The Access Network Map of England is a national composite dataset of Access layers, showing analysis of extent of Access provision for each Lower Super Output Area (LSOA), as a percentage or area coverage of access in England. The ‘Access Network Map’ was developed by Natural England to inform its work to improve opportunities for people to enjoy the natural environment. This map shows, across England, the relative abundance of accessible land in relation to where people live. Due to issues explained below, the map does not, and cannot, provide a definitive statement of where intervention is necessary. Rather, it should be used to identify areas of interest which require further exploration. Natural England believes that places where people can enjoy the natural environment should be improved and created where they are most wanted. Access Network Maps help support this work by providing means to assess the amount of accessible land available in relation to where people live. They combine all the available good quality data on access provision into a single dataset and relate this to population. This provides a common foundation for regional and national teams to use when targeting resources to improve public access to greenspace, or projects that rely on this resource. The Access Network Maps are compiled from the datasets available to Natural England which contain robust, nationally consistent data on land and routes that are normally available to the public and are free of charge. Datasets contained in the aggregated data:•
Agri-environment scheme permissive access (routes and open access)•
CROW access land (including registered common land and Section 16)•
Country Parks•
Cycleways (Sustrans Routes) including Local/Regional/National and Link Routes•
Doorstep Greens•
Local Nature Reserves•
Millennium Greens•
National Nature Reserves (accessible sites only)•
National Trails•
Public Rights of Way•
Forestry Commission ‘Woods for People’ data•
Village Greens – point data only Due to the quantity and complexity of data used, it is not possible to display clearly on a single map the precise boundary of accessible land for all areas. We therefore selected a unit which would be clearly visible at a variety of scales and calculated the total area (in hectares) of accessible land in each. The units we selected are ‘Lower Super Output Areas’ (LSOAs), which represent where approximately 1,500 people live based on postcode. To calculate the total area of accessible land for each we gave the linear routes a notional width of 3 metres so they could be measured in hectares. We then combined together all the datasets and calculated the total hectares of accessible land in each LSOA. For further information about this data see the following links:Access Network Mapping GuidanceAccess Network Mapping Metadata Full metadata can be viewed on data.gov.uk.

*This dataset is authored by ESRI and is being shared as a direct link to the feature service by Pend Oreille County. NHD is a primary hydrologic reference used by our organization.The National Hydrography Dataset Plus High Resolution (NHDplus High Resolution) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US Geological Survey, NHDPlus High Resolution provides mean annual flow and velocity estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.For more information on the NHDPlus High Resolution dataset see the User’s Guide for the National Hydrography Dataset Plus (NHDPlus) High Resolution.Dataset SummaryPhenomenon Mapped: Surface waters and related features of the United States and associated territoriesCoordinate System: Web Mercator Auxiliary Sphere Extent: The Contiguous United States, Hawaii, portions of Alaska, Puerto Rico, Guam, US Virgin Islands, Northern Marianas Islands, and American Samoa Visible Scale: Visible at all scales but layer draws best at scales larger than 1:1,000,000Source: USGSPublication Date: July 2022This layer was symbolized in the ArcGIS Map Viewer and while the features will draw in the Classic Map Viewer the advanced symbology will not.Prior to publication, the network and non-network flowline feature classes were combined into a single flowline layer. Similarly, the Area and Waterbody feature classes were merged under a single schema.Attribute fields were added to the flowline and waterbody layers to simplify symbology and enhance the layer's pop-ups. Fields added include Pop-up Title, Pop-up Subtitle, Esri Symbology (waterbodies only), and Feature Code Description. All other attributes are from the original dataset. No data values -9999 and -9998 were converted to Null values.What can you do with this Feature Layer?Feature layers work throughout the ArcGIS system. Generally your work flow with feature layers will begin in ArcGIS Online or ArcGIS Pro. Below are just a few of the things you can do with a feature service in Online and Pro.ArcGIS OnlineAdd this layer to a map in the map viewer. The layer or a map containing it can be used in an application. Change the layer’s transparency and set its visibility rangeOpen the layer’s attribute table and make selections. Selections made in the map or table are reflected in the other. Center on selection allows you to zoom to features selected in the map or table and show selected records allows you to view the selected records in the table.Apply filters. For example you can set a filter to show larger streams and rivers using the mean annual flow attribute or the stream order attribute.Change the layer’s style and symbologyAdd labels and set their propertiesCustomize the pop-upUse as an input to the ArcGIS Online analysis tools. This layer works well as a reference layer with the trace downstream and watershed tools. The buffer tool can be used to draw protective boundaries around streams and the extract data tool can be used to create copies of portions of the data.ArcGIS ProAdd this layer to a 2d or 3d map.Use as an input to geoprocessing. For example, copy features allows you to select then export portions of the data to a new feature class.Change the symbology and the attribute field used to symbolize the dataOpen table and make interactive selections with the mapModify the pop-upsApply Definition Queries to create sub-sets of the layerThis layer is part of the ArcGIS Living Atlas of the World that provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.

