In 2007, the California Ocean Protection Council initiated the California Seafloor Mapping Program (CSMP), designed to create a comprehensive seafloor map of high-resolution bathymetry, marine benthic habitats, and geology within California’s State Waters. The program supports a large number of coastal-zone- and ocean-management issues, including the California Marine Life Protection Act (MLPA) (California Department of Fish and Wildlife, 2008), which requires information about the distribution of ecosystems as part of the design and proposal process for the establishment of Marine Protected Areas. A focus of CSMP is to map California’s State Waters with consistent methods at a consistent scale. The CSMP approach is to create highly detailed seafloor maps through collection, integration, interpretation, and visualization of swath sonar data (the undersea equivalent of satellite remote-sensing data in terrestrial mapping), acoustic backscatter, seafloor video, seafloor photography, high-resolution seismic-reflection profiles, and bottom-sediment sampling data. The map products display seafloor morphology and character, identify potential marine benthic habitats, and illustrate both the surficial seafloor geology and shallow (to about 100 m) subsurface geology. It is emphasized that the more interpretive habitat and geology data rely on the integration of multiple, new high-resolution datasets and that mapping at small scales would not be possible without such data. This approach and CSMP planning is based in part on recommendations of the Marine Mapping Planning Workshop (Kvitek and others, 2006), attended by coastal and marine managers and scientists from around the state. That workshop established geographic priorities for a coastal mapping project and identified the need for coverage of “lands” from the shore strand line (defined as Mean Higher High Water; MHHW) out to the 3-nautical-mile (5.6-km) limit of California’s State Waters. Unfortunately, surveying the zone from MHHW out to 10-m water depth is not consistently possible using ship-based surveying methods, owing to sea state (for example, waves, wind, or currents), kelp coverage, and shallow rock outcrops. Accordingly, some of the data presented in this series commonly do not cover the zone from the shore out to 10-m depth. This data is part of a series of online U.S. Geological Survey (USGS) publications, each of which includes several map sheets, some explanatory text, and a descriptive pamphlet. Each map sheet is published as a PDF file. Geographic information system (GIS) files that contain both ESRI ArcGIS raster grids (for example, bathymetry, seafloor character) and geotiffs (for example, shaded relief) are also included for each publication. For those who do not own the full suite of ESRI GIS and mapping software, the data can be read using ESRI ArcReader, a free viewer that is available at http://www.esri.com/software/arcgis/arcreader/index.html (last accessed September 20, 2013). The California Seafloor Mapping Program is a collaborative venture between numerous different federal and state agencies, academia, and the private sector. CSMP partners include the California Coastal Conservancy, the California Ocean Protection Council, the California Department of Fish and Wildlife, the California Geological Survey, California State University at Monterey Bay’s Seafloor Mapping Lab, Moss Landing Marine Laboratories Center for Habitat Studies, Fugro Pelagos, Pacific Gas and Electric Company, National Oceanic and Atmospheric Administration (NOAA, including National Ocean Service–Office of Coast Surveys, National Marine Sanctuaries, and National Marine Fisheries Service), U.S. Army Corps of Engineers, the Bureau of Ocean Energy Management, the National Park Service, and the U.S. Geological Survey. These web services for the Offshore of Point Conception map area includes data layers that are associated to GIS and map sheets available from the USGS CSMP web page at https://walrus.wr.usgs.gov/mapping/csmp/index.html. Each published CSMP map area includes a data catalog of geographic information system (GIS) files; map sheets that contain explanatory text; and an associated descriptive pamphlet. This web service represents the available data layers for this map area. Data was combined from different sonar surveys to generate a comprehensive high-resolution bathymetry and acoustic-backscatter coverage of the map area. These data reveal a range of physiographic including exposed bedrock outcrops, large fields of sand waves, as well as many human impacts on the seafloor. To validate geological and biological interpretations of the sonar data, the U.S. Geological Survey towed a camera sled over specific offshore locations, collecting both video and photographic imagery; these “ground-truth” surveying data are available from the CSMP Video and Photograph Portal at https://doi.org/10.5066/F7J1015K. The “seafloor character” data layer shows classifications of the seafloor on the basis of depth, slope, rugosity (ruggedness), and backscatter intensity and which is further informed by the ground-truth-survey imagery. The “potential habitats” polygons are delineated on the basis of substrate type, geomorphology, seafloor process, or other attributes that may provide a habitat for a specific species or assemblage of organisms. Representative seismic-reflection profile data from the map area is also include and provides information on the subsurface stratigraphy and structure of the map area. The distribution and thickness of young sediment (deposited over the past about 21,000 years, during the most recent sea-level rise) is interpreted on the basis of the seismic-reflection data. The geologic polygons merge onshore geologic mapping (compiled from existing maps by the California Geological Survey) and new offshore geologic mapping that is based on integration of high-resolution bathymetry and backscatter imagery seafloor-sediment and rock samplesdigital camera and video imagery, and high-resolution seismic-reflection profiles. The information provided by the map sheets, pamphlet, and data catalog has a broad range of applications. High-resolution bathymetry, acoustic backscatter, ground-truth-surveying imagery, and habitat mapping all contribute to habitat characterization and ecosystem-based management by providing essential data for delineation of marine protected areas and ecosystem restoration. Many of the maps provide high-resolution baselines that will be critical for monitoring environmental change associated with climate change, coastal development, or other forcings. High-resolution bathymetry is a critical component for modeling coastal flooding caused by storms and tsunamis, as well as inundation associated with longer term sea-level rise. Seismic-reflection and bathymetric data help characterize earthquake and tsunami sources, critical for natural-hazard assessments of coastal zones. Information on sediment distribution and thickness is essential to the understanding of local and regional sediment transport, as well as the development of regional sediment-management plans. In addition, siting of any new offshore infrastructure (for example, pipelines, cables, or renewable-energy facilities) will depend on high-resolution mapping. Finally, this mapping will both stimulate and enable new scientific research and also raise public awareness of, and education about, coastal environments and issues. Web services were created using an ArcGIS service definition file. The ArcGIS REST service and OGC WMS service include all Offshore of Point Conception map area data layers. Data layers are symbolized as shown on the associated map sheets.

