This dataset holds all materials for the Inform E-learning GIS course

Have you ever wanted to create your own maps, or integrate and visualize spatial datasets to examine changes in trends between locations and over time? Follow along with these training tutorials on QGIS, an open source geographic information system (GIS) and learn key concepts, procedures and skills for performing common GIS tasks – such as creating maps, as well as joining, overlaying and visualizing spatial datasets. These tutorials are geared towards new GIS users. We’ll start with foundational concepts, and build towards more advanced topics throughout – demonstrating how with a few relatively easy steps you can get quite a lot out of GIS. You can then extend these skills to datasets of thematic relevance to you in addressing tasks faced in your day-to-day work.

ArcGIS Dashboards Training Videos for COVID-19With the current COVID-19 situation across the world, there’s been a proliferation of corona virus themed dashboards emerging over the last few weeks in ArcGIS Online. Many of these were created with ArcGIS Dashboards, which enables users to convey information by presenting location-based analytics using intuitive and interactive data visualizations on a single screen._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...

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

This vector dataset provides polygons that represent significant golf course facility locations in Suffolk County. These courses can be publicly (State, County, Town, Village) or privately owned. This dataset can be linked with the GolfCoursePoint feature class by the FACILITYID field. In some cases, there may be multiple Golf Course Points for a single Golf Course Polygon. These data are organized for consumption in desktop and web applications.

In successful geoinformatics education, students’ active role in the learning process, e.g. through applying self-assessment, show an increasing interest but the evidence of benefits and challenges of self-assessment are sporadic. In this article, we examine the usefulness of an online self-assessment tool developed for geoinformatics education. We gathered data in two Finnish universities on five courses (n = 11–73 students/course) between 2019 and 2021. We examined 1) how the students’ self-assessed knowledge and understanding in geoinformatics subject topics changed during a course, 2) how the competencies at the end of a course changed between the years in different courses, and 3) what was the perceived usefulness of the self-assessment approach among the students. The results indicate support for the implementation of self-assessment, both as a formative and summative assessment. However, it is crucial to ensure that the students understand the contents of the self-assessment subject topics. To increase students’ motivation to take a self-assessment, it is crucial that the teacher actively highlights how it supports their studying and learning. As the teachers of the examined courses, we discuss the benefits and challenges of the self-assessment approach and the applied tool for the future development of geoinformatics education.

This course demonstrates how to select, modify, create, and share web applications using ArcGIS Online. ArcGIS Online offers many different options for creating web applications that share web maps, web scenes, and spatial functions. But how do you decide which web application best meets your requirements? Each web application option implements different functions and showcases a specific look and feel. You can choose a web application that meets your organization's functional requirements, apply your organization's look and feel, and share your web map without writing any code.Two workflows will be introduced for creating web applications using ArcGIS Online:Applying your web map to an existing template applicationCreating your own web application using Web AppBuilder for ArcGISAfter completing this course, you will be able to do the following:Identify the components of a web application.Create a web application from an existing configurable app template.Create a web application using Web AppBuilder for ArcGIS.Use ArcGIS Online to deploy a web application.