Spatial data supporting the England Woodland Creation Offer (EWCO) additional contribution targeting for Nature Recovery, where the layer indicates ‘Premium’, ‘Higher’ and ‘Lower’ priority areas for woodland network expansion. Data input sources: National Forest Inventory (NFI) 2012 Map plus additional woodland habitat network modelling provided by Forest Research. Habitat Networks (England) - Ancient Semi Natural Woodland 2019 map by Natural England Attributes: ‘cat’ – ‘Premium’, ‘Higher’ and ‘Lower’ priority area for woodland network expansion ‘ewco_score’ – the number of points the additional contribution provides per hectare, if awarded ‘ewco_val’ – £ value the additional contribution provides per hectare, if awarded Lineage: First published to support the woodland creation grant under Countryside Stewardship (CS), launched in 2015. The layers attributes were updated in 2021 to cater for the new England Woodland Creation Offer (EWCO) scheme. The layer was edited in 2024 to add the ‘Premium’ priority area, based on data from the Habitat Networks (England) - Ancient Semi Natural Woodland map and the Countryside Stewardship elements removed. Attribution statement: © Forestry Commission copyright and/or database right 2024. All rights reserved.

The European Copernicus Coastal Flood Awareness System (ECFAS) project aimed at contributing to the evolution of the Copernicus Emergency Management Service (https://emergency.copernicus.eu/) by demonstrating the technical and operational feasibility of a European Coastal Flood Awareness System. Specifically, ECFAS provides a much-needed solution to bolster coastal resilience to climate risk and reduce population and infrastructure exposure by monitoring and supporting disaster preparedness, two factors that are fundamental to damage prevention and recovery if a storm hits.

The ECFAS Proof-of-Concept development ran from January 2021 to December 2022. The ECFAS project was a collaboration between Scuola Universitaria Superiore IUSS di Pavia (Italy, ECFAS Coordinator), Mercator Ocean International (France), Planetek Hellas (Greece), Collecte Localisation Satellites (France), Consorzio Futuro in Ricerca (Italy), Universitat Politecnica de Valencia (Spain), University of the Aegean (Greece), and EurOcean (Portugal), and was funded by the European Commission H2020 Framework Programme within the call LC-SPACE-18-EO-2020 - Copernicus evolution: research activities in support of the evolution of the Copernicus services.

Description of the containing files inside the Dataset.

The ECFAS Coastal Dataset represents a single access point to publicly available Pan-European datasets that provide key information for studying coastal areas. The publicly available datasets listed below have been clipped to the coastal area extent, quality-checked and assessed for completeness and usability in terms of coverage, accuracy, specifications and access. The dataset was divided at European country level, except for the Adriatic area which was extracted as a region and not at the country level due to the small size of the countries. The buffer zone of each data was 10km inland in order to be correlated with the new Copernicus product Coastal Zone LU/LC.

Specifically, the dataset includes the new Coastal LU/LC product which was implemented by the EEA and became available at the end of 2020. Additional information collected in relation to the location and characteristics of transport (road and railway) and utility networks (power plants), population density and time variability. Furthermore, some of the publicly available datasets that were used in CEMS related to the above mentioned assets were gathered such as OpenStreetMap (building footprints, road and railway network infrastructures), GeoNames (populated places but also names of administrative units, rivers and lakes, forests, hills and mountains, parks and recreational areas, etc.), the Global Human Settlement Layer (GHS) and Global Human Settlement Population Grid (GHS-POP) generated by JRC. Also, the dataset contains 2 layers with statistics information regarding the population of Europe per sex and age divided in administrative units at NUTS level 3. The first layer includes information for the whole of Europe and the second layer has only the information regarding the population at the Coastal area. Finally, the dataset includes the global database of Floods protection standards. Below there are tables which present the dataset.

