All of the ERS mapping applications, such as the Food Environment Atlas and the Food Access Research Atlas, use map services developed and hosted by ERS as the source for their map content. These map services are open and freely available for use outside of the ERS map applications. Developers can include ERS maps in applications through the use of the map service REST API, and desktop GIS users can use the maps by connecting to the map server directly.

OpenStreetMap - The Map API

https://upload.wikimedia.org/wikipedia/commons/thumb/b/b0/Openstreetmap_logo.svg/100px-Openstreetmap_logo.svg.png" alt="">

Browsing our map is easy. Have a look around and see what you think of our coverage and detail. Over the years we've progressed quite spectacularly, achieving many mapping milestones. Individuals, governments and commercial companies have already begun putting this data to use, and in many countries, for many uses, OpenStreetMap is a viable alternative to other map providers. However the map isn't finished yet. The world is a big place. How does your neighbourhood look on OSM? There's lots of other ways to start using OpenStreetMap too.

Development

Extensive software development work is taking this project in many different directions. As mentioned above, we have created various map editing tools. In fact OpenStreetMap is powered by open-source software from its slippy map interface to the underlying data access API (a web service interface for reading and writing map data). There is opportunity for subprojects that work with or use our data, but we also need help fixing bugs and adding features to our core components.

Developers and translators are always welcome!

The OpenStreetMap Foundation

The OpenStreetMap Foundation is an organization that performs fund-raising. One major expense is acquiring and maintaining the servers that host the OpenStreetMap project. While the foundation supports the project, it does not control the project or "own" the OSM data. The foundation is dedicated to encouraging the growth, development and distribution of free geospatial data and to providing geospatial data for anyone to use and share.

Inspiration

I LOVE IT!

This is an API service classification table provided by NGTN (OpenAPI), which includes the functions, classification codes, and descriptions of each API, and is data that helps with API search and integrated management in disaster and spatial information utilization systems. 1. Format: CSV 2. Summary of contents ■ class_cd: API classification code (e.g. Q, R, I, etc.) ■ class_nm: API classification name (e.g. map control tool API, damage prediction information display 2D API, etc.) ■ class_dc: Description of API function (e.g. map movement, zoom in/out, damage prediction information display, etc.) ■ delete_at: Whether to delete the corresponding API classification (Y/N) ■ indict_at: Whether to display in the system (Y/N) ■ class_se: API classification (e.g. visualization and data processing type such as 2D, 3D, DP, etc.) 3. Usage examples ■ When public institutions or private companies classify various API services of NGTN by function and build an integrated linkage system, it is utilized for automatic classification and call management. ■ When building an automated API catalog documentation, map service and disaster information visualization platform, API classification criteria are used as a reference for function mapping and UI design. ■ When developing a new API or linking with open source, it can be utilized for function similarity analysis, duplication removal, and function classification re-establishment by referring to the existing API classification criteria.

We provide background maps, image maps, and hybrid maps provided by Open Platform. You can use it by adding the request URL to the user client as a Javascript source with the authenticated key value. Supports OpenLayers 2.7 ~ 2.13. If you want to use the latest version of Openlayers, you can use the WMTS API. For related inquiries, please link to the relevant site and contact the relevant customer center and we will respond.

The GRASS GIS database containing the input raster layers needed to reproduce the results from the manuscript entitled:

"Mapping forests with different levels of naturalness using machine learning and landscape data mining" (under review)

Abstract:

To conserve biodiversity, it is imperative to maintain and restore sufficient amounts of functional habitat networks. Hence, locating remaining forests with natural structures and processes over landscapes and large regions is a key task. We integrated machine learning (Random Forest) and wall-to-wall open landscape data to scan all forest landscapes in Sweden with a 1 ha spatial resolution with respect to the relative likelihood of hosting High Conservation Value Forests (HCVF). Using independent spatial stand- and plot-level validation data we confirmed that our predictions (ROC AUC in the range of 0.89 - 0.90) correctly represent forests with different levels of naturalness, from deteriorated to those with high and associated biodiversity conservation values. Given ambitious national and international conservation objectives, and increasingly intensive forestry, our model and the resulting wall-to-wall mapping fills an urgent gap for assessing fulfilment of evidence-based conservation targets, spatial planning, and designing forest landscape restoration.

