The ArcGIS Javascript API lets developers build GIS web applications. The Javascript API is one of many that could be used but it's a great starting place. Students may also be interested in the Python API or others!

RTB Maps is a cloud-based electronic Atlas. We used ArGIS 10 for Desktop with Spatial Analysis Extension, ArcGIS 10 for Server on-premise, ArcGIS API for Javascript, IIS web services based on .NET, and ArcGIS Online combining data on the cloud with data and applications on our local server to develop an Atlas that brings together many of the map themes related to development of roots, tubers and banana crops. The Atlas is structured to allow our participating scientists to understand the distribution of the crops and observe the spatial distribution of many of the obstacles to production of these crops. The Atlas also includes an application to allow our partners to evaluate the importance of different factors when setting priorities for research and development. The application uses weighted overlay analysis within a multi-criteria decision analysis framework to rate the importance of factors when establishing geographic priorities for research and development.Datasets of crop distribution maps, agroecology maps, biotic and abiotic constraints to crop production, poverty maps and other demographic indicators are used as a key inputs to multi-objective criteria analysis.Further metadata/references can be found here: http://gisweb.ciat.cgiar.org/RTBmaps/DataAvailability_RTBMaps.htmlDISCLAIMER, ACKNOWLEDGMENTS AND PERMISSIONS:This service is provided by Roots, Tubers and Bananas CGIAR Research Program as a public service. Use of this service to retrieve information constitutes your awareness and agreement to the following conditions of use.This online resource displays GIS data and query tools subject to continuous updates and adjustments. The GIS data has been taken from various, mostly public, sources and is supplied in good faith.RTBMaps GIS Data Disclaimer• The data used to show the Base Maps is supplied by ESRI.• The data used to show the photos over the map is supplied by Flickr.• The data used to show the videos over the map is supplied by Youtube.• The population map is supplied to us by CIESIN, Columbia University and CIAT.• The Accessibility map is provided by Global Environment Monitoring Unit - Joint Research Centre of the European Commission. Accessibility maps are made for a specific purpose and they cannot be used as a generic dataset to represent "the accessibility" for a given study area.• Harvested area and yield for banana, cassava, potato, sweet potato and yam for the year 200, is provided by EarthSat (University of Minnesota’s Institute on the Environment-Global Landscapes initiative and McGill University’s Land Use and the Global Environment lab). Dataset from Monfreda C., Ramankutty N., and Foley J.A. 2008.• Agroecology dataset: global edapho-climatic zones for cassava based on mean growing season, temperature, number of dry season months, daily temperature range and seasonality. Dataset from CIAT (Carter et al. 1992)• Demography indicators: Total and Rural Population from Center for International Earth Science Information Network (CIESIN) and CIAT 2004.• The FGGD prevalence of stunting map is a global raster datalayer with a resolution of 5 arc-minutes. The percentage of stunted children under five years old is reported according to the lowest available sub-national administrative units: all pixels within the unit boundaries will have the same value. Data have been compiled by FAO from different sources: Demographic and Health Surveys (DHS), UNICEF MICS, WHO Global Database on Child Growth and Malnutrition, and national surveys. Data provided by FAO – GIS Unit 2007.• Poverty dataset: Global poverty headcount and absolute number of poor. Number of people living on less than $1.25 or $2.00 per day. Dataset from IFPRI and CIATTHE RTBMAPS GROUP MAKES NO WARRANTIES OR GUARANTEES, EITHER EXPRESSED OR IMPLIED AS TO THE COMPLETENESS, ACCURACY, OR CORRECTNESS OF THE DATA PORTRAYED IN THIS PRODUCT NOR ACCEPTS ANY LIABILITY, ARISING FROM ANY INCORRECT, INCOMPLETE OR MISLEADING INFORMATION CONTAINED THEREIN. ALL INFORMATION, DATA AND DATABASES ARE PROVIDED "AS IS" WITH NO WARRANTY, EXPRESSED OR IMPLIED, INCLUDING BUT NOT LIMITED TO, FITNESS FOR A PARTICULAR PURPOSE. By accessing this website and/or data contained within the databases, you hereby release the RTB group and CGCenters, its employees, agents, contractors, sponsors and suppliers from any and all responsibility and liability associated with its use. In no event shall the RTB Group or its officers or employees be liable for any damages arising in any way out of the use of the website, or use of the information contained in the databases herein including, but not limited to the RTBMaps online Atlas product.APPLICATION DEVELOPMENT:• Desktop and web development - Ernesto Giron E. (GeoSpatial Consultant) e.giron.e@gmail.com• GIS Analyst - Elizabeth Barona. (Independent Consultant) barona.elizabeth@gmail.comCollaborators:Glenn Hyman, Bernardo Creamer, Jesus David Hoyos, Diana Carolina Giraldo Soroush Parsa, Jagath Shanthalal, Herlin Rodolfo Espinosa, Carlos Navarro, Jorge Cardona and Beatriz Vanessa Herrera at CIAT, Tunrayo Alabi and Joseph Rusike from IITA, Guy Hareau, Reinhard Simon, Henry Juarez, Ulrich Kleinwechter, Greg Forbes, Adam Sparks from CIP, and David Brown and Charles Staver from Bioversity International.Please note these services may be unavailable at times due to maintenance work.Please feel free to contact us with any questions or problems you may be having with RTBMaps.

