This mapping tool provides a representation of the general watershed boundaries for stream systems declared fully appropriated by the State Water Board. The boundaries were created by Division of Water Rights staff by delineating FASS critical reaches and consolidating HUC 12 sub-watersheds to form FASS Watershed boundaries. As such, the boundaries are in most cases conservative with respect to the associated stream system. However, users should check neighboring FASS Watersheds to ensure the stream system of interest is not restricted by other FASS listings. For more information regarding the Declaration of Fully Appropriated Stream Systems, visit the Division of Water Rights’ Fully Appropriated Streams webpage. How to Use the Interactive Mapping Tool: If it is your first time viewing the map, you will need to click the “OK” box on the splash screen and agree to the disclaimer before continuing. Navigate to your point of interest by either using the search bar or by zooming in on the map. You may enter a stream name, street address, or watershed ID in the search bar. Click on the map to identify the location of interest and one or more pop-up boxes may appear with information about the fully appropriated stream systems within the general watershed boundaries of the identified location. The information provided in the pop-up box may include: (a) stream name, (b) tributary, (c) season declared fully appropriated, (d) Board Decisions/Water Right Orders, and/or (e) court references/adjudications. You may toggle the FAS Streams reference layer on and off to find representative critical reaches associated with the FASS Watershed layer. Please note that this layer is for general reference purposes only and ultimately the critical reach listed in Appendix A of Water Rights Order 98-08 and Appendix A together with any associated footnotes controls. Note: A separate FAS Watershed boundary layer was created for the Bay-Delta Watershed. The Bay-Delta Watershed layer should be toggled on to check if the area of interest is fully appropriated under State Water Board Decision 1594.

Map containing the layers for the Parcel Report tool on the Planning Dept website. Location: https://www.countyofnapa.org/589/Planning-Building-Environmental-Services\Business\Parcel Report ToolInstant App:Id: fcbc684ce24f4220b1fa383bdeed2371Url: https://napacounty.maps.arcgis.com/apps/instant/nearby/index.html?appid=fcbc684ce24f4220b1fa383bdeed2371Layers:Both Parcels and Address are Enterprise Hosted Layers.

To view and download tax plat maps, click on the DATA tab above. To find a map, select a column heading to sort the table by zone or section number. Or refine your search by the clicking the filter icon at the top of each column. To view or download the tax plat map, click on the URL under ViewMap next to the ZSP map number. Alternatively, use the online Parcel and Zoning Map to search for tax plat maps by address, tax map key (TMK), or using a map interface.

Scanned Pipeline Maps Query - You can now request these same well files, well logs, and well data as a free download through the File Request System ( https://www.data.bsee.gov/Other/FileRequestSystem/Default.aspx ). The Disc Media Store will be removed at some point in the future.

Are you looking to identify B2B leads to promote your business, product, or service? Outscraper Google Maps Scraper might just be the tool you've been searching for. This powerful software enables you to extract business data directly from Google's extensive database, which spans millions of businesses across countless industries worldwide.

Outscraper Google Maps Scraper is a tool built with advanced technology that lets you scrape a myriad of valuable information about businesses from Google's database. This information includes but is not limited to, business names, addresses, contact information, website URLs, reviews, ratings, and operational hours.

Whether you are a small business trying to make a mark or a large enterprise exploring new territories, the data obtained from the Outscraper Google Maps Scraper can be a treasure trove. This tool provides a cost-effective, efficient, and accurate method to generate leads and gather market insights.

By using Outscraper, you'll gain a significant competitive edge as it allows you to analyze your market and find potential B2B leads with precision. You can use this data to understand your competitors' landscape, discover new markets, or enhance your customer database. The tool offers the flexibility to extract data based on specific parameters like business category or geographic location, helping you to target the most relevant leads for your business.

