Summary data of fixed broadband coverage by geographic area. License and Attribution: Broadband data from FCC Form 477, and data from the U.S. Census Bureau that are presented on this site are offered free and not subject to copyright restriction. Data and content created by government employees within the scope of their employment are not subject to domestic copyright protection under 17 U.S.C. § 105. See, e.g., U.S. Government Works. While not required, when using content, data, documentation, code and related materials from fcc.gov or broadbandmap.fcc.gov in your own work, we ask that proper credit be given. Examples include: • Source data: FCC Form 477 • Map layer based on FCC Form 477 • Code data based on broadbandmap.fcc.gov The geography look ups are created from the US census shapefiles, which are in Global Coordinate System North American Datum of 1983 (GCS NAD83). The coordinates do not get reprojected during processing. The "centroid_lng", "centroid_lat" columns in the lookup table are the exact values from the US census shapefile (INTPTLON, INTPTLAT). The "bbox_arr" column is calculated from the bounding box/extent of the original geometry in the shapefile; no reprojection or transformations are done to the geometry.

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address. To learn more about the Low-Income Housing Tax Credit Program visit: https://www.hud.gov/program_offices/public_indian_housing/programs/ph/, for questions about the spatial attribution of this dataset, please reach out to us at GISHelpdesk@hud.gov. Data Dictionary: DD_Low Income Tax Credit Program

Analysis of ‘💳 CFPB Credit Card History’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/cfpb-credit-card-historye on 13 February 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Lending levels
Monitoring developments in overall activity helps us identify new developments in the markets we regulate. These graphs show the number and aggregate dollar volume of new credit cards opened each month. Aggregated monthly originations are displayed along with a seasonally-adjusted series, which adjust for expected seasonal variation in lending activity.

Year-over-year changes
These graphs show the percentage change in the number of new credit cards originated in the month, compared to lending activity from one year ago. Positive changes indicate that lending activity is higher than it was last year and negative values indicate that lending has declined.

Geographic changes
This map shows the percentage change in the volume of new credit cards originated in each state, compared to lending activity from one year ago. Positive changes mean that the volume of credit cards originated in the state during the month are higher than they were one year ago and negative values indicate that the volume of credit cards has declined.

Source: https://www.consumerfinance.gov/data-research/consumer-credit-trends/credit-cards/origination-activity/#anchor_geographic-changes

This dataset was created by Adam Helsinger and contains around 300 samples along with Group, Month, technical information and other features such as: - Group - Month - and more.

How to use this dataset

• Analyze Group in relation to Month
• Study the influence of Group on Month
• More datasets

Acknowledgements

If you use this dataset in your research, please credit Adam Helsinger

Start A New Notebook!

--- Original source retains full ownership of the source dataset ---

This dataset is built from the Overture 2023-07-26-alpha.0 version of open map data by the Overture Maps Foundation. This dataset compiles points of interests (POIs) for individual cities for convenient and lightweight spatial sampling.Credits: Overture Maps FoundationLicense: https://cdla.dev/permissive-2-0/

UNESCO World Heritage Sites Dataset

UNESCO World Heritage Sites Dataset

By Throwback Thursday [source]

About this dataset

How to use the dataset

Here are some tips on how to make the most out of this dataset:

• Data Exploration:

  • Begin by understanding the structure and contents of the dataset. Evaluate the number of rows (sites) and columns (attributes) available.
  • Check for missing values or inconsistencies in data entry that may impact your analysis.
  • Assess column descriptions to understand what information is included in each attribute.

• Geographical Analysis:

  • Leverage geographical features such as latitude and longitude coordinates provided in this dataset.
  • Plot these sites on a map using any mapping software or library like Google Maps or Folium for Python. Visualizing their distribution can provide insights into patterns based on location, climate, or cultural factors.

• Analyzing Attributes:

  • Familiarize yourself with different attributes available for analysis. Possible attributes include Name, Description, Category, Region, Country, etc.
  • Understand each attribute's format and content type (categorical, numerical) for better utilization during data analysis.

