For each park, the ParkServe® team created a 10-minute walkable service area using a nationwide walkable road network dataset provided by Esri. We use the Esri Network Analyst extension to create the 10-minute walk service area. The analysis identifies physical barriers such as highways, train tracks, and rivers without bridges and chooses routes without barriers.In order to create service areas, we created access points for each park using an auto-generation model. The model generates access points at any location where a walkable road is within a predefined distance of the park boundary. ParkServe® does incorporate the verified access points for the 100 cities in ParkScore®. Using the 10-minute walk service areas, overall access statistics are generated for each park, place, and urban area included in the database. Access is then disaggregated by several demographic variables – race/ethnicity, age, and income. In communities with an exceptionally small number of block groups, 10-minute walk demographic calculations are not available. This information is attached for the park locations. Visit https://www.tpl.org/parkserve/downloads for field descriptions.All calculated population statistics are based on 2018 US Census Block Group estimates provided by Esri.For more information on ParkServe methodology, visit: https://www.tpl.org/parkserve/about

Reason for Selection Protected natural areas help foster a conservation ethic by providing opportunities for people to connect with nature, and also support ecosystem services like offsetting heat island effects (Greene and Millward 2017, Simpson 1998), water filtration, stormwater retention, and more (Hoover and Hopton 2019). In addition, parks, greenspace, and greenways can help improve physical and psychological health in communities (Gies 2006). However, parks are not equitably distributed within easy walking distance for everyone. It also complements the urban park size indicator by capturing the value of potential new parks. Input Data

  The Trust for Public Land (TPL) ParkServe database, accessed 8-8-2021: Park priority areas (ParkServe_ParkPriorityAreas_08062021) 
    From the TPL ParkServe documentation:

The ParkServe database maintains an inventory of parks for every urban area in the U.S., including Puerto Rico. This includes all incorporated and Census-designated places that lie within any of the country’s 3,000+ census-designated urban areas. All populated areas in a city that fall outside of a 10-minute walk service area are assigned a level of park priority, based on a comprehensive index of six equally weighted demographic and environmental metrics:Population densityDensity of low-income households – which are defined as households with income less than 75 percent of the urban area median household incomeDensity of people of colorCommunity health – a combined index based on the rate of poor mental health and low physical activity from the 2020 CDC PLACES census tract datasetUrban heat islands – surface temperature at least 1.25o greater than city mean surface temperature from The Trust for Public Land, based on Landsat 8 satellite imageryPollution burden - Air toxics respiratory hazard index from 2020 EPA EJScreen The 10-minute walkFor each park, we create a 10-minute walkable service area using a nationwide walkable road network dataset provided by Esri. The analysis identifies physical barriers such as highways, train tracks, and rivers without bridges and chooses routes without barriers.

  CDC Social Vulnerability Index 2018: RPL_Themes 

Social vulnerability refers to the capacity for a person or group to “anticipate, cope with, resist and recover from the impact” of a natural or anthropogenic disaster such as extreme weather events, oil spills, earthquakes, and fires. Socially vulnerable populations are more likely to be disproportionately affected by emergencies (Wolkin et al. 2018).

In this indicator, we use the “RPL_THEMES” attribute from the Social Vulnerability Index, described here. “The Geospatial Research, Analysis, and Services Program (GRASP) at Centers for Disease Control and Prevention/Agency for Toxic Substances and Disease Registry developed the Social Vulnerability Index (SVI). The SVI is a dataset intended to help state, local, and tribal disaster management officials identify where the most socially vulnerable populations occur (Agency for Toxic Substances and Disease Registry [ATSDR] 2018)” (Flanagan et al. 2018).

“The SVI database is regularly updated and includes 15 census variables (ATSDR 2018). Each census variable was ranked from highest to lowest vulnerability across all census tracts in the nation with a nonzero population. A percentile rank was calculated for each census tract for each variable. The variables were then grouped among four themes.... A tract-level percentile rank was also calculated for each of the four themes. Finally, an overall percentile rank for each tract as the sum of all variable rankings was calculated. This process of percentile ranking was then repeated for the individual states” (Flanagan et al. 2018).

Base Blueprint 2022 extent
Southeast Blueprint 2023 extent

Mapping Steps

Convert the ParkServe park priority areas layer to a raster using the ParkRank field. Note: The ParkRank scores are calculated using metrics classified relative to each city. Each city contains park rank values that range from 1-3. For the purposes of this indicator, we chose to target potential park areas to improve equity. Because the ParkRank scores are relative for each city, a high score in one city is not necessarily comparable to a high score from another city. In an effort to try to bring more equity into this indicator, we also use the CDC Social Vulnerability Index to narrow down the results.
Reclassify the ParkServe raster to make NoData values 0. 
Convert the SVI layer from vector to raster based on the “RPL_Themes” field. 
To limit the ParkRank layer to areas with high SVI scores, first identify census tracts with an “RPL_Themes” field value >0.65. Make a new raster that assigns a value of 1 to census tracts that score >0.65, and a value of 0 to everything else. Take the resulting raster times the ParkRank layer.
Reclassify this raster into the 4 classes seen in the final indicator below.
Clip to the spatial extent of Base Blueprint 2022.
As a final step, clip to the spatial extent of Southeast Blueprint 2023. 