Pursuant to the provisions of Minnesota Statutes, section 103G.223, this database contains points that represent calcareous fens as defined in Minnesota Rules, part 8420.0935, subpart 2. These calcareous fens have been identified by the commissioner by written order published in the State Register on June 2, 2008 (32 SR 2148-2154), August 31, 2009 (34 SR 278) and December 7, 2009 (34 SR 823-824). The current list of fens (DNR List of Known Calcareous Fens) is posted on the DNR's web site at: http://files.dnr.state.mn.us/eco/wetlands/calcareous_fen_list.pdf

This data set is a GIS point shapefile derived from the Natural Heritage "Biotics" Database. Data in the Biotics Database are maintained according to established Natural Heritage Methodology developed by NatureServe and The Nature Conservancy. The core of the Biotics Database is made up of Element Occurrence (EO) records of rare plant and animal species, animal aggregations, native plant communities, and geologic features. An Element is a unit of biological diversity, such as a species, subspecies, or a native plant community. An EO is an area of land and/or water in which an Element is, or was, present, and which has practical conservation value for the Element (e.g. species or community) as evidenced by potential continued (or historical) presence and/or regular recurrence at a given location. Source Features are the mapped representation of observations of rare features. Source Features are then evaluated using biological standards, and grouped into EOs as appropriate.

This data set contains a point for each Calcareous Fen (DNR List of Known Calcareous Fens) Source Feature in the Biotics database, and selected attributes from the Source Feature record and it’s linked Element Occurrence (EO) record.

The National Tunnel Inventory Elements dataset was compiled on September 02, 2025 and published on August 26, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Tunnel Inventory (NTI) is a collection of information (database) describing the more than 500 of the Nation's tunnels located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible tunnels on Federal lands. The element data present a breakdown of the condition of each structural and civil element for each tunnel on the National Highway System (NHS). A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529051

There is a total of 32, 537 elements (centroids) within the C2VSimFG model, which make up the finite element mesh of the model. The elements are subdivided into 21 subregions. Each element is composed of corresponding groundwater nodes within the model domain. The model domain area is 20, 742 square miles, and the average element size is roughly 407 acres. The boundaries of the model grid were developed using a set of control points at important locations of the model area. The finite grid mesh was created using GIS and several Excel and FORTRAN utilities. The grid size was refined in areas of higher groundwater gradient and/or areas that are more critical from hydrogeological viewpoints. The grid lines are designed parallel to the streamflow direction, when possible, as well as the groundwater streamlines, to capture the surface and subsurface drainage patterns. Nine major faults in the Central Valley are represented by thin strip of elements of around 500 feet. The dataset excludes three geologic outcrops: Sutter Buttes, Kettleman Hills and Capay Valley Hills, which are areas not included in the Bulletin 118. The dataset is maintained by the Sustainable Groundwater Management Office, Modeling and Tools Support Section.

