Free U.S. ZIP Code Database with 7 essential data fields for personal use. Includes all 42,000+ ZIP codes with city, state, latitude, longitude, classification, and 2020 Census population. Updated monthly with lifetime access. Download in CSV, Excel, Access, and SQL formats at no cost. Perfect for educational projects, address validation, basic mapping, and personal applications. No credit card required.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F1842206%2F4408fd0c0561e4a48a03776b784ed650%2Fzip2.jpeg?generation=1728526740859651&alt=media" alt="">

US Zip Codes Database We're proud to offer a simple, accurate and up-to-date database of US Zip Codes. It's been built from the ground up using authoritative sources including the U.S. Postal Service™, U.S. Census Bureau, National Weather Service, American Community Survey, and the IRS. - Up-to-date: Data updated as of October 8, 2024. Includes data from the most recent American Community Survey (2022)! - Comprehensive: 41,618 unique zip codes including ZCTA, unique, military, and PO box zips. - Useful fields: From latitude and longitude to household income. - Accurate: Aggregated from official sources and precisely geocoded to latitude and longitude. - Simple: A single CSV file, concise field names, only one entry per zip code.

From https://simplemaps.com/data/us-zips

Image

Generated with Bing Image Generator

Note

I just downloaded and uploaded it here. All credits to https://simplemaps.com/data/us-zips

Context

I'm working on some visualizations of US geographic data and need the lat, long of various US zip codes for plotting some values on a map.

Content

This data set consists of a 3-column csv: zip code, latitude and longitude

Acknowledgements

Thanks to gist.github.com/erichurst/

Inspiration

Dataset

This dataset was created by Pranay

Contents

UK Postcode Directory

Geocode UK Postcodes & Identify Geographical Hierarchies

By GetTheData [source]

About this dataset

This Open Postcode Geo dataset contains a wealth of information about UK postcodes. For each postcode, there are several geospace attributes you can use to refine your analysis such as latitude, longitude, easting and northing. Moreover, the positional quality indicator provides a range of accuracy levels for each geospace attribute.

In addition to positioning data, this dataset has been derived from the Office for National Statistics' Postcode Directory which gives users extra insights into postcodes such as postcode areas, districts and sectors — enabling them to accurately group records into geographic hierarchies: perfect for mapping applications and statistical analysis!

And with data coming from multiple sources — The Crown Copyright & Database Right (2016), Royal Mail Copyright & Database Right (2016) & ONS ™ - users can be assured that Open Postcode Geo provides accurate and up-to-date results that cover terminated archives as well as smaller user-generated postcodes! All released under the UK Government's Open Government Licence v3; with attribution required pursuant to ONS Licences info... Now you too have access to powerful spatial information about the United Kingdom; helping you gain unparalleled insight in record time

More Datasets

For more datasets, click here.

Featured Notebooks

• 🚨 Your notebook can be here! 🚨!

Research Ideas

• Use this dataset to combine with other datasets to accurately geocode addresses in a variety of formats, such as full postcodes or postcodes with only one digit missing.
• Utilise the different hierarchy levels including postcode area, district and sector for data visualization and analysis on census data collected by specific area in the UK.
• Feed this dataset into a route optimization algorithm so delivery routes can be quickly optimized between different locations using accurate lat-long coordinates from each address

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

File: open_postcode_geo.csv | Column name | Description | |:---------------|:------------------------------| | AB1 0AA | Postcode (String) | | terminated | Terminated postcode (String) | | small | Small postcode (String) | | 385386 | Easting coordinate (Integer) | | 801193 | Northing coordinate (Integer) | | Scotland | Country name (String) | | 57.101474 | Latitude coordinate (Float) | | -2.242851 | Longitude coordinate (Float) | | AB10AA | Postcode area (String) | | AB1 0AA.1 | Postcode district (String) | | AB1 0AA | Postcode sector (String) | | AB1.1 | Postcode area (String) |

Acknowledgements

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

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.

The provided datasets contain information related to various aspects of an e-commerce site. Here is a description of each dataset:

1. order.csv: This dataset, named "olist_orders_dataset.csv," contains information about the orders made on the e-commerce site. It likely includes details such as order ID, customer ID, order status, purchase timestamp, and other relevant order-related information.

2. customer.csv: This dataset, named "olist_customers_dataset.csv," contains information about the customers who have made purchases on the e-commerce site. It likely includes customer ID, customer name, customer location, and other related customer information.

3. payment.csv: This dataset, named "olist_order_payments_dataset.csv," contains information about the payments made for the orders. It likely includes order ID, payment ID, payment type, payment value, and other relevant payment-related information.