Important Note: This item is in mature support as of February 2023 and will be retired 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 layer displays change in pixels of the Sentinel-2 10m Land Use/Land Cover product developed by Esri, Impact Observatory, and Microsoft. Available years to compare with 2021 are 2018, 2019 and 2020. By default, the layer shows all comparisons together, in effect showing what changed 2018-2021. But the layer may be changed to show one of three specific pairs of years, 2018-2021, 2019-2021, or 2020-2021.Showing just one pair of years in ArcGIS Online Map ViewerTo show just one pair of years in ArcGIS Online Map viewer, create a filter. 1. Click the filter button. 2. Next, click add expression. 3. In the expression dialogue, specify a pair of years with the ProductName attribute. Use the following example in your expression dialogue to show only places that changed between 2020 and 2021:ProductNameis2020-2021By default, places that do not change appear as a transparent symbol in ArcGIS Pro. But in ArcGIS Online Map Viewer, a transparent symbol may need to be set for these places after a filter is chosen. To do this:4. Click the styles button. 5. Under unique values click style options. 6. Click the symbol next to No Change at the bottom of the legend. 7. Click the slider next to "enable fill" to turn the symbol off.Showing just one pair of years in ArcGIS ProTo show just one pair of years in ArcGIS Pro, choose one of the layer's processing templates to single out a particular pair of years. The processing template applies a definition query that works in ArcGIS Pro. 1. To choose a processing template, right click the layer in the table of contents for ArcGIS Pro and choose properties. 2. In the dialogue that comes up, choose the tab that says processing templates. 3. On the right where it says processing template, choose the pair of years you would like to display. The processing template will stay applied for any analysis you may want to perform as well.How the change layer was created, combining LULC classes from two yearsImpact Observatory, Esri, and Microsoft used artificial intelligence to classify the world in 10 Land Use/Land Cover (LULC) classes for the years 2017-2021. Mosaics serve the following sets of change rasters in a single global layer: Change between 2018 and 2021Change between 2019 and 2021Change between 2020 and 2021To make this change layer, Esri used an arithmetic operation combining the cells from a source year and 2021 to make a change index value. ((from year * 16) + to year) In the example of the change between 2020 and 2021, the from year (2020) was multiplied by 16, then added to the to year (2021). Then the combined number is served as an index in an 8 bit unsigned mosaic with an attribute table which describes what changed or did not change in that timeframe. Variable mapped: Change in land cover between 2018, 2019, or 2020 and 2021 Data Projection: Universal Transverse Mercator (UTM)Mosaic Projection: WGS84Extent: GlobalSource imagery: Sentinel-2Cell Size: 10m (0.00008983152098239751 degrees)Type: ThematicSource: Esri Inc.Publication date: January 2022What can you do with this layer?Global LULC maps provide information on conservation planning, food security, and hydrologic modeling, among other things. This dataset can be used to visualize land cover anywhere on Earth. This layer can also be used in analyses that require land cover input. For example, the Zonal Statistics tools allow a user to understand the composition of a specified area by reporting the total estimates for each of the classes. Land Cover processingThis map was produced by a deep learning model trained using over 5 billion hand-labeled Sentinel-2 pixels, sampled from over 20,000 sites distributed across all major biomes of the world. The underlying deep learning model uses 6 bands of Sentinel-2 surface reflectance data: visible blue, green, red, near infrared, and two shortwave infrared bands. To create the final map, the model is run on multiple dates of imagery throughout the year, and the outputs are composited into a final representative map. Processing platformSentinel-2 L2A/B data was accessed via Microsoft’s Planetary Computer and scaled using Microsoft Azure Batch.Class definitions1. WaterAreas where water was predominantly present throughout the year; may not cover areas with sporadic or ephemeral water; contains little to no sparse vegetation, no rock outcrop nor built up features like docks; examples: rivers, ponds, lakes, oceans, flooded salt plains.2. TreesAny significant clustering of tall (~15-m or higher) dense vegetation, typically with a closed or dense canopy; examples: wooded vegetation,
clusters of dense tall vegetation within savannas, plantations, swamp or mangroves (dense/tall vegetation with ephemeral water or canopy too thick to detect water underneath).4. Flooded vegetationAreas of any type of vegetation with obvious intermixing of water throughout a majority of the year; seasonally flooded area that is a mix of grass/shrub/trees/bare ground; examples: flooded mangroves, emergent vegetation, rice paddies and other heavily irrigated and inundated agriculture.5. CropsHuman planted/plotted cereals, grasses, and crops not at tree height; examples: corn, wheat, soy, fallow plots of structured land.7. Built AreaHuman made structures; major road and rail networks; large homogenous impervious surfaces including parking structures, office buildings and residential housing; examples: houses, dense villages / towns / cities, paved roads, asphalt.8. Bare groundAreas of rock or soil with very sparse to no vegetation for the entire year; large areas of sand and deserts with no to little vegetation; examples: exposed rock or soil, desert and sand dunes, dry salt flats/pans, dried lake beds, mines.9. Snow/IceLarge homogenous areas of permanent snow or ice, typically only in mountain areas or highest latitudes; examples: glaciers, permanent snowpack, snow fields. 10. CloudsNo land cover information due to persistent cloud cover.11. Rangeland Open areas covered in homogenous grasses with little to no taller vegetation; wild cereals and grasses with no obvious human plotting (i.e., not a plotted field); examples: natural meadows and fields with sparse to no tree cover, open savanna with few to no trees, parks/golf courses/lawns, pastures. Mix of small clusters of plants or single plants dispersed on a landscape that shows exposed soil or rock; scrub-filled clearings within dense forests that are clearly not taller than trees; examples: moderate to sparse cover of bushes, shrubs and tufts of grass, savannas with very sparse grasses, trees or other plants.CitationKarra, Kontgis, et al. “Global land use/land cover with Sentinel-2 and deep learning.” IGARSS 2021-2021 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2021.AcknowledgementsTraining data for this project makes use of the National Geographic Society Dynamic World training dataset, produced for the Dynamic World Project by National Geographic Society in partnership with Google and the World Resources Institute.For questions please email environment@esri.com

