Miami-Dade County Plastic Free 305. Implementing successful local programs that paved the way for this county-wide initiative!The Plastic Free 305 program will ensure the continuation of the County's commitment to resiliency by providing support to businesses as they transition to plastic free alternatives

The Street_and_Address_Composite will return a geographic coordinate when a street address is entered. A user can enter an address either manually or by bulk input from a database or other source.The geocoder returns a coordinate pair and standardized address for each input address it is able to match. The NYS ITS Geospatial Services geocoder uses a series of combinations of reference data and configuration parameters to optimize both the likelihood of a match and the quality of the results. The reference data supporting the geocoder is stored in Federal Geographic Data Committee (FGDC) standard.The first composite locator (Street_and_Address_Composite) is made up of the following set of locators which are most likely to return a high quality hit. The locators are listed in the order in which they will be accessed along with a brief description of the locator's source data. These six locators will generate the majority of the results when geocoding addresses.Locator NameSource DataDescription1A_SAM_AP_ZipNameSAM Address PointsSAM address points using the postal zip code name for the city name in the locator.1B_SAM_AP_CTNameSAM Address PointsSAM address points. The city or town name is used for the city name in the locator.1C_SAM_AP_PlaceNameSAM Address PointsSAM address points. The city name is populated using the NYS Villages and Indian Reservations, the Census Designated Places and Alternate Acceptable Zip Code Names from the USPS. These names do not exist everywhere so there will be a limited number of points in this locator.3A_SS_ZipNameNYS Street SegmentsNYS Street Segments dataset using the postal zip code name for the city name in the locator. The location is interpolated from an address range on the street segment. The city name can be different for the left and right sides of the streets.3B_SS_CTNameNYS Street SegmentsNYS Street Segments using the city or town name for the city name in the locator. The location is interpolated from an address range on the street segment.3C_SS_PlaceNameNYS Street SegmentsNYS Street Segments using an alternate place name for the city field. This field is populated using the NYS Villages and Indian Reservations, the Census Designated Places and Alternate Acceptable Zip Code Names from the USPS. These areas do not exist everywhere so there will be a limited number of segments with this attribute. The location is interpolated from an address range on the street segment.For more information about the geocoding service, please visit: https://gis.ny.gov/address-geocoder.For documentation on how to add these locators to ArcGIS, please reference Adding the Statewide Geocoding Web Service. If you would like these locators to be your default locators in ArcGIS, copy DefaultLocators.xml to C:\Users<username>\AppData\Roaming\ESRI\Desktop10.X\Locators, where

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

This 3D basemap presents OpenStreetMap (OSM) and other data sources and is hosted by Esri using the Topographic style.The Buildings layer references the Esri 3D Buildings scene layer, which includes commercial 3D buildings data acquired from TomTom and Maxar, in addition to Esri Community Maps and Overture Maps Foundation data. The Esri 3D Buildings scene layer is an alternative to the OpenStreetMap (OSM) 3D Buildings scene layer, particularly for areas where the OSM data is missing accurate 3D attributes.Esri created the Places and Labels, Trees, and Topographic layers from the Daylight map distribution of OSM data, which was supported by Meta and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new scene available to the OSM, GIS, and Developer communities.

This 3D basemap presents OpenStreetMap (OSM) data and other data sources and is hosted by Esri using the Navigation (Dark) style.The Buildings layer references the Esri 3D Buildings scene layer, which includes commercial 3D buildings data acquired from TomTom and Maxar, in addition to Esri Community Maps and Overture Maps Foundation data. The Esri 3D Buildings scene layer is an alternative to the OpenStreetMap (OSM) 3D Buildings scene layer, particularly for areas where the OSM data is missing accurate 3D attributes.Esri created the Places and Labels, and Navigation Dark layers from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new scene available to the OSM, GIS, and Developer communities.

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GIS In Utility Industry Market Size 2025-2029

The gis in utility industry market size is forecast to increase by USD 3.55 billion, at a CAGR of 19.8% between 2024 and 2029.

The utility industry's growing adoption of Geographic Information Systems (GIS) is driven by the increasing need for efficient and effective infrastructure management. GIS solutions enable utility companies to visualize, analyze, and manage their assets and networks more effectively, leading to improved operational efficiency and customer service. A notable trend in this market is the expanding application of GIS for water management, as utilities seek to optimize water distribution and reduce non-revenue water losses. However, the utility GIS market faces challenges from open-source GIS software, which can offer cost-effective alternatives to proprietary solutions. These open-source options may limit the functionality and support available to users, necessitating careful consideration when choosing a GIS solution. To capitalize on market opportunities and navigate these challenges, utility companies must assess their specific needs and evaluate the trade-offs between cost, functionality, and support when selecting a GIS provider. Effective strategic planning and operational execution will be crucial for success in this dynamic market.