This is a dataset download, not a document. The Open Document button will start the download.This data layer is an element of the Oregon GIS Framework. This data layer represents the Existing Vegetation data element. This statewide grid was created by combining four independently-generated datasets: one for western Oregon (USGS zones 2 and 7), and two for eastern Oregon (USGS zones 8 and 9; forested and non-forested lands), and selected wetland types from the Oregon Wetlands geodatabase. The landcover grid for zones 2 and 7 was produced using a modification of Breiman's Random Forest classifier to model landcover. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to build two predictive models for the forested landcover classes, and the nonforested landcover classes. The grids resulting from the models were then modified to improve the distribution of the following classes: volcanic systems and wetland vegetation. Along the eastern edge, the sagebrush systems were modified to help match with the map for the adjacent region. Additional classes were then layered on top of the modified models from other sources. These include disturbed classes (harvested and burned), cliffs, riparian, and NLCD's developed, agriculture, and water classes. A validation for forest classes was performed on a withheld of the sample data to assess model performance. Due to data limitations, the nonforest classes were evaluated using the same data that were used to build the original nonforest model. Two independent grids were combined to map landcover in adjacent zones 8 and 9. Tree canopy greater than 10% (from NLCD 2001), complemented with a disturbance grid, served as a mask to delineate forested areas. A grid of non-forested areas was extracted from a larger, regional grid (Sagemap) created using decision tree classifier and other techniques. Multi-season satellite imagery (Landsat ETM+, 1999-2003) and digital elevation model (DEM) derived datasets (e.g. elevation, landform, aspect, etc.) were utilized to derive rule sets for the various landcover classes. Eleven mapping areas, each characterized by similar ecological and spectral characteristics, were modeled independently of one another and mosaicked. An internal validation for modeled classes was performed on a withheld 20% of the sample data to assess model performance. The portion of this original grid corresponding to USGS map zones 8 and 9 was extracted and split into three mapping areas (one for USGS zone 8, two for USGS zone 9: Northern Basin and Range in the south, Blue Mountains in the north) and modified to improve the distribution of the following classes: cliffs, subalpine zone, dunes, lava flows, silver sagebrush, ash beds, playas, scabland, and riparian vegetation. Agriculture and urban areas were extracted from NLCD 2001. A forest grid was generated using Gradient Nearest Neighbor (GNN) imputation process. GNN uses multivariate gradient modeling to integrate data from regional grids of field plots with satellite imagery and mapped environmental data. A suite of fine-scale plot variables is imputed to each pixel in a digital map, and regional maps can be created for most of the same vegetation attributes available from the field plots. However, due to lack of sampling plots in the southern half of zone 9, the GNN model proved unreliable there; forest data from Landfire were used instead. To compensate for known under-representation of wetlands, selected wetland types from the Oregon Wetlands Geodatabase (version 2009-1030) were converted to raster and overlaid (replaced) pixel value assignments from the previous steps just detailed. See Process Steps for more information. The ecological systems were crosswalked to landcover (based on Oregon landcover standard, modified from NLCD 2001) and to wildlife habitats (based on integrated habitats used in the Oreg

The National Bridge Inventory Elements dataset is as of June 20, 2025 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 620,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The element data present a breakdown of the condition of each structural and bridge management element for each bridge on the National Highway System (NHS). The Specification for the National Bridge Inventory Bridge Elements contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519106. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1519106

Meet Earth EngineGoogle Earth Engine combines a multi-petabyte catalog of satellite imagery and geospatial datasets with planetary-scale analysis capabilities and makes it available for scientists, researchers, and developers to detect changes, map trends, and quantify differences on the Earth's surface.SATELLITE IMAGERY+YOUR ALGORITHMS+REAL WORLD APPLICATIONSLEARN MOREGLOBAL-SCALE INSIGHTExplore our interactive timelapse viewer to travel back in time and see how the world has changed over the past twenty-nine years. Timelapse is one example of how Earth Engine can help gain insight into petabyte-scale datasets.EXPLORE TIMELAPSEREADY-TO-USE DATASETSThe public data archive includes more than thirty years of historical imagery and scientific datasets, updated and expanded daily. It contains over twenty petabytes of geospatial data instantly available for analysis.EXPLORE DATASETSSIMPLE, YET POWERFUL APIThe Earth Engine API is available in Python and JavaScript, making it easy to harness the power of Google’s cloud for your own geospatial analysis.EXPLORE THE APIGoogle Earth Engine has made it possible for the first time in history to rapidly and accurately process vast amounts of satellite imagery, identifying where and when tree cover change has occurred at high resolution. Global Forest Watch would not exist without it. For those who care about the future of the planet Google Earth Engine is a great blessing!-Dr. Andrew Steer, President and CEO of the World Resources Institute.CONVENIENT TOOLSUse our web-based code editor for fast, interactive algorithm development with instant access to petabytes of data.LEARN ABOUT THE CODE EDITORSCIENTIFIC AND HUMANITARIAN IMPACTScientists and non-profits use Earth Engine for remote sensing research, predicting disease outbreaks, natural resource management, and more.SEE CASE STUDIESREADY TO BE PART OF THE SOLUTION?SIGN UP NOWTERMS OF SERVICE PRIVACY ABOUT GOOGLE