This resource contains data inputs and a Jupyter Notebook that is used to introduce Hydrologic Analysis using Terrain Analysis Using Digital Elevation Models (TauDEM) and Python. TauDEM is a free and open-source set of Digital Elevation Model (DEM) tools developed at Utah State University for the extraction and analysis of hydrologic information from topography. This resource is part of a HydroLearn Physical Hydrology learning module available at https://edx.hydrolearn.org/courses/course-v1:Utah_State_University+CEE6400+2019_Fall/about

In this activity, the student learns how to (1) derive hydrologically useful information from Digital Elevation Models (DEMs); (2) describe the sequence of steps involved in mapping stream networks, catchments, and watersheds; and (3) compute an approximate water balance for a watershed-based on publicly available data.

Please note that this exercise is designed for the Logan River watershed, which drains to USGS streamflow gauge 10109000 located just east of Logan, Utah. However, this Jupyter Notebook and the analysis can readily be applied to other locations of interest. If running the terrain analysis for other study sites, you need to prepare a DEM TIF file, an outlet shapefile for the area of interest, and the average annual streamflow and precipitation data. - There are several sources to obtain DEM data. In the U.S., the DEM data (with different spatial resolutions) can be obtained from the National Elevation Dataset available from the national map (http://viewer.nationalmap.gov/viewer/). Another DEM data source is the Shuttle Radar Topography Mission (https://www2.jpl.nasa.gov/srtm/), an international research effort that obtained digital elevation models on a near-global scale (search for Digital Elevation at https://www.usgs.gov/centers/eros/science/usgs-eros-archive-products-overview?qt-science_center_objects=0#qt-science_center_objects). - If not already available, you can generate the outlet shapefile by applying basic terrain analysis steps in geospatial information system models such as ArcGIS or QGIS. - You also need to obtain average annual streamflow and precipitation data for the watershed of interest to assess the annual water balance and calculate the runoff ratio in this exercise. In the U.S., the streamflow data can be obtained from the USGS NWIS website (https://waterdata.usgs.gov/nwis) and the precipitation from PRISM (https://prism.oregonstate.edu/normals/). Note that using other datasets may require preprocessing steps to make data ready to use for this exercise.