What will be the Size of the GIS In Utility Industry Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
Request Free SampleThe Global Utilities Industry Market for Geographic Information Systems (GIS) continues to evolve, driven by the increasing demand for advanced data management and analysis solutions. GIS services play a crucial role in utility infrastructure management, enabling asset management, data integration, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage management, and spatial analysis. These applications are not static but rather continuously unfolding, with new patterns emerging in areas such as energy efficiency, smart grid technologies, renewable energy integration, network optimization, and transmission lines. Spatial statistics, data privacy, geospatial databases, and remote sensing are integral components of this evolving landscape, ensuring the effective management of utility infrastructure. Moreover, the adoption of mobile GIS, infrastructure planning, customer service, asset lifecycle management, metering systems, regulatory compliance, GIS data management, route planning, environmental impact assessment, mapping software, GIS consulting, GIS training, smart metering, workforce management, location intelligence, aerial imagery, construction management, data visualization, operations and maintenance, GIS implementation, and IoT sensors is transforming the industry. The integration of these technologies and services facilitates efficient utility infrastructure management, enhancing network performance, improving customer service, and ensuring regulatory compliance. The ongoing evolution of the utilities industry market for GIS reflects the dynamic nature of the sector, with continuous innovation and adaptation to meet the changing needs of utility providers and consumers.

How is this GIS In Utility Industry Industry segmented?

The gis in utility industry industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments. ProductSoftwareDataServicesDeploymentOn-premisesCloudGeographyNorth AmericaUSCanadaEuropeFranceGermanyRussiaMiddle East and AfricaUAEAPACChinaIndiaJapanSouth AmericaBrazilRest of World (ROW).

By Product Insights

The software segment is estimated to witness significant growth during the forecast period.In the utility industry, Geographic Information Systems (GIS) play a pivotal role in optimizing operations and managing infrastructure. Utilities, including electricity, gas, water, and telecommunications providers, utilize GIS software for asset management, infrastructure planning, network performance monitoring, and informed decision-making. The GIS software segment in the utility industry encompasses various solutions, starting with fundamental GIS software that manages and analyzes geographical data. Additionally, utility companies leverage specialized software for field data collection, energy efficiency, smart grid technologies, distribution grid design, renewable energy integration, network optimization, transmission lines, spatial statistics, data privacy, geospatial databases, GIS services, project management, demand forecasting, data modeling, data analytics, grid modernization, data security, field data capture, outage ma