Note: For more details on the mapping steps, code used to create this layer is available in the Southeast Blueprint Data Download under > 6_Code. Final indicator values Indicator values are assigned as follows: 3 = Very high priority for a new park that would create nearby equitable access

2 = High priority for a new park that would create nearby equitable access1 = Moderate priority for a new park that would create nearby equitable access 0 = Not identified as a priority for a new park that would create nearby equitable access (within urban areas) Known Issues

This indicator could overestimate park need in areas where existing parks are missing from the ParkServe database. TPL regularly updates ParkServe to incorporate the best available park data. If you notice missing parks or errors in the park boundaries or attributes, you can submit corrections through the ParkReviewer tool or by contacting TPL staff.
Within a given area of high park need, the number of people served by the creation of a new park depends on its size and how centrally located it is. This indicator does not account for this variability. Similarly, while creating a new park just outside an area of high park need would create access for some people on the edge, the indicator does not capture the benefits of new parks immediately adjacent to high-need areas. For a more granular analysis of new park benefits, ParkServe’s ParkEvaluator tool allows you to draw a new park, view its resulting 10-minute walk service area, and calculate who would benefit.
Beyond considering distance to a park and whether it is open to the public, this indicator does not account for other factors that might limit park access, such as park amenities or public safety. The TPL analysis excludes private or exclusive parks that restrict access to only certain individuals (e.g., parks in gated communities, fee-based sites). The TPL data includes a wide variety of parks, trails, and open space as long as there is no barrier to entry for any portion of the population.
The indicator does not incorporate inequities in access to larger versus smaller parks. In predicting where new parks would benefit nearby people who currently lack access, this indicator treats all existing parks equally.
This indicator identifies areas where parks are needed, but does not consider whether a site is available to become a park. We included areas of low intensity development in order to capture vacant lots, which can serve as new park opportunities. However, as a result, this indicator also captures some areas that are already used for another purpose (e.g., houses, cemeteries, and businesses) and are unlikely to become parks. In future updates, we would like to use spatial data depicting vacant lots to identify more feasible park opportunities.
This indicator underestimates places in rural areas where many people within a socially vulnerable census tract would benefit from a new park. ParkServe covers incorporated and Census-designated places within census-designated urban areas, which leaves out many rural areas. We acknowledge that there are still highly socially vulnerable communities in rural areas that would benefit from the development of new parks. However, based on the source data, we were not able to capture those places in this version of the indicator. 

Other Things to Keep in MindThe zero values in this indicator contain three distinct types of areas that we were unable to distinguish between in the legend: 1) Areas that are not in a community analyzed by ParkServe (ParkServe covers incorporated and Census-designated places within census-designated urban areas); 2) Areas in a community analyzed by ParkServe that were not identified as a priority; 3) Areas that ParkServe identifies as a priority but do not meet the SVI threshold used to represent areas in most need of improved equitable access.This indicator only includes park priority areas that fall within the 65th percentile or above from the Social Vulnerability Index. We did not perform outreach to community leaders or community-led organizations for feedback on this threshold. This indicator is intended to generally help identify potential parks that can increase equitable access but should not be solely used to inform the creation of new parks. As the social equity component relies on information summarized by census tract, it should only be used in conjunction with local knowledge and in discussion with local communities (NRPA 2021, Manuel-Navarete et al. 2004). Disclaimer: Comparing with Older Indicator Versions There are numerous problems with using Southeast Blueprint indicators for change analysis. Please consult Blueprint staff if you would like to do this (email hilary_morris@fws.gov). Literature Cited Centers for

Polygons in this layer represent low food access areas: areas of the District of Columbia which are estimated to be more than a 10-minute walk from the nearest full-service grocery store. These have been merged with Census poverty data to estimate how much of the population within these areas is food insecure (below 185% of the federal poverty line in addition to living in a low food access area).Office of Planning GIS followed several steps to create this layer, including: transit analysis, to eliminate areas of the District within a 10-minute walk of a grocery store; non-residential analysis, to eliminate areas of the District which do not contain residents and cannot classify as low food access areas (such as parks and the National Mall); and Census tract division, to estimate population and poverty rates within the newly created polygon boundaries.Fields contained in this layer include:Intermediary calculation fields for the aforementioned analysis, and:PartPop2: The total population estimated to live within the low food access area polygon (derived from Census tract population, assuming even distribution across the polygon after removing non-residential areas, followed by the removal of population living within a grocery store radius.)PrtOver185: The portion of PartPop2 which is estimated to have household income above 185% of the federal poverty line (the food secure population)PrtUnd185: The portion of PartPop2 which is estimated to have household income below 185% of the federal poverty line (the food insecure population)PercentUnd185: A calculated field showing PrtUnd185 as a percent of PartPop2. This is the percent of the population in the polygon which is food insecure (both living in a low food access area and below 185% of the federal poverty line).Note that the polygon representing Joint Base Anacostia-Bolling was removed from this analysis. While technically classifying as a low food access area based on the OP Grocery Stores layer (since the JBAB Commissary, which only serves military members, is not included in that layer), it is recognized that those who do live on the base have access to the commissary for grocery needs.Last updated November 2017.