Note: This dataset is updated weekly. Gas Service Stations are points representing the locations of gasoline services stations regulated by NJDEP. The NJDEP New Jersey Environmental Management System (NJEMS) serves as the database that supplies coordinates and descriptive attributes from several tables used to generate this GIS layer. This layer is produced primarily for the NJDEP i-MapNJ ArcIMS interactive mapping web application and ArcGIS (ArcView, ArcInfo) users. Program interests included in NJEMS are: Air, Communications Center, Discharge Prevention, Exams and Licensing, Fish Game and Wildlife, Green Acres, Hazardous Waste, Lab Certification, Land Use, Landscape Irrigation, Parks and Forestry, Pesticides, Pinelands, Planning, Radiation, Right-to-Know, Site Remediation, Soil Conservation, Solid Waste, TCPA, Water Quality, Water Supply, and Watershed Management. The locations derived using GPS represent main entrance or front door locations for the sites. Users should note that not every site in NJEMS presently (as of February 2009) has an established coordinate (GPS or otherwise). NJDEP is continually working to acquire these with GPS, location data submitted to permitting programs, and through address matching techniques.

Operational level forest inventory data was acquired in 2019 and provided the basis for mapping, quantifying and assessing area-wide forest and commercial timber resources and for establishing the AAC for SSE. Forest inventory data from 2019 and the analysis in 2020 provides the following forest management benefits: Updated Timber Type data layer (map) contained in the State’s GIS for SSE Data acquired and analyzed through the forest inventory project was entered into the State’s GIS to create an updated timber type layer (map) of the commercial forest timber base in SSE containing individual timber stands. Updated timber type descriptors for each individual stand include stand species composition, stand density and per acre timber volume. SSE Forest Inventory Report July 17, 2020 4 Using the GIS to analyze the relationships between the commercial timber resource and other forest resources (transportation network, fish and wildlife habitat, cultural resources, etc.) allows the DOF to undertake and complete complex forest planning documents such as the Five-Year Schedules of Timber Sales (FYSTS), and Forest Land Use Plans (FLUPs) used to guide both broad scale and site-specific forest management activities. The GIS also allows DOF to track changes to the commercial timber base resulting from management activities including timber harvest, stand regeneration/reforestation, and timber stand improvement projects such as precommercial tree thinning. Updated Annual Allowable Cut for SSE The GIS timber type map for SSE, updated with the 2019 forest inventory data, formed the basis for area (acreage) and timber volume (board feet) figures necessary to calculate an updated AAC. The new GIS timber type map and associated data files along with newly available LiDAR data provided the raw data necessary to perform the growth and yield modelling to estimate timber volume and characteristics in the developing young growth stands over the course of the rotation.

The Minnesota Department of Agriculture’s “Minnesota Agricultural Water Quality Certification Program” (MAWQCP) launched an assessment tool online application to provide program participants a common online format in which to streamline the certification process.
The intended application audience includes the producer, licensed certifier, crop advisor, or other agronomic and conservation professionals. The assessment tool, one of three steps necessary to obtain certification, is a risk assessment tool which aggregates factors relating to nutrient management, tillage, soil properties, pest management and conservation practices into a unitless index score on a 1 thru 10 scores. Each field and cropping scenario is assessed.
The online application increases the efficiency of information gathering necessary to run the assessment tool. Features include mapping queries, data organization into field libraries, summary report generation and data packaging into small, easily transmittable formats. The application uses GIS map and geoprocessing services to calculate some of the summary data.
The online application ensures producer privacy by requiring users to store the information on a local drive; the hosting website and server does not store any information.