(Link to Metadata) EmergencyE911_RDS was originally derived from RDSnn (now called TransRoad_RDS). "Zero-length ranges" in the ROADS layer pertain to grand-fathered towns that have not yet provided the Enhanced 9-1-1 Board road segment range information. RDSnn was originally developed using a combination of paper and RC Kodak RF 5000 orthophotos (visual image interpretation and manual digitizing of centerlines). Road attributes (RTNO and CLASS) were taken from the official VT Agency of Transportation (VTrans) highway maps. New roads not appearing on the photos were digitized with locations approximated from the VTrans highway maps. State Forest maps were used to determine both location and attributes of state forest roads. Some data updates have used RF 2500 or RF 1250 orthophotos and GPS, or other means for adding new roads and improving road locations. The Enhanced E911 program added new roads from GPS and orthos between 1996-1998. Also added road name and address geocoding. VCGI PROCESSING (Tiling and Added items); E911 provides the EmergencyE911_RDS data to VCGI in a statewide format. It lacks FIPS6 coding, making it difficult to extract data on the basis of town/county boundaries. As a result, VCGI has added FIPS6 to the attribute table. This field was originally populated by extracting MCODE value from RDNAME and relating to TBPOLY.PAT to bring over matching MCODE values. FIPS6 problems along the interstates and "Gores & Grants" in the Northeast Kingdom, were corrected. All features with an MCODE equal to 200 or 579 were assigned a FIPS6 equal to 0. The center point of these arcs were then intersected with BoundaryTown_TBHASH to assign a FIPS6 value. This information was then transfered back into the RDS.AAT file via a relate. A relate was established between the ROADNAMES.DBF file (road name lookup table) and the RDS.AAT file. The RDFLNAME attribute was populated by transfering the NAME value in the ROADNAMES.DBF table. The RDFLNAME item was then parsed into SUF.DIR, STREET.NAME, STREET.TYPE, and PRE.DIR, making addressing matching functions a little easier. See the "VT Road Centerline Data FAQ" for more information about TransRoad_RDS and EmergencyE911_RDS. https://vcgi.vermont.gov/techres/?page=./white_papers/default_content.cfmField Descriptions:OBJECTID: Internal feature number, automatically generated by Esri software.SEGMENTID: Unique segment ID.ARCID: Arc identifier, unique statewide. The ARCID is a unique identifier for every ARC in the EmergencyE911_RDS data layer.PD: Prefix Direction, previously name PRE.DIR.PT: Prefix Type.SN: Street Name. Previously named STREET.ST: Street Type.SD: Suffix Direction, i.e., W for West, E for East, etc.GEONAMEID: Unique ID for each road name.PRIMARYNAME: Primary name.ALIAS1: Alternate road name 1.ALIAS2: Alternate road name 2.ALIAS3: Alternate road name 3.ALIAS4: Alternate road name 4.ALIAS5: Alternate road name 5.COMMENTS: Free text field for miscellaneous comments.ONEWAY: One-way street. Uses the Oneway domain*.NO_MSAG:MCODE: Municipal code.LESN: Left side of road Emergency Service Number.RESN: Right side of road Emergency Service Number.LTWN: Left side of road town.RTWN: Right side of road town.LLO_A: Low address for left side of road.RLO_A: Low address for right side of road.LHI_A: High address for left side of road.RHI_A: High address for right side of road.LZIP: Left side of road zip code.RZIP: Right side of road zip code.LLO_TRLO_TLHI_TRHI_TRTNAME: Route name.RTNUMBER: Route number.HWYSIGN: Highway sign.RPCCLASSAOTCLASS: Agency of Transportation class. Uses AOTClass domain**.ARCMILES: ESRI ArcGIS miles.AOTMILES: Agency of Transportation miles.AOTMILES_CALC:UPDACT:SCENICHWY: Scenic highway.SCENICBYWAY: Scenic byway.FORMER_RTNAME: Former route name.PROVISIONALYEAR: Provisional year.ANCIENTROADYEAR: Ancient road year.TRUCKROUTE: Truck route.CERTYEAR:MAPYEAR:UPDATEDATE: Update date.GPSUPDATE: Uses GPSUpdate domain***.GlobalID: GlobalID.STATE: State.GAP: Gap.GAPMILES: Gap miles.GAPSTREETID: Gap street ID.FIPS8:FAID_S:RTNUMBER_N:LCOUNTY:RCOUNTY:PRIMARYNAME1:SOURCEOFDATA: Source of data.COUNTRY: Country.PARITYLEFT:PARITYRIGHT:LFIPS:RFIPS:LSTATE:RSTATE:LESZ:RESZ:SPEED_SOURCE: Speed source.SPEEDLIMIT: Speed limit.MILES: Miles.MINUTES: Minutes.Shape: Feature geometry.Shape_Length: Length of feature in internal units. Automatically computed by Esri software.*Oneway Domain:N: NoY: Yes - Direction of arcX: Yes - Opposite direction of arc**AOTClass Domain:1: Town Highway Class 1 - undivided2: Town Highway Class 2 - undivided3: Town Highway Class 3 - undivided4: Town Highway Class 4 - undivided5: State Forest Highway6: National Forest Highway7: Legal Trail. Legal Trail Mileage Approved by Selectboard after the enactment of Act 178 (July 1, 2006). Due to the introduction of Act 178, the Mapping Unit needed to differentiate between officially accepted and designated legal trail versus trails that had traditionally been shown on the maps. Towns have until 2015 to map all Class 1-4 and Legal Trails, based on new changes in VSA Title 19.8: Private Road - No Show. Private road, but not for display on local maps. Some municipalities may prefer not to show certain private roads on their maps, but the roads may need to be maintained in the data for emergency response or other purposes.9: Private road, for display on local maps10: Driveway (put in driveway)11: Town Highway Class 1 - North Bound12: Town Highway Class 1 - South Bound13: Town Highway Class 1 - East Bound14: Town Highway Class 1 - West Bound15: Town Highway Class 1 - On/Off Ramp16: Town Highway Class 1 - Emergency U-Turn20: County Highway21: Town Highway Class 2 - North Bound22: Town Highway Class 2 - South Bound23: Town Highway Class 2 - East Bound24: Town Highway Class 2 - West Bound25: Town Highway Class 2 - On/Off Ramp30: State Highway31: State Highway - North Bound32: State Highway - South Bound33: State Highway - East Bound34: State Highway - West Bound35: State Highway - On/Off Ramp40: US Highway41: US Highway - North Bound42: US Highway - South Bound43: US Highway - East Bound44: US Highway - West Bound45: US Highway - On/Off Ramp46: US Highway - Emergency U-Turn47: US Highway - Rest Area50: Interstate Highway51: Interstate Highway - North Bound52: Interstate Highway - South Bound53: Interstate Highway - East Bound54: Interstate Highway - West Bound55: Interstate Highway - On/Off Ramp56: Interstate Highway - Emergency U-Turn57: Interstate Highway - Rest Area59: Interstate Highway - Other65: Ferry70: Unconfirmed Legal Trail71: Unidentified Corridor80: Proposed Highway Unknown Class81: Proposed Town Highway Class 182: Proposed Town Highway Class 283: Proposed Town Highway Class 384: Proposed State Highway85: Proposed US Highway86: Proposed Interstate Highway87: Proposed Interstate Highway - Ramp88: Proposed Non-Interstate Highway - Ramp89: Proposed Private Road91: New - Class Unknown92: Military - no public access93: Public - Class Unknown95: Class Under Review96: Discontinued Road97: Discontinued Now Private98: Not a Road99: Unknown***GPSUpdate Domain:Y: Yes - Needs GPS UpdateN: No - Does not need GPS UpdateG: GPS Update CompleteV: GPS Update Complete - New RoadX: Unresolved Segment

