ZIP Code Tabulation Areas (ZCTAs) are generalized representations of United States Postal Service (USPS) ZIP Code service areas. The USPS ZIP Codes identify the individual post office or metropolitan area delivery station associated with mailing addresses. USPS ZIP Codes are not areal features but a collection of mail delivery routes.

The City of Austin provides this zip code dataset for general use, designed to support a variety of research and analysis needs. Please note that while we facilitate access to this data, the dataset is owned and produced by the United States Postal Service (USPS). Users are encouraged to acknowledge USPS as the source when utilizing this dataset in their work. U.S. ZIP Code Areas (Five-Digit) represents five-digit ZIP Code areas used by the U.S. Postal Service to deliver mail more effectively. The first digit of a five-digit ZIP Code divides the United States into 10 large groups of states numbered from 0 in the Northeast to 9 in the far West. Within these areas, each state is divided into an average of 10 smaller geographical areas, identified by the second and third digits. These digits, in conjunction with the first digit, represent a sectional center facility or a mail processing facility area. The fourth and fifth digits identify a post office, station, branch or local delivery area. This product is for informational purposes and may not have been prepared for or be suitable for legal, engineering, or surveying purposes. It does not represent an on-the-ground survey and represents only the approximate relative location of property boundaries. This product has been produced by the City of Austin for the sole purpose of geographic reference. No warranty is made by the City of Austin regarding specific accuracy or completeness. City of Austin Open Data Terms of Use: https://datahub.austintexas.gov/stories/s/ranj-cccq

India Pin/Zip Code (City, Area, District, State) Contain pincode/zipcode with city, area, district, state information Source: India post gov in website

Our International Zip Code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas for numerous countries worldwide. This global dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

1. Zip code boundaries change, in ways small and large - these are not a stable analysis unit. Data you received keyed to zip codes may have used an earlier and very different boundary for your zip codes of interest.
2. Historically, the United States Postal Service has not published zip code boundaries, and instead, boundary datasets are compiled by third party vendors from address data. That means that the boundary data are not authoritative, and any data you have keyed to zip codes may use a different, vendor-specific method for generating boundaries from the data here.
3. Zip codes are designed to optimize mail delivery, not social, environmental, or demographic characteristics. Analysis using zip codes is subject to create issues with the Modifiable Areal Unit Problem that will bias any results because your units of analysis aren't designed for the data being studied.

IRS US Income Data by Zip Code

Number of Returns, Adjusted Gross Income, Total Income, and Taxable Income

By Jon Loyens [source]

About this dataset

This dataset provides a unique insight into the US income patterns in 2013, by zip code. With this data, you can explore how taxes and adjusted gross income (AGI) vary according to geographic area. The data includes total and average incomes reported, number of returns filed in each ZIP code and taxable incomes reported. This dataset is ideal for studying how economic trends have shifted geographically over time or examining regional economic disparities within the US. In addition, this dataset has been cleansed from data removed from items such as ZIP codes with fewer than 100 returns or those identified as a single building or nonresidential ZIP codes that were categorized as “other” (99999) by the IRS. Finally, dollar amounts for all variables are in thousands for better accuracy

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Research Ideas

• Using this dataset to identify potential locations for commercial developments by maping taxable incomes, total income amounts, and average incomes in different zip codes.
• Comparing the number of returns with total income, taxes payable, and income variance between different zip codes to gain insights into areas with higher financial prosperity or disparities between zip codes due to wider economic trends.
• Analyzing average adjusted gross incomes on a state-by-state basis to identify states where high net worth citizens or individuals earning high wages live in order to target marketing campaigns or develop high-end service offerings

Acknowledgements

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

License

License: Dataset copyright by authors - You are free to: - Share - copy and redistribute the material in any medium or format for any purpose, even commercially. - Adapt - remix, transform, and build upon the material for any purpose, even commercially. - You must: - Give appropriate credit - Provide a link to the license, and indicate if changes were made. - ShareAlike - You must distribute your contributions under the same license as the original. - Keep intact - all notices that refer to this license, including copyright notices.

