Google Data for Market Intelligence, Business Validation & Lead Enrichment Google Data is one of the most valuable sources of location-based business intelligence available today. At Canaria, we’ve built a robust, scalable system for extracting, enriching, and delivering verified business data from Google Maps—turning raw location profiles into high-resolution, actionable insights.

Our Google Maps Company Profile Data includes structured metadata on businesses across the U.S., such as company names, standardized addresses, geographic coordinates, phone numbers, websites, business categories, open hours, diversity and ownership tags, star ratings, and detailed review distributions. Whether you're modeling a market, identifying leads, enriching a CRM, or evaluating risk, our Google Data gives your team an accurate, up-to-date view of business activity at the local level.

This dataset is updated daily and is fully customizable, allowing you to pull exactly what you need, whether you're targeting a specific geography, industry segment, review range, or open-hour window.

What Makes Canaria’s Google Data Unique? • Location Precision – Every business record is enriched with latitude/longitude, ZIP code, and Google Plus Code to ensure exact geolocation • Reputation Signals – Review tags, star ratings, and review counts are included to allow brand sentiment scoring and risk monitoring • Diversity & Ownership Tags – Capture public-facing declarations such as “women-owned” or “Asian-owned” for DEI, ESG, and compliance applications • Contact Readiness – Clean, standardized phone numbers and domains help teams route leads to sales, support, or customer success • Operational Visibility – Up-to-date open hours, categories, and branch information help validate which locations are active and when

Our data is built to be matched, integrated, and analyzed—and is trusted by clients in financial services, go-to-market strategy, HR tech, and analytics platforms.

What This Google Data Solves Canaria Google Data answers critical operational, market, and GTM questions like:

• Which businesses are actively operating in my target region or category? • Which leads are real, verified, and tied to an actual physical branch? • How can I detect underperforming companies based on review sentiment? • Where should I expand, prospect, or invest based on geographic presence? • How can I enhance my CRM, enrichment model, or targeting strategy using location-based data?

Key Use Cases for Google Maps Business Data Our clients leverage Google Data across a wide spectrum of industries and functions. Here are the top use cases:

Lead Scoring & Business Validation • Confirm the legitimacy and physical presence of potential customers, partners, or competitors using verified Google Data • Rank leads based on proximity, star ratings, review volume, or completeness of listing • Filter spammy or low-quality leads using negative review keywords and tag summaries • Validate ABM targets before outreach using enriched business details like phone, website, and hours

Location Intelligence & Market Mapping • Visualize company distributions across geographies using Google Maps coordinates and ZIPs • Understand market saturation, density, and white space across business categories • Identify underserved ZIP codes or local business deserts • Track presence and expansion across regional clusters and industry corridors

Company Risk & Brand Reputation Scoring • Monitor Google Maps reviews for sentiment signals such as “scam”, “spam”, “calls”, or service complaints • Detect risk-prone or underperforming locations using star rating distributions and review counts • Evaluate consistency of open hours, contact numbers, and categories for signs of listing accuracy or abandonment • Integrate risk flags into investment models, KYC/KYB platforms, or internal alerting systems

CRM & RevOps Enrichment • Enrich CRM or lead databases with phone numbers, web domains, physical addresses, and geolocation from Google Data • Use business category classification for segmentation and routing • Detect duplicates or outdated data by matching your records with the most current Google listing • Enable advanced workflows like field-based rep routing, localized campaign assignment, or automated ABM triggers

Business Intelligence & Strategic Planning • Build dashboards powered by Google Maps data, including business counts, category distributions, and review activity • Overlay business presence with population, workforce, or customer base for location planning • Benchmark performance across cities, regions, or market verticals • Track mobility and change by comparing past and current Google Maps metadata

DEI, ESG & Ownership Profiling • Identify minority-owned, women-owned, or other diversity-flagged companies using Google Data ownership attributes • Build datasets aligned with supplier diversity mandates or ESG investment strategies • Segment location insights by ownership type ...

