Income InequalityThe level of income inequality among households in a county can be measured using the Gini index. A Gini index varies between zero and one. A value of one indicates perfect inequality, where only one household in the county has any income. A value of zero indicates perfect equality, where all households in the county have equal income.The United States, as a country, has a Gini Index of 0.47 for this time period. For comparision in this map, the purple counties have greater income inequality, while orange counties have less inequality of incomes. For reference, Brazil has an index of 0.58 (relatively high inequality) and Denmark has an index of 0.24 (relatively low inequality).The 5-year Gini index for the U.S. was 0.4695 in 2007-2011 and 0.467 in 2006-2010. Appalachian Regional Commission, September 2013Data source: U.S. Census Bureau, 5-Year American Community Survey, 2006-2010 & 2007-2011

Rethinking, prioritizing and supporting the way care tasks are performed in urban and rural environments can contribute to reducing inequality in cities and territories, especially in Latin America and the Caribbean (LAC), the most unequal region in the world. To achieve this, gender mainstreaming must come to the forefront in urban policies, at all scales and phases of the policy cycle: from planning, regulation, and legislation, to design, construction and management of both cities and the services they provide. The concept of the “city of care” overcomes traditional visions of urban realities based on the dichotomy between the productive and reproductive spheres, by appropriately supporting care work, which is essential for the reproduction of society and for sustaining life and the economy. This article addresses gender mainstreaming in urban policies as a tool to shaping cities in ways that their physical, social, economic, cultural, and power dimensions can contribute to facilitating the realization of care work, by looking first into what the provision of care as a right can entail. Secondly, it looks at the spatial dimensions of care, particularly as mobility and facilities, also referred to as infrastructure, are concerned. Thirdly, it emphasizes the importance of gender mainstreaming in urban planning and legislation to achieve urban transformations that support care work. Fourthly, it showcases three examples from Latin America, two from Mexico City (Utopías and Pilares) and one in Bogotá (Manzanas del Cuidado), which have set out to advance access to rights in Latin America, including the right to care.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers,

"HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous."

Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. "

HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas."

These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality."

Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

Most of the text in this description originally appeared on the Mapping Inequality Website. Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, "HOLC staff members, using data and evaluations organized by local real estate professionals--lenders, developers, and real estate appraisers--in each city, assigned grades to residential neighborhoods that reflected their "mortgage security" that would then be visualized on color-coded maps. Neighborhoods receiving the highest grade of "A"--colored green on the maps--were deemed minimal risks for banks and other mortgage lenders when they were determining who should received loans and which areas in the city were safe investments. Those receiving the lowest grade of "D," colored red, were considered "hazardous." Conservative, responsible lenders, in HOLC judgment, would "refuse to make loans in these areas [or] only on a conservative basis." HOLC created area descriptions to help to organize the data they used to assign the grades. Among that information was the neighborhood's quality of housing, the recent history of sale and rent values, and, crucially, the racial and ethnic identity and class of residents that served as the basis of the neighborhood's grade. These maps and their accompanying documentation helped set the rules for nearly a century of real estate practice. " HOLC agents grading cities through this program largely "adopted a consistently white, elite standpoint or perspective. HOLC assumed and insisted that the residency of African Americans and immigrants, as well as working-class whites, compromised the values of homes and the security of mortgages. In this they followed the guidelines set forth by Frederick Babcock, the central figure in early twentieth-century real estate appraisal standards, in his Underwriting Manual: "The infiltration of inharmonious racial groups ... tend to lower the levels of land values and to lessen the desirability of residential areas." These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today. Mapping Inequality, we hope, will allow and encourage you to grapple with this history of government policies contributing to inequality." Data was copied from the Mapping Inequality Website for communities in Western Pennsylvania where data was available. These communities include Altoona, Erie, Johnstown, Pittsburgh, and New Castle. Data included original and georectified images, scans of the neighborhood descriptions, and digital map layers. Data here was downloaded on June 9, 2020.