• Exploring Categories & Regions:

  • Look at unique categories mentioned in the Category column (e.g., Cultural Site, Natural Site) to explore specific interests. This could help identify clusters within particular heritage types across countries/regions worldwide.
  • Analyze regions with high concentrations of heritage sites using data visualizations like bar plots or word clouds based on frequency counts.

• Identify Trends & Patterns:

  • Discover recurring themes across various sites by analyzing descriptive text attributes such as names and descriptions.
  • Identify patterns and correlations between attributes by performing statistical analysis or utilizing machine learning techniques.

• Comparison:

  • Compare different attributes to gain a deeper understanding of the sites.
  • For example, analyze the number of heritage sites per country/region or compare the distribution between cultural and natural heritage sites.

• Additional Data Sources:

  • Use this dataset as a foundation to combine it with other datasets for in-depth analysis. There are several sources available that provide additional data on UNESCO World Heritage Sites, such as travel blogs, official tourism websites, or academic research databases.

Remember to cite this dataset appropriately if you use it in

Research Ideas

• Travel Planning: This dataset can be used to identify and plan visits to UNESCO World Heritage sites around the world. It provides information about the location, category, and date of inscription for each site, allowing users to prioritize their travel destinations based on personal interests or preferences.
• Cultural Preservation: Researchers or organizations interested in cultural preservation can use this dataset to analyze trends in UNESCO World Heritage site listings over time. By studying factors such as geographical distribution, types of sites listed, and inscription dates, they can gain insights into patterns of cultural heritage recognition and protection.
• Statistical Analysis: The dataset can be used for statistical analysis to explore various aspects related to UNESCO World Heritage sites. For example, it could be used to examine the correlation between a country's economic indicators (such as GDP per capita) and the number or type of World Heritage sites it possesses. This analysis could provide insights into the relationship between economic development and cultural preservation efforts at a global scale

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Throwback Thursday.

Analysis of ‘Geography Lookup Table’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/801e1bd7-e19b-4cb9-8959-b34b8fc61ab7 on 12 February 2022.

--- Dataset description provided by original source is as follows ---

Summary data of fixed broadband coverage by geographic area. License and Attribution: Broadband data from FCC Form 477, and data from the U.S. Census Bureau that are presented on this site are offered free and not subject to copyright restriction. Data and content created by government employees within the scope of their employment are not subject to domestic copyright protection under 17 U.S.C. § 105. See, e.g., U.S. Government Works.

While not required, when using content, data, documentation, code and related materials from fcc.gov or broadbandmap.fcc.gov in your own work, we ask that proper credit be given. Examples include: • Source data: FCC Form 477 • Map layer based on FCC Form 477 • Code data based on broadbandmap.fcc.gov

The geography look ups are created from the US census shapefiles, which are in Global Coordinate System North American Datum of 1983 (GCS NAD83). The coordinates do not get reprojected during processing. The "centroid_lng", "centroid_lat" columns in the lookup table are the exact values from the US census shapefile (INTPTLON, INTPTLAT). The "bbox_arr" column is calculated from the bounding box/extent of the original geometry in the shapefile; no reprojection or transformations are done to the geometry.