Economic Development Web Map. Auto-generated by Grow your Local Economy Initiative, for Economic Development App (Business)

Discover the booming GIS mapping tools market! This in-depth analysis reveals a $15B market in 2025 projected to reach $39B by 2033, driven by cloud adoption, AI integration, and surging demand across sectors. Explore key trends, leading companies (Esri, ArcGIS, QGIS, etc.), and regional growth forecasts.

Using the coronavirus infographic template in Business/Community Analyst Web (ArcGIS Blog).Business Analyst (BA) Web infographics are a powerful way to understand demographics and other information in context. This blog article explains how your organization can use the Coronavirus infographic template that was added to the infographics gallery on March 1, 2020._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

The Global GIS Mapping Tools Market is poised for significant expansion, projected to reach a substantial market size of $10 billion by 2025, with an anticipated Compound Annual Growth Rate (CAGR) of 12.5% through 2033. This robust growth trajectory is fueled by the increasing demand for advanced spatial analysis and visualization capabilities across a multitude of sectors. Key drivers include the escalating need for accurate geological exploration to identify and manage natural resources, the critical role of GIS in planning and executing complex water conservancy projects for sustainable water management, and the indispensable application of GIS in urban planning for efficient city development and infrastructure management. Furthermore, the burgeoning adoption of cloud-based and web-based GIS solutions is democratizing access to powerful mapping tools, enabling broader use by organizations of all sizes. The market is also benefiting from advancements in data processing, artificial intelligence integration, and the growing availability of open-source GIS platforms. Despite the optimistic outlook, certain restraints could temper the market's full potential. High initial investment costs for sophisticated GIS software and hardware, coupled with a shortage of skilled GIS professionals in certain regions, may pose challenges. However, the overwhelming benefits of enhanced decision-making, improved operational efficiency, and the ability to gain deep insights from spatial data are compelling organizations to overcome these hurdles. The competitive landscape is dynamic, featuring established players like Esri and Autodesk alongside innovative providers such as Mapbox and CARTO, all vying for market share by offering specialized features, user-friendly interfaces, and integrated solutions. The continuous evolution of GIS technology, driven by the integration of remote sensing data, big data analytics, and real-time information, will continue to shape the market's future. Here's a comprehensive report description on GIS Mapping Tools, incorporating your specified requirements:

