The map shows population density in Tioga County NY using a quantile classification with 5 data breaks each rounded to the nearest 10 people. The population data is census block level data from the 2010 U.S. Census.

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

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

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 map includes the 2010 U.S. Census block boundaries in Tioga County NY. Attached attributes include population density, total population, and voting age population.

Estimates of human population for the year 2015 by 2.5 arc-minute grid cells. 2015 global population density from CIESIN Gridded Population of the World version 4. Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ Accessed 5 April 2017.

The population density maps presented here for the UNDESERT study areas in Burkina Faso, Benin, Niger and Senegal for 1990, 2000 and 2010 were produced by the Columbia University Center for International Earth Science Information Network (CIESIN) in collaboration with the Centro Internacional de Agricultura Tropical (CIAT). CIESIN/CIAT population density grids are available for the entire globe at a 2.5 arc-minutes resolution (http://sedac.ciesin.columbia.edu/data/collection/gpw-v3/sets/browse). The UNDESERT project (EU FP7 243906), financed by the European Commission, Directorate General for Research and Innovation, Environment Program, aims to improve the Understanding and Combating of Desertification to Mitigate its Impact on Ecosystem Services in West Africa. Humans originate and contribute significantly to desertification processes. Based on the CIESIN/CIAT population density grids we want to illustrate how population density changed in the UNDESERT study areas and countries during the last 20 years. Data for 1990 and 2000 were downloaded from the Gridded Population of the World, Version 3 (GPWv3) consisting of estimates of human population by 2.5 arc-minute grid cells and associated data sets dated circa 2000. Data for 2010 were copied from the Gridded Population of the World, Version 3 (GPWv3) consisting in a future estimate of human population by 2.5 arc-minute grid cells. The future estimate population values are extrapolated based on a combination of subnational growth rates from census dates and national growth rates from United Nations statistics.

Source: http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density 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): Population Density Grid. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density. Accessed 28/10/2013 And http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates 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): Population Density Grid, Future Estimates. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://sedac.ciesin.columbia.edu/data/set/gpw-v3-population-density-future-estimates. Accessed 28/10/2013

The 2015 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. The records in this file allow users to map the parts of Urban Areas that overlap a particular county. After each decennial census, the Census Bureau delineates urban areas that represent densely developed territory, encompassing residential, commercial, and other nonresidential urban land uses. In general, this territory consists of areas of high population density and urban land use resulting in a representation of the "urban footprint." There are two types of urban areas: urbanized areas (UAs) that contain 50,000 or more people and urban clusters (UCs) that contain at least 2,500 people, but fewer than 50,000 people (except in the U.S. Virgin Islands and Guam which each contain urban clusters with populations greater than 50,000). Each urban area is identified by a 5-character numeric census code that may contain leading zeroes. The primary legal divisions of most states are termed counties. In Louisiana, these divisions are known as parishes. In Alaska, which has no counties, the equivalent entities are the organized boroughs, city and boroughs, municipalities, and for the unorganized area, census areas. The latter are delineated cooperatively for statistical purposes by the State of Alaska and the Census Bureau. In four states (Maryland, Missouri, Nevada, and Virginia), there are one or more incorporated places that are independent of any county organization and thus constitute primary divisions of their states. These incorporated places are known as independent cities and are treated as equivalent entities for purposes of data presentation. The District of Columbia and Guam have no primary divisions, and each area is considered an equivalent entity for purposes of data presentation. The Census Bureau treats the following entities as equivalents of counties for purposes of data presentation: Municipios in Puerto Rico, Districts and Islands in American Samoa, Municipalities in the Commonwealth of the Northern Mariana Islands, and Islands in the U.S. Virgin Islands. The entire area of the United States, Puerto Rico, and the Island Areas is covered by counties or equivalent entities. The boundaries for counties and equivalent entities are as of January 1, 2010.

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.

In 2008, the Quality Deer Management Association (QDMA) developed a map of white-tailed deer density with information obtained from state wildlife agencies. The map contains information from 2001 to 2005, with noticeable changes since the development of the first deer density map made by QDMA in 2001. The University of Minnesota, Forest Ecosystem Health Lab and the US Department of Agriculture, Forest Service-Northern Research Station have digitized the deer density map to provide information on the status and trends of forest health across the eastern United States. The QDMA spatial map depicting deer density (deer per square mile) was digitized across the eastern United States. Estimates of deer density were: White = rare, absent, or urban area with unknown population, Green = less than 15 deer per square mile, Yellow = 15 to 30 deer per square mile, Orange = 30 to 40 deer per square mile, or Red = greater than 45 deer per square mile. These categories represent coarse deer density levels as identified in the QDMA report in 2009 and should not be used to represent current or future deer densities across the study region. Sponsorship: Quality Deer Management Association; US Department of Agriculture, Forest Service-Northern Research Station; Minnesota Agricultural Experiment Station. Resources in this dataset:Resource Title: Link to DRUM catalog record. File Name: Web Page, url: https://conservancy.umn.edu/handle/11299/178246

