribas-Bel et al., 2025; Batty 20... more Intelligence (AI) is impacting all aspects of our lives (e.g., Arribas-Bel et al., 2025; Batty 2025). While there is much promise with respect to AI for studying cities, we also need to proceed with caution. There are dangers of using AI, especially when it comes to multimodal large language models (LLMs) for research such as performing literature reviews and writing papers or grants, as it raises ethical questions with respect to plagiarism or just being inaccurate (e.g., Batty 2023; Parrilla 2023). However, there is no mistaking that Generative AI (GenAI) is changing and impacting our research. GenAI has a specific emphasis on creating and generating new content or information like text, images, or code based on prompts. We are seeing a proliferation of GenAI from Chatbots (e.g., ChatGPT and Gemini), website, and app creation tools (e.g., 10Web and Imagica) to coding (e.g., Amazon's CodeWhisperer and GitHub's Copilot). In a previous editorial, we discussed how GenAI could be used to lower the barrier for researchers wishing to study urban problems through the lens of urban analytics, especially with respect to street view images (Crooks and Chen, 2024). This got us to thinking about how else GenAI could be used in urban analytics. In a recent survey by Van Noorden and Perkel (2023), it was noted that many researchers commented that LLM is making coding easier and faster, which dovetails nicely into one area of urban analytics that often requires coding: that of urban modeling. Urban modeling has been a constant theme in this journal over the years (see Crooks et al., 2024). To some extent, this editorial builds on that of Arribas-Bel et al. (2025), which asked for a more focused discussion about tangible areas where AI could be leveraged in urban analytics. However, before exploring this, it should be noted that over the years, AI and machine learning have been used in many urban modeling applications, especially cellular automata models for calibration and validation (see Liu et al., 2021), but we see GenAI as offering new capabilities (and challenges) for urban modeling. To focus this editorial a little, we will choose just one area of urban modeling, that of agent-based modeling, the rationale being that the way we model cities has changed with the growth of complexity science. It has been noted that cities emerge from the interactions of many individuals, and agent-based models can capture such emergence (see Batty (2005)). This style of model attempts to mimic the behaviors of key decision makers for a specific application through their interactions with each other and their environment, and as a result, more aggregate patterns emerge. The classic example is Schelling's (1971) model of residential segregation, where agents have a preference for living in neighborhoods composed of similar types of people to themselves; if their preference is not met, they move. Through these interactions, segregated neighborhoods emerge. Generally speaking, the agent-based modeling process can be considered as three distinct phases: designing, building, and running the model. With this in mind, we were curious how GenAI could help with these three phases. Traditionally, the designing of models was based on a specific research question at hand such as how will people evacuate a building or how do people navigate around a city? Once a research question has been formulated, the next stage is building the model. While it is possible to create an agent-based model from scratch using a programming language of choice, over
GeoJournal, 2025
to dust events, which can be attributed to the sparsely populated nature of the region. Furthermo... more to dust events, which can be attributed to the sparsely populated nature of the region. Furthermore, seasonal analysis reveals that dust events are prevalent during the Summer months followed by Spring. These results are consistent with previous traditional studies that did not use social media of dust occurrences in the U.S., and Flickr-identified images of dust events show substantial co-occurrence with regions of NWS dust warnings. This paper highlights the potential of using crowdsourced data for the often overlooked field of dust monitoring that has substantial health and economic impacts.
Annals of the American Association of Geographers , 2025
Smells can shape people's perceptions of urban spaces, influencing how individuals relate themsel... more Smells can shape people's perceptions of urban spaces, influencing how individuals relate themselves to the environment both physically and emotionally. Although the urban environment has long been conceived as a multisensory experience, research has mainly focused on the visual dimension, leaving smell largely understudied. This article aims to construct a flexible and efficient bottom-up framework for capturing and classifying perceived urban smells from individuals based on geosocial media data, thus, increasing our understanding of this relatively neglected sensory dimension in urban studies. We take New York City as a case study and decode perceived smells by teasing out specific smell-related indicator words through text mining techniques from a historical set of geosocial media data (i.e., Twitter/X). The data set consists of more than 56 million data points sent by more than 3.2 million users. The results demonstrate that this approach, which combines quantitative analysis with qualitative insights, can not only reveal "hidden" places with clear spatial smell patterns, but also capture elusive smells that might otherwise be overlooked. By making perceived smells measurable and visible, we can gain a more nuanced understanding of smellscapes and people's sensory experiences within the urban environment. Overall, we hope our study opens up new possibilities for understanding urban spaces through an olfactory lens and, more broadly, multisensory urban experience research.
