This map contains NYC administrative boundaries enriched with various demographics datasets.Learn more about Esri's Enrich Layer / Geoenrichment analysis tool.Learn more about Esri's Demographics, Psychographic, and Socioeconomic datasets.Search for a specific location or site using the search bar. Toggle layer visibility with the layer list. Click on a layer to see more information about the feature.

This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

"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.

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,

This EnviroAtlas dataset is a summary of key demographic groups for the EnviroAtlas community. This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

A dataset listing New York cities by population for 2024.

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.

Census Tracts from the 2020 US Census for New York City clipped to the shoreline. These boundary files are derived from the US Census Bureau's TIGER project and have been geographically modified to fit the New York City base map. Because some census tracts are under water not all census tracts are contained in this file, only census tracts that are partially or totally located on land have been mapped in this file. All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE. Current version: 25c

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

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

Summary: Essential Question: How can geospatial data be utilized to determine the degree of racial segregation during the height of Jim Crow?Storymap metadata page: URL forthcoming Possible K-12 Next Generation Science standards addressed:Grade level(s) 9-12: Standard HS-PS4-4 - Waves and Their Applications in Technologies for Information Transfer - Evaluate the validity and reliability of claims in published materials of the effects that different frequencies of electromagnetic radiation have when absorbed by matter.Grade level(s) 9-12: Standard HS-ESS2-2 - Earth’s Systems - Analyze geoscience data to make the claim that one change to Earth’s surface can create feedbacks that cause changes to other Earth systemsMost frequently used words:yorkcitynygreenbookApproximate Flesch-Kincaid reading grade level: 9.7. The FK reading grade level should be considered carefully against the grade level(s) in the NGSS content standards above.

This shapefile, in conjunction with layer files:May2013_Richness_All_Invertebrates.lyr, May2013_Richness_Biogenic_features.lyr, May2013_Richness_Habitat_forming_species.lyr, May2013_Richness_Habitatforming_Biogenic_features.lyr, May2013_Richness_Inverts_biogenic_features.lyr, May2013_Shannon_Diversity_All_Invertebrates.lyr, May2013_Shannon_Diversity_Biogenic_Features.lyr, May2013_Shannon_Diversity_Habitat_forming_species.lyr, May2013_Shannon_Diversity_Habitatforming_Biogenic_features.lyr, May2013_Shannon_Diversity_Inverts_Biogenic_features.lyr, shows the distribution of diversity [Species Richness (S), Shannon Diversity (H’log10)] in the Stratford Shoal region of Long Island Soundfor invertebrates, habitat forming species, and biogenic features.View Dataset on the Gateway

Note: These layers were compiled by Esri's Demographics Team using data from the Census Bureau's American Community Survey. These data sets are not owned by the City of Rochester.Overview of the map/data: This map shows the percentage of the population living below the federal poverty level over the previous 12 months, shown by tract, county, and state boundaries. Estimates are from the 2018 ACS 5-year samples. This service is updated annually to contain the most currently released American Community Survey (ACS) 5-year data, and contains estimates and margins of error. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Current Vintage: 2019-2023ACS Table(s): B17020, C17002Data downloaded from: Census Bureau's API for American Community Survey Date of API call: December 12, 2024National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer will be updated automatically when the most current vintage of ACS data is released each year, usually in December. The layer always contains the latest available ACS 5-year estimates. It is updated annually within days of the Census Bureau's release schedule. Click here to learn more about ACS data releases.Boundaries come from the US Census TIGER geodatabases. Boundaries are updated at the same time as the data updates (annually), and the boundary vintage appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto Rico.Census tracts with no population are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -555555...) have been set to null. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small. NOTE: any calculated percentages or counts that contain estimates that have null margins of error yield null margins of error for the calculated fields.

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).

