This dataset compiles a comprehensive database containing 90,327 street segments in New York City, covering their street design features, streetscape design, Vision Zero treatments, and neighborhood land use. It has two scales-street and street segment group (aggregation of same type of street at neighborhood). This dataset is derived based on all publicly available data, most from NYC Open Data. The detailed methods can be found in the published paper, Pedestrian and Car Occupant Crash Casualties Over a 9-Year Span of Vision Zero in New York City. To use it, please refer to the metadata file for more information and cite our work. A full list of raw data source can be found below:

Motor Vehicle Collisions – NYC Open Data: https://data.cityofnewyork.us/Public-Safety/Motor-Vehicle-Collisions-Crashes/h9gi-nx95

Citywide Street Centerline (CSCL) – NYC Open Data: https://data.cityofnewyork.us/City-Government/NYC-Street-Centerline-CSCL-/exjm-f27b

NYC Building Footprints – NYC Open Data: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh

Practical Canopy for New York City: https://zenodo.org/record/6547492

New York City Bike Routes – NYC Open Data: https://data.cityofnewyork.us/Transportation/New-York-City-Bike-Routes/7vsa-caz7

Sidewalk Widths NYC (originally from Sidewalk – NYC Open Data): https://www.sidewalkwidths.nyc/

LION Single Line Street Base Map - The NYC Department of City Planning (DCP): https://www.nyc.gov/site/planning/data-maps/open-data/dwn-lion.page

NYC Planimetric Database Median – NYC Open Data: https://data.cityofnewyork.us/Transportation/NYC-Planimetrics/wt4d-p43d

NYC Vision Zero Open Data (including multiple datasets including all the implementations): https://www.nyc.gov/content/visionzero/pages/open-data

NYS Traffic Data - New York State Department of Transportation Open Data: https://data.ny.gov/Transportation/NYS-Traffic-Data-Viewer/7wmy-q6mb

Smart Location Database - US Environmental Protection Agency: https://www.epa.gov/smartgrowth/smart-location-mapping

Race and ethnicity in area - American Community Survey (ACS): https://www.census.gov/programs-surveys/acs

"Neighborhood Financial Health (NFH) Digital Mapping and Data Tool provides neighborhood financial health indicator data for every neighborhood in New York City. DCWP's Office of Financial Empowerment (OFE) also developed NFH Indexes to present patterns in the data within and across neighborhoods. NFH Index scores describe relative differences between neighborhoods across the same indicators; they do not evaluate neighborhoods against fixed standards. OFE intends for the NFH Indexes to provide an easy reference tool for comparing neighborhoods, and to establish patterns in the relationship of NFH indicators to economic and demographic factors, such as race and income. Understanding these connections is potentially useful for uncovering systems that perpetuate the racial wealth gap, an issue with direct implications for OFE’s mission to expand asset building opportunities for New Yorkers with low and moderate incomes. This data tool was borne out of the Collaborative for Neighborhood Financial Health, a community-led initiative designed to better understand how neighborhoods influence the financial health of their residents.

Report on Demographic Data in New York City Public Schools, 2020-21Enrollment counts are based on the November 13 Audited Register for 2020. Categories with total enrollment values of zero were omitted. Pre-K data includes students in 3-K. Data on students with disabilities, English language learners, and student poverty status are as of March 19, 2021. Due to missing demographic information in rare cases and suppression rules, demographic categories do not always add up to total enrollment and/or citywide totals. NYC DOE "Eligible for free or reduced-price lunch” counts are based on the number of students with families who have qualified for free or reduced-price lunch or are eligible for Human Resources Administration (HRA) benefits. English Language Arts and Math state assessment results for students in grade 9 are not available for inclusion in this report, as the spring 2020 exams did not take place. Spring 2021 ELA and Math test results are not included in this report for K-8 students in 2020-21. Due to the COVID-19 pandemic’s complete transformation of New York City’s school system during the 2020-21 school year, and in accordance with New York State guidance, the 2021 ELA and Math assessments were optional for students to take. As a result, 21.6% of students in grades 3-8 took the English assessment in 2021 and 20.5% of students in grades 3-8 took the Math assessment. These participation rates are not representative of New York City students and schools and are not comparable to prior years, so results are not included in this report. Dual Language enrollment includes English Language Learners and non-English Language Learners. Dual Language data are based on data from STARS; as a result, school participation and student enrollment in Dual Language programs may differ from the data in this report. STARS course scheduling and grade management software applications provide a dynamic internal data system for school use; while standard course codes exist, data are not always consistent from school to school. This report does not include enrollment at District 75 & 79 programs. Students enrolled at Young Adult Borough Centers are represented in the 9-12 District data but not the 9-12 School data. “Prior Year” data included in Comparison tabs refers to data from 2019-20. “Year-to-Year Change” data included in Comparison tabs indicates whether the demographics of a school or special program have grown more or less similar to its district or attendance zone (or school, for special programs) since 2019-20. Year-to-year changes must have been at least 1 percentage point to qualify as “More Similar” or “Less Similar”; changes less than 1 percentage point are categorized as “No Change”. The admissions method tab contains information on the admissions methods used for elementary, middle, and high school programs during the Fall 2020 admissions process. Fall 2020 selection criteria are included for all programs with academic screens, including middle and high school programs. Selection criteria data is based on school-reported information. Fall 2020 Diversity in Admissions priorities is included for applicable middle and high school programs. Note that the data on each school’s demographics and performance includes all students of the given subgroup who were enrolled in the school on November 13, 2020. Some of these students may not have been admitted under the admissions method(s) shown, as some students may have enrolled in the school outside the centralized admissions process (via waitlist, over-the-counter, or transfer), and schools may have changed admissions methods over the past few years. Admissions methods are only reported for grades K-12. "3K and Pre-Kindergarten data are reported at the site level. See below for definitions of site types included in this report. Additionally, please note that this report excludes all students at District 75 sites, reflecting slightly lower enrollment than our total of 60,265 students

