Context

NYC Open Data is an opportunity to engage New Yorkers in the information that is produced and used by City government. We believe that every New Yorker can benefit from Open Data, and Open Data can benefit from every New Yorker. Source: https://opendata.cityofnewyork.us/overview/

Content

Thanks to NYC Open Data, which makes public data generated by city agencies available for public use, and Citi Bike, we've incorporated over 150 GB of data in 5 open datasets into Google BigQuery Public Datasets, including:

• Over 8 million 311 service requests from 2012-2016

• More than 1 million motor vehicle collisions 2012-present

• Citi Bike stations and 30 million Citi Bike trips 2013-present

• Over 1 billion Yellow and Green Taxi rides from 2009-present

• Over 500,000 sidewalk trees surveyed decennially in 1995, 2005, and 2015

This dataset is deprecated and not being updated.

Fork this kernel to get started with this dataset.

Acknowledgements

https://opendata.cityofnewyork.us/

https://cloud.google.com/blog/big-data/2017/01/new-york-city-public-datasets-now-available-on-google-bigquery

This dataset is publicly available for anyone to use under the following terms provided by the Dataset Source - https://data.cityofnewyork.us/ - and is provided "AS IS" without any warranty, express or implied, from Google. Google disclaims all liability for any damages, direct or indirect, resulting from the use of the dataset.

By accessing datasets and feeds available through NYC Open Data, the user agrees to all of the Terms of Use of NYC.gov as well as the Privacy Policy for NYC.gov. The user also agrees to any additional terms of use defined by the agencies, bureaus, and offices providing data. Public data sets made available on NYC Open Data are provided for informational purposes. The City does not warranty the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set made available on NYC Open Data, nor are any such warranties to be implied or inferred with respect to the public data sets furnished therein.

The City is not liable for any deficiencies in the completeness, accuracy, content, or fitness for any particular purpose or use of any public data set, or application utilizing such data set, provided by any third party.

Banner Photo by @bicadmedia from Unplash.

Inspiration

On which New York City streets are you most likely to find a loud party?

Can you find the Virginia Pines in New York City?

Where was the only collision caused by an animal that injured a cyclist?

What’s the Citi Bike record for the Longest Distance in the Shortest Time (on a route with at least 100 rides)?

https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png" alt="enter image description here"> https://cloud.google.com/blog/big-data/2017/01/images/148467900588042/nyc-dataset-6.png

Our dataset provides detailed and precise insights into the business, commercial, and industrial aspects of any given area in the USA (Including Point of Interest (POI) Data and Foot Traffic. The dataset is divided into 150x150 sqm areas (geohash 7) and has over 50 variables. - Use it for different applications: Our combined dataset, which includes POI and foot traffic data, can be employed for various purposes. Different data teams use it to guide retailers and FMCG brands in site selection, fuel marketing intelligence, analyze trade areas, and assess company risk. Our dataset has also proven to be useful for real estate investment.- Get reliable data: Our datasets have been processed, enriched, and tested so your data team can use them more quickly and accurately.- Ideal for trainning ML models. The high quality of our geographic information layers results from more than seven years of work dedicated to the deep understanding and modeling of geospatial Big Data. Among the features that distinguished this dataset is the use of anonymized and user-compliant mobile device GPS location, enriched with other alternative and public data.- Easy to use: Our dataset is user-friendly and can be easily integrated to your current models. Also, we can deliver your data in different formats, like .csv, according to your analysis requirements. - Get personalized guidance: In addition to providing reliable datasets, we advise your analysts on their correct implementation.Our data scientists can guide your internal team on the optimal algorithms and models to get the most out of the information we provide (without compromising the security of your internal data).Answer questions like: - What places does my target user visit in a particular area? Which are the best areas to place a new POS?- What is the average yearly income of users in a particular area?- What is the influx of visits that my competition receives?- What is the volume of traffic surrounding my current POS?This dataset is useful for getting insights from industries like:- Retail & FMCG- Banking, Finance, and Investment- Car Dealerships- Real Estate- Convenience Stores- Pharma and medical laboratories- Restaurant chains and franchises- Clothing chains and franchisesOur dataset includes more than 50 variables, such as:- Number of pedestrians seen in the area.- Number of vehicles seen in the area.- Average speed of movement of the vehicles seen in the area.- Point of Interest (POIs) (in number and type) seen in the area (supermarkets, pharmacies, recreational locations, restaurants, offices, hotels, parking lots, wholesalers, financial services, pet services, shopping malls, among others). - Average yearly income range (anonymized and aggregated) of the devices seen in the area.Notes to better understand this dataset:- POI confidence means the average confidence of POIs in the area. In this case, POIs are any kind of location, such as a restaurant, a hotel, or a library. - Category confidences, for example"food_drinks_tobacco_retail_confidence" indicates how confident we are in the existence of food/drink/tobacco retail locations in the area. - We added predictions for The Home Depot and Lowe's Home Improvement stores in the dataset sample. These predictions were the result of a machine-learning model that was trained with the data. Knowing where the current stores are, we can find the most similar areas for new stores to open.How efficient is a Geohash?Geohash is a faster, cost-effective geofencing option that reduces input data load and provides actionable information. Its benefits include faster querying, reduced cost, minimal configuration, and ease of use.Geohash ranges from 1 to 12 characters. The dataset can be split into variable-size geohashes, with the default being geohash7 (150m x 150m).