This 3D basemap presents OpenStreetMap (OSM) and other data sources and is hosted by Esri using the Dark Gray Canvas style.The Buildings layer references the Esri 3D Buildings scene layer, which includes commercial 3D buildings data acquired from TomTom and Maxar, in addition to Esri Community Maps and Overture Maps Foundation data. The Esri 3D Buildings scene layer is an alternative to the OpenStreetMap (OSM) 3D Buildings scene layer, particularly for areas where the OSM data is missing accurate 3D attributes.Esri created the Places and Labels, and Dark Gray Canvas layers from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new vector basemap available available to the OSM, GIS, and Developer communities.

This 3D basemap presents OpenStreetMap (OSM) and other data sources and is hosted by Esri using the Light Gray Canvas style.The Buildings layer references the Esri 3D Buildings scene layer, which includes commercial 3D buildings data acquired from TomTom and Maxar, in addition to Esri Community Maps and Overture Maps Foundation data. The Esri 3D Buildings scene layer is an alternative to the OpenStreetMap (OSM) 3D Buildings scene layer, particularly for areas where the OSM data is missing accurate 3D attributes.Esri created the Places and Labels, and Light Gray Canvas layers from the Daylight map distribution of OSM data, which was supported by Meta and supplemented with additional data from Microsoft. OpenStreetMap (OSM) is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this new scene available to the OSM, GIS, and Developer communities.

As part of the Blue W program this dataset contains the business name and location of businesses within Waterloo Region that offer clean, free public and commercial sources to fill reusable bottles. The Blue W Program is a community-based program dedicated to promoting municipal tap water as a healthy, easily accessible alternative to purchasing bottled drinks. For more information visit www.bluew.org .

This map displays the Apparent and Expected Air Temperature forecast over the next 72 hours across the Contiguous United States, Alaska, Guam, Hawaii, and Puerto Rico in 3 hour increments. The original raster data has been processed into 1-degree contours.Two layers are included: apparent and expected temperature, both include a Time Series set to a 3-hour time interval. The apparent temperature is the perceived (or feels like) temperature derived from either a combination of temperature and wind (wind chill) or temperature and humidity (heat index) for the indicated hour. When the temperature at a particular grid point falls to 50 °F or less, wind chill will be used for that point for the apparent temperature. When the temperature at a grid point rises above 80 °F, the heat index will be used for apparent temperature.
Between 51 and 80 °F, the apparent temperature will be the ambient air temperature.The expected temperature is the forecasted ambient air temperature in °F.See sister data product for Min and Max Daily TemperaturesRevisionsApr 21, 2022: Added Forecast Period Number 'Interval' field for an alternate query method to the Timeline of data. Disabled Time Series by default to improve initial Map Viewer exprience and added a Filter for 'interval = 1' to display initial forecast time data (current time period).Apr 22, 2022: Set 'Apparent Temperature' layer visibility to True by default, so content is visible when initially viewed.Sep 1, 2022: Updated renderer Arcade logic on layers to correctly symbolize on values greater than 120 and less than -60 degrees.DetailService Data update interval is: HourlyWhere is the data coming from?The National Digital Forecast Database (NDFD) was designed to provide access to weather forecasts in digital form from a central location. The NDFD produces gridded forecasts of sensible weather elements. NDFD contains a seamless mosaic of digital forecasts from National Weather Service (NWS) field offices working in collaboration with the National Centers for Environmental Prediction (NCEP). All of these organizations are under the administration of the National Oceanic and Atmospheric Administration (NOAA).Apparent Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.apt.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.apt.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.apt.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.apt.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.apt.binExpected Temperature Source:CONUS: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.conus/VP.001-003/ds.temp.binALASKA: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.alaska/VP.001-003/ds.temp.binHAWAII: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.hawaii/VP.001-003/ds.temp.binGUAM: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.guam/VP.001-003/ds.temp.binPUERTO RICO: https://tgftp.nws.noaa.gov/SL.us008001/ST.opnl/DF.gr2/DC.ndfd/AR.puertori/VP.001-003/ds.temp.binWhere can I find other NDFD data?The Source data is downloaded and parsed using the Aggregated Live Feeds methodology to return information that can be served through ArcGIS Server as a map service or used to update Hosted Feature Services in Online or Enterprise.What can you do with this layer?This feature service is suitable for data discovery and visualization. Identify features by clicking on the map to reveal the pre-configured pop-ups. View the time-enabled data using the time slider by Enabling Time Animation or add a Filter using the 'Forecast Period Number'.This map is provided for informational purposes and is not monitored 24/7 for accuracy and currency.If you would like to be alerted to potential issues or simply see when this Service will update next, please visit our Live Feed Status Page.