GPWv4 is a gridded data product that depicts global population data from the 2010 round of Population and Housing Censuses. The Population Density, 2015 layer represents persons per square kilometer for year 2015. Data SummaryGPWv4 is constructed from national or subnational input areal units of varying resolutions. The native grid cell size is 30 arc-seconds, or ~1 km at the equator. Separate grids are available for population count, population density, estimated land area, and data quality indicators; which include the water mask represented in this service. Population estimates are derived by extrapolating the raw census counts to estimates for the 2010 target year. The development of GPWv4 builds upon previous versions of the data set (Tobler et al., 1997; Deichmann et al., 2001; Balk et al., 2006).The full GPWv4 data collection will consist of population estimates for the years 2000, 2005, 2010, 2015, and 2020, and will include grids for estimates of total population, age, sex, and urban/rural status. However, this release consists only of total population estimates for the year 2015. This data is being released now to allow users access to the population grids.Recommended CitationCenter for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ. Accessed DAY MONTH YEAR

This map was designed as an overview map of the Lake Tanganika Basin. Many of the data are of coarse resolution and should be verified before used in an research or planning efforts.Sources by Layer GroupsAdmin: Populations retrieved from worldpopulationreview.com.Town and village names and locations retrieved the NGA GEOnet Names Server (GNS) http://geonames.nga.mil/gns/html/. These data may be incomplete or show incorrect spellings. Refugee camp names and locations provided by Frankfurt Zoological Society. TNC Tuungane Project Villages GPS point locations collected by TNC staff. For more information about the Tuungane Project please visit: https://www.nature.org/ourinitiatives/regions/africa/wherewework/tuungane-project.xml.Interntaional Boundaries retrieved from GADM database (www.gadm.org).Admin Level 1 & 2 subnational boundaries below the country level. This varies by country. Infrastructure:Liemba stops: Derived from https://en.wikivoyage.org/wiki/MV_Liemba\Airport names: Derived from NGA GEOnet Names Server (GNS) http://geonames.nga.mil/gns/html/Roads: The Global Roads Open Access Data Set, Version 1 (gROADSv1) was developed under the auspices of the CODATA Global Roads Data Development Task Group. The data set combines the best available roads data by country into a global roads coverage, using the UN Spatial Data Infrastructure Transport (UNSDI-T) version 2 as a common data model. All country road networks have been joined topologically at the borders, and many countries have been edited for internal topology. Source data for each country are provided in the documentation, and users are encouraged to refer to the readme file for use constraints that apply to a small number of countries. Because the data are compiled from multiple sources, the date range for road network representations ranges from the 1980s to 2010 depending on the country (most countries have no confirmed date), and spatial accuracy varies. The baseline global data set was compiled by the Information Technology Outreach Services (ITOS) of the University of Georgia. Updated data for 27 countries and 6 smaller geographic entities were assembled by Columbia University's Center for International Earth Science Information Network (CIESIN), with a focus largely on developing countries with the poorest data coverage.Credits: http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1Dams: Lehner, B., C. Reidy Liermann, C. Revenga, C. Vorosmarty, B. Fekete, P. Crouzet, P. Doll, M. Endejan, K. Frenken, J. Magome, C. Nilsson, J.C. Robertson, R. Rodel, N. Sindorf, and D. Wisser. 2011. Global Reservoir and Dam Database, Version 1 (GRanDv1): Reservoirs, Revision 01. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC).http://dx.doi.org/10.7927/H4HH6H08. Accessed 28 August 2016.Credits: http://sedac.ciesin.columbia.edu/pfs/grand.htmlPower Plants: Data for power plants with total installed generating capacity > 10 mw from the Platts World Electric Power Plants Database (WEPP 2006). Plants were georeferenced using location information from the WEPP, auxiliary GIS datasets, World Bank project documents and the internet. Locations are approximate, precision varies greatly by point, based on the source of coordinate information.The following attributes are included:PLANT: power plant name,STATUS: status (OPR, CON, PLN, OTHER, UNK),SUM_MW: total installed generating capacity,LATITUDE: approximate location, latitude,LONGITUDE: approximate location, longitude,GEN_TYPE: type of electricity generation (HYDRO, THERMAL, OTHER)Credits: http://www.infrastructureafrica.org/Transmission Lines & Railroads: Africa Infrastructure Knowledge Program http://www.infrastructureafrica.org/.Socioeconomic: FEWS Livelihood Zones, Lean Times Livelihood Hazards: These were derived form country level livelihood zones information at the Famine Early Warning System Network. : Data for individual countries with detailed descriptions of livelihood zones, inclkuding crop calendars and hazards, can be found at http://www.fews.net/.Distance to Markets:HarvestChoice, 2015. "Travel time to nearest town over 20K (mean, hours, 2000)." International Food Policy Research Institute, Washington, DC., and University of Minnesota, St. Paul, MN. Available online at http://harvestchoice.org/data/tt_20k.Lean Times: Lean Times refer to times of the year when food shortages may occur. These were derived form country level livelihood zones information at the Famine Early Warning System Network. NOTE: None of the regions within Lake Tanganyika indicated July as a time of food shortages; therefore, July is excluded as a seperate layer.http://www.fews.net/Population Density: Center for International Earth Science Information Network - CIESIN - Columbia University. 