This graph shows the population density in the federal state of New York from 1960 to 2018. In 2018, the population density of New York stood at 414.7 residents per square mile of land area.

New York City Population By Community Districts

The data was collected from Census Bureaus' Decennial data dissemination (SF1) for the years 1970, 1980, 1990, 2000 and 2010.

Compiled by the Population Division – New York City Department of City Planning

Population Numbers By New York City Neighborhood Tabulation Areas The data was collected from Census Bureaus' Decennial data dissemination (SF1). Neighborhood Tabulation Areas (NTAs), are aggregations of census tracts that are subsets of New York City's 55 Public Use Microdata Areas (PUMAs). Primarily due to these constraints, NTA boundaries and their associated names may not definitively represent neighborhoods. This report shows change in population from 2000 to 2010 for each NTA. Compiled by the Population Division – New York City Department of City Planning.

In 2023, Washington, D.C. had the highest population density in the United States, with 11,130.69 people per square mile. As a whole, there were about 94.83 residents per square mile in the U.S., and Alaska was the state with the lowest population density, with 1.29 residents per square mile. The problem of population density Simply put, population density is the population of a country divided by the area of the country. While this can be an interesting measure of how many people live in a country and how large the country is, it does not account for the degree of urbanization, or the share of people who live in urban centers. For example, Russia is the largest country in the world and has a comparatively low population, so its population density is very low. However, much of the country is uninhabited, so cities in Russia are much more densely populated than the rest of the country. Urbanization in the United States While the United States is not very densely populated compared to other countries, its population density has increased significantly over the past few decades. The degree of urbanization has also increased, and well over half of the population lives in urban centers.

The 2022 cartographic boundary KMLs are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

This dataset contains the New York City Population By Community Districts.The community boards of the New York City government are the appointed advisory groups of the community districts of the five boroughs. There are currently 59 community districts, including twelve in Manhattan, twelve in the Bronx, eighteen in Brooklyn, fourteen in Queens, and three in Staten Island.

The Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11 consists of estimates of human population density (number of persons per square kilometer) based on counts consistent with national censuses and population registers. A proportional allocation gridding algorithm, utilizing approximately 13.5 million national and sub-national administrative units, was used to assign population counts to 30 arc-second grid cells. The population density rasters were created by dividing the population count raster for a given target year by the land area raster. The data files were produced as global rasters at 30 arc-second (~1 km at the equator) resolution. To enable faster global processing, and in support of research communities, the 30 arc-second count data were aggregated to 2.5 arc-minute, 15 arc-minute, 30 arc-minute and 1-degree resolutions to produce density rasters at these resolutions.Source: Center for International Earth Science Information Network - CIESIN - Columbia University. 2018. Gridded Population of the World, Version 4 (GPWv4): Population Density, Revision 11. Palisades, New York: NASA Socioeconomic Data and Applications Center (SEDAC). Available at https://doi.org/10.7927/H49C6VHW. (October 2022)Files Download

A dataset listing New York cities by population for 2024.

The codes attached are used to support our study. Each of these codes is exported from ArcMap where they were constructed using ModelBuilder.Our study area focuses on New York City, which provides a data-rich urban environment with extreme variations in local population density and diverse types of input data in which to construct multiple methods. In this study area we can then compare the efficacy of multiple methodologies, which employ a strong binary mask paired with a density variable directly derived from the binary mask. We test the following methodologies:

1. Land areas binary mask

2. Building footprint binary mask

3. Building footprint binary mask and area density variable

4. Building footprints binary mask and volume density variable

5. Residential building footprint binary mask

6. Residential building footprint binary mask and area density variable

7. Residential building footprint binary mask and volume density variable

Includes the error tables, ESRI ArcMap document, accompanying ESRI Geodatabase, ESRI Toolkit and the Python scripts/codes used in the analysis. The error tables are by Census Block for each tested method as well as the calculated grouped error statistics.Our study area focuses on New York City, which provides a data-rich urban environment with extreme variations in local population density and diverse types of input data in which to construct multiple methods. In this study area we can then compare the efficacy of multiple methodologies, which employ a strong binary mask paired with a density variable directly derived from the binary mask. We test the following methodologies:1. Land areas binary mask2. Building footprint binary mask3. Building footprint binary mask and area density variable4. Building footprints binary mask and volume density variable5. Residential building footprint binary mask6. Residential building footprint binary mask and area density variable7. Residential building footprint binary mask and volume density variable

This resource is a member of a series. 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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

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. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some States and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Harvard Forest (HFR) contains human population density measurements in numberPerKilometerSquared units and were aggregated to a yearly timescale.