4. product.csv: This dataset, named "olist_products_dataset.csv," contains information about the products available for sale on the e-commerce site. It likely includes product ID, product name, product category, product price, and other relevant product-related information.

5. geo.csv: This dataset, named "olist_geolocation_dataset.csv," contains geolocation information related to Brazilian zip codes. It likely includes information such as zip code, latitude, longitude, and other relevant geographic details.

6. sellers.csv: This dataset, named "olist_sellers_dataset.csv," contains information about the sellers who are associated with the e-commerce platform. It likely includes seller ID, seller name, seller location, and other relevant seller-related information.

Each of these datasets provides data from different perspectives of the e-commerce platform, including orders, customers, payments, products, geolocation, and sellers. These datasets can be used together to gain insights about the sales performance, customer behavior, product analysis, payment patterns, and geographic distribution of the e-commerce site's operations.

Make Open Data is an open source initiative to facilitate the transformation of public data by centralizing logic.

Here, provides a table with information about the municipalities (code/common name/department/region/district, latitude/longitude, population, postal code).

Data catalogue: Catalogue link Make Open Data

source_url: https://www.insee.fr/statistics/file/7705908/RP2020_LOGEMT_csv.zip source_reference: https://www.insee.fr/statistics/7705908?summary=7637890

This is a construction and collaborative project: Repo Make Open Data link

Any star, request for improvement (issues) or contribution is welcome.

mail_file: source_url: https://datanova.laposte.fr/data-fair/api/v1/datasets/laposte-hexasmal/metadata-attachments/base-official-codes-postaux.csv source_reference: https://www.data.gouv.fr/en/datasets/official database of postal codes/#/resources

cog_municipalities: source_url: https://unpkg.com/@etalab/decoupage-administratif@4.0.0/data/communes.json source_reference: https://github.com/datagouv/decoupage-administratif

cog_arrondissements: source_url: https://unpkg.com/@etalab/decoupage-administratif@4.0.0/data/arrondissements.json source_reference: https://github.com/datagouv/decoupage-administratif

cog_departments: source_url: https://unpkg.com/@etalab/decoupage-administratif@4.0.0/data/departements.json source_reference: https://github.com/datagouv/decoupage-administratif

cog_regions: source_url: https://unpkg.com/@etalab/decoupage-administratif@4.0.0/data/regions.json source_reference: https://github.com/datagouv/decoupage-administratif

Postal Codes Dataset for Japan, JP including name of the city, town, or place, various administrative divisions and alternative city names.

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.

The property level flood risk statistics generated by the First Street Foundation Flood Model Version 2.0 come in CSV format.

The data that is included in the CSV includes:

• An FSID; a First Street ID (FSID) is a unique identifier assigned to each location.

• The latitude and longitude of a parcel as well as the zip code, census block group, census tract, county, congressional district, and state of a given parcel.

• The property’s Flood Factor as well as data on economic loss.

• The flood depth in centimeters at the low, medium, and high CMIP 4.5 climate scenarios for the 2, 5, 20, 100, and 500 year storms this year and in 30 years.

• Data on the cumulative probability of a flood event exceeding the 0cm, 15cm, and 30cm threshold depth is provided at the low, medium, and high climate scenarios for this year and in 30 years.

• Information on historical events and flood adaptation, such as ID and name.

This dataset includes First Street's aggregated flood risk summary statistics. The data is available in CSV format and is aggregated at the congressional district, county, and zip code level. The data allows you to compare FSF data with FEMA data. You can also view aggregated flood risk statistics for various modeled return periods (5-, 100-, and 500-year) and see how risk changes due to climate change (compare FSF 2020 and 2050 data). There are various Flood Factor risk score aggregations available including the average risk score for all properties (flood factor risk scores 1-10) and the average risk score for properties with risk (i.e. flood factor risk scores of 2 or greater). This is version 2.0 of the data and it covers the 50 United States and Puerto Rico. There will be updated versions to follow.

If you are interested in acquiring First Street flood data, you can request to access the data here. More information on First Street's flood risk statistics can be found here and information on First Street's hazards can be found here.

The data dictionary for the parcel-level data is below.

Listing of low carbon energy generators installed on GLA group properties as requested in question 2816/2010 to the Mayor during the September 2010 Mayor's Question Time.

To date information has been provided by the London Fire and Emergency Planning Authority, the GLA and the Metropolitan Police Service (MPS). Transport for London has provided interim data, and further data will follow.

