Abstract

A crosswalk table from US postal ZIP codes to geo-points (latitude, longitude)

Documentation

Data source: public.opendatasoft.

The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not).

This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".The ZIP code database contained in 'zipcode.csv' contains 43204 ZIP codes for the continental United States, Alaska, Hawaii, Puerto Rico, and American Samoa. The database is in comma separated value format, with columns for ZIP code, city, state, latitude, longitude, timezone (offset from GMT), and daylight savings time flag (1 if DST is observed in this ZIP code and 0 if not). This database was composed using ZIP code gazetteers from the US Census Bureau from 1999 and 2000, augmented with additional ZIP code information The database is believed to contain over 98% of the ZIP Codes in current use in the United States. The remaining ZIP Codes absent from this database are entirely PO Box or Firm ZIP codes added in the last five years, which are no longer published by the Census Bureau, but in any event serve a very small minority of the population (probably on the order of .1% or less). Although every attempt has been made to filter them out, this data set may contain up to .5% false positives, that is, ZIP codes that do not exist or are no longer in use but are included due to erroneous data sources. The latitude and longitude given for each ZIP code is typically (though not always) the geographic centroid of the ZIP code; in any event, the location given can generally be expected to lie somewhere within the ZIP code's "boundaries".

The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.The database and this README are copyright 2004 CivicSpace Labs, Inc., and are published under a Creative Commons Attribution-ShareAlike license, which requires that all updates must be released under the same license. See http://creativecommons.org/licenses/by-sa/2.0/ for more details. Please contact schuyler@geocoder.us if you are interested in receiving updates to this database as they become available.

Address ranges describe a label given to a unique collection of addresses that fall along a road or path. Address ranges provide a way of locating homes and businesses based on their street addresses when no other location information is available.Using a house number, street name, street side and ZIP code, address ranges can locate the address to the geographic area associated to that side of the street. Once geocoded, the U.S. Census Bureau can assign the address to a field assignment area or tabulate the data for that address. In addition, academics, researchers, professionals and government agencies outside of the Census Bureau use MAF/TIGER address ranges to transform tabular addresses into geographical datasets for decision-making and analytical purposes.Address ranges must be unique to geocode addresses to the correct location and avoid geocoding conflicts. Multiple elements in MAF/TIGER are required to make an address range unique including street names, address house numbers and street feature geometries, such as street centerlines. The address range data model is designed to maximize geocoding matches with their correct geographic areas in MAF/TIGER by allowing an unlimited number of address range-to-street feature relationships.The Census Bureau’s Geography Division devises numerous operations and processes to build and maintain high quality address ranges so that:Address ranges accurately describe the location of addresses on the ground.Address All possible city-style addresses are geocoded.Address ranges can handle all known address and street name variations.Address ranges conform with current U.S. Postal Service ZIP codes.Address ranges are reliable and free from conflicts.Automated software continually updates existing address ranges, builds new address ranges and corrects errors. An automated operation links address location points and tabular address information to street feature edges with matching street names in the same block to build and modify address ranges.Many business rules and legal value checks ensure quality address range data in MAF/TIGER. For example, business rules prevent adding or modifying address ranges that overlap another house number range with the same street name and ZIP code. Legal value checks verify that address ranges include mandatory attribute information, valid data types and valid character values.Some of the TIGER/Line products for the public include address ranges and give the public the ability to geocode addresses to MAF/TIGER address ranges for the user’s own purpose. The address range files are available for the nation, Puerto Rico and the U.S. Island Areas at the county level. TIGER/Line files require geographic information system (GIS) software to use.The Census Bureau Geocoder Service is a web service provided to the public. The service accepts up to 1,000 input addresses and, based on Census address ranges, returns the interpolated geocoded location and census geographies. Users can access the service a web interface or a representational state transfer (REST) application program interface (API) web service. See the Census Geocoder for more information on this process. Directions on how to use the Census Geocoder available: Geocoding Services Web Application Programming Interface (API)Download: https://www2.census.gov/geo/tiger/TGRGDB24/tlgdb_2024_a_us_addr.gdb.zip

DASC provides a free, statewide geocoding service that uses NG911 address points and road centerline data as a base. For directions on using the geocoding service, see this guide.

If you do not see the Web Service you are looking for, or still having trouble connecting to your service, please contact us at dasc@ku.edu.