--- Original source retains full ownership of the source dataset ---

Created by the Tax Reform Act of 1986, the Low-Income Housing Tax Credit program (LIHTC) gives State and local LIHTC-allocating agencies the equivalent of nearly $8 billion in annual budget authority to issue tax credits for the acquisition, rehabilitation, or new construction of rental housing targeted to lower-income households. Although some data about the program have been made available by various sources, HUD's database is the only complete national source of information on the size, unit mix, and location of individual projects. With the continued support of the national LIHTC database, HUD hopes to enable researchers to learn more about the effects of the tax credit program.HUD has no administrative authority over the LIHTC program. IRS has authority at the federal level and it is structured so that the states truly administer the program. The LIHTC property locations depicted in this map service represent the general location of the property. The locations of individual buildings associated with each property are not depicted here. The location of the property is derived from the address of the building with the most units. Location data for HUD-related properties and facilities are derived from HUD's enterprise geocoding service. While not all addresses are able to be geocoded and mapped to 100% accuracy, we are continuously working to improve address data quality and enhance coverage. Please consider this issue when using any datasets provided by HUD. When using this data, take note of the field titled “LVL2KX” which indicates the overall accuracy of the geocoded address using the following return codes:‘R’ - Interpolated rooftop (high degree of accuracy, symbolized as green)‘4’ - ZIP+4 centroid (high degree of accuracy, symbolized as green)‘B’ - Block group centroid (medium degree of accuracy, symbolized as yellow)‘T’ - Census tract centroid (low degree of accuracy, symbolized as red)‘2’ - ZIP+2 centroid (low degree of accuracy, symbolized as red)‘Z’ - ZIP5 centroid (low degree of accuracy, symbolized as red)‘5’ - ZIP5 centroid (same as above, low degree of accuracy, symbolized as red)Null - Could not be geocoded (does not appear on the map)For the purposes of displaying the location of an address on a map only use addresses and their associated lat/long coordinates where the LVL2KX field is coded ‘R’ or ‘4’. These codes ensure that the address is displayed on the correct street segment and in the correct census block.The remaining LVL2KX codes provide a cascading indication of the most granular level geography for which an address can be confirmed. For example, if an address cannot be accurately interpolated to a rooftop (‘R’), or ZIP+4 centroid (‘4’), then the address will be mapped to the centroid of the next nearest confirmed geography: block group, tract, and so on. When performing any point-in polygon analysis it is important to note that points mapped to the centroids of larger geographies will be less likely to map accurately to the smaller geographies of the same area. For instance, a point coded as ‘5’ in the correct ZIP Code will be less likely to map to the correct block group or census tract for that address.

land and oceanic climate variables. The data cover the Earth on a 31km grid and resolve the atmosphere using 137 levels from the surface up to a height of 80km. ERA5 includes information about uncertainties for all variables at reduced spatial and temporal resolutions.

Analysis of ‘Climate Change Dataset’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/yamqwe/climate-change-datae on 28 January 2022.

--- Dataset description provided by original source is as follows ---

About this dataset

Data from World Development Indicators and Climate Change Knowledge Portal on climate systems, exposure to climate impacts, resilience, greenhouse gas emissions, and energy use.

In addition to the data available here and through the Climate Data API, the Climate Change Knowledge Portal has a web interface to a collection of water indicators that may be used to assess the impact of climate change across over 8,000 water basins worldwide. You may use the web interface to download the data for any of these basins.

Here is how to navigate to the water data:

• Go to the Climate Change Knowledge Portal home page (http://climateknowledgeportal.worldbank.org/)
• Click any region on the map Click a country In the navigation menu
• Click "Impacts" and then "Water" Click the map to select a specific water basin
• Click "Click here to get access to data and indicators" Please be sure to observe the disclaimers on the website regarding uncertainties and use of the water data.

Attribution: Climate Change Data, World Bank Group.

World Bank Data Catalog Terms of Use

Source: http://data.worldbank.org/data-catalog/climate-change

This dataset was created by World Bank and contains around 10000 samples along with 2009, 1993, technical information and other features such as: - 1994 - Series Code - and more.

How to use this dataset

• Analyze 1995 in relation to Scale
• Study the influence of 1998 on Country Code
• More datasets

Acknowledgements

If you use this dataset in your research, please credit World Bank

--- Original source retains full ownership of the source dataset ---

The following is data collected by Africa Data Hub covering vaccination data. It covers: Vaccine types | Africa/global vaccination rollouts | SARS-CoV-2 variants in Africa | Covax deliveries to Africa | Our World in Data - vaccinations | Media Hack Collective - vaccinations | As well as Africa Maps as additional resources. Example uses of this data: Africa vaccine tracker | Vaccine types and vaccination use | South African vaccinations widget | South African vaccination calculator | Daily confirmed cases in Africa You may use this data in your own projects on the condition that you credit the appropriate sources. All data must be credited to Media Hack Collective and Africa Data Hub, with the exception of the Our World in Data - Vaccinations data which must be credited to Our World in Data. Documentation for the datasets, including variables and methodology can be found here.