The 2023 cartographic boundary shapefiles are simplified representations of selected geographic areas from the U.S. Census Bureau's Master Address File / Topologically Integrated Geographic Encoding and Referencing (MAF/TIGER) Database (MTDB). These boundary files are specifically designed for small-scale thematic mapping. When possible, generalization is performed with the intent to maintain the hierarchical relationships among geographies and to maintain the alignment of geographies within a file set for a given year. Geographic areas may not align with the same areas from another year. Some geographies are available as nation-based files while others are available only as state-based files. Census tracts are small, relatively permanent statistical subdivisions of a county or equivalent entity, and were defined by local participants as part of the 2020 Census Participant Statistical Areas Program. The Census Bureau delineated the census tracts in situations where no local participant existed or where all the potential participants declined to participate. The primary purpose of census tracts is to provide a stable set of geographic units for the presentation of census data and comparison back to previous decennial censuses. Census tracts generally have a population size between 1,200 and 8,000 people, with an optimum size of 4,000 people. When first delineated, census tracts were designed to be homogeneous with respect to population characteristics, economic status, and living conditions. The spatial size of census tracts varies widely depending on the density of settlement. Physical changes in street patterns caused by highway construction, new development, and so forth, may require boundary revisions. In addition, census tracts occasionally are split due to population growth, or combined as a result of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division (MCD) or incorporated place boundaries in some states and situations to allow for census tract-to-governmental unit relationships where the governmental boundaries tend to remain unchanged between censuses. State and county boundaries always are census tract boundaries in the standard census geographic hierarchy. In a few rare instances, a census tract may consist of noncontiguous areas. These noncontiguous areas may occur where the census tracts are coextensive with all or parts of legal entities that are themselves noncontiguous. For the 2010 Census and beyond, the census tract code range of 9400 through 9499 was enforced for census tracts that include a majority American Indian population according to Census 2000 data and/or their area was primarily covered by federally recognized American Indian reservations and/or off-reservation trust lands; the code range 9800 through 9899 was enforced for those census tracts that contained little or no population and represented a relatively large special land use area such as a National Park, military installation, or a business/industrial park; and the code range 9900 through 9998 was enforced for those census tracts that contained only water area, no land area.

New York has become one of the worst-affected COVID-19 hotspots and a pandemic epicenter due to the ongoing crisis. This paper identifies the impact of the pandemic and the effectiveness of government policies on human mobility by analyzing multiple datasets available at both macro and micro levels for New York City. Using data sources related to population density, aggregated population mobility, public rail transit use, vehicle use, hotspot and non-hotspot movement patterns, and human activity agglomeration, we analyzed the inter-borough and intra-borough movement for New York City by aggregating the data at the borough level. We also assessed the internodal population movement amongst hotspot and non-hotspot points of interest for the month of March and April 2020. Results indicate a drop of about 80% in people’s mobility in the city, beginning in mid-March. The movement to and from Manhattan showed the most disruption for both public transit and road traffic. The city saw its first case on March 1, 2020, but disruptions in mobility can be seen only after the second week of March when the shelter in place orders was put in effect. Owing to people working from home and adhering to stay-at-home orders, Manhattan saw the largest disruption to both inter- and intra-borough movement. But the risk of spread of infection in Manhattan turned out to be high because of higher hotspot-linked movements. The stay-at-home restrictions also led to an increased population density in Brooklyn and Queens as people were not commuting to Manhattan. Insights obtained from this study would help policymakers better understand human behavior and their response to the news and governmental policies.