iScience, 2025
As more satellite imagery has become openly available, efforts in mapping the Earth’s surface hav... more As more satellite imagery has become openly available, efforts in mapping the Earth’s surface have accelerated. Yet the accuracy of these maps is still limited by the lack of in-situ data needed to train machine learning algorithms. Citizen science has proven to be a valuable approach for collecting in-situ data through applications like Geo-Wiki and Picture Pile, but better approaches for optimizing volunteer time are still required. Although machine learning is being used in some citizen science projects, advances in generative Artificial Intelligence (AI) are yet to be fully exploited. This paper discusses how generative AI could be harnessed for land cover/land use mapping by enhancing citizen science approaches with multi-modal large language models (MLLMs), including improvements to the spatial awareness of AI.
PLOS Digital Health, 2025
In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and indivi... more In the face of the unprecedented COVID-19 pandemic, various government-led initiatives and individual actions (e.g., lockdowns, social distancing, and masking) have resulted in diverse pandemic experiences. This study aims to explore these varied experiences to inform more proactive responses for future public health crises. Employing a novel "bigthick" data approach, we analyze and compare key pandemic-related topics that have been disseminated to the public through newspapers with those collected from the public via interviews. Specifically, we utilized 82,533 U.S. newspaper articles from January 2020 to December 2021 and supplemented this "big" dataset with "thick" data from interviews and focus groups for topic modeling. Identified key topics were contextualized, compared and visualized at different scales to reveal areas of convergence and divergence. We found seven key topics from the "big" newspaper dataset, providing a macro-level view that covers public health, policies and economics. Conversely, three divergent topics were derived from the "thick" interview data, offering a micro-level view that focuses more on individuals' experiences, emotions and concerns. A notable finding is the public's concern about the reliability of news information, suggesting the need for further investigation on the impacts of mass media in shaping the public's perception and behavior. Overall, by exploring the convergence and divergence in identified topics, our study offers new insights into the complex impacts of the pandemic and enhances our understanding of key issues both disseminated to and resonating with the public, paving the way for further health communication and policy-making.
Proceedings of the 7th ACM SIGSPATIAL Workshop on Location-based Recommendations, Geosocial Networks and Geoadvertising , 2023
Generating synthetic social networks is an important task for many problems that study humans, th... more Generating synthetic social networks is an important task for many problems that study humans, their behavior, and their interactions. Geosocial networks enrich social networks with location information. Commonly used models to generate synthetic social networks include the classical Erdős-Rényi, Barabási-Albert, and Watts-Strogatz models. However, these classic social network models do not consider the location of individuals. Real-world geosocial networks do exhibit a strong spatial autocorrelation, thus having a higher likelihood of a social connection between agents that are spatially close. As such, recent variants of the three classical models have been proposed to consider location information. Yet, these existing solutions assume that individuals are located on a uniform lattice and exhibit certain limitations when applied to real-world data that exhibits clusters. In this work, we discuss these limitations and propose new approaches to extend the three classic social network generation models to geosocial networks. Our experiments show that our generated synthetic geosocial networks address the shortcomings of the state-of-the-art models and generate realistic geosocial networks that exhibit high similarity to real-world geosocial networks.
Environmental Research Letters, 2021
Flood risk is increasing in urban communities due to climate change and socioeconomic development... more 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.
Scientific Reports, 2021
Floods are among the costliest natural hazards and their consequences are expected to increase fu... more 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.
Transactions in GIS, 2022
Calibration of agent-based models (ABMs) is a major challenge due to the complex nature of the sy... more 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.