NOTE: The 311 dataset is currently showing incorrect values in the "Agency Name" column. Please use the "Agency" column in the interim while this is being resolved.

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

Snapshot img

Government Open Data Management Platform Market Size 2025-2029

The government open data management platform market size is forecast to increase by USD 189.4 million at a CAGR of 12.5% between 2024 and 2029.

The market is witnessing significant growth, driven by the increasing demand for digitalization in government operations. This trend is leading to an increased adoption of advanced technologies, such as artificial intelligence (AI) and machine learning, in open data management platforms. These technologies enable more efficient data processing, analysis, and dissemination, making it easier for governments to provide accessible and actionable data to the public. However, the market faces challenges related to data privacy concerns.
Additionally, there is a need for clear guidelines and regulations regarding the collection, storage, and sharing of open data to maintain transparency and trust with the public. Companies operating in this market can capitalize on the growing demand for digitalization and advanced technologies while addressing data privacy concerns to gain a competitive edge. With the growing availability of open data, ensuring the security and confidentiality of sensitive information is a major concern. Governments must implement robust security measures to protect data from unauthorized access, misuse, or theft. Computer vision and image recognition are transforming industries like healthcare and education.

What will be the Size of the Government Open Data Management Platform Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market for government open data management platforms continues to evolve, driven by the increasing importance of public data infrastructure and the need for effective data governance policies. Data privacy regulations are shaping the landscape, with a growing emphasis on data reuse promotion and performance benchmarking. Data aggregation methods and data usage patterns are under constant review, as transparency and system scalability become essential. Data storytelling techniques and data usability assessments are gaining traction, while data platform architecture and data integration tools are being refined. A recent study revealed a 25% increase in data accessibility features adoption among government agencies.

Industry growth is expected to reach 15% annually, as open data licensing, role-based access control, and data modeling techniques become standard. Data quality monitoring, data consistency, and data reliability remain key concerns, with data audit procedures and data integrity measures being implemented to address these challenges. Data contextualization and data visualization dashboards are essential for making sense of the vast amounts of data being generated, while open government initiatives continue to drive innovation and collaboration. Data security remains a priority, with privacy concerns driving the need for data mining and edge computing.

How is this Government Open Data Management Platform Industry segmented?

The government open data management platform industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  Large enterprises
  SMEs


Deployment

  On-premises
  Cloud-based


Component

  Solutions
  Services


Geography

  North America

    US
    Canada
    Mexico


  Europe

    France
    Germany
    Italy
    UK


  APAC

    Australia
    China
    India


  Rest of World (ROW)

By End-user Insights

The Large enterprises segment is estimated to witness significant growth during the forecast period. In today's data-driven business landscape, large enterprises are increasingly turning to government open data management platforms to unlock valuable insights and fuel innovation. These platforms enable organizations to access, manage, and analyze vast amounts of data published by government agencies. By integrating government open data with their internal data, businesses can gain a deeper understanding of market trends, consumer behavior, and emerging opportunities. Data interoperability and version control ensure seamless integration of diverse data sources, while data migration strategies facilitate the transfer of data between systems. Data lineage tracking and metadata management provide transparency into the origin and evolution of data, enabling data provenance management and data discovery. Advanced process control and time series forecasting are integral to this evolution, with machine learning algorithms and deep learning frameworks powering predictive analytics tools.

