• This dataset contains the postal codes of Berlin grouped by districts and neighborhoods.
• It allows you to create graphs and filters at these two levels.
• It is especially useful for plotting maps in Tableau, but you can also use it with other tools.
• Please visit the project: https://github.com/santiagocandia/data-viz/tree/main/berlin-zip-codes and the dashboard: https://public.tableau.com/app/profile/santiago.candia/viz/Berlinspostalcodes/Berlinspostalcodes

🌍 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.

The 2020 Zip Code Boundaries dataset contains boundary information for Zip Code Tabulation Areas (ZCTAs) as of the 2020 Census, sourced from the United States Census Bureau. ZCTAs are statistical representations of USPS ZIP Code service areas and are used for demographic analysis, data aggregation, and geographic reference purposes. This dataset includes boundary polygons for each ZCTA, allowing users to visualize and analyze ZIP Code boundaries within Montgomery County, Texas.Data Fields Included:ZCTA CodeAreaThis dataset is sourced from the United States Census Bureau.Data source: United States Census Bureau TIGER Data

Demographic statistics broken down by zip code

Context

I used this publicly available data for making interactive map visualization of NYC. Zipcode geodata is useful for building interactive maps with each zip code area representing a separate area on the map.

Content

NYC zipcode geodata in geojson format

Acknowledgements

The rights belong to the original authors.

This Zipcode GIS Layer is a spatial dataset that outlines the boundaries of ZIP code areas across York County, Pennsylvania. This layer is used in Geographic Information Systems (GIS) to support mapping, analysis, and decision-making based on location. Each ZIP code area is represented as a shape on the map and includes basic information such as the ZIP code, city, and state. This data is useful for a wide range of applications including business planning, public services, marketing, transportation, and emergency response. The Zipcode GIS Layer allows users to visualize and analyze geographic patterns, such as population distribution, service coverage, and regional trends. It can be used on its own or combined with other spatial data for more detailed studies.

Spatial Mapping of HITECH Grants

Visualizing Health and Community Grant Locations in the U.S

By US Open Data Portal, data.gov [source]

About this dataset

This dataset provides crucial geographic data related to two of the U.S. Health Information Technology for Economic and Clinical Health (HITECH) Act programs: the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. As part of the American Recovery and Reinvestment Act (ARRA), these grants were made available to provide citizens with access to health IT infrastructure investments throughout diverse communities across the United States. This crosswalk is an essential resource for anyone looking to link regional, state, county and zip code level program financials with performance metrics for visualization or comparison. With detailed information about region counties, codes, states, FIPS codes and ZIP codes associated with local HITECH grantees, this data presentation helps shed light on a financially impactful initiative from our federal government that can drastically improve healthcare delivery in thousands of cities nationwide!

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How to use the dataset

This dataset provides geographic data for the service areas of two of the HITECH programs, the Health IT Regional Extension Centers (REC) Program and the Beacon Communities Program. This can be used to map and visualize data related to those programs. Here is a guide on how to use this dataset:

• Get familiar with key columns: Familiarize yourself with the columns included in this dataset, including column names and descriptions for each column such as region, region_code, county_name, state_fips, county_fips and zip.
• Review data formats: If there are any discrepancies between your current format of data presented in this dataset versus what you may have currently in your system or within other sources of information - make sure to review those discrepancies prior exploring more from here onwards.
• Understand regional coverage: Refine the analysis by filtering out different grantee located based on specific regions or states - use necessary filters such as Region code or County FIPs code that will give you an easier view on which region/county grantee has been provided funding through these HHS programs as part of Hitech Act program distribution.
• Map & Visualize grantees: We can visualise geographically where are REC-Program & Beacon Communities Grants distributed across US by making a heatmap while taking desired geolocation coordinates like zip codes; query all available details under columns we need like zip codes along their respective countyp location & state value so that grants can be highlighted after it renders practical Map visuals for us giving an ease if further status / details required about entities who had taken these grants within certain area / regions!

