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.

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.

The prevalence of genetic diversity in switchgrass germplasm can be exploited to capture favorable alleles that increase its range of adaptation and biomass yield. The objectives of the study were to analyze the extent of polymorphism and patterns of segregation distortion in two F1 populations and use the linkage maps to locate QTL for biomass yield. We conducted genotyping-by-sequencing on two populations derived from crosses between the allotetraploid lowland genotype AP13 (a selection from “Alamo”) and coastal genotype B6 (a selection from PI 422001) with 285 progeny (AB population) and between B6 and the allotetraploid upland VS16 (a selection from “Summer”) with 227 progeny (BV population). As predictable from the Euclidean distance between the parents, a higher number of raw variants was discovered in the coastal × upland BV cross (6 M) compared to the lowland × coastal AB cross (2.5 M). The final number of mapped markers was 3,107 on the BV map and 2,410 on the AB map. More segregation distortion of alleles was seen in the AB population, with 75% distorted loci compared to 11% distorted loci in the BV population. The distortion in the AB population was seen across all chromosomes in both the AP13 and B6 maps and likely resulted from zygotic or post-zygotic selection for increased levels of heterozygosity. Our results suggest lower genetic compatibility between the lowland AP13 and the coastal B6 ecotype than between B6 and the upland ecotype VS16. Four biomass QTLs were mapped in the AB population (LG 2N, 6K, 6N, and 8N) and six QTLs in the BV population [LG 1N (2), 8N (2), 9K, and 9N]. The QTL, with the largest and most consistent effect across years, explaining between 8.4 and 11.5% of the variation, was identified on 6N in the AP13 map. The cumulative effect of all the QTLs explained a sizeable portion of the phenotypic variation in both AB and BV populations and the markers associated with them may potentially be used for the marker-assisted improvement of biomass yield. Since switchgrass improvement is based on increasing favorable allele frequencies through recurrent selection, the transmission bias within individuals and loci needs to be considered as this may affect the genetic gain if the favorable alleles are distorted.

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

The population of some areas in the United States is dominated heavily by one racial or ethnic group. These areas stand out on this map. In other areas, one group may be the majority, but the population is much more evenly balanced. Other areas have one group claiming a plurality, but not a majority.In each neighborhood, county, and state, this map shows which race or ethnicity is predominant, and by how much. It uses map colors to identify the predominant racial or ethnic group in specific areas by county and tract. The strength of the color indicates the extent to which one group is dominant over the next most populous.The data shown is from the U.S. Census Bureau's SF1 and TIGER data sets for 2010, and Esri. Concept and colors by Andrew Skinner.Original data sourced from: https://nation.maps.arcgis.com/apps/OnePane/splash/index.html?appid=602849530f5d4b6781ba37393144728c

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.

Learn more about Skye Lam's StoryMap project goals at: https://www.arcgis.com/home/item.html?id=c61ac50131594a4fb2ff371e2bce7517

Genotypic Data (VCFs):All VCF files contain imputed, biallelic SNPs derived from the same population but differ in the filtering strategies applied.Approach1_...vcf.gz: Filtered using hard thresholds (minQ ≥ 100, max Missing ≤ 0.75, MAF ≥ 0.05, inferred single copy).Approach2_...vcf.gz: Applies the same hard filters as Approach 1, with an additional Chi-Square filter (p-value ≤ 0.1).Approach3_...vcf.gz: Filtered using only a Chi-Square filter (p-value ≤ 0.1) on imputed, biallelic SNPs.Phenotypic Data (XLSX):nectar_phenotype.xlsx: Contains phenotypic measurements for the population, including individual identifiers (ID) and nectar volume data (nectar_mm_T, nectar_mm).

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.

Maps of Dutch municipalities representing the spatial distribution of 2 social groups, along with regions obtained with a regionalization method.

Based on a two-way pseudo-testcross strategy, high density and complete coverage linkage maps were constructed for the maternal and paternal parents of an intraspecific F2 pedigree of Populus deltoides. A total of 1,107 testcross markers were obtained, and the mapping population consisted of 376 progeny. Among these markers, 597 were from the mother, and were assigned into 19 linkage groups, spanning a total genetic distance of 1,940.3 cM. The remaining 519 markers were from the father, and were also were mapped into 19 linkage groups, covering 2,496.3 cM. The genome coverage of both maps was estimated as greater than 99.9% at 20 cM per marker, and the numbers of linkage groups of both maps were in accordance with the 19 haploid chromosomes in Populus. Marker segregation distortion was observed in large contiguous blocks on some of the linkage groups. Subsequently, we mapped the segregation distortion loci in this mapping pedigree. Altogether, eight segregation distortion loci with significant logarithm of odds supports were detected. Segregation distortion indicated the uneven transmission of the alternate alleles from the mapping parents. The corresponding genome regions might contain deleterious genes or be associated with hybridization incompatibility. In addition to the detection of segregation distortion loci, the established genetic maps will serve as a basic resource for mapping genetic loci controlling traits of interest in future studies.

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.

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.

Quantitative trait locus (QTL) mapping is often conducted in line crossing experiments where progeny are derived randomly from the original crosses. The detected QTL from such experiments are rarely relevant to breeding populations because they are not detected from the breeding populations. We developed generalized linear model methods to perform QTL mapping in directionally selected populations using a segregation distortion approach. A selected population is often small and thus has low power for QTL detection. The segregation distortion approach actually takes advantage of the small populations because small selected populations often reflected strong selection and thus possess a high degree of segregation distortion. We also developed methods to combine results of several populations and results from different types of data analyses from the same populations. Such a combined analysis can boost statistical powers. Simulation studies showed that the new methods of QTL mapping in sele...

My ArcGIS StoryMap is centered around The Green Book, an annual travel guide that allowed African Americans to travel safely during the height of the Jim Crow Era in the United States. More specifically, The Green Book listed establishments, such as hotels and restaurants, that would openly accept and welcome black customers into their businesses. As someone who is interested in the intersection between STEM and the humanities, I wanted to utilize The Science of Where to formulate a project that would reveal important historical implications to the public. Therefore, my overarching goal was to map each location in The Green Book in order to draw significant conclusions regarding racial segregation in one of the largest cities in the entire world.Although a more detailed methodology of my work can be found in the project itself, the following is a step by step walkthrough of my overall scientific process:Develop a question in relation to The Green Book to be solved through the completion of the project.Perform background research on The Green Book to gain a more comprehensive understanding of the subject matter.Formulate a hypothesis that answers the proposed question based on the background research.Transcribe names and addresses for each of the hotel listings in The Green Book into a comma separated values file.Transcribe names and addresses for each of the restaurants listings in The Green Book into a comma separated values file.Repeat Steps 4 and 5 for the 1940, 1950, 1960, and 1966 publications of The Green Book. In total, there should be eight unique database files (1940 New York City Hotels, 1940 New York City Restaurants, 1950 New York City Hotels, 1950 New York City Restaurants, 1960 New York City Hotels, 1960 New York City Restaurants, 1966 New York City Hotels, and 1966 New York City Restaurants.)Construct an address locator that references a New York City street base map to plot the information from the databases in Step 6 as points on a map.Manually plot locations that the address locator did not automatically match on the map.Repeat Steps 7 and 8 for all eight database files.Find and match the point locations for each listing in The Green Book with historical photographs.Generate a map tour using the geotagged images for each point from Step 10.Create a point density heat map for the locations in all eight database files.Research and obtain professional and historically accurate racial demographic data for New York City during the same time period as when The Green Book was published.Generate a hot spot map of the black population percentage using the demographic data.Analyze any geospatial trends between the point density heat maps for The Green Book and the black population percentage hot spot maps from the demographic data.Research and obtain professional and historically accurate redlining data for New York City during the same time period as when The Green Book was published.Overlay the points from The Green Book listings from Step 9 on top of the redlining shapefile.Count the number of point features completely located within each redlining zone ranking utilizing the spatial join tool.Plot the data recorded from Step 18 in the form of graphs.Analyze any geospatial trends between the listings for The Green Book and its location relative to the redlining ranking zones.Draw conclusions from the analyses in Steps 15 and 20 to present a justifiable rationale for the results._Student Generated Maps:New York City Pin Location Maphttps://arcg.is/15i4nj1940 New York City Hotels Maphttps://arcg.is/WuXeq1940 New York City Restaurants Maphttps://arcg.is/L4aqq1950 New York City Hotels Maphttps://arcg.is/1CvTGj1950 New York City Restaurants Maphttps://arcg.is/0iSG4r1960 New York City Hotels Maphttps://arcg.is/1DOzeT1960 New York City Restaurants Maphttps://arcg.is/1rWKTj1966 New York City Hotels Maphttps://arcg.is/4PjOK1966 New York City Restaurants Maphttps://arcg.is/1zyDTv11930s Manhattan Black Population Percentage Enumeration District Maphttps://arcg.is/1rKSzz1930s Manhattan Black Population Percentage Hot Spot Map (Same as Previous)https://arcg.is/1rKSzz1940 Hotels Point Density Heat Maphttps://arcg.is/jD1Ki1940 Restaurants Point Density Heat Maphttps://arcg.is/1aKbTS1940 Hotels Redlining Maphttps://arcg.is/8b10y1940 Restaurants Redlining Maphttps://arcg.is/9WrXv1950 Hotels Redlining Maphttps://arcg.is/ruGiP1950 Restaurants Redlining Maphttps://arcg.is/0qzfvC01960 Hotels Redlining Maphttps://arcg.is/1KTHLK01960 Restaurants Redlining Maphttps://arcg.is/0jiu9q1966 Hotels Redlining Maphttps://arcg.is/PXKn41966 Restaurants Redlining Maphttps://arcg.is/uCD05_Bibliography:Image Credits (In Order of Appearance)Header/Thumbnail Image:Student Generated Collage (Created Using Pictures from the Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library, https://digitalcollections.nypl.org/collections/the-green-book#/?tab=about.)Mob Violence Image:Kelley, Robert W. “A Mob Rocks an out of State Car Passing.” Life Magazine, www.life.com/history/school-integration-clinton-history, The Green Book Example Image:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library Digital Collections, https://images.nypl.org/index.php?id=5207583&t=w. 1940s Borough of Manhattan Hotels and Restaurants Photographs:“Manhattan 1940s Tax Photos.” NYC Municipal Archives Collections, The New York City Department of Records & Information Services, https://nycma.lunaimaging.com/luna/servlet/NYCMA~5~5?cic=NYCMA~5~5.Figure 1:Student Generated GraphFigure 2:Student Generated GraphFigure 3:Student Generated GraphGIS DataThe Green Book Database:Student Generated (See Above)The Green Book Listings Maps:Student Generated (See Above)The Green Book Point Density Heat Maps:Student Generated (See Above)The Green Book Road Trip Map:Student GeneratedLION New York City Single Line Street Base Map:https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page 1930s Manhattan Census Data:https://s4.ad.brown.edu/Projects/UTP2/ncities.htm Mapping Inequality Redlining Data:https://dsl.richmond.edu/panorama/redlining/#loc=12/40.794/-74.072&city=manhattan-ny&text=downloads 1940 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Negro Motorist Green-Book: 1940" The New York Public Library Digital Collections, 1940, https://digitalcollections.nypl.org/items/dc858e50-83d3-0132-2266-58d385a7b928. 1950 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Negro Motorist Green-Book: 1950" The New York Public Library Digital Collections, 1950, https://digitalcollections.nypl.org/items/283a7180-87c6-0132-13e6-58d385a7b928. 1960 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "The Travelers' Green Book: 1960" The New York Public Library Digital Collections, 1960, https://digitalcollections.nypl.org/items/a7bf74e0-9427-0132-17bf-58d385a7b928. 1966 The Green Book Document:Schomburg Center for Research in Black Culture, Manuscripts, Archives and Rare Books Division, The New York Public Library. "Travelers' Green Book: 1966-67 International Edition" The New York Public Library Digital Collections, 1966, https://digitalcollections.nypl.org/items/27516920-8308-0132-5063-58d385a7bbd0. Hyperlink Credits (In Order of Appearance)Referenced Hyperlink #1: Coen, Ross. “Sundown Towns.” Black Past, 23 Aug. 2020, blackpast.org/african-american-history/sundown-towns.Referenced Hyperlink #2: Foster, Mark S. “In the Face of ‘Jim Crow’: Prosperous Blacks and Vacations, Travel and Outdoor Leisure, 1890-1945.” The Journal of Negro History, vol. 84, no. 2, 1999, pp. 130–149., doi:10.2307/2649043. Referenced Hyperlink #3:Driskell, Jay. “An Atlas of Self-Reliance: The Negro Motorist's Green Book (1937-1964).” National Museum of American History, Smithsonian Institution, 30 July 2015, americanhistory.si.edu/blog/negro-motorists-green-book. Referenced Hyperlink #4:Kahn, Eve M. “The 'Green Book' Legacy, a Beacon for Black Travelers.” The New York Times, The New York Times, 6 Aug. 2015, www.nytimes.com/2015/08/07/arts/design/the-green-book-legacy-a-beacon-for-black-travelers.html. Referenced Hyperlink #5:Giorgis, Hannah. “The Documentary Highlighting the Real 'Green Book'.” The Atlantic, Atlantic Media Company, 25 Feb. 2019, www.theatlantic.com/entertainment/archive/2019/02/real-green-book-preserving-stories-of-jim-crow-era-travel/583294/. Referenced Hyperlink #6:Staples, Brent. “Traveling While Black: The Green Book's Black History.” The New York Times, The New York Times, 25 Jan. 2019, www.nytimes.com/2019/01/25/opinion/green-book-black-travel.html. Referenced Hyperlink #7:Pollak, Michael. “How Official Is Official?” The New York Times, The New York Times, 15 Oct. 2010, www.nytimes.com/2010/10/17/nyregion/17fyi.html. Referenced Hyperlink #8:“New Name: Avenue Becomes a Boulevard.” The New York Times, The New York Times, 22 Oct. 1987, www.nytimes.com/1987/10/22/nyregion/new-name-avenue-becomes-a-boulevard.html. Referenced Hyperlink #9:Norris, Frank. “Racial Dynamism in Los Angeles, 1900–1964.” Southern California Quarterly, vol. 99, no. 3, 2017, pp. 251–289., doi:10.1525/scq.2017.99.3.251. Referenced Hyperlink #10:Shertzer, Allison, et al. Urban Transition Historical GIS Project, 2016, https://s4.ad.brown.edu/Projects/UTP2/ncities.htm. Referenced Hyperlink #11:Mitchell, Bruce. “HOLC ‘Redlining’ Maps: The Persistent Structure Of Segregation And Economic Inequality.” National Community Reinvestment Coalition, 20 Mar. 2018,

FalsePositiveEstimationSee ReadMe file.SublineCreationSegDistSee ReadMe file.SterilityREADME

This layer contains block level 2020 Decennial Census redistricting data as reported by the U.S. Census Bureau. The attributes come from the 2020 Public Law 94-171 (P.L. 94-171) tables. To protect the privacy and confidentiality of respondents, data has been protected using differential privacy techniques by the U.S. Census Bureau. This means that some individual blocks will have values that are inconsistent or improbable. However, when aggregated up, these issues become minimized. Citation: U.S. Census, 2020Census tables: P1, P2, P3, P4, H1, P5, HeaderData Source: https://hub.arcgis.com/datasets/d1105f1e65a743cc84fc12c034625fc7/exploreData downloaded from source: 11/28/2022

Georectified HOLC map of greater Boston.

Segregation distortion is the phenomenon in which genotypes deviate from expected Mendelian ratios in the progeny of a cross between two varieties or species. There is not currently a widely used consensus for the appropriate statistical test, or more specifically the multiple testing correction procedure, used to detect segregation distortion for high-density single-nucleotide polymorphism (SNP) data. Here we examine the efficacy of various multiple testing procedures, including chi-square test with no correction for multiple testing, false-discovery rate correction and Bonferroni correction using an in-silico simulation of a biparental mapping population. We find that the false discovery rate correction best approximates the traditional p-value threshold of 0.05 for high-density marker data. We also utilize this simulation to test the effect of segregation distortion on the genetic mapping process, specifically on the formation of linkage groups during marker clustering. Only extreme segregation distortion was found to effect genetic mapping. In addition, we utilize replicate empirical mapping populations of wheat varieties Avalon and Cadenza to assess how often segregation distortion conforms to the same pattern between closely related wheat varieties.

An endogenous meiotic driver in the dengue and yellow fever vector mosquito Aedes aegypti can cause highly male-biased sex ratio distortion in crosses from suitable genetic backgrounds. We previously selected a strain that carries a strong meiotic drive gene (D) linked with the male-determining allele (M) on chromosome 1 in A. aegypti. Here we performed segregation analysis of the MD locus among backcross (BC1) progeny from a driver male and drive-sensitive females. Assessment of sex ratios among BC2 progeny showed ~5.2% recombination between the MD locus and the sex determination locus. Multipoint linkage mapping across this region revealed consistent marker orders and recombination frequencies with the existing reference linkage map and placed the MD locus within a 6.5 cM interval defined by the LF159 locus and microsatellite marker 446GAA, which should facilitate future positional cloning efforts.