@article{li2025incorporating,
  title = {Incorporating environmental considerations into infrastructure inequality evaluation using interpretable machine learning},
  author = {Li, Bo and Mostafavi, Ali},
  journal = {Computers, Environment and Urban Systems},
  volume = {120},
  pages = {102301},
  year = {2025},
  publisher = {Elsevier},
  doi = {10.1016/j.compenvurbsys.2025.102301}
}

A growing body of literature has recognized the importance of characterizing infrastructure inequality in cities and provided quantified metrics to inform urban development plans. However, the majority of existing approaches suffered from two limitations. First, prior research has provided empirical evidence of negative environmental impacts that infrastructure can incur, while infrastructure provision inequality assessment has not taken those environmental concerns into consideration. Second, comprehensive provision assessment for multi-infrastructure system calls for a proper weight assignment, while current studies either determine the infrastructure components as equal weights or rely on subjective methods (e.g. AHP), which may be affected by potential biases. This study proposes a novel approach for incorporating environmental considerations into quantifying and assessing infrastructure provision in cities based on a data-driven method. We applied an interpretable machine learning method (XGBoost + SHAP) to capture the relationship between infrastructure features and environmental hazards (i.e., air pollution and urban heat), and then determined feature weights as their relative contributions towards environmental hazards when calculating infrastructure provision. The implementation of the model in five metropolitan areas in the U.S. demonstrates the capability of the proposed approach in characterizing inequality in infrastructure. Further the study reveals both spatial and income inequality regarding infrastructure provision. Environmentally integrated infrastructure provision proposed in this study can better capture the intersection of infrastructure development and environmental justice in measuring and characterizing infrastructure inequality in cities. This study could be used effectively to inform integrated urban design strategies to promote infrastructure equity and environmental justice based on data-driven and machine learning-based insights.

@article{ma2024characterizing,
  title = {Characterizing urban lifestyle signatures using motif properties in network of places},
  author = {Ma, Junwei and Li, Bo and Mostafavi, Ali},
  journal = {Environment and Planning B: Urban Analytics and City Science},
  volume = {51},
  number = {4},
  pages = {889--903},
  year = {2024},
  publisher = {SAGE Publications Sage UK: London, England},
  doi = {10.1177/23998083231206171},
  cover = {ma2024characterizing_cover.jpg}
}

The lifestyles of urban dwellers could reveal important insights regarding the dynamics and complexity of cities. The availability of human movement data captured from cell phones enables characterization of distinct and recurrent human daily visitation patterns. Despite growing research on analysis of lifestyle patterns in cities, little is known about the characteristics of people’s lifestyle patterns at urban scale. This limitation is primarily due to challenges in restriction of human movement data to protect the privacy of users. To address this gap, this study constructed networks of places to model cities based on location-based human visitation data. We examined the motifs in the networks of places to map and characterize lifestyle patterns at urban scale. The results show that (1) people’s lifestyles in cities can be well depicted and quantified based on distribution and attributes of motifs in networks of places; (2) motifs show stability in quantity and distance as well as periodicity on weekends and weekdays indicating the stability of lifestyle patterns in cities; (3) networks of places and lifestyle patterns show similarities across different metropolitan areas implying the universality of lifestyle signatures across cities; (4) lifestyles represented by attributed motifs are spatially heterogeneous suggesting variations of lifestyle patterns within different population groups based on where they live in a city. The findings provide deeper insights into urban lifestyle signatures and significant implications for data-informed urban planning and management.

@article{li2024mobility,
  title = {Mobility behaviors shift disparity in flood exposure in US population groups},
  author = {Li, Bo and Fan, Chao and Chien, Yu-Heng and Hsu, Chia-Wei and Mostafavi, Ali},
  journal = {International Journal of Disaster Risk Reduction},
  volume = {108},
  pages = {104545},
  year = {2024},
  publisher = {Elsevier},
  doi = {10.1016/j.ijdrr.2024.104545},
  cover = {li2024mobility_cover.jpg}
}