Research Ideas

• Creating an interactive map to visualize grant program performance by region and county.
• Using the data to create a color-coded scatterplot graphic to show active grant program sites in the US.
• Generating reports on HITECH Grantee performance over time, grouped by geographic area or region

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: healthit-dashboard-areatype-crosswalk-csv-1.csv | Column name | Description | |:------------------------|:---------------------------------------------------------------------------------------------------------------------------------------------| | region | The region of the grantee. (String) | | region_code | The code for the region of the grantee. (String) | | county_name | The name of county where the grantee is located. (String) | | state_fips | The Federal Information Processing Standard (FIPS) code for knowledge of which state it is located in. (String) | | county_fips | The Federa...

The 2010 Zip Code Boundaries dataset contains boundary information for Zip Code Tabulation Areas (ZCTAs) as of the 2010 Census, sourced from the United States Census Bureau. ZCTAs are statistical representations of USPS ZIP Code service areas and are used for demographic analysis, data aggregation, and geographic reference purposes. This dataset includes boundary polygons for each ZCTA, allowing users to visualize and analyze ZIP Code boundaries within Montgomery County, Texas.Data Fields Included:ZCTA CodeAreaThis dataset is sourced from the United States Census Bureau.Data source: United States Census Bureau TIGER Data

** A Newer Version of this data is available here: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6The United States Postal Service (USPS) does not define ZIP codes as fixed geographic boundaries, such as polygons on a map. Instead, ZIP codes are structured as collections of carrier routes designed to optimize mail delivery. These routes are established based on logistical considerations, such as population density, delivery efficiency, and infrastructure changes, rather than adhering to precise geographic outlines.When ZIP codes are mapped, the resulting visualization is essentially an estimation of these delivery routes. However, these approximations are inherently subject to change, as the Postal Service frequently adjusts routes to accommodate new developments, address shifts in demand, or enhance operational efficiency. Consequently, any representation of ZIP codes on a map should be understood as a general reference and not as an exact or permanent delineation.National ZipCodes: https://dallasgis.maps.arcgis.com/home/item.html?id=0a2fde8aa7404187917488bafcbc77e6

Context

Those data came from data.iledefrance.fr, which collects open data for Paris and its region

Content

Four files are in the dataset :

base_comparateur_de_territoire : give information on the number of firms, population, residence, employment....
  CODGEO : geographique code for the town 
  LIBGEO : name of the town (in french)
  REG : region number
  DEP : depatment number
  P14_POP : population size in 2014
  P09_POP : population size in 2009
  SUPERF : area size in km2
  NAIS0914 : births between 2009 and 2014
  DECE0914 : deaths between 2009 and 2014
  P14_MEN : family number in 2014
  NAISD16 : number of birth in 2016
  DECESD16 : number of death in 2016
  P14_LOG : number of residence in 2014
  P14_RP : number of main residence in 2014
  P14_RSECOCC : number of secondary residence in 2014
  P14_LOGVAC : number of free residence in 2014
  P14_RP_PROP : number of main residence occupied by their owner in 2014
  NBMENFISC14 : number of familly who are paying tax on their earning
  NAISD16 : number of birth in 2016
  DECESD16 : number of death in 2016
  P14_LOG : number of residence in 2014
  P14_RP : number of main residence in 2014

more information on https://data.iledefrance.fr/explore/dataset/base-comparateur-de-territoires/information/

entreprises-immatriculees-en-2017: give information on created firms in 2017
  more information on https://data.iledefrance.fr/explore/dataset/entreprises-immatriculees-en-2017/information/

entreprises-radiees-en-2017: give information on deleted firms in 2017
  more information on https://data.iledefrance.fr/explore/dataset/entreprises-radiees-en-2017/information/

laposte_hexasmal : contains correspondence between zip codes and insee codes (CODGEO)

These datasets can be merged by : CODGEO = code_insee or zip codes = code postal

Acknowledgements

Thanks to data.iledefrance.fr and more generally to data.gouv.fr which provides many interesting open data.

Inspiration

Main idea is to check firms evolution in paris neighborhood

A self-hosted location dataset containing all administrative divisions, cities, and zip codes for Croatia. All geospatial data is updated weekly to maintain the highest data quality, including coverage of complex regions within the country.

Use cases for the Global Zip Code Database (Geospatial data) - Address capture and validation - Map and visualization - Reporting and Business Intelligence (BI) - Master Data Management - Logistics and Supply Chain Management - Sales and Marketing

Data export methodology Our location data packages are offered in variable formats, including .csv. All geospatial data are optimized for seamless integration with popular systems like Esri ArcGIS, Snowflake, QGIS, and more.