* Adriatic folder contains the countries: Slovenia, Croatia, Montenegro, Albania, Bosnia and Herzegovina

* Malta was added to the dataset

Copernicus Land Monitoring Service:

Coastal LU/LC

Scale 1:10.000; A Copernicus hotspot product to monitor landscape dynamics in coastal zones

EU-Hydro - Coastline

Scale 1:30.000; EU-Hydro is a dataset for all European countries providing the coastline

Natura 2000

Scale 1: 100000; A Copernicus hotspot product to monitor important areas for nature conservation

European Settlement Map

Resolution 10m; A spatial raster dataset that is mapping human settlements in Europe

Imperviousness Density

Resolution 10m; The percentage of sealed area

Impervious Built-up

Resolution 10m; The part of the sealed surfaces where buildings can be found

Grassland 2018

Resolution 10m; A binary grassland/non-grassland product

Tree Cover Density 2018

Resolution 10m; Level of tree cover density in a range from 0-100%

Joint Research Center:

Global Human Settlement Population Grid
GHS-POP)

Resolution 250m; Residential population estimates for target year 2015

GHS settlement model layer
(GHS-SMOD)

Resolution 1km: The GHS Settlement Model grid delineates and classify settlement typologies via a logic of population size, population and built-up area densities

GHS-BUILT

Resolution 10m; Built-up grid derived from Sentinel-2 global image composite for reference year 2018

ENACT 2011 Population Grid

(ENACT-POP R2020A)

Resolution 1km; The ENACT is a population density for the European Union that take into account major daily and monthly population variations

JRC Open Power Plants Database (JRC-PPDB-OPEN)

Europe's open power plant database

GHS functional urban areas
(GHS-FUA R2019A)

Resolution 1km; City and its commuting zone (area of influence of the city in terms of labour market flows)

GHS Urban Centre Database
(GHS-UCDB R2019A)

Resolution 1km; Urban Centres defined by specific cut-off values on resident population and built-up surface

Additional Data:

Open Street Map (OSM)

BF, Transportation Network, Utilities Network, Places of Interest

CEMS

Data from Rapid Mapping activations in Europe

GeoNames

Populated places, Adm. units, Hydrography, Forests, Hills/Mountains, Parks, etc.

Global Administrative Areas

Administrative areas of all countries, at all levels of sub-division

NUTS3 Population Age/Sex Group

Eurostat population by age and sex statistics interescted with the NUTS3 Units

FLOPROS

A global database of FLOod PROtection Standards, which comprises information in the form of the flood return period associated with protection measures, at different spatial scales

Disclaimer:

ECFAS partners provide the data "as is" and "as available" without warranty of any kind. The ECFAS partners shall not be held liable resulting from the use of the information and data provided.

This project has received funding from the Horizon 2020 research and innovation programme under grant agreement No. 101004211

Notice - Replacement of the English and French Web services (WMS and ESRI REST) with a bilingual one. The NRN product is distributed in the form of thirteen provincial or territorial datasets and consists of two linear entities (Road Segment and Ferry Connection Segment) and three punctual entities (Junction, Blocked Passage, Toll Point) with which is associated a series of descriptive attributes such as, among others: First House Number, Last House Number, Street Name Body, Place Name, Functional Road Class, Pavement Status, Number Of Lanes, Structure Type, Route Number, Route Name, Exit Number. The development of the NRN was realized by means of individual meetings and national workshops with interested data providers from the federal, provincial, territorial and municipal governments. In 2005, the NRN edition 2.0 was alternately adopted by members from the Inter-Agency Committee on Geomatics (IACG) and the Canadian Council on Geomatics (CCOG). The NRN content largely conforms to the ISO 14825 from ISO/TC 204.

Spatial Data layers referenced in City Development Plan Policy and Proposals & Supplementary Guidance Maps. Third party data displayed in the above mentioned maps are not included herein.