The current characterization of flood exposure is largely based on residential location of populations; however, location of residence only partially captures the extent to which populations are exposed to flood hazards. An important, yet under-recognized aspect of flood exposure is associated with human mobility patterns and population visitation to places located in flood prone areas. In this study, we analyzed large-scale, high-resolution location-intelligence data collected from anonymous mobile phone users to characterize human mobility patterns and the resulting flood exposure in coastal counties of the United States. We developed the metric of mobility-based exposure based on dwell time in places located in the 100-year floodplain. The results of examining the extent of mobility-based flood exposure across demographic groups reveal significant disparities across race, income, and education level groups. The results show that Black and Asian, economically disadvantaged, and undereducated populations in US coastal cities are disproportionally exposed to flood due to their daily mobility activities, indicating a pattern contrary to that of residential flood exposure. The results suggest that mobility behaviors play an important role in extending flood exposure reach disproportionally among different socio-demographic groups. The results highlight that urban flood risk assessments should not only focus on the level of flood exposure to residences, but also should consider mobility-based exposure to better learn the disparities in flood exposure among social groups. Mobility-based flood exposure provides a new perspective regarding the extent to which floods could disrupt people’s life activities and enable a better characterization of disparity in populations’ exposure to flood hazards beyond their place of residence. The findings of this study have important implications for urban planners, flood managers, and city officials in terms of accounting for mobility-based flood exposure in flood risk management plans and actions.

@article{li2024human,
  title = {Human mobility disproportionately extends pm2. 5 emission exposure for low income populations},
  author = {Li, Bo and Fan, Chao and Chien, Yu-Heng and Mostafavi, Ali},
  journal = {Sustainable Cities and Society},
  pages = {106063},
  year = {2024},
  publisher = {Elsevier},
  doi = {10.1016/j.scs.2024.106063},
  cover = {li2024human_cover.jpg}
}

Ambient exposure to fine particulate matter with diameters smaller than 2.5 μm (PM2.5) has been identified as one critical cause for respiratory disease. Disparities in exposure to PM2.5 among income groups at individual residences are known to exist and are easy to calculate. Existing approaches for exposure assessment, however, do not capture the exposure implied by the dynamic mobility of city dwellers that accounts for a large proportion of the exposure outside homes. To overcome the challenge of gauging the exposure to PM2.5 for city dwellers, we analyzed billions of anonymized and privacy-enhanced location-based data generated by mobile phone users in Harris County, Texas, to characterize the mobility patterns of the populations and associated exposure. We introduce the metric for exposure extent based on the time people spent at places with the air pollutant and examine the disparities in mobility-based exposure across income groups. Our results show that PM2.5 concentrations disproportionately expose low-income populations due to their mobility activities. People with higher-than-average income are exposed to lower levels of PM2.5 concentrations. These disparities in mobility-based exposure are the result of frequent visits of low-income people to the industrial sectors of urban areas with high PM2.5 concentrations, and the larger mobility scale of these people for life needs. The results inform environmental justice and public health strategies, not only to reduce the overall PM2.5 exposure but also to mitigate the disproportional impacts on low-income populations. The findings also suggest that an integration of extensive fine-scale population mobility and pollution concentration data can unveil new insights into inequality in air pollution exposures at urban scale.

@article{li2024unraveling,
  title = {Unraveling fundamental properties of power system resilience curves using unsupervised machine learning},
  author = {Li, Bo and Mostafavi, Ali},
  journal = {Energy and AI},
  volume = {16},
  pages = {100351},
  year = {2024},
  publisher = {Elsevier},
  doi = {10.1016/j.egyai.2024.100351},
  cover = {li2024unraveling_cover.jpg}
}

Power system is vital to modern societies, while it is susceptible to hazard events. Thus, analyzing resilience characteristics of power system is important. The standard model of infrastructure resilience, the resilience triangle, has been the primary way of characterizing and quantifying resilience in infrastructure systems for more than two decades. However, the theoretical model provides a one-size-fits-all framework for all infrastructure systems and specifies general characteristics of resilience curves (e.g., residual performance and duration of recovery). Little empirical work has been done to delineate infrastructure resilience curve archetypes and their fundamental properties based on observational data. Most of the existing studies examine the characteristics of infrastructure resilience curves based on analytical models constructed upon simulated system performance. There is a dire dearth of empirical studies in the field, which hindered our ability to fully understand and predict resilience characteristics in infrastructure systems. To address this gap, this study examined more than two hundred power-grid resilience curves related to power outages in three major extreme weather events in the United States. Through the use of unsupervised machine learning, we examined different curve archetypes, as well as the fundamental properties of each resilience curve archetype. The results show two primary archetypes for power grid resilience curves, triangular curves, and trapezoidal curves. Triangular curves characterize resilience behavior based on three fundamental properties: 1. critical functionality threshold, 2. critical functionality recovery rate, and 3. recovery pivot point. Trapezoidal archetypes explain resilience curves based on 1. duration of sustained function loss and 2. constant recovery rate. The longer the duration of sustained function loss, the slower the constant rate of recovery. The findings of this study provide novel perspectives enabling better understanding and prediction of resilience performance of power system infrastructure in extreme weather events.

