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

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.

Map of historical demographics and means of segregation in 1940s Minneapolis.

Much recent attention has been paid to the interaction between poverty and conflict in developing countries. However, it is surprising that neither the academic nor the international development community has as of yet, systematically examined the influence of international inequalities upon poverty and conflict. The project proposes that the prevalence of poverty and conflict is strongly conditioned by countries' position within the international economic system. The nature of a country's economic ties with the rest of the world - often deeply unequal - can create significant dependencies and / or incentives to challenge the status quo, resulting in poverty-provoked violence. The project uses network analysis and matching methods. The network analysis is used to map out key international economic networks (aid, trade, and FDI) and generate measures of countries' direct and indirect relations with other states plus their position within the overall structure. These network measures are then used in a statistical method of matching countries to infer whether dependent countries are more likely to succumb to poverty-provoked conflict. The findings from the project will identify the extent to which international inequality traps lead to poverty and conflict traps in developing countries, and help to draw out the policy implications of this.

GEM's Global Socio-Economic Vulnerability Maps

The Global Social Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-human-vulnerability) is a composite index that was developed to measure characteristics or qualities of social systems that create the potential for loss or harm. Here, social vulnerability helps to explain why some countries will experience adverse impacts from earthquakes differentially where the linking of social capacities with demographic attributes suggests that communities with higher percentages of age-dependent populations, homeless, disabled, under-educated, and foreign migrants are likely to exhibit higher social vulnerability than communities lacking these characteristics. Other relevant factors that affect the social vulnerability of populations include in-migration from foreign countries, population density, an accounting of slum populations, and international tourist arrivals.

The Global Economic Vulnerability Map (viewable here: https://maps.openquake.org/map/sv-global-economic-vulnerability) is a composite index that was designed primarily to measure the potential for economic losses from earthquakes due to a country’s macroeconomic exposure. This index is also an appraisal of the ability of countries to respond to shocks to their economic systems. Relevant indicators include the density of exposed economic assets such as commercial and industrial infrastructure. Metrics used to measure the ability of a country to withstand shocks to its economic system include reliance on imports/exports, government debt, and purchasing power. The economic vulnerability category also considers the economic vitality of countries since the economic vitality of a country can be directly related to the vulnerability and resilience of its populations. The latter includes measurements of single-sector economic dependence, income inequality, and employment status.

The Recovery/Reconstruction Potential Map (viewable here: https://maps.openquake.org/map/sv-global-recovery-and-reconstruction) is closely aligned with the concept of disaster resilience. Enhancing a country’s resilience to earthquakes is to improve its capacity to anticipate threats, to reduce its overall vulnerability, and to allow its communities to recover from adverse impacts from earthquakes when they occur. The measurement of recovery and reconstruction potential includes capturing inherent conditions that allow communities within a country to absorb impacts and cope with a damaging earthquake event, such as the density of the built environment, education levels, and political participation. It also encompasses post-event processes that facilitate a population’s ability to reorganize, change, and learn in response to a damaging earthquake.

Criteria for indicator selection
To choose indicators contextually exclusive for use in each map, the starting point was an exhaustive review of the literature on earthquake social vulnerability and resilience. For a variable to be considered appropriate and selected, three equally important criteria were met:

- variables were justified based on the literature regarding its relevance to one or more of the indices.
- variables needed to be of consistent quality and freely available from sources such as the United Nations and the World Bank; and
- variables must be scalable or available at various levels of geography to promote sub-country level analyses.

This procedure resulted in a ‘wish list’ of approximately 300 variables of which 78 were available and fit for use based on the three criteria.

Process for indicator selection
For variables to be allocated to an index, a two-tiered validation procedure was utilized. For the first tier, variables were assigned to each of the respective indices based on how each variable was cited within the literature, i.e., as being part of an index of social vulnerability, economic vulnerability, or recovery/resilience. For the second tier, machine learning and a multivariate ordinal logistic regression modelling procedure was used for external validation. Here, focus was placed on the statistical association between the socio-economic vulnerability indicators and the adverse impacts from historical earthquakes on a country-by country-basis.