The Google Maps dataset is ideal for getting extensive information on businesses anywhere in the world. Easily filter by location, business type, and other factors to get the exact data you need. The Google Maps dataset includes all major data points: timestamp, name, category, address, description, open website, phone number, open_hours, open_hours_updated, reviews_count, rating, main_image, reviews, url, lat, lon, place_id, country, and more.

The Alabama Department of Transportation (ALDOT) and the U.S. Geological Survey (USGS) studied several sites in the northern East Gulf Coastal Plain of Alabama to investigate effects of newly installed box culverts on the natural conditions of the streams they are traversing (Pugh and Gill, 2021). Data collection for the study spanned approximately 10 years and included before-, during-, and after-construction phases of box culvert installations at selected stream sites. The objectives of the project were to (1) assess the degree and extent of changes in geomorphic conditions, suspended-sediment concentrations, turbidity, and benthic macroinvertebrate populations at selected small streams following box culvert installation and (2) identify any substantial relationships between observed changes in geomorphology and benthic macroinvertebrate populations. Aerial imagery for each study site, taken before, during and after culvert construction, was downloaded from Google Earth (https://earth.google.com/web/) and are presented as separate Portable Document Format (PDF) files labeled by site name and imagery date. Aerial imagery was examined to see if any natural or anthropogenic changes occurred in the areas surrounding the study sites. For example, examination of the High Log Creek imagery from 2013 and 2015 shows the forested area northwest of the study site was clear cut and the start of culvert construction occurred sometime between when the two images were taken.

This dataset details all data used for a manuscript submission entitled "Spotting green tides over Brittany from space: three decades of monitoring with Landsat imagery". It presents data derived from Earth observation detection on the macroalgae surface on four studied sites in Brittany, France. These estimates were made using Landsat 5 and 8 satellite imagery, using the Google Earth Engine environment. Spectral signatures of natural features found on the study sites (sand, water and algae) are also presented. Additional datasets include 1) green macroalgae surface estimates made by an external source, CEVA (French Algae Technology and Innovation Center) and derived from aerial photography. This data was used for comparison with our results 2) nitrogen concentrations for four water stations close to the study sites. Nitrogen is considered the main physico-chemical factor controlling algae growth.

Elevate your B2B marketing strategy with B2B Email Databases' premier Google Maps Data Extraction Service. Our cutting-edge solution offers direct access to a wealth of business information from Google's extensive database, encompassing millions of businesses across a multitude of industries worldwide.

B2B Email Databases' service is meticulously designed to harvest a vast array of business information. This includes but is not limited to, business names, addresses, contact details, website URLs, customer reviews, ratings, and operational hours. Whether you're a burgeoning small business or a well-established enterprise, the data gleaned from our Google Maps Data Extraction Service is an invaluable asset.

Our service empowers your business with the ability to efficiently and accurately generate leads and gather critical market insights. It's an essential tool for analyzing market dynamics, identifying potential B2B leads with precision, and comprehending the competitive landscape. Tailor your data extraction to specific business categories or geographic locations, ensuring you target the most relevant leads for your endeavors.

In today's data-centric business world, utilizing a service like B2B Email Databases' Google Maps Data Extraction is crucial for maintaining a competitive edge. It streamlines the data collection process, allowing you to focus on what's truly important – leveraging this data for your business growth.

Explore the depth of information you can access through our service, which provides comprehensive business insights including contact details, ratings, operational hours, and much more.

To further enhance your data sets with additional details such as social media accounts, consider integrating this service with our Domain Contact Scraper. This supplementary tool can offer deeper insights into a business's digital footprint across various platforms, including Facebook, Instagram, LinkedIn, and more.

Opt for B2B Email Databases' Google Maps Data Extraction Service to gain a strategic advantage in your market. Our solution is designed to simplify your data collection process, enabling your business to flourish in an increasingly competitive and data-driven world.

• Mussels spread in concise, close to Les Argilliez, with anomaly detection.
• S. Stefano fires, here only the most serious one caused by a lit cigarette on the 28^th of June June 2022.