@article{ma2024attributed,
  title = {Attributed network embedding model for exposing COVID-19 spread trajectory archetypes},
  author = {Ma, Junwei and Li, Bo and Li, Qingchun and Fan, Chao and Mostafavi, Ali},
  journal = {International Journal of Data Science and Analytics},
  pages = {1--18},
  year = {2024},
  publisher = {Springer},
  doi = {10.1007/s41060-024-00627-5},
  cover = {ma2024attributed_cover.jpg}
}

The spread of COVID-19 revealed that transmission risk patterns are not homogenous across different cities and communities, and various heterogeneous features can influence the spread trajectories. Hence, for predictive pandemic monitoring, it is essential to explore latent heterogeneous features in cities and communities that distinguish their specific pandemic spread trajectories. To this end, this study creates a network embedding model capturing cross-county visitation networks, as well as heterogeneous features related to population activities, human mobility, socio-demographic features, disease attribute, and social interaction to uncover clusters of counties in the USA based on their pandemic spread transmission trajectories. We collected and computed location intelligence features from 2787 counties from March 3 to June 29, 2020 (initial wave). Second, we constructed a human visitation network, which incorporated county features as node attributes, and visits between counties as network edges. Our attributed network embeddings approach integrates both typological characteristics of the cross-county visitation network, as well as heterogeneous features. We conducted clustering analysis on the attributed network embeddings to reveal four archetypes of spread risk trajectories corresponding to four clusters of counties. Subsequently, we identified four features—population density, GDP, minority status, and POI visits—as important features underlying the distinctive transmission risk patterns among the archetypes. The attributed network embedding approach and the findings identify and explain the non-homogenous pandemic risk trajectories across counties for predictive pandemic monitoring. The study also contributes to data-driven and deep learning-based approaches for pandemic analytics to complement the standard epidemiological models for policy analysis in pandemics.

@article{wang2022trust,
  title = {Trust repair in the aftermath of conflict occurrence in construction subcontracting: an intergroup contact perspective},
  author = {Wang, Conghan and Zhang, Shuibo and Gao, Ying and Li, Bo},
  journal = {Construction management and economics},
  volume = {40},
  number = {10},
  pages = {781--795},
  year = {2022},
  publisher = {Taylor \& Francis},
  doi = {10.1080/01446193.2022.2110272},
  cover = {wang2022trust_cover.jpg}
}

Although trust repair after conflict occurrence is significant for effective work and sustainable relationships in construction subcontracting and contact is inevitable, researchers have yet to examine the relationship between contact quality and trust repair after conflict occurrence. This study investigates the effect of contact quality on Party A’s trust repair after conflict occurrence and the mediating mechanisms of that effect. The authors conducted a questionnaire survey to collect data for hypotheses testing, receiving 310 valid questionnaires from general contractors and subcontractors engaged in construction projects. The results reveal the positive effect of contact quality on Party A’s trust repair after conflict occurrence and the mediating roles of Party A’s feeling of threat and Party B’s self-disclosure in that effect. This study contributes to the trust research and intergroup contact theory. It also offers suggestions for construction subcontracting practitioners to facilitate trust repair after conflict occurrence.

@article{li2022location,
  title = {Location intelligence reveals the extent, timing, and spatial variation of hurricane preparedness},
  author = {Li, Bo and Mostafavi, Ali},
  journal = {Scientific reports},
  volume = {12},
  number = {1},
  pages = {16121},
  year = {2022},
  publisher = {Nature Publishing Group UK London},
  doi = {10.1038/s41598-022-20571-3},
  cover = {li2022location_cover.jpg}
}

Hurricanes are one of the most catastrophic natural hazards faced by residents of the United States. Improving the public’s hurricane preparedness is essential to reduce the impact and disruption of hurricanes on households. Inherent in traditional methods for quantifying and monitoring hurricane preparedness are significant lags, which hinder effective monitoring of residents’ preparedness in advance of an impending hurricane. This study establishes a methodological framework to quantify the extent, timing, and spatial variation of hurricane preparedness at the census block group level using high-resolution location intelligence data. Anonymized cell phone data on visits to points of interest for each census block group in Harris County before 2017 Hurricane Harvey were used to examine residents’ hurricane preparedness. Four categories of points of interest, grocery stores, gas stations, pharmacies, and home improvement stores, were identified as they have a close relationship with hurricane preparedness, and the daily number of visits from each census block group to these four categories of points of interest were calculated during the preparation period. Two metrics, extent of preparedness and proactivity, were calculated based on the daily visit percentage change compared to the baseline period. The results show that peak visits to pharmacies often occurred in the early stage of preparation, whereas the peak of visits to gas stations happened closer to hurricane landfall. The spatial and temporal patterns of visits to grocery stores and home improvement stores were quite similar. However, correlation analysis demonstrates that extent of preparedness and proactivity are independent of each other. Combined with synchronous evacuation data, census block groups in Harris County were divided into four clusters in terms of extent of preparedness and evacuation rate. The clusters with low preparedness and low evacuation rate were identified as hotspots of vulnerability for shelter-in-place households that would need urgent attention during response. Hence, the research findings provide a new data-driven approach to quantify and monitor the extent, timing, and spatial variations of hurricane preparedness. Accordingly, the study advances data-driven understanding of human protective actions during disasters. The study outcomes also provide emergency response managers and public officials with novel data-driven insights to more proactively monitor residents’ disaster preparedness, making it possible to identify under-prepared areas and better allocate resources in a timely manner.