The Global Significant Earthquake Database provided the external validation metrics that were used as dependent variables in the statistical analysis. To include both severe and moderate earthquakes within the dependent variables, adverse impact data was collected from damaging earthquake events that conformed to at least one of five criteria: 1) caused deaths, 2) caused moderate damage (approximately 1 million USD or more), 3) had a magnitude 7.5 or greater 4) had a Modified Mercalli Intensity (MMI) X or greater, or 5) generated a tsunami. This database was chosen because it considers low magnitude earthquakes that were damaging (e.g., MW >=2.5 & MW<=5.5) and contains socio-economic data such as the total number of fatalities, injuries, houses damaged or destroyed, and dollar loss estimates in USD.

Countries not demonstrating at least a minimal earthquake risk, i.e., seismicity <0.05 PGA (Pagani et al. 2018) and <$10,000 USD in predicted average annual losses (Silva et al. 2018) were eliminated from the analyses so as not to include countries with minimal to no earthquake risk. A total study area consists of 136 countries.

From:Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers, accessed May 26, 2021

Examples of factors provided by workshop participants.

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.

This report aims to map poverty and inequality in Sudan and would be representative of the 18

states and 131 localities of Sudan. The poverty mapping technique is based on a small area

estimation (SAE) technique developed by the World Bank to derive estimates of geographic

poverty and inequality. It combines data from the 2014/15 National Household Budget and

Poverty Survey (NHBPS) and the 2008 Population and Housing Census data to build spatially

disaggregated poverty maps.

Although household surveys usually include measures of income and wealth, they are not

representative beyond the state level. Yet, allowing lower levels of disaggregation is important

for policy interventions, particularly for countries like Sudan that have state governments, which

manage the activities of the state while reporting to the federal government. This study uses a

model of household expenditure from a survey data set to estimate household welfare at the

lower levels and apply it to the census data set which does not provide information on household

income or expenditure. These maps illustrate the information gains provided by SAE, show there

is a substantial spatial heterogeneity within the localities, and highlight the small areas most likely

to exhibit the highest risk of poverty.

Between 1935 and 1940 the federal government’s Home Owners’ Loan Corporation (HOLC) classified the neighborhoods of 239 cities according to their perceived investment risk. This practice has since been referred to as “redlining,” as the neighborhoods classified as being the highest risk for investment were often colored red on the resultant maps. The Mapping Inequality project, a collaboration of faculty at the University of Richmond’s Digital Scholarship Lab, the University of Maryland’s Digital Curation Innovation Center, Virginia Tech, and Johns Hopkins University has digitized and georectified all 239 HOLC maps and made them publicly available, including the HOLC map of Boston from 1938. The Boston Area Research Initiative has coordinated (i.e., spatial joined) the districts from the 1938 HOLC map of Boston with census tracts from the 2010 U.S. Census. This dataset contains the original shapefile and the spatially joined tract-level data.

MAPPING INEQUALITY Redlining in New Deal America Atlanta How Owners' Loan Corporation 1938 Mapping Inequality introduces viewer to the records of the Home Owners' Loan Corporation on a scale that is unprecedented. Here you can browse more than 150 interactive maps and thousands of "area descriptions." These materials afford an extraordinary view of the contours of wealth and racial inequality in Depression-era American cities and insights into discriminatory policies and practices that so profoundly shaped cities that we feel their legacy to this day.https://dsl.richmond.edu/panorama/redlining/

CYP subpopulations and determinants impacting outside influence of local level.