Columns

File: IRSIncomeByZipCode.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abbreviation for the state in which the zip code is located. (String) | | ZIPCODE | The five-digit US zip code. (Integer) | | Number of returns | The total number of tax returns filed in the zip code. (Integer) | | Adjusted gross income (AGI) | The total amount of adjusted gross income reported in the zip code. (Integer) | | Avg AGI | The average amount of adjusted gross income reported in the zip code. (Integer) | | Number of returns with total income | The total number of returns with total income reported in the zip code. (Integer) | | Total income amount | The total amount of income reported in the zip code. (Integer) | | Avg total income | The average amount of total income reported in the zip code. (Integer) | | Number of returns with taxable income | The total number of returns with taxable income reported in the zip code. (Integer) | | Taxable income amount | The total amount of taxable income reported in the zip code. (Integer) | | Avg taxable income | The average amount of taxable income reported in the zip code. (Integer) |

File: IRSIncomeByZipCode_NoStateTotalsNoSmallZips.csv | Column name | Description | |:------------------------------------------|:-------------------------------------------------------------------------------------| | STATE | The two-letter abb...

This dataset provides the zip codes in the region. One resource provides a shapefile of the zip codes polygons from the USPS while another resource provides a shapefile of the...

Our Europe Zip Code Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas for numerous European countries. This dataset contains accurate and up-to-date information on all administrative divisions, cities, and zip codes, making it an invaluable resource for various applications such as address capture and validation, map and visualization, reporting and business intelligence (BI), master data management, logistics and supply chain management, and sales and marketing. Our location data packages are available in various formats, including CSV, optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more. Product features include fully and accurately geocoded data, multi-language support with address names in local and foreign languages, comprehensive city definitions, and the option to combine map data with UNLOCODE and IATA codes, time zones, and daylight saving times. Companies choose our location databases for their enterprise-grade service, reduction in integration time and cost by 30%, and weekly updates to ensure the highest quality.

GeoJunxion‘s ZIP+4 is a complete dataset based on US postal data consisting of plus 35 millions of polygons​. The dataset is NOT JUST a table of spot data, which can be downloaded as csv or other text file as it happens with other suppliers​. The data can be delivered as shapefile through a single RAW data delivery or through an API​.

The January 2021 USPS data source has significantly changed since the previous delivery. Some States have sizably lower ZIP+4 totals across all counties when compared with previous deliveries due to USPS parcelpoint cleanup, while other States have a significant increase in ZIP+4 totals across all counties due to cleanup and other rezoning. California and North Carolina in particular have several new ZIP5s, contributing to the increase in distinct ZIPs and ZIP+4s​.

GeoJunxion‘s ZIP+4 data can be used as an additional layer on an existing map to run customer or other analysis, e.g. who is my customer who not, what is the density of my customer base in a certain ZIP+4 etc.

Information can be put into visual context, due to the polygons, which is good for complex overviews or management decisions. ​CRM data can be enriched with the ZIP+4 to have more detailed customer information​.

Key specifications:

Topologized ZIP polygons​

GeoJunxion ZIP+4 polygons follow USPS postal codes ​

ZIP+4 code polygons: ​

ZIP5 attributes ​

State codes. ​

Overlapping ZIP+4 ​

boundaries for multiple ZIP+4 addresses on one area​

Updated USPS source (January 2021) ​

Distinct ZIP5 codes: 34 731​

Distinct ZIP+4 codes: 35 146 957 ​

The ZIP + 4 polygons are delivered in Esri shapefile format. This format allows the storage of geometry and attribute information for each of the features. ​

The four components for the shapefile data are: ​

.shp – This file stores the geometry of the feature​

.shx –This file stores an index that stores the feature geometry​

.dbf –This file stores attribute information relating to individual features​

.prj –This file stores projection information associated with features​

​Current release version 2021. Earlier versions from previous years available on request.

​

This is the ONS Postcode Directory (ONSPD) for the United Kingdom as at February 2023 in Comma Separated Variable (CSV) and ASCII text (TXT) formats. This file contains the multi CSVs so that postcode areas can be opened in MS Excel. To download the zip file click the Download button. The ONSPD relates both current and terminated postcodes in the United Kingdom to a range of current statutory administrative, electoral, health and other area geographies. It also links postcodes to pre-2002 health areas, 1991 Census enumeration districts for England and Wales, 2001 Census Output Areas (OA) and Super Output Areas (SOA) for England and Wales, 2001 Census OAs and SOAs for Northern Ireland and 2001 Census OAs and Data Zones (DZ) for Scotland. It now contains 2021 Census OAs and SOAs for England and Wales. It helps support the production of area based statistics from postcoded data. The ONSPD is produced by ONS Geography, who provide geographic support to the Office for National Statistics (ONS) and geographic services used by other organisations. The ONSPD is issued quarterly. (File size - 234 MB)NOTE: The 2022 ONSPDs included an incorrect update of the ITL field with two LA changes in Northamptonshire. This error has been corrected from the February 2023 ONSPD.NOTE: There was an issue with the originally published file where some change orders yet to be included in OS Boundary-LineÔ (including The Cumbria (Structural Changes) Order 2022, The North Yorkshire (Structural Changes) Order 2022 and The Somerset (Structural Changes) Order 2022) were mistakenly implemented for terminated postcodes. Version 2 corrects this, so that ward codes E05014171–E05014393 are not yet included. Please note that this product contains Royal Mail, Gridlink, LPS (Northern Ireland), Ordnance Survey and ONS Intellectual Property Rights.