Visit Mapping Inequality for full details.History of this spatial dataThe majority of this dataset is derived from maps made by the Home Owners’ Loan Corporation (HOLC), a New Deal agency. Using data and evaluations gathered from local real estate professionals, HOLC created color-coded maps for more than 200 American cities. The maps used four colors to represent the “security,” or the determined relative riskiness of mortgage lending, for residential areas of each city.Green areas on the maps were called "A," "First Grade," or "Best" and were considered to be safest for loans. These areas were typically populated with wealthy, white residents that were born in the United States.Blue areas were called "B," "Second Grade," or "Still Desirable". Yellow areas were called "C," "Third Grade," or "Definitely Declining".Red areas were called "D," "Fourth Grade," or "Hazardous". HOLC recommended lenders "refuse to make loans in these areas [or] only on a conservative basis." These areas typically overlapped with Black and immigrant communities, which usually had lower incomes.These grades were a tool for redlining: making it difficult or impossible for people in certain areas to access mortgage financing and thus become homeowners. Redlining directed both public and private capital to native-born white families and away from African American and immigrant families. As homeownership was arguably the most significant means of intergenerational wealth building in the United States in the twentieth century, these redlining practices from eight decades ago had long-term effects in creating wealth inequalities that we still see today.This dataset also includes spatial data for more than 100 municipalities from redlining maps that were not made as part of HOLC’s City Survey Program. These places were typically smaller in size, falling below the population threshold of 40,000 that HOLC used to determine which cities they would survey. As these maps were not made as part of HOLC’s City Survey Program, the vast majority use different categories and colors than those used by HOLC.The Residential Security Map created by the Home Owner's Loan Corporation for Decatur, IL.A new version of Mapping InequalityThe University of Richmond Digital Scholarship Lab began the Mapping Inequality project in 2016. Using scanned images of HOLC City Survey maps, a team of students and scholars georeferenced the images and digitally traced their color-coded residential areas, creating a spatial dataset that has since been used in numerous studies and research projects. Over the course of the project, more cities and their maps were added, including redlining maps of smaller cities that were not a part of HOLC’s City Survey. Non-residential areas shown on the maps, such as industrial and commercial areas, were also traced and added to the spatial database. The spatial dataset has grown, and now contains 10,000 polygons that were created from maps of 328 cities in 43 states.Previous versions of this feature layer, which are missing cities and non-residential areas, are available here and here.Images of the redlining maps maps, and their derived data, as well as more in-depth reading on the history are available on the the Mapping Inequality website. The site also includes a searchable archive of the detailed area descriptions that accompanied redlining maps. These texts provide important nuance in the grades and are invaluable for laying bare the racist, nativist, and often anti-semitic prejudices underlying real estate practice and federal housing policy during the Great Depression.What's in this feature layerEach feature in this dataset is a polygon that represents an area that was drawn on a 1930s redlining map. They include the following fields:area_id (integer) is a unique identifier for each area.city (string) is the name of the city, town, county, etc.state (string) is the 2-letter U.S. Postal Service abbreviation for the state.city_survey (boolean, true=1, false=0) denotes whether the map was created as part of the HOLC City Survey Program or not.category (string) is the assigned category from a redlining map. On standard HOLC City Survey Program maps, the category values are “Best”, “Still Desirable”, “Declining”, or “Hazardous.”grade (string) is the letter grade used to grade the area. For non-residential areas and most cities that were not part of the City Survey, the value is null.label (string) is the label from a redlining map. For most HOLC City Survey Program maps, this value is a letter and number, which often corresponds to an area description viewable on the Mapping Inequality website. commercial (boolean, true=1, false=0) denotes whether or not an area is labeled explicitly as commercial or inferred to be commercial from a redlining map.industrial (boolean, true=1, false=0) denotes whether or not an area is labeled explicitly as industrial, or inferred to be industrial.residential (boolean, true=1, false=0) denotes whether or not an area is labeled explicitly as residential or inferred to be residential.fill (string) is a hexadecimal color code for symbology. The value is typically an approximation of the color shown on a redlining map. You can use this attribute field as a feature symbology in ArcGIS Pro.This spatial dataset is available in geojson and geopackage format on the Mapping Inequality downloads page. Images of the scanned redlining maps, including in spatially referenced geotiff format are also available. UpdatesMarch 1st, 2024Fixed category errors on areas in Hudson County, NJAdded missing areas to Mt. Morris, MI (inset of Flint MI)

Map Flint - Feature Service layer(s) : ACS5YR 2012-2016 estimates for City of Flint, Michigan, USA by tract of Gini Index of Income Inequality.