This dataset contains the recreation opportunity information that the Forest Service collects through the Recreation Portal and shares with the public on https://www.recreation.gov, the Forest Service World Wide Web pages (https://www.fs.usda.gov/) and the Interactive Visitor Map. This recreation data contains detailed descriptions of recreational sites, areas, activities & facilities. This published dataset consists of one point feature class for recreational areas, one spatial view and three related tables such as activities, facilities & rec area advisories. The purpose of each related table is described below RECAREAACTIVITIES: This related table contains information about the activities that are associated with the rec area.RECAREAFACILITIES: This related table contains information about the amenities that are associated with the rec area. RECAREAADVISORIES: This table contains events, news, alerts and warnings that are associated with the rec area.RECAREAACTIVITIES_V: This spatial view/feature class is generated by joining the RECAREAACTIVITIES table to the RECREATION OPPORTUNITIES Feature Class. Please note that the RECAREAID is the unique identifier present in point feature class and in the related tables as well. The RECAREAID is used as foreign key to access relate records.This published data is updated nightly from an XML feed maintained by the CIO Rec Portal team. This data is intended for public use and distribution. MetadataThis record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML https://apps.fs.usda.gov/arcx/rest/services/EDW/EDW_RecreationOpportunities_01/MapServer/0 Metadata For complete information, please visit https://data.gov.

The dataset and the validation are fully described in a Nature Scientific Data Descriptor https://www.nature.com/articles/s41597-019-0265-5

If you want to use this dataset in an interactive environment, then use this link https://mybinder.org/v2/gh/GeographerAtLarge/TravelTime/HEAD

The following text is a summary of the information in the above Data Descriptor.

The dataset is a suite of global travel-time accessibility indicators for the year 2015, at approximately one-kilometre spatial resolution for the entire globe. The indicators show an estimated (and validated), land-based travel time to the nearest city and nearest port for a range of city and port sizes.

The datasets are in GeoTIFF format and are suitable for use in Geographic Information Systems and statistical packages for mapping access to cities and ports and for spatial and statistical analysis of the inequalities in access by different segments of the population.

These maps represent a unique global representation of physical access to essential services offered by cities and ports.

The datasets travel_time_to_cities_x.tif (where x has values from 1 to 12) The value of each pixel is the estimated travel time in minutes to the nearest urban area in 2015. There are 12 data layers based on different sets of urban areas, defined by their population in year 2015 (see PDF report).

travel_time_to_ports_x (x ranges from 1 to 5)

The value of each pixel is the estimated travel time to the nearest port in 2015. There are 5 data layers based on different port sizes.

Format Raster Dataset, GeoTIFF, LZW compressed Unit Minutes

Data type Byte (16 bit Unsigned Integer)

No data value 65535

Flags None

Spatial resolution 30 arc seconds

Spatial extent

Upper left -180, 85

Lower left -180, -60 Upper right 180, 85 Lower right 180, -60 Spatial Reference System (SRS) EPSG:4326 - WGS84 - Geographic Coordinate System (lat/long)

Temporal resolution 2015

Temporal extent Updates may follow for future years, but these are dependent on the availability of updated inputs on travel times and city locations and populations.

Methodology Travel time to the nearest city or port was estimated using an accumulated cost function (accCost) in the gdistance R package (van Etten, 2018). This function requires two input datasets: (i) a set of locations to estimate travel time to and (ii) a transition matrix that represents the cost or time to travel across a surface.

The set of locations were based on populated urban areas in the 2016 version of the Joint Research Centre’s Global Human Settlement Layers (GHSL) datasets (Pesaresi and Freire, 2016) that represent low density (LDC) urban clusters and high density (HDC) urban areas (https://ghsl.jrc.ec.europa.eu/datasets.php). These urban areas were represented by points, spaced at 1km distance around the perimeter of each urban area.

Marine ports were extracted from the 26th edition of the World Port Index (NGA, 2017) which contains the location and physical characteristics of approximately 3,700 major ports and terminals. Ports are represented as single points

The transition matrix was based on the friction surface (https://map.ox.ac.uk/research-project/accessibility_to_cities) from the 2015 global accessibility map (Weiss et al, 2018).

Code The R code used to generate the 12 travel time maps is included in the zip file that can be downloaded with these data layers. The processing zones are also available.

Validation The underlying friction surface was validated by comparing travel times between 47,893 pairs of locations against journey times from a Google API. Our estimated journey times were generally shorter than those from the Google API. Across the tiles, the median journey time from our estimates was 88 minutes within an interquartile range of 48 to 143 minutes while the median journey time estimated by the Google API was 106 minutes within an interquartile range of 61 to 167 minutes. Across all tiles, the differences were skewed to the left and our travel time estimates were shorter than those reported by the Google API in 72% of the tiles. The median difference was −13.7 minutes within an interquartile range of −35.5 to 2.0 minutes while the absolute difference was 30 minutes or less for 60% of the tiles and 60 minutes or less for 80% of the tiles. The median percentage difference was −16.9% within an interquartile range of −30.6% to 2.7% while the absolute percentage difference was 20% or less in 43% of the tiles and 40% or less in 80% of the tiles.

This process and results are included in the validation zip file.

Usage Notes The accessibility layers can be visualised and analysed in many Geographic Information Systems or remote sensing software such as QGIS, GRASS, ENVI, ERDAS or ArcMap, and also by statistical and modelling packages such as R or MATLAB. They can also be used in cloud-based tools for geospatial analysis such as Google Earth Engine.

The nine layers represent travel times to human settlements of different population ranges. Two or more layers can be combined into one layer by recording the minimum pixel value across the layers. For example, a map of travel time to the nearest settlement of 5,000 to 50,000 people could be generated by taking the minimum of the three layers that represent the travel time to settlements with populations between 5,000 and 10,000, 10,000 and 20,000 and, 20,000 and 50,000 people.