The Distance Sailing Race layer depicts race routes as mapped in the Northeast Coastal and Marine Recreational Use Characterization Study which was conducted by SeaPlan, the Surfrider Foundation, and Point 97 under the direction of the Northeast Regional Planning Body. Routes were mapped using a combination of outside research, leveraging existing data sources such as the Rhode Island Ocean Special Area Management Plan (RI OSAMP), and gathering input from race organizers and other industry experts through participatory mapping. For more information, users are encouraged to consult the metadata and final report.View Dataset on the Gateway

AbstractUrbanization often substantially influences animal movement and gene flow. However, few studies to date have examined gene flow of the same species across multiple cities. In this study, we examine brown rats (Rattus norvegicus) to test hypotheses about the repeatability of neutral evolution across four cities: Salvador, Brazil; New Orleans, USA; Vancouver, Canada; New York City, USA. At least 150 rats were sampled from each city and genotyped for a minimum of 15,000 genome-wide SNPs. Levels of genome-wide diversity were similar across cities, but varied across neighborhoods within cities. All four populations exhibited high spatial autocorrelation at the shortest distance classes (< 500 m) due to limited dispersal. Coancestry and evolutionary clustering analyses identified genetic discontinuities within each city that coincided with a resource desert in New York City, major waterways in New Orleans, and roads in Salvador and Vancouver. Such replicated studies are crucial to assessing the generality of predictions from urban evolution, and have practical applications for pest management and public health. Future studies should include a range of global cities in different biomes, incorporate multiple species, and examine the impact of specific characteristics of the built environment and human socioeconomics on gene flow. Usage notesPLINK .map file for New Orleans rat SNP GenotypesPLINK .map file for New Orleans SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.NOL.plink.mapPLINK .ped file for New Orleans rat SNP GenotypesPLINK .ped file for New Orleans SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.NOL.plink.pedPLINK .map file for New York City rat SNP GenotypesPLINK .map file for New York City SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.NYC.plink.mapPLINK .ped file for New York City rat SNP GenotypesPLINK .ped file for New York City SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.NYC.plink.pedPLINK .map file for Salvador, Brazil rat SNP GenotypesPLINK .map file for Salvador, Brazil SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.SAL.plink.mapPLINK .ped file for Salvador, Brazil rat SNP GenotypesPLINK .ped file for Salvador, Brazil SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.SAL.plink.pedPLINK .map file for Vancouver rat SNP GenotypesPLINK .map file for Vancouver SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.VAN.plink.mapPLINK .ped file for Vancouver rat SNP GenotypesPLINK .ped file for Vancouver SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.VAN.plink.ped

List Price: $20.99

From journalist and New York Times bestselling author Charles Blow comes a powerful manifesto and call to action for Black Americans to amass political power and fight white supremacy.

Race, as we have come to understand it, is a fiction; but, racism, as we have come to live it, is a fact. The point here is not to impose a new racial hierarchy, but to remove an existing one. After centuries of waiting for white majorities to overturn white supremacy, it seems to me that it has fallen to Black people to do it themselves.