Product Features - Fully and accurately geocoded - Administrative areas with a level range of 0-4 - Multi-language support including address names in local and foreign languages - Comprehensive city definitions across countries

For additional insights, you can combine the map data with: - UNLOCODE and IATA codes - Time zones and Daylight Saving Times

Why do companies choose our location databases - Enterprise-grade service - Reduce integration time and cost by 30% - Weekly updates for the highest quality

Note: Custom geospatial data packages are available. Please submit a request via the above contact button for more details.

Traffic Count Viewer is an online mapping application, which users can use to explore traffic count reports in different locations within the Delaware Valley, including Philadelphia. Users search by location (address, city, zip code, or place name) to view point features on the interactive mapping visualization of traffic records. Clicking on a point of interest or grouping multiple points on the map yields traffic count information tables, which includes: Date of Counnt ; DVRPC File # ; Type ; Annual Average Daily Traffic (AADT) ; Municipality ; Route Number ; Road Name ; Count Direction ; and From/To Locations, as well as a link to the detailed (hourly) report. Data tables are exportable as .CSV and detailed reports are available for export in multiple formats (including basic .doc and .rtf outputs.) Traffic count data is collected by the Delaware Valley Regional Planning Commission and other agencies.

Spotzi’s premium 3-5 digit postal code data for Ukraine (UKR) gives marketers, retailers, and OOH professionals everything they need to understand and reach their audience more effectively. Our high-quality boundary data lets you visualize every postal code area, build precise targeting strategies, create sales territories, and uncover valuable demographic and campaign insights—all in one easy-to-use dashboard.

With a Spotzi Premium account, you can explore, customize, and export Ukrainian postal code boundaries for activation across your channels. Whether you're planning a national campaign or analyzing store visitors by location, Spotzi eliminates the hassle of complex data management so you can focus on driving results.

US Broadband Usage Across Counties

Utilizing Microsoft's Data to Estimate Access

By Amber Thomas [source]

About this dataset

This dataset provides an estimation of broadband usage in the United States, focusing on how many people have access to broadband and how many are actually using it at broadband speeds. Through data collected by Microsoft from our services, including package size and total time of download, we can estimate the throughput speed of devices connecting to the internet across zip codes and counties.

According to Federal Communications Commission (FCC) estimates, 14.5 million people don't have access to any kind of broadband connection. This data set aims to address this contrast between those with estimated availability but no actual use by providing more accurate usage numbers downscaled to county and zip code levels. Who gets counted as having access is vastly important -- it determines who gets included in public funding opportunities dedicated solely toward closing this digital divide gap. The implications can be huge: millions around this country could remain invisible if these number aren't accurately reported or used properly in decision-making processes.

This dataset includes aggregated information about these locations with less than 20 devices for increased accuracy when estimating Broadband Usage in the United States-- allowing others to use it for developing solutions that improve internet access or label problem areas accurately where no real or reliable connectivity exists among citizens within communities large and small throughout the US mainland.. Please review the license terms before using these data so that you may adhere appropriately with stipulations set forth under Microsoft's Open Use Of Data Agreement v1.0 agreement prior to utilizing this dataset for your needs-- both professional and educational endeavors alike!

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How to use the dataset

How to Use the US Broadband Usage Dataset

This dataset provides broadband usage estimates in the United States by county and zip code. It is ideally suited for research into how broadband connects households, towns and cities. Understanding this information is vital for closing existing disparities in access to high-speed internet, and for devising strategies for making sure all Americans can stay connected in a digital world.

The dataset contains six columns: - County – The name of the county for which usage statistics are provided. - Zip Code (5-Digit) – The 5-digit zip code from which usage data was collected from within that county or metropolitan area/micro area/divisions within states as reported by the US Census Bureau in 2018[2].
- Population (Households) – Estimated number of households defined according to [3] based on data from the US Census Bureau American Community Survey's 5 Year Estimates[4].
- Average Throughput (Mbps)- Average Mbps download speed derived from a combination of data collected anonymous devices connected through Microsoft services such as Windows Update, Office 365, Xbox Live Core Services, etc.[5]
- Percent Fast (> 25 Mbps)- Percentage of machines with throughput greater than 25 Mbps calculated using [6]. 6) Percent Slow (< 3 Mbps)- Percentage of machines with throughput less than 3Mbps calculated using [7].