This in-depth report provides a panoramic view of the global GIS Mapping Tools market, meticulously analyzing its landscape from the Historical Period (2019-2024) through to the Forecast Period (2025-2033), with 2025 serving as both the Base Year and the Estimated Year. The study period encompasses 2019-2033, offering a robust historical context and forward-looking projections. The market is valued in the millions of US dollars, with detailed segment-specific valuations and growth trajectories. The report is structured to deliver actionable intelligence to stakeholders, covering market concentration, key trends, regional dominance, product insights, and critical industry dynamics. It delves into the intricate interplay of companies such as Esri, Hexagon, Autodesk, CARTO, and Mapbox, alongside emerging players like Geoway and Shenzhen Edraw Software, across diverse applications including Geological Exploration, Water Conservancy Projects, and Urban Planning. The analysis also differentiates between Cloud Based and Web Based GIS solutions, providing a granular understanding of market segmentation.

U.S. State Plane Zones (NAD 1983) represents the State Plane Coordinate System (SPCS) Zones for the 1983 North American Datum within United States.

Economic Development Web Map. Auto-generated by Grow your Local Economy Testing Initiative, for Economic Development App (Business)

Image Visit is a configurable app template that allows users to quickly review the attributes of a predetermined sequence of locations in imagery. The app optimizes workflows by loading the next image while the user is still viewing the current image, reducing the delay caused by waiting for the next image to be returned from the server.Image Visit users can do the following:Navigate through a predetermined sequence of locations two ways: use features in a 'Visit' layer (an editable hosted feature layer), or use a web map's bookmarks.Use an optional 'Notes' layer (a second editable hosted feature layer) to add or edit features associated with the Visit locations.If the app uses a Visit layer for navigation, users can edit an optional 'Status' field to set the status of each Visit location as it's processed ('Complete' or 'Incomplete,'' for example).View metadata about the Imagery, Visit, and Notes layers in a dialog window (which displays information based on each layer's web map popup settings).Annotate imagery using editable feature layersPerform image measurement on imagery layers that have mensuration capabilitiesExport an imagery layer to the user's local machine, or as layer in the user’s ArcGIS accountUse CasesAn insurance company checking properties. An insurance company has a set of properties to review after an event like a hurricane. The app would drive the user to each property, and allow the operator to record attributes (the extent of damage, for example). Image analysts checking control points. Organizations that collect aerial photography often have a collection of marked or identifiable control points that they use to check their photographs. The app would drive the user to each of the known points, at a suitable scale, then allow the user to validate the location of the control point in the image. Checking automatically labeled features. In cases where AI is used for object identification, the app would drive the user to identified features to review/correct the classification. Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsCreating an app with this template requires a web map with at least one imagery layer.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageClick the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

ASTHO created an Essential and Non-Essential Business Designations layer using Esri’s ArcGIS online mapping tool. Data was sourced from jurisdictional websites, executive orders and guidance documents. The layer displays information on essential and non-essential business designations from state/territorial websites. Please note, local authorities may also issue declarations or executive orders that are more restrictive in nature. This information is not included on this layer. Information is assessed regularly by ASTHO staff for relevance to state/territorial health officials’ priorities in their COVID-19 response. Updates to this layer will occur peridically.Data Definitions:Essential and Non-Essential Business Designations - States and territories that have issued designations on essential and non-essential businesses in response to COVID-19.Terms of UseIf you plan to use this map to advance your own research or to disseminate the information we’ve presented here, please reference the below data citation, using DataCite’s format for citing.ASTHO. April 16, 2020. Essential and Non-Essential Business Designations. Esri ArcGIS Layer. https://coronavirus-astho.hub.arcgis.com/.Originally published April 16, 2020 on https://coronavirus-astho.hub.arcgis.com/Workbook details: 1 attribute table in ArcGisOriginal author: ASTHO