Acclaimed columnist and author Charles Blow never wanted to write a “race book.” But as violence against Black people—both physical and psychological—seemed only to increase in recent years, culminating in the historic pandemic and protests of the summer of 2020, he felt compelled to write a new story for Black Americans. He envisioned a succinct, counterintuitive, and impassioned corrective to the myths that have for too long governed our thinking about race and geography in America. Drawing on both political observations and personal experience as a Black son of the South, Charles set out to offer a call to action by which Black people can finally achieve equality, on their own terms.

So what will it take to make lasting change when small steps have so frequently failed? It’s going to take an unprecedented shift in power. The Devil You Know is a groundbreaking manifesto, proposing nothing short of the most audacious power play by Black people in the history of this country. This book is a grand exhortation to generations of a people, offering a road map to true and lasting freedom.

ISBN: 9780062914699 Published: Jan. 26, 2021 By: Charles M. Blow Read by: JD Jackson

Data shows polygon locations of Potential Environmental Justice Areas (PEJA) and is defined in the PEJA field. PEJA's have been identified based on data from the 2014-2018 5-year American Community Survey (ACS), conducted by the US Census Bureau. Environmental justice efforts focus on improving the environment in communities, specifically minority and low-income communities, and addressing disproportionate adverse environmental impacts that may exist in those communities. The information balloon for each census block group area displays the census block group ID, population, percent minority, percent below poverty level, county, municipality, and a link to more information on the Department of Environmental Conservation's website https://www.dec.ny.gov/public/333.html The data was collected by the US Census Bureau as part of the American Community Survey. Reported income and race/ethnicity data were analyzed by OEJ to determine the presence of Potential Environmental Justice Areas. The designated areas are then considered for additional outreach within the permitting process, for grant eligibility, and for targeted enforcement of Environmental Conservation Law violations. Utilized established methods as originally detailed in the Interim Environmental Justice Policy, US EPA Region 2, December 2000, and recommended by the Environmental Justice Advisory Group, Recommendations for the New York State Department of Environmental Conservation Environmental Justice Program, January 2, 2002. Individual thresholds for low-income populations (statewide), minority populations (rural communities), and minority populations (urban communities) were determined by using ArcGIS 10.3 (used to indicate if census block groups overlapped Census designated urban areas) and IBM SPSS Statistics 26 (to conduct a K-means clustering algorithm on ACS data for the three categories). More detail is provided under processing steps. Service updated annually. For more information or to download layer see https://gis.ny.gov/gisdata/inventories/details.cfm?DSID=1273Download the metadata to learn more information about how the data was created and details about the attributes. Use the links within the metadata document to expand the sections of interest see http://gis.ny.gov/gisdata/metadata/nysdec.PEJA.xml

Improvement in performance times for the Boston (BOS), London (LON), Berlin (BER), Chicago (CHI) and New York (NYC) Marathons.