PRISM data converted into FIPS, ZIP Code, and census tract summaries in the USA Introduction: Parameter-elevation Regressions on Independent Slopes Model (PRISM) by PRISM Climate group Oregon State temperature, precipitation 4km daily weather variable grids that I have converted to daily county FIPS, ZIP Code, and census tract summaries for use in several papers. Available for download (see Data below) in RDS (compact) format. CSV available on request. In Python it is easy to load RDS files and much more compact files than CSVs too. Note that ZIP Code throughout is actually ZIP Code Tabulation Area (ZCTA), which was developed to overcome the difficulties in precisely defining the land area covered by each ZIP Code. Defining the extent of an area is necessary in order to tabulate census data for that area.

Files contain 5000 samples of AWARE characterization factors, as well as sampled independent data used in their calculations and selected intermediate results.

AWARE is a consensus-based method development to assess water use in LCA. It was developed by the WULCA UNEP/SETAC working group. Its characterization factors represent the relative Available WAter REmaining per area in a watershed, after the demand of humans and aquatic ecosystems has been met. It assesses the potential of water deprivation, to either humans or ecosystems, building on the assumption that the less water remaining available per area, the more likely another user will be deprived.

The code used to generate the samples can be found here: https://github.com/PascalLesage/aware_cf_calculator/

Samples were updated from v1.0 in 2020 to include model uncertainty associated with the choice of WaterGap as the global hydrological model (GHM).

The following datasets are supplied:

1) AWARE_characterization_factor_samples.zip

Actual characterization factors resulting from the Monte Carlo Simulation. Contains 4 zip files:

* monthly_cf.zip: contains 116,484 arrays of 5000 monthly characterization factor samples for each of 9707 watershed and for each month, in csv format. Names are cf_.csv, where is the watershed id and is the first three letters of the month ('jan', 'feb', etc.).


* average_agri_cf.zip: contains 9707 arrays of 5000 annual average, agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_agri_.csv.


* average_non_agri_cf.zip: contains 9707 arrays of 5000 annual average, non-agricultural use, characterization factor samples for each watershed, in csv format. Names are cf_average_non_agri_.csv.


* average_unknown_cf.zip: contains 9707 arrays of 5000 annual average, unspecified use, characterization factor samples for each watershed, in csv format. Names are cf_average_unknown_.csv..

2) AWARE_base_data.xlsx

Excel file with the deterministic data, per watershed and per month, for each of the independent variables used in the calculation of AWARE characterization factors. Specifically, it includes:

  Monthly irrigation
    Description: irrigation water, per month, per basin
    Unit: m3/month
    Location in Excel doc: Irrigation
    File name once imported: irrigation.pickle
    table shape: (11050, 12)


  Non-irrigation hwc: electricity, domestic, livestock, manufacturing
    Description: non-irrigation uses of water
    Unit: m3/year
    Location in Excel doc: hwc_non_irrigation
    File name once imported: electricity.pickle, domestic.pickle,
      livestock.pickle, manufacturing.pickle
    table shape: 3 x (11050,)


  avail_delta
    Description: Difference between "pristine" natural availability
      reported in PastorXNatAvail and natural availability calculated
      from "Actual availability as received from WaterGap - after
      human consumption" (Avail!W:AH) plus HWC.
      This should be added to calculated water availability to
      get the water availability used for the calculation of EWR
    Unit: m3/month
    Location in Excel doc: avail_delta
    File name once imported: avail_delta.pickle
    table shape: (11050, 12)


  avail_net
    Description: Actual availability as received from WaterGap - after human consumption
    Unit: m3/month
    Location in Excel doc: avail_net
    File name once imported: avail_net.pickle
    table shape: (11050, 12)


  pastor
    Description: fraction of PRISTINE water availability that should be reserved for environment
    Unit: unitless
    Location in Excel doc: pastor
    File name once imported: pastor.pickle
    table shape: (11050, 12)


  area
    Description: area
    Unit: m2
    Location in Excel doc: area
    File name once imported: area.pickle
    table shape: (11050,)