GIS-datasets for the Street networks of Stockholm, Gothenburg and Eskilstuna produced as part of the Spatial Morphology Lab (SMoL). The goal of the SMoL project is to develop a strong theory and methodology for urban planning & design research with an analytical approach. Three frequently recurring variables of spatial urban form are studied that together quite well capture and describe the central characteristics and qualities of the built environment: density, diversity and proximity. The first measure describes how intensive a place can be used depending on how much built up area is found there. The second measure captures how differentiated the use of a place can be depending on the division in smaller units such as plots. The third measure describes how accessible a place is depending on how it relates with other places. Empirical studies have shown strong links between these metrics and people's use of cities such as pedestrian movement patterns. To support this goal, a central objective of the project is the establishment of an international platform of GIS data models for comparative studies in spatial urban form comprising three European capitals: London in the UK, Amsterdam in the Netherlands and Stockholm in Sweden, as well as two additional Swedish cities of smaller size than Stockholm: Gothenburg and Eskilstuna. The result of the project is a GIS database for the five cities covering the three basic layers of urban form: street network (motorised and non-motorised), buildings and plots systems. The data is shared via SND to create a research infrastructure that is open to new study initiatives. The datasets for Amsterdam will also be uploaded to SND. The datasets of London cannot be uploaded because of licensing restrictions. The street network GIS-maps include motorised and non-motorised networks. The non-motorized networks include all streets and paths that are accessible for people walking or cycling, including those that are shared with vehicles. All streets where walking or cycling is forbidden, such as motorways, highways, or high-speed tunnels, are not included in the network. The non-motorised network layers for Stockholm and Eskilstuna are based on the Swedish national road database, NVDB (Nationell Vägdatabas), downloaded from Trafikverket (https://lastkajen.trafikverket.se, date of download 15-5-2016, last update 8-11-2015) . For Gothenburg, it is based on Open Street Maps (openstreetmap.org, http://download.geofabrik.de, date of download 29-4-2016), because the NVDB did not provide enough detail for the non-motorized network, as in the other cities. The original road-centre-line maps of all cities were edited based on the same basic representational principles and were converted into line-segment maps, using the following software: FME, Mapinfo professional and PST (Place Syntax Tool). The coordinate system is SWEREF99TM. In the final line-segment maps (GIS-layers) all streets or paths are represented with one line irrespectively of the number of lanes or type, meaning that parallel lines representing a street and a pedestrian or a cycle path running on the side, are reduced to one line. The reason is that these parallel lines are nor physically or perceptually separated, and thus are accessible and recognized from pedestrians as one “line of movement” in the street network. If there are obstacles or great distance between parallel streets and paths, then the multiple lines remain. The aim is to make a skeletal network that better represents the total space, which is accessible for pedestrians to move, irrespectively of the typical separations or distinctions of streets and paths. This representational choice follows the Space Syntax methodology in representing the public space and the street network. We followed the same editing and generalizing procedure for all maps aiming to remove errors and to increase comparability between networks. This process included removing duplicate and isolated lines, snapping and generalizing. The snapping threshold used was 2m (end points closer than 2m were snapped together). The generalizing threshold used was 1m (successive line segments with angular deviation less than 1m were merged into one). In the final editing step, all road polylines were segmented to their constituting line-segments. The aim was to create appropriate line-segment maps to be analysed using Angular Segment Analysis, a network centrality analysis method introduced in Space Syntax. All network layers are complemented with an “Unlink points” layer; a GIS point layer with the locations of all non-level intersections, such as pedestrian bridges and tunnels. The Unlink point layer is necessary to conduct network analysis that takes into account the non-planarity of the street network, using such software as PST (Place Syntax Tool). For more detailed documentation on the creation of the non-motorised network of Gothenburg, please download the specific documentation file.

The Network Model digitally represents England’s Strategic Road Network. The model contains critical information about our road’s location, names, lanes and widths. The Network Model was derived from Ordnance Survey (OS) Highways data and enriched with internal datasets. It reflects National Highways roads that are open for traffic and have been validated against our Operational Highway Boundary (RedLine). To ensure the model remains accurate, we have implemented processes to track changes across the network. However, if you have noticed any inaccuracies in the data, please report it here. This form is to be used to report data issues only. In this initial release, speed limit and smart motorway information has been removed pending data validation. To download a file geodatabase containing all layers of the network model and their relationships please use this link. For more information about the Network Model please visit our landing page and technical hub. For maintenance issues on the network please report here. For non-emergency incidents please contact our Customer Contact Centre on 0300 123 5000.The data is published under an Open Government Licence.