See Sound Transit

The Department of Information Technology and Telecommunications, GIS Unit, has created a series of Map Tile Services for use in public web mapping & desktop applications. The link below describes the Basemap, Labels, & Aerial Photographic map services, as well as, how to utilize them in popular JavaScript web mapping libraries and desktop GIS applications. A showcase application, NYC Then&Now (https://maps.nyc.gov/then&now/) is also included on this page.

There is a total of 32, 537 elements within the C2VSimFG model, which make-up the finite grid mesh of the model. The elements are subdivided into 21 subregions. Each element is composed of corresponding groundwater nodes within the model domain. The model domain area is 20, 742 square miles, and each element is roughly 407 acres. The boundaries of the model grid were developed using a set of control points at important locations of the model area. The finite element mesh was created using GIS and several Excel and FORTRAN utilities. The grid size was refined in areas of higher groundwater gradient and/or areas that are more critical from hydrogeological viewpoints. The grid lines are designed parallel to the streamflow direction, when possible, as well as the groundwater streamlines, to capture the surface and subsurface drainage patterns. Nine major faults in the Central Valley are represented by thin strip of elements of around 500 feet. The dataset excludes three geologic outcrops: Sutter Buttes, Kettleman Hills and Capay Valley Hills, which are areas not included in the Bulletin 118. The dataset is maintained by the Sustainable Groundwater Management Office, Modeling and Tools Support Section. The areas calculated for this data using the WGS 1984 Web Mercator projection may not reflect the actual areas used in the C2VSimFG model.

Author: Megan Banaski (mbanaski@esri.com) and Max Ozenberger (mozenberger@esri.com)Last Updated: 1/1/2024Intended Environment: WebPurpose:Exercise F6: Create a JS API 4.x WebMap App or Create a JS API 4.x WebScene App This lab is part of GitHub repository that contains short labs that step you through the process of developing a web application with ArcGIS API for JavaScript.The labs start from ground-zero and work through the accessing different aspects of the API and how to begin to build an application and add functionality.Requirements: Here are the resources you will use for the labs.ArcGIS for Developers - Account, Documentation, Samples, Apps, DownloadsEsri Open Source Projects - More source codeA simple guide for setting up a local web server (optional)Help with HTML, CSS, and JavaScript

Retained-Earnings Time Series for Beijing SuperMap Software Co Ltd. Beijing SuperMap Software Co., Ltd. provides geographic information system and spatial intelligence software products and services in China and internationally. The company offers SuperMap iServer, which provides web services for geospatial big data; GeoAI, an 3D to support massive vector/raster data publishing; SuperMap iPortal that offers Web applications; and SuperMap iManager to monitor various GIS data storage, computing, service nodes, or other Web sites, as well as occupancy of hardware resources, map access hotspots, node health, and other indicators to achieve integrated operation and maintenance management of GIS system. It also provides Edge GIS Server for service publishing and real-time analysis and calculation, reduces response latency and bandwidth consumption, and reduces the pressure of cloud GIS center; Terminal GIS for Components, a large-scale full-component GIS development platform; SuperMap iDesktop and SuperMap iDesktopX, which are 2D and 3D integrated desktop GIS software platforms; SuperMap iExplorer3D, a 3D scene browsing software; SuperMap iMaritimeEditor, a cross-platform electronic chart production desktop software; and SuperMap ImageX Pro, a cross-platform remote sensing image processing desktop software. In addition, the company offers SuperMap iClient JavaScript, a GIS web terminal development platform; SuperMap iClient3D for WebGL, a 3D web terminal development platform; SuperMap iClient3D for WebGPU, a 3D GIS network client development platform; SuperMap iMobile, a mobile GIS software development platform based on map browsing, data collection, data analysis, and route navigation and combined with AR maps, mobile 3D, cloud collaboration, etc.; and SuperMap online GIS platform that integrates GIS data management, service management, data mining, and display. Beijing SuperMap Software Co., Ltd. was founded in 1997 and is based in Beijing, the People's Republic of China.