HOLC, in consultation with local real estate professionals and local policymakers, categorized neighborhoods in hundreds of cities in the United States into four types: Best (A), Still Desirable (B), Definitely Declining (C), and Hazardous (D). So-called “hazardous” zones were colored red on these maps. These zones were then used to approve or deny credit-lending and mortgage-backing by banks and the Federal Housing Administration. The descriptions provided by HOLC in their reports rely heavily on race and ethnicity as critical elements in assigning these grades. According to the University of Richmond's Mapping Inequality project, “Arguably the HOLC agents in the other two hundred-plus cities graded through this program 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” (Mapping Inequality). HOLC’s classifications were one contributory factor in underinvestment in a neighborhood, and generally, although not always, closed off many, especially people of color, from the credit necessary to purchase their own homes.The 15 Worcester neighborhood zones included on the map are ordered from Zone 1 (categorized as "Best") to Zone 15, with the highest numbered zones included in the least desirable "Hazardous" category. The exact descriptions used by HOLC to classify the neighborhoods in 1936 are included, and therefore may contain some disturbing language. Many scholars and institutions have focused their efforts on tracking the effects the 1930s redlining maps still have today. The Mapping Inequality project by the University of Richmond has collected and analyzed a comprehensive set of redlining maps for more than 200 cities in the U.S. One of their conclusions is that, for most cities, there are striking and persistent geographic similarities between redlined zones and currently vulnerable areas even after eighty years. See the Mapping Inequality website for more information (https://dsl.richmond.edu/panorama/redlining).This digitized version prepared by the Worcester Regional Research Bureau was based on a scanned copy from the National Archives, obtained thanks to Dr. Robert Nelson, the Digital Scholarship Lab, and the rest of his team at Mapping Inequality at the University of Richmond. Dr. Nelson worked with The Research Bureau directly to track it down in the Archives.Informing Worcester is the City of Worcester's open data portal where interested parties can obtain public information at no cost.

What does the data show?

The data shows the S80/S20 income quintile ratio from the UK Climate Resilience Programme UK-SSPs project. The data is available for each ONS NUTS3 shape simplified to a 10m resolution.

The S80/S20 ratio is a measure of the inequality of income distribution. The ratio is the total income received by the 20% of the population with the highest income (the top quintile) against the total income received by the 20% of the population with the lowest income (the bottom quintile).

The data is available for the end of each decade. This dataset contains SSP1, SSP2, SSP3, SSP4 and SSP5. For more information see the table below.

Indicator

Inequality

Metric

S80/S20 income quintile ratio

Unit

Ratio [unitless]

Spatial Resolution

NUTS 3

Temporal Resolution

Decadal

Sectoral Categories

N/A

Baseline Data Source

OECD 2011

Projection Trend Source

Stakeholder process

What are the naming conventions and how do I explore the data?

This data contains a field for the year at the end of each decade. A separate field for 'Scenario' allows the data to be filtered, e.g. by scenario 'SSP3'.

To understand how to explore the data, see this page: https://storymaps.arcgis.com/stories/457e7a2bc73e40b089fac0e47c63a578

Please note, if viewing in ArcGIS Map Viewer, the map will default to 2020 values.

What are Shared Socioeconomic Pathways (SSPs)?

The global SSPs, used in Intergovernmental Panel on Climate Change (IPCC) assessments, are five different storylines of future socioeconomic circumstances, explaining how the global economy and society might evolve over the next 80 years. Crucially, the global SSPs are independent of climate change and climate change policy, i.e. they do not consider the potential impact climate change has on societal and economic choices.

Instead, they are designed to be coupled with a set of future climate scenarios, the Representative Concentration Pathways or ‘RCPs’. When combined together within climate research (in any number of ways), the SSPs and RCPs can tell us how feasible it would be to achieve different levels of climate change mitigation, and what challenges to climate change mitigation and adaptation might exist.

Until recently, UK-specific versions of the global SSPs were not available to combine with the RCP-based climate projections. The aim of the UK-SSPs project was to fill this gap by developing a set of socioeconomic scenarios for the UK that is consistent with the global SSPs used by the IPCC community, and which will provide the basis for further UK research on climate risk and resilience.

Useful links: Further information on the UK SSPs can be found on the UK SSP project site and in this storymap.Further information on RCP scenarios, SSPs and understanding climate data within the Met Office Climate Data Portal.

Women and girls make up more than half the world’s population, but they are also more deeply impacted than men and boys by poverty, violence, discrimination, and economic crises. This inequality holds us all back. Each and every one of the UN's Global Goals for Sustainable Development sets out to address this inequality in some way. Check out this Cascade story map to see some of the issues we face and what the future could look like if we achieve these Goals (click here for more information on the maps, graphics, and data behind the story map).We created this layer using datasets from the World Bank.

Abstract

The Home Owners’ Loan Corporation (HOLC) was a U.S. federal agency that graded mortgage investment risk of neighborhoods across the U.S. between 1935 and 1940. HOLC residential security maps standardized neighborhood risk appraisal methods that included race and ethnicity, pioneering the institutional logic of residential “redlining.”

Methodology

The Mapping Inequality Project digitized the HOLC mortgage security risk maps from the 1930s. We overlaid the HOLC maps with 2010 and 2020 census tracts for 142 cities across the U.S. using ArcGIS and determined the proportion of HOLC residential security grades contained within the boundaries. We assigned a numerical value to each HOLC risk category as follows: 1 for “A” grade, 2 for “B” grade, 3 for “C” grade, and 4 for “D” grade. We calculated a historic redlining score from the summed proportion of HOLC residential security grades multiplied by a weighting factor based on area within each census tract. A higher score means greater redlining of the census tract. Continuous historic redlining score, assessing the degree of “redlining,” as well as 4 equal interval divisions of redlining, can be linked to existing data sources by census tract identifier allowing for one form of structural racism in the housing market to be assessed with a variety of outcomes.

The 2010 files are set to census 2010 tract boundaries. The 2020 files use the new census 2020 tract boundaries, reflecting the increase in the number of tracts from 12,888 in 2010, to 13,488 in 2020. Use the 2010 HRS with decennial census 2010 or ACS 2010-2019 data. As of publication (10/15/2020) decennial census 2020 data for the P1 (population) and H1 (housing) files are available from census.

Personal domain factors.

Climate change poses a greater threat for more exposed and vulnerable countries, communities and social groups. People whose livelihood depends on the agriculture and food sector, especially in low- and middle-income countries (LMICs), face significant risk. In contexts with gendered roles in agri-food systems or where structural constraints to gender equality underlie unequal access to resources and services and constrain women’s agency, local climate hazards and stressors, such as droughts, floods, or shortened crop-growing seasons, tend to negatively affect women more than men and women’s adaptive capacities tend to be more restrained than men’s. Transformation toward just and sustainable agri-food systems in the face of climate change will not only depend on reducing but also on averting aggravated gender inequality in agri-food systems. In this paper, we developed and applied an accessible and versatile methodology to identify and map localities where climate change poses high risk especially for women in agri-food systems because of gendered exposure and vulnerability. We label these localities climate-agriculture-gender inequality hotspots. Applying our methodology to LMICs reveals that the countries at highest risk are majorly situated in Africa and Asia. Applying our methodology for agricultural activity-specific hotspot subnational areas to four focus countries, Mali, Zambia, Pakistan and Bangladesh, for instance, identifies a cluster of districts in Dhaka and Mymensingh divisions in Bangladesh as a hotspot for rice. The relevance and urgency of identifying localities where climate change hits agri-food systems hardest and is likely to negatively affect population groups or sectors that are particularly vulnerable is increasingly acknowledged in the literature and, in the spirit of leaving no one behind, in climate and development policy arenas. Hotspot maps can guide the allocation of scarce resources to most-at-risk populations. The climate-agriculture-gender inequality hotspot maps show where women involved in agri-food systems are at high climate risk while signaling that reducing this risk requires addressing the structural barriers to gender equality.

Workshop participants.