Welcome to Apiscrapy, your ultimate destination for comprehensive location-based intelligence. As an AI-driven web scraping and automation platform, Apiscrapy excels in converting raw web data into polished, ready-to-use data APIs. With a unique capability to collect Google Address Data, Google Address API, Google Location API, Google Map, and Google Location Data with 100% accuracy, we redefine possibilities in location intelligence.

Key Features:

Unparalleled Data Variety: Apiscrapy offers a diverse range of address-related datasets, including Google Address Data and Google Location Data. Whether you seek B2B address data or detailed insights for various industries, we cover it all.

Integration with Google Address API: Seamlessly integrate our datasets with the powerful Google Address API. This collaboration ensures not just accessibility but a robust combination that amplifies the precision of your location-based insights.

Business Location Precision: Experience a new level of precision in business decision-making with our address data. Apiscrapy delivers accurate and up-to-date business locations, enhancing your strategic planning and expansion efforts.

Tailored B2B Marketing: Customize your B2B marketing strategies with precision using our detailed B2B address data. Target specific geographic areas, refine your approach, and maximize the impact of your marketing efforts.

Use Cases:

Location-Based Services: Companies use Google Address Data to provide location-based services such as navigation, local search, and location-aware advertisements.

Logistics and Transportation: Logistics companies utilize Google Address Data for route optimization, fleet management, and delivery tracking.

E-commerce: Online retailers integrate address autocomplete features powered by Google Address Data to simplify the checkout process and ensure accurate delivery addresses.

Real Estate: Real estate agents and property websites leverage Google Address Data to provide accurate property listings, neighborhood information, and proximity to amenities.

Urban Planning and Development: City planners and developers utilize Google Address Data to analyze population density, traffic patterns, and infrastructure needs for urban planning and development projects.

Market Analysis: Businesses use Google Address Data for market analysis, including identifying target demographics, analyzing competitor locations, and selecting optimal locations for new stores or offices.

Geographic Information Systems (GIS): GIS professionals use Google Address Data as a foundational layer for mapping and spatial analysis in fields such as environmental science, public health, and natural resource management.

Government Services: Government agencies utilize Google Address Data for census enumeration, voter registration, tax assessment, and planning public infrastructure projects.

Tourism and Hospitality: Travel agencies, hotels, and tourism websites incorporate Google Address Data to provide location-based recommendations, itinerary planning, and booking services for travelers.

Discover the difference with Apiscrapy – where accuracy meets diversity in address-related datasets, including Google Address Data, Google Address API, Google Location API, and more. Redefine your approach to location intelligence and make data-driven decisions with confidence. Revolutionize your business strategies today!

This tutorial will teach you how to take time-series data from many field sites and create a shareable online map, where clicking on a field location brings you to a page with interactive graph(s).

The tutorial can be completed with a sample dataset (provided via a Google Drive link within the document) or with your own time-series data from multiple field sites.

Part 1 covers how to make interactive graphs in Google Data Studio and Part 2 covers how to link data pages to an interactive map with ArcGIS Online. The tutorial will take 1-2 hours to complete.

An example interactive map and data portal can be found at: https://temple.maps.arcgis.com/apps/View/index.html?appid=a259e4ec88c94ddfbf3528dc8a5d77e8

KML file of DGB sites in the Mandara Mountains, Cameroon.

The Digital Geologic-GIS Map of Roosevelt-Vanderbilt National Historic Sites and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (rova_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (rova_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (rova_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (rova_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (rova_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (rova_geology_metadata_faq.pdf). Please read the rova_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: New York Geological Survey and New York State Department of Transportation. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (rova_geology_metadata.txt or rova_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:24,000 and United States National Map Accuracy Standards features are within (horizontally) 12.2 meters or 40 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The SEN12 Global Urban Mapping (SEN12_GUM) dataset consists of Sentinel-1 SAR (VV + VH band) and Sentinel-2 MSI (10 spectral bands) satellite images acquired over the same area for 96 training and validation sites and an additional 60 test sites covering unique geographies across the globe. The satellite imagery was acquired as part of the European Space Agency's Earth observation program Copernicus and was preprocessed in Google Earth Engine. Built-up area labels for the 30 training and validation sites located in the United States, Canada, and Australia were obtained from Microsoft's open-access building footprints. The other 66 training sites located outside of the United States, Canada, and Australia are unlabeled but can be used for semi-supervised learning. Labels obtained from the SpaceNet7 dataset are provided for all 60 test sites.