The accessibility layers also permit user-defined hierarchies that go beyond computing the minimum pixel value across layers. A user-defined complete hierarchy can be generated when the union of all categories adds up to the global population, and the intersection of any two categories is empty. Everything else is up to the user in terms of logical consistency with the problem at hand.

The accessibility layers are relative measures of the ease of access from a given location to the nearest target. While the validation demonstrates that they do correspond to typical journey times, they cannot be taken to represent actual travel times. Errors in the friction surface will be accumulated as part of the accumulative cost function and it is likely that locations that are further away from targets will have greater a divergence from a plausible travel time than those that are closer to the targets. Care should be taken when referring to travel time to the larger cities when the locations of interest are extremely remote, although they will still be plausible representations of relative accessibility. Furthermore, a key assumption of the model is that all journeys will use the fastest mode of transport and take the shortest path.

The Walk to a Park initiative focuses on increasing access to parks and open spaces, concentrating on areas of the city that are under-resourced and where residents are living further than a walk to a park. NYC Parks calculates the number of New Yorkers within walking distance of a park and reports on this as part of the Mayor’s Management Report. “Walking distance” is defined as a 1/4-mile or less for sites such as small playgrounds and sitting areas; or a 1/2-mile or less for larger parks that serve a wider region, typically over 8 acres or situated on the waterfront. Certain properties in NYC Parks' portfolio, such as cemeteries, community gardens, or sites with no recreational equipment were not included in this analysis. Similarly, some parks and open-space amenities not under the jurisdiction of NYC Parks were included in this analysis, as they provide recreational value.

This dataset includes two ESRI shapefiles of access points, one for sites that get a 1/4-mile buffer and one for sites that get a 1/2-mile buffer. It also includes an ESRI shapefile of the combined polygon that is the output of this analysis. This polygon represents the total area within walking distance of a park. To generate the number of New Yorkers within that distance, the polygon is compared with data from the U.S. Census.

This information is only current as of the publication date. For more information about this analysis and the Walk to a Park Initiative, visit: https://www.nycgovparks.org/planning-and-building/planning/walk-to-a-park

The agsview extension for CKAN provides view plugins designed to display Esri ArcGIS Server data directly within CKAN resources. Specifically, it enables visualization of ArcGIS Map services and Feature layer services, leveraging an Esri Leaflet Viewer for interactive display. As such, this extension enhances CKAN by providing native support for displaying commonly used geospatial data formats, increasing the usability of CKAN for geospatial data catalogs through built-in rendering capabilities when used by your organization's CKAN end users. Key Features: ArcGIS Feature Layer Viewer (agsfsview): Allows visualization of ArcGIS Feature Layers found within either MapServices or FeatureServices, offering a means by which you or your end users can expose specific layers, enabling selective display of datasets. Configuration option: ags_url: Specifies the ArcGIS Server layer endpoint, including the layer ID for targeted data access ensuring the correct layer or service is connected to your CKAN resource. Configuration option: basemapurl: Allows customization of the basemap by specifying either an Esri basemap name or a generic tile URL template, to tailor the visual context of the displayed ArcGIS data. ArcGIS MapService Viewer (agsmsview): Provides functionality to render ArcGIS MapServices, giving control over which layers within the service are displayed. Configuration option: ags_url: Defines the ArcGIS Server MapService endpoint, directing the viewer to the desired MapService resource for inclusion in your CKAN resource. Configuration option: list_ids: Enables filtering of layers within the MapService by providing a comma-delimited list of layer IDs for selective display. An empty list will display all layers, offering you flexibility in configuring the data viewed in CKAN. Configuration option: basemapurl: Permits customization of the basemap, accepting either an Esri basemap name or a generic tile URL template, ensuring flexibility in the map presentation. Configurable Default Basemap: Using your CKAN .ini configuration, you can set a default basemap for all ArcGIS views, providing consistency and improving usability. You can specify either an Esri basemap name or a tile URL template as the default. Technical Integration: The agsview extension integrates with CKAN by adding view plugins (agsfsview and agsmsview). To enable the extension, you must add the plugin names to the ckan.plugins setting in the CKAN configuration file (e.g., production.ini). After updating you CKAN file, and restarting the CKAN instance, the ArcGIS viewers become available options when creating a CKAN resource in the 'View' section assuming the resource has URLs that are supported by the viewing feature. Benefits & Impact: By implementing the agsview extension, CKAN instances can natively display ArcGIS Server MapServices and Feature Layers, eliminating the need for external viewers or custom development. This significantly enhances CKAN's utility for organizations managing and sharing geospatial data, as users can readily visualize Esri ArcGIS data directly within the CKAN interface. The configuration options further allow for customization of the display, improving the user experience.