This GIS web browser contains stratigraphic information from the southern flank of the Murphy Inlier, Lawn Hill Platform and Leichhardt River Fault Trough in the Western Succession of the Mount Isa Block. The principal lithostratigraphic units covered by this dataset include the Surprise Creek Formation, Mount Isa and McNamara Groups. The images are pixilated versions of those contained on CD Records AGSO Record 1999/10, AGSO Record 1999/15, GA Record 2002/3. The data contained on the CD's is more comprehensive, at a better resolution and also contains cross sections that are not available over the web. No drill hole data is supplied in this viewer. The data compiled for this viewer was collected during the course of the NABRE, AMIRA P552 and pmd*CRC projects. The respective CD's also provide measured sections at different scales with slightly varying information available at 1:400, 1:1000, 1:2500 and 1:5000 scales. The information at each scale is slightly different. Plot files ready for printing also accompany the measured sections. Each of the measured sections contains primary observational data (grainsize, lithology, bed thickness, sedimentary structure and gamma ray curve) map-based lithostratigraphic units as shown on the 1:100,000 geological sheets, interpreted facies and sequence stratigraphic surfaces. Sections were measured using a Jacobs Staff and Abney Level and the rocks were marked in 1.5 m intervals of true thickness. Gamma ray data was collected at either 50 cm or 75 cm intervals of true thickness using hand-held Scintrex GRS 500 spectrometers that measured total gamma ray counts. A beryllium standard was used to calibrate each spectrometer. Each machine was calibrated at intervals of two to three hours. Each gamma reading was averaged over an interval of ten seconds. Outcrop discontinuities prevented the collection of stratigraphic data in a line of continuous section. As a result most of the sections present in this data set comprise a series of segments combined to form a single composite section. The single sections were all measured within a radius of several kilometres of each other. Individual sections were spliced together at prominent marker beds (outcrop tracing of strata), or by the use of overlapping gamma ray curves in conjunction with facies descriptions. Section locations shown in the web browser depict the base of each composite section. Grid coordinates for the base each composite section can be found in the header block of the appropriate section. The geological maps used in this web browser depict the approximate position of supersequence boundaries. Not all the geology for the region has been included and only the geology relevant to the measured sections has been used. The supersequences provided are based on the most appropriate lithostratigraphic boundaries and no new geological polygons have been created. It should be noted that the Torpedo Creek and Warrina Park Quartzites have been placed in the Prize Supersequence. However, we acknowledge that due to mis-mapping of these sand bodies the Torpedo Creek and Warrina Park Quartzites from the basal part of the Gun Supersequence at some locations.

The SSURGO database contains information about soil as collected by the National Cooperative Soil Survey over the course of a century. The information can be displayed in tables or as maps and is available for most areas in the United States and the Territories, Commonwealths, and Island Nations served by the USDA-NRCS (Natural Resources Conservation Service). The information was gathered by walking over the land and observing the soil. Many soil samples were analyzed in laboratories. The maps outline areas called map units. The map units describe soils and other components that have unique properties, interpretations, and productivity. The information was collected at scales ranging from 1:12,000 to 1:63,360. More details were gathered at a scale of 1:12,000 than at a scale of 1:63,360. The mapping is intended for natural resource planning and management by landowners, townships, and counties. Some knowledge of soils data and map scale is necessary to avoid misunderstandings. The maps are linked in the database to information about the component soils and their properties for each map unit. Each map unit may contain one to three major components and some minor components. The map units are typically named for the major components. Examples of information available from the database include available water capacity, soil reaction, electrical conductivity, and frequency of flooding; yields for cropland, woodland, rangeland, and pastureland; and limitations affecting recreational development, building site development, and other engineering uses. SSURGO datasets consist of map data, tabular data, and information about how the maps and tables were created. The extent of a SSURGO dataset is a soil survey area, which may consist of a single county, multiple counties, or parts of multiple counties. SSURGO map data can be viewed in the Web Soil Survey or downloaded in ESRI® Shapefile format. The coordinate systems are Geographic. Attribute data can be downloaded in text format that can be imported into a Microsoft® Access® database. A complete SSURGO dataset consists of:

GIS data (as ESRI® Shapefiles) attribute data (dbf files - a multitude of separate tables) database template (MS Access format - this helps with understanding the structure and linkages of the various tables) metadata

Resources in this dataset:Resource Title: SSURGO Metadata - Tables and Columns Report. File Name: SSURGO_Metadata_-_Tables_and_Columns.pdfResource Description: This report contains a complete listing of all columns in each database table. Please see SSURGO Metadata - Table Column Descriptions Report for more detailed descriptions of each column.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Metadata - Table Column Descriptions Report. File Name: SSURGO_Metadata_-_Table_Column_Descriptions.pdfResource Description: This report contains the descriptions of all columns in each database table. Please see SSURGO Metadata - Tables and Columns Report for a complete listing of all columns in each database table.