GLA csv

LFEPA csv

MPS csv

TfL csv

LFEPA Data
Details of low carbon energy generators located at fire stations in London operated by the London Fire Brigade (London Fire and Emergency Planning Authority). The data provides the location of the fire stations (including post code, grid reference and latitude/longitude) and the type of generators at those premises including photovoltaic (PV) array, combined heat and power (CHP), wind turbines (WT) and solar thermal panels (STU). Data correct as of December 2012. The previous LFEPA data from October 2010 is available in csv, tab and shp formats. Previous LFEPA data from May 2011 and April 2014 is available.

For further information please contact david.wyatt@london-fire.gov.uk

GLA Data Details of the photovoltaic (PV) installation at City Hall. Data correct as of 4th May 2011.

MPS Data The table provides details of low carbon energy generation installations on MPS buildings in London. The data provides the site locations (including post code, grid reference and latitude/longitude) and the type of generators at those premises which includes Photovoltaic (PV) arrays, Combined Heat and Power (CHP), Ground Source Heat Pumps (GSHP) and Solar Thermal panels (STU). This data is correct as at the 20th May 2011.

TfL Data Details of low carbon energy generators located at Transport for London’s buildings such as stations, depots, crew accommodation and head offices are provided. The data includes the postcode of the buildings and the type of generators at those premises including photovoltaic (PV) array, combined heat and power (CHP), wind turbines (WT) and solar thermal panels (STU). Data correct as of 24th May 2011.

For further information please contact helenwoolston@tfl.gov.uk

The Inventory of Owned and Leased Properties (IOLP) allows users to search properties owned and leased by the General Services Administration (GSA) across the United States, Puerto Rico, Guam and American Samoa.

The Owned and Leased Data Sets include the following data except where noted below for Leases:

• Location Code - GSA’s alphanumeric identifier for the building
• Real Property Asset Name - Allows users to find information about a specific building
• Installation Name - Allows users to identify whether a property is part of an installation, such as a campus
• Owned or Leased - Indicates the building is federally owned (F) or leased (L)
• GSA Region - GSA assigned region for building location
• Street Address/City/State/Zip Code - Building address
• Longitude and Latitude - Map coordinates of the building (only through .csv export)
• Rentable Square Feet - Total rentable square feet in building
• Available Square Feet - Vacant space in building
• Construction Date (Owned Only) - Year built
• Congressional District - Congressional District building is located
• Senator/Representative/URL - Senator/Representative of the Congressional District and their URL
• Building Status (Owned Only) - Indicates building is active
• Lease Number (Leased Only) - GSA’s alphanumeric identifier for the lease
• Lease Effective/Expiration Dates (Leased Only) - Date lease starts/expires
• Real Property Asset Type - Identifies a property as land, building, or structure

Real world driving trajectory dataset in Nanshan and Futian districts, Shenzhen, China, collected in October, 2017 by Amap platform. The dataset is used to predict trajectory destinations. Road network and POI statistics are utilized in this dataset, serving as urban contexts. The geometries are in Gauss-Kruger zone 38 (epsg:4526) with GCJ-02 latitude-longitude coordinate confusion.The published article is available (Hu et al., 2024) on International Journal of Digital Earth.The latest version of our code is available on GithubFile description:code.zip: code for model structure, data pipeline and training, testing procedure.data.zip: dataset and code for this study, including:data.zip/embedding/: the trained embeddings of road topology by LINE method.data.zip/predict_model/: the trained parameters of our model and baselines, with *.pth suffix for pytorch framework.data.zip/roads/: the shp file of road network. POI statistics are contained in road_input.csvdata.zip/trajectories/: driving trajectories of each day. metadata.csv contains the departure time, destination and other statistics.

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 data file for each of these countries:

• Bermuda - bermuda.csv
• Canada - canada.csv
• Curaçao - curaçao.csv
• Jamaica - jamaica.csv
• Mexico - mexico.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 to map weather, crime, or how your next canoing trip.

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the November 2025 release

• Nationally, the November 2025 update of G-NAF shows an increase of 32,773 addresses overall (0.21%). The total number of addresses in G-NAF now stands at 15,827,416 of which 14,983,358 or 94.67% are principal.

