Bike Share's Impact on COVID-19 Transmission and Bike Share's Responses to COVID-19: A case study of Washington DC

Beigi, Pedram; Haque, M. Aminul; Rajabi, Mohammad Sadra; Hamdar, Samer H.

doi:10.48550/arxiv.2205.05011

Cited by 4 publications

(4 citation statements)

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“…For most employees, it is hard to accept changes in common procedures. (6) Interoperability: Lack of interoperability is one of the most common obstacles to BIM implementation.…”

Section: Organizational Bim Research In Malaysiamentioning

confidence: 99%

“…Engineering and construction challenges are being addressed with technological advances, especially in project management, scheduling, and safety concerns [1][2][3][4][5][6][7]. In this regard, Building Information Models have recently attracted increasing attention from the Mahamadu et al [24] displayed that one of the principal factors in BIM success is staff experience.…”

Section: Introductionmentioning

confidence: 99%

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Key Assessment Criteria for Organizational BIM Capabilities: A Cross-Regional Study

Rajabi

Radzi²,

Rezaeiashtiani³

et al. 2022

Buildings

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Building information modeling (BIM) is an emerging process for managing the design, construction, operation, and maintenance of a facility. While BIM has developed in diverse aspects, the lack of organizational BIM capabilities remains a barrier to its implementation across the global architecture, engineering, and construction (AEC) industry. Accordingly, AEC organizations need to understand their organizational BIM capabilities and those of other organizations to realize the benefits of implementing BIM. This study examines the key criteria for assessing organizational BIM capabilities across two countries—Malaysia and Iran. For this purpose, the study compares the assessment criteria for BIM capabilities among the two countries based on the following elements: (1) criticality of the criteria; (2) degree of centrality of the criteria; and (3) underlying groups of the criteria. A systematic literature review of 26 articles and semi-structured interviews with BIM professionals provided nineteen criteria. A total of 121 and 126 BIM professionals evaluated the criticality of the criteria through a survey in Malaysia and Iran. The collected data were analyzed using the contextual disparities test (Mann–Whitney U test, Kruskal–Wallis H test, and rank agreement factor), network analysis, and exploratory factor analysis (EFA). The leading key criteria in both countries are “the company has the necessary infrastructure to implement BIM”, “the company has a good attitude towards new technology”, and “the company understands its expertise”. However, the subsequent key criteria differ between countries. Furthermore, while the level of agreement on the ranking of the criteria is at a neutral level, the Mann–Whitney U test indicates that the level of criticality significantly differs between countries for most criteria. There are also changes in the level of criticality of the criteria between countries. Finally, criteria with a high degree of centrality differ between countries. On the contrary, although the criteria slightly differ between countries, the overarching groups of the criteria are similar (i.e., the criteria are related to organizational BIM capabilities and organizational capabilities). Understanding these criteria can help researchers and industry practitioners develop the optimal tool for assessing organizational BIM capabilities for the local industry.

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“…For most employees, it is hard to accept changes in common procedures. (6) Interoperability: Lack of interoperability is one of the most common obstacles to BIM implementation.…”

Section: Organizational Bim Research In Malaysiamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Key Assessment Criteria for Organizational BIM Capabilities: A Cross-Regional Study

Rajabi

Radzi²,

Rezaeiashtiani³

et al. 2022

Buildings

Self Cite

View full text Add to dashboard Cite

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“…The interactions between human mobility and epidemic spread have been studied unprecedentedly during the COVID-19 pandemic [1][2][3][4][5][6][7][8]. With these efforts, nonpharmaceutical interventions (such as national lockdowns) have been evaluated for their effectiveness and socioeconomic impact on different groups [9][10][11], models have been developed to predict disease spatial diffusion [12,13], and scenarios have been modeled to assess their outcomes [14][15][16][17].…”

Section: Introductionmentioning

confidence: 99%

A fairness assessment of mobility-based COVID-19 case prediction models

Erfani,

Frias-Martinez

2023

PLoS ONE

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In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, and urban counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, less educated and people from rural regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these areas. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

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“…The interactions between human mobility and epidemic spread have been studied unprecedentedly during the COVID-19 pandemic [1][2][3][4][5][6][7][8] . With these efforts, nonpharmaceutical interventions (such as national lockdowns) have been evaluated for their effectiveness and socio-economic impact on different groups [9][10][11] , models have been developed to predict disease spatial diffusion 12,13 , and scenarios have been modeled to assess their outcomes [14][15][16][17] . Studies have demonstrated that mobility data are a meaningful proxy measure of social distancing 18 , affect viral spreading 19,20 , and are useful for predicting the spread of COVID-19 [21][22][23] .…”

Section: Introductionmentioning

confidence: 99%

A fairness assessment of mobility-based COVID-19 case prediction models

Erfani

Frías-Martínez

2022

Preprint

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In light of the outbreak of COVID-19, analyzing and measuring human mobility has become increasingly important. A wide range of studies have explored spatiotemporal trends over time, examined associations with other variables, evaluated non-pharmacologic interventions (NPIs), and predicted or simulated COVID-19 spread using mobility data. Despite the benefits of publicly available mobility data, a key question remains unanswered: are models using mobility data performing equitably across demographic groups? We hypothesize that bias in the mobility data used to train the predictive models might lead to unfairly less accurate predictions for certain demographic groups. To test our hypothesis, we applied two mobility-based COVID infection prediction models at the county level in the United States using SafeGraph data, and correlated model performance with sociodemographic traits. Findings revealed that there is a systematic bias in models’ performance toward certain demographic characteristics. Specifically, the models tend to favor large, highly educated, wealthy, young, urban, and non-black-dominated counties. We hypothesize that the mobility data currently used by many predictive models tends to capture less information about older, poorer, non-white, and less educated regions, which in turn negatively impacts the accuracy of the COVID-19 prediction in these regions. Ultimately, this study points to the need of improved data collection and sampling approaches that allow for an accurate representation of the mobility patterns across demographic groups.

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Bike Share's Impact on COVID-19 Transmission and Bike Share's Responses to COVID-19: A case study of Washington DC

Cited by 4 publications

References 0 publications

Key Assessment Criteria for Organizational BIM Capabilities: A Cross-Regional Study

Key Assessment Criteria for Organizational BIM Capabilities: A Cross-Regional Study

A fairness assessment of mobility-based COVID-19 case prediction models

A fairness assessment of mobility-based COVID-19 case prediction models

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