The Navigable Waterway Network Lines dataset is periodically updated by the United States Army Corp of Engineers (USACE) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The National Waterway Network (Lines) is a comprehensive network database of the Nation's navigable waterways. The dataset covers the 48 contiguous states plus the District of Columbia, Hawaii, Alaska, Puerto Rico and water links between. It consists of a line feature class of the National Waterway Network (NWN), which is based on a route feature class for the NWN update regions (“1” through “7”, as well as the open ocean region “0”) and route event table with linear referencing system measures for NWN links. This dataset is a feature class with associated measures (in miles) that are used for finding distances, locating features, and displaying route event layers. It was exported from this route event layer. The nominal scale of the dataset varies with the source material. The majority of the information is at 1:100,000 with larger scales used in harbor/bay/port areas and smaller scales used in open waters. These data could be used for analytical studies of waterway performance, for compiling commodity flow statistics, and for mapping purposes. A data dictionary, or other source of attribute information, is accessible at https://doi.org/10.21949/1529053

GIS-datasets for the Street networks of Stockholm, Gothenburg and Eskilstuna produced as part of the Spatial Morphology Lab (SMoL). The goal of the SMoL project is to develop a strong theory and methodology for urban planning & design research with an analytical approach. Three frequently recurring variables of spatial urban form are studied that together quite well capture and describe the central characteristics and qualities of the built environment: density, diversity and proximity. The first measure describes how intensive a place can be used depending on how much built up area is found there. The second measure captures how differentiated the use of a place can be depending on the division in smaller units such as plots. The third measure describes how accessible a place is depending on how it relates with other places. Empirical studies have shown strong links between these metrics and people's use of cities such as pedestrian movement patterns. To support this goal, a central objective of the project is the establishment of an international platform of GIS data models for comparative studies in spatial urban form comprising three European capitals: London in the UK, Amsterdam in the Netherlands and Stockholm in Sweden, as well as two additional Swedish cities of smaller size than Stockholm: Gothenburg and Eskilstuna. The result of the project is a GIS database for the five cities covering the three basic layers of urban form: street network (motorised and non-motorised), buildings and plots systems. The data is shared via SND to create a research infrastructure that is open to new study initiatives. The datasets for Amsterdam will also be uploaded to SND. The datasets of London cannot be uploaded because of licensing restrictions. The street network GIS-maps include motorised and non-motorised networks. The motorised networks exclude all streets that are pedestrian-only and were cars are excluded. The network layers are based on the Swedish national road database, NVDB (Nationell Vägdatabas), downloaded from Trafikverket (https://lastkajen.trafikverket.se, date of download 15-5-2016, last update 8-11-2015). The original road-centre-line maps of all cities were edited based on the same basic representational principles and were converted into line-segment maps, using the following software: FME, Mapinfo professional and PST (Place Syntax Tool). The coordinate system is SWEREF99TM. In the final line-segment maps (GIS-layers) all roads are represented with one line irrespectively of the number of lanes, except from Motorways and Highways which are represented with two lines, one for each direction, again irrespectively of the number of lanes. We followed the same editing and generalizing procedure for all maps aiming to remove errors and to increase comparability between networks. This process included removing duplicate and isolated lines, snapping and generalizing. The snapping threshold used was 2m (end points closer than 2m were snapped together). The generalizing threshold used was 1m (successive line segments with angular deviation less than 1m were merged into one). In the final editing step, all road polylines were segmented to their constituting line-segments. The aim was to create appropriate line-segment maps to be analysed using Angular Segment Analysis, a network centrality analysis method introduced in Space Syntax. All network layers are complemented with an “Unlink points” layer; a GIS point layer with the locations of all non-level intersections, such as overpasses and underpasses, bridges, tunnels, flyovers and the like. The Unlink point layer is necessary to conduct network analysis that takes into account the non-planarity of the street network, using such software as PST (Place Syntax Tool).