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides maps depicting the latest global forecast guidance of water currents, water temperature, and salinity at forecast projections: 0, 12, 24, 36, 48, 60, 72, 84, and 96-hours from the NWS/NCEP Global Real-Time Ocean Forecast System (GRTOFS). The surface water currents velocity maps display the direction using white or black streaklets. The magnitude of the current is indicated by the length and width of the streaklet. The maps of the GRTOFS surface forecast guidance are updated on the nowCOAST™ map service once per day. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

GRTOFS is based on the Hybrid Coordinates Ocean Model (HYCOM), an eddy resolving, hybrid coordinate numerical ocean prediction model. GRTOFS has global coverge and a horizontal resolution of 1/12 degree and 32 hybrid vertical layers. It has one forecast cycle per day (i.e. 0000 UTC) which generates forecast guidance out to 144 hours (6 days). However, nowCOAST™ only provides guidance out to 96 hours (4 days). The forecast cycle uses 3-hourly momentum and radiation fluxes along with precipitation predictions from the NCEP Global Forecast System (GFS). Each forecast cycle is preceded with a 48-hr long nowcast cycle. The nowcast cycle uses daily initial 3-D fields from the NAVOCEANO operational HYCOM-based forecast system which assimilates situ profiles of temperature and salinity from a variety of sources and remotely sensed SST, SSH and sea-ice concentrations. GRTOFS was developed by NCEP/EMC/Marine Modeling and Analysis Branch. GRTOFS is run once per day (0000 UTC forecast cycle) on the NOAA Weather and Climate Operational Supercomputer System (WCOSS) operated by NWS/NCEP Central Operations.

The maps are generated using a visualization technique developed by the Data Visualization Research Lab at The University of New Hampshire's Center for Coastal and Ocean Mapping (http://www.ccom.unh.edu/vislab/). The method combines two techniques. First, equally spaced streamlines are computed in the flow field using Jobard and Lefer's (1977) algorithm. Second, a series of "streaklets" are rendered head to tail along each streamline to show the direction of flow. Each of these varies along its length in size, color and transparency using a method developed by Fowler and Ware (1989), and later refined by Mr. Pete Mitchell and Dr. Colin Ware (Mitchell, 2007).

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

Fowler, D. and C. Ware, 1989: Strokes for Representing Vector Field Maps. Proceedings: Graphics Interface '98 249-253. Jobard, B and W. Lefer,1977: Creating evenly spaced streamlines of arbitrary density. Proceedings: Eurographics workshop on Visualization in Scientific Computing. 43-55. Mitchell, P.W., 2007: The Perceptual optimization of 2D Flow Visualizations Using Human in the Loop Local Hill Climbing. University of New Hampshire Masters Thesis. Department of Computer Science. NWS, 2013: About Global RTOFS, NCEP/EMC/MMAB, College Park, MD (Available at http://polar.ncep.noaa.gov/global/about/). Chassignet, E.P., H.E. Hurlburt, E.J. Metzger, O.M. Smedstad, J. Cummings, G.R. Halliwell, R. Bleck, R. Baraille, A.J. Wallcraft, C. Lozano, H.L. Tolman, A. Srinivasan, S. Hankin, P. Cornillon, R. Weisberg, A. Barth, R. He, F. Werner, and J. Wilkin, 2009: U.S. GODAE: Global Ocean Prediction with the HYbrid Coordinate Ocean Model (HYCOM). Oceanography, 22(2), 64-75. Mehra, A, I. Rivin, H. Tolman, T. Spindler, and B. Balasubramaniyan, 2011: A Real-Time Operational Global Ocean Forecast System, Poster, GODAE OceanView –GSOP-CLIVAR Workshop in Observing System Evaluation and Intercomparisons, Santa Cruz, CA.