@article{lee2022quantitative,
  title = {Quantitative measures for integrating resilience into transportation planning practice: Study in Texas},
  author = {Lee, Cheng-Chun and Rajput, Akhil Anil and Hsu, Chia-Wei and Fan, Chao and Yuan, Faxi and Dong, Shangjia and Esmalian, Amir and Farahmand, Hamed and Patrascu, Flavia Ioana and Liu, Chia-Fu and Li, Bo and Ma, Junwei and Mostafavi, Ali},
  journal = {Transportation Research Part D: Transport and Environment},
  volume = {113},
  pages = {103496},
  year = {2022},
  publisher = {Elsevier},
  doi = {10.1016/j.trd.2022.103496},
  cover = {lee2022quantitative_cover.jpg}
}

Using quantitative measures to assess road network resilience, this study proposes a system-level framework that offers insights into existing road network resilience that could inform the planning and development processes of transportation agencies. This study identified and implemented four quantitative metrics to classify the criticality of road segments based on dimensions of road network resilience. Integrating the four metrics of classification using two mathematical approaches, this study arrived at overall resilience performance metrics for assessing road segment criticality. A case study was conducted on Texas road networks to demonstrate the effectiveness of implementing this framework in a practical scenario. The data used in this study is available to other states and countries; thus, the framework presented in this study can be adopted by transportation agencies for regional transportation resilience assessments.

@article{li2021understanding,
  title = {Understanding the effects of trust and conflict event criticality on conflict resolution behavior in construction projects: Mediating role of social motives},
  author = {Li, Bo and Gao, Ying and Zhang, Shuibo and Wang, Conghan},
  journal = {Journal of Management in Engineering},
  volume = {37},
  number = {6},
  pages = {04021066},
  year = {2021},
  publisher = {American Society of Civil Engineers},
  doi = {10.1061/(ASCE)ME.1943-5479.0000962},
  cover = {li2021understanding_cover.jpg}
}

Because conflicts are unavoidable in construction projects, resolving them constructively is essential. The purpose of this paper is to examine whether trust and conflict event criticality can affect conflict resolution behaviors, and, if so, how. This research proposes that trust and conflict event criticality influence conflict resolution behaviors mediated by prosocial motive and proself motive, respectively. An empirical study using 253 valid questionnaires was conducted to test the hypotheses. The results verify that trust has positive impacts on integrating, compromising, and obliging behaviors mediated by a prosocial motive, while conflict event criticality positively affects obliging and dominating behaviors via a proself motive. Regarding the direct effects, trust and conflict event criticality are positively associated with integrating behavior, while trust also has a negative effect on dominating behavior. This study highlights how trust and conflict event criticality affects conflict resolution behaviors and uncovers the underlying psychological mechanisms. This paper also provides implications for practitioners.

@unpublished{li2024recent,
  title = {Recent Decade's Power Outage Data Reveals the Increasing Vulnerability of US Power Infrastructure},
  author = {Li, Bo and Ma, Junwei and Omitaomu, Femi and Mostafavi, Ali},
  journal = {arXiv preprint arXiv:2408.15882},
  doi = {2408.15882},
  year = {2024},
  cover = {li2024recent_cover.jpg}
}

