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.

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.

Throughout history, government and industries have neglected investments in some neighborhoods, especially communities of color, who are more likely to have fewer resources.

This is a historical measure for Strategic Direction 2023. For more data on Austin demographics please visit austintexas.gov/demographics. The purpose of this dataset is to account for the number and percentage of Census tracts that are economically and/or racially segregated. The data was derived from a calculation originating with data from the 2019 U.S. Census Bureau, American Communities Survey (5yr). The row level data indicates the count or percentage of Census tracts. This data can be used to show the share of Census tracts that are not demographically reflective of the City of Austin’s demographics. View more details and insights related to this measure on the story page: https://data.austintexas.gov/stories/s/Percentage-of-Census-tracts-that-are-economically-/xf9r-hzjn/

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.

The aim of this article and associate Main Map is to highlight the social and economic diversity of the Ruhr area in Germany through the use of multivariate analysis and visualization. To this end we combine two different datasets. Demographic parameters stemming from the 2011 German census and socioeconomic indicators obtained from the microdialog of the German post service. Due to the different spatial resolution of the two datasets, we aggregated the data at the neighbourhood (Stadtteil) level. The multivariate analysis was carried out at this scale using Self-Organizing Maps (SOM), an artificial neuron network, which uses an unsupervised learning mechanism for projecting multidimensional data in a low (in our case two) dimensional space. First we used a visualization technique to better comprehend the relationship between our observations via reducing the dimensionality or complexity of our input data. At the same time, we established a global statistical relationships between the indicators. Finally, using these results we built clusters for revealing the distribution of socioeconomic profiles over the whole region. Our results demonstrate that structural inequalities resulting from the processes of industrialization/deindustrialization in the region are still widely persistent and result in characteristic patterns along the three main rivers, the Lippe, Emscher and the Ruhr. In close connection with this, three types of societal segregation patterns become clearly evident in the Ruhr area, namely nationality, age and economic power.

This map shows the diversity index of the population in the USA in 2010 by block group. "The diversity index summarizes racial and ethnic diversity. The index shows the likelihood that two people, chosen at random from the same area, belong to different race or ethnic groups. The index ranges from 0 (no diversity) to 100 (complete diversity). For example, a diversity index of 59 means there is a 59 percent probability that two people randomly chosen would belong to different race or ethnic groups." -Esri DemographicsIt calls to the 2010 Census service with attributes related to race and ethnicity. The field PctNonWhite calculates the total percentage of non-white population by subtracting the Total white population from the reported population total. This yields the total non-white population (Field "TotNonWhite"). This number was then divided by the total reported population and multipled by 100 to yield a percetage of the population that is non-white (Field "PctNonWhite"). Original data sourced from: https://tpc.maps.arcgis.com/home/item.html?id=04a8fbbf59aa48ebbc646ba2bc8d9b1c

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

Note: Effect span indicates the genetic length that a SDL would cause segregation distortion of makers within the region. “+” represents segregation distortion skews to more visible alleles for markers in the affected genome region, and “-” indicates segregation distortion skews to less visible alleles for markers in the affected genome region.The SDLs detected based on the established genetic maps.

In 1934, the Federal Housing Administration created a financial mortgage system that rated mortgage risks for properties based on various criteria but was centered on race and ethnicity. This rating system propagated racial segregation that in many ways persists today.

The FHA Underwriting Handbook incorporated color-coded real estate investment maps that classified neighborhoods based on assumptions about a community, primarily their racial and ethnic composition, and not on the financial ability of the residents to satisfy the obligations of a mortgage loan. These maps, created by the Home Owners Loan Corporation (HOLC) were used to determine where mortgages could or could not be issued.

The neighborhoods were categoriezed into four types:
Type A : Best - newer or areas stil in demand
Type B : Still Desirable - areas expected to remain stable for many years
Type C : Definitely Declining - areas in transition
Type D : Hazardous - older areas considered risky

Neighborhoods shaded red were deemed too hazardous for federally-back loans. These "red-lined" neighborhoods were where most African American residents lived.

Many have argued tha the HOLC maps institutionalized discriminating lending practices which not only perpetuated racial segregation but also led to neighborhood disinvestment. Today, neighborhoods classified as Type C and Type D in 2934 make up the majority of neighborhoods in 2016 that are Areas of Concentrated Poverty where 50% or More are People of Color.

Home Owners’ Loan Corporation (HOLC) maps illustrated patterns of segregation in United States cites in the 1930s. As the causes and drivers of demographic and land use segregation vary over years, these maps provide an important spatial lens in determining how patterns of segregation spatially and temporally developed during the course of the past century. Using a high-resolution land-use time series (1937-2018) of Denver Colorado USA, in conjunction with 80 years of U.S. Census data, we found divergent land-use and demographics patterns across HOLC categories were both pre-existent to the establishment of HOLC mapping, and continued to develop over time. Over this period, areas deemed “declining” or “hazardous” had more diverse land use compared “desirable” areas. “Desirable” areas were dominated by one land-use type (single-family residential), while single-family residential diminished in prominence in the “declining/hazardous” areas. This divergence became more established decades after HOLC mapping, with impact to racial metrics and low-income households. We found changes in these demographic patterns also occurred between 2000 and 2019, highlighting how processes like gentrification can develop from both rapid demographic and land-use changes. This study demonstrates how the legacy of urban segregation develops over decades and can simultaneously persist in some neighborhoods while providing openings for fast-paced gentrification in others.

1930's HOLC grades in greater Boston.Robert K. Nelson, LaDale Winling, Richard Marciano, Nathan Connolly, et al., “Mapping Inequality,” American Panorama, ed. Robert K. Nelson and Edward L. Ayers

This component of the Philadelphia Social History Project examines the demographic composition of city grid squares using census data from years 1850, 1860, 1870, and 1880. The collection consists of two types of data files: (1) grid tallies, and (2) grid dictionaries. The grid tally files consist of counts of individuals living in PSHP grid squares, with totals broken down by race/ethnicity, sex, and age. The grid dictionary files link lines in the census manuscripts to PSHP grid squares, allowing users to follow the movements of census-takers as they moved house-to-house on foot, adding individuals to the printed census manuscript forms. The "grid" network consists of a set of vertical and horizontal lines drawn at fixed intervals across a city map, forming the foundation for the spatial organization of the data. The grid dictionary files show when census-takers crossed from one grid square to another; each row in the grid dictionary describes a set of rows that are in a specific grid square by listing the starting page/line and the ending page/line.

This repository contains the code used for the study: "Variant Filters Using Segregation Information Improve Mapping of Nectar-Production Genes in Sunflower (Helianthus annuus L.)". The study evaluates the impact of biologically informed variant filtering strategies on QTL mapping, demonstrating improved identification of candidate genes related to nectar production.ContentsCandidateGeneGetter.shThis shell script extracts candidate genes from a GFF annotation file (HAN412_Eugene_curated_v1_1.gff3) based on genomic regions specified in the Windows file. For each region (defined by chromosome, start position, and end position), it identifies all genes falling entirely within that window, counts them, and outputs the region information along with a comma-separated list of gene IDs to AshleyCandidateGenes.txt.Chi_square_template.RThis R script filters genomic markers using a chi-square test based on expected segregation ratios. The script is designed as a template that can be adjusted for different population types by modifying the expected ratios. The default values (48.4375% homozygous for each allele and 3.125% heterozygous) are set for F6 inbred lines, but can be modified to match the segregation expectations of any population being filtered. It retains markers whose observed genotype frequencies do not significantly deviate from expectations (p > 0.1), removing markers with segregation distortion that could interfere with accurate QTL identification.mapping.RThis R script performs QTL (Quantitative Trait Locus) mapping using the qtl package. It includes code for three distinct "Approaches," likely representing analyses performed on different datasets or using varied marker filtering strategies (Approach1.csv, Approach2.csv, Approach3.csv). The script covers data loading, genetic map estimation and refinement (including custom marker thinning functions and visualization of recombination frequencies), calculation of genotype probabilities, performing 1D (scanone), Composite Interval (cim), and 2D (scantwo) QTL scans, significance testing via permutations, and refining QTL models (fitqtl, refineqtl).marker_filt_dist.RThis R script filters genomic markers from a VCF file by removing markers within 125,000 bp of each other. It optimizes marker density while maintaining genome-wide coverage, ensuring the filtered set is suitable for QTL mapping and identifying genomic regions linked to nectar-production traits in sunflower.proc freq marker data.sasThis SAS script filters genetic markers based on segregation patterns. It utilizes PROC FREQ to calculate genotype frequencies for biallelic markers (assuming three genotype classes) and performs chi-square tests against expected segregation ratios (e.g., specified test probabilities like 0.484375, 0.03125, 0.484375, corresponding to F6 expectations). Markers significantly deviating from these expectations (p < 0.10 in this script) are identified and potentially excluded from downstream analyses, similar in principle to Chi_square_template.R but implemented within the SAS environment for specific datasets (markers.bialw).thinning_loop.RThis R script thins genomic markers based on inter-marker distance thresholds, identifying and removing redundant or closely spaced markers. It helps refine marker sets to balance genome coverage and computational efficiency, improving QTL mapping precision in the study of sunflower nectar-production traits. (Note: Similar custom functions are also included within mapping.R).WindowsThis plain text file serves as input for the CandidateGeneGetter.sh script. Each line defines a genomic window with three columns: Chromosome, Start Position, and End Position. These windows likely represent regions of interest identified through QTL mapping or other analyses.CitationBarstow, A.C., McNellie, J.P., Smart, B.C., Keepers, K.G., Prasifka, J.R., Kane, N.C., & Hulke, B.S. (2025). Variant filters using segregation information improve mapping of nectar-production genes in sunflower (Helianthus annuus L.). The Plant Genome.

FalsePositiveEstimationSee ReadMe file.SublineCreationSegDistSee ReadMe file.SterilityREADME

This dataset was created primarily to map and track socioeconomic and demographic variables from the US Census Bureau from year 1940 to year 2010, by decade, within the City of Baltimore's Mayor's Office of Information Technology (MOIT) year 2010 neighborhood boundaries. The socioeconomic and demographic variables include the percent White, percent African American, percent owner occupied homes, percent vacant homes, the percentage of age 25 and older people with a high school education or greater, and the percentage of age 25 and older people with a college education or greater. Percent White and percent African American are also provided for year 1930. Each of the the year 2010 neighborhood boundaries were also attributed with the 1937 Home Owners' Loan Corporation (HOLC) definition of neighborhoods via spatial overlay. HOLC rated neighborhoods as A, B, C, D or Undefined. HOLC categorized the perceived safety and risk of mortgage refinance lending in metropolitan areas using a hierarchical grading scale of A, B, C, and D. A and B areas were considered the safest areas for federal investment due to their newer housing as well as higher earning and racially homogenous households. In contrast, C and D graded areas were viewed to be in a state of inevitable decline, depreciation, and decay, and thus risky for federal investment, due to their older housing stock and racial and ethnic composition. This policy was inherently a racist practice. Places were graded based on who lived there; poor areas with people of color were labeled as lower and less-than. HOLC's 1937 neighborhoods do not cover the entire extent of the year 2010 neighborhood boundaries. The neighborhood boundaries were also augmented to include which of the year 2017 Housing Market Typology (HMT) the 2010 neighborhoods fall within. Finally, the neighborhood boundaries were also augmented to include tree canopy and tree canopy change year 2007 to year 2015.

This study uses a boundary design and propensity score methods to study the effects of the 1930s-era HOLC “redlining” maps on the long-run trajectories of urban neighborhoods. The maps led to reduced homeownership rates, house values, and rents and increased racial segregation in later decades. A comparison on either side of a city-level population cutoff that determined whether maps were drawn finds broadly similar conclusions. These results suggest the HOLC maps had meaningful and lasting effects on the development of urban neighborhoods through reduced credit access and subsequent disinvestment.

Percent changes to demographic metrics in Home Owners’ Loan Corporation (HOLC) categories. Education and racial data start in 1940 and income data start in 1960.

Shea et al (1993) studied the small-scale spatial patterning of water tupelo, a gynodioecious tree. Locations of all trees in 50m x 50m quadrats were mapped using compass and tape. By inspection of flowers in spring, each tupelo was classified as male, female, or juvenile (not flowering). The biological questions concern spatial segregation. Do males tend to occur in the vicinity of other males. Do females tend to occur in the vicinity of other females? There are two data sets: tupall.dat and wtrlvl.dat. The data (tupall.dat) are records of all water tupelo trees in 3 approx. 50 m x 50m plots in bottomland hardwood forests. They are formatted as plot, sex (F = 1, M = 2, or juvenile = 3), index number of nearest neighbor (If a tree 1 in a plot has NN of 185, tree 185 is it's NN), x and y (the spatial location in meters). Also available, but not used in the tests of spatial relationships are water depth data collected on a grid (wtrlvl.dat). These observations almost certainly include considerable measurement error and small scale spatial variation.

This map was created to illustrate the residential patterns of 'Mexican' and 'Negro' demographics within the City of Dallas, serving as a historical record of segregation and racial demographics in the city. By visually documenting where these communities lived, the map provides a critical lens into the social, economic, and political landscape of Dallas during that period.This historical artifact is a key feature in the Racial Equity Storymap, an initiative aimed at uncovering and sharing the city’s complex history of racial inequity. By situating this map within the broader context of systemic policies and practices, the Storymap encourages reflection on how these historical dynamics continue to shape the city’s present-day communities and challenges.In addition to its historical significance, the map serves as an educational tool, helping residents, researchers, and policymakers better understand the enduring impacts of segregation, redlining, and discriminatory housing policies. Its inclusion in the Racial Equity Storymap underscores the importance of confronting and learning from the past to inform ongoing efforts to foster equity and inclusion in Dallas.By preserving and sharing this visual documentation, the City of Dallas aims to promote transparency, education, and dialogue as part of a larger commitment to racial equity and justice.