This layer comprises road centrelines for all roads controlled by Main Roads (state Roads) that are assigned road numbers in the state of Western Australia. The State Road Network are attributed with a road number as identified in Main Roads' corporate Integrated Road Information System (IRIS). The purpose of this layer is to identify state roads as identified in IRIS.Note that you are accessing this data pursuant to a Creative Commons (Attribution) Licence which has a disclaimer of warranties and limitation of liability. You accept that the data provided pursuant to the Licence is subject to changes.Pursuant to section 3 of the Licence you are provided with the following notice to be included when you Share the Licenced Material:- The Commissioner of Main Roads is the creator and owner of the data and Licenced Material, which is accessed pursuant to a Creative Commons (Attribution) Licence, which has a disclaimer of warranties and limitation of liability.Creative Commons CC BY 4.0 https://creativecommons.org/licenses/by/4.0/

Connecticut Roads and Trails is 1:24,000-scale base map data. It depicts the location of all roads and trails published on the USGS topographic quadrangle maps. For base map purposes, use this layer with other 1:24,000-scale base map data such as Hydrography, Railroads, Airports, and Towns. The Roads and Trails layer includes information within Connecticut and is derived from the Roads and Trails Master layer, which includes all road and trail features depicted on all of the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps that cover the State of Connecticut. This layer may be used as a possible data source for other 1:24,000-scale layers having features that should coincide with the roads and trails on the USGS topographic quadrangle maps. Not intended for maps printed at map scales greater or more detailed than 1:24,000 scale (1 inch = 2,000 feet.)

Connecticut Roads and Trails is a 1:24,000-scale, feature-based layer that includes road and trail features on the U.S. Geological Survey (USGS) 7.5 minute topographic quadrangle maps for the State of Connecticut. This layer only includes features located in Connecticut. The layer is based on information from USGS topographic quadrangle maps published between 1969 and 1984 and does not represent the road network in Connecticut at any one particular point in time. The layer does not depict current conditions and excludes many roads that have been built, modified, or removed since the time these topographic quadrangle maps were published. The layer includes Interstate highways, US routes, state routes, local roads, unpaved roads, traffic circles, bridges, cul-de-sacs, trails, etc. It does not include route number, street name, house address, traffic direction, or traffic volume information for these features. Nor does it represent a complete or current network of hiking trails. Features are linear and represent road centerlines. Attribute information is comprised of codes to cartographically represent (symbolize) road and trail features on a map. This layer was originally published in 1994. The 2005 edition includes the same road features published in 1994, but the attribute information has been slightly modified and made easier to use.