The Traffic Resources web map is used to author the Traffic Resources web app at https://ebrgis.maps.arcgis.com/home/item.html?id=822fdc16e55e4410b5ee200a5a4a184c. Objective:The data displayed in this web map is intended to provide motorists in the Baton Rouge area with near real-time traffic data.Details:The traffic incidents are being handled by the Baton Rouge Police Department and the East Baton Rouge Sheriff's Office. The information is gathered from the 911 Computer Assisted Dispatch System, and it does not include data from Baker Police, Central Police, Zachary Police, Louisiana State Police, LSU, or Southern University. The map will refresh automatically every 60 seconds.Disclaimer:This page contains raw data and unconfirmed information about traffic incidents as they have been reported to the East Baton Rouge Parish 911 computer aided dispatch (CAD) system. The possibility exists that an incident may have been reported or classified incorrectly.The information on this site is intended only as a general guide to possible traffic related situations being investigated by the Baton Rouge Police Department, East Baton Rouge Sheriff's Office, Emergency Medical Services, or Baton Rouge Fire Department. Incidents located in East Baton Rouge Parish that are being investigated by other agencies, including Baker Police, Central Police, Zachary Police, Louisiana State Police, or campus police at LSU and Southern University incidents are not reflected in this map. Because this information is derived from the 911 CAD system, the incident will remain on the page until the responding officer has been taken off the call. This may occur after the actual incident has been cleared from the roadway. Also, traffic incident points should be considered approximate, not exact locations.The City-Parish Department of Transportation and Drainage maintains the local road closure data which is displayed in this web map. More information about local road closures can be found on the EBR Road Closures webpage. This map displays data from the Louisiana Department of Transportation and Development. Included are the traffic camera locations and LA-DOTD road closures. These datasets are provided as a convenience for users. The City-Parish is not responsible for the information provided by LA-DOTD or its affiliates.The live and predictive traffic data comes directly from HERE. HERE collects data per month, and where available, users sensors to augment the collected data. An algorithm compiles the data and computes accurate speeds. The imagery is not live. Imagery is provided as a service from Esri, Inc. and image tiles are not updated each time the map is displayed.This site uses web mapping software from Esri, Inc. which has been purchased by the Baton Rouge City-Parish Government.Users are encouraged to submit their questions and comments related to the EBRGIS Program by sending an email to gis@brla.gov or contacting the Department of Information Services at (225) 389-3070.

The planimetric data was compiled by The Sanborn Map Company, Inc for the Metropolitan District and is based on an aerial flight performed in April 2015. In addition, the City's GIS staff has been updating limited planimetric features based on information on file in various City departments. The planimetric data has also been updated in 2016 and yearly to current based on spring aerial flights by EagleView.

