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!

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.

📊 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 weekly, 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 insi...

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.

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.

National Library of Scotland Historic Maps APIHistorical Maps of Great Britain for use in mashups and ArcGIS Onlinehttps://nls.tileserver.com/https://maps.nls.uk/projects/api/index.htmlThis seamless historic map can be:embedded in your own websiteused for research purposesused as a backdrop for your own markers or geographic dataused to create derivative work (such as OpenStreetMap) from it.The mapping is based on out-of-copyright Ordnance Survey maps, dating from the 1920s to the 1940s.The map can be directly opened in a web browser by opening the Internet address: https://nls.tileserver.com/The map is ready for natural zooming and panning with finger pinching and dragging.How to embed the historic map in your websiteThe easiest way of embedding the historical map in your website is to copy < paste this HTML code into your website page. Simple embedding (try: hello.html):You can automatically position the historic map to open at a particular place or postal address by appending the name as a "q" parameter - for example: ?q=edinburgh Embedding with a zoom to a place (try: placename.html):You can automatically position the historic map to open at particular latitude and longitude coordinates: ?lat=51.5&lng=0&zoom=11. There are many ways of obtaining geographic coordinates. Embedding with a zoom to coordinates (try: coordinates.html):The map can also automatically detect the geographic location of the visitor to display the place where you are right now, with ?q=auto Embedding with a zoom to coordinates (try: auto.html):How to use the map in a mashupThe historic map can be used as a background map for your own data. You can place markers on top of it, or implement any functionality you want. We have prepared a simple to use JavaScript API to access to map from the popular APIs like Google Maps API, Microsoft Bing SDK or open-source OpenLayers or KHTML. To use our map in your mashups based on these tools you should include our API in your webpage: ... ...

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.

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).

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

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

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 contains a single vector shapefile and two text documents with code used to generate the data product. This vector shapefile contains the locations of 365 “plugged and abandoned” well sites from across the Colorado Plateau with their respective relative fractional vegetation cover (RFVC) values. Oil and gas pads are often developed for production, and then capped, reclaimed, and left to recover when no longer productive (collectively termed “plugged and abandoned”). Understanding the rates, controls, and degree of recovery of these reclaimed well sites (well pads) to a state similar to pre-development conditions is critical for energy development and land management decision processes. We used the Soil-Adjusted Total Vegetation Index (SATVI) to measure post-abandonment vegetation cover relative to pre-drilling condition as a metric of recovery: relative fractional vegetation cover (RFVC). The Google Earth Engine cloud computing platform allows for the automated processing of hundreds of images for each of the hundreds of sites, permitting time series analyses that were not easily achieved with earlier image processing methods. The time-series package BFAST in R statistical software enables the efficient detection of breaks in temporal trends, helping to identify when vegetation was cleared from the site and the magnitudes and rates of vegetation change after abandonment. The code text documents include: 1) Google Earth Engine Script: Well Pad Means, Medians, and DART Percentile Time Series Collection 2) R Script: Generation of BFAST time series models and calculation of RFVC The Google Earth Engine and R code used for data processing, and the final shapefile were used for statistical analysis in the following paper: Waller, E.K., Villarreal, M.L., Poitras, T.B., Nauman, T.W., Duniway, M.C. 2018. Landsat time series analysis of fractional plant cover changes on abandoned energy development sites. International Journal of Applied Earth Observation and Geoinformation 10.1016/j.jag.2018.07.008

Abstract

This is a source dataset created by the Bioregional Assessment Programme without the use of source data.

This dataset contains all of the surface water footprint polygons that were created from mining reports that were used in the surface water modelling. There is also a document with the source references for all of the footprints included in the dataset.

Dataset History

Environmental impact statements and similar documents were downloaded from New South Wales Department of Planning and Environment Major Projects website, and from mining companies' websites. To obtain mine footprints for surface water modelling, the mining reports were searched for past and future projected mine layouts and surface water contributing areas. Each figure was digitised and georeferenced using one of four methods:

1. The preferred method was to use maps or plans with coordinates already on them.

2. If there were no coordinates, then three point locations were matched with points on Google Earth and the latitude and longitude from Google Earth were used to georeference the image.

3. If there were not three clearly identifiable point locations in the image, then supplementary points were found by matching contour information to the Shuttle Radar Topography Mission Smoothed Digital Elevation Model (SRTM DEM-S) grid

Dataset GUID - 12e0731d-96dd-49cc-aa21-ebfd65a3f67a

1. Other site-specific approaches: a. Mangoola Coal Mine did not have adequate georeferencing points in the Year 10 and Final Landform images so these images were georeferenced to the matching project boundary in the other Mangoola Coal Mine images.

b. The West Wallsend Colliery existing pit top surface facilities image, containing a satellite photo background, was georeferenced using Google Earth. The West Wallsend Colliery pit top facility outline was used to georeference the water management system image as they both contained the same outline.

These areas were exported as polygon files (*.poly) using Geosoft Oasis Montaj software.

