🌍 Ultimate Geographic Data Collection | Cities & Zip Codes

📌 Overview

Welcome to the Ultimate Geographic Data Collection, a comprehensive dataset providing valuable geographic insights. This dataset includes U.S. Zip Codes, U.S. Cities, and World Cities data, making it an essential resource for developers, data analysts, and researchers. Whether you're building location-based applications, conducting geographic analysis, or working on machine learning projects, this dataset offers an extensive and curated collection of location-based information.

📊 What's Inside?

• U.S. Zip Codes Database (Free Version) 🏙️

  • Includes ZIP codes, state associations, and geographic coordinates.
  • 🔗 Usage Condition: Requires a visible backlink to SimpleMaps US Zip Code Database.

• U.S. Cities Database (Free Version) 🌆

  • Includes city names, state information, latitude, longitude, and population data.
  • 🔗 Usage Condition: Requires a visible backlink to SimpleMaps US Cities Database.

• Basic World Cities Database 🗺️

  • Provides global city data licensed under Creative Commons Attribution 4.0.
  • 📜 Learn more: CC BY 4.0 License.

• Comprehensive & Pro World Cities Database (Density Data) 🌎

  • Population density estimates sourced from CIESIN - Columbia University.
  • 🔗 Licensed under Creative Commons Attribution 4.0 with no additional restrictions.

⚖️ License & Usage Terms

• ✅ You CAN:

  • Use this dataset in private and public-facing applications.
  • Create copies and backups for your projects.
  • Transfer the license (with provider approval via email).

• 🚫 You CANNOT:

  • Redistribute the dataset publicly without written permission.
  • Use it in a way that violates any laws.
  • Bypass the backlink requirement (for free U.S. Zip Code & Cities Databases).

🛠️ How to Use

1. Download the dataset 📥.
2. Ensure compliance with licensing terms.
3. Use it in your projects for analysis, visualization, or machine learning.
4. Provide attribution (if applicable) for free datasets.

⚠️ Disclaimer

• This dataset is provided "AS IS", without any warranties.
• The provider is not liable for any issues arising from usage.
• Users are responsible for ensuring legal compliance in their jurisdiction.

🔥 Get Started!

Enhance your geographic projects with this powerful dataset today! 🚀

📩 For any inquiries, licensing requests, or attribution clarifications, contact the dataset provider.

This metadata record provides the latitude, longitude, and a unique ID value (PRISMID) for all raster cells used by the PRISM group at Oregon State University to characterize climate throughout the conterminous United States. The three column format of comma-separated data can be readily joined to the comma-separated climate and water-balance variables described in the associated metadata file "Water Balance Model Inputs and Outputs for the Conterminous United States".

This .cvs file contains the latitude and longitude, along with closest geographic names, for the 15 study sites visited by projects projects ANT-1744550, -1744570, -1744584, and -1744602 during ARSV Laurence M. Gould cruise LMG 19-04 in April and May 2019.

This dataset, worldcities.csv is a comprehensive collection of geographic and demographic data for 44,383 cities worldwide, spanning 127+ countries. Sourced independently, it includes 10 columns: city, city_ascii, lat, lng, country, iso2, iso3, admin_name, capital, and population. With India (15%) and the US (11%) leading the pack, and cities like Tokyo (37.7M) and Jakarta (33.8M) topping population charts, it’s a goldmine of real-world coordinates (lat: -54.9 to 81.7, lng: -180 to 179) and stats. I used this as a foundation to generate synthetic geo-tagged tweet data (geo_gpt_tweets_8.2-10.json), pulling lat, lng, and country to anchor 1,000 GPT-related posts in realistic locations—achieving an 82% real-world chaos vibe. Perfect for mapping, clustering, or seeding your own geo-based projects!

Dataset

This dataset was created by Mohamed Y Abdelaziz

Released under Database: Open Database, Contents: Database Contents

Contents

This dataset includes the 2018 latitude and longitude information for habitat and fish reach locations on the Santa Ana and San Gabriel Rivers.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

CORDEX (http://wcrp-cordex.ipsl.jussieu.fr) is a WCRP initiative to coordinate the downscaling of CMIP5 simulation results produced in support of the IPCC/AR5. About 30 institutes worldwide contributed to the CORDEX data base with more than 20 RCMs in various configurations and versions. CORDEX defined 13 domains (http://wcrp-cordex.ipsl.jussieu.fr/images/pdf/cordex_regions.pdf) with acronyms AFR(Africa), EUR(Europe), ARC(Arctic), ANT(Antarctica), MED(Mediterranean), SAM(South America), CAM(Central America), NAM(North America), WAS(South Asia), EAS(East Asia), CAS(Central Asia), AUS(Austral-Asia), ARB(Mediterranean-North Africa). The priority domain is the African continent. The data are requested to be generated on model grids with an approximate grid cell distance of 50/25/12 km (acronyms -44i/-22i/-11i). More data '- on native model grids '- are provided in project CORDEX_DDS-CMIP5_native-grid. RCM evaluation with the ERA-Interim re-analyses data (1989-2008) is mandatory. Besides, downscaling of historical experiments (1950-2005) and of climate change scenarios RCP 4.5 and 8.5 is requested. Many groups also used RCP 2.6 climate projections. CORDEX data are formatted following the CMIP5 data and metadata protocol where possible. For more details see http://cordex.dmi.dk/joomla/images/CORDEX/cordex_archive_specifications.pdf . A list of the variables requested by CORDEX can be downloaded from this project (CORDEX_variables_requirements_table.pdf). Not all variables will be provided by all RCMs though. Note that 3 and 6 hourly data are also available, however, only at the modelling centres on request.