IMPORTANT NOTICE This item has moved to a new organization and will be entering Mature Support in Fall 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here Electric charging stations where vehicles can be charged in Canada. Public, private, existing and planned charging stations are included. The data was obtained from the Electric Charging and Alternative Fueling Stations Locator website collected by Natural Resources Canada and contains metadata such as collection methods, station update schedule, mapping and counting methods, and notes about specific station types. For more information about the data, please contact the webmaster.Update Frequency:The hosted feature service is created from the electric charging stations csv provided on the main website and is updated daily using a Notebook.

This digital geographic dataset contains line features representing the established routes of the City of Miami Trolley program. These features contain attributes that show the name of the route. The trolley program was established as an alternate form of transportation, free of charge and it provides great connectivity among the major business and tourist hubs within the City of Miami.

The Prediction Of Worldwide Energy Resource (POWER) Project gathers NASA Earth Observation data and parameters related to the fields of surface solar irradiance and meteorology to serve the public in several free, easy-to-access, and easy-to-use methods. POWER helps communities become resilient amid observed climate variability by improving data accessibility, aiding research in renewable energy development, building energy efficiency, and agriculture sustainability. POWER is funded through the NASA Earth Action Program within the Earth Science Mission Directorate at NASA Langley Research Center (LaRC).---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------This annual radiation service provides time-enabled global Analysis Ready Data (ARD) parameters from 1984 to 2023 for POWER's communities.Time Interval: AnnualTime Extent: 1984/01/01 to 2023/12/31Time Standard: Local Sidereal Time (LST)Grid Size: 1.0 X 1.0 DegreeProjection: GCS WGS84Extent: GlobalSource: NASA Prediction Of Worldwide Energy Resources (POWER)For questions or issues please email: larc-power-project@mail.nasa.govRadiation Data Sources:NASA's GEWEX/SRB release 4.0 archive (Jan. 1, 1984 – Dec. 31, 2000)NASA's CERES SYN1deg (Jan. 1, 2001 – Dec. 31, 2023)Radiation Data Parameters:ALLSKY_KT (All Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the all sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.ALLSKY_SFC_LW_DWN (All Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under all sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.ALLSKY_SFC_LW_UP (All Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under all sky conditions.ALLSKY_SFC_PAR_TOT (All Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DIFF (All Sky Surface Shortwave Diffuse Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under all sky conditions.ALLSKY_SFC_SW_DNI (All Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under all sky conditions.ALLSKY_SFC_SW_DWN (All Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under all sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.ALLSKY_SFC_SW_UP (All Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under all sky conditions.ALLSKY_SFC_UV_INDEX (All Sky Surface UV Index): The ultraviolet radiation exposure index.ALLSKY_SFC_UVA (All Sky Surface UVA Irradiance): The ultraviolet A (UVA 315nm-400nm) irradiance under all sky conditions.ALLSKY_SFC_UVB (All Sky Surface UVB Irradiance): The ultraviolet B (UVB 280nm-315nm) irradiance under all sky conditions.ALLSKY_SRF_ALB (All Sky Surface Albedo): The all sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.AOD_55 (Aerosol Optical Depth 55): The optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.AOD_55_ADJ (Adjusted Aerosol Optical Depth 55): The adjusted optical thickness at 0.55 um measured vertically; the component of the atmosphere to quantify the removal of radiant energy from an incident beam.CLOUD_AMT (Cloud Amount): The average percent of cloud amount during the temporal period.CLOUD_AMT_DAY (Cloud Amount at Daytime): The average percent of cloud amount during daylight.CLOUD_AMT_NIGHT (Cloud Amount at Nighttime): The average percent of cloud amount during nighttime.CLOUD_OD (Cloud Optical Visible Depth): The vertical optical thickness between the top and bottom of a cloud.CLRSKY_DAYS (Clear Sky Day): The number of Clear Sky Days if the daytime cloud amount is less than 10 percent.CLRSKY_KT (Clear Sky Insolation Clearness Index): A fraction representing clearness of the atmosphere; the clear sky insolation that is transmitted through the atmosphere to strike the surface of the earth divided by the average of top of the atmosphere total solar irradiance incident.CLRSKY_SFC_LW_DWN (Clear Sky Surface Longwave Downward Irradiance): The downward thermal infrared irradiance under clear sky conditions reaching a horizontal plane the surface of the earth. Also known as Horizontal Infrared Radiation Intensity from Sky.CLRSKY_SFC_LW_UP (Clear Sky Surface Longwave Upward Irradiance): The upward thermal infrared irradiance under clear sky conditions.CLRSKY_SFC_PAR_TOT (Clear Sky Surface PAR Total): The total Photosynthetically Active Radiation (PAR) incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DIFF (Clear Sky Surface Shortwave Downward Diffuse Horizontal Irradiance): The diffuse (light energy scattered out of the direction of the sun) solar irradiance incident on a horizontal plane at the surface of the earth under clear sky conditions.CLRSKY_SFC_SW_DNI (Clear Sky Surface Shortwave Downward Direct Normal Irradiance): The direct solar irradiance incident to a horizontal plane normal (perpendicular) to the direction of the sun's position under clear sky conditions.CLRSKY_SFC_SW_DWN (Clear Sky Surface Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the surface of the earth under clear sky conditions. An alternative term for the total solar irradiance is the "Global Horizontal Irradiance" or GHI.CLRSKY_SFC_SW_UP (Clear Sky Surface Shortwave Upward Irradiance): The upward shortwave irradiance under clearsky conditions.CLRSKY_SRF_ALB (Clear Sky Surface Albedo): The clear sky rate of reflectivity of the earth's surface; the ratio of the solar energy reflected by the surface of the earth compared to the total solar energy incident reaching the surface of the earth.MIDDAY_INSOL (Midday Insolation Incident): The total amount of solar irradiance (i.e. direct plus diffuse) incident on a horizontal plane at the earth's surface during the solar noon hour midday period.PW (Precipitable Water): The total atmospheric water vapor contained in a vertical column of the atmosphere.TOA_SW_DNI (Top-Of-Atmosphere Shortwave Direct Normal Radiation): The total solar irradiance incident (direct plus diffuse) on a horizontal plane where oriented to the sun's position at the top of the atmosphere (extraterrestrial radiation).TOA_SW_DWN (Top-Of-Atmosphere Shortwave Downward Irradiance): The total solar irradiance incident (direct plus diffuse) on a horizontal plane at the top of the atmosphere (extraterrestrial radiation).TS_ADJ (Earth Skin Temperature Adjusted): The adjusted average temperature at the earth's surface.