This database was compiled from the following sources:

1. HCVF. A database of High Conservation Value Forests in Sweden. Swedish Environmental Protection Agency.

source: https://geodata.naturvardsverket.se/nedladdning/skogliga_vardekarnor_2016.zip

1. NMD. National Land Cover Data. Swedish Environmental Protection Agency.

source: https://www.naturvardsverket.se/en/services-and-permits/maps-and-map-services/national-land-cover-database/

1. DEM. Terrain Model Download, grid 50+. Lantmateriet, Swedish Ministry of Finance.

source: https://www.lantmateriet.se/en/geodata/geodata-products/product-list/terrain-model-download-grid-50/

1. GFC. Global Forest Change. Global Land Analysis and Discovery, University of Maryland.

source: https://glad.earthengine.app

1. LIGHTS. A harmonized global nighttime light dataset 1992–2018. Land pollution with night-time lights expressed as calibrated digital numbers (DN).

source: https://doi.org/10.6084/m9.figshare.9828827.v2

1. POPULATION. Total Population in Sweden. Statistics Sweden.

source: https://www.scb.se/en/services/open-data-api/open-geodata/grid-statistics/

To learn more about the GRASS GIS database structure, see:

https://grass.osgeo.org/grass82/manuals/grass_database.html

Last update: October 16, 2025 OverviewThis point data was generated and filtered from OpenStreetMap and is intended to represent places of interest in the state of Utah. These may include businesses, restaurants, places of worship, airports, parks, schools, event centers, apartment complexes, hotels, car dealerships…almost anything that you can find in OpenStreetMap (OSM). There are over 23,000 features in the original dataset (March 2022) and users can directly contribute to it through openstreetmap.org. This data is updated approximately once every month and will likely continue to grow over time with user activity. Data SourcesThe original bulk set of OSM data for the state of Utah is downloaded from Geofabrik: https://download.geofabrik.de/north-america/us/utah-latest-free.shp.zipAdditional attributes for the Utah features are gathered via the Overpass API using the following query: https://overpass-turbo.eu/s/1geRData Creation ProcessThe Open Source Places layer is created by a Python script that pulls statewide OSM data from a nightly archive provided by Geofabrik (https://www.geofabrik.de/data/download.html). The archive data contains nearly 20 shapefiles, some that are relevant to this dataset and some that aren't. The Open Source Places layer is built by filtering the polygon and point data in those shapefiles down to a single point feature class with specific categories and attributes that UGRC determines would be of widest interest. The polygon features (buildings, areas, complexes, etc.) are converted to points using an internal centroid. Spatial filtering is done as the data from multiple shapefiles is combined into a single layer to minimize the occurrence of duplicate features. (For example, a restaurant can be represented in OSM as both a point of interest and as a building polygon. The spatial filtering helps reduce the chances that both of these features are present in the final dataset.) Additional de-duplication is performed by using the 'block_id' field as a spatial index, to ensure that no two features of the same name exist within a census block. Then, additional fields are created and assigned from UGRC's SGID data (county, city, zip, nearby address, etc.) via point-in-polygon and near analyses. A numeric check is done on the 'name' field to remove features where the name is less than 3 characters long or more than 50% numeric characters. This eliminates several features derived from the buildings layer where the 'name' is simply an apartment complex building number (ex: 3A) or house number (ex: 1612). Finally, additional attributes (osm_addr, opening_hours, phone, website, cuisine, etc.) are pulled from the Overpass API (https://wiki.openstreetmap.org/wiki/Overpass_API) and joined to the filtered data using the 'osm_id' field as the join key. Field Descriptionsaddr_dist - the distance (m) to the nearest UGRC address point within 25 mosm_id - the feature ID in the OSM databasecategory - the feature's data class based on the 4-digit code and tags in the OSM databasename - the name of the feature in the OSM databasecounty - the county the feature is located in (assigned from UGRC's county boundaries)city - the city the feature is located in (assigned from UGRC's municipal boundaries)zip - the zip code of the feature (assigned from UGRC's approximation of zip code boundaries)block_id - the census block the feature is located in (assigned from UGRC's census block boundaries)ugrc_addr - the nearest address (within 25 m) from the UGRC address point databasedisclaimer - a note from UGRC about the ugrc_near_addr fieldlon - the approximate longitude of the feature, calculated in WGS84 EPSG:4326lat - the approximate latitude of the feature, calculated in WGS84 EPSG:4326amenity - the amenity available at the feature (if applicable), often similar to the categorycuisine - the type of food available (if applicable), multiple types are separated by semicolons (;)tourism - the type of tourist location, if applicable (zoo, viewpoint, hotel, attraction, etc.)shop - the type of shop, if applicablewebsite - the feature's website in the OSM database, if availablephone - the feature's phone number(s) in the OSM database, if availableopen_hours - the feature's operating hours in the OSM database, if availableosm_addr - the feature's address in the OSM database, if availableMore information can be found on the UGRC data page for this layer:https://gis.utah.gov/data/society/open-source-places/

