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.

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.

A dataset listing New York cities by population for 2024.

The 2022 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.

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.

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.

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 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 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.

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) System (MTS). The MTS 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 because of substantial population decline. Census tract boundaries generally follow visible and identifiable features. They may follow legal boundaries such as minor civil division 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 Bureau 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.

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

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 percent urban population measurements in percent units and were aggregated to a yearly timescale.

Values of parameters.

SeeClickFix data (issue IDs & statistics) along with socioeconomic, demographic and walkability information: - Neighborhood names (nei_final_simple) - Number of unique users - Number of issue reports - Number of thanks and votes - Number of anonymous issue reports - Number of non-anonymous reports and reporters - Response times, in seconds - Median household incomes - Household types count - Population and population density - Race and ethnic population - Age range population - Marital statuses count - Employment statuses count - Food stamps count - Educational attainments count - Walk, transit, bike scores SeeClickFix data was collected in April 2018 and includes reported civil issues between January 5, 2010 and February 10, 2018. Socioeconomic and demographic information was collected from Statistical Atlas (https://statisticalatlas.com/place/New-York/Albany/Overview), which obtains its data from the US Census Bureau, and Walk, Bike and Transit Scores were collected from the WalkScore website (https://www.walkscore.com/).

The Safe Streets for Seniors program is an initiative aimed at increasing safety for older New Yorkers. Based on factors such as senior population density, injury crashes, and senior trip generators, DOT has selected and studied Senior Pedestrian Focus Areas. Within these areas, DOT evaluates potential safety improvements and also conducts educational outreach to senior centers.

For a complete list of Vision Zero maps, please follow this link

COVID-19 Data for Analysis and Machine Learning

There are lots of datasets online, more growing every day, to help us all get a handle on this pandemic. Here are just a few links to data we've found that students in ECE 657A, and anyone else who finds their way here, can play with and practice their machine learning skills. The main dataset is the COVID-19 dataset from John Hopkins university. This data is perfect for time series analysis and Recurrent Neural Networks, the final topic in the course. This dataset will be left public so anyone can see it but to join you must request the link from Prof. Crowley or be in the ECE 657A W20 course at the University of Waterloo.

For ECE 657A W20 Students

Your bonus grade for assignment 4 comes from creating a kernel from this dataset and writing up some useful analysis and publishing that notebook. You can do any kind of analysis you like but some good places to start are - Analysis: feature extraction and analysis of the data to look for patterns that aren't evident from the original features (this is hard for the simple spread/infection/death data since there aren't that many features) - Other Data: utilize any other datasets in your kernels by loading data about the countries themselves (population, density, wealthy etc.) or their responses to the situation. Tip: If you open a New Notebook related to this dataset you can easily add new data available on Kaggle and link that to you analysis. - HOW'S MY FLATTENING COVID19 DATASET - This dataset has a lot more files and includes a lot of what I was talking about, so if you produce good kernels there you can also count them for your asg4 grade. https://www.kaggle.com/howsmyflattening/covid19-challenges - Predict: make predictions about confirmed cases, deaths, recoveries or other metrics for the future. You can test you models by training on the past and predicting on the following days, then post a prediction for tomorrow or the next few days given ALL the data up to this point. Hopefully the datasets we've linked here will updated automatically so your kernels would update as well. - Create Tasks: you can make your own "Tasks" as part of this kaggle and propose your own solution to it. Then others can try solving it as well. - Groups: students can do this assignment either in the same groups they had for assignment 3 or individually.

Suggest other datasets

We're happy to add other relevant data to this Kaggle, in particular it would be great to integrate live data on the following: - Progression of each country/region/city in "days since X Level" such as Days since 100 confirmed cases, see the link for a great example such a dataset being plotted. I haven't see a live link to a csv of that data, but we could generate. - Mitigation Policies enacted by local governments in each city/region/country. These are dates when that region first enacted Level 1, 2, 3, 4 containment, or started encouraging social distancing or the date when they closed different levels of schools, pubs, restaurants etc. - The hidden positives: this would be a dataset, or method for estimating, as described by Emtiyaz Khan in this twitter thread. The idea is, how many unreported or unconfirmed cases are there in any region, and can we build an estimate of that number using other regions with widespread testing as a baseline and the death rates which are like an observation of a process with a hidden variable or true infection rate. - Paper discussing one way to compute this : https://cmmid.github.io/topics/covid19/severity/global_cfr_estimates.html