The nine-banded Armadillo (Dasypus novemcinctus) is the only species of Armadillo in the United States and alters ecosystems by excavating extensive burrows used by many other wildlife species. Relatively little is known about its habitat use or population densities, particularly in developed areas, which may be key to facilitating its range expansion. We evaluated Armadillo occupancy and density in relation to anthropogenic and landcover variables in the Ozark Mountains of Arkansas along an urban to rural gradient. Armadillo detection probability was best predicted by temperature (positively) and precipitation (negatively). Contrary to expectations, occupancy probability of Armadillos was best predicted by slope (negatively) and elevation (positively) rather than any landcover or anthropogenic variables. Armadillo density varied considerably between sites (ranging from a mean of 4.88 – 46.20 Armadillos per km2) but was not associated with any environmental or anthropogenic variables. Methods Site Selection Our study took place in Northwest Arkansas, USA, in the greater Fayetteville metropolitan area. We deployed trail cameras (Spypoint Force Dark (Spypoint Inc, Victoriaville, Quebec, Canada) and Browning Strikeforce XD cameras (Browning, Morgan, Utah, USA) over the course of two winter seasons, December 2020-March 2021, and November 2021-March 2022. We sampled 10 study sites in year one, and 12 study sites in year two. All study sites were located in the Ozark Mountains ecoregion in Northwest Arkansas. Sites were all Oak Hickory dominated hardwood forests at similar elevation (213.6 – 541 m). Devils Eyebrow and ONSC are public natural areas managed by the Arkansas Natural heritage Commission (ANHC). Devil’s Den and Hobbs are managed by the Arkansas state park system. Markham Woods (Markham), Ninestone Land Trust (Ninestone) and Forbes, are all privately owned, though Markham has a publicly accessible trail system throughout the property. Lake Sequoyah, Mt. Sequoyah Woods, Kessler Mountain, Lake Fayetteville, and Millsaps Mountain are all city parks and managed by the city of Fayetteville. Lastly, both Weddington and White Rock are natural areas within Ozark National Forest and managed by the U.S. Forest Service. We sampled 5 sites in both years of the study including Devils Eyebrow, Markham Hill, Sequoyah Woods, Ozark Natural Science Center (ONSC), and Kessler Mountain. We chose our study sites to represent a gradient of human development, based primarily on Anthropogenic noise values (Buxton et al. 2017, Mennitt and Fristrup 2016). We chose open spaces that were large enough to accommodate camera trap research, as well as representing an array of anthropogenic noise values. Since anthropogenic noise is able to permeate into natural areas within the urban interface, introducing human disturbance that may not be detected by other layers such as impervious surface and housing unit density (Buxton et al. 2017), we used dB values for each site as an indicator of the level of urbanization. Camera Placement We sampled ten study sites in the first winter of the study. At each of the 10 study sites, we deployed anywhere between 5 and 15 cameras. Larger study areas received more cameras than smaller sites because all cameras were deployed a minimum of 150m between one another. We avoided placing cameras on roads, trails, and water sources to artificially bias wildlife detections. We also avoided placing cameras within 15m of trails to avoid detecting humans. At each of the 12 study areas we surveyed in the second winter season, we deployed 12 to 30 cameras. At each study site, we used ArcGIS Pro (Esri Inc, Redlands, CA) to delineate the trail systems and then created a 150m buffer on each side of the trail. We then created random points within these buffered areas to decide where to deploy cameras. Each random point had to occur within the buffered areas and be a minimum of 150m from the next nearest camera point, thus the number of cameras at each site varied based upon site size. We placed all cameras within 50m of the random points to ensure that cameras were deployed on safe topography and with a clear field of view, though cameras were not set in locations that would have increased animal detections (game trails, water sources, burrows etc.). Cameras were rotated between sites after 5 or 10 week intervals to allow us to maximize camera locations with a limited number of trail cameras available to us. Sites with more than 25 cameras were active for 5 consecutive weeks while sites with fewer than 25 cameras were active for 10 consecutive weeks. We placed all cameras on trees or tripods 50cm above ground and at least 15m from trails and roads. We set cameras to take a burst of three photos when triggered. We used Timelapse 2.0 software (Greenberg et al. 2019) to extract metadata (date and time) associated with all animal detections. We manually identified all species occurring in photographs and counted the number of individuals present. Because density estimation requires the calculation of detection rates (number of Armadillo detections divided by the total sampling period), we wanted to reduce double counting individuals. Therefore, we grouped photographs of Armadillos into “episodes” of 5 minutes in length to reduce double counting individuals that repeatedly triggered cameras (DeGregorio et al. 2021, Meek et al. 2014). A 5 min threshold is relatively conservative with evidence that even 1-minute episodes adequately reduces double counting (Meek et al. 2014). Landcover Covariates To evaluate occupancy and density of Armadillos based on environmental and anthropogenic variables, we used ArcGIS Pro to extract variables from 500m buffers placed around each camera (Table 2). This spatial scale has been shown to hold biological meaning for Armadillos and similarly sized species (DeGregorio et al. 2021, Fidino et al. 2016, Gallo et al. 2017, Magle et al. 2016). At each camera, we extracted elevation, slope, and aspect from the base ArcGIS Pro map. We extracted maximum housing unit density (HUD) using the SILVIS housing layer (Radeloff et al. 2018, Table 2). We extracted anthropogenic noise from the layer created by Mennitt and Fristrup (2016, Buxton et al. 2017, Table 2) and used the “L50” anthropogenic sound level estimate, which was calculated by taking the difference between predicted environmental noise and the calculated noise level. Therefore, we assume that higher levels of L50 sound corresponded to higher human presence and activity (i.e. voices, vehicles, and other sources of anthropogenic noise; Mennitt and Fristrup 2016). We derived the area of developed open landcover, forest area, and distance to forest edge from the 2019 National Land Cover Database (NLDC, Dewitz 2021, Table 2). Developed open landcover refers to open spaces with less than 20% impervious surface such as residential lawns, cemeteries, golf courses, and parks and has been shown to be important for medium-sized mammals (Gallo et al. 2017, Poessel et al. 2012). Forest area was calculated by combing all forest types within the NLCD layer (deciduous forest, mixed forest, coniferous forest), and summarizing the total area (km2) within the 500m buffer. Distance to forest edge was derived by creating a 30m buffer on each side of all forest boundaries and calculating the distance from each camera to the nearest forest edge. We calculated distance to water by combining the waterbody and flowline features in the National Hydrogeography Dataset (U.S. Geological Survey) for the state of Arkansas to capture both permanent and ephemeral water sources that may be important to wildlife. We measured the distance to water and distance to forest edge using the geoprocessing tool “near” in ArcGIS Pro which calculates the Euclidean distance between a point and the nearest feature. We extracted Average Daily Traffic (ADT) from the Arkansas Department of Transportation database (Arkansas GIS Office). The maximum value for ADT was calculated using the Summarize Within tool in ArcGIS Pro. We tested for correlation between all covariates using a Spearman correlation matrix and removed any variable with correlation greater than 0.6. Pairwise comparisons between distance to roads and HUD and between distance to forest edge and forest area were both correlated above 0.6; therefore, we dropped distance to roads and distance to forest edge from analyses as we predicted that HUD and forest area would have larger biological impacts on our focal species (Kretser et al. 2008). Occupancy Analysis In order to better understand habitat associations while accounting for imperfect detection of Armadillos, we used occupancy modeling (Mackenzie et al. 2002). We used a single-species, single-season occupancy model (Mackenzie et al. 2002) even though we had two years of survey data at 5 of the study sites. We chose to do this rather than using a multi-season dynamic occupancy model because most sites were not sampled during both years of the study. Even for sites that were sampled in both years, cameras were not placed in the same locations each year. We therefore combined all sampling into one single-season model and created unique site by year combinations as our sampling locations and we used year as a covariate for analysis to explore changes in occupancy associated with the year of study. For each sampling location, we created a detection history with 7 day sampling periods, allowing presence/absence data to be recorded at each site for each week of the study. This allowed for 16 survey periods between 01 December 2020, and 11 March 2021 and 22 survey periods between 01 November 2021 and 24 March 2022. We treated each camera as a unique survey site, resulting in a total of 352 sites. Because not all cameras were deployed at the same time and for the same length of time, we used a staggered entry approach. We used a multi-stage fitting approach in which we