Liabilities-and-Stockholders-Equity Time Series for Beijing SuperMap Software Co Ltd. Beijing SuperMap Software Co., Ltd. provides geographic information system and spatial intelligence software products and services in China and internationally. The company offers SuperMap iServer, which provides web services for geospatial big data; GeoAI, an 3D to support massive vector/raster data publishing; SuperMap iPortal that offers Web applications; and SuperMap iManager to monitor various GIS data storage, computing, service nodes, or other Web sites, as well as occupancy of hardware resources, map access hotspots, node health, and other indicators to achieve integrated operation and maintenance management of GIS system. It also provides Edge GIS Server for service publishing and real-time analysis and calculation, reduces response latency and bandwidth consumption, and reduces the pressure of cloud GIS center; Terminal GIS for Components, a large-scale full-component GIS development platform; SuperMap iDesktop and SuperMap iDesktopX, which are 2D and 3D integrated desktop GIS software platforms; SuperMap iExplorer3D, a 3D scene browsing software; SuperMap iMaritimeEditor, a cross-platform electronic chart production desktop software; and SuperMap ImageX Pro, a cross-platform remote sensing image processing desktop software. In addition, the company offers SuperMap iClient JavaScript, a GIS web terminal development platform; SuperMap iClient3D for WebGL, a 3D web terminal development platform; SuperMap iClient3D for WebGPU, a 3D GIS network client development platform; SuperMap iMobile, a mobile GIS software development platform based on map browsing, data collection, data analysis, and route navigation and combined with AR maps, mobile 3D, cloud collaboration, etc.; and SuperMap online GIS platform that integrates GIS data management, service management, data mining, and display. Beijing SuperMap Software Co., Ltd. was founded in 1997 and is based in Beijing, the People's Republic of China.

Shows SB 35 determination status for cities and counties throughout the state, based on data reported on the annual progress report (APR).SB 35 (Wiener) Streamline Approval Process Opt-in program for developersFinal Streamlined Ministerial Approval Process Guidelines (PDF)Creates a streamlined approval process for developments in localities that have not yet met their housing targets, provided that the development is on an infill site and complies with existing residential and mixed use zoning. Participating developments must provide at least 10 percent of units for lower-income families. All projects over 10 units must be prevailing wage and larger projects must provide skilled and trained labor.For more information, visit the Annual Progress Reports on HCD's website.