In a world that's growing increasingly data-driven, utilizing a tool like Outscraper Google Maps Scraper could be instrumental to your business' success. If you're looking to get ahead in your market and find B2B leads in a more efficient and precise manner, Outscraper is worth considering. It streamlines the data collection process, allowing you to focus on what truly matters – using the data to grow your business.

https://outscraper.com/google-maps-scraper/

As a result of the Google Maps scraping, your data file will contain the following details:

Query Name Site Type Subtypes Category Phone Full Address Borough Street City Postal Code State Us State Country Country Code Latitude Longitude Time Zone Plus Code Rating Reviews Reviews Link Reviews Per Scores Photos Count Photo Street View Working Hours Working Hours Old Format Popular Times Business Status About Range Posts Verified Owner ID Owner Title Owner Link Reservation Links Booking Appointment Link Menu Link Order Links Location Link Place ID Google ID Reviews ID

If you want to enrich your datasets with social media accounts and many more details you could combine Google Maps Scraper with Domain Contact Scraper.

Domain Contact Scraper can scrape these details:

Email Facebook Github Instagram Linkedin Phone Twitter Youtube

This Guide is designed to assist you with adding and viewing data on a map within the Department of Climate Change, Energy, the Environment and Water's Find Environmental Data (FED) geospatial data catalogue.This Guide assumes that you are familiar with locating data within FED. For further assistance see the Finding Data Guide.

The National Flood Hazard Layer (NFHL) is a geospatial database that contains current effective flood hazard data. FEMA provides the flood hazard data to support the National Flood Insurance Program. You can use the information to better understand your level of flood risk and type of flooding.The NFHL is made from effective flood maps and Letters of Map Change (LOMC) delivered to communities. NFHL digital data covers over 90 percent of the U.S. population. New and revised data is being added continuously. If you need information for areas not covered by the NFHL data, there may be other FEMA products which provide coverage for those areas.In the NFHL Viewer, you can use the address search or map navigation to locate an area of interest and the NFHL Print Tool to download and print a full Flood Insurance Rate Map (FIRM) or FIRMette (a smaller, printable version of a FIRM) where modernized data exists. Technical GIS users can also utilize a series of dedicated GIS web services that allow the NFHL database to be incorporated into websites and GIS applications. For more information on available services, go to the NFHL GIS Services User Guide.You can also use the address search on the FEMA Flood Map Service Center (MSC) to view the NFHL data or download a FIRMette. Using the “Search All Products” on the MSC, you can download the NFHL data for a County or State in a GIS file format. This data can be used in most GIS applications to perform spatial analyses and for integration into custom maps and reports. To do so, you will need GIS or mapping software that can read data in shapefile format.FEMA also offers a download of a KMZ (keyhole markup file zipped) file, which overlays the data in Google Earth™. For more information on using the data in Google Earth™, please see Using the National Flood Hazard Layer Web Map Service (WMS) in Google Earth™.

These data provide an accurate high-resolution shoreline compiled from imagery of LAKE HURON, ST VITAL POINT TO SEARCH BAY, MI . This vector shoreline data is based on an office interpretation of imagery that may be suitable as a geographic information system (GIS) data layer. This metadata describes information for both the line and point shapefiles. The NGS attribution scheme 'Coastal Cartographic Object Attribute Source Table (C-COAST)' was developed to conform the attribution of various sources of shoreline data into one attribution catalog. C-COAST is not a recognized standard, but was influenced by the International Hydrographic Organization's S-57 Object-Attribute standard so the data would be more accurately translated into S-57. This resource is a member of https://www.fisheries.noaa.gov/inport/item/39808

The Cumberland County GIS Data Viewer provides the general public with parcel, zoning, hydrology, soils, utilities and topographic data. You can search for a specific address, street name, parcel number (PIN), or by the owner's name.