Data Dictionary: https://mapflint.org/dictionaries/2016_Flint_by_tract_ACS5YR_Gini_Index_of_Income_Inequality_vars001_data_dictionary.pdf

Note: Layer(s) not initially visible and must be turned on.

This feature layer is an American Community Survey (ACS) estimate (U.S. Census Bureau) that is derived from the National Historical Geographic Information System (NHGIS) and has been customized for various Map Flint analyses and projects pertaining to the City of Flint, Genesee County, Michigan U.S.A. and other surrounding counties - e.g., counties and communities in the greater Flint vicinity that also overlap with the mission of the University of Michigan-Flint EDA University Center for Community and Economic Development. All NHGiS layers in Map Flint projects maintain the uniquely-valued GISJOIN geographic ID assigned by the NHGIS in order to work with multiple data sets.

For more information, visit https://mapflint.org

Understanding the extent to which systemic biases influence local ecological communities is essential for developing just and equitable environmental practices. With over 270 million people across the United States living in urban areas, understanding the socio-ecological consequences of racially-targeted zoning, such as redlining, provides crucial information for urban planning. There is a growing body of literature documenting the relationships between redlining and disparities in the distribution of environmental harms and goods, including inequities in green space cover and pollutant exposure. Yet, it remains unknown whether noise pollution is also inequitably distributed, and whether inequitable noise is an important driver of ecological change in urban environments. We conducted 1) a spatial analysis of urban noise to determine the extent to which noise overlaps with the distribution of redlining categories and 2) a systematic literature review to summarize the effects of noise on wildlife in urban landscapes. We found strong evidence that noise is inequitably distributed in cities across the United States, and that inequitable noise may drive complex biological responses across diverse urban wildlife. These findings lay a foundation for future research that advances acoustic and urban ecology by centering equity and challenging systems of oppression. Methods Spatial Analysis of Urban Noise Pollution To evaluate noise exposure across HOLC redlining grades for 83 U.S. cities in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map 2018. The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to calculate descriptive statistics (median, minimum, maximum, area) for the 30m x 30m pixels in the transportation noise models with non-null noise values (i.e., values > 35 dBA). We used the resulting zonal statistics estimates to calculate an area-corrected measure of excess noise: N = (r * Md)/a where N is excess noise in each HOLC grade (with units of dBA/900 m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Literature Review Methodology To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson’s ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted Shannon et al. (2016) search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of ‘Acoustics’, ‘Zoology’, ‘Ecology’, ‘Environmental Sciences’, ‘Ornithology’, ‘Biodiversity Conservation’, ‘Evolutionary Biology’, and ‘Marine Freshwater Biology’. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set. We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise. We also verified the noise levels that caused a significant biological response, noting each noise level if multiple responses were recorded. For any new articles published since the Shannon et al. (2016) dataset or those published between 1990 and 2013 but not reviewed by Shannon et al. (2016) (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper and also recorded the weightings for each noise level.

The Home Owners' Loan Corporation was established in 1933 by the U.S Congress to refinance mortgages in default and prevent foreclosures. In 1935 they created residential security maps for 239 cities to indicate the level of security for real-estate investments. The maps were graded such as the newest areas, which were considered desirable for lending received a "Type A" grade. These areas were primarily wealthy suburbs on the outskirts of town. Still Desirable neighborhoods were given a "Type B" grade and older neighborhoods were given a "Type C" grade and considered Declining. Lastly "Type D" neighborhoods were regarded as most risky for mortgage lending.If you are citing Mapping Inequality or acknowledge the source of any of the following data, we recommend the following format using the Chicago Manual of Style.Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed September 16, 2020, https://dsl.richmond.edu/panorama/redlining/[YOUR VIEW].