The Places of Worship dataset is composed of any type of building or portion of a building that is used, constructed, designed, or adapted to be used as a place for religious and spiritual activities. These facilities include, but are not limited to, the following types: chapels, churches, mosques, shrines, synagogues, and temples. The license free Large Protestant Churches, Mosques, Jewish Synagogues, and Roman Catholic Churches in Large Cities datasets were merged together to create the initial data for the Places of Worship dataset. Additional entities have been added from TGS research. This dataset contains Buddhist, Christian, Hindu, Islamic, Judaic, and Sikh places of worship. Unitarian places of worship have been included when a congregation from one of these religions meets at a church owned by a Unitarian congregation. Some Protestant denominations are not currently represented in this dataset. The Places of Worship dataset is not intended to include homes of religious leaders (unless they also serve as a place of organized worship), religious schools (unless they also serve as a place of organized worship for people other than those enrolled in the school), Jewish Mikvahs or Hillel facilities, and buildings that serve a purely administrative purpose. If a building's primary purpose is something other than worship (e.g., a community center, a public school), but a religious group uses the building for worship on a regular basis, it was included in this dataset if it otherwise met the criteria for inclusion. Convents and monasteries are included in this dataset, regardless of whether or not the facilities are open to the public, because religious services are regularly held at these locations. On 08/07/2007, TGS ceased making phone calls to verify information about religious locations. Therefore, most entities in this dataset were verified using alternative reference sources such as topographic maps, parcel maps, various sources of imagery, and internet research.