Computational Urban Science, 2022
Geographically-explicit simulations have become crucial in understanding cities and are playing a... more Geographically-explicit simulations have become crucial in understanding cities and are playing an important role in Urban Science. One such approach is that of agent-based modeling which allows us to explore how agents interact with the environment and each other (e.g., social networks), and how through such interactions aggregate patterns emerge (e.g., disease outbreaks, traffic jams). While the use of agent-based modeling has grown, one challenge remains, that of creating realistic, geographically-explicit, synthetic populations which incorporate social networks. To address this challenge, this paper presents a novel method to create a synthetic population which incorporates social networks using the New York Metro Area as a test area. To demonstrate the generalizability of our synthetic population method and data to initialize models, three different types of agent-based models are introduced to explore a variety of urban problems: traffic, disaster response, and the spread of disease. These use cases not only demonstrate how our geographically-explicit synthetic population can be easily utilized for initializing agent populations which can explore a variety of urban problems, but also show how social networks can be integrated into such populations and large-scale simulations.
Socio-Environmental Systems Modelling, 2022
Agricultural water reuse is gaining momentum to address freshwater scarcity worldwide. The main o... more Agricultural water reuse is gaining momentum to address freshwater scarcity worldwide. The main objective of this paper was to investigate the micro-level dynamics of water reuse adoption by farmers at the watershed scale. An agent-based model was developed to simulate agricultural water consumption and socio-hydrological dynamics. Using a case study in California, the developed model was tested, and the results showed that agricultural water reuse adoption by farmers is a gradual and time-consuming process. In addition, results also showed that agricultural water reuse could significantly decrease the water shortage (by 57.7%) and groundwater withdrawal (by 74.1%). Furthermore, our results suggest that recycled water price was the most influential factor in total recycled water consumption by farmers. Results also showed how possible freshwater shortage or groundwater withdrawal regulations could increase recycled water use by farmers. The developed model can significantly help assess how the current water reuse management practices and strategies would affect the sustainability of agricultural water resources. Keywords Water reuse; agent-based modelling; agricultural water management; recycled water for irrigation Code availability The WRAF (water reuse adoption by farmers) model presented in this paper and its complete description following the Overview, Design concepts, Details, and Decision-making (ODD) (Grimm et al., 2006) protocol can be found at https://www.comses.net/codebase-release/cc6d551e-cf0f-472e-a54b-28591cd39b4d/.
Computational and Mathematical Organization Theory, 2023
The employment of drone strikes has been ongoing and the public continues to debate their perceiv... more The employment of drone strikes has been ongoing and the public continues to debate their perceived benefits. A question that persists is whether drone strikes contribute to an increase in radicalization. This paper presents a data-driven approach to explore the relationship between drone strikes conducted in Pakistan and subsequent responses, often in the form of terrorist attacks carried out by those in the communities targeted by these particular counterterrorism measures. Our exploration and analysis of news reports which discussed drone strikes and radicalization suggest that government-sanctioned drone strikes in Pakistan appear to drive terrorist events with a distributed lag that can be determined analytically. We leverage news reports to inform and calibrate an agent-based model grounded in radicalization and opinion dynamics theory. This enabled us to simulate terrorist attacks that approximated the rate and magnitude observed in Pakistan from 2007 through 2018. We argue that this research effort advances the field of radicalization and lays the foundation for further work in the area of data-driven modeling and drone strikes.
Computational and Mathematical Organization Theory, 2023
We introduce the Urban Life agent-based simulation used by the Ground Truth program to capture th... more 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.
Journal of Environmental Management, 2023
Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon f... more Soil organic carbon (SOC) can be increased by cultivating bioenergy crops to produce low-carbon fuels, improving soil quality and agricultural productivity. This study evaluates the incentives for farmers to sequester SOC by adopting a bioenergy crop, carinata. Two agricultural management scenariosbusiness as usual (BaU) and a climate-smart (no-till) practicewere simulated using an agent-based modeling approach to account for farmers' carinata adoption rates within their context of traditional crop rotations, the associated profitability, influences of neighboring farmers, as well as their individual attitudes. Using the state of Georgia, US, as a case study, the results show that farmers allocated 1056 × 10 3 acres (23.8%; 2.47 acres is equivalent to 1 ha) of farmlands by 2050 at a contract price of $6.5 per bushel of carinata seeds and with an incentive of $50 Mg − 1 CO2e SOC sequestered under the BaU scenario. In contrast, at the same contract price and SOC incentive rate, farmers allocated 1152 × 10 3 acres (25.9%) of land under the no-till scenario, while the SOC sequestration was 483.83 × 10 3 Mg CO2e, which is nearly four times the amount under the BaU scenario. Thus, this study demonstrated combinations of seed prices and SOC incentives that encourage farmers to adopt carinata with climate-smart practices to attain higher SOC sequestration benefits.
Environment and Planning B, 2024
Today we are awash with data, especially when it comes to studying cities from a diverse data eco... more Today we are awash with data, especially when it comes to studying cities from a diverse data ecosystem ranging from demographic to remotely sensed imagery and social media. This has led to the growth of urban analytics providing new ways to conduct quantitative research within cities. One area that has seen significant growth is using natural language processing techniques on text data from social media to explore various issues relating to urban morphology. However, we would argue that social media only provides limited insights when dealing with longer-term urban phenomena, such as the growth and shrinkage of cities. This relates to the fact that social media is a relatively recent phenomenon compared to longer-term urban problems that take decades to emerge. Concerning longer-term coverage, newspapers, which are increasingly becoming digitized, provide the possibility to overcome the limitations of social media and provide insights over a timeframe that social media does not. To demonstrate the utility of newspapers for urban analytics and to study longer-term urban issues, we utilize an advanced topic modeling technique (i.e., BERTopic) on a large number of newspaper articles from 1975 to 2021 to explore urban shrinkage in Detroit. Our topic modeling results reveal insights related to how Detroit shrinks. For example, side effects of 2007 to 2009 economic recessions on Detroit's automobile industry, local employment status, and the housing market.
Computers, Environment and Urban Systems, 2024
Disasters have been a long-standing concern to societies at large. With growing attention being p... more Disasters have been a long-standing concern to societies at large. With growing attention being paid to resilient communities, such concern has been brought to the forefront of resilience studies. However, there is a wide variety of definitions with respect to resilience, and a precise definition has yet to emerge. Moreover, much work to date has often focused only on the immediate response to an event, thus investigating the resilience of an area over a prolonged period of time has remained largely unexplored. To overcome these issues, we propose a novel framework utilizing network analysis and concepts from disaster science (e.g., the resilience triangle) to quantify the long-term impacts of wildfires. Taking the Mendocino Complex and Camp wildfires-the largest and most deadly wildfires in California to date, respectively-as case studies, we capture the robustness and vulnerability of communities based on human mobility data from 2018 to 2019. The results show that demographic and socioeconomic characteristics alone only partially capture community resilience, however, by leveraging human mobility data and network analysis techniques, we can enhance our understanding of resilience over space and time, providing a new lens to study disasters and their long-term impacts on society.
International Journal of Geographical Information Science, 2024
This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a... more This study addresses the challenge of equifinality in agent-based modeling (ABM) by introducing a novel sequential calibration approach. Equifinality arises when multiple models equally fit observed data, risking the selection of an inaccurate model. In the context of ABM, such a situation might arise due to limitations in data, such as aggregating observations into coarse spatial units. It can lead to situations where successfully calibrated model parameters may still result in reliability issues due to uncertainties in accurately calibrating the inner mechanisms. To tackle this, we propose a method that sequentially calibrates model parameters using diverse outcomes from multiple datasets. The method aims to identify optimal parameter combinations while mitigating computational intensity. We validate our approach through indoor pedestrian movement simulation, utilizing three distinct outcomes: (1) the count of grid cells crossed by individuals, (2) the number of people in each grid cell over time (fine grid) and (3) the number of people in each grid cell over time (coarse grid). As a result, the optimal calibrated parameter combinations were selected based on high test accuracy to avoid overfitting. This method addresses equifinality while reducing computational intensity of parameter calibration for spatially explicit models, as well as ABM in general.