Web App. Use the tabs provided to discover information about map features and capabilities. Link to Metadata. A variety of searches can be performed to find the parcel of interest. Use the Query Tool to build searches. Click Apply button at the bottom of the tool.Query by Name (Last First) (e.g. Bond James)Query by Address (e.g. 41 S Central)Query by Locator number (e.g. 21J411046)Search results will be listed under the Results tab. Click on a parcel in the list to zoom to that parcel. Click on the parcel in the map and scroll through the pop-up to see more information about the parcel. Click the ellipse in the Results tab or in the pop-up to view information in a table. Attribute information can be exported to CSV file. Build a custom Filter to select and map properties by opening the Parcels attribute table:1. Click the arrow tab at the bottom middle of the map to expand the attribute table window2. Click on the Parcels tab3. Check off Filter by map extent4. Open Options>Filter5. Build expressions as needed to filter by owner name or other variables6. Select the needed records from the returned list7. Click Zoom to which will zoom to the selected recordsPlease note that as the map zooms out detailed layers, such as the parcel boundaries will not display.In addition to Search capabilities, the following tools are provided:MeasureThe measure tool provides the capabilities to draw a point, line, or polygon on the map and specify the unit of measurement.DrawThe draw tool provides the capabilities to draw a point, line, or polygon on the map as graphics. PrintThe print tool exports the map to either a PDF or image file. Click Settings button to configure map or remove legend.Map navigation using mouse and keyboard:Drag to panSHIFT + CTRL + Drag to zoom outMouse Scroll Forward to zoom inMouse Scroll Backward to zoom outUse Arrow keys to pan+ key to zoom in a level- key to zoom out a levelDouble Click to Zoom inFAQsHow to select a parcel: Click on a parcel in the map, or use Query Tool to search for parcel by owner, address or parcel id.How to select more than one parcel: Go to Select Tool and choose options on Select button.How to clear selected parcel(s): Go to Select Tool and click Clear.

Components: Hardware: Includes mobile mapping systems, sensors, and other equipment Software: Includes software for data collection, processing, and visualization Services: Includes data collection, processing, and analysis servicesSolutions: Location-based: Provides location-based information and services Indoor mapping: Creates maps of indoor spaces Asset management: Helps manage assets and track their location 3D mapping: Creates 3D models of buildings and infrastructureApplications: Land surveys: Used for surveying land and creating maps Aerial surveys: Used for surveying areas from the air Real estate & construction: Used for planning and designing buildings and infrastructure IT & telecom: Used for network planning and management Recent developments include: One of the pioneers in wearable mobile mapping technology, NavVis, revealed the NavVis VLX 3, their newest generation of wearable technology. As the name suggests, this is the third version of their wearable VLX system; the NavVis VLX 2 was released in July of 2021, which is over two years ago. In their news release, NavVis emphasises the NavVis VLX 3's improved accuracy in point clouds by highlighting the two brand-new, 32-layer lidars that have been "meticulously designed and crafted" to minimise noise and drift in point clouds while delivering "high detail at range.", According to the North American Mach9 Software Platform, mobile Lidar will produce 2D and 3D maps 30 times faster than current systems by 2023., Even though this is Mach9's first product launch, the business has already begun laying the groundwork for future expansion by updating its website, adding important engineering and sales professionals, relocating to new headquarters in Pittsburgh's Bloomfield area, and forging ties in Silicon Valley., In order to make search more accessible to more users in more useful ways, Google has unveiled a tonne of new search capabilities for 2022 spanning Google Search, Google Lens, Shopping, and Maps. These enhancements apply to Google Maps, Google Shopping, Google Leons, and Multisearch., A multi-year partnership to supply Velodyne Lidar, Inc.'s lidar sensors to GreenValley International for handheld, mobile, and unmanned aerial vehicle (UAV) 3D mapping solutions, especially in GPS-denied situations, was announced in 2022. GreenValley is already receiving sensors from Velodyne., The acquisition of UK-based GeoSLAM, a leading provider of mobile scanning solutions with exclusive high-productivity simultaneous localization and mapping (SLAM) programmes to create 3D models for use in Digital Twin applications, is expected to close in 2022 and be completed by FARO® Technologies, Inc., a global leader in 4D digital reality solutions., November 2022: Topcon donated to TU Dublin as part of their investment in the future of construction. Students learning experiences will be improved by instruction in the most cutting-edge digital building techniques at Ireland's first technical university., October 2022: Javad GNSS Inc has released numerous cutting-edge GNSS solutions for geospatial applications. The TRIUMPH-1M Plus and T3-NR smart antennas, which employ upgraded Wi-Fi, Bluetooth, UHF, and power management modules and integrate the most recent satellite tracking technology into the geospatial portfolio, are two examples of important items.. Key drivers for this market are: Improvements in GPS, LiDAR, and camera technologies have significantly enhanced the accuracy and efficiency of mobile mapping systems. Potential restraints include: The initial investment required for mobile mapping equipment, including sensors and software, can be a barrier for small and medium-sized businesses.. Notable trends are: Mobile mapping systems are increasingly integrated with cloud platforms and AI technologies to process and analyze large datasets, enabling more intelligent mapping and predictive analytics.