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 at the DCP Website: BYTES of the BIG APPLE.

Human population density in 2000, by terrestrial ecoregion.

We summarized human population density by ecoregion using the Gridded Population of the World database and projections for 2015 (CIESIN et al. 2005). The mean for each ecoregion was extracted using a zonal statistics algorithm.

These data were derived by The Nature Conservancy, and were displayed in a map published in The Atlas of Global Conservation (Hoekstra et al., University of California Press, 2010). More information at http://nature.org/atlas.

Data derived from:

Center for International Earth Science Information Network (CIESIN), Columbia University; and Centro Internacional de Agricultura Tropical (CIAT). 2005. Gridded Population of the World Version 3 (GPWv3). Socioeconomic Data and Applications Center (SEDAC), Columbia University Palisades, New York. Available at http://sedac.ciesin.columbia.edu/gpw. Digital media.

United Nations Population Division (UNPD). 2007. Global population, largest urban agglomerations and cities of largest change. World Urbanization Prospects: The 2007 Revision Population Database. Available at http://esa.un.org/unup/index.asp.

For more about The Atlas of Global Conservation check out the web map (which includes links to download spatial data and view metadata) at http://maps.tnc.org/globalmaps.html. You can also read more detail about the Atlas at http://www.nature.org/science-in-action/leading-with-science/conservation-atlas.xml, or buy the book at http://www.ucpress.edu/book.php?isbn=9780520262560

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,

Urbanization can have profound consequences for mammalian biodiversity and is thought to contribute to patterns of species richness and community composition. Large cities can be particularly challenging environments for mammals because these habitats are often impacted by anthropogenic perturbations, including high human population density, fragmented habitats, and extensive human development. In this study, we investigated mammalian species richness, Shannon–Wiener diversity, and evenness in the most densely populated region in the United States: the New York metropolitan area. Specifically, we deployed camera traps from 2015 to 2019 to investigate six drivers of mammalian diversity across 31 greenspaces: (1) human population density, (2) patch size, (3) habitat type, (4) surrounding land cover, (5) geographical barriers to dispersal, and (6) habitat heterogeneity. We found that mammal community composition is largely influenced by a multitude of anthropogenic factors. Specifically, mammal species richness was higher in greenspaces with larger patch sizes and lower in greenspaces surrounded by more development. Moreover, Shannon–Wiener diversity and evenness were higher in urban natural landscapes than human-altered landscapes. In a subset of data that only included carnivores, we found that carnivore Shannon–Wiener diversity was higher in urban natural habitats and in sites with lower human population densities. Finally, we found that geographical barriers to dispersal contributed to both patterns of mammalian diversity and patterns of carnivore diversity: mammal taxa richness, Shannon–Wiener diversity, and evenness were all significantly higher on the continent (Bronx/Westchester) than on Long Island. These results suggest that preserving urban greenspaces is important for maintaining both mammalian and carnivore biodiversity and that management of mammals in cities should concentrate on maintaining large, connected, natural greenspaces.

Classical epidemiological models assume mass action. However, this assumption is violated when interactions are not random. With the recent COVID-19 pandemic, and resulting shelter in place social distancing directives, mass action models must be modified to account for limited social interactions. In this paper we apply a pairwise network model with moment closure to study the early transmission of COVID-19 in New York and San Francisco and to investigate the factors determining the severity and duration of outbreak in these two cities. In particular, we consider the role of population density, transmission rates and social distancing on the disease dynamics and outcomes. Sensitivity analysis shows that there is a strongly negative correlation between the clustering coefficient in the pairwise model and the basic reproduction number and the effective reproduction number. The shelter in place policy makes the clustering coefficient increase thereby reducing the basic reproduction number and the effective reproduction number. By switching population densities in New York and San Francisco we demonstrate how the outbreak would progress if New York had the same density as San Francisco and vice-versa. The results underscore the crucial role that population density has in the epidemic outcomes. We also show that under the assumption of no further changes in policy or transmission dynamics not lifting the shelter in place policy would have little effect on final outbreak size in New York, but would reduce the final size in San Francisco by 97%.