The full Kansas geospatial catalog is administered by the Kansas Data Access & Support Center (DASC) and can be found at the following URL: https://hub.kansasgis.org/

Big “p” policy changes at the state and federal level are certainly important to health equity, such as eligibility for and generosity of Medicaid benefits. Medicaid expansion has significantly expanded the number of people who are eligible for Medicaid and the creation of the health insurance exchanges (Marketplace) under the Affordable Care Act created a very visible avenue through which people can learn that they are eligible. Although many applications are now submitted online, physical access to state, county, and tribal government Medicaid offices still plays a critical role in understanding eligibility, getting help in applying, and navigating required documentation for both initial enrollment and redetermination of eligibility. However, as more government functions have moved online, in-person office locations and/or staff may have been cut to reduce costs, and gentrification has shifted where minoritized, marginalized, and/or low-income populations live, it is unclear if this key local connection point between residents and Medicaid has been maintained. Our objective was to identify and geocode all Medicaid offices in the United States for pairing with other spatial data (e.g., demographics, Medicaid participation, health care use, health outcomes) to investigate policy-relevant research questions. Three coders identified Medicaid office addresses in all 50 states and the District of Columbia by searching state government websites (e.g., Department of Health and Human Services or analogous state agency) during late 2021 and early 2022 for the appropriate Medicaid agency and its office locations, which were then reviewed for accuracy by a fourth coder. Our corpus of Medicaid office addresses was then geocoded using the Census Geocoder from the US Census Bureau (https://geocoding.geo.census.gov/geocoder/) with unresolved addresses investigated and/or manually geocoded using Google Maps. The corpus was updated in August through December 2023 following the end of the COVID-19 public health emergency by a fifth coder as several states closed and/or combined offices during the pandemic. After deduplication (e.g., where multiple counties share a single office) and removal of mailing addresses (e.g., PO Boxes), our dataset includes 3,027 Medicaid office locations. 1 (December 19, 2023) – original version 2 (January 25, 2024) – added related publication (Data in Brief), corrected two records that were missing negative signs in longitude 3 (February 6, 2024) – corrected latitude and longitude for one office (1340 State Route 9, Lake George, NY 12845) 4 (March 4, 2024) – added one office for Vermont after contacting relevant state agency (280 State Road, Waterbury, VT 05671)

This dataset provides a list of hospitals that include medical centers in the USA with detailed geolocation identifiers, such as latitude and longitude of each hospital. The information about the location of hospitals was obtained by geocoding of addresses and then was corrected using satellite images. It includes only hospital facilities and does not include nursing homes. The dataset represents the location of hospitals for 50 states and Washington D.C., Puerto Rico and US territories.

Data and Maps for ArcGIS provides several address locator files for geocoding addresses in Canada and United States with ArcMap and ArcCatalog. NOTE: To use the composite locator files, you will need to download their component locators first (indicated in the file description on the download screen).

Context

Federal Superfund sites are some of the most polluted in the United States. This dataset contains a multifaceted view of Superfunds, including free-form text descriptions, geography, demographics and socioeconomics.

Content

The core data was scraped from the National Priorities List (NPL) provided by the U.S. Environmental Protection Agency (EPA). This table provides basic information such as site name, site score, date added, and links to a site description and current status. Apache Tika was used to extract text from the site description pdfs. The addresses were scraped from site status pages, and used to geocode to latitude and longitude and Census block group. The block group assignment was used to join with the Census Bureau's planning database, a rich source of nationwide demographic and socioeconomic data. The full source code used to generate the data can be found here, on github.

I have provided three separate downloads to explore:

• priorities_list_full.json: the NPL containing all geographic, site information, text descriptions, and Census Bureau data from the relevant block groups.
• pdb_tract.csv: the planning database aggregated on the tract level with an additional indicator (has_superfund) noting whether or not the tract contains the address of a Superfund site.
• pdb_block_group.csv: the planning database aggregated on the block group level with an additional indicator (has_superfund) noting whether or not the block group contains the address of a Superfund site.

Some caveats:

1. The planning database contains 300+ columns. For a full description of these columns, please see the documentation here.
2. Since the Google geocoder is relatively aggressive in providing address matches, geocoding was done through a hierarchy of queries (full address, city-state-zip, and zipcode only) to prevent gross errors. The address string used to geocode is noted through the 'geocode_source' column.
3. While this data is linked to demographic and socioeconomic data based on either the block group (tract for pdb_tract.csv), the impacts of a particular site's pollution may extend beyond these geographic regions.

Acknowledgements

I would like to thank the EPA and the Census Bureau for making such detailed information publicly available. For relevant academic work, please see Burwell-Naney et al. (2013) and references, both to and therein.

Please let me know if you have any suggestions for improving the dataset!