simulated agents can be created to emulate the behavior of the lost person. Within this study

This layer of the map based index (GeoIndex) shows the availability of 1:63360 scale geological maps. The maps are available for most of England and Wales and show early geological mapping covering the OS Old Series one inch map sheet areas.

Brand performance data collected from AI search platforms for the query "customer journey mapping tools comparison".

The Webis Query-Task-Mapping Corpus 2019 (Webis-QTM-19) comprises three benchmark datasets on the query-task-mapping problem, which consists of finding the correct task for a new query in a given task-split background query log.

It comprises three subdatasets in separate CSV files, each of which has three columns:

• Query. The query string.
• Source. The source of the query. In all datasets, a source field with value 'google' or 'bing' indicates that the query was derived from query suggestions from the respective search engine; otherwise, the query is from one of the underlying base corpora:
  • 'lucc' : lucchese:2011
  • 'webis' : stein:2013b
  • 'trc' : stein:2016a
  • 'trec' : various collections of TREC queries
  • 'wikihow' : based on titles of wikiHow questions
• Task. The ID of the ground-truth task for the corresponding query.

Further details can be found in reference:Michael Völske, Ehsan Fatehifar, Benno Stein, and Matthias Hagen. Query-Task Mapping. In 42nd International ACM Conference on Research and Development in Information Retrieval (SIGIR 2019), July 2019. ACM.http://doi.acm.org/10.1145/3331184.3331286

The index shows the availability of county series geological maps, 1:10560 scale. The maps themselves were produced on OS County Series sheets between approximately 1860 and 1960. The list indicates whether the map has been revised or re-surveyed and gives details of any later versions that have been produced. It is advisable to discuss your requirements before ordering or travelling to view these maps.

The San Bernardino County map viewer is a collection of maps and apps related to various administrative boundaries in San Bernardino County. All data is publicly available. The San Bernardino County map viewer contains the following maps:Parcels: Find and identify parcels by APN or address.Flood Control: Find and identify Flood Control facilities within San Bernardino CountyBoundaries: Explore various administrative boundaries in San Bernardino County, such as Supervisor districts, city limits, US Senate districts and moreHistorical Imagery: Imagery archives for the years 2008 - 2023Power Outages: Power outage data from CalOES showing power outages within San Bernardino County3D Scene: Interactively explore San Bernardino County geographic data in 3D.DIY Map Viewer: Create your own map using a variety of provided datasets, or add your ownThe San Bernardino County Map viewer was created by San Bernardino County's Information Services Department. For more information please contact the Information Services Department (ISD) Help Desk at (909)884-4884.

This package contains data on five text analysis types (term extraction, contract analysis, topic modeling, network mapping), based on the survey data where researchers selected research output that are related to the 17 Sustainable Development Goals (SDGs). This is used as input to improve the current SDG classification model v4.0 to v5.0

Sustainable Development Goals are the 17 global challenges set by the United Nations. Within each of the goals specific targets and indicators are mentioned to monitor the progress of reaching those goals by 2030. In an effort to capture how research is contributing to move the needle on those challenges, we earlier have made an initial classification model than enables to quickly identify what research output is related to what SDG. (This Aurora SDG dashboard is the initial outcome as proof of practice.)