This dataverse hosts maps and data from Levy, Brian L., Nolan E. Phillips, and Robert J. Sampson. 2020. Triple Disadvantage: Neighborhood Networks of Everyday Urban Mobility and Violence in U.S. Cities. American Sociological Review.

This map provides a spatial illustration of different means by which racial segregation was historically reinforced across the cities of Minneapolis and Saint Paul. The map focuses largely on data from the 1940s, and includes the following data layers:Population by Race - Data based on 1940 US Census that shows the percentage of the non-white population at the census tract level. This data was downloaded from NHGIS, with a spatial join performed to combine the census table and historic tracts (Citation: Steven Manson, Jonathan Schroeder, David Van Riper, Katherine Knowles, Tracy Kugler, Finn Roberts, and Steven Ruggles, IPUMS National Historical Geographic Information System: Version 18.0. Minneapolis, MN: IPUMS. 2023).HOLC Map Zones by Number of Covenants - This layer displays a summary of the number of racially exclusive covenants within the area of zones designated by grade on HOLC redlining maps. The polygons of each grade zone were digitized by the Mapping Inequality Project (University of Richmond Digital Scholarship Lab) and are symbolized by the grade colors on the original maps. The data on racially exclusive covenants in Twin Cities neighborhoods was downloaded from the Mapping Prejudice Project (University of Minnesota) and is symbolized by the size of each feature.Greenbook Locations - This layer displays locations included on Greenbook travel guides from the 1940s, which indicate safe businesses for African American travelers to American Cities. This data comes from a service layer created by Shana Crosson (University of Minnesota).This spatial extent of this map is limited to the cities of Minneapolis and Saint Paul. It was created as part of an in-class exercise in February of 2024.

Disclaimer: This application is a DRAFT and is still under development. Data source: Mapping Inequality: Redlining in New Deal America, https://dsl.richmond.edu/panorama/redliningThe Home Owners Loan CorporationThe Home Owners' Loan Corporation (HOLC) was created in 1933. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. Grading:A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Year: 2023Provider: Nelson, Robert K., LaDale Winling, et al. "Mapping Inequality: Redlining in New Deal America." Edited by Robert K. Nelson and Edward L. Ayers. American Panorama: An Atlas of United States History, 2023. https://dsl.richmond.edu/panorama/redlining.

Spatial Analysis of Urban Noise Pollution: We conducted a spatial analysis of the distribution of noise pollution across HOLC grades for 83 cities in the United States (data in HOLC_Noise_City_results.csv). To be included in the study, the city needed to be included in both datasets used in the analysis: 1) the Mapping Inequality Project dataset on the distribution of HOLC grades across cities, and 2) the U.S. Department of Transportation, National Transportation Noise Map 2018. Any cities in which the distribution of HOLC grades did not include all four grades (A-D) were excluded from the analysis, which largely excluded cities with population sizes below 100,000 people. To evaluate noise exposure across HOLC grades for each city in our study, we acquired spatial data on the distribution of HOLC grades across U.S. cities from the Mapping Inequality Project. We also acquired data on road, rail, and aircraft noise (hereafter transportation noise models), from the U.S. Department of Transportation, National Transportation Noise Map (2018). The transportation noise models represent potential exposure to transportation noise reported on a decibel scale in a 30m x 30m pixel resolution. Here noise represents the average noise energy produced by road, rail, and aviation networks over a 24-hour period, measured in A-weighted decibels (dBA) (LAeq, 24h) at sampling locations deployed across a uniform grid in each city at an elevation of 1.5 m above ground level. Noise levels below 35 dBA are assumed to have minimal negative impacts to humans and the environment and thus are represented with null values in the transportation noise models. For each HOLC grade and each city, we used zonal statistics in ArcGIS Desktop v. 10.7 to summarize the median noise levels and area covered by excess noise (i.e., values > 35 dBA). We used the resulting zonal statistics estimates and the formula from Collins et al. (2019) to calculate an area-corrected measure of excess noise:

N = (r * Md)/a

where N is excess noise in each HOLC grade (with units of dBA/900m2); r is the area covered by the 30m x 30m pixels with noise values >35 dBA across all polygons of the same HOLC grade in each city; Md is the median transportation noise value (in dBA) for those same pixels; and a is the total area of all polygons of the same HOLC grade in each city. Thus, N represents a measure of both the level of noise and the area covered by excess noise in a given HOLC grade for each city.