This dataset includes basic data about all US cities with a population over 100.000 (333 cities)

Source: https://en.wikipedia.org/wiki/List_of_United_States_cities_by_population

Coordinates of cities have been geocoded using https://rapidapi.com/GeocodeSupport/api/forward-reverse-geocoding/

Rows description:

City: Name of city State: Name of state Latitude, Longitude, Population_estimate_2022: Estimated population in 2022 Population_2020: Population figure from 2020 census Change_population: % change in population between 2022 and 2020 Land_area: City land area in sq. mi. Population_density_2020: density of population per sq. mi. in 2020

HUD furnishes technical and professional assistance in planning, developing and managing these developments. Public Housing Developments are depicted as a distinct address chosen to represent the general location of an entire Public Housing Development, which may be comprised of several buildings scattered across a community. The building with the largest number of units is selected to represent the location of the development. 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. In an effort to protect Personally Identifiable Information (PII), the characteristics for each building are suppressed with a -4 value when the “Number_Reported” is equal to, or less than 10. To learn more about Public Housing 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_Public Housing Developments Date Updated: Q1 2025

Context

Created for use in the Renewable and Appropriate Energy Lab at UC Berkeley and Lawrence Berkeley National Laboratory.

Content

Geography: All 58 Counties of the American State of California

Time period: 2015

Unit of analysis: Tons per year

Variables:

• CO: County ID as numbered in the County dropdown menu on the California Air Resources Board Facility Search Tool
• AB
• FACID
• DIS
• FNAME
• FSTREET
• FCITY
• FZIP
• FSIC: Facility Standard Industrial Classification Code specified by the US Department of Labor
• COID
• DISN
• CHAPIS
• CERR_CODE
• TOGT: Total organic gases consist of all hydrocarbons, i.e. compounds containing hydrogen and carbon with or without other chemical elements.
• ROGT: Reactive organic gases include all the organic gases exclude methane, ethane, acetone, methyl acetate, methylated siloxanes, and number of low molecular weight halogenated organics that have a low rate of reactivity.
• COT: The emissions of CO are for the single species, carbon monoxide.
• NOXT: The emissions of NOx gases (mostly nitric oxide and nitrogen dioxide) are reported as equivalent amounts of NO2.
• SOXT: The emissions of SOx gases (sulfur dioxide and sulfur trioxide) are reported as equivalent amounts of SO2.
• PMT: Particulate matter refers to small solid and liquid particles such as dust, sand, salt spray, metallic and mineral particles, pollen, smoke, mist and acid fumes.
• PM10T: PM10 refers to the fraction of particulate matter with an aerodynamic diameter of 10 micrometer and smaller. These particles are small enough to penetrate the lower respiratory tract.
• PM2.5T: PM2.5 refers to the fraction of particulate matter with an aerodynamic diameter of 2.5 micrometer and smaller. These particles are small enough to penetrate the lower respiratory tract.
• lat: Facility latitude geocoded by inputting FSTREET, FCITY, California FZIP into Bing’s geocoding service.
• lon: Facility longitude geocoded in the same way.

Sources: All columns except for lat and lon were scraped from the California Air Resources Board Facility Search Tool using the Request module from Python’s Urllib library. The script used is included below in scripts in case you would like to get additional columns.

The lat and lon columns were geocoded using the Geocoder library for Python with the Bing provider.

Scripts

download.py

import pandas as pd
out_dir = 'ARB/'
file_ext = '.csv'
for i in range(1, 59):
  facilities = pd.read_csv("https://www.arb.ca.gov/app/emsinv/facinfo/faccrit_output.csv?&dbyr=2015&ab_=&dis_=&co_=" + str(i) + "&fname_=&city_=&sort=FacilityNameA&fzip_=&fsic_=&facid_=&all_fac=C&chapis_only=&CERR=&dd=")
  for index, row in facilities.iterrows():
    curr_facility = pd.read_csv("https://www.arb.ca.gov/app/emsinv/facinfo/facdet_output.csv?&dbyr=2015&ab_=" + str(row['AB']) + "&dis_=" + str(row['DIS']) + "&co_=" + str(row['CO']) + "&fname_=&city_=&sort=C&fzip_=&fsic_=&facid_=" + str(row['FACID']) + "&all_fac=&chapis_only=&CERR=&dd=")
    facilities.set_value(index, 'PM2.5T', curr_facility.loc[curr_facility['POLLUTANT NAME'] == 'PM2.5'].iloc[0]['EMISSIONS_TONS_YR'])
  facilities.to_csv(out_dir + str(i) + file_ext)