Mature Support Notice: This item is in mature support as of February 2025. A new version of this item is available for your use. This web application highlights some of the capabilities for accessing Sentinel-2 imagery layers, powered by ArcGIS for Server, accessing Landsat Public Datasets running on the Amazon Web Services Cloud. The layers are updated with new Sentinel-2 images on a daily basis.Created for you to visualize our planet and understand how the Earth has changed over time, the Esri Sentinel-2 Explorer app provides the power of Sentinel-2 satellites, which gather data beyond what the eye can see. Use this app to draw on Sentinel's different bands to better explore the planet's geology, vegetation, agriculture, and cities. Additionally, access the Sentinel-2 archive to visualize how the Earth's surface has changed over the last fourteen monthsQuick access to the following band combinations and indices is provided: BandDescriptionWavelength (µm)Resolution (m)1Coastal aerosol0.433 - 0.453602Blue0.458 - 0.523103Green0.543 - 0.578104Red0.650 - 0.680105Vegetation Red Edge0.698 - 0.713206Vegetation Red Edge0.733 - 0.748207Vegetation Red Edge0.773 - 0.793208NIR0.785 - 0.900108ANarrow NIR0.855 - 0.875209Water vapour0.935 - 0.9556010SWIR – Cirrus1.365 - 1.3856011SWIR-11.565 - 1.6552012SWIR-22.100 - 2.28020 Agriculture : Highlights vigorous vegetation in bright green, stressed vegetation dull green and bare areas brown; Bands 11, 8, 2Natural Color : Bands 4, 3, 2Color Infrared : Healthy vegetation is bright red while stressed vegetation is dull red; Bands 8, 4 ,3 SWIR (Short-wave Infrared) : Highlights rock formations; Bands 12, 11, 4Geology : Highlights geologic features; Bands 12, 11, 2Bathymetric : Highlights underwater features; Bands 4, 3, 1Vegetation Index : Normalized Difference Vegetation Index(NDVI) with Colormap ; (Band 8 - Band 4)/(Band 8 + Band 4)Moisture Index : Normalized Difference Moisture Index (NDMI); (Band 8 - Band 11)/(Band 8 + Band 11)Normalized Burn Ratio : (Band 8 - Band 12)/(Band 8 + Band 12)Built-Up Index : (Band 11 - Band 8)/(Band 11 + Band 8)NDVI Raw : Normalized Difference Vegetation Index(NDVI); (Band 8 - Band 4)/(Band 8 + Band 4)NDVI - VRE only Raw : NDVI with VRE bands only; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - VRE only Colorized : NDVI with VRE bands only with Colormap; (Band 6 - Band 5)/(Band 6 + Band 5)NDVI - with VRE Raw : Also known as NDRE. NDVI with VRE band 5 and NIR band 8; (Band 8 - Band 5)/(Band 8 + Band 5)NDVI - with VRE Colorized : Also known as NDRE with Colormap; (Band 8 - Band 5)/(Band 8 + Band 5)NDWI Raw : Normalized Difference Water index with Green band and NIR band; (Band 3 - Band 8)/(Band 3 + Band 8)NDWI - with VRE Raw : Normalized Difference Water index with VRE band 5 and Green band 3; (Band 3 - Band 5)/(Band 3 + Band 5)NDWI - with VRE Colorized : NDWI index with VRE band 5 and Green band 3 with Colormap; (Band 3 - Band 5)/(Band 3 + Band 5)Custom SAVI : (Soil Adjusted Veg. Index); Offset + Scale*(1.5*(Band 8 - Band 4)/(Band 8 + Band 4 + 0.5))Custom Water Index : Offset + Scale*(Band 3 - Band 12)/(Band 3 + Band 12) Custom Burn Index : Offset + Scale*(Band 8 - Band 13)/(Band 8 + Band 13)Urban Index : Offset + Scale*(Band 8 - Band 12)/(Band 8 + Band 12)Optionally, you can also choose the "Custom Bands" or "Custom Index" option to create your own band combinations The Time tool enables access to a temporal time slider and a temporal profile of different indices for a selected point. The Time tool is only accessible at larger zoom scales. It provides temporal profiles for indices like NDVI (Normalized Difference Vegetation Index), NDMI (Normalized Difference Moisture Index) and Urban Index. The Identify tool enables access to information on the images, and can also provide a spectral profile for a selected point. The Bookmark tool will direct you to pre-selected interesting locations.NOTE: Using the Time tool to access imagery in the Sentinel-2 archive requires an ArcGIS account. The application is written using Web AppBuilder for ArcGIS accessing imagery layers using ArcGIS API for JavaScript. The following Imagery Layer are being accessed : Sentinel-2 - Provides access to 10, 20, and 60m 13-band multispectral imagery and a range of functions that provide different band combinations and indices.

The ORN is a provincewide geographic database of over 250,000 kilometres of municipal roads, provincial highways, and resource and recreational roads. The ORN is the source of roads data for the Government of Ontario. Road names in the ORN are the official names provided by the authoritative jurisdiction for each road segment, such as a municipality or the Ontario Government. You can also find the authoritative jurisdiction for a specific road feature in the Jurisdiction table in ORN Road Net Element. ORN Road Net Element requires an advanced knowledge of GIS including LRS and complex table relationships. This dataset contains the following related tables:official street namealternate street nameaddress informationroad classificationnumber of lanesroad surfacespeed limitstructuretoll pointblocked passageroute nameroute numberjurisdictionsourceunderpassjunction The ORN contains information licensed from the parties listed in in the “Ontario Road Network – Licenced Sources” document in the Additional Documentation section below. Additional Documentation Ontario Road Network - Road Net Element - User Guide (Word)Data Capture Specifications for Road Net Elements - Guide to Best Practices for Acquisition (Word)GO-ITS 29 - Ontario Road Network StandardOntario Road Network - Licensed Sources (Word) Status On going: data is being continually updated Maintenance and Update Frequency Weekly: data is updated on a weekly basis Contact Ontario Ministry of Natural Resources - Geospatial Ontario, geospatial@ontario.ca