Prior to the civil war in the 1990’s ethnic tension caused many rivalries between groups. This was common between the Temne, with their allies the Limba, and the Mende, with their allies the Sherbro, Kissi, and Gola groups. Even with this history of ethnic conflict it does not appear to be a significant factor that contributed to the civil war as the war focused on control of diamond mines. With the civil war over for more than a decade the country is relatively peaceful. There are no serious ethnic conflicts or rivalries. Limba – Limba populations are found in other West African countries although 90% reside in Sierra Leone. The majority are Muslim, having been introduced to Islam in the late nineteenth century. This is much later than their neighbors. To prevent too much Westernization, the Limba often send their children to Islamic schools. Mande – The Mande are a large ethnic group in West Africa that is comprised of many smaller groups. The Mande people speak a variety of Mande languages. Most practice agriculture, animal husbandry, and trade. They practice a patrilineal society having the eldest male serve as lineage head. With so many Mande groups spread over West Africa there is much variation among language and culture. Mel – The Mel within Sierra Leone are comprised of the Gola and the Kissi. Similar to other West Africa groups, the Gola participate in secret societies. The most important occurs around the age of puberty and these societies seek to socialize youth with Gola culture. The Kissi are increasingly becoming culturally influenced by the Mende people. Soso - The Soso were introduced to Islam in the seventeenth century and they are now overwhelmingly Sunni Muslim, of the Maliki School. Many still perform ritual ceremonies from indigenous religions. They are often influenced by neighboring groups. Temne – The Temne are one of the largest ethnic groups in the country. While the capital of Freetown is home to many groups, the largest number of people belong to the Temne ethnicity. The majority are Muslim, having been introduced to Islam in the seventeenth century. Some Temne still practice indigenous religions or incorporate them into their practice of Islam. Similar to other groups in the country, the Temne also have secret socieites. The Temne use these socieites to learn about the Temne culture. Although many have convertered to Islam or Christianity, it is common to incorporate indigenous religious beliefs. Attribute Table Field DescriptionsISO3-International Organization for Standardization 3-digit country codeADM0_NAME-Administration level zero identification / namePEOPLEGP_1-People Group level 1PEOPLEGP_2-People Group level 2PEOPLEGP_3-People Group level 3PEOPLEGP_4-People Group level 4PEOPLEGP_5-People Group level 5ALT_NAMES-Alternative names or spellings for a people groupCOMMENTS-Comments or notes regarding the people groupSOURCE_DT-Source one creation dateSOURCE-Source oneSOURCE2_DT-Source two creation dateSOURCE2-Source twoCollectionThis feature class was constructed by referencing and combining information from Murdock’s Map of Africa (1959) with other anthropological literature pertaining to Sierra Leone ethnicity. The information was then processed through DigitalGlobe’s AnthropMapper program to generate more accurate ethnic coverage boundaries. Anthromapper uses geographical terrain features, combined with a watershed model, to predict the likely extent of ethnic and linguistic influence.Metadata and data pertaining to the feature class was collected from the review of Murdock’s Map of Africa (1959) in conjunction with information from anthropological research pertaining to ethnicity in northern Africa. While efforts were made to secure the accuracy of the geographic location of existing ethnicities, many are transient in nature and continue to migrate. Further, it should be stressed that ethnic groups listed represent the prominent people groups in Sierra Leone; however, numerous subgroups may exist below this tier. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Anthromapper. DigitalGlobe, September 2014.Gonen, Amiram. The Encyclopedia of the Peoples of the World. New York: Henry Holt and Company, 1993.Levinson, David. Encyclopedia of World Cultures: Africa and the Middle East. Boston: G.K. Hall and Co., 1995.Murdock, George Peter. Tribal Map of Africa from Africa: Its Peoples and Their Culture History. New York: McGraw-Hill Book Co., January 1959.Olson, James S. The Peoples of Africa: An Ethnohistorical Dictionary. Westport: Greenworod Press, 1996.The Diagram Group. Encyclopedia African Peoples. London: Diagram Visual Information, 2000.Yakan, Mohamad Z. Almanac of African Peoples and Nations. New Brunswick: Transaction Publishers, 1999.Sources (Metadata)Gonen, Amiram. The Encyclopedia of the Peoples of the World. New York: Henry Holt and Company, 1993.Levinson, David. Encyclopedia of World Cultures: Africa and the Middle East. Boston: G.K. Hall and Co., 1995.Murdock, George Peter. Tribal Map of Africa from Africa: Its Peoples and Their Culture History. New York: McGraw-Hill Book Co., January 1959.Notholt, Stuart A. Fields of Fire: An atlas of ethnic conflict. London: Stuart Notholt Communications Ltd, 2008.Olson, James S. The Peoples of Africa: An Ethnohistorical Dictionary. Westport: Greenworod Press, 1996.The Diagram Group. Encyclopedia African Peoples. London: Diagram Visual Information, 2000.University of Iowa Museum of Art, “Sierra Leone; Gola or Vai peoples, Lansana Ngumoi”. January 2006. Accessed December 2014. http://uima.uiowa.edu.Yakan, Mohamad Z. Almanac of African Peoples and Nations. New Brunswick: Transaction Publishers, 1999.

PLINK .map file for New Orleans rat SNP GenotypesPLINK .map file for New Orleans SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.NOL.plink.mapPLINK .ped file for New Orleans rat SNP GenotypesPLINK .ped file for New Orleans SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.NOL.plink.pedPLINK .map file for New York City rat SNP GenotypesPLINK .map file for New York City SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.NYC.plink.mapPLINK .ped file for New York City rat SNP GenotypesPLINK .ped file for New York City SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.NYC.plink.pedPLINK .map file for Salvador, Brazil rat SNP GenotypesPLINK .map file for Salvador, Brazil SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.SAL.plink.mapPLINK .ped file for Salvador, Brazil rat SNP GenotypesPLINK .ped file for Salvador, Brazil SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.SAL.plink.pedPLINK .map file for Vancouver rat SNP GenotypesPLINK .map file for Vancouver SNP genotypes. The genotypes themselves are in the .ped file of the same name, and the .map file contains the chromosomal coordinates for each SNP.VAN.plink.mapPLINK .ped file for Vancouver rat SNP GenotypesPLINK .ped file for Vancouver SNP genotypes. The genotypes themselves are in the .ped file, and the .map file contains the chromosomal coordinates for each SNP.VAN.plink.ped Urbanization often substantially influences animal movement and gene flow. However, few studies to date have examined gene flow of the same species across multiple cities. In this study, we examine brown rats (Rattus norvegicus) to test hypotheses about the repeatability of neutral evolution across four cities: Salvador, Brazil; New Orleans, USA; Vancouver, Canada; New York City, USA. At least 150 rats were sampled from each city and genotyped for a minimum of 15,000 genome-wide SNPs. Levels of genome-wide diversity were similar across cities, but varied across neighborhoods within cities. All four populations exhibited high spatial autocorrelation at the shortest distance classes (< 500 m) due to limited dispersal. Coancestry and evolutionary clustering analyses identified genetic discontinuities within each city that coincided with a resource desert in New York City, major waterways in New Orleans, and roads in Salvador and Vancouver. Such replicated studies are crucial to assessing the generality of predictions from urban evolution, and have practical applications for pest management and public health. Future studies should include a range of global cities in different biomes, incorporate multiple species, and examine the impact of specific characteristics of the built environment and human socioeconomics on gene flow.