This web map created by the Colorado Governor's Office of Information Technology GIS team, serves as a basemap specific to the state of Colorado. The basemap includes general layers such as counties, municipalities, roads, waterbodies, state parks, national forests, national wilderness areas, and trails.Layers:Layer descriptions and sources can be found below. Layers have been modified to only represent features within Colorado and are not up to date. Layers last updated February 23, 2023. Colorado State Extent: Description: “This layer provides generalized boundaries for the 50 States and the District of Columbia.” Notes: This layer was filtered to only include the State of ColoradoSource: Esri Living Atlas USA States Generalized Boundaries Feature LayerState Wildlife Areas:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state wildlife areas layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer hosted in ArcGIS Online Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerMunicipal Boundaries:Description: "Boundaries data from the State Demography Office of Colorado Municipalities provided by the Department of Local Affairs (DOLA)"Source: Colorado Information Marketplace Municipal Boundaries in ColoradoCounties:Description: “This layer presents the USA 2020 Census County (or County Equivalent) boundaries of the United States in the 50 states and the District of Columbia. It is updated annually as County (or County Equivalent) boundaries change. The geography is sources from US Census Bureau 2020 TIGER FGDB (National Sub-State) and edited using TIGER Hydrology to add a detailed coastline for cartographic purposes. Geography last updated May 2022.” Notes: This layer was filtered to only include counties in the State of ColoradoSource: Esri USA Census Counties Feature LayerInterstates:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: Interstates are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointU.S. Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: U.S. Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointState Highways:Description: Authoritative data from the Colorado Department of Transportation representing Highways Notes: State Highways are filtered by route sign from this CDOT Highways layer Source: Colorado Department of Transportation Highways REST EndpointMajor Roads:Description: Authoritative data from the Colorado Department of Transportation representing major roads Source: Colorado Department of Transportation Major Roads REST EndpointLocal Roads:Description: Authoritative data from the Colorado Department of Transportation representing local roads Source: Colorado Department of Transportation Local Roads REST EndpointRail Lines:Description: Authoritative data from the Colorado Department of Transportation representing rail lines Source: Colorado Department of Transportation Rail Lines REST EndpointCOTREX Trails:Description: “The Colorado Trail System, now titled the Colorado Trail Explorer (COTREX), endeavors to map every trail in the state of Colorado. Currently their are nearly 40,000 miles of trails mapped. Trails come from a variety of sources (USFS, BLM, local parks & recreation departments, local governments). Responsibility for accuracy of the data rests with the source.These data were last updated on 2/5/2019” Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerNHD Waterbodies:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include waterbodies in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerNHD Flowlines:Description: “The National Hydrography Dataset Plus (NHDplus) maps the lakes, ponds, streams, rivers and other surface waters of the United States. Created by the US EPA Office of Water and the US Geological Survey, the NHDPlus provides mean annual and monthly flow estimates for rivers and streams. Additional attributes provide connections between features facilitating complicated analyses.”Notes: This layer was filtered to only include flowline features in the State of ColoradoSource: National Hydrography Dataset Plus Version 2.1 Feature LayerState Parks:Description: “This data was created by the CPW GIS Unit. Property boundaries are created by dissolving CDOWParcels by the property name, and property type and appending State Park boundaries designated as having public access. All parcel data correspond to legal transactions made by the CPW Real Estate Unit. The boundaries of the CDOW Parcels were digitized using metes and bounds, BLM's GCDB dataset, the PLSS dataset (where the GCDB dataset was unavailable) and using existing digital data on the boundaries.” Notes: The state parks layer in this basemap is filtered from the CPW Managed Properties (public access only) layer from this feature layer Source: Colorado Parks and Wildlife CPW Admin Data Feature LayerDenver Parks:Description: "This dataset should be used as a reference to locate parks, golf courses, and recreation centers managed by the Department of Parks and Recreation in the City and County of Denver. Data is based on parcel ownership and does not include other areas maintained by the department such as medians and parkways. The data should be used for planning and design purposes and cartographic purposes only."Source: City and County of Denver Parks REST EndpointNational Wilderness Areas:Description: “A parcel of Forest Service land congressionally designated as wilderness such as National Wilderness Area.”Notes: This layer was filtered to only include National Wilderness Areas in the State of ColoradoSource: United States Department of Agriculture National Wilderness Areas REST EndpointNational Forests: Description: “A depiction of the boundaries encompassing the National Forest System (NFS) lands within the original proclaimed National Forests, along with subsequent Executive Orders, Proclamations, Public Laws, Public Land Orders, Secretary of Agriculture Orders, and Secretary of Interior Orders creating modifications thereto, along with lands added to the NFS which have taken on the status of 'reserved from the public domain' under the General Exchange Act. The following area types are included: National Forest, Experimental Area, Experimental Forest, Experimental Range, Land Utilization Project, National Grassland, Purchase Unit, and Special Management Area.”Notes: This layer was filtered to only include National Forests in the State of ColoradoSource: United States Department of Agriculture Original Proclaimed National Forests REST Endpoint

Last Revised: February 2016

Map Information

This nowCOAST™ time-enabled map service provides maps depicting the NWS Multi-Radar Multi-Sensor (MRMS) quantitative precipitation estimate mosaics for 1-, 3-, 6-, 12-, 24-, 48-, and 72-hr time periods at a 1 km (0.6 miles) horizontal resolution for CONUS and southern part of Canada. The precipitation estimates are based only on radar data. The total precipitation amount is indicated by different colors at 0.01, 0.10, 0.25 inches and then at 1/4 inch intervals up to 4.0 inches (e.g. 0.50, 0.75, 1.00, 1.25, etc.), at 1-inch intervals from 4 to 10 inches and then at 2-inch intervals up to 14+ inches. The increments from 0.01 to 1.00 or 2.00 inches are similar to what are used on NCEP's Weather Prediction Center QPF products and the NWS River Forecast Center (RFC) daily precipitation analysis. The 1-hr mosaic is updated every 4 minutes with a latency on nowCOAST™ of about 6-7 minutes from valid time. The 3-, 6-, 12-, and 24-hr QPEs are updated on nowCOAST™ every hour for the period ending at the top of the hour. The 48- and 72-hr QPEs are generated daily for the period ending at 12 UTC (i.e. 7AM EST) and available on nowCOAST™ shortly afterwards. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

The NWS Multi-Radar Multi-Sensor System (MRMS)/Q3 QPEs are radar-only based quantitative precipitation analyses. The 1-hr precipitation accumulation is obtained by aggregating 12 instantaneous rate fields. Missing rate fields are filled with the neighboring rate fields if the data gap is not significantly large (e.g.<=15 minutes). The instantaneous rate is computed from the hybrid scan reflectivity and the precipitation flag fields (both are 2-D derivative products from the National 3-D Reflectivity Mosaic grid which has a 1-km horizontal resolution, 31 vertical levels and a 5-minute update cycle). The instantaneous rate currently uses four Z-R relationships (i.e. tropical, convective, stratiform, or snow). The particular ZR relationship used in any grid cell depends on precipitation type which is indicated by the precipitation flag. The other accumulation products are derived by aggregating the hourly accumulations. The 1-hr QPE are generated every 4 minutes, while the 3-, 6-, 12-, and 24-hr accumulations are generated every hour at the top of the hour. The 48- and 72-hr QPEs are updated daily at approximately 12 UTC. MRMS was developed by NOAA/OAR/National Severe Storms Laboratory and migrated into NWS operations at NOAA Integrated Dissemination Program.

Time Information

This map service is time-enabled, meaning that each individual layer contains time-varying data and can be utilized by clients capable of making map requests that include a time component.

In addition to ArcGIS Server REST access, time-enabled OGC WMS 1.3.0 access is also provided by this service.