2016. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4NP22DQ.http://sedac.ciesin.columbia.edu/data/set/gpw-v4-population-density2011 Fishiereis Frame Survey sites: Indicates at the regional or district level, the percentage of fish landing sites with described properties. Citation: LTA Secretariat, 2012.Lake Tanganyika Regional Fisheries Frame Survey 2011, Bujumbura, Burundi, 30 pFamily Planning, HIV Statistics, Women Issues, Childrens Health, Water and Sanitation,Houshold Fuel Source: Socioeconomic data from USAID-funded The Demographic and Health Surveys (DHS) Program: Produced by ICF International. Spatial Data Repository, The Demographic and Health Surveys Program. ICF International. Available from spatialdata.dhsprogram.com [Accessed 18 August 2016]. Fishiereis Frame Survey :All Datasets Indicates at the regional or district level, the percentage of fish landing sites with described properties. Citation: LTA Secretariat, 2012.Lake Tanganyika Regional Fisheries Frame Survey 2011, Bujumbura, Burundi, 30 pFishieries Frame SurveyConservation:Human Disturbance Index:Simple Human Disturbance Index to assess the relative levels of human disturbance along the lakeshore of Lake Tanganyika. Evidence from Britton et al.(2017) indicates that human activity in the nearshore environment will significantly influence fish populations along the lakeshore. For detailed methods see https://tnc.box.com/s/k65bdhh72gjjv7f3v0gwvn2856onh9h9.Credits: Dr. Tracy Baker, The Nature Conservancy Africa Program: tracy.baker@tnc.orgHydroBASINS Level 08 Average HDI:Average level of human disturbance at the HydroBASINS Level 8. This level correxpond to the unit of analysis for IUCN Red List data. Credits: Dr. Tracy Baker, The Nature Conservancy Africa Program: tracy.baker@tnc.orgProtected Areas:IUCN and UNEP-WCMC (year), The World Database on Protected Areas (WDPA) [On-line], [January, 2017], Cambridge, UK: UNEP-WCMC. Available at: www.protectedplanet.net.Priority Aquatic Sites: Aquaruim trade watch fish: Estimated ranges of cichlids considered to be endangered or critically endagered, Credit Ad KoningsProposed Lake Key Biodiversity Area & Key Biodiversity Area Trigger Species Ranges: The Nature Conservancy staff worked with IUCN and other experts to compile and analyze available spatial data for Lake Tanganyika, to identify candidate areas within the lake that have exceptional potential to meet the revised KBAcriteria and thresholds based on the new standard, as well as having practical potential for application of local and regional management and conservation strategies. This layer represents a draft version of this work. The work still must undergo a national level stakeholder consultation. Credits: Dr. Kristen Blann, The Nature Conservancy - Freshwater Ecologist, Minnesota Priority Fisheries Conservation Sites - TAFIRI: TAFIRI Conservation Priorities derived from 2013 presentation by Dr. Ismael Kimirei, TAFIRI Director, Kigoma. Priorities were ranked by a quatitative assessment at each site. Priority Fisheries Conservation Sites - Zambia Fisheries: Zambia Fisheries priority sites acquired via personal communication with Mr. Taylor Banda, Senior Fisheries Officer at Mpulungu. The sites represent the current planning scenario alon the Zambia side of the lake. Lake & Freshwater Species & Basin Freshwater Species: Known and accessible information on freshwater species within Lake Tanganyika. Data may not include all known species for a taxon. Spatial unit used to calcuate total freshwater species richness is the HydroBASINS Level 11 dataset boundaries.Species level data were derived from the IUCN Red List of Threatened Species (http://www.iucnredlist.org), the Lake Tanganyika Biodiversity Program (http://www.ltbp.org/), and Ad Konings. Zambia Terrestrial Species Distributions: Mean probability of species presence, conditioned on environmental variables.See: https://tnc.box.com/s/hvqdyawz26i75lm5lnlj7dh0uut65rk7Credits: Dr. Anne Trainor, The Nature Conservancy Africa Program - Smart Growth Director anne.trainor@tnc.orgMammals & Amphibians : Modeled number of mammal species across the Lake Tangnayika Basin. This is a surface layer with no individual species level information given. International Union for Conservation of Nature - IUCN, and Center for International Earth Science Information Network - CIESIN - Columbia University. 2015. Gridded Species Distribution: Global Mammal Richness Grids, 2015 Release. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). http://dx.doi.org/10.7927/H4N014G5.Credits: http://sedac.ciesin.columbia.edu/data/set/species-global-mammal-richness-2015Terrestrial Ecoregions & Greater Mahale Ecosystem: Olson, D. M. and E. Dinerstein. 2002. The Global 200: Priority ecoregions for global conservation. (PDF file) Annals of the Missouri Botanical Garden 89:125-126. -The Nature Conservancy, USDA Forest Service and U.S. Geological Survey, based on Bailey, Robert G. 1995. Description of the ecoregions of the United States (2nd ed.). Misc. Pub. No. 1391, Map scale

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,