The Bicycle and E-Mobility Element of the STP will help create a safer, more bikeable Seattle. It provides a foundation for the City of Seattle to grow our investment in bicycling and e-mobility to achieve STP goals. The STP and the Bicycle and E-Mobility Element build on and supersede the 2014 Bicycle Master Plan (BMP). The bicycle and e-mobility network serves not only people riding traditional bicycles, but also people using adaptive bikes, cargo bicycles for both personal use and deliveries, trikes, scooters, skateboards, roller skates, wheelchairs or other wheeled mobility devices, and “e-mobility” devices, which refers to personal and shared electric-powered bicycles, scooters, and other electric-powered devices. It serves people bicycling and taking e-mobility to serve a variety of trip purposes, such as getting to work, school, transit, the gym or doctor's office, recreating, making urban goods deliveries, and more.The Bike+ network consists of bikeways suitable for people of all ages and abilities (AAA), including protected bike lanes, Neighborhood Greenways, Healthy Streets, and bike lanes where vehicle speeds and volumes are sufficiently low. The Bike+ network is envisioned to seamlessly integrate with the multi-use trail network, which provides connections through or on the edges of parks and opens spaces, where an off-street connection is preferred, or is more feasible than an on-street connection. Diagram of an umbrella titled "What is Bike+?" Underneath the umbrella, the following are bulleted - protected bike lane, bike lane (if vehicle speed and volumes low. See Table 4), neighborhood greenway, and healthy street. Many planned projects from the 2014 BMP have been implemented and are shown on the existing bicycle and e-mobility network map. The Bike+ network shows existing and proposed AAA bikeways on Seattle’s arterial and non-arterial (i.e., neighborhood streets) networks.Refresh Cycle: None, Static. Manually as required.Original Publish: 5/23/2024Update Publish: 7/11/2024 per Policy and Planning teamContact: Policy and Planning team

The financially constrained element of Visualize 2045 identifies all the regionally significant capital improvements to the region’s highway and transit systems that transportation agencies expect to make and to be able to afford through 2045.For more information on Visualize 2045, visit https://www.mwcog.org/visualize2045/.To view the web map, visit https://www.mwcog.org/maps/map-listing/visualize-2045-project-map/.Download the ZIP file that contains a File Geodatabase

Retirement Notice: This item is in mature support as of July 2024 and will retire in December 2025. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version.This web application enables the exploration of Arctic elevation based on the 2m resolution Arctic Digital Elevation Models (DEM) created by the Polar Geospatial Center. The app displays multiple different renderings as well as profiles of the data. In many areas the coverage is available from multiple dates and the app displays temporal profiles as well as computing the differences. The current datasets consisting of 2m DEMs, cover the Arctic from 60*N to the Pole and will gradually, and incrementally be replaced with better 2m versions as they are produced during 2018. The elevations are digital surface models photogrammetrically generated from stereo satellite imagery and have not been edited to create terrain heights. The current datasets are preliminary and are known to contain some errors and artifacts. As more control becomes available, the elevation values will be refined and adjusted. The original PGC datasets have been adjusted according to the PGC proposed correction parameters, to give WGS84 ellipsoidal heights, but available in this service also as orthometric heights computed using the EGM2008 geoid separation. Details on how the DEMs are generated and their use can be found in ArcticDEM datasets. The DEMs were created from DigitalGlobe, Inc., imagery and funded under National Science Foundation awards 1043681, 1559691, and 1542736.The app also provides access to the Arctic Landsat imagery that is updated daily and also served through ArcGIS Online.Quick access to server functions defined for the following elevation derivatives are provided:Hillshade – Hillshaded surface generated dynamically on elevation layer, with a solar azimuth of 315 degrees and solar altitude of 45 degreesMulti-Directional Hillshade – Multi-directional hillshaded surface generated dynamically on elevation layer, computing hillshade from 6 different directionsElevation Tinted Hillshade – Elevation tinted hillshade surface generated dynamically on elevation layerSlopeMap – A color visualization of Slope surface generated dynamically on elevation layer, where flat surfaces is gray, shallow slopes are yellow and steep slopes are orangeAspectMap - A color visualization of aspect generated dynamically on elevation layerContour – Dynamically generated contours with specified contour intervals and options for smoothing to create more cartographically pleasing contours.The Time tool enables access to a temporal time slider and temporal profile for a selected point. The Time tool is only accessible at larger zoom scales. The Identify tool enables access to elevation, slope and aspect values for the specified point as well as information on the source image and links to download the source data. From the app it is also possible to export defined areas of the DEMs. These can be exported in user defined projections and resolutions. The Bookmark tool link to pre-selected interesting locations.For more information on the underlying services see Arctic DEM layer.The application is written using Web AppBuilder for ArcGIS accessing imagery layers using the ArcGIS API for JavaScript.