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      A multisource database tracking the impact of the COVID-19 pandemic on the communities of Boston, MA, USA

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          Abstract

          A pandemic, like other disasters, changes how systems work. In order to support research on how the COVID-19 pandemic impacted the dynamics of a single metropolitan area and the communities therein, we developed and made publicly available a “data-support system” for the city of Boston. We actively gathered data from multiple administrative (e.g., 911 and 311 dispatches, building permits) and internet sources (e.g., Yelp, Craigslist), capturing aspects of housing and land use, crime and disorder, and commercial activity and institutions. All the data were linked spatially through BARI’s Geographical Infrastructure, enabling conjoint analysis. We curated the base records and aggregated them to construct ecometric measures (i.e., descriptors of a place) at various geographic scales, all of which were also published as part of the database. The datasets were published in an open repository, each accompanied by a detailed documentation of methods and variables. We anticipate updating the database annually to maintain the tracking of the records and associated measures.

          Abstract

          Measurement(s) Neighborhood context
          Technology Type(s) Naturally-occurring data
          Sample Characteristic - Environment City neighborhoods
          Sample Characteristic - Location Boston, MA, USA

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          Most cited references21

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          Prevalence of Depression Symptoms in US Adults Before and During the COVID-19 Pandemic

          Key Points Question What is the burden of depression symptoms among US adults during the coronavirus disease 2019 (COVID-19) pandemic compared with before COVID-19, and what are the risk factors associated with depression symptoms? Findings In this survey study that included 1441 respondents from during the COVID-19 pandemic and 5065 respondents from before the pandemic, depression symptom prevalence was more than 3-fold higher during the COVID-19 pandemic than before. Lower income, having less than $5000 in savings, and having exposure to more stressors were associated with greater risk of depression symptoms during COVID-19. Meaning These findings suggest that there is a high burden of depression symptoms in the US associated with the COVID-19 pandemic and that this burden falls disproportionately on individuals who are already at increased risk.
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            Differential occupational risk for COVID‐19 and other infection exposure according to race and ethnicity

            Abstract Background There are racial and ethnic disparities in the risk of contracting COVID‐19. This study sought to assess how occupational segregation according to race and ethnicity may contribute to the risk of COVID‐19. Methods Data about employment in 2019 by industry and occupation and race and ethnicity were obtained from the Bureau of Labor Statistics Current Population Survey. This data was combined with information about industries according to whether they were likely or possibly essential during the COVID‐19 pandemic and the frequency of exposure to infections and close proximity to others by occupation. The percentage of workers employed in essential industries and occupations with a high risk of infection and close proximity to others by race and ethnicity was calculated. Results People of color were more likely to be employed in essential industries and in occupations with more exposure to infections and close proximity to others. Black workers in particular faced an elevated risk for all of these factors. Conclusion Occupational segregation into high‐risk industries and occupations likely contributes to differential risk with respect to COVID‐19. Providing adequate projection to workers may help to reduce these disparities.
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              Impact of social distancing during COVID-19 pandemic on crime in Los Angeles and Indianapolis

              Governments have implemented social distancing measures to address the ongoing COVID-19 pandemic. The measures include instructions that individuals maintain social distance when in public, school closures, limitations on gatherings and business operations, and instructions to remain at home. Social distancing may have an impact on the volume and distribution of crime. Crimes such as residential burglary may decrease as a byproduct of increased guardianship over personal space and property. Crimes such as domestic violence may increase because of extended periods of contact between potential offenders and victims. Understanding the impact of social distancing on crime is critical for ensuring the safety of police and government capacity to deal with the evolving crisis. Understanding how social distancing policies impact crime may also provide insights into whether people are complying with public health measures. Examination of the most recently available data from both Los Angeles, CA, and Indianapolis, IN, shows that social distancing has had a statistically significant impact on a few specific crime types. However, the overall effect is notably less than might be expected given the scale of the disruption to social and economic life.
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                Author and article information

                Contributors
                d.obrien@northeastern.edu
                Journal
                Sci Data
                Sci Data
                Scientific Data
                Nature Publishing Group UK (London )
                2052-4463
                20 June 2022
                20 June 2022
                2022
                : 9
                : 330
                Affiliations
                [1 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, School of Public Policy and Urban Affairs, , Northeastern University, ; Boston, MA 02115 United States
                [2 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, Boston Area Research Initiative, , Northeastern University, ; Boston, MA 02115 United States
                [3 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, School of Criminology and Criminal Justice, , Northeastern University, ; Boston, MA 02115 United States
                [4 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, Department of Sociology and Anthropology, , Northeastern University, ; Boston, MA 02115 United States
                [5 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, Network Science Institute, College of Social Sciences and Humanities, , Northeastern University, ; Boston, MA 02115 United States
                [6 ]GRID grid.38142.3c, ISNI 000000041936754X, Harvard T.H. Chan School of Public Health, ; Boston, MA 02115 United States
                [7 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, College of Engineering, , Northeastern University, ; Boston, MA 02115 United States
                [8 ]GRID grid.38142.3c, ISNI 000000041936754X, Department of Sociology, , Harvard University, ; Cambridge, MA 02138 United States
                [9 ]GRID grid.38142.3c, ISNI 000000041936754X, Kennedy School of Government, , Harvard University, ; Cambridge, MA 02138 United States
                [10 ]GRID grid.17088.36, ISNI 0000 0001 2150 1785, Department of Epidemiology and Biostatistics, , Michigan State University, ; East Lansing, MI 48824 United States
                [11 ]GRID grid.261112.7, ISNI 0000 0001 2173 3359, Kostas Research Institute, , Northeastern University, ; Burlington, MA 01803 United States
                Author information
                http://orcid.org/0000-0003-2682-1416
                Article
                1378
                10.1038/s41597-022-01378-3
                9209523
                35725848
                bc210923-74ba-4e8d-aaa8-f5c900804558
                © The Author(s) 2022

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 15 August 2021
                : 10 May 2022
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000001, National Science Foundation (NSF);
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
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                Award ID: #SES-1637124
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                Award ID: #SES-1637124
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                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
                Award ID: #BCS-2032384
                Award ID: #SES-1637124
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                © The Author(s) 2022

                social sciences,geography,society
                social sciences, geography, society

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