It also includes:

• information (k values) on the distributions used for each variable (uncertainty tab)

• information (k values) on the model uncertainty (model uncertainty tab)

• two filters used to exclude watersheds that are either in Greenland (polar filter) or without data from the Pastor et al. (2014) method (122 cells), representing small coastal cells with no direct overlap (pastor filter). (filters tab)

3) independent_variable_samples.zip

Samples for each of the independent variables used in the calculation of characterization factors. Only random variables are contained. For all watershed or watershed-months without samples, the Monte Carlo simulation used the deterministic values found in the AWARE_base_data.xlsx file.

The files are in csv format. The first column contains the watershed id (BAS34S_ID) if the data is annual or the (BAS34S_ID, month) for data with a monthly resolution. the other 5000 columns contain the sampled data.

The names of the files are .

4) intermediate_variables.zip

Contains results of intermediate calculations, used in the calculation of characterization factors. The zip file contains 3 zip files:

* AMD_world_over_AMD_i.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the ratio between the AMD (Availability Minus Demand) for the watershed-month and AMD_glo, the world weighted AMD average. Format is csv.
* AMD_world.zip: contains one array of 5000 calculated values of the world average AMD. Format is csv.


* HWC.zip: contains 116,484 arrays (for each watershed-month) of 5000 calculated values of the total Human Water Consumption. Format is csv.

5) watershedBAS34S_ID.zip

Contains the GIS files to link the watershed ids (BAS34S_ID) to actual spatial data.

Comprehensive Canadian Postal Code Database with complete PCCF-equivalent Statistics Canada census geography linkage. Includes 900,000+ postal codes with latitude/longitude coordinates, census demographic data, Federal Electoral Districts, and 17 supplemental reference tables. Available in Standard (8 fields), Deluxe (15 fields), and Business (43 fields) editions. Business edition includes pre-integrated Census Metropolitan Areas (CMA), Census Divisions (CD), Census Subdivisions (CSD), Dissemination Areas (DA), Census Tracts (CT), Economic Regions (ER), Population Centres, and Federal Electoral Districts-eliminating the need for separate PCCF file management. All editions include monthly updates from Canada Post, bilingual municipality names, accent supplement tables, and geocoding coordinates with ~99% coverage. Multiple formats: Microsoft Access, Excel, and CSV. Includes free FTP access, U.S.-based phone and email support, and 30-day money-back guarantee.

Dataset

This dataset was created by haakakak

Contents

This dataset contains information on antibody testing for COVID-19: the number of people who received a test, the number of people with positive results, the percentage of people tested who tested positive, and the rate of testing per 100,000 people, stratified by modified ZIP Code Tabulation Area (ZCTA) of residence. Modified ZCTA reflects the first non-missing address within NYC for each person reported with an antibody test result. This unit of geography is similar to ZIP codes but combines census blocks with smaller populations to allow more stable estimates of population size for rate calculation. It can be challenging to map data that are reported by ZIP Code. A ZIP Code doesn’t refer to an area, but rather a collection of points that make up a mail delivery route. Furthermore, there are some buildings that have their own ZIP Code, and some non-residential areas with ZIP Codes. To deal with the challenges of ZIP Codes, the Health Department uses ZCTAs which solidify ZIP codes into units of area. Often, data reported by ZIP code are actually mapped by ZCTA. The ZCTA geography was developed by the U.S. Census Bureau. These data can also be accessed here: https://github.com/nychealth/coronavirus-data/blob/master/totals/antibody-by-modzcta.csv Exposure to COVID-19 can be detected by measuring antibodies to the disease in a person’s blood, which can indicate that a person may have had an immune response to the virus. Antibodies are proteins produced by the body’s immune system that can be found in the blood. People can test positive for antibodies after they have been exposed, sometimes when they no longer test positive for the virus itself. It is important to note that the science around COVID-19 antibody tests is evolving rapidly and there is still much uncertainty about what individual antibody test results mean for a single person and what population-level antibody test results mean for understanding the epidemiology of COVID-19 at a population level. These data only provide information on people tested. People receiving an antibody test do not reflect all people in New York City; therefore, these data may not reflect antibody prevalence among all New Yorkers. Increasing instances of screening programs further impact the generalizability of these data, as screening programs influence who and how many people are tested over time. Examples of screening programs in NYC include: employers screening their workers (e.g., hospitals), and long-term care facilities screening their residents. In addition, there may be potential biases toward people receiving an antibody test who have a positive result because people who were previously ill are preferentially seeking testing, in addition to the testing of persons with higher exposure (e.g., health care workers, first responders) Rates were calculated using interpolated intercensal population estimates updated in 2019. These rates differ from previously reported rates based on the 2000 Census or previous versions of population estimates. The Health Department produced these population estimates based on estimates from the U.S. Census Bureau and NYC Department of City Planning. Antibody tests are categorized based on the date of specimen collection and are aggregated by full weeks starting each Sunday and ending on Saturday. For example, a person whose blood was collected for antibody testing on Wednesday, May 6 would be categorized as tested during the week ending May 9. A person tested twice in one week would only be counted once in that week. This dataset includes testing data beginning April 5, 2020. Data are updated daily, and the dataset preserves historical records and source data changes, so each extract date reflects the current copy of the data as of that date. For example, an extract date of 11/04/2020 and extract date of 11/03/2020 will both contain all records as they were as of that extract date. Without filtering or grouping by extract date, an analysis wi