• There is one new locality for the November 2025 Release of G-NAF, the locality of Southwark in South Australia.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

Sentinel2GlobalLULC is a deep learning-ready dataset of RGB images from the Sentinel-2 satellites designed for global land use and land cover (LULC) mapping. Sentinel2GlobalLULC v2.1 contains 194,877 images in GeoTiff and JPEG format corresponding to 29 broad LULC classes. Each image has 224 x 224 pixels at 10 m spatial resolution and was produced by assigning the 25th percentile of all available observations in the Sentinel-2 collection between June 2015 and October 2020 in order to remove atmospheric effects (i.e., clouds, aerosols, shadows, snow, etc.). A spatial purity value was assigned to each image based on the consensus across 15 different global LULC products available in Google Earth Engine (GEE). Our dataset is structured into 3 main zip-compressed folders, an Excel file with a dictionary for class names and descriptive statistics per LULC class, and a python script to convert RGB GeoTiff images into JPEG format. The first folder called "Sentinel2LULC_GeoTiff.zip" contains 29 zip-compressed subfolders where each one corresponds to a specific LULC class with hundreds to thousands of GeoTiff Sentinel-2 RGB images. The second folder called "Sentinel2LULC_JPEG.zip" contains 29 zip-compressed subfolders with a JPEG formatted version of the same images provided in the first main folder. The third folder called "Sentinel2LULC_CSV.zip" includes 29 zip-compressed CSV files with as many rows as provided images and with 12 columns containing the following metadata (this same metadata is provided in the image filenames): Land Cover Class ID: is the identification number of each LULC class Land Cover Class Short Name: is the short name of each LULC class Image ID: is the identification number of each image within its corresponding LULC class Pixel purity Value: is the spatial purity of each pixel for its corresponding LULC class calculated as the spatial consensus across up to 15 land-cover products GHM Value: is the spatial average of the Global Human Modification index (gHM) for each image Latitude: is the latitude of the center point of each image Longitude: is the longitude of the center point of each image Country Code: is the Alpha-2 country code of each image as described in the ISO 3166 international standard. To understand the country codes, we recommend the user to visit the following website where they present the Alpha-2 code for each country as described in the ISO 3166 international standard:https: //www.iban.com/country-codes Administrative Department Level1: is the administrative level 1 name to which each image belongs Administrative Department Level2: is the administrative level 2 name to which each image belongs Locality: is the name of the locality to which each image belongs Number of S2 images : is the number of found instances in the corresponding Sentinel-2 image collection between June 2015 and October 2020, when compositing and exporting its corresponding image tile For seven LULC classes, we could not export from GEE all images that fulfilled a spatial purity of 100% since there were millions of them. In this case, we exported a stratified random sample of 14,000 images and provided an additional CSV file with the images actually contained in our dataset. That is, for these seven LULC classes, we provide these 2 CSV files: A CSV file that contains all exported images for this class A CSV file that contains all images available for this class at spatial purity of 100%, both the ones exported and the ones not exported, in case the user wants to export them. These CSV filenames end with "including_non_downloaded_images". To clearly state the geographical coverage of images available in this dataset, we included in the version v2.1, a compressed folder called "Geographic_Representativeness.zip". This zip-compressed folder contains a csv file for each LULC class that provides the complete list of countries represented in that class. Each csv file has two columns, the first one gives the country code and the second one gives the number of images provided in that country for that LULC class. In addition to these 29 csv files, we provided another csv file that maps each ISO Alpha-2 country code to its original full country name. © Sentinel2GlobalLULC Dataset by Yassir Benhammou, Domingo Alcaraz-Segura, Emilio Guirado, Rohaifa Khaldi, Boujemâa Achchab, Francisco Herrera & Siham Tabik is marked with Attribution 4.0 International (CC-BY 4.0)

Auxiliary files, code, and data for paper published in The Cryosphere:

Observed snow depth trends in the European Alps 1971 to 2019

https://doi.org/10.5194/tc-15-1343-2021

Auxiliary files:

• aux_paper.zip: Auxiliary figures to the paper (time series showing the consistency of averaging monthly mean snow depth of stations within 500 m elevation bins; times of seasonal snow depth and snow cover duration indices).
• aux_paper_crocus_comparison.zip: Time series comparing spatial statistical gap filling from paper to gap filling using snow depth assimilation into Crocus snow model (only for subset of stations in the French Alps)
• aux_paper_monthly_time_series.zip: Plots of monthly time series of snow depth, for each station.
• aux_paper_spatial_consistency.zip: Aggregate results from spatial consistency (statistical simulation using neighboring stations), and time series of observed versus simulated monthly snow depths.