Structured data management, data clea

Evaluation of my dataset with my .915 baseline:

F5 score = .690 - Recall = .692, Precision = .639

Distribution of data:

• 843x Address (ca. 500 US)
• 496x Names (Incl. Middle Names, Pronounciation or Nicknames)
• 537x Userid
• 704x Username (Incl. Name)
• 531x Phone
• 755x Email (Incl. Name)
• 501x URL

See linked notebook for generation.

Remarks on labels:

EMAIL:

1. Email is always based on name, but random domains
2. Prompt was to also write about their favourite book, they are heavily favouring “to kill a mockingbird”

PHONE:

1. Generated from multiple countries for diversity
2. Labelling of phone numbers should only include the full number (not parts of it)

ADDRESSES:

1. From multiple countries for diversity
2. For US Addresses, State abbreviations are mapped to full name, so these are labeled as well
3. Addresses are only labelled as such if it starts with either of the first two words of the full address (e.g., if house number misses for us address, it is still labelled)

NAMES:

1. Middle names are sometimes generated, either separeted with " " or "-"
2. Pronounciations and nicknames were generated and labelled
3. However, “t’oma” as in my name Thomas is derived from the arameic word “t’oma” was not tagged. Let me know if this is wrong. They are relatively easy to identify in the names dataset by looking for “derived from”

URL:

1. Short domains, full websites and full URIs

USERID:

1. Mostly random generated string, number combination - not oriented on other formats
2. Can mostly easily be augmented by replacing the userid
3. Userid is sometimes split in text into parts - these splits are not labelled (not sure if this is right)

USERNAMES:

1. either generated based on name OR animal+birthyear OR colour+fruit

Open data and its content are those that can be freely used, modified, and shared by anyone for any purpose (Open Knowledge Foundation). At the University of Granada, we want to promote the reuse of public information, in a way that generates additional value for citizens by providing transparency and greater citizen participation through the exposure of data generated by the university. The University of Granada has enabled this portal so that the general public and members of the university in particular (Administrative and Service Staff, Teaching and Research Staff, and students) have access to the main data related to the activity of this university in an accessible, reusable, and shareable format. In the University of Granada's open data portal, you will find the data generated by the university (academic, research, services, budgets, etc.) in a format and with a license suitable for you to access, analyze, reuse, and share them. The exploitation of data generated by the public institution can be used by the private sector and citizens to generate additional value by building new services and applications with the data, conducting studies and analyses, or imagining new innovative uses of this data. Tools, apps, reports, analyses, studies, articles... Think, use, and help us create a catalog of applications with the data from the University of Granada. Tim Berners-Lee, inventor of the web and initiator of "Linked Data," defined a quality model to measure the reusability of open data, known as the five-star open data model (5 - star Open Data). This model establishes five levels of quality labeled with stars according to their suitability for the purpose of being reused. You can consult this model at 5 Star Data. This portal is maintained by the Free Software Office of the University of Granada. For any questions, you can write to us at the email address opendata@ugr.es or through our Twitter account. Translated from Spanish Original Text: Los datos abiertos y su contenido son aquellos que pueden ser libremente utilizados, modificados y compartidos por cualquier persona para cualquier propósito (Open Knowledge Foundation). Desde la Universidad de Granada queremos impulsar la reutilización de la información pública, de manera que genere un valor adicional para la ciudadanía aportando transparencia y mayor participación ciudadana a través de la exposición de los datos generados desde la universidad. La Universidad de Granada ha habilitado este portal para que el público en general y los miembros de la universidad en particular (PAS, PDI y estudiantes) tengan acceso a los principales datos relacionados con la actividad de esta universidad en un formato accesible, reutilizable y compartible. En el portal de datos abiertos de la Universidad de Granada encontrarás los datos que genera la universidad (académicos, investigación, servicios, presupuestos, etc. ) en un formato y con una licencia adecuados para que puedas acceder a ellos, analizarlos, reutilizarlos y compartirlos. La explotación de los datos generados por la institución pública puede ser utilizado por el sector privado y la ciudadanía para generar un valor adicional construyendo con los datos nuevos servicios y aplicaciones, realizando estudios y análisis o imaginando nuevos usos innovadores de estos datos. Herramientas, apps, informes, análisis, estudios, artículos... Piensa, usa y ayúdanos a crear un catálogo de aplicaciones con los datos de la Universidad de Granada. Tim Berners-Lee, inventor de la web e iniciador del “Linked Data”, definió un modelo de calidad para medir la capacidad de reutilización de los datos abiertos, conocido como el modelo de cinco estrellas de los datos abiertos (5 - star Open Data). Este modelo establece cinco niveles de calidad etiquetados con estrellas según su adecuación al objetivo de ser reutilizados. Puedes consultar este modelo en 5 Star Data. Este portal está mantenido por la Oficina de Sofware Libre de la Universidad de Granada. Para cualquier consulta puedes escribirnos al correo electrónico opendata@ugr.es o a través de nuestra cuenta de Twitter.