IntroductionOur study explores how New York City (NYC) communities of various socioeconomic strata were uniquely impacted by the COVID-19 pandemic.MethodsNew York City ZIP codes were stratified into three bins by median income: high-income, middle-income, and low-income. Case, hospitalization, and death rates obtained from NYCHealth were compared for the period between March 2020 and April 2022.ResultsCOVID-19 transmission rates among high-income populations during off-peak waves were higher than transmission rates among low-income populations. Hospitalization rates among low-income populations were higher during off-peak waves despite a lower transmission rate. Death rates during both off-peak and peak waves were higher for low-income ZIP codes.DiscussionThis study presents evidence that while high-income areas had higher transmission rates during off-peak periods, low-income areas suffered greater adverse outcomes in terms of hospitalization and death rates. The importance of this study is that it focuses on the social inequalities that were amplified by the pandemic.

A dataset listing Illinois zip codes by population for 2024.

The data set contains a list of address locations in CSV format per municipality. For each address location, the following attributes are given: code of address, code and name of the municipality, code and name of the district/urban area (only in the case of territorially divided statutory cities), code and name of the city district of Prague (only for Prague), code and name of part of the municipality, code and name of the street (if specified), type of building (with number number number/with number no.), house number number (if introduced), indicative number (only if assigned to the indicative number), postal code, coordinates Y and X of the address point (in the S-JTSK coordinate system) and the date of validity. The data set is provided as open data (CC-BY 4.0 license). The data is based on RÚIAN (Registry of Territorial Identification, Addresses and Real Estate). The data covers the entire territory of the Czech Republic. They are provided by municipalities in compressed form (ZIP). Files are created on the first day of each month with data in force on the last day of the previous month. More in Act No. 111/2009 Coll., on basic registers, in Decree No. 359/2011 Coll., on the basic register of territorial identification, addresses and real estate. The data set contains a list of address locations in CSV format per municipality. For each address location, the following attributes are given: code of address, code and name of the municipality, code and name of the district/urban area (only in the case of territorially divided statutory cities), code and name of the city district of Prague (only for Prague), code and name of part of the municipality, code and name of the street (if specified), type of building (with number number number/with number no.), house number number (if introduced), indicative number (only if assigned to the indicative number), postal code, coordinates Y and X of the address point (in the S-JTSK coordinate system) and the date of validity. The data set is provided as open data (CC-BY 4.0 license). The data is based on RÚIAN (Registry of Territorial Identification, Addresses and Real Estate). The data covers the entire territory of the Czech Republic. They are provided by municipalities in compressed form (ZIP). Files are created on the first day of each month with data in force on the last day of the previous month. More in Act No. 111/2009 Coll., on basic registers, in Decree No. 359/2011 Coll., on the basic register of territorial identification, addresses and real estate.