The "Major Cities" layer is derived from the "World Cities" dataset provided by ArcGIS Data and Maps group as part of the global data layers made available for public use. "Major cities" layer specifically contains National and Provincial capitals that have the highest population within their respective country. Cities were filtered based on the STATUS (“National capital”, “National and provincial capital”, “Provincial capital”, “National capital and provincial capital enclave”, and “Other”). Majority of these cities within larger countries have been filtered at the highest levels of POP_CLASS (“5,000,000 and greater” and “1,000,000 to 4,999,999”). However, China for example, was filtered with cities over 11 million people due to many highly populated cities. Population approximations are sourced from US Census and UN Data. Credits: ESRI, CIA World Factbook, GMI, NIMA, UN Data, UN Habitat, US Census Bureau Disclaimer: The designations employed and the presentation of material at this site do not imply the expression of any opinion whatsoever on the part of the Secretariat of the United Nations concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries.

ARCOS Database provided by the Washington Post. We use the data for the empirical analysis in our article`` Retail Pharmacies and Drug Diversion during the Opioid Epidemic’’.

2006--2012 data from the Automation of Reports and Consolidated Orders System (ARCOS), maintained by the Diversion Control Division of the US Drug Enforcement Administration (DEA).

The data can be downloaded from https://www.washingtonpost.com/national/2019/07/18/how- download-use-dea-pain-pills-database/ in raw format and until 2021 through an R package (API) on https://github.com/wpinvestigative/arcos. Please follow the requirement of the Washington Post: ‘If you publish an online story, graphic, map or other piece of journalism based on this data set, please credit The Washington Post, link to the original source, and send us an email when you’ve hit publish. We want to learn what you discover and will attempt to link to your work as part of cataloguing the impact of this project.” (The Washington Post, 2019)

The Washington Post. How to download and use the DEA pain pills database, 2019. https://www.washingtonpost.com/national/2019/07/18/how-download-use-dea-pain-pills-database/

The following is data collected by Africa Data Hub covering vaccination data. It covers: Vaccine types | Africa/global vaccination rollouts | SARS-CoV-2 variants in Africa | Covax deliveries to Africa | Our World in Data - vaccinations | Media Hack Collective - vaccinations |

As well as Africa Maps as additional resources.

Example uses of this data: Africa vaccine tracker | Vaccine types and vaccination use | South African vaccinations widget | South African vaccination calculator | Daily confirmed cases in Africa

You may use this data in your own projects on the condition that you credit the appropriate sources. All data must be credited to Media Hack Collective and Africa Data Hub, with the exception of the Our World in Data - Vaccinations data which must be credited to Our World in Data.

Documentation for the datasets, including variables and methodology can be found here.

Reason for Selection This indicator captures the recreational value and opportunities to connect with nature provided by greenways and trails. Greenways and trails provide many well-established social and economic benefits ranging from improving human health, reducing traffic congestion and air and noise pollution, increasing property values, and generating new jobs and business revenue (ITRE 2018). The locations of greenways and trails are regularly updated through the open-source database OpenStreetMap. Input Data

Southeast Blueprint 2023 subregions: Caribbean
Southeast Blueprint 2023 extent
2012 NOAA Coastal Change Analysis Program (C-CAP) land cover files for the U.S. Virgin Islands (St. Thomas, St. John, and St. Croix are provided as separate rasters), accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
2010 NOAA C-CAP land cover files for Puerto Rico, accessed 11-10-2022; learn more about C-CAP high resolution land cover and change products
OpenStreetMap data “lines” layer, accessed 2-26-2023 