Comprehensive dataset of 15 Public defender's offices in Indiana, United States as of July, 2025. Includes verified contact information (email, phone), geocoded addresses, customer ratings, reviews, business categories, and operational details. Perfect for market research, lead generation, competitive analysis, and business intelligence. Download a complimentary sample to evaluate data quality and completeness.

USGS Structures from The National Map (TNM) consists of data to include the name, function, location, and other core information and characteristics of selected manmade facilities across all US states and territories. The types of structures collected are largely determined by the needs of disaster planning and emergency response, and homeland security organizations. Structures currently included are: School, School:Elementary, School:Middle, School:High, College/University, Technical/Trade School, Ambulance Service, Fire Station/EMS Station, Law Enforcement, Prison/Correctional Facility, Post Office, Hospital/Medical Center, Cabin, Campground, Cemetery, Historic Site/Point of Interest, Picnic Area, Trailhead, Vistor/Information Center, US Capitol, State Capitol, US Supreme Court, State Supreme Court, Court House, Headquarters, Ranger Station, White House, and City/Town Hall. Structures data are designed to be used in general mapping and in the analysis of structure related activities using geographic information system technology. Included is a feature class of preliminary building polygons provided by FEMA, USA Structures. The National Map structures data is commonly combined with other data themes, such as boundaries, elevation, hydrography, and transportation, to produce general reference base maps. The National Map viewer allows free downloads of public domain structures data in either Esri File Geodatabase or Shapefile formats. For additional information on the structures data model, go to https://www.usgs.gov/ngp-standards-and-specifications/national-map-structures-content.

OPT provides transportation service to many different kinds of locations. Many of these locations are schools but they also include offices or other sites that may be part of certain students’ educational plans. The schools may be public, private or religious. OPT provides busing to some Pre-K sites for students who have an IEP for curb-to-curb busing because of medical condition. Transportation service is not limited to school bus service; it includes distribution of MetroCards and approved reimbursement services. Bus service can be conducted on a yellow school bus, an ambulance, or even a coach bus. Yellow school buses are available in a number of sizes and seating configurations. This dataset includes schools, offices or Pre-K/EI sites that currently receive any transportation services from OPT. These sites may be within the New York City limits or up to fifty miles from the city limits in the states of New York, New Jersey or Connecticut. This dataset does not include field trip destinations.

Presents the number of cases and individuals for each shelter case type by borough and community district

The PAD (Property Address Directory) file contains additional geographic information at the tax lot level not found in the PLUTO files. This data includes alias addresses and Building Identification Numbers (BINs). It consists of two ASCII, comma delimited files: a tax lot file and an address file.

All previously released versions of this data are available on the DCP Website: BYTES of the BIG APPLE.

Xverum’s Point of Interest (POI) Data is a comprehensive dataset containing 230M+ verified locations across 5000 business categories. Our dataset delivers structured geographic data, business attributes, location intelligence, and mapping insights, making it an essential tool for GIS applications, market research, urban planning, and competitive analysis.

With regular updates and continuous POI discovery, Xverum ensures accurate, up-to-date information on businesses, landmarks, retail stores, and more. Delivered in bulk to S3 Bucket and cloud storage, our dataset integrates seamlessly into mapping, geographic information systems, and analytics platforms.

🔥 Key Features:

Extensive POI Coverage: ✅ 230M+ Points of Interest worldwide, covering 5000 business categories. ✅ Includes retail stores, restaurants, corporate offices, landmarks, and service providers.