Find the Soil Survey Geographic (SSURGO) web site at https://www.nrcs.usda.gov/wps/portal/nrcs/detail/vt/soils/?cid=nrcs142p2_010596#Datamart Title: SSURGO Data Dictionary. File Name: SSURGO 2.3.2 Data Dictionary.csvResource Description: CSV version of the data dictionary

Note: This web page provides data on health facilities only. To file a complaint against a facility, please see: https://www.cdph.ca.gov/Programs/CHCQ/LCP/Pages/FileAComplaint.aspxThe California Department of Public Health (CDPH), Center for Health Care Quality, Licensing and Certification (L&C) Program licenses and certifies more than 30 types of healthcare facilities. The Electronic Licensing Management System (ELMS) is a CDPH data system created to manage state licensing-related data and enforcement actions. This file includes California healthcare facilities that are operational and have a current license issued by the CDPH and/or a current U.S. Department of Health and Human Services’ Centers for Medicare and Medicaid Services (CMS) certification.To link the CDPH facility IDs with those from other Departments, like HCAI, please reference the "Licensed Facility Cross-Walk" Open Data table at https://data.chhs.ca.gov/dataset/licensed-facility-crosswalk. Facility geographic variables are updated monthly, if latitude/longitude information is missing at any point in time, it should be available when the next time the Open Data facility file is refreshed.Please note that the file contains the data from ELMS as of the 11th business day of the month. See DATA_DATE variable for the specific date of when the data was extracted.

Bucknell's summer 2015 "New Orleans in 12 Movements" course aims to help students view New Orleans' natural environment, built infrastructure, and human experience in an integrated way. The course is co-taught by faculty from 3 departments and includes a week of field work in New Orleans. In this course, students will develop an integrated, holistic understanding of how the city of New Orleans has evolved over time. To support this learning, students have been provided an ArcGIS Online web-based map containing key cultural and historic information about New Orleans selected by their instructors. This interactive tool will enable them to explore New Orleans’ natural environment, built infrastructure and human experience through a variety of lenses. Faculty will use the map to deliver presentations and course materials to students. Students will use their own copy of the map to take notes, complete and deliver course assignments, and add their own materials to the course collection. Link to ArcGIS Online resource guide for class: click hereLink to data dictionary for NOLA class map layers: click hereLink to class website/blog: click here

Low-Res boundaries of the native regional corporations created by the Alaska Native Claims Settlement Act (ANCSA). As of early 2025 these are still very old legacy boundaries digitized off of small scale maps - and are only suitable at scales of 1:500,000 or smaller. Review indicates in some cases that they should follow Township, Range, or Drainage boundaries but set has not been touched likely since the 90's. Also note that of course of the entire polygons portrayed the actual acreage that actually was conveyed or is yet to be conveyed to ANCSA Corporations and Village Corporations is a very small fraction of the total acreage.Provided as a frame of reference. Master set of actual status of conveyances are tracked by BLM Alaska - see group page https://www.blm.gov/programs/lands-and-realty/regional-information/alaska/land-transferFor BLM Alaska GIS data as a Service take a look at their LaMAR Web App