geocode.py

import geocoder
import csv
directory = 'ARB/'
outdirectory = 'ARB_OUT/'
for i in range(1, 59):
  with open(directory + str(i) + ".csv", 'rb') as csvfile, open(outdirectory + str(i) + '.csv', 'a') as csvout:
    reader = csv.DictReader(csvfile)
    fieldnames = reader.fieldnames + ['lat'] + ['lon'] # Add new columns
    writer = csv.DictWriter(csvout, fieldnames)
    writer.writeheader()
    for row in reader:
      address = row['FSTREET'] + ', ' + row['FCITY'] + ', California ' + row['FZIP']
      g = geocoder.bing(address, key='API_KEY')
      newrow = dict(row)
      if g.latlng:
        newrow['lat'] = g.json['lat']
        newrow['lon'] = g.json['lng']
        writer.writerow(newrow) # Only write row if successfully geocoded

This dataset contains points which represent the location for each ZIP+4® range in Arkansas. This base data serves a variety of public functions that include index layer for address match/geocoding applications, and Streamlines Sales and Tax source jurisdiction assignment. The location of each point was determined by geocoding either the low, high, or mid value for each ZIP+4® address range. All attribute data is drawn from the USPS® (United States Postal Service®) ZIP+4® product - see also supplementary information This information is published by the Arkansas Geographic Information Office, an Arkansas State Government Agency, which holds a non-exclusive license from the United States Postal Service® to publish the information. The price of the PRODUCT or information is neither established, controlled, or approved by the United States Postal Service®. Product advertisement is neither approved nor endorsed by the United States Postal Service®

QDGC tables delivered in geopackage file
- - - - - - - - - - - - - - - - - - - - - -
QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.


Within each geopackage file you will find a number of tables with these names:
-tbl_qdgc_01
-tbl_qdgc_02
-tbl_qdgc_03
-tbl_qdgc_04
-tbl_qdgc_05
-etc


The attributes for each table are:
qdgc Unique Quarter Degree Grid Cell reference string
level_qdgc QDGC level
cellsize degrees decimal degree for the longitudal and latitudal length of the cell
lon_center Longitude center of the cell
lat_center Latitudal center of the cell
area_km2 Calculated area for the cell
geom Geometry


Metadata
--------
Geodata GCS_WGS_1984
Datum: D_WGS_1984
Prime Meridian: 0


Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:
- st_area(st_transform(geom, 102022))/1000000)


Conditions
----------
Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc


Thankyou!
--------
The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:
- TAWIRI (http://tawiri.or.tz/)
- Dept of Biology, NTNU, Norway
- Norwegian Environment Agency
- Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey


References
----------
* http://en.wikipedia.org/wiki/QDGC
* http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
* http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
* http://www.safe.com

description: This database contains locations of day care centers for 39 states which include the states of AZ, CA, , NV, NY, HI. This is a work in progress and data for remaining states will be added as they become available. The dataset only includes center based day care locations (including schools and religious institutes) and does not include home and family based day cares. All the data was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database, data for Washington D.C., Puerto Rico, Delaware and Louisiana was obtained in a GIS format. Information on the source of data for each state is available in the database itself. After geocoding the exact spatial location of each point is being verified using high resolution imagery and ancillary dataset and points are being moved to rooftops wherever possible, this is an ongoing work and points which have been physically verified have been labeled "Geocode", "Imagery", "Imagery with other" and "Unverified" depending on the methodology used to move the points. "Unverified" data points have still not being physically examined even though each of the points has been street geocoded as mentioned above. "Unverified" points for Puerto Rico, Washington DC and the states of Louisiana and Delaware may have better positional accuracy as data for these was obtained in GIS format. The "TYPE" attribute has not been populated yet, this will be populated once a common classification of day care for all states has been decided. The "O_TYPE" attribute contains the classification provided by individual states.; abstract: This database contains locations of day care centers for 39 states which include the states of AZ, CA, , NV, NY, HI. This is a work in progress and data for remaining states will be added as they become available. The dataset only includes center based day care locations (including schools and religious institutes) and does not include home and family based day cares. All the data was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database, data for Washington D.C., Puerto Rico, Delaware and Louisiana was obtained in a GIS format. Information on the source of data for each state is available in the database itself. After geocoding the exact spatial location of each point is being verified using high resolution imagery and ancillary dataset and points are being moved to rooftops wherever possible, this is an ongoing work and points which have been physically verified have been labeled "Geocode", "Imagery", "Imagery with other" and "Unverified" depending on the methodology used to move the points. "Unverified" data points have still not being physically examined even though each of the points has been street geocoded as mentioned above. "Unverified" points for Puerto Rico, Washington DC and the states of Louisiana and Delaware may have better positional accuracy as data for these was obtained in GIS format. The "TYPE" attribute has not been populated yet, this will be populated once a common classification of day care for all states has been decided. The "O_TYPE" attribute contains the classification provided by individual states.