Geographic & Location Intelligence Data: ✅ Latitude & longitude coordinates for mapping and navigation applications. ✅ Geographic classification, including country, state, city, and postal code. ✅ Business status tracking – Open, temporarily closed, or permanently closed.

Continuous Discovery & Regular Updates: ✅ New POIs continuously added through discovery processes. ✅ Regular updates ensure data accuracy, reflecting new openings and closures.

Rich Business Insights: ✅ Detailed business attributes, including company name, category, and subcategories. ✅ Contact details, including phone number and website (if available). ✅ Consumer review insights, including rating distribution and total number of reviews (additional feature). ✅ Operating hours where available.

Ideal for Mapping & Location Analytics: ✅ Supports geospatial analysis & GIS applications. ✅ Enhances mapping & navigation solutions with structured POI data. ✅ Provides location intelligence for site selection & business expansion strategies.

Bulk Data Delivery (NO API): ✅ Delivered in bulk via S3 Bucket or cloud storage. ✅ Available in structured format (.json) for seamless integration.

🏆Primary Use Cases:

Mapping & Geographic Analysis: 🔹 Power GIS platforms & navigation systems with precise POI data. 🔹 Enhance digital maps with accurate business locations & categories.

Retail Expansion & Market Research: 🔹 Identify key business locations & competitors for market analysis. 🔹 Assess brand presence across different industries & geographies.

Business Intelligence & Competitive Analysis: 🔹 Benchmark competitor locations & regional business density. 🔹 Analyze market trends through POI growth & closure tracking.

Smart City & Urban Planning: 🔹 Support public infrastructure projects with accurate POI data. 🔹 Improve accessibility & zoning decisions for government & businesses.

💡 Why Choose Xverum’s POI Data?

• 230M+ Verified POI Records – One of the largest & most detailed location datasets available.
• Global Coverage – POI data from 249+ countries, covering all major business sectors.
• Regular Updates – Ensuring accurate tracking of business openings & closures.
• Comprehensive Geographic & Business Data – Coordinates, addresses, categories, and more.
• Bulk Dataset Delivery – S3 Bucket & cloud storage delivery for full dataset access.
• 100% Compliant – Ethically sourced, privacy-compliant data.

Access Xverum’s 230M+ POI dataset for mapping, geographic analysis, and location intelligence. Request a free sample or contact us to customize your dataset today!

About this Dataset

Get access to a comprehensive list of medical supply stores across the USA. Our CSV file contains around 4700 rows of data with each row containing name, type of service, rating, phone number, website, address, state, city. The rating field consists of two parts. For example, consider the rating 4.3(1350). The first part 4.3 shows the score 4.3 out of 5. The second part is very important and shows the number of reviews submitted for this store. Get your hands on this data today and make informed decisions for your business.

Category

Health & Medicine

Keywords

United State Medical,Healthcare,Company,United states

Row Count

4718

Price

$100.00

Success.ai’s Phone Number Data offers direct access to over 50 million verified phone numbers for professionals worldwide, extracted from our expansive collection of 170 million profiles. This robust dataset includes work emails and key decision-maker profiles, making it an essential resource for companies aiming to enhance their communication strategies and outreach efficiency. Whether you're launching targeted marketing campaigns, setting up sales calls, or conducting market research, our phone number data ensures you're connected to the right professionals at the right time.

Why Choose Success.ai’s Phone Number Data?

Direct Communication: Reach out directly to professionals with verified phone numbers and work emails, ensuring your message gets to the right person without delay. Global Coverage: Our data spans across continents, providing phone numbers for professionals in North America, Europe, APAC, and emerging markets. Continuously Updated: We regularly refresh our dataset to maintain accuracy and relevance, reflecting changes like promotions, company moves, or industry shifts. Comprehensive Data Points:

Verified Phone Numbers: Direct lines and mobile numbers of professionals across various industries. Work Emails: Reliable email addresses to complement phone communications. Professional Profiles: Decision-makers’ profiles including job titles, company details, and industry information. Flexible Delivery and Integration: Success.ai offers this dataset in various formats suitable for seamless integration into your CRM or sales platform. Whether you prefer API access for real-time data retrieval or static files for periodic updates, we tailor the delivery to meet your operational needs.

Competitive Pricing with Best Price Guarantee: We provide this essential data at the most competitive prices in the industry, ensuring you receive the best value for your investment. Our best price guarantee means you can trust that you are getting the highest quality data at the lowest possible cost.

Targeted Applications for Phone Number Data:

Sales and Telemarketing: Enhance your telemarketing campaigns by reaching out directly to potential customers, bypassing gatekeepers. Market Research: Conduct surveys and research directly with industry professionals to gather insights that can shape your business strategy. Event Promotion: Invite prospects to webinars, conferences, and seminars directly through personal calls or SMS. Customer Support: Improve customer service by integrating accurate contact information into your support systems. Quality Assurance and Compliance:

Data Accuracy: Our data is verified for accuracy to ensure over 99% deliverability rates. Compliance: Fully compliant with GDPR and other international data protection regulations, allowing you to use the data with confidence globally. Customization and Support:

Tailored Data Solutions: Customize the data according to geographic, industry-specific, or job role filters to match your unique business needs. Dedicated Support: Our team is on hand to assist with data integration, usage, and any questions you may have. Start with Success.ai Today: Engage with Success.ai to leverage our Phone Number Data and connect with global professionals effectively. Schedule a consultation or request a sample through our dedicated client portal and begin transforming your outreach and communication strategies today.

Remember, with Success.ai, you don’t just buy data; you invest in a partnership that grows with your business needs, backed by our commitment to quality and affordability.

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What is the phone number for Windstar booking?

The Windstar Cruises booking phone number is +1-855-732-4023 (USA) or +44-289-708-0062 (UK) or 1-855-Windstar Cruises. You can also reach them via their website's live chat or email for assistance.

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How do I contact Windstar Cruises?

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How do I call Windstar Cruises?

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The City of Naperville's address point file contains over forty thousand address points within the City's boundary. The file's attributes consists of the address number (unincorporated and incorporated), direction, street name, street suffix, location ID, property ID, and what city that the point rests in. The points are associated with the parcel that it pertains to and not the buildings.

Dataset of 700,000 restaurants in the United States complete with detailed contact and geolocation data. The database includes multiple data points such as restaurant name, address, phone number, website, email, opening hours, latitude, longitude, and cuisine.

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UPS Facilities Locations Dataset

Locations and Details of UPS Facilities across the United States

By Homeland Infrastructure Foundation [source]

About this dataset

The UPS Facilities dataset is a comprehensive collection of information about UPS (United Parcel Service) facilities located across the United States. This dataset provides details on the location, placement, and contact information of each facility.

The dataset includes various columns such as X and Y coordinates, which indicate the longitude and latitude coordinates respectively. These coordinates pinpoint the exact geographic location of each UPS facility. Additionally, there are columns for the name of each facility, address including street address and additional information (ADDRESS2 and ADDRESS3), city, state, ZIP code, phone number for contact purposes.

Furthermore, this dataset provides insightful information about each facility's match status in terms of its address accuracy or completeness. It also includes details about the specific business associated with each UPS facility.

In addition to these data points, there are columns that provide census codes for each facility location. These codes offer additional contextual information related to demographic and socio-economic characteristics associated with each area where a UPS facility is situated.

Overall, this extensive dataset serves as a comprehensive resource for researchers or businesses looking to analyze or utilize information regarding UPS facilities across different states in the United States

How to use the dataset

Introduction:

• Understanding the Dataset Structure: The dataset consists of several columns that provide relevant information about each UPS facility location. Here is a brief overview of the key columns:

• NAME: The name of the UPS facility.

• ADDRESS: The street address of the UPS facility.

• ADDRESS2/ADDRESS3: Additional address information for the facility.

• CITY/STATE/ZIP: The city, state, and ZIP code where the facility is located.

• PHONE: The contact phone number for the facility.

Additionally, there are geographic coordinates (LATITUDE and LONGITUDE) representing each facility's precise location on a map. Other columns such as PLACEMENT, MATCHSTATU, CENSUSCODE, and BUSINESSNA provide further context regarding placement status, address matching status, census codes for locations, and associated business names.

• Potential Use Cases:

a) Visualizing Facility Distribution: Using latitude and longitude coordinates from this dataset with mapping tools like Python's Folium or Tableau can help create interactive maps that showcase spatial distributions across different regions.

b) Analyzing Facility Density: By aggregating data at regional levels (e.g., state-wise), you can analyze which areas have higher concentrations of UPS facilities compared to others. This analysis may offer insights into patterns related to population density or commercial activity.

c) Optimizing Transportation Routes: Understanding where these facilities are located can be beneficial for route optimization. By analyzing facility placements and their proximity to transportation networks, you can identify potential areas for streamlining logistics operations.

d) Market Research: The dataset's additional columns (such as BUSINESSNA) allow researchers to analyze UPS facilities within the context of associated businesses. This information can be useful for market research, identifying industry clusters, or studying supply chain dynamics.

• Data Cleaning and Preprocessing: Before utilizing this dataset, it is recommended to perform standard data cleaning procedures, such as handling missing or incorrect values. Pay attention to any inconsistencies in column names or encoding formats that may require normalization.

• Combining with Other Datasets: To

Research Ideas

• Geospatial analysis: This dataset can be used for geospatial analysis to analyze the distribution and concentration of UPS facilities across different states or cities. It can help identify areas with high or low availability of UPS services and assist logistics planning and decision making.
• Customer segmentation: By combining this dataset with customer data, businesses can segment their customers based on proximity to UPS facilities. This can help companies optimize their delivery routes, improve customer service, and target marketing efforts more effectively.
• Benchmarking and competition analysis: The dataset can also be used for benchmarking purposes by comparing the number of UPS facilities in different regions or against competito...

This dataset is a compilation of address point data from Minnesota suppliers that have opted-in for their address point data to be included in this dataset.

It includes the following 44 suppliers that have opted-in to share their data openly as of the publication date of this dataset: Aitkin County, Anoka County, Benton County, Carver County, Cass County, Chippewa County, Chisago County, Clay County, Cook County, Dakota County, Douglas County, Fillmore County, Grant County, Hennepin County, Houston County, Isanti County, Itasca County, Koochinching County, Lac qui Parle County, Lake County, Le Sueur County, Lyon County, Marshall County, McLeod County, Morrison County, Mower County, Murray County, Otter Tail County, Pipestone County, Polk County, Pope County, Ramsey County, Renville County, Rock County, Saint Louis County, Scott County, Sherburne County, Stearns, Stevens County, Waseca County, Washington County, Wright County, and Yellow Medicine County.

The two sources of address point data are the Minnesota Next Generation 9-1-1 (NG9-1-1) Program, in collaboration with local data suppliers, and the MetroGIS Metro Address Points Dataset which is on the Minnesota Geospatial Commons:

The Minnesota NG9-1-1 Program enterprise database provides the data outside of the Metro Region which is provide by the suppliers. The data have been aggregated into a single dataset which implements the MN NG9-1-1 GIS Data Model (https://ng911gis-minnesota.hub.arcgis.com/documents/79beb1f9bde84e84a0fa9b74950f7589/about ).

Only data which have meet the requirements for supporting NG9-1-1 are in the statewide aggregate GIS data. MnGeo extracts the available data, applies domain translations, and transforms it to UTM Zone 15 to comply with the GAC Address Point attribute schema: https://www.mngeo.state.mn.us/committee/address/address_standard.html.

The MetroGIS Metro Address Points Dataset was created by a joint collaborative project involving the technical and managerial GIS staff from the ten Metropolitan Counties (Anoka, Carver, Chisago, Dakota, Hennepin, Isanti, Ramsey, Scott, Sherburne, and Washington), the Metropolitan Emergency Services Board, MetroGIS and the Metropolitan Council. The data are pulled in from the Minnesota Geospatial Commons: https://gisdata.mn.gov/dataset/us-mn-state-metrogis-loc-address-points

‘Supplier’ is a term used throughout this document. A supplier will typically be a county, but it could also be a public safety answering point (PSAP), region, or tribal nation. The supplier is the agency which provides the individual datasets for the aggregated dataset. The loc_addresses_open_metadata feature layer will contain the geometry/shape of the supplier boundaries, supplier name, supplier type, and feature count.

Aggregation Process:
1. Transfer NG9-1-1 data from the DPS Enterprise database.
2. Download the latest data from the Geospatial Commons for MetroGIS.
3. Extract, Translate, and Load (ETL) the data to the GAC Address Point Standard schema.
4. Combine NG9-1-1 data with MetroGIS data.
5. Filter the data for the Opt-In suppliers