This web map displays discrete tidal zoning generated by the National Ocean Service (NOS) Center for Operational Oceanographic Products and Services (CO-OPS). Zoned tides, relative to a tidal datum, can be constructed by applying time and range correctors to observed water level data from a NOAA tide station. These correctors and the recommended tide stations are contained within discrete tide zones. Each zone was constructed with an ideal uncertainty of less than 0.45m at 95% Confidence Interval.Content: Discrete Tide Zones: Discrete tide zones delineate geographic areas of similar tidal characteristics. For each discrete zone, a tide curve can be constructed by applying a time (AvgTimeCorr) and range (RangeRatio) corrector to the observed water level data for the zone's assigned control water level station. Zones are grouped by geographic region. Table attributes contain tidal information for each zone:ControlStn: Operating water level station referenced by the zone AvgTimeCorr: Average of high and low tide time corrections in 6 minute intervalsRangeRatio: Range ratio (multiplier used to scale the tidal value read for the observation file)ControlStn2 (where available): Alternate operating water level station referenced by the zoneAvgTimeCorr2 (where available): Alternate average of high and low tide time corrections in 6 minute intervalsRangeRatio2 (where available): Alternate range ratio (multiplier used to scale the tidal value read for the observation file)NOAA CO-OPS Active Water Level Stations (REST service on the NOAA IDP): Layer represents the geographic locations at which water level observations are presently being collected. "Water level" is defined as the height of the water surface relative to a specific datum (base elevation). Most stations with water level observations provide readings every 6 minutes. CO-OPS measures water levels at over 200 tidal and non-tidal stations along the coast of the United States and its territories and around the Great Lakes. "Tide" is defined as the periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth. The time series on the CO-OPS website that are associated with these point locations contain both verified and unverified data. Unverified, or raw, data have not been subjected to the National Ocean Service's quality control or quality assurance procedures and do not meet the criteria and standards of official National Ocean Service data. They are released for limited public use as preliminary data to be used only with appropriate caution. More information can be found at https://tidesandcurrents.noaa.gov/stations.html?type=Water+Levels.NOAA CO-OPS Historic/Active Water Level Stations (REST service on the NOAA IDP): Layer represents any geographic location at which tidal (water level) observations have been collected and verified, including stations presently collecting observations. "Tide" is defined as the periodic rise and fall of a body of water resulting from gravitational interactions between Sun, Moon, and Earth. "Water level" is defined as the height of the water surface relative to a specific datum (reference elevation). Most stations with water level observations provide readings every 6 minutes. CO-OPS measures water levels at over 200 stations along the coast of the United States and its territories and around the Great Lakes. More information can be found at https://tidesandcurrents.noaa.gov/stations.html?type=Historic+Water+LevelsPlease download data using the following method:To export data, click on one of the “CO-OPS Regional Zoning” layers below to be taken to the hosted Feature Layer page. Here you can export the layer of your choice to Shapefile, CSV, FGDB, GeoJSON or Feature Collection by clicking on the "Export To" drop-down menu.Note: Please be aware that you must create a free ArcGIS Online account before you can download the data.Tide Zone Water Level Correction for Hydrographic Data: To create a tide zone correction file for use with hydrographic processing software you will need the following information from the shapefiles contained in this map:Name (OBJECTID) and number of vertices for each zone that overlaps your area of coverageCoordinates of all vertices within each tide zone polygonReference/Control tide station (ControlStn or ControlStn2) for each zoneAverage tide time corrector (AvgTimeCorr or AvgTimeCorr2) and tide zone range ratio (RangeRatio or RangeRatio2)Reference/Control tide station name and coordinatesSix minute preliminary and verified water level data may be retrieved in one month increments over the internet from the CO-OPS web services at https://opendap.co-ops.nos.noaa.gov/axis/ by clicking on “Six Minute Data”.More Resources: https://tidesandcurrents.noaa.gov/hydro.html

Purpose: Displays geographic area populated by city owned public parking garages.Intended Use: Intended for use in a public parking application that can be used by parking staff to inventory parking assets and promote safe and affordable public parking (lots, garages, on-street spaces, and pay stations) alternatives in a vibrant downtown and/or neighborhood.Department: WPB Parking DepartmentData Source: Geodatabase layer in parking dataset.How was the Data Manipulated: GIS layer was created by GIS team for the Parking departmentHow the Data is Modified: Input from Parking DepartmentUpdate Frequency: As-Needed

IMPORTANT NOTICE This item has moved to a new organization and will be entering Mature Support in Fall 2025. We encourage you to switch to using the item on the new organization as soon as possible to avoid any disruptions within your workflows. If you have any questions, please feel free to leave a comment below or email our Living Atlas Curator (livingatlascurator@esri.ca) The new version of this item can be found here The Open Database of Healthcare Facilities (ODHF) is a collection of open data containing the names, types, and locations of health facilities across Canada. It is released under the Open Government License - Canada.

The ODHF compiles open, publicly available, and directly-provided data on health facilities across Canada. Data sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. This database aims to provide enhanced access to a harmonized listing of health facilities across Canada by making them available as open data. This database is a component of the Linkable Open Data Environment (LODE).To access the hosted (downloadable) version of the dataset, go to https://services.arcgis.com/zmLUiqh7X11gGV2d/ArcGIS/rest/services/Open_Database_of_Healthcare_Facilities_Canada_(Hosted)/FeatureServer/0Version: 1.1 (May to June 2020)Data sources and methodology

The inputs for the ODHF are datasets whose sources include regional health authorities, provincial, territorial and municipal governments, and public health and professional healthcare bodies. These datasets were available either under one of the various types of open data licences, e.g., in an open government portal, or as publicly available data. In certain cases, data were obtained directly from administrative sources. Details of the sources used are available in the ODHF metadata.