This particular service can be queried with or without the use of a time component. If the time parameter is specified in a request, the data or imagery most relevant to the provided time value, if any, will be returned. If the time parameter is not specified in a request, the latest data or imagery valid for the present system time will be returned to the client. If the time parameter is not specified and no data or imagery is available for the present time, no data will be returned.

This service is configured with time coverage support, meaning that the service will always return the most relevant available data, if any, to the specified time value. For example, if the service contains data valid today at 12:00 and 12:10 UTC, but a map request specifies a time value of today at 12:07 UTC, the data valid at 12:10 UTC will be returned to the user. This behavior allows more flexibility for users, especially when displaying multiple time-enabled layers together despite slight differences in temporal resolution or update frequency.

When interacting with this time-enabled service, only a single instantaneous time value should be specified in each request. If instead a time range is specified in a request (i.e. separate start time and end time values are given), the data returned may be different than what was intended.

Care must be taken to ensure the time value specified in each request falls within the current time coverage of the service. Because this service is frequently updated as new data becomes available, the user must periodically determine the service's time extent. However, due to software limitations, the time extent of the service and map layers as advertised by ArcGIS Server does not always provide the most up-to-date start and end times of available data. Instead, users have three options for determining the latest time extent of the service:

  Issue a returnUpdates=true request (ArcGIS REST protocol only)
  for an individual layer or for the service itself, which will return
  the current start and end times of available data, in epoch time format
  (milliseconds since 00:00 January 1, 1970). To see an example, click on
  the "Return Updates" link at the bottom of the REST Service page under
  "Supported Operations". Refer to the
  ArcGIS REST API Map Service Documentation
  for more information.


  Issue an Identify (ArcGIS REST) or GetFeatureInfo (WMS) request against
  the proper layer corresponding with the target dataset. For raster
  data, this would be the "Image Footprints with Time Attributes" layer
  in the same group as the target "Image" layer being displayed. For
  vector (point, line, or polygon) data, the target layer can be queried
  directly. In either case, the attributes returned for the matching
  raster(s) or vector feature(s) will include the following:


      validtime: Valid timestamp.


      starttime: Display start time.


      endtime: Display end time.


      reftime: Reference time (sometimes referred to as
      issuance time, cycle time, or initialization time).


      projmins: Number of minutes from reference time to valid
      time.


      desigreftime: Designated reference time; used as a
      common reference time for all items when individual reference
      times do not match.


      desigprojmins: Number of minutes from designated
      reference time to valid time.




  Query the nowCOAST™ LayerInfo web service, which has been created to
  provide additional information about each data layer in a service,
  including a list of all available "time stops" (i.e. "valid times"),
  individual timestamps, or the valid time of a layer's latest available
  data (i.e. "Product Time"). For more information about the LayerInfo
  web service, including examples of various types of requests, refer to
  the 
  nowCOAST™ LayerInfo Help Documentation

References

For more information about the MRMS/Q3 system, please see http://nmq.ou.edu and http://www.nssl.noaa.gov/projects/mrms.

Statewide 2016 Lidar points colorized with 2018 NAIP imagery as a scene created by Esri using ArcGIS Pro for the entire State of Connecticut. This service provides the colorized Lidar point in interactive 3D for visualization, interaction of the ability to make measurements without downloading.Lidar is referenced at https://cteco.uconn.edu/data/lidar/ and can be downloaded at https://cteco.uconn.edu/data/download/flight2016/. Metadata: https://cteco.uconn.edu/data/flight2016/info.htm#metadata. The Connecticut 2016 Lidar was captured between March 11, 2016 and April 16, 2016. Is covers 5,240 sq miles and is divided into 23, 381 tiles. It was acquired by the Captiol Region Council of Governments with funding from multiple state agencies. It was flown and processed by Sanborn. The delivery included classified point clouds and 1 meter QL2 DEMs. The 2016 Lidar is published on the Connecticut Environmental Conditions Online (CT ECO) website. CT ECO is the collaborative work of the Connecticut Department of Energy and Environmental Protection (DEEP) and the University of Connecticut Center for Land Use Education and Research (CLEAR) to share environmental and natural resource information with the general public. CT ECO's mission is to encourage, support, and promote informed land use and development decisions in Connecticut by providing local, state and federal agencies, and the public with convenient access to the most up-to-date and complete natural resource information available statewide.Process used:Extract Building Footprints from Lidar1. Prepare Lidar - Download 2016 Lidar from CT ECO- Create LAS Dataset2. Extract Building Footprints from LidarUse the LAS Dataset in the Classify Las Building Tool in ArcGIS Pro 2.4.Colorize LidarColorizing the Lidar points means that each point in the point cloud is given a color based on the imagery color value at that exact location.1. Prepare Imagery- Acquire 2018 NAIP tif tiles from UConn (originally from USDA NRCS).- Create mosaic dataset of the NAIP imagery.2. Prepare and Analyze Lidar Points- Change the coordinate system of each of the lidar tiles to the Projected Coordinate System CT NAD 83 (2011) Feet (EPSG 6434). This is because the downloaded tiles come in to ArcGIS as a Custom Projection which cannot be published as a Point Cloud Scene Layer Package.- Convert Lidar to zlas format and rearrange. - Create LAS Datasets of the lidar tiles.- Colorize Lidar using the Colorize LAS tool in ArcGIS Pro. - Create a new LAS dataset with a division of Eastern half and Western half due to size limitation of 500GB per scene layer package. - Create scene layer packages (.slpk) using Create Cloud Point Scene Layer Package. - Load package to ArcGIS Online using Share Package. - Publish on ArcGIS.com and delete the scene layer package to save storage cost.Additional layers added:Visit https://cteco.uconn.edu/projects/lidar3D/layers.htm for a complete list and links. 3D Buildings and Trees extracted by Esri from the lidarShaded Relief from CTECOImpervious Surface 2012 from CT ECONAIP Imagery 2018 from CTECOContours (2016) from CTECOLidar 2016 Download Link derived from https://www.cteco.uconn.edu/data/download/flight2016/index.htm