The Terrestrial 30x30 Conserved Areas map layer was developed by the CA Nature working group, providing a statewide perspective on areas managed for the protection or enhancement of biodiversity. Understanding the spatial distribution and extent of these durably protected and managed areas is a vital aspect of tracking and achieving the “30x30” goal of conserving 30% of California's lands and waters by 2030.Terrestrial and Freshwater Data• The California Protected Areas Database (CPAD), developed and managed by GreenInfo Network, is the most comprehensive collection of data on open space in California. CPAD data consists of Holdings, a single parcel or small group of parcels which comprise the spatial features of CPAD, generally corresponding to ownership boundaries. • The California Conservation Easement Database (CCED), managed by GreenInfo Network, aggregates data on lands with easements. Conservation Easements are legally recorded interests in land in which a landholder sells or relinquishes certain development rights to their land in perpetuity. Easements are often used to ensure that lands remain as open space, either as working farm or ranch lands, or areas for biodiversity protection. Easement restrictions typically remain with the land through changes in ownership. •The Protected Areas Database of the United States (PAD-US), hosted by the United States Geological Survey (USGS), is developed in coordination with multiple federal, state, and non-governmental organization (NGO) partners. PAD-US, through the Gap Analysis Project (GAP), uses a numerical coding system in which GAP codes 1 and 2 correspond to management strategies with explicit emphasis on protection and enhancement of biodiversity. PAD-US is not specifically aligned to parcel boundaries and as such, boundaries represented within it may not align with other data sources. • Numerous datasets representing designated boundaries for entities such as National Parks and Monuments, Wild and Scenic Rivers, Wilderness Areas, and others, were downloaded from publicly available sources, typically hosted by the managing agency.Methodology1.CPAD and CCED represent the most accurate location and ownership information for parcels in California which contribute to the preservation of open space and cultural and biological resources.2. Superunits are collections of parcels (Holdings) within CPAD which share a name, manager, and access policy. Most Superunits are also managed with a generally consistent strategy for biodiversity conservation. Examples of Superunits include Yosemite National Park, Giant Sequoia National Monument, and Anza-Borrego Desert State Park. 3. Some Superunits, such as those owned and managed by the Bureau of Land Management, U.S. Forest Service, or National Park Service , are intersected by one or more designations, each of which may have a distinct management emphasis with regards to biodiversity. Examples of such designations are Wilderness Areas, Wild and Scenic Rivers, or National Monuments.4. CPAD Superunits and CCED easements were intersected with all designation boundary files to create the operative spatial units for conservation analysis, henceforth 'Conservation Units,' which make up the Terrestrial 30x30 Conserved Areas map layer. Each easement was functionally considered to be a Superunit. 5. Each Conservation Unit was intersected with the PAD-US dataset in order to determine the management emphasis with respect to biodiversity, i.e., the GAP code. Because PAD-US is national in scope and not specifically parcel aligned with California assessors' surveys, a direct spatial extraction of GAP codes from PAD-US would leave tens of thousands of GAP code data slivers within the 30x30 Conserved Areas map. Consequently, a generalizing approach was adopted, such that any Conservation Unit with greater than 80% areal overlap with a single GAP code was uniformly assigned that code. Additionally, the total area of GAP codes 1 and 2 were summed for the remaining uncoded Conservation Units. If this sum was greater than 80% of the unit area, the Conservation Unit was coded as GAP 2. 6.Subsequent to this stage of analysis, certain Conservation Units remained uncoded, either due to the lack of a single GAP code (or combined GAP codes 1&2) overlapping 80% of the area, or because the area was not sufficiently represented in the PAD-US dataset. 7.These uncoded Conservation Units were then broken down into their constituent, finer resolution Holdings, which were then analyzed according to the above workflow. 8. Areas remaining uncoded following the two-step process of coding at the Superunit and then Holding levels were assigned a GAP code of 4. This is consistent with the definition of GAP Code 4: areas unknown to have a biodiversity management focus. 9. Greater than 90% of all areas in the Terrestrial 30x30 Conserved Areas map layer were GAP coded at the level of CPAD Superunits intersected by designation boundaries, the coarsest land units of analysis. By adopting these coarser analytical units, the Terrestrial 30X30 Conserved Areas map layer avoids hundreds of thousands of spatial slivers that result from intersecting designations with smaller, more numerous parcel records. In most cases, individual parcels reflect the management scenario and GAP status of the umbrella Superunit and other spatially coincident designations.10. PAD-US is a principal data source for understanding the spatial distribution of GAP coded lands, but it is national in scope, and may not always be the most current source of data with respect to California holdings. GreenInfo Network, which develops and maintains the CPAD and CCED datasets, has taken a lead role in establishing communication with land stewards across California in order to make GAP attribution of these lands as current and accurate as possible. The tabular attribution of these datasets is analyzed in addition to PAD-US in order to understand whether a holding may be considered conserved. Tracking Conserved Areas The total acreage of conserved areas will increase as California works towards its 30x30 goal. Some changes will be due to shifts in legal protection designations or management status of specific lands and waters. However, shifts may also result from new data representing improvements in our understanding of existing biodiversity conservation efforts. The California Nature Project is expected to generate a great deal of excitement regarding the state's trajectory towards achieving the 30x30 goal. We also expect it to spark discussion about how to shape that trajectory, and how to strategize and optimize outcomes. We encourage landowners, managers, and stakeholders to investigate how their lands are represented in the Terrestrial 30X30 Conserved Areas Map Layer. This can be accomplished by using the Conserved Areas Explorer web application, developed by the CA Nature working group. Users can zoom into the locations they understand best and share their expertise with us to improve the data representing the status of conservation efforts at these sites. The Conserved Areas Explorer presents a tremendous opportunity to strengthen our existing data infrastructure and the channels of communication between land stewards and data curators, encouraging the transfer of knowledge and improving the quality of data. CPAD, CCED, and PAD-US are built from the ground up. Data is derived from available parcel information and submissions from those who own and manage the land. So better data starts with you. Do boundary lines require updating? Is the GAP code inconsistent with a Holding’s conservation status? If land under your care can be better represented in the Terrestrial 30X30 Conserved Areas map layer, please use this link to initiate a review.The results of these reviews will inform updates to the California Protected Areas Database, California Conservation Easement Database, and PAD-US as appropriate for incorporation into future updates to CA Nature and tracking progress to 30x30.