The initiative started from the Aurora Universities Network in 2017, in the working group "Societal Impact and Relevance of Research", to investigate and to make visible 1. what research is done that are relevant to topics or challenges that live in society (for the proof of practice this has been scoped down to the SDGs), and 2. what the effect or impact is of implementing those research outcomes to those societal challenges (this also have been scoped down to research output being cited in policy documents from national and local governments an NGO's).

Context of this dataset | classification model improvement workflow

The classification model we have used are 17 different search queries on the Scopus database.

SDG search queries version 4.0 (SQv4) have been created, Published here:

Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v4.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817443

A survey has been distributed to senior researchers to test the robustness of SQv4. Published here:

Survey data of "Mapping Research output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3798385

This text analysis has been made as one of the inputs to improve the classification model. Published here:

Text Analyses of Survey Data on "Mapping Research Output to the Sustainable Development Goals SDGs" by Aurora Universities Network (AUR) doi:10.5281/zenodo.3832090

Improved SDG search queries version 5.0 (SQv5) have been created, Published here:

Search Queries for "Mapping Research Output to the Sustainable Development Goals (SDGs)" v5.0 by Aurora Universities Network (AUR) doi:10.5281/zenodo.3817445

Methods used to do the text analysis

Term Extraction: after text normalisation (stemming, etc) we extracted 2 terms in bigrams and trigrams that co-occurred the most per document, in the title, abstract and keyword

Contrast analysis: the co-occurring terms in publications (title, abstract, keywords), of the papers that respondents have indicated relate to this SDG (y-axis: True), and that have been rejected (x-axis: False). In the top left you'll see term co-occurrences that a clearly relate to this SDG. The bottom-right are terms that are appear in papers that have been rejected for this SDG. The top-right terms appear frequently in both and cannot be used to discriminate between the two groups.

Network map: This diagram shows the cluster-network of terms co-occurring in the publications related to this SDG, selected by the respondents (accepted publications only).

Topic model: This diagram shows the topics, and the related terms that make up that topic. The number of topics is related to the number of of targets of this SDG.

Contingency matrix: This diagram shows the top 10 of co-occurring terms that correlate the most.

Software used to do the text analyses

CorTexT: The CorTexT Platform is the digital platform of LISIS Unit and a project launched and sustained by IFRIS and INRAE. This platform aims at empowering open research and studies in humanities about the dynamic of science, technology, innovation and knowledge production.

Resource with interactive visualisations

Based on the text analysis data we have created a website that puts all the SDG interactive diagrams together. For you to scrall through. https://sites.google.com/vu.nl/sdg-survey-analysis-results/

Data set content

In the dataset root you'll find the following folders and files:

/sdg01-17/

This contains the text analysis for all the individual SDG surveys.

/methods/

This contains the step-by-step explanations of the text analysis methods using Cortext.

/images/

images of the results used in this README.md.

LICENSE.md

terms and conditions for reusing this data.

README.md

description of the dataset; each subfolders contains a README.md file to futher describe the content of each sub-folder.

Inside an /sdg01-17/-folder you'll find the following:

This contains the step-by-step explanations of the text analysis methods using Cortext.

/sdg01-17/sdg04-sdg-survey-selected-publications-combined.db

his contains the title, abstract, keywords, fo the publications in the survey, including the and accept or rejection status and the number of respondents

/sdg01-17/sdg04-sdg-survey-selected-publications-combined-accepted-accepted-custom-filtered.db

same as above, but only the accepted papers

/sdg01-17/extracted-terms-list-top1000.csv

the aggregated list of co-occuring terms (bigrams and trigrams) extracted per paper.