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

Access APIGeocoded Addressing Theme - Address Point Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in …Show full description Access APIGeocoded Addressing Theme - Address Point Please Note WGS 84 service aligned to GDA94 This dataset has spatial reference [WGS 84 ≈ GDA94] which may result in misalignments when viewed in GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS 84 ≈ GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that the original service name will adopt the new multiCRS functionally. The Address point feature is a point feature class used to spatially locate an address / addressstring. The Address Point Layer includes the below subtypes: · Building · Homestead · Monument · Property · Unit/Strata · Other Metadata Type Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets NSW Geocoded Addressing Theme of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, the Lot and Property digital datasets. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS 84 Geographic 2D WGS 84 Equivalent To GDA94 Spatial Extent Full State Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Information about the “Feature Class” and “Domain Name” descriptions for the NSW Administrative Boundaries Theme can be found in the GURAS Delivery Model Data DictionarySome of Spatial Services Datasets are designed to work together for example “NSW Address Point” and “NSW Address String Table”, NSW Property (Polygon) and NSW Property Lot Table and NSW Lot (polygons). To do this you need to add a “Spatial Join”. A Spatial join is a GIS operation that affixes data from one feature layer’s attribute table to another from a spatial perspective. To see how Address, Property and Lot Geometry data and Tables can be joined together download the Data Model Document. This will show what attributes in the datasets can be linked. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795 Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795

Access APINSW Features of Interest Category - General Cultural Area Please Note WGS 84 = GDA94 service This dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into …Show full description Access APINSW Features of Interest Category - General Cultural Area Please Note WGS 84 = GDA94 service This dataset has a spatial reference of [WGS 84 = GDA94] and can NOT be easily consumed into GDA2020 environments. A similar service with a ‘multiCRS’ suffix is available which can support GDA2020, GDA94 and WGS84 = GDA2020 environments. In due course, and allowing time for user feedback and testing, it is intended that these original services will adopt the new multiCRS functionally. General Cultural Area is a man-made polygon feature defining general cultural data feature types. Feature Types in this layer include: Building Area: Dwelling - A main place of residence. This polygon feature dataset is part of the Physiography Category. Where possible, polygon geometries of the building dwelling dataset align to the topographic and cadastral databases. Pondage: Swimming Pool - An enclosed or artificial pool for swimming in for public use. This does not include domestic swimming pools. · Settling Pond - Shallow beds, usually segmented by constructed walls, for the treatment of sewage or other wastes. Water Filtration Bed - Shallow beds, usually segmented by constructed walls, for the treatment of water. · Restricted Area - A restricted area designated for defense purposes. MetadataType Esri Feature Service Update Frequency As required Contact Details Contact us via the Spatial Services Customer Hub Relationship to Themes and Datasets Features of Interest Category of the Foundation Spatial Data Framework (FSDF) Accuracy The dataset maintains a positional relationship to, and alignment with, a range of themes from the NSW FSDF including, transport, imagery, positioning, water and land cover. This dataset was captured by digitising the best available cadastral mapping at a variety of scales and accuracies, ranging from 1:500 to 1:250 000 according to the National Mapping Council of Australia, Standards of Map Accuracy (1975). Therefore, the position of the feature instance will be within 0.5mm at map scale for 90% of the well-defined points. That is, 1:500 = 0.25m, 1:2000 = 1m, 1:4000 = 2m, 1:25000 = 12.5m, 1:50000 = 25m and 1:100000 = 50m. A program of positional upgrade (accuracy improvement) is currently underway. Spatial Reference System (dataset) Geocentric Datum of Australia 1994 (GDA94), Australian Height Datum (AHD) Spatial Reference System    (web service) EPSG 4326: WGS84 Geographic 2D WGS84 Equivalent To GDA94 Spatial Extent Full state Standards and Specifications Open Geospatial Consortium (OGC) implemented and compatible for consumption by common GIS platforms. Available as either cache or non-cache, depending on client use or requirement. Distributors Service Delivery, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795Dataset Producers and Contributors Administrative Spatial Programs, DCS Spatial Services 346 Panorama Ave Bathurst NSW 2795