The Port and Port Statistical Area web service allows users to visualize and access two USACE enterprise-wide feature classes: the Port Feature Class and the Port Statistical Area Feature Class, both of which include polygon geometries used to generate statistics for commerce data and vessel movements. The GIS service includes attributes such as port name, boundary description, and associated legislative documentation.

USACE works with port authorities from across the United States to develop the statistical port boundaries through an iterative and collaborative process. Port boundary information is prepared by USACE to increase transparency on public waterborne commerce statistic reporting, as well as to modernize how the data type is stored, analyzed, and reported.

A Port Area is defined by the limits set by overarching legislative enactments of state, county, or city governments, or the corporate limits of a municipality. A port typically refers to a geographical area that includes operational activities related to maritime transport as well as acquisition, operation, and management of port infrastructure and property, such as might be associated with ownership, concession, construction approval, or policy decision-making authority.

A Port Statistical Area (PSA) is a region with formally justified shared economic interests and collective reliance on infrastructure related to waterborne movements of commodities that is formally recognized by legislative enactments of state, county, or city governments. PSAs generally contain groups of county legislation for the sole purpose of statistical reporting. Through GIS mapping, legislative boundaries, and stakeholder collaboration, PSAs often serve as the primary unit for aggregating and reporting commerce statistics for broader geographical areas.

Per Engineering Regulation 1130-2-520, the U.S. Army Corps of Engineers' Navigation Data Center is responsible to collect, compile, publish, and disseminate waterborne commerce statistics. This task has subsequently been charged to the Waterborne Commerce Statistics Center to perform. Performance of this work is in accordance with the Rivers and Harbors Appropriation Act of 1922. Included in this work is the definition of a port area. A port area is defined in Engineering Pamphlet 1130-2-520 as: (1) Port limits defined by legislative enactments of state, county, or city governments. (2) The corporate limits of a municipality. The USACE enterprise-wide port and port statistical area feature classes per EP 1130-2-520 are organized in SDSFIE 4.0.2 format.

This webmap a subset of World Bioclimate Feature Service.Climate plays a major role in determining the distribution of plants and animals. Bioclimatology, the study of climate as it affects and is affected by living organisms, is key to understanding the patterns of forests and deserts on the landscape, where productive agricultural lands may be found, and how changes in the climate will affect rare species. This layer is part of the Ecophysiographic Project and is one of the four input layers used to create the World Ecological Land Units Map.Dataset Summary This layer provides access to a 250m cell-sized raster with a bioclimatic stratification. The source dataset was a 30-arcsecond resolution raster (equivalent to 0.86 km2 at the equator or about a 920m pixel size). The layer has the following attributes: Temperature Description - Seven classes based on the number of growing degree days (the monthly mean temperature multiplied by number of days in the month summed for all months). The 1950 to 2000 monthly average temperature was used to calculate growing degree days. Values in this field and associated number of growing degree days are:Temperature DescriptionGrowing Degree DaysVery Hot9,000 – 13,500Hot7,000 – 9,000Warm4,500 – 7,000Cool2,500 – 4,500Cold1,000 – 2,500Very Cold300 – 1,000Arctic0 - 300Aridity Description - Six classes based on an index of aridity calculated by dividing precipitation by evapotranspiration. Precipitation and evapotranspiration are average values from 1950 to 2000.Aridity DescriptionAridity IndexVery Wet1.5 – 70Wet1.0 – 1.5Moist0.6 – 1.0Semi-dry0.3 – 0.6Dry0.1 – 0.3Very Dry0.01 – 0.1Bioclimate Class - a 2-part description that combines the value of the Temperature Description field and the Aridity Description field. The alias for this field is ELU Bioclimate Reclass. This layer was created by modifying the dataset documented in the publication: Metzger and others. 2012. A high-resolution bioclimate map of the world: a unifying framework for global biodiversity research and monitoring. What can you do with this layer? This layer is suitable for both visualization and analysis. It can be used in ArcGIS Online in web maps and applications and can be used in ArcGIS Desktop.This layer has query, identify, and export image services available. This layer is restricted to a maximum area of 16,000 x 16,000 pixels - an area 4,000 kilometers on a side or an area approximately the size of Europe. A service is available providing access to the data table associated with this layer. The data table services can be used by developers to quickly and efficiently query the data and to create custom applications. For more information see the World Ecophysiographic Tables.This layer is part of a larger collection of landscape layers that you can use to perform a wide variety of mapping and analysis tasks.The Living Atlas of the World provides an easy way to explore the landscape layers and many other beautiful and authoritative maps on hundreds of topics.Geonet is a good resource for learning more about landscape layers and the Living Atlas of the World. To get started see the Living Atlas Discussion Group.The Esri Insider Blog provides an introduction to the Ecophysiographic Mapping project.

The Land