These data were automated to provide an accurate high-resolution historical shoreline of Twelve Mile Speed Trial Course, NY suitable as a geographic information system (GIS) data layer. These data are derived from shoreline maps that were produced by the NOAA National Ocean Service including its predecessor agencies which were based on an office interpretation of imagery and/or field survey. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://inport.nmfs.noaa.gov/inport/item/39808

This is a list of authorized providers who offer the TLC Driver License 24 hour TLC Driver Education Course and exam. All TLC Driver License applicants must complete the course and pass an 80-question multiple choice exam on a computer with a grade of 70% or higher (you must answer 56 out of 80 questions correctly in order to pass). The course covers the following topics: TLC rules and regulations; geography; safe driving skills; traffic rules; and customer service.

As of March 2025, the men's course record for the London Marathon was held by Kelvin Kiptum, who broke the record with a time of ******* in 2023. Meanwhile, Paula Radcliffe has held the women's course record since 2003, with a time of *******. Marcel Hug, who also holds the course records in the New York and Boston marathons, set the fastest men's wheelchair time of ******* in 2023.

The Board and Paddle Events layer depicts the point locations of competitive board and paddle events as mapped in the Northeast Coastal and Marine Recreational Use Characterization Study which was conducted by SeaPlan, the Surfrider Foundation, and Point 97 under the direction of the Northeast Regional Planning Body. This study mapped event locations through an online opt-in survey, which allowed participants to map the locations of stand up paddleboard (SUP) races, surf contests, triathlons, and kayak, canoe or row boat races. Additional points were mapped based on additional research. Users are encouraged to consult the metadata and final report for additional details.View Dataset on the Gateway

Data in PolicyMap COVID-19 Quick Maps includes:Severe COVID-19 Health Risk Index, created by PolicyMap for the New York Times.COVID-19 Daily Cases and Deaths (counts, rates and weekly averages) as reported by the New York Times.COVID-19 Testing Rates as reported by the COVID Tracking ProjectSocial Vulnerability from the Centers for Disease Control. This includes an overall index created by the CDC, as well as the underlying four categories of indicators used by the CDC in the creation of this index: socioeconomic status, household composition and disability status, minority status and language and, housing and transportation.Underlying Health Conditions, such as asthma and COPD, as estimated by PolicyMap using CDC’s Behavioral Risk Factor Surveillance System.Basic demographics including age, race and incomes from the Census’ American Community Survey.Homeless Population counts from the Department of Housing and Urban Development.Computer and Internet Access from the Census’ American Community Survey.ICU Beds as reported by Kaiser Health News.Hospital Capacity and Federally Qualified Health Centers from the Health Resources and Services Administration.Insured and Uninsured Populations from the Census’ American Community Survey.See also - https://www.policymap.com/2020/05/policymap-covid19-quick-maps/