Research Ideas

• Targeting marketing campaigns based on broadband use. Companies can use the geographic and demographic data in this dataset to create targeted advertising campaigns that are tailored to individuals living in areas where broadband access is scarce or lacking.
• Creating an educational platform for those without reliable access to broadband internet. By leveraging existing technologies such as satellite internet, media streaming services like Netflix, and platforms such as Khan Academy or EdX, those with limited access could gain access to new educational options from home.
• Establishing public-private partnerships between local governments and telecom providers need better data about gaps in service coverage and usage levels in order to make decisions about investments into new infrastructure buildouts for better connectivity options for rural communities

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: broadband_data_2020October.csv

Acknowledgements

If you use this dataset in your research,...

Spotzi's premium 6-digit postal code data for The Netherlands (NLD) gives marketers, retailers, and OOH professionals everything they need to understand and reach their audience more effectively. Our high-quality boundary data lets you visualize every postal code area, build precise targeting strategies, create sales territories, and uncover valuable demographic and campaign insights—all in one easy-to-use dashboard.

With a Spotzi Premium account, you can explore, customize, and export Dutch postal code boundaries for activation across your channels. Whether you're planning a national campaign or analyzing store visitors by location, Spotzi eliminates the hassle of complex data management so you can focus on driving results.

Spotzi's premium 5-digit postal code data for Estonia (EST) gives marketers, retailers, and OOH professionals everything they need to understand and reach their audience more effectively. Our high-quality boundary data lets you visualize every postal code area, build precise targeting strategies, create sales territories, and uncover valuable demographic and campaign insights—all in one easy-to-use dashboard.

With a Spotzi Premium account, you can explore, customize, and export Estonian postal code boundaries for activation across your channels. Whether you're planning a national campaign or analyzing store visitors by location, Spotzi eliminates the hassle of complex data management so you can focus on driving results.

Spotzi's premium 5-digit postal code data for Italy (ITA) gives marketers, retailers, and OOH professionals everything they need to understand and reach their audience more effectively. Our high-quality boundary data lets you visualize every postal code area, build precise targeting strategies, create sales territories, and uncover valuable demographic and campaign insights—all in one easy-to-use dashboard.

With a Spotzi Premium account, you can explore, customize, and export Italian postal code boundaries for activation across your channels. Whether you're planning a national campaign or analyzing store visitors by location, Spotzi eliminates the hassle of complex data management so you can focus on driving results.

Data is scraped from OpenWeather, National Weather Service, and Zip-Codes.com APIs to retrieve and display JSON weather information for U.S. cities. Additional information is scraped from the web and manipulated using the Beautiful Soup and Pandas libraries. | Column | Description | | --- | --- | | City | The name of the city. | | State | The state in which the city is located.. | |Date | The date on which the information was requested.| |Time| The time at which the information was requested.| |Weather | A general description of the weather at the current location.| | Current Temperature (Farenheit) |The current temperature of the location in Farenheit. | | High (Farenheit) |The current maximum recorded temperature at the current location.| |Low (Farenheit) | The current minimum recorded temperature at the current location.| | Atmospheric Pressure (hPa) | The atmospheric pressure of the current location. | |Humidity (Percentage) |The relative humidity of the current location. |

Our World Administrative Boundaries Database offers comprehensive postal code data for spatial analysis, including postal and administrative areas. 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.

Spotzi's premium 5-digit postal code data for Montenegro (MNE) gives marketers, retailers, and OOH professionals everything they need to understand and reach their audience more effectively. Our high-quality boundary data lets you visualize every postal code area, build precise targeting strategies, create sales territories, and uncover valuable demographic and campaign insights—all in one easy-to-use dashboard.

With a Spotzi Premium account, you can explore, customize, and export Montenegrin postal code boundaries for activation across your channels. Whether you're planning a national campaign or analyzing store visitors by location, Spotzi eliminates the hassle of complex data management so you can focus on driving results.