The Geologic Atlas of the United States is a set of 227 folios published by the U.S. Geological Survey between 1894 and 1945. Each folio includes both topographic and geologic maps for each quad represented in that folio, as well as description of the basic and economic geology of the area.

Includes a link to a Google Earth overlay which includes links to sites with raster information as well as a map on the webpage with the links present. A viewer can use the links displayed on page (inside numbers) to be led to sites with lists/catalogs of downloadable data. Includes JPEG, TIFF, and GIS data.

This data release provides tidally corrected shoreline positions for three sites of western Long Island, NY (Rockaway Peninsula, Long Beach, and Jones Beach Island). GeoJSON files are derived from CoastSeg version 1.1.35 (Fitzpatrick and others, 2024) with settings derived from config files. These files contain the region of interests (ROIs), transects, and reference shorelines for each section. CoastSeg collects satellite images from Google Earth Engine to create shoreline data along with user-supplied inputs based on the CoastSat methodology (Vos and others, 2019). Data have been tidally corrected based on beach foreshore slopes (Farris and Webber, 2024). Data can be viewed in a GIS software such as QGIS or ArcGIS.

The Satellite image (Pléiades ©CNES 2017, Distribution Airbus DS) as of 26 October 2017 converted to .kmz for using through Google Earth on a computer, Earth for Android for easy viewing in the field, OSMNavigator for offline, Google Maps-style real-time GPS position overlay on top of the image, Garmin handheld GPS units etc.

The database BDFGeotherm, containing physical, chemical and hydrogeological information on more than 200 deep fluids from 84 sites in Switzerland and some neighbouring regions, was first compiled on ACCESS code and was later modified to improve its availability and attractiveness by using Google Earth free software and the CREGE website (www.crege.ch/BDFGeotherm/). BDFGeotherm is a functional tool for various phases of a geothermal project such as exploration, production or fluid re-injection. This database allows gathering existing geothermal data, generally widely dispersed and often difficult to reach, towards a users friendly tool. Downloading the file BDFGeotherm.kmz from the CREGE website makes possible to visualize the 84 geothermal sites from Switzerland and neighbouring areas. Each one is represented with a pinpoint of different colour, for diverse temperature ranges.A large majority of sites is located in the northern part of the Jura Mountain and in the upper Rhone Valley. General information about water use, geology, flow rate, temperature and mineralization are given in a small window by clicking on the desired pinpoint. Moreover, two links to Internet addresses are available for each site in each window, allowing returning to the CREGE website or providing more details on each sampling point such as: geographical description, reservoir geology, hydraulics, hydrochemistry, isotopes and geothermal parameters. For a limited number of sites, photos and a geological log can be viewed and exported (Sonney et al., 2009).

Material S3: Google Earth archive of the sampled palaeomagnetic sites in Myanmar.

Context

KMZ file for the global list of ports. Very useful if you want to map logistics, get distances between certain ports, find the optimal port/node for shipping goods. File is great for visualization using Google Earth Pro or My Maps.

Content

Found the dataset from intensely searching for datasets I'm interested to map for my Geospatial Visualization and Geospatial Information Systems Class. Unfortunately, I couldn't find the source anymore. Found this around September 2021.

KMZ zip file with: -port id's -latitude and longitude -website

Acknowledgements

Thank you to everyone who will contribute to improve this dataset. I realize that finding files to work with for some specific programs for Geospatial Information Science can be hard, so I'm paying it forward to help beginners out there. If you find the data source you can let me know so we can credit them properly! 😄

👍 give this dataset a like, to help other people find it!