A line from this dataset is considered a potential greenway/trail if the “highway” tag attribute is either bridleway, cycleway, footway, or path. In OpenStreetMap, a highway refers to “any road, route, way, or thoroughfare on land which connects one location to another and has been paved or otherwise improved to allow travel by some conveyance, including motorized vehicles, cyclists, pedestrians, horse riders, and others (but not trains)”. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF). Additional credit to OSM contributors. Read more on the OSM copyright page. Mapping Steps The greenways and trails indicator score reflects both the natural condition and connected length of the greenway/trail. Natural condition Natural condition is based on the amount of impervious surface surrounding the greenway/trail. Since perceptions of a greenway’s “naturalness” are influenced both by the immediate surroundings adjacent to the path, and the greater viewshed, natural condition is calculated by averaging two measurements: local impervious and nearby impervious.

Local impervious is defined as the percent impervious surface of the 30 m pixel that intersects the trail. Nearby impervious is defined as the average impervious surface within a 300 m radius circle surrounding the path (note: along a 300 m stretch of trail, we only count the impervious surface within a 45 m buffer on either side of the trail, since pixels nearer the trail have a bigger impact on the greenway/trail experience). The natural classes are defined as follows: 3 = Mostly natural: average of local and nearby impervious is ≤1% 2 = Partly natural: average of local and nearby impervious is >1 and <10% 1 = Developed: average of local and nearby impervious is ≥10%

To create a percent impervious layer, start by converting the C-CAP land cover rasters for Puerto Rico (2 m resolution) and the U.S. Virgin Islands (separate downloads for St. Thomas, St. John, and St. Croix with 2.4 m resolution) from .img format to .tif using the Copy Raster function.
For each individual C-CAP layer, use the ArcPy Conditional function to make a binary raster assigning the impervious class a value of 100 (representing fully impervious) and all other classes a value of 0 (representing fully permeable). This mimics the data format of the 2019 National Land Cover Database (NLCD) used in the continental Southeast permeable surface indicator, which provides a continuous impervious surface value ranging from 0 to 100. Use focal statistics to calculate the percent of cells in a 30 m square that are identified as impervious in the C-CAP data, then reproject and resample the result to a 30 m resolution. 
Use the Cell Statistics “MAX” function to combine the resulting four 30 m C-CAP impervious rasters. This creates an approximation of the percent developed impervious score from the 2019 NLCD.

Connected length The connected length of the path is calculated using the entire extent of the potential greenways/trails dataset. A trail is considered connected to another trail if it is within 2 m of the other trail. Length thresholds are defined by typical lengths of three common recreational greenway activities: walking, running, and biking. The 40 km threshold for biking is based on the standard triathlon biking segment of 40 km (~25 mi). Because a 5K is the most common road race distance, the running threshold is set at 5 km (~3.1 mi) (Running USA 2017). The 1.9 km (1.2 mi) walking threshold is based on the average walking trip on a summer day (U.S. DOT 2002).

Using the statistics software R, download the OpenStreetMap data for Puerto Rico and the US Virgin Islands.
Select all lines from the OpenStreetMap data that have a highway tag of either footway, cycleway, bridleway, or path. These are all considered potential trails. 
Removed all lines marked as private.
Identify lines from the potential trails that are tagged as sidewalks. Assign them a value of 1 in the indicator.

Final scores If the potential greenway/trail was tagged as a sidewalk in the “other tags” field, it is given a value of 1 to separate sidewalks from what most people think of as a trail or greenway. If a pixel does not intersect a potential greenway/trail but is covered by the C-CAP landcover data, it is coded with a value of 0. Clip to the Caribbean Blueprint 2023 subregion. As a final step, clip to the spatial extent of Southeast Blueprint 2023.