This dataset contains a topologically connected representation of the European high-voltage grid (220 kV to 750 kV) constructed using OpenStreetMap data. Input data was retrieved using the Overpass turbo API (https://overpass-turbo.eu). A heurisitic cleaning process was used to for lines and links where electrical parameters are incomplete, missing, or ambiguous. Close substations within a radius of 500 m are aggregated to single buses, exact locations of underlying substations is preserved. Unique identifiers for lines and links are preserved, e.g. an AC line/cable with the ID way/83742802-1 can be viewed on OpenStreetMap using the query https://www.openstreetmap.org/way/83742802. A DC line/cable with the ID relation/15781671 can be accessed using the query https://www.openstreetmap.org/relation/15781671

A detailed explanation on the background, methodology, and validation can be found in the article published in Nature Scientific Data:

Xiong, B., Fioriti, D., Neumann, F., Riepin, I., Brown, T. Modelling the high-voltage grid using open data for Europe and beyond. Sci Data 12, 277 (2025). https://doi.org/10.1038/s41597-025-04550-7

Countries included in the dataset:

Albania (AL), Austria (AT), Belgium (BE), Bosnia and Herzegovina (BA), Bulgaria (BG), Croatia (HR), Czech Republic (CZ), Denmark (DK), Estonia (EE), Finland (FI), France (FR), Germany (DE), Greece (GR), Hungary (HU), Ireland (IE), Italy (IT), Kosovo (XK), Latvia (LV), Lithuania (LT), Luxembourg (LU), Moldova (MD), Montenegro (ME), Netherlands (NL), North Macedonia (MK), Norway (NO), Poland (PL), Portugal (PT), Romania (RO), Serbia (RS), Slovakia (SK), Slovenia (SI), Spain (ES), Sweden (SE), Switzerland (CH), Ukraine (UA), United Kingdom (GB)

The dataset was constructed as part of the workflow within the open-source, sector-coupling model PyPSA-Eur and will be updated continuously as data and/or the cleaning process improves.

PyPSA-Eur is an open model dataset of the European power system at the transmission network level that covers the full ENTSO-E area. It can be built using the code provided at https://github.com/PyPSA/PyPSA-eur.

Not all data dependencies are shipped with the code repository, since git is not suited for handling large changing files. Instead we provide separate data bundles to be downloaded and extracted as noted in the documentation.

While the code and provided dataset in PyPSA-Eur is released as free software under the MIT, different licenses and terms of use apply to the underlying input data.

Extract from OpenStreetMap Terms of Use

OpenStreetMap® is open data, licensed under the Open Data Commons Open Database License (ODbL) by the OpenStreetMap Foundation (OSMF).

You are free to copy, distribute, transmit and adapt our data, as long as you credit OpenStreetMap and its contributors. If you alter or build upon our data, you may distribute the result only under the same licence. The full legal code explains your rights and responsibilities.

Our documentation is licensed under the Creative Commons Attribution-ShareAlike 2.0 license (CC BY-SA 2.0).

This processed dataset is provided under the Open Data Commons Open Database License (ODbL 1.0) license.

Changelog from version 0.5 to 0.6:

Added electric parameters to lines (e.g. nominal current, resistance r, reactance x, susceptance b). This allows the dataset to be used outside of PyPSA/PyPSA-Eur.

Interactive map.html now bundled with the dataset.

Tags columns include what the element contains (e.g. merged lines contain lines that were aggregated together).

Changelog from version 0.4 to 0.5:

Exact locations of original substations and converter stations (interior point/Pole of Inaccessibility) are preserved.

Clustering resolution improved from 5000 to 500 meters.

Lines of same electric parameters are merged, if they cross a virtual bus (that is not a real substation).

Information from OSM relations are used, wherever applicable. To avoid doubling, members (ways) of the relation are dropped in the set of lines, accordingly.

There are now unique transformers for each voltage level in each station. Transformers now have a nominal capacity, representing the maximum of line capacities connected to either side/bus of the transformer (n-0, nominal capacity).

Wherever applicable, OSM IDs are preserved and used in the index of the network components.

The National Archives have some 300,000 maps in the collection. A limited part of this (6200 pieces) is available digitally. Due to the age of the material and its origin (government), the card material is a collection that is eligible to be made available as open data. The map collection is diverse from world maps and panoramas on exotic cities and coasts, to 19th century cadastral maps of South Holland.

Due to the age of the material, the majority is available under Public Domain but here and there are some younger map material available under CC0.

The center of gravity of the open data cards is based on the Collection Binnenland Hingman, the South-Holland maps (Cadaster and Polder Regulations) and the Collection Buitenland Leupe.

Earlier, Open Culture Data published thatablogs about the Collection Buitenland Leupe and the Polder Regulations Cards at the time under Public Domain and CC0 of these datasets. In the two datablogs, the background and origin of the maps are discussed in greater depth.

The total of map archives containing open data material can be viewed in the following overview:

https://www.gahetna.nl/sites/default/files/bijlagen/kaarten_beschikbaar_onder_een_creative_commons_verklaring_0.pdf

(the overview of maps available under a Public Domain or CC0 statement | PDF, 92 KB)

The maps from the card collection are available via the Open Search Api. These can be accessed via the URL of the Open Search Api. The description document of the API indicates which fields can be searched.

Vrstva prístreškov je exportovaná z databázy OpenStreetMap. OpenStreetMap je otvorený projekt, ktorého cieľom je tvorba voľných geografických dát. Používa predovšetkým dáta z prijímačov GPS (v režime automatického zaznamenávania súradníc prechádzanej trasy), ktoré sú následne kontrolované a editované. Je založený na kolektívnej spolupráci a na koncepcii Open source.

The AEM method measures regolith and rocks' bulk subsurface electrical conductivity, typically to a depth of several hundred meters. AEM survey data is widely used in Australia for mineral exploration (i.e. mapping undercover and detection of mineralisation), groundwater assessment (i.e. hydro-stratigraphy and water quality) and natural resource management (i.e. salinity assessment). Geoscience Australia (GA) has flown Large regional AEM surveys over Northern Australia, including Queensland, Northern Territory and Western Australia. The surveys were flown nominally at 20-kilometre line spacing, using the airborne electromagnetic systems that have signed technical deeds of staging with GA to ensure they can be modelled quantitatively. Geoscience Australia commissioned the survey as part of the Exploring for the Future (EFTF) program. The EFTF program is led by Geoscience Australia (GA), in collaboration with the Geological Surveys of the Northern Territory, Queensland, South Australia and Western Australia, and is investigating the potential mineral, energy and groundwater resources in northern Australia and South Australia.

We have used a machine learning modelling approach that establishes predictive relationships between the inverted flight-line modelled conductivity with a suite of national environmental and geological covariates. These covariates include terrain derivatives, gamma-ray radiometric, geological maps, climate derived surfaces and satellite imagery. Conductivity-depth values were derived from a single model using GA's deterministic 1D smooth-30-layer layered-earth-inversion algorithm. (Brodie and Richardson 2015). Three conductivity depth interval predictions are generated to interpolate the actual modelled conductivity data, which is 20km apart. These depth slices include a 0-50cm, 9-11m and 22-27m depth prediction. Each depth interval was modelled and individually optimised using the gradient boosted tree algorithm. The training cross-validation step used label clusters or groups to minimise over-fitting. Many hundreds of conductivity models are generated (i.e. ensemble modelling). Here we use the median of the models as the conductivity prediction and the upper and lower percentiles (95th and 5th) to measure model uncertainty. Grids show conductivity (S/m) in log 10 units.

Reported out-of-sample r-squares for each interval in order of increasing depth are 0.74, 0.64, and 0.67. A decline in model performance with increasing depth was expected due to the decrease in suitable covariates at greater depths. Modelled conductivities seem to be consistent with the geological, regolith, geomorphological, and climate processes in the study area. The conductivity grids are at the resolution of the covariates, which have a nominal pixel size of 85 meters.

Datasets in this data package include;

1. 0-50cm depth interval 0_50cm_median.tif; 0_50_upper.tif; 0_50_lower.tif

2. 9-11m depth interval 9_11m_median.tif; 9_11m_upper.tif; 9_11m_lower.tif

3. 22-27m depth interval 22_27_median.tif; 22_27_upper.tif; 22_27_lower.tif

4. Covariate shift; Cov_shift.tif (higher values = great shift in covariates)

Reference: Ross C Brodie & Murray Richardson (2015) Open Source Software for 1D Airborne Electromagnetic Inversion, ASEG Extended Abstracts, 2015:1, 1-3, DOI: 10.1071/ ASEG2015ab197

This dataset consists of 24-hour traffic volumes which are collected by the City of Tempe high (arterial) and low (collector) volume streets. Data located in the tabular section shares with its users total volume of vehicles passing through the intersection selected along with the direction of flow.Historical data from this feature layer extends from 2016 to present day.Contact: Sue TaaffeContact E-Mail: sue_taaffe@tempe.govContact Phone: 480-350-8663Link to embedded web map:http://www.tempe.gov/city-hall/public-works/transportation/traffic-countsLink to site containing historical traffic counts by node: https://gis.tempe.gov/trafficcounts/Folders/Data Source: SQL Server/ArcGIS ServerData Source Type: GeospatialPreparation Method: N/APublish Frequency: As information changesPublish Method: AutomaticData Dictionary

This feature service contains live traffic event data collected from the Waze for Cities (Waze CCP) Live Map, updated continuously to reflect current roadway conditions across Arizona. Events include accidents, road closures, construction, traffic jams, and other hazards reported by Waze users and system sensors.The dataset is intended for situational awareness, traffic management, emergency response coordination, and public information mapping. Each feature includes event details such as type, location, confidence level, reliability score, timestamp, and user reports.Update Frequency: Live / near real-time (refreshed every 5 minutes).Geographic Coverage: State of Arizona.Data Source: Waze for Cities Partnership Program (Waze Live Map API).Usage Notes:This data is dynamic and reflects current conditions; events may appear or disappear as reports are verified or expire.Suitable for monitoring, visualization, and decision support, but not recommended for archival or long-term analysis.Attribution required: "Data provided by Waze through the Waze for Cities Program."

This layer portrays elevation as an artistic hillshade. The map is designed to be used as a backdrop for topographical, soil, hydro, landcover or other outdoor recreational maps. It’s a default relief background in various basemaps such as Topographic, Terrain with Labels.The map is compiled from a variety of data sources from commercial, community maps and many authoritative organizations across the globe. The basemap has global coverage down to a scale of ~1:72k. In the United States, parts of Europe, Asia and Africa coverage goes down to ~1:9k. To see the coverage and sources of various datasets comprising this map layer, view the World Elevation Coverage Map. Additionally, this layer uses data from Vantor’s Precision 3D Digital Terrain Models for parts of the globe.The map is based on the Multi-directional hillshade algorithm.

Vrstva miest s pekným výhľadom je exportovaná z databázy OpenStreetMap. OpenStreetMap je otvorený projekt, ktorého cieľom je tvorba voľných geografických dát. Používa predovšetkým dáta z prijímačov GPS (v režime automatického zaznamenávania súradníc prechádzanej trasy), ktoré sú následne kontrolované a editované. Je založený na kolektívnej spolupráci a na koncepcii Open source.

Surface current data measured by the CNR-ISMAR HF radar network are made available in graphical format for the last 48 hours and in real time and delayed mode via a THREDDS catalog which provides metadata and data access. The web site gives information on HF radar technology, sites position and operational parameters, and links to the THREDDS catalog. The catalog offers different remote-data-access protocols such as Open-source Project for a Network Data Access Protocol (OpenDaP), Web Coverage Service (WCS), Web Map Service (WMS) (OGS standards), as well as pure HTTP or NetCDF-Subsetter. They allow for metadata interrogation and data download (even sub-setting the dataset in terms of time and space) while embedded clients, such as GODIVA2, NetCDF-JavaToolsUI and Integrated Data Viewer, grant real-time data visualization directly via browser and allow for navigating within the plotted maps, saving images, exporting-importing on Google Earth, generating animations in selected time intervals. The data on the THREDDS catalog are organized in two folders, collecting the hourly current files of the last five days and grouping all the historical data. The two folders are accessible both in aggregated and in non-aggregated configuration.

The data set consists of maps of radial and total velocity of the sea water surface current collected by the HF radars within the Italian Coastal Radar Network established in the framework of the Italian flagship project RITMARE. Surface ocean velocities estimated by HF Radar are representative of the upper 0.3-2.5 meters of the ocean. The radar sites are operated according to Quality Assessment procedures and data are processed for Quality Control. Data access tools are compliant to Open Geospatial Consortium (OGC), Climate and Forecast (CF) convention and INSPIRE directive. The use of netCDF format allows an easy implementation of all the open source services developed by UNIDATA.

Geoscape G-NAF is the geocoded address database for Australian businesses and governments. It’s the trusted source of geocoded address data for Australia with over 50 million contributed addresses distilled into 15.4 million G-NAF addresses. It is built and maintained by Geoscape Australia using independently examined and validated government data.

From 22 August 2022, Geoscape Australia is making G-NAF available in an additional simplified table format. G-NAF Core makes accessing geocoded addresses easier by utilising less technical effort.

G-NAF Core will be updated on a quarterly basis along with G-NAF.

Further information about contributors to G-NAF is available here.

With more than 15 million Australian physical address record, G-NAF is one of the most ubiquitous and powerful spatial datasets. The records include geocodes, which are latitude and longitude map coordinates. G-NAF does not contain personal information or details relating to individuals.

Updated versions of G-NAF are published on a quarterly basis. Previous versions are available here

Users have the option to download datasets with feature coordinates referencing either GDA94 or GDA2020 datums.

Changes in the November 2025 release

• Nationally, the November 2025 update of G-NAF shows an increase of 32,773 addresses overall (0.21%). The total number of addresses in G-NAF now stands at 15,827,416 of which 14,983,358 or 94.67% are principal.

• There is one new locality for the November 2025 Release of G-NAF, the locality of Southwark in South Australia.

• Geoscape has moved product descriptions, guides and reports online to https://docs.geoscape.com.au.

Further information on G-NAF, including FAQs on the data, is available here or through Geoscape Australia’s network of partners. They provide a range of commercial products based on G-NAF, including software solutions, consultancy and support.

Additional information: On 1 October 2020, PSMA Australia Limited began trading as Geoscape Australia.

License Information

Use of the G-NAF downloaded from data.gov.au is subject to the End User Licence Agreement (EULA)

The EULA terms are based on the Creative Commons Attribution 4.0 International license (CC BY 4.0). However, an important restriction relating to the use of the open G-NAF for the sending of mail has been added.

The open G-NAF data must not be used for the generation of an address or the compilation of an address for the sending of mail unless the user has verified that each address to be used for the sending of mail is capable of receiving mail by reference to a secondary source of information. Further information on this use restriction is available here.

End users must only use the data in ways that are consistent with the Australian Privacy Principles issued under the Privacy Act 1988 (Cth).

Users must also note the following attribution requirements:

Preferred attribution for the Licensed Material:

_G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the _Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

Preferred attribution for Adapted Material:

Incorporates or developed using G-NAF © Geoscape Australia licensed by the Commonwealth of Australia under the Open Geo-coded National Address File (G-NAF) End User Licence Agreement.

What to Expect When You Download G-NAF

G-NAF is a complex and large dataset (approximately 5GB unpacked), consisting of multiple tables that will need to be joined prior to use. The dataset is primarily designed for application developers and large-scale spatial integration. Users are advised to read the technical documentation, including product change notices and the individual product descriptions before downloading and using the product. A quick reference guide on unpacking the G-NAF is also available.

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy. Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca Available Products: ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/ Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/ Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022 Central Ontario Orthophotography Project (COOP) 2021 South-Western Ontario Orthophotography Project (SWOOP) 2020 Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020 South Central Ontario Orthophotography Project (SCOOP) 2018 North-Western Ontario Orthophotography Project (NWOOP) 2017 Central Ontario Orthophotography Project (COOP) 2016 South-Western Ontario Orthophotography Project (SWOOP) 2015 Algonquin Orthophotography Project (2015) Additional Documentation: Ontario Web Raster Services User Guide (Word) Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every year Contact:Geospatial Ontario (GEO), geospatial@ontario.ca

This App includes 3,141 summary records by U.S. County for all Federal disaster declarations between 1964 and 2013. The initial map display shows the All Incidents later, but there are 12 Additional layers users can display showing the county declaration summary data by Incident Type (Fire, Flood, Tornado, etc...]. Click on an disaster declaration highlighted on the map and a custom pop-up attribute display will show you the disaster declaration information and census population estimates for reference. This App also includes a number of map tools to help visualize and analyze the data. NSGIC Data Citation:This project uses existing FEMA data resources that are the authoritative sources of information on this topic, including geospatial data files and open data APIs that were used to access available FEMA Federally-declared Natural Disaster data in the United States available from 1964 to 2014 (through 2013).To support our mapping needs, NSGIC downloaded a snapshot of FEMA data and published our own data Service Definitions and Feature Layers on NSGIC’s ArcGIS Online Mapping Platform to create the unfiltered Feature Layer Services we needed to support our mapping needs of the FEMA Federally Declared Disaster data.Note: These original data sources reflect a variety of inconsistencies and completeness is data collection, as well as changing definitions and priorities in FEMA’s disaster declaration information collection since record-keeping began in 1964. The original data was not modified.To publish the new Feature Layers on ArcGIS Online, NSGIC joined the FEMA Natural Disaster data with an Esri US County polygon shapefile with county population and demographic attributes from the U.S. Census Bureau’s American Community Survey. NSGIC added the 2010 and 2015 population estimates from the Census Bureau’s American Community Survey to relate the impacts of every declared natural disaster to current time frame.A significant portion of the available attribute data is not displayed in the NSGIC interactive maps, but is accessible through the site by experienced users.More recent data may be available from the original sourcesFEMA Data Citation:Data for this project was downloaded from FEMA in April 2016 and reflects the data available at that time using the available APIs.This product uses the Federal Emergency Management Agency’s API, but is not endorsed by FEMA.FEMA cannot verify the quality and/or timeliness of any data or any analysis derived therefrom after the data has been retrieved from FEMA.gov.

Data holdings of polygonal habitat maps in European waters.

Data are collated by EMODnet Seabed Habitats partners from a variety of source datasets and conformed and standardised into the portal's INSPIRE-compliant schema (for more information, please see https://emodnet-seabedhabitats.eu/def .

Habitats are described in a variety of classification systems, including EUNIS (European Nature Information System), Habitats Directive Annex I and local/other classifcation systems.

Ownership of the individual maps is retained by the original owners, for more information please see the individual metadata record tied to the map, which can be seen in the query response.

Maps are available indivudually through EMODnet Seabed Habitats' "maplibrary" OGC service endpoints: For WMS (view) access to maps, please use https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_view_maplibrary/wms? For WFS (download) access to open maps, please use https://ows.emodnet-seabedhabitats.eu/geoserver/emodnet_open_maplibrary/wfs?

Content

The following dataset includes "Active Benchmarks," which are provided to facilitate the identification of City-managed standard benchmarks. Standard benchmarks are for public and private use in establishing a point in space. Note: The benchmarks are referenced to the Chicago City Datum = 0.00, (CCD = 579.88 feet above mean tide New York). The City of Chicago Department of Water Management’s (DWM) Topographic Benchmark is the source of the benchmark information contained in this online database. The information contained in the index card system was compiled by scanning the original cards, then transcribing some of this information to prepare a table and map. Over time, the DWM will contract services to field verify the data and update the index card system and this online database.This dataset was last updated September 2011. Coordinates are estimated. To view map, go to https://data.cityofchicago.org/Buildings/Elevation-Benchmarks-Map/kmt9-pg57 or for PDF map, go to http://cityofchicago.org/content/dam/city/depts/water/supp_info/Benchmarks/BMMap.pdf. Please read the Terms of Use: http://www.cityofchicago.org/city/en/narr/foia/data_disclaimer.html.

Context

This is a dataset hosted by the City of Chicago. The city has an open data platform found here and they update their information according the amount of data that is brought in. Explore the City of Chicago using Kaggle and all of the data sources available through the City of Chicago organization page!

• Update Frequency: This dataset is updated quarterly.

Acknowledgements

This dataset is maintained using Socrata's API and Kaggle's API. Socrata has assisted countless organizations with hosting their open data and has been an integral part of the process of bringing more data to the public.

Cover photo by Vlad Shapochnikov on Unsplash
Unsplash Images are distributed under a unique Unsplash License.