Inspiration & Things you can do with it:

🗺️ Visit all the ports using tours in Google Earth Pro 🧭 Locate ports and find ports nearby 🚢 Find Optimal paths 📍Every port has an ID and a website to find it 🌎 Create your map and visualize it 🔍 Create a categorized shipping map

Resource Mapping data was collected from field survey and all points such as markets, atms, schools were located and appropriate tags were given.

Data was uploaded on Google sheets and addons of Fusion Mas and point map were installed and addons were run to form virtual maps in their own particular webpages.

Source link of those webpages are determined and were added in a iframe in src link.

In web html design a table was made and all three iframe are added in table.

The final html was added as html element in sites.google.com to create a custom website.

The website link: www.sites.google.com/site/pranavrsmap

Webpage and Sheets are the most important data here. Other data are optional and are uploaded for your Geospatial Location research

This point map provides the locations of DEC-owned or operated boat launching sites. By clicking on the blue points on the point map, users are provided the name of a boat launch site, restroom availability, vehicle access information and the option of viewing the details for that point from the associated dataset and/or opening a page in Google Maps for that location. Users may also use the next buttons, which appear at the bottom of the page after selecting 'details,' to find and view other boat launch sites.

This dataset contains identifiers, metadata, and a map of the locations where field measurements have been conducted at the East River Community Observatory located in the Upper Colorado River Basin, United States. This is version 3.0 of the dataset and replaces the prior version 2.0, which should no longer be used (see below for details on changes between the versions). Dataset description: The East River is the primary field site of the Watershed Function Scientific Focus Area (WFSFA) and the Rocky Mountain Biological Laboratory. Researchers from several institutions generate highly diverse hydrological, biogeochemical, climate, vegetation, geological, remote sensing, and model data at the East River in collaboration with the WFSFA. Thus, the purpose of this dataset is to maintain an inventory of the field locations and instrumentation to provide information on the field activities in the East River and coordinate data collected across different locations, researchers, and institutions. The dataset contains (1) a README file with information on the various files, (2) three csv files describing the metadata collected for each surface point location, plot and region registered with the WFSFA, (3) csv files with metadata and contact information for each surface point location registered with the WFSFA, (4) a csv file with with metadata and contact information for plots, (5) a csv file with metadata for geographic regions and sub-regions within the watershed, (6) a compiled xlsx file with all the data and metadata which can be opened in Microsoft Excel, (7) a kml map of the locations plotted in the watershed which can be opened in Google Earth, (8) a jpeg image of the kml map which can be viewed in any photo viewer, and (9) a zipped file with the registration templates used by the SFA team to collect location metadata. The zipped template file contains two csv files with the blank templates (point and plot), two csv files with instructions for filling out the location templates, and one compiled xlsx file with the instructions and blank templates together. Additionally, the templates in the xlsx include drop down validation for any controlled metadata fields. Persistent location identifiers (Location_ID) are determined by the WFSFA data management team and are used to track data and samples across locations. Dataset uses: This location metadata is used to update the Watershed SFA’s publicly accessible Field Information Portal (an interactive field sampling metadata exploration tool; https://wfsfa-data.lbl.gov/watershed/), the kml map file included in this dataset, and other data management tools internal to the Watershed SFA team. Version Information: The latest version of this dataset publication is version 3.0. The latest version contains a breaking change to the Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml), If you had previously downloaded the map file prior to version 3.0, it will no longer work. Use the updated Location Map (EastRiverCommunityObservatory_Map_v3_0_20220613.kml) in this version of the dataset. This version also contains a total of 51 new point locations, 8 new plot locations, and 1 new geographic region. Additionally, it corrects inconsistencies in existing metadata. Refer to methods for further details on the version history. This dataset will be updated on a periodic basis with new measurement location information. Researchers interested in having their East River measurement locations added in this list should reach out to the WFSFA data management team at wfsfa-data@googlegroups.com. Acknowledgements: Please cite this dataset if using any of the location metadata in other publications or derived products. If using the location metadata for the NEON hyperspectral campaign, additionally cite Chadwick et al. (2020). doi:10.15485/1618130.