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

This dataset contains ecological characteristics of infaunal Fishers diversity (prediction of the number of species at different levels of abundance) in the central Long Island Sound Pilot Area based on grab samples and analyses of high definition photos and video.View Dataset on the Gateway

This map shows a simple summary of the social vulnerability of populations in the United States. Using Census 2010 information, the map answers the question “Where are the areas of relatively greater potential impact from disaster events within the U.S.?” from the perspective of social vulnerability to hazards. In other words, all areas of the U.S. are assessed relative to each other. Local and regional assessments of social vulnerability should apply the same model to their multi-county or multi-state region. For emergency response planning and hazard mitigation, populations can be assessed by their vulnerability to various hazards (fire, flood, etc). Physical vulnerability refers to a population’s exposure to specific potential hazards, such as living in a designated flood plain. There are various methods for calculating the potential or real geographic extents for various types of hazards. Social vulnerability refers to sensitivity to this exposure due to population and housing characteristics: age, low income, disability, home value or other factors. The social vulnerability score presented in this web service is based upon a 2000 article from the Annals of the Association of American Geographers which sums the values of 8 variables as a surrogate for "social vulnerability". For example, low-income seniors may not have access to a car to simply drive away from an ongoing hazard such as a flood. A map of the flood’s extent can be overlaid on the social vulnerability layer to allow planners and responders to better understand the demographics of the people affected by the hazard. This map depicts social vulnerability at the block group level. A high score indicates an area is more vulnerable. This web service provides a simplistic view of social vulnerability. There are more recent methods and metrics for determining and displaying social vulnerability, including the Social Vulnerability Index (SoVI) which capture the multi-dimensional nature of social vulnerability across space. See www.sovius.org for more information on SoVI. The refereed journal article used to guide the creation of the model in ModelBuilder was: Cutter, S. L., J. T. Mitchell, and M. S. Scott, 2000. "Revealing the Vulnerability of People and Places: A Case Study of Georgetown County, South Carolina." Annals of the Association of American Geographers 90(4): 713-737. Additionally, a white paper used to guide creation of the model in ModelBuilder was "Handbook for Conducting a GIS-Based Hazards Assessment at the County Level" by Susan L. Cutter, Jerry T. Mitchell, and Michael S. Scott.Off-the-shelf software and data were used to generate this index. ModelBuilder in ArcGIS 10.1 was used to connect the data sources and run the calculations required by the model.-------------------------The Civic Analytics Network collaborates on shared projects that advance the use of data visualization and predictive analytics in solving important urban problems related to economic opportunity, poverty reduction, and addressing the root causes of social problems of equity and opportunity. For more information see About the Civil Analytics Network.

A shapefile for mapping data by Modified Zip Code Tabulation Areas (MODZCTA) in NYC, based on the 2010 Census ZCTA shapefile. MODZCTA are being used by the NYC Department of Health & Mental Hygiene (DOHMH) for mapping COVID-19 Data.

Many residents of New York City speak more than one language; a number of them speak and understand non-English languages more fluently than English. This dataset, derived from the Census Bureau's American Community Survey (ACS), includes information on over 1.7 million limited English proficient (LEP) residents and a subset of that population called limited English proficient citizens of voting age (CVALEP) at the Community District level. There are 59 community districts throughout NYC, with each district being represented by a Community Board.

The TIGER/Line shapefiles and related database files (.dbf) are an extract of selected geographic and cartographic information from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). The MTDB represents a seamless national file with no overlaps or gaps between parts, however, each TIGER/Line shapefile is designed to stand alone as an independent data set, or they can be combined to cover the entire nation. The TIGER/Line shapefiles include both incorporated places (legal entities) and census designated places or CDPs (statistical entities). An incorporated place is established to provide governmental functions for a concentration of people as opposed to a minor civil division (MCD), which generally is created to provide services or administer an area without regard, necessarily, to population. Places always nest within a state, but may extend across county and county subdivision boundaries. An incorporated place usually is a city, town, village, or borough, but can have other legal descriptions. CDPs are delineated for the decennial census as the statistical counterparts of incorporated places. CDPs are delineated to provide data for settled concentrations of population that are identifiable by name, but are not legally incorporated under the laws of the state in which they are located. The boundaries for CDPs often are defined in partnership with state, local, and/or tribal officials and usually coincide with visible features or the boundary of an adjacent incorporated place or another legal entity. CDP boundaries often change from one decennial census to the next with changes in the settlement pattern and development; a CDP with the same name as in an earlier census does not necessarily have the same boundary. The only population/housing size requirement for CDPs is that they must contain some housing and population. The boundaries of most incorporated places in this shapefile are as of January 1, 2020, as reported through the Census Bureau's Boundary and Annexation Survey (BAS). The boundaries of all CDPs were delineated as part of the Census Bureau's Participant Statistical Areas Program (PSAP) for the 2020 Census.