Despite significant anecdotal evidence regarding the vulnerability of the U.S. power infrastructure, there is a dearth of longitudinal and nation-level characterization of the spatial and temporal patterns in the frequency and extent of power outages. A data-driven national-level characterization of power outage vulnerability is particularly essential for understanding the urgency and formulating policies to promote the resilience of power infrastructure systems. Recognizing this, we retrieved 179,053,397 county-level power outage records with a 15-minute interval across 3,022 US counties during 2014-2023 to capture power outage characteristics. We focus on three dimensions—power outage intensity, frequency, and duration—and develop multiple metrics to quantify each dimension of power outage vulnerability. The results show that in the past ten years, the vulnerability of U.S. power system has consistently been increasing. The national cumulative user outage time reached 7.86 billion user-hours, with a mean of 2.55 million user-hours at the county level, highlighting a significant disruption to customer service. Counties experienced an average of 999.4 outages over the decade, affecting an average of more than 540,000 customers per county, with disruptions occurring approximately every week. Coastal areas, particularly in California, Florida and New Jersey, faced more frequent and prolonged outages, while inland regions showed higher outage rates. A concerning increase in outage frequency and intensity was noted, especially after 2017, with a sharp rise in prolonged outages since 2019. The research also found positive association between social vulnerability and outage metrics, with the association becoming stronger over the years under study. Areas with higher social vulnerability experienced more severe and frequent outages, exacerbating challenges in these regions. These findings reveal the much-needed empirical evidence for infrastructure owners and operators, policymakers, and community leaders to inform policy formulation and program development for enhancing the resilience of the U.S. power infrastructure.

@unpublished{ma2024establishing,
  title = {Establishing Nationwide Power System Vulnerability Index across US Counties Using Interpretable Machine Learning},
  author = {Ma, Junwei and Li, Bo and Omitaomu, Olufemi A and Mostafavi, Ali},
  journal = {arXiv preprint arXiv:2410.19754},
  doi = {2410.19754},
  year = {2024},
  cover = {ma2024establishing_cover.jpg}
}

Power outages have become increasingly frequent, intense, and prolonged in the US due to climate change, aging electrical grids, and rising energy demand. However, largely due to the absence of granular spatiotemporal outage data, we lack data-driven evidence and analytics-based metrics to quantify power system vulnerability. This limitation has hindered the ability to effectively evaluate and address vulnerability to power outages in US communities. Here, we collected  179 million power outage records at 15-minute intervals across 3022 US contiguous counties (96.15% of the area) from 2014 to 2023. We developed a power system vulnerability assessment framework based on three dimensions (intensity, frequency, and duration) and applied interpretable machine learning models (XGBoost and SHAP) to compute Power System Vulnerability Index (PSVI) at the county level. Our analysis reveals a consistent increase in power system vulnerability over the past decade. We identified 318 counties across 45 states as hotspots for high power system vulnerability, particularly in the West Coast (California and Washington), the East Coast (Florida and the Northeast area), the Great Lakes megalopolis (Chicago-Detroit metropolitan areas), and the Gulf of Mexico (Texas). Heterogeneity analysis indicates that urban counties, counties with interconnected grids, and states with high solar generation exhibit significantly higher vulnerability. Our results highlight the significance of the proposed PSVI for evaluating the vulnerability of communities to power outages. The findings underscore the widespread and pervasive impact of power outages across the country and offer crucial insights to support infrastructure operators, policymakers, and emergency managers in formulating policies and programs aimed at enhancing the resilience of the US power infrastructure.

@unpublished{esparza5072677ai,
  title = {Ai Meets Natural Hazard Risk: A Nationwide Vulnerability Assessment of Data Centers to Natural Hazards and Power Outages},
  author = {Esparza, Miguel Tobias and Li, Bo and Ma, Junwei and Mostafavi, Ali},
  journal = {arXiv preprint arXiv:2501.14760},
  doi = {2501.14760},
  year = {2024},
  cover = {esparza5072677ai_cover.jpg}
}

Our society is on the verge of a revolution powered by Artificial Intelligence (AI) technologies. With increasing advancements in AI, there is a growing expansion in data centers (DCs) serving as critical infrastructure for this new wave of technologies. This technological wave is also on a collision course with exacerbating climate hazards which raises the need for evaluating the vulnerability of DCs to various hazards. Hence, the objective of this research is to conduct a nationwide vulnerability assessment of DCs in the United States of America (USA). DCs provide such support; however, if an unplanned disruption (like a natural hazard or power outage) occurs, the functionality of DCs is in jeopardy. Unplanned downtime in DCs causes severe economic and social repercussions. With the Local Indicator of Spatial Association (LISA) test, the research found that there are a large percentage of DCs that are in non-vulnerable areas of disruption; however, there is still a notable percentage in disruption-prone areas. For example, earthquakes, hurricanes, and tornadoes have the most DCs in vulnerable areas. After identifying these vulnerabilities, the research identified areas within the USA that have minimal vulnerabilities to both the aforementioned natural hazards and power outages with the BI-LISA test. After doing a composite vulnerability score on the cold spots from the BI-LISA analysis, the research found three counties with low vulnerability scores. These are Koochiching, Minnesota (0.091), Schoolcraft, Michigan (0.095), and Houghton, Michigan (0.096).