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 6 = Mostly natural and connected for 5 to <40 km or partly natural and connected for ≥40 km 5 = Mostly natural and connected for 1.9 to <5 km, partly natural and connected for 5 to <40 km, or developed and connected for ≥40 km 4 = Mostly natural and connected for <1.9 km, partly natural and connected for 1.9 to <5 km, or developed and connected for 5 to <40 km 3 = Partly natural and connected for <1.9 km or developed and connected for 1.9 to <5 km 2 = Developed and connected for <1.9 km 1 = Sidewalk 0 = Not identified as a trail, sidewalk, or other path Known Issues

This indicator sometimes misclassifies sidewalks as greenways and trails because they are not tagged as a sidewalk in the OpenStreetMap data.
This indicator occasionally misclassifies driveways as “sidewalks and other paths” in places where they are not correctly tagged as private in OpenStreetMap. These typically appear as isolated pixels receiving a score of 1 on the indicator.
OpenStreetMap does not provide a complete inventory of greenways and trails in the U.S. Caribbean. Paths that are missing from the source data will be underprioritized in this indicator. For example, some trails are missing within National Wildlife Refuges.
This indicator includes trails and sidewalks from OpenStreetMap, which is a crowdsourced dataset. While members of the OpenStreetMap community often verify map features to check for accuracy and completeness, there is the potential for spatial errors (e.g., misrepresenting the path of a greenway) or incorrect tags (e.g., mislabeling a path as a footway that is actually a road for vehicles). However, using a crowdsourced dataset gives on-the-ground experts, Blueprint users, and community members the power to fix errors and add new greenways and trails to improve the accuracy and coverage of this indicator in the future.
This indicator sometimes underestimates greenway length when connections route under bridges or along abandoned dirt roads. Some of these issues have been fixed through active testing and improvement, but some likely remain.
Some greenways and trails continue along roadways that allow motorized vehicles, which are excluded from this indicator. As a result, certain trails may appear incomplete because the indicator only captures the sections dedicated for cyclists, pedestrians, and horseback riders.
When calculating nearby impervious for one greenway, if there’s another greenway within 300 m, impervious surface from the different but overlapping greenway buffer area is also used to compute natural condition. This is an unintended issue with the analysis methods. Investigation into potential fixes is ongoing.
The indicator doesn’t currently include areas where future greenways are planned.
This indicator doesn’t include Mona Island, even though there are important and popular trails, due to the lack of landcover data. 

Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited American Planning Association. 2018. Recommendations for Future Enhancements to the Blueprint. [https://secassoutheast.org/pdf/Recommendations-for-Future-Enhancements-to-the-Blueprint-FINAL.pdf].

Institute for Transportation Research and Education (ITRE) & Alta Planning and Design. February 2018. Evaluating the Economic Impact of Shared Use Paths in North Carolina: 2015-2017 Final Report. [https://itre.ncsu.edu/wp-content/uploads/2018/03/NCDOT-2015-44_SUP-Project_Final-Report_optimized.pdf].

National Oceanic and Atmospheric Administration, Office for Coastal Management. “C-CAP Land Cover Files for Puerto Rico and US Virgin Islands”. Coastal Change Analysis Program (C-CAP) High-Resolution Land Cover. Charleston, SC: NOAA Office for Coastal Management. Accessed November 2022. [https://www.coast.noaa.gov/htdata/raster1/landcover/bulkdownload/hires/].

OpenStreetMap. Highways. Data extracted through Geofabrik downloads. Accessed February 26,