/sdg01-17/contrast-analysis/

This contains the data and visualisation of the terms appearing in papers that have been accepted (true) and rejected (false) to be relating to this SDG.

/sdg01-17/topic-modelling/

This contains the data and visualisation of the terms clustered in the same number of topics as there are 'targets' within that SDG.

/sdg01-17/network-mapping/

This contains the data and visualisation of the terms clustered in co-occuring proximation of appearance in papers

/sdg01-17/contingency-matrix/

This contains the data and visualisation of the top 10 terms co-occuring

note: the .csv files are actually tab-separated.

Contribute and improve the SDG Search Queries

We welcome you to join the Github community and to fork, branch, improve and make a pull request to add your improvements to the new version of the SDG queries. https://github.com/Aurora-Network-Global/sdg-queries

There are a variety of resources available via The National Map homepage, such as static maps, interactive map viewers, and geospatial data. Some of these maps and apps include, the National Map Viewer, the 3D Elevation Program, the National Hydrography Dataset and Hydrography Viewer, the Historical Topographic Map and the US Topo. Via The National Map, historical topographic maps are available to search and download via a variety of options. The 3D Elevation Program (3DEP) provides information about, and access to elevation data meeting the 3DEP guidelines. Users can also access and view the National Hydrography Dataset via the Hydrography viewer; this is similar to the National Map Viewer, however the basemap is based on HUC watersheds. Using the National Map Viewer, users can search for, access and download current 7.5 minute US Topos for the entire country; users can also explore and view other data for their area of interest. Below, find links to the different The National Map resources that were described above. The National Map also provides access to other data and viewers, such as the National Land Cover Database, and The National Map Corps.

This dataset contains the Version 2 MRO MARCI Mars Daily Global Maps (MDGMs) [Wang and Gonzalez Abad, 2021]. Each RGB color map (0.1 degree longitude by 0.1 degree latitude, covering 180W-180E, 90S-90N) was made from 13 consecutive sets of global map swaths taken during a time period of about a sol by the Mars Color Imager (MARCI) onboard the Mars Reconnaissance Orbiter (MRO) spacecraft. The MARCI mapping data have been collected since November 2006. The current MDGMs cover MARCI subphases from P01 to N14 (Mars Year 28-35, data collected during 2006-2021). New maps will be appended as they become available. The MDGMs in this release are organized into the "P" (for P01-P22), "B" for (B01-B22), "G" (for G01-G23), "D" (for D01-D22), "F" (for F01-F23), "J" (for J01-J22), "K" (for K01-K23) and "N" (for N01-N14) MARCI phase folders in time order. The folders are tarred and gzipped. The MDGMs before subphase K11 in TIFF format with both 0.1x0.1 degree and 0.05x0.05 degree resolutions are also archived at the Planetary Data System (PDS) Annex Astropedia (https://astrogeology.usgs.gov/search/map/mars_mro_marci_daily_global_weather_maps_pds4_archive). Information for the Mars year and solar longitude (Ls) value of each MDGM can be found from a .txt file within each subphase folder. The list of images used to construct the MDGMs can be found in the list/ subdirectory of each subphase folder. The MARCI filenames can be used to obtain more information (e.g., imaging time, dimension, etc.) from the cumindex.tab file in the MARCI archive released by the PDS. The time range covered by each subphase can be found in the summary_subphases.txt file. The documents (readme.pdf, processing.pdf and errata.pdf, replicated from the PDS) and Notes associated with this dataset provide additional information. The MDGMs are intended for a quick look of time-variable phenomena on Mars from day to day and for keeping a long-term record. The color of MDGMs results from an arbitrary color scheme that is applied across all maps, but users can apply their own color stretch. The MDGMs are provided in PNG format. Filenames are in the format of png_Xxx_Xyy.tar.gz, where X denotes MARCI mission phase, xx and yy denote the start and end subphases. NOTE: the (0,0) pixel is at the south pole (90S, 180W). Depending on the software used, the image may need to be flipped.