Literature Review on the Impacts of Noise to Urban Wildlife: To assess the effects of noise on wildlife in urban environments, we conducted a literature review using Thompson’s ISI Web of Science and adapting the methods of Shannon et al. (2016). We adjusted of Shannon et al.’s search criteria to include urban phrases, resulting in the following search terms (TS=(WILDLIFE OR ANIMAL OR MAMMAL OR REPTILE OR AMPHIBIAN OR BIRD OR FISH OR INVERTEBRATE) AND TS=(NOISE OR SONAR) AND TS=(CITY OR *URBAN OR METROPOLITAN)). We only selected papers published between 1990 and 23 June 2021 (i.e., the date we conducted our search) within the ISI Web of Science categories of ‘Acoustics’, ‘Zoology’, ‘Ecology’, ‘Environmental Sciences’, ‘Ornithology’, ‘Biodiversity Conservation’, ‘Evolutionary Biology’, and ‘Marine Freshwater Biology’. This returned 691 peer-reviewed papers, which we filtered so only empirical studies focused on documenting the effects of anthropogenic noise on wildlife in urban or suburban ecosystems or the effects of urban noise on wildlife in rural environments were included in the final data set (n = 207). We excluded reviews, meta-analyses, methods papers, and research that took place outside of urban or suburban areas where the noise was not explicitly denoted as urban (e.g., omitted studies that measured traffic noise by parks and reserves in rural areas). For the 241 articles previously analyzed in of Shannon et al. (2016), one of our authors reviewed each paper to determine which studies were focused on urban noise (n = 46). We then verified whether there were significant biological responses to a particular noise level threshold, noting each noise level if multiple biological responses were recorded. We recorded responses to noise into one of eight possible biological response categories, many of which were taken or modified from the biological response categories utilized in Shannon et al. (2016). The following were the biological response categorical values: movement behavior, vocal behavior, physiological, population, mating behavior, foraging behavior, vigilance behavior, life history / reproduction, and ecosystem. For any new articles published since the Shannon et al. (2016) dataset (n = 354) or those published between 1990 and 2013 but not reviewed by Shannon et al. (n = 96), two of our authors reviewed each paper to first determine which studies met our criteria (n = 161) and then compiled data on a number of variables of interest, including the noise levels and their resulting biological responses that were statistically significant. For this subset of papers, one author was randomly assigned a list of papers and then a second author was randomly assigned to assess the accuracy of the data collected by the first author. Any discrepancies were discussed as a group until an agreement was reached. Noise categories (environmental, transportation, industrial, multiple, other) were chosen for each paper by noting the explicitly stated source or description of urban noise described in the methodology. Noise levels and their units were reported for each paper, with only noise levels reported in decibels (dB) being used in data analysis. All terrestrial papers used a reference pressure of 20 microPascals (μPa). Due to the low sample size of aquatic studies (n = 4), differences in reference pressures, and varying sound intensities amongst aquatic studies, we only included terrestrial studies in statistical analyses and figures. We recorded the sound metric used (i.e., SPL, SPL Max, Leq) for each paper, but were unable to convert the various sound metrics given to a single sound metric for standardization during analysis. Thus, there were various sound metrics used in the analysis of the data extracted from the literature search, in particular for the cumulative weight-of-evidence curve, which poses a limitation in the comparison of noise levels amongst papers. Additionally, we recorded the weightings for each noise level, with many of the papers being A-weighted (dBA; n = 100) and Z-weighted (dBZ; n = 4). These weightings relate to typical characteristics of sounds as observed by humans. Many papers, however, did not record the weighting and/or the exact sound metric used.