Code (working copy, not cleaned, all written in R statistical software): code.zip

• to read in the different data sources
• to do quality checks and data processing
• to perform statistical analyses as in paper
• to produce figures and tables as in paper

Data:

• Daily and monthly stations snow depth and depth of snowfall, as .zips, grouped by data provider.
• Information on column content is provided in separate files "data_[daily|monthly]_00_column_names_content.txt".
• > 2000 stations from Austria, Germany, France, Italy, Switzerland, and Slovenia
• Meta data (name, latitude, longitude, elevation) in "meta_all.csv", along with an interactive map "meta_interactive_map.html", and column information in "meta_00_column_names_content.txt".
• If you use the data you agree to adhere to the respective data provider's terms as listed in "00_DATA_LICENSE_AND_TERMS.PDF"
• The license terms especially (and additionally to any other terms of the single data providers) include: Attribution — You must give appropriate credit, provide a link to the license, and indicate if changes were made. You may do so in any reasonable manner, but not in any way that suggests the licensor endorses you or your use. [from CC BY 4.0]

Version history:

v1.2: uploaded data

v1.1: changes to aux-paper.zip and code.zip as consequence from submitting a revised manuscript

v1.0: initial upload

This dataset contains supplementary files uploaded as part of the above journal article.

5 sub-datasets have been uploaded separately. The first sub-dataset contains the peak delay times data. A few
records contain negative peak delay times due to change in waveform shape from filtering, these
were excluded during further calculations. The other 4 sub-datasets contain files as well as the script
to generate the results of Δlog t_p , κ, ε_param and P(m_l ) as shown in figures 7, 8, 9 and 10 respectively
of the main article.

Sub-Dataset S1: File “ds01.csv” contains the list of peak delay times (t_p ) in 2-4 Hz, 4-8 Hz, 8-16 Hz
and 16-32 Hz bands for the waveforms used in this study. The columns in the file represent
origin time (in year-month-day’T’hour:minute:seconds.microseconds format), event latitude,
event longitude, event depth, station code, station latitude, station longitude, t_p in 2-4 Hz, t_p in 4-
8 Hz, t_p in 8-16 Hz and t_p in 16-32 Hz in a sequential manner.

Sub-Dataset S2: File “ds02.zip” contains four text files (nodes_24e.txt, nodes_48e.txt, and
nodes_816e.txt) and one GMT (Generic Mapping Tools) script file (plot_final_comb.gmt)
written in BASH. The text files contain Δlog t_p values in 2-4 Hz, 4-8 Hz and 8-16 Hz bands
respectively. The columns in the text files represent node index, node latitude, node longitude,
node depth and Δlog t_p value in a sequential manner. The GMT script uses GSHHG coastline
data which is freely available for download from http://www.soest.hawaii.edu/wessel/gshhg/ .
Once downloaded and extracted its path can be added to the variable “GDIR” at the beginning of
the script. The GMT script file can be run to see the spatial distribution of Δlog t p using GMT-5
(Wessel et al., 2013) and above.

Sub-Dataset S3: File “ds03.zip” contains four text files (kappa_f10.txt, kappa_f30.txt,
kappa_f50.txt, kappa_f70.txt) and one GMT script file (inv_kappa.gmt) written in BASH. The
text files contain κ values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively.
The columns in the text files represent node latitude, node longitude and κ value of the node
sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the
script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution
of κ using GMT-5 (Wessel et al., 2013) and above.

Sub-Dataset S4: File “ds04.zip” contains four text files (aetal_f10.txt, aetal_f30.txt, aetal_f50.txt,
aetal_f70.txt) and one GMT script file (inv_aetal.gmt) written in BASH. The text files contain
ε_param values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The columns in
the text files represent node latitude, node longitude and ε_param value of the node sequentially.
This GMT script also uses GSHHG coastline data whose path can be added to the script, same as
in data set S2 case. The GMT script file can be run to see the spatial distribution of ε_param using
GMT-5 (Wessel et al., 2013) and above.

Sub-Dataset S5: File “ds05.zip” contains four text files (psdf_f10.txt, psdf_f30.txt, psdf_f50.txt,
psdf_f70.txt) and one GMT script file (inv_psdf.gmt) written in BASH. The text files contain
psdf (P(m_l )) values for 0-20 km, 20-40 km, 40-60 km and 60-80 km range respectively. The
columns in the text files represent node latitude, node longitude and P(m_l ) value of the node
sequentially. This GMT script also uses GSHHG coastline data whose path can be added to the
script, same as in data set S2 case. The GMT script file can be run to see the spatial distribution
of P(m_l ) using GMT-5 (Wessel et al., 2013) and above.

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. West region.

States included in this dataset:

• Alaska - ak.csv
• Arizona - az.csv
• California - ca.csv
• Colorado - co.csv
• Hawaii - hi.csv
• Idaho - id.csv
• Montana - mt.csv
• New Mexico - nm.csv
• Nevada - nv.csv
• Oregon - or.csv
• Utah - ut.csv
• Washington - wa.csv
• Wyoming - wy.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