Polygons in this feature service are derived from many sources. These include state divisions' data, irrigation company websites and other online information, shapefiles received from various entities, and some unknown sources. The hope is to discover the unknown sources, or to replace them with new ones. Ideally, all boundaries will be derived with input from irrigation companies. This feature service is a work in progress, and known to not be complete.

Template site provided as a starting point for a new ArcGIS Hub initiative.

This web map shows the location of shipping companies or companies engaging in shipping related business with office(s) in Hong Kong. It is a set of data made available by the Marine Department under the Government of Hong Kong Special Administrative Region (the "Government") at https://portal.csdi.gov.hk ("CSDI Portal"). The source data has been processed and converted into Esri File Geodatabase format and uploaded to Esri's ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of Hong Kong CSDI Portal at https://portal.csdi.gov.hk.

A listing of current business licenses in the City of Alpharetta. Most items in this dataset are associated with a spatial location and can be plotted in GIS software, however some features may not be tied to a location, and therefore may appear to plot outside of the Alpharetta city limits.Important: If you are downloading a dataset from https://open-alpharetta.opendata.arcgis.com, please disregard the Updated, Created, and Published dates on the web page. Most datasets are refreshed nightly. At times, however, the website may provide you with an older cached copy of the data. To ensure you are downloading the most current dataset, we recommend using the "Request new file" option that may appear after you have downloaded a stale dataset.