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

This dataset provides country code, postal code, latitude, longitude, as well as names of state, county/province, community etc. for all countries where the data is available.

Context

This dataset is meant to be used with other datasets that have features like country and city but no latitude/longitude. It is simply a list of cities in the world. Being able to put cities on a map will help people tell their stories more effectively. Another way to think about it is that you can use this make more pretty graphs!

Content

Fields:

• city
• region
• country
• population
• latitude
• longitude

Acknowledgements

These data come from Maxmind.com and have not been altered. The original source can be found by clicking here

Additionally, the Maxmind sharing license has been included.

Inspiration

I wanted to analyze a dataset and make a map, but I was only given a city name without any latitude or longotude coordinates. I found this dataset very helpful and I hoe you do too!

OpenStreetMap (OSM) is a collaborative project to create a free editable map of the world. Created in 2004, it was inspired by the success of Wikipedia and more than two million registered users who can add data by manual survey, GPS devices, aerial photography, and other free sources.

OSM is produced as a public good by volunteers, and there are no guarantees about data quality. OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

OSM represents physical features on the ground (e.g. roads or buildings) using tabs attached to its basic data structure (its nodes, ways, and relations). Each tag describes a geographic attribute of the feature being shown by the specific node, way or relation.

Nodes are one of the core elements in the OSM data model. It consists of a single point in space defined by its latitude, longitude and node id. Nodes can be used to define standalone point features.

The GeoJunxion Airports are part of the GeoJunxion Geo-Boundaries data suite. The GeoJunxion Airports are land use boundaries and are often used to define areas. Airport boundaries are shown in geographical data by making use of Polygons. Polygons form the geometry of boundary features. The GeoJunxion Airports Boundaries have attributes such as: Name, IATA name, type of airport, latitude/longitude and centre point.

With the GeoJunxion Airports, also named Area of Interest (AOI) and Points of Interest (POI), you can add relevant information to an area such as the name, latitude/longitude, road network and centre point. The GeoJunxion Airports database covers the geographic boundaries of Airports across the globe.

In select areas the detailed Runway and Taxiway boundaries are also available.

Additional attributes with more detailed information optionally available.

The prices vary depending on the application and individual requirements. Just talk to us, we'll be happy to make you an offer.

This API returns the US Census Block geography ID information given a passed Latitude and Longitude.

The dataset includes addresses, latitudes, longitudes, streets, and neighborhoods only of the city of São Paulo (SP). All the data were web scrapped from Cep Brasil, hence the source is not an official one, It could have inconsistencies such as zip codes excluded and new ones added to the base. Bear in mind that the latitude and longitude might not always be faithful to the 'exact' location.

The source code: Github

This dataset mainly provides a matching table of 3-digit postal codes and the coordinates of administrative district centers.

Crowdsourced georeference data for over three thousand maps, annotated online by participants in the BL Georeferencer project. http://britishlibrary.typepad.co.uk/magnificentmaps/2014/08/success.html "In just 28 days from release, 3,220 maps have been geo-located online by participants in the BL Georeferencer project. For this quantity of maps to be completed at such a speed is truly impressive, and testifies to much scrutiny of maps and online research by many people."

NB The image identifier refers to its Flickr identifier. "11000447924" refers to the image located at https://flickr.com/photos/biritishlibrary/11000447924

TIGER road data for the MSA. When compared to high-resolution imagery and other transportation datasets positional inaccuracies were observed. As a result caution should be taken when using this dataset. TIGER, TIGER/Line, and Census TIGER are registered trademarks of the U.S. Census Bureau. ZCTA is a trademark of the U.S. Census Bureau. The Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States, Puerto Rico, and the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The boundary information in the TIGER/Line files are for statistical data collection and tabulation purposes only; their depiction and designation for statistical purposes does not constitute a determination of jurisdictional authority or rights of ownership or entitlement. The Census 2000 TIGER/Line files do NOT contain the Census 2000 urban areas which have not yet been delineated. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful. This is part of a collection of 221 Baltimore Ecosystem Study metadata records that point to a geodatabase. The geodatabase is available online and is considerably large. Upon request, and under certain arrangements, it can be shipped on media, such as a usb hard drive. The geodatabase is roughly 51.4 Gb in size, consisting of 4,914 files in 160 folders. Although this metadata record and the others like it are not rich with attributes, it is nonetheless made available because the data that it represents could be indeed useful.

Dataset

This dataset was created by Nadia R

Released under Database: Open Database, Contents: © Original Authors

Contents

A dataset containing the longitude and latitude of all countries typically includes the geographic coordinates for each country. The coordinates usually represent the geographic center, capital city, or another significant location within the country. Such a dataset can be useful for various applications, including mapping, geographic analysis, and location-based services.