Web Map contains all layers of Saskatchewan Upgraded Road Network (SURN).

Saskatchewan Highway Network map contains current Highway Network layer with highway classification based on varied criteria

Open in: [Superset)] [ArcGIS] or [Request format]Open in: [Superset)] or [Request format]The Act51 Map Layer represents the road network that the City of Detroit reports to the Michigan Department of Transportation on a annual basis. The Act51 Map Layer identifies road jurisdictions for permitting and planning.Public Act 51 of 1951 created the Michigan Transportation Fund (MTF), and it is the main road funding source for most cities and villages. This Act defines the formula by which Michigan distributes money for road maintenance to cities, villages, and counties.Under Act 51, county roads can be classified as either primary or local roads. City and village streets can be classified as either major or local streets. Primary roads and major streets are selected by the counties, cities and villages on the basis of the greatest general importance to each respective local agency.

To integrate government resources, the Ministry of Transportation's "Transportation Network Digital Map" will be incorporated into our center's "Taiwan Universal Electronic Map" update starting from the 106th fiscal year. Based on the jointly established layer structure, the "Taiwan Area Transportation Network Map Digital Data File" will be created and classified as one of the thematic layers of the Taiwan Universal Electronic Map. In addition, to comply with the government's open data policy, our center will open part of the digital map data file of the "Taiwan Area Transportation Network" for public browsing and use, promoting inter-agency data flow, enhancing governance effectiveness, meeting public needs, and using government open data to create intellectual assets and convenient services.

This StoryMap series contains a collection of four Dashboards used to display active project data on the Connecticut road network. Dashboards are used to display Capital Projects, Maintenance Resurfacing Program (MRP) projects, and Local Transportation Capital Improvement Program (LOTCIP) projects, as well as a dashboard to display all data together.Dashboards are listed by tabs at the top of the display. Each dashboard has similar capabilities. Projects are displayed in a zoomable GIS interface and a Project List. As the map is zoomed and the extent changes, the Project List will update to only display projects on the map. Projects selected from the Map or Project List will display a Project Details popup. Additional components of each dashboard include dynamic project counts, a Map Zoom By Town function and a Project Number Search.Capital Project data is sourced from the CTDOT Project Work Areas feature layer. The data is filtered to display active projects only, and categorized as "Pre-Construction" or "Construction." Pre-Construction is defined as projects with a CurrentSchedulePhase value of Planning, Pre-Design, Final Design, or Contract Processing.Maintenance Project data is sourced from the MRP Active feature layer. Central Maintenance personnel coordinate with the four districts to develop an annual statewide resurfacing program based upon a variety of factors (age, condition, etc.) that prioritize paving locations. Active MRP projects are incomplete projects for the current year.LOTCIP Project data is sourced from the CTDOT LOTCIP Projects feature layer. The data updates from LOTCIP database nightly. The geometry of the LOTCIP projects represent the approximate outline of the projects limits and does not represent the actual limits of the projects.

Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study. omo-turkana.gpkg: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap: basin: Hydrological basin of the Omo-Turkana river (case study boundary) subbasin: Basins of the main tributaries waterbodies: Natural lakes and reservoirs boundaries rivers: River network dams: Existing dams protected_areas: Protected areas aei_pct_cells: Area equipped for irrigation, expressed as percentage of total area. roads: Main roads network cities: Main cities in the riparian countries countries: Administrative borders of riparian countries markers: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas. zambezi_raster.zip: raster dataset including the following layers: srtm_90m: Digital Elevation Model Global Surface Water: change: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015 extent: Maximum Water Extent shows all the locations ever detected as water over period 1984-2015 occurence: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics. recurrence: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year. seasonality: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present. transitions: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water. Original data sources include: AQUASTAT, the FAO global information system on water resources and agricultural water management; Natural Earth, a public domain map dataset available at different scales; Protected Planet, the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN; OpenStreetMap, a collaborative project to create a free editable map of the world; NASA's Shuttle Radar Topography Mission (SRTM) Digital Elevation Database; Global Water Surface, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.