List of businesses operating in the City of Kitchener in 2017.FieldsIDUNIQUE ID FOR COMPANYCOMPANY_NAMENAME OF COMPANYSTREET_NUMBERCIVIC NUMBER OF ADDRESSSTREET_NAMENAME OF STREETUNITUNITPOSTAL_CODEPOSTAL CODEBUISNESS_PARK/BIAASSOCIATED BUSINESS PARK OR BUSINESS IMPROVEMENT AREAIN_CIPINDICATES IF COMPANY IS LOCATED WITHIN A COMMUNITY IMPROVEMENT BOUNDARYDOWNTOWNINDICATES IF COMPANY IS LOCATED WITHIN DOWNTOWN BOUNDARYDOWNTOWN_PLANNING_DISCTRICTINDICATES WHICH DOWNTOWN PLANNING DISCTRICTSPACE_SIZE_SQFTSIZE OF LOCATION IN SQUARE FEETTOTAL_EMPLOYEESNUMBER OF PERSONS EMPLOYED AT COMPANYPROFILEBRIEF DESCRIPTION OF COMPANYPRIMARY_NAICS_SECTORASSOCIATED NORTH AMERICAN INDUSTRY CLASSIFICATION SYSTEM CODEPRIMARY_NAICS_DESCRIPTIONPROVIDES A BREIF DESCRIPTION OF THE NAICS CODESECONDARY_NAICSSECONDARY NAICSYEAR_ESTABLISHED_ORIGINALLYYEAR COMPANY WAS ORIGINALLY ESTABLISHEDEXPORTINGINDICATES IF COMPANY IS INVOLVED IN EXPORTINGPHONECOMPANY PHONE NUMBERTOLL FREECOMPANY TOLL FREE PHONE NUMBEREMAILCOMPANY E-MAIL ADDRESSWEBSITECOMPANY WEBSITE ADDRESSFIRST NAME 1FIRST NAME OF PRIMARY COMPANY REPRESENTATIVELAST NAME 1LAST NAME OF PRIMARY COMPANY REPRESENTATIVETITLE 1TITLE OF PRIMARY COMPANY RESPRESENTATIVEFIRST NAME 2FIRST NAME OF SECONDARY COMPANY REPRESENTATIVELAST NAME 2LAST NAME OF SECONDARY COMPANY REPRESENTATIVETITLE 2TITLE OF SECONDARY COMPANY RESPRESENTATIVE

Check out the PHL Taking Care of Business Clean Corridors website for more information about this program. View metadata for key information about this dataset. PHL TCB has four main goals: 1. Maintain clean commercial districts in Philadelphia neighborhoods.2. Promote the economic success of neighborhood businesses by creating an inviting environment for shoppers.3. Create work opportunities for Philadelphians.4. Grow the capacity of local small businesses and organizations that provide cleaning services. For questions about this dataset, contact samuel.hall@phila.gov. For technical assistance, email maps@phila.gov.

The Digital Geomorphic-GIS Map of Cumberland Island National Seashore, Georgia is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (cuis_geomorphology.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 3.X map file (.mapx) file (cuis_geomorphology.mapx) and individual Pro 3.X layer (.lyrx) 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 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 readme file (cuis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (cuis_geomorphology.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 (cuis_geomorphology_metadata_faq.pdf). Please read the cuis_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: 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: RWParkinson Inc. and MDA Information Systems, Inc. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (cuis_geomorphology_metadata.txt or cuis_geomorphology_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 1:60,000 and United States National Map Accuracy Standards features are within (horizontally) 30.5 meters or 100 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 Pro, 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).

Data OriginThe dataset provided by Ofwat is rooted in legal records. The dataset is digitised from the official appointments of companies as water and sewage undertakers, which include legally binding documents and maps. These documents establish the specific geographic areas each water company is responsible for. The dataset was sourced from Constituency information: Water companiesData TriageAnonymisation is not required for this dataset, since the data is publicly available and focuses on geographical boundaries of water companies rather than individual or sensitive information. The shapefile serves a specific purpose related to geospatial analysis and regulatory compliance, offering transparent information about the service areas of different water companies as designated by Ofwat.Further ReadingBelow is a curated selection of links for additional reading, which provide a deeper understanding of the water company boundaries datasetOfwat (The Water Services Regulation Authority): As the regulatory body for water and wastewater services in England and Wales, Ofwat's website is a primary source for detailed information about the water industry, including company boundaries.Data.gov.uk: This site provides access to national datasets, including the Water Resource Zone GIS Data (WRMP19), which covers all water resource zones in England. This dataset is crucial for understanding geographical boundaries related to water management.Water UK: As a trade body representing UK water and wastewater service providers, Water UK's website offers insights into the industry's workings, including aspects related to geographical boundaries.Specifications and CaveatsWhen compiling the dataset, the following specifications and caveats were made:This shapefile is intended solely for geospatial analysis. The authoritative legal delineation of areas is maintained in the maps and additional details specified in the official appointments of companies as water and/or sewerage undertakers, along with any alterations to their areas.The shapefile does not encompass data on any structures or properties that, despite being outside the designated boundary, are included in the area, or those within the boundary yet excluded from the area.In terms of geospatial analysis and visual representation, the Mean High Water Line has been utilized to define any boundary extending into the sea, though it's more probable that the actual boundary aligns with the low water mark. Furthermore, islands that are incorporated into the area might not be included in this representation.Ofwat’s data was last updated on 25th May 2022Contact Details If you have a query about this dataset, please email foi@ofwat.gov.uk