There is a newer and more authoritative version of this layer here! It is owned by the University of Richmond's Digital Scholarship Lab and contains data on many more cities.The Home Owners' Loan Corporation (HOLC) was created in the New Deal Era and trained many home appraisers in the 1930s. The HOLC created a neighborhood ranking system infamously known today as redlining. Local real estate developers and appraisers in over 200 cities assigned grades to residential neighborhoods. These maps and neighborhood ratings set the rules for decades of real estate practices. The grades ranged from A to D. A was traditionally colored in green, B was traditionally colored in blue, C was traditionally colored in yellow, and D was traditionally colored in red. A (Best): Always upper- or upper-middle-class White neighborhoods that HOLC defined as posing minimal risk for banks and other mortgage lenders, as they were "ethnically homogeneous" and had room to be further developed.B (Still Desirable): Generally nearly or completely White, U.S. -born neighborhoods that HOLC defined as "still desirable" and sound investments for mortgage lenders.C (Declining): Areas where the residents were often working-class and/or first or second generation immigrants from Europe. These areas often lacked utilities and were characterized by older building stock.D (Hazardous): Areas here often received this grade because they were "infiltrated" with "undesirable populations" such as Jewish, Asian, Mexican, and Black families. These areas were more likely to be close to industrial areas and to have older housing.Banks received federal backing to lend money for mortgages based on these grades. Many banks simply refused to lend to areas with the lowest grade, making it impossible for people in many areas to become homeowners. While this type of neighborhood classification is no longer legal thanks to the Fair Housing Act of 1968 (which was passed in large part due to the activism and work of the NAACP and other groups), the effects of disinvestment due to redlining are still observable today. For example, the health and wealth of neighborhoods in Chicago today can be traced back to redlining (Chicago Tribune). In addition to formerly redlined neighborhoods having fewer resources such as quality schools, access to fresh foods, and health care facilities, new research from the Science Museum of Virginia finds a link between urban heat islands and redlining (Hoffman, et al., 2020). This layer comes out of that work, specifically from University of Richmond's Digital Scholarship Lab. More information on sources and digitization process can be found on the Data and Download and About pages. This layer includes 7,148 neighborhoods spanning 143 cities across the continental United States. NOTE: As mentioned above, over 200 cities were redlined and therefore this is not a complete dataset of every city that experienced redlining by the HOLC in the 1930s. More cities are available in this feature layer from University of Richmond.Cities included in this layerAlabama: Birmingham, Mobile, MontgomeryCalifornia: Fresno, Los Angeles, Sacramento, San Diego, San Francisco, San Jose, StocktonColorado: DenverConnecticut: East Hartford, New Britain, New Haven, StamfordFlorida: Jacksonville, Miami, St. Petersburg, TampaGeorgia: Atlanta, Augusta, Chattanooga, Columbus, MaconIllinois: Aurora, Chicago, Decatur, Joliet, GaryIndiana: Evansville, Fort Wayne, Indianapolis, Gary, Muncie, South Bend, Terre HauteKansas: Greater Kansas City, WichitaKentucky: Lexington, LouisvilleLouisiana: New OrleansMassachusetts: Arlington, Belmont, Boston, Braintree, Brockton, Brookline, Cambridge, Chelsea, Dedham, Everett, Haverhill, Holyoke Chicopee, Lexington, Malden, Medford, Melrose, Milton, Needham, Newton, Quincy, Revere, Saugus, Somerville, Waltham, Watertown, Winchester, WinthropMaryland: BaltimoreMichigan: Battle Creek, Bay City, Detroit, Flint, Grand Rapids, Kalamazoo, Muskegon, Pontiac, Saginaw, ToledoMinnesota: Duluth, MinneapolisMissouri: Greater Kansas City, Springfield, St. Joseph, St. LouisNorth Carolina: Asheville, Charlotte, Durham, Greensboro, Winston SalemNew Hampshire: ManchesterNew Jersey: Atlantic City, Bergen Co., Camden, Essex County, Hudson County, TrentonNew York: Bronx, Brooklyn, Buffalo, Elmira, Binghamton/Johnson City, Lower Westchester Co., Manhattan, Niagara Falls, Poughkeepsie, Queens, Rochester, Staten Island, Syracuse, UticaOhio: Akron, Canton, Cleveland, Columbus, Dayton, Hamilton, Lima, Lorrain, Portsmouth, Springfield, Toledo, Warren, YoungstownOregon: PortlandPennsylvania: Altoona, Erie, Johnstown, New Castle, Philadelphia, PittsburghSouth Carolina: AugustaTennessee: Chattanooga, KnoxvilleTexas: DallasVirginia: Lynchburg, Norfolk, Richmond, RoanokeWashington: Seattle, Spokane, TacomaWisconsin: Kenosha, Milwaukee, Oshkosh, RacineWest Virginia: Charleston, WheelingAn example of a map produced by the HOLC of Philadelphia: