This record contains the underlying research data for the publication "People in more racially diverse neighborhoods are more prosocial" and the full-text is available from: https://ink.library.smu.edu.sg/lkcsb_research/5359Five studies tested the hypothesis that people living in more diverse neighborhoods would have more inclusive identities, and would thus be more prosocial. Study 1 found that people residing in more racially diverse metropolitan areas were more likely to tweet prosocial concepts in their everyday lives. Study 2 found that following the 2013 Boston Marathon bombings, people in more racially diverse neighborhoods were more likely to spontaneously offer help to individuals stranded by the bombings. Study 3 found that people living in more ethnically diverse countries were more likely to report having helped a stranger in the past month. Providing evidence of the underlying mechanism, Study 4 found that people living in more racially diverse neighborhoods were more likely to identify with all of humanity, which explained their greater likelihood of having helped a stranger in the past month. Finally, providing causal evidence for the relationship between neighborhood diversity and prosociality, Study 5 found that people asked to imagine that they were living in a more racially diverse neighborhood were more willing to help others in need, and this effect was mediated by a broader identity. The studies identify a novel mechanism through which exposure to diversity can influence people, and document a novel consequence of this mechanism.

The percent chance that two people picked at random within an area will be of a different race/ethnicity. This number does not reflect which race/ethnicity is predominant within an area. The higher the value, the more racially and ethnically diverse an area. Source: U.S. Bureau of the Census, American Community Survey Years Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2017-2021, 2018-2022, 2019-2023

The percent chance that two people picked at random within an area will be of a different race/ethnicity. This number does not reflect which race/ethnicity is predominant within an area. The higher the value, the more racially and ethnically diverse an area. Source: U.S. Bureau of the Census, American Community Survey Years Available: 2010, 2011-2015, 2012-2016, 2013-2017, 2014-2018, 2015-2019, 2017-2021, 2018-2022, 2019-2023Please note: We do not recommend comparing overlapping years of data due to the nature of this dataset. For more information, please visit: https://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data.html

Diversity index information by neighborhoods in Johns Creek, GA.Neighborhood boundaries are created and maintained by Johns Creek, GA.Demographics data is from Esri GeoEnrichment Services.

This dataset tracks annual diversity score from 2005 to 2023 for University Neighborhood Middle School vs. New York and New York City Geographic District # 1 School District

In the map, each dot represents 100 people in four race categories: white (non-Hispanic), black (non-Hispanic), Hispanic/Latino, and Asian/Pacific Islander. Thus, the map also depicts population densities throughout the region. While the rural/ suburban areas in the region have largely white populations, many urban/densely populated areas in the region are racially diverse, with two or more ethnicities living in relatively non-segregated neighborhoods.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

Comprehensive demographic data including income distribution, education levels, age distribution, and household composition

Abstract: The term “diversity,” though widely used, can mean different things. Diversity can refer to heterogeneity, i.e., the distribution of people across groups, or to the representation of specific minority groups. We use a conjoint experiment with a race-balanced, national sample to uncover which properties— heterogeneity or minority representation—Americans use to evaluate how racially diverse a neighborhood is and whether this varies by participants’ race. We show that perceived diversity is strongly associated with heterogeneity. This association is stronger for Whites than for Blacks, Latinos, or Asians. In addition, Blacks, Latinos, and Asians view neighborhoods where their own group is largest as more diverse. Whites vary in their tendency to associate diversity with representation, and Whites who report conservative stances on diversity- related policy issues view predominately White neighborhoods as more diverse than predominately Black neighborhoods. People can agree that diversity is desirable while disagreeing on what makes a community diverse.

Schools in gentrifying neighborhoods often experience demographic changes in enrollment. The purpose of this qualitative holistic case study is to describe how leaders and teachers in a diversifying elementary school in a gentrifying neighborhood perceive and experience diversity. Drawing on Turner’s (2017) value of diversity framework, we use inductive coding to analyze interviews and also use documents to inform our findings. Although Greenleaf was striving to be intentionally diverse, consensus did not exist about the meaning of “diversity” or the desired form of diversity. Challenges associated with decentering Whiteness and resisting upholding the racial contract existed as educators worked to establish a shared mission, ensure diverse staff voice and representation with a White leader, and navigate complications of power and privilege among White families. Educators highlighted the value of diversity for developing students’ multicultural capital and global cosmopolitanism as well as the collective benefit of reducing divisiveness for our nation.

The percent chance that two people picked at random within an area will be of a different income bracket. This number does not reflect which income bracket is predominant within an area. The higher the value, the more economically diverse an area. Source: American Community Survey Years Available: 2019-2023

This dataset tracks annual diversity score from 2019 to 2023 for Our World Neighborhood Charter School 2 School District vs. New York

This collection contains replication material for Dahir et al. “Surveillance Cameras Are Most Prevalent in Racially Diverse Neighborhoods Across Ten US Cities". Our analysis code is available at Github (https://github.com/Changing-Cities-Research-Lab/surveillance-replication).

This dataset was generated through the Diversity Barometer, a study tracking attitudes towards ethnic diversity and immigration in Sweden since 2005. The data were collected annually between 2005 and 2014, and biennially thereafter. Unweighted samples, consisting of adults aged between 18 and 75 years were used. The data can be managed and analyzed in the statistical program SPSS. The dataset includes the following variable categories: 1. Respondent descriptives 2. Interaction with persons with foreign background at school, work and in the neighborhood. 3. Cultural rights for persons with foreign background. 4. Social rights for persons with foreign background. 5. Immigration as beneficial to the Swedish society. 6. Immigration as a threat to the Swedish society. 7. Attitudes towards Swedish immigration policies. 8. Immigrants are exploited in the Swedish labor market. 9. Interest in interacting with immigrants and learning foreign cultures. 10. Attitudes towards religion in general, and Islam in particular. 11. Willingness to live in the same neighborhood as immigrants. 12. Immigrant-neighborhoods are problem neighborhoods.

Background: Little is known about the role of perceived neighborhood environment as a determinant of physical activity (PA) and sedentary time (ST) in understanding obesity-related health behaviors. We focus on a biracial, socioeconomically diverse population using objectively measured ST, which is under-represented in the literature.Methods: We examined the association between self-reported neighborhood perception (Likert-scale questions), PA using the Baecke questionnaire, and both non-sedentary time and ST using accelerometry from wave 4 of the Healthy Aging in Neighborhoods of Diversity across the Life Span (HANDLS) study (n = 2,167). After applying exclusion criteria, the sample size was n = 1,359 for analyses of self-reported PA and n = 404 for analyses of accelerometry data. Factor analysis identified key neighborhood characteristics to develop a total neighborhood perception score (NPS). Higher NPS indicated less favorable neighborhood perception. Linear regression was used to determine the relationship between NPS, PA, non-sedentary time, and ST.Results: Complete data were available for n = 1,359 [age 56.6(9.0) years, 59.5% female, 62.2% African American] for whom we identified four neighborhood perception factors: (1) concern about crime, (2) physical environment, (3) location of violent crime, and (4) social environment. Worsening perception of the overall neighborhood [β = −0.13 (SE = 0.03); p = 0.001], the physical environment [−0.11 (0.05); p = 0.03], and the social environment [−0.46 (0.07); p < 0.0001] were associated with decreased PA. Worsening perception of the overall neighborhood [1.14 (0.49); p = 0.02] and neighborhood social environment [3.59 (1.18); p = 0.003] were associated with increased ST over the day. There were no interactions for race, sex, poverty status, or economic index.Conclusion: Poor overall neighborhood perception, perceived social environment, and perceived neighborhood physical environment are associated with PA and ST in a multi-racial, socioeconomically diverse cohort of urban adults.Clinical Trial Registration: The HANDLS study is registered at ClinicalTrials.gov as NCT01323322.

This dataset tracks annual diversity score from 2009 to 2023 for Ampark Neighborhood vs. New York and New York City Geographic District #10 School District

Using records of growth of tree individuals from 15 tree-diversity experiments across four biomes. We examine how neighborhood-scale (defined as a focal tree and the adjacent trees) taxonomic and functional diversity effects on tree growth vary with climate spatially (across sites) and temporally (within sites).The dataset contains information for each experiment, interannual climate data, and species-level trait data used in this study. The climate data of annual climate precipitation (P) and potential evapotranspiration (PET) were downloaded from ERA5Land, and SPEI were accessed from the global SPEI dataset. Trait data were mainly obtained from the Plant Trait Database (TRY), Botanical Information and Ecology Network80, and the global wood density database.The R code files are also attached for running hierarchical Bayesian models, using Markov chain Monte Carlo (MCMC) sampling techniques in JAGS (version 4.3.2) and R (version 4.4.0) via the rjags package.

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information: https://catalog.data.gov/dataset/smart-location-database7 If you have questions about the underlying data stored here, please contact Thomas John (thomas.john@epa.gov). If you have questions or recommendations related to this metadata entry and extracted data, please contact the CAFE Data Management team at: climatecafe@bu.edu. "The Smart Location Database is a nationwide geographic data resource for measuring location efficiency. It includes more than 90 attributes summarizing characteristics, such as housing density, diversity of land use, neighborhood design, destination accessibility, transit service, employment and demographics. Most attributes are available for every census block group in the United States. A large body of research has demonstrated that land use and urban form can have a significant effect on transportation outcomes. People who live and/or work in compact neighborhoods with a walkable street grid and easy access to public transit, jobs, stores, and services are more likely to have several transportation options to meet their everyday needs. As a result, they can choose to drive less, which reduces their emissions of greenhouse gases and other pollutants compared to people who live and work in places that are not location efficient. Walking, biking, and taking public transit can also save people money and improve their health by encouraging physical activity. The Smart Location Database summarizes several demographic, employment, and built environment variables for every census block group (CBG) in the United States. The database includes indicators of the commonly cited “D” variables shown in the transportation research literature to be related to travel behavior. The Ds include residential and employment density, land use diversity, design of the built environment, access to destinations, and distance to transit. SLD variables can be used as inputs to travel demand models, baseline data for scenario planning studies, and combined into composite indicators characterizing the relative location efficiency of CBG within U.S. metropolitan regions. EPA first released a beta version of the Smart Location Database in 2011. The initial full version was released in 2013, and the database was updated to its current version in 2021." Quote from https://www.epa.gov/smartgrowth/smart-location-mapping and https://catalog.data.gov/dataset/smart-location-database7

A Neighborhood Improvement Association is dedicated to providing neighborhood input to City officials regarding development, crime prevention, street conditions, lighting, preservation, and revitalization. NIAs encourage neighborhood participation by working to maintain the value, beauty, safety, and diversity of their neighborhoods. The overall goal of an NIA is to form positive relationships with neighbors and local organizations to beautify, safeguard, and create a vibrant neighborhood community. The association also exists to provide information and assistance to its residents on matters of general interest, especially with regards to safety.

Data productivityData from FORBIO biodiversity experiment (Belgium) including six-years proudctivity data. Please consider the main manuscript and supporting information for more details about data collection and processing.Analyses 6y DRYAD.xlsx