Wie zahlreiche andere Küstenstädte ist New York einem starken Risiko durch Extremereignisse ausgesetzt, die sich durch den Klimawandel erhöht haben. Dazu gehören vor allem Sturmfluten, wie sie das moderne New York durch den Hurrikan Sandy 2012 erlebt hat, der die Gefährdung der Stadt zu einem politischen Thema auch für die Zukunft gemacht hat. Die Hochwassergefahr vom Meer her wird in New York auch durch einen - im globalen Vergleich ungewöhnlich starken - Meeresspiegelanstieg forciert. Zusätzlich leidet die Stadt durch die dichte Bebauung unter Hitzewellen, deren Häufigkeit und Intensität infolge der globalen Erwär- mung in den nächsten Jahrzehnten zunehmen werden. Angesichts dieser Lage hat die Verwaltung der Stadt ein umfangreiches Forschungs- und Planungsprogramm aufgelegt, das die Folgen des Klimawandels für die Bevölkerung feststellen und Anpassungsmaßnahmen entwickeln soll. The Coastal Cities - The example New York: Like many other coastal cities, New York is exposed to a high risk of extreme weather, which has increased as a result of climate change. This includes in particular storm surges, as the modern-day New York has experienced during Hurricane Sandy in 2012, which has made the endangerment of the city a political issue for the future as well. The threat of flooding from the sea in New York is further promoted by an unusually high sea level rise in global comparison. In addition, as a result of the high building density the city suffers from heat waves, whose frequency and intensity will increase as a result of global warming in the coming decades. In view of this situation, the administration of the city of New York has launched a comprehensive research and planning program to determine the impact of climate change on the population and to develop adaptation measures.

Urban rats are notorious invasive pests that thrive in cities by exploiting the resources accompanying high human population density. Identifying long-term trends in rat numbers and how they are shaped by environmental changes is critical for understanding their ecology, and projecting future vulnerabilities and mitigation needs. Here, we use trend analyses of public complaint and inspection data in 16 cities around the world to estimate trends in commensal rat populations. Eleven of 16 cities (69%) had significant increasing trends in rat numbers, including Washington D.C., New York, and Amsterdam. Just three cities experienced declines. Cities experiencing greater temperature increases over time saw larger increases in rat numbers. Cities with more dense human populations and more urbanization also saw larger increases in rats. Warming temperatures and more people living in cities may be expanding the seasonal activity periods and food resource availability for urban rats. Cities will..., , , # Increasing rat numbers in cities are linked to climate warming, urbanization and human population

https://doi.org/10.5061/dryad.3xsj3txrq

Description of the data and file structure

The dataset consists of an Excel file (with two sheets such as data and metadata).

Files and variables

File: Richardson_et_al_ScienceAdv_wild_rat_trend_analysis_data_19Apr24.xlsx

Description:Â Please see the "Metadata" sheet tab within this data file for more information on each variable, abbreviations, etc.Â

Code/Software

This is a basic spreadsheet file, viewable in Excel or Google Sheets. All subsequent analyses with these data were done in R.

Urbanization exposes species to novel environments and selection pressures that may change morphological traits within a population. We investigated how the shape and size of crania and mandibles changed over time within a population of brown rats (Rattus norvegicus) living in Manhattan, New York, USA, a highly urbanized environment. We measured 3D landmarks on the cranium and mandible of 62 adult individuals sampled in the 1890s and 2010s. Static allometry explained approximately 22% of shape variation in crania and mandible datasets, while time accounted for approximately 14% of variation. We did not observe significant changes in skull size through time or between the sexes. Estimating the P-matrix revealed that directional selection explained temporal change of the crania but not the mandible. Specifically, rats from the 2010s had longer noses and shorter upper molar tooth rows, traits identified as adaptive to colder environments and higher quality or softer diets, respectively. Our results highlight the continual evolution to selection pressures. We acknowledge that urban selection pressures impacting cranial shape likely began in Europe prior to the introduction of rats to Manhattan. Yet, our study period spanned changes in intensity of artificial lighting, human population density, and human diet, thereby altering various aspects of rat ecology and hence pressures on the skull.

Methods 3D landmark data was taken with a microscribe on brown rat crania and mandibles. Dorsal and ventral landmarks were merged into a single shape using MorphoJ. Data were analyzed in R with the geomorph package.

SFS of ddRAD-Seq data for 248 rats from NYC included for estimation of Ne.

A comparison of three city types.