A list of documents used for creating these polygon files are also included in the dataset

Dataset Citation

Bioregional Assessment Programme (2016) HUN SW footprint shapefiles v01. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/2a9520c8-1569-4e0e-8bd8-26e2c7b9e9e0.

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.

Analysis of ‘San Joaquin County Land Use Survey 2017’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/34320867-1a92-4422-98e2-4f68d26cff40 on 28 January 2022.

--- Dataset description provided by original source is as follows ---

--- Original source retains full ownership of the source dataset ---

The MCD12Q1 V6 product provides global land cover types at yearly intervals (2001-2016) derived from six different classification schemes. It is derived using supervised classifications of MODIS Terra and Aqua reflectance data. The supervised classifications then undergo additional post-processing that incorporate prior knowledge and ancillary information to further refine specific classes.

The band selected in this dataset is the LC_Prop1 which represents the FAO-Land Cover Classification System 1 (LCCS1) land cover layer. The LC_Prop1 Class Table is provided by Google Earth Engine website at https://developers.google.com/earth-engine/datasets/catalog/MODIS_006_MCD12Q1#bands

Citation:

Please visit LP DAAC 'Citing Our Data' page for information on citing LP DAAC datasets

Contact points:

Resource Contact: NASA LP DAAC at the USGS EROS Center

Metadata Contact: FAO-Data

Resource constraints:

MODIS data and products acquired through the LP DAAC have no restrictions on subsequent use, sale, or redistribution.

Online resources:

LPDAAC-USGS (NASA Earth Data) Website onMODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid

Earth Engine Data Catalog

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

In the most recently reported fiscal year, Google's revenue amounted to 348.16 billion U.S. dollars. Google's revenue is largely made up by advertising revenue, which amounted to 264.59 billion U.S. dollars in 2024. As of October 2024, parent company Alphabet ranked first among worldwide internet companies, with a market capitalization of 2,02 billion U.S. dollars. Google’s revenue Founded in 1998, Google is a multinational internet service corporation headquartered in California, United States. Initially conceptualized as a web search engine based on a PageRank algorithm, Google now offers a multitude of desktop, mobile and online products. Google Search remains the company’s core web-based product along with advertising services, communication and publishing tools, development and statistical tools as well as map-related products. Google is also the producer of the mobile operating system Android, Chrome OS, Google TV as well as desktop and mobile applications such as the internet browser Google Chrome or mobile web applications based on pre-existing Google products. Recently, Google has also been developing selected pieces of hardware which ranges from the Nexus series of mobile devices to smart home devices and driverless cars. Due to its immense scale, Google also offers a crisis response service covering disasters, turmoil and emergencies, as well as an open source missing person finder in times of disaster. Despite the vast scope of Google products, the company still collects the majority of its revenue through online advertising on Google Site and Google network websites. Other revenues are generated via product licensing and most recently, digital content and mobile apps via the Google Play Store, a distribution platform for digital content. As of September 2020, some of the highest-grossing Android apps worldwide included mobile games such as Candy Crush Saga, Pokemon Go, and Coin Master.

The California Department of Fish and Wildlife (Department) Vegetation Classification and Mapping Program (VegCAMP) created a fine-scale vegetation classification and map of the southern addition to the Departments Knoxville Wildlife Area (WA), Napa County, California following State Vegetation Survey, Federal Geographic Data Committee (FGDC), and National Vegetation Classification (NVC) Standards (Grossman et al 1998). The vegetation classification was derived from data collected in the field following the Combined Rapid Assessment and Relevé Protocol during the periods November 18''20, 2013 and April 28''May 1, 2014. Vegetation polygons were drawn using heads-up manual digitizing using the 2011 Napa County 30-cm resolution color infrared (CIR) imagery as the base imagery. Supplemental imagery included National Agricultural Imagery Program (NAIP) true color and CIR 1-meter resolution data from 2009''2012, BING imagery, and current and historical imagery from Google Earth. The minimum mapping unit (MMU) is 1 acre, with the exception of wetland types, which have an MMU of 1/2 acre. Ponds, riparian types, and the one vernal pool on the WA that were visible on the imagery were mapped regardless of size, and streams were generally mapped if greater than 10 m wide (narrower portions may have been mapped to maintain the continuity of the streams). Mapping is to the NVC hierarchy association, alliance, or group level based on the ability of the photointerpreters to distinguish types based on all imagery available and on the field data. Both the existing (northern) and new addition (southern) portions of the Knoxville WA were mapped in 2002 as part of the Napa County vegetation map (https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=14660). The 2002 map is at a coarse thematic resolution (alliance through macrogroup level) and vegetation in portions of the WA has changed since the 2004 Rumsey Fire, necessitating this map update. We have produced an updated version of the KWA portion of the 2002 map layer that uses the same spatial data, but added a crosswalk to the current classification and the upper levels of the current hierarchy. This map layer is included in the downloaded dataset for this map and an expanded metadata report for that crosswalk can be found at https://nrm.dfg.ca.gov/FileHandler.ashx?DocumentID=164825.