The list of communes of France contains 38 data allowing to identify the communes and to link the French communes with files from the Open Data, using the Insee code of the commune, or the postal code(s), codes of departments, regions, cantons or academy. The files also contain data on population, area, density, coordinates (of the town hall and geographical center), altitude (average, minimum and maximum) and various information. The simplified geography of the territory of the communes is present in the files marked "with geography" or "with polygon". The names of the cities are offered in 5 different formats (with or without article and/or preposition, in lower or upper case...). The municipalities of the overseas departments, regions and collectivities (DROM-COM) are included in the files but some data may be missing. ### Available file formats The files are available in csv, csv.gz and json on data.gouv.fr. Files in Excel (xlsx), Parquet (.parquet) and Feather (.feather) formats are not accepted on data.gouv.fr but are freely available on villedereve.fr/open-data-donnees-libres-sur-les-communes. ### Years Available The files are available for the years 2022, 2023, 2024 and 2025. The geographies used are those of year N-1 (e.g. 1 January 2024 for file 2025). The differences between the files from one year to the next mainly concern the population as well as administrative changes (groupings or deletions of municipalities, mainly). ### List of data available in files - insee_code: Common code, INSEE code, code assigned by INSEE to the municipality - standard_name: Standard name of the municipality, with its article (e.g.: Le Havre) - name_without_pronoun: Name of the municipality, without its article if applicable (e.g. Havre) - name_a: Name of the municipality, preceded by the preposition to, to or from and article of the municipality, if applicable (e.g.: Le Havre) - name of: Name of the municipality, preceded by the preposition of the municipality's article(s), if any (e.g. Le Havre) - name_without_accent: Name of the municipality without accent, special characters or spaces - Standard_name: Name of municipality in capital letters (e.g.: THE HAVRE) - typecom: Type of municipality in abbreviated version (COM, COMA, COMD, ARM) - typecom_text: Type of municipality in text version - reg_code: Region code assigned by INSEE to the region of the municipality - reg_name: Name of the region where the municipality is located - dep_code: Department code assigned by INSEE to the department of the commune - dep_nom: Name of the department where the municipality is located - canton_code: Canton code of the commune - canton_name: Name of the canton of the municipality - epci_code: EPCI code (public institutions of inter-municipal cooperation) assigned by INSEE to the region of the municipality - epci_name: Name of the EPCI where the municipality is located - postal_code: Main postal code of the municipality - postal_codes: Postal codes attached to the municipality - academie_code: Code of the academy of attachment of the schools of the commune - academie_nom: Name of the home academy - employment_zone: Area of use of the municipality, defined by INSEE - code_insee_centre_zone_emploi: INSEE code of the municipality centre of the area of employment - population: Municipal population - area_hectare: Area of the municipality, in hectare - area_km2: Area of the municipality, in km2 - density: Density of the municipality, inhabitant per km2 - average_altitude: Average altitude, m - minimum_altitude: Minimum altitude, m - maximum_altitude: Maximum altitude, m - latitude_mairie: Latitude of the town hall - longitude_mairie: Longitude of the town hall - latitude_centre: Latitude of the centroid of the communal territory - longitude_centre: Longitude of the centroid of the communal territory - densite_grid: Communal grid of density at 7 levels, according to INSEE - nice: Gentile (names of inhabitants) - url_wikipedia: URL of the wikipedia page of the municipality - url_villedereve: URL of the page City of dream of the municipality ### Data source - INSEE - geo.api.gouv.fr - Ministry of Education - La Poste

Context

OpenAddresses's goal is to connect the digital and physical worlds by sharing geographic coordinates, street names, house numbers and postal codes.

Content

This dataset contains one datafile for each state in the U.S. South region (although some are arguably not in the South).

States included in this dataset:

• Alabama - al.csv
• Arkansas - ar.csv
• Washington D.C. - dc.csv
• Delaware - de.csv
• Florida - fl.csv
• Georgia - ga.csv
• Kentucky - ky.csv
• Louisiana - la.csv
• Maryland - md.csv
• Mississippi - ms.csv
• North Carolina - nc.csv
• Oklahoma - ok.csv
• South carolina - sc.csv
• Tennessee - tn.csv
• Texas - tx.csv
• Virginia - va.csv
• West Virginia - wv.csv

Field descriptions:

• LON - Longitude
• LAT - Latitude
• NUMBER - Street number
• STREET - Street name
• UNIT - Unit or apartment number
• CITY - City name
• DISTRICT - ?
• REGION - ?
• POSTCODE - Postcode or zipcode
• ID - ?
• HASH - ?

Acknowledgements

Data collected around 2017-07-25 by OpenAddresses (http://openaddresses.io).

Address data is essential infrastructure. Street names, house numbers and postal codes, when combined with geographic coordinates, are the hub that connects digital to physical places.

Data licenses can be found in LICENSE.txt.

Data source information can be found at https://github.com/openaddresses/openaddresses/tree/9ea72b079aaff7d322349e4b812eb43eb94d6d93/sources

Inspiration

Use this dataset to create maps in conjunction with other datasets for crime or weather