Link to this report's codebookUnfulfilled Promise of Racial EqualityUS states unequally distribute resources, services, and opportunities by raceThe US is failing to deliver on its promise of racial equality. While the US founding documents assert that ‘all men are created equal,’ this value is not demonstrated in outcomes across areas as diverse and varied as education, justice, health, gender, and pollution. On average, white communities receive resources and services at a rate approximately three times higher, than the least-served racial community (data on Asian, Black, Indigenous, Hawaiian and Pacific Islander, Hispanic, Multiracial and ‘Other’ racial communities, were used as available). Evidence shows that unequal treatment impacts each of these communities, however, it is most often Black and Indigenous communities that are left the furthest behind. When states are scored on how well they deliver the United Nations Sustainable Development Goals (SDGs) to the racial group least served, no state is even halfway to achieving the SDGs by 2030 (see Figure 1). To learn more about the Sustainable Development Goals, see the section “SDGs & Accountability.”One example of this inequality is in life expectancy. In Figure 2, the scatter plot on the left demonstrates a pattern in which Black and Indigenous communities, represented by orange and green dots closest to the bottom of the graph, are consistently the communities with least access to years of life. In the graph on the right, each box represents a racial population in a specific state, the boxes are organized from left to right, lowest to highest, according to the life expectancy for that group and state. The graph shows how large the gap is in life expectancy across racial communities and states, with green and orange boxes, representing Indigenous and Black communities respectively, clustered to the left of the graph.Patterns like this one, demonstrating both deep and wide racial inequalities, occur across the 51 indicators this analysis includes, covering 12 of 17 SDGs. In a similar example (Figure 3), a pattern emerges where white students are least likely to attend a school where 75 percent or more of its students receive free or reduced cost lunch when compared to all other racial groups. In the most unequal state, North Dakota, Indigenous students attend high poverty schools at a rate 42 times higher than white students. As Figure 3 shows, although the percentage of students from the least served racial group attending high poverty schools ranges from 2 percent in Vermont to 73 percent in Mississippi, the group least served, represented by the dots closest to the top of the graph, are most often Hispanic and Indigenous communities.Lack of Racial DataMore, and better, racially and ethnically disaggregated data are needed to assess delivery of racial equalityA significant barrier to evaluating progress is the unavailability of racial data across all areas of measurement. For too many important topic areas, such as food insecurity, maternal mortality and lead in drinking water, there is no racial data available at the state level. Even in the areas where there is some racial data, it is often not available for all groups (see Figure 4). Particularly missing, were measures of environmental justice; in Goals focusing on Water, Clean Energy, and Life on Land (Goals 6, 7, and 15), racial data was not found for any indicators, despite the fact that there is research indicating that clean water, for example, is unequally distributed across racial groups. The reasons for these gaps vary. For some indicators, data is not tracked through a nationally organized database, for other indicators, the data is old and out of date, and in many cases, surveys are not large enough to disaggregate by race. As was made clear with the disparate impacts of COVID-19 (for example, see CDC 2020), understanding to whom resources are being distributed has real life implications and is an important part of holding democratic institutions accountable to promises of equality.People are often left behind due to a combination of intersecting identities and factors; they remain hidden in averages. Evaluating the Leave No One Behind Agenda through the lens of gender, ability, class and other identities are undoubtedly important and urgent. Disaggregating data along two axes such as race and location—is revealing. But an even more refined analysis using multilevel disaggregation, such as looking at women and race in urban settings, would likely reveal even starker inequalities. Those are not included here and are important areas for future work. Other areas for further exploration include the use of longitudinal data to understand how these inequalities are changing over time.Though the full extent of this unequal treatment is unknown, this analysis sheds some light on the clouded story told by state averages. Whole group averages leave out important