The data sources used do not deploy a uniform classification system. The ODHF harmonizes facility type by assigning one of three types to each health facility. This was done based on the facility type provided in the source data as well as using other research carried out for the purpose. The 3 facility types used in the ODHF include:Ambulatory Health Care ServicesHospitalsNursing and Residential Care FacilitiesHowever, alternative medicine (e.g., herbalists) and specialist areas (e.g., chiropractors, dentists, mental health specialists, etc.) are not in scope for the current ODHF version (version 1.1).

The ODHF does not assert having exhaustive coverage and may not contain all facilities in scope for the current version. While efforts have been made to minimize these, facility type classification and geolocation errors are also possible. While all data are released on the same date, the dates as of which data are current depends on the update dates of the sources used.

A subset of geo-coordinates available in the source data were validated using the internet and updated as needed. When latitude and longitude were not available, geocoding was performed for some sources using address data in the source. Some coordinates were also removed from the original sources when it was determined they were derived from postal codes or other aggregate geographic areas as opposed to street address.

Deduplication was done to remove duplicates for cases where sources overlapped in coverage.

This current version of the database (version 1.1) contains approximately 7,000 records. Data were collected by accessing sources between November 2019 and March 2020 for the initial release, with additional data collected or otherwise updated from May to June 2020 for version 1.1.

The variables included in the ODHF are as follows:

Index Facility Name Source Facility Type ODHF Facility Type Provider Unit Street Number Street Name Postal Code City Province or Territory Source-Format Street Address Census Subdivision Name Census Subdivision Unique Identifier Province or Territory Unique Identifier Latitude Longitude

For more information on how the addresses and variables were compiled, see the metadata that accompanies the ODHF.

This is a republishing of the data that is freely available from Statistics Canada at https://www.statcan.gc.ca/eng/lode/databases/odhf. Records that did not have a latitude and longitude value (about 484) were geocoded using the Esri World Geocoder. For more information on this data set please review the Statistics Canada metadata document.

Update Frequency: As needed

These data represent the Wild and Scenic Rivers Inventory Evaluation stream segments for Alternatives B, C, D, and E.The Wild and Scenic River evaluation associated with Forest Plan Revisions on the Sierra and Sequoia National Forests determined rivers that met the eligibility requirements. This information has been compiled into an appendix in the revised draft environmental impact statement. All eligible rivers have also been classified as “wild,” “scenic,” or “recreational” based on the type and intensity of existing development. Wild: Those rivers or sections of rivers free of impoundments and generally inaccessible except by trail, with watersheds or shorelines essentially primitive and waters unpolluted. These represent vestiges of primitive America.Scenic: Those rivers or sections of rivers free of impoundments, with shorelines or watersheds still largely primitive and shorelines largely undeveloped, but accessible in places by roads.Recreational: Those rivers or sections of rivers readily accessible by road or railroad that may have some development along their shorelines, and that may have undergone some impoundment or diversion in the past.Further details about the Wild and Scenic River process can be found in Forest Service Handbook 1909.12 - Land Management Planning Handbook Chapter 80 - Wild and Scenic Rivers.

Neighborhood Map Atlas neighborhoods are derived from the Seattle City Clerk's Office Geographic Indexing Atlas. These are the smallest neighborhood areas and have been supplemented with alternate names from other sources in 2020. They roll up to the district areas. The sub-neighborhood field contains the most common name and the alternate name field is a comma delimited list of all the alternate names.The original atlas is designed for subject indexing of legislation, photographs, and other documents and is an unofficial delineation of neighborhood boundaries used by the City Clerks Office. Sources for this atlas and the neighborhood names used in it include a 1980 neighborhood map produced by the Department of Community Development, Seattle Public Library indexes, a 1984-1986 Neighborhood Profiles feature series in the Seattle Post-Intelligencer, numerous parks, land use and transportation planning studies, and records in the Seattle Municipal Archives. Many of the neighborhood names are traditional names whose meaning has changed over the years, and others derive from subdivision names or elementary school attendance areas.Disclaimer: The Seattle City Clerk's Office Geographic Indexing Atlas is designed for subject indexing of legislation, photographs, and other records in the City Clerk's Office and Seattle Municipal Archives according to geographic area. Neighborhoods are named and delineated in this collection of maps in order to provide consistency in the way geographic names are used in describing records of the Archives and City Clerk, thus allowing precise retrieval of records. The neighborhood names and boundaries are not intended to represent any "official" City of Seattle neighborhood map.

The Office of the City Clerk makes no claims as to the completeness, accuracy, or content of any data contained in the Geographic Indexing Atlas; nor does it make any representation of any kind, including, but not limited to, warranty of the accuracy or fitness for a particular use; nor are any such warranties to be implied or inferred with respect to the representations furnished herein. The maps are subject to change for administrative purposes of the Office of the City Clerk. Information contained in the site, if used for any purpose other than as an indexing and search aid for the databases of the Office of the City Clerk, is being used at one's own risk.