QDGC tables delivered in geopackage file
- - - - - - - - - - - - - - - - - - - - - -
QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.


Within each geopackage file you will find a number of tables with these names:
-tbl_qdgc_01
-tbl_qdgc_02
-tbl_qdgc_03
-tbl_qdgc_04
-tbl_qdgc_05
-etc


The attributes for each table are:
qdgc Unique Quarter Degree Grid Cell reference string
level_qdgc QDGC level
cellsize degrees decimal degree for the longitudal and latitudal length of the cell
lon_center Longitude center of the cell
lat_center Latitudal center of the cell
area_km2 Calculated area for the cell
geom Geometry


Metadata
--------
Geodata GCS_WGS_1984
Datum: D_WGS_1984
Prime Meridian: 0


Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:
- st_area(st_transform(geom, 102022))/1000000)


Conditions
----------
Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc


Thankyou!
--------
The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:
- TAWIRI (http://tawiri.or.tz/)
- Dept of Biology, NTNU, Norway
- Norwegian Environment Agency
- Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey


References
----------
* http://en.wikipedia.org/wiki/QDGC
* http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
* http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
* http://www.safe.com

This dataset denotes ZIP Code centroid locations weighted by population. Population weighted centroids are a common tool for spatial analysis, particularly when more granular data is unavailable or researchers lack sophisticated geocoding tools. The ZIP Code Population Weighted Centroids allows researchers and analysts to estimate the center of population in a given geography rather than the geometric center.

description: This database contains locations of Hospitals for 50 states and Washington D.C. , Puerto Rico and US territories. The dataset only includes hospital facilities and does not include nursing homes. Data for all the states was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database. After geocoding the exact spatial location of each point was moved to rooftops wherever possible and points which have been physically verified have been labelled 'Geocode', 'Imagery', 'Imagery with other' and 'Unverified' depending on the methodology used to move the points. 'Unverified' data points have still not been physically examined even though each of the points has been street geocoded as mentioned above. Missing records are denoted by 'Not Available' or NULL values. Not Available denotes information that was either missing in the source data or data that has not been populated current version.; abstract: This database contains locations of Hospitals for 50 states and Washington D.C. , Puerto Rico and US territories. The dataset only includes hospital facilities and does not include nursing homes. Data for all the states was acquired from respective states departments or their open source websites and then geocoded and converted into a spatial database. After geocoding the exact spatial location of each point was moved to rooftops wherever possible and points which have been physically verified have been labelled 'Geocode', 'Imagery', 'Imagery with other' and 'Unverified' depending on the methodology used to move the points. 'Unverified' data points have still not been physically examined even though each of the points has been street geocoded as mentioned above. Missing records are denoted by 'Not Available' or NULL values. Not Available denotes information that was either missing in the source data or data that has not been populated current version.

DGC tables delivered in geopackage file
- - - - - - - - - - - - - - - - - - - - - -
QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.


Within each geopackage file you will find a number of tables with these names:
-tbl_qdgc_01
-tbl_qdgc_02
-tbl_qdgc_03
-tbl_qdgc_04
-tbl_qdgc_05
-etc


The attributes for each table are:
qdgc Unique Quarter Degree Grid Cell reference string
level_qdgc QDGC level
cellsize degrees decimal degree for the longitudal and latitudal length of the cell
lon_center Longitude center of the cell
lat_center Latitudal center of the cell
area_km2 Calculated area for the cell
geom Geometry


Metadata
--------
Geodata GCS_WGS_1984
Datum: D_WGS_1984
Prime Meridian: 0


Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:
- st_area(st_transform(geom, 102022))/1000000)


Conditions
----------
Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc


Thankyou!
--------
The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:
- TAWIRI (http://tawiri.or.tz/)
- Dept of Biology, NTNU, Norway
- Norwegian Environment Agency
- Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey


References
----------
* http://en.wikipedia.org/wiki/QDGC
* http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
* http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
* http://www.safe.com

This dataset comprises SITUS address points (as opposed to owner or mailing addresses) for jurisdictions within the County of San Diego. Addresses include the corresponding Assessor Parcel Number (APN), address type, placement location, and US National Grid (USNG) value.

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All 311 Service Requests from 2010 to present. This information is automatically updated daily.