information, particularly about inequality. Racially disaggregated data is essential for holding governments accountable to the promise of racial equity. Without it, it is too easy to hide who is being excluded and left behind.SDGs and AccountabilitySDGs and AccountabilityThe SDGs can be an accountability tool to address racial inequality. This would not be the first time UN frameworks have been used to call attention to racial inequality in the US. In 1951, the Civil Rights Congress (CRC) led by William L. Patterson and Paul Robeson put a petition to the UN, named: “We Charge Genocide,” which charged that the United States government was in violation of the Charter of the United Nations and the Convention on the Prevention and Punishment of the Crime of Genocide (Figure 5). While this attempt did not succeed in charging the US government with genocide, it is a central example of how international instruments can be used to apply localized pressure to advance civil rights.All 193 member countries of the UN, including the United States, signed on to the Sustainable Development Goals in 2015, to be achieved by 2030. The Goals cover 17 wide-ranging topics, with 169 specific targets for action (Figure 6). The first agenda of the SDGs, the Leave No One Behind Agenda (LNOB), requires that those left furthest behind by governments must have the SDGs delivered to them first. The results of this project demonstrate that in a US-context, those left furthest behind would undoubtedly include Asian, Black, Indigenous, Hawaiian and Pacific Islander, Hispanic, Multiracial and ‘Other’ racial communities. The SDGs can offer a template for US states attempting to deliver on their promise of racial equality. The broad topic areas covered by the SDGs, in combination with the Leave No One Behind agenda, can be a tool to hold states accountable for addressing racial inequalities when and through developing solutions for clean water, quality education, ending hunger, delivering justice and more. This highlights an important implication of the Leave No One Behind Agenda, it is not meant to pit communities against each other, but rather to remind us how much everyone has to gain by building and advocating for sustainable communities that serve us all.Explore ResultsExplore the data from the In the Red: the US failure to deliver on a promise of racial equality in our interactive dashboards.These maps display how US states are delivering sustainability across different racial and ethnic groups. As part of the Leave No One Behind Agenda, which maintains that those who have been least served by development progress must be those first addressed through the SDGs, progress toward the goals in each state is displayed based on the racial group with the least access to resources, programs, and services in that state. In other words, the “Overall scores’’ map shows the score for the racial group least served in each state. Click on a state to toggle through the state’s performance by different SDGs, and click on an indicator to view how a state performs on a given indicator. At the indicator level, horizontal bar charts show the racial disparity in the selected indicator and state, when data is available.AboutIn the Red: the US Failure to Deliver on a Promise of Racial EqualityIn the Red: the US Failure to Deliver on a Promise of Racial Equality project highlights measurable gaps in how states deliver sustainability to different racial groups. The full report can be read here. It extends an earlier report, Never More Urgent, looking at policies and practices that have led to the inequalities described in this project. It was prepared by a group of independent experts at SDSN and Howard University.UN Sustainable Development Solutions Network (SDSN)The UN Sustainable Development Solutions Network (SDSN) mobilizes scientific and technical expertise from academia, civil society, and the private sector to support practical problem solving for sustainable development at local, national, and global scales. The SDSN has been operating since 2012 under the auspices of the UN Secretary-General Antonio Guterres. The SDSN is building national and regional networks of knowledge institutions, solution-focused thematic networks, and the SDG Academy, an online university for sustainable development.SDSN USASDSN USA is a network of 150+ research institutions across the United States and unincorporated territories. The network builds pathways toward achievement of the UN Sustainable Development Goals (SDGs) in the United States by mobilizing research, outreach, collective action, and global cooperation. SDSN USA is one of more than 40 national and regional SDSN networks globally. It is hosted by the UN Sustainable Development Solutions Network (SDSN) in New York City, and is chaired by Professors Jeffrey Sachs (Columbia University), Helen Bond (Howard University), Dan Esty (Yale University), and Gordon McCord (UC San Diego).

(UNCLASSIFIED) There are three main ethnolinguistic groups that made up ethnicity in Liberia; Mel, Mande, and Kru. The ethnic mix of Liberia has contributed to a rich culture as well as ethnic tension. It is common for politics in West Africa to divide along ethnic lines. Ethnic tension along with poor economic and social conditions and political instability were the leading causes for the two recent civil wars in the country. This first began in 1989 when the National Patriotic Front of Liberia, led by Charles Taylor, rose up against the Kran dominant government lead by Samuel Doe. The first civil war ended in 1997 with Charles Taylor formally voted into power. During the civil war Taylor commonly targeted Muslim Mande populations and the Kran for being the two groups most associated with the Doe regime. The opposition to Taylor retaliated by attacking Christian sites. Taylor’s regime was chaotic which led to a second civil war that began in 1999 with full scale war in 2003; a cease fire was signed the same year which ended the civil war. The actions during both civil wars show how politics and ethnicity go hand in hand and can produce ethnoreligious violence. Many in Liberia participate in secret societies known as hale, this is the most controlling and unifying force in Liberian culture with most participants belonging to one or more societies. They are both religious and political in nature and lay out acceptable and unacceptable behavior. There are numerous different hale societies offering regulations on how someone should act in society. The two most important hale societies are the men’s Poro and the female’s Sande, with participants joining at puberty to be taught the ideals of manhood and womanhood. Initiations are secret and performed in the forest. Reports state that initiation into the Sande society often includes female genital mutilation while boys undergo circumcisions in the Poro society. Belonging to either the Poro or Sande society is so important among traditional communities that those who do not join are not considered a member of the village, clan, or tribe. Mande - The Mande people group is the largest ethnicity in Liberia and has multiple subgroups. Agriculture, trade, and animal husbandry are common economic activities among the Mande people. They are patrilineal and the oldest male serves as the lineage head. Class structure is also common among Mande people typically consisting of royal, noble, commoner, artisan, and former slave classes. The largest Mande subgroup are the Kpelle and alone they account for 20.3 percent of the total Liberian population. The Kpelle organize themselves into many chiefdoms each of which are led by a paramount chief. While mass conversion to Christianity happened in the nineteenth century many still practice indigenous belief systems either alone or in combination with Christianity. Mel - The Mel group in Liberia is comprised of the Kissi and the Gola, 4.8 percent and 4.4 percent of the population respectively. Most Kissi are either Christian, animists, or a combination of the two. A small population, roughly 9 percent, is Muslim. Most are subsistence farmers or urban laborers. During the first civil war they were in conflict with the Kran. Kru – The Kru are organized based on patrilineal relationships and divided in many subgroups. As with many other ethnic groups in the region, while many have converted to Christianity there is still a significant portion that still adheres to indigenous beliefs or incorporates them into Christianity. Indigenous beliefs are passed through folklores and proverbs. Attribute Table Field DescriptionsISO3 - International Organization for Standardization 3-digit country code ADM0_NAME - Administration level zero identification / name PEOPLEGP_1 - People Group level 1 PEOPLEGP_2 - People Group level 2 PEOPLEGP_3 - People Group level 3 PEOPLEGP_4 - People Group level 4 PEOPLEGP_5 - People Group level 5 ALT_NAMES - Alternative names or spellings for a people group COMMENTS - Comments or notes regarding the people group SOURCE_DT - Source one creation date SOURCE - Source one SOURCE2_DT - Source two creation date SOURCE2 - Source two CollectionThe feature class was constructed by combining information from Murdock’s Map of Africa (1959) with other anthropological literature pertaining to Liberian ethnicity. The information was then processed through DigitalGlobe’s AnthropMapper program to generate more accurate ethnic coverage boundaries. Anthromapper uses geographical terrain features, combined with a watershed model, to predict the likely extent of ethnic and linguistic influence. The data included herein have not been derived from a registered survey and should be considered approximate unless otherwise defined. While rigorous steps have been taken to ensure the quality of each dataset, DigitalGlobe is not responsible for the accuracy and completeness of data compiled from outside sources.Sources (HGIS)Anthromapper. DigitalGlobe, September 2014.Gonen, Amiram. The Encyclopedia of the Peoples of the World. New York: Henry Holt and Company, 1993.Levinson, David. Encyclopedia of World Cultures: Africa and the Middle East. Boston: G.K. Hall and Co., 1995.Minority Rights Group International. World Directory of Minorities and Indigenous Peoples, “Liberia Overview.” January 2005. Accessed September 23, 2014. http://www.minorityrights.org/directory.Murdock, George Peter. Tribal Map of Africa from Africa: Its Peoples and Their Culture History. New York: McGraw-Hill Book Co., January 1959.Olson, James S. The Peoples of Africa: An Ethnohistorical Dictionary. Westport: Greenworod Press, 1996.The Diagram Group. Encyclopedia African Peoples. London: Diagram Visual Information, 2000.Yakan, Mohamad Z. Almanac of African Peoples and Nations. New Brunswick: Transaction Publishers, 1999.Sources (Metadata)Central Intelligence Agency. The World FactBook, “Liberia.” June 20, 2014. Accessed September 22, 2014. https://www.cia.gov/index.html.Gonen, Amiram. The Encyclopedia of the Peoples of the World. New York: Henry Holt and Company, 1993.Humanitarian News and Analysis, “Liberia: FGM continues in rural secrecy.” September 24, 2008. Accessed September 23, 2014. http://www.irinnews.org/.Levinson, David. Encyclopedia of World Cultures: Africa and the Middle East. Boston: G.K. Hall and Co., 1995.Olson, James S. The Peoples of Africa: An Ethnohistorical Dictionary. Westport: Greenworod Press, 1996.The Diagram Group. Encyclopedia African Peoples. London: Diagram Visual Information, 2000.Vogel, Health. Blogging without Maps: a Journey through Liberia, “Societies within Society – The Secret Societies of Liberia.” June 16, 2012. Accessed September 23, 2014. http://bloggingwithoutmaps.blogspot.com/.Yakan, Mohamad Z. Almanac of African Peoples and Nations. New Brunswick: Transaction Publishers, 1999.

This dataset contains the parcel boundaries of land owned by the New Jersey Sports and Exposition Authority.The NJSEA was established on May 10, 1971. The Authority was given the mission of constructing and managing a new state-of-the-art sports complex. The Complex would provide a home for the New York Giants football team. A 750-acre site in East Rutherford was chosen as the location for a racetrack and football stadium to be funded through revenue bonds backed by proceeds created by the racetrack itself. The NJSEA added an arena, now previously named the IZOD CENTER, now named the American Dream to the Meadowlands Complex in 1981. The Atlantic City Convention Center was added in 1997. The Wildwodds Convention Center was added in 2002.They also own the Atlantic City Boardwalk Hall and Monmoth Park Race Track. Their mission is to help citizens, and guests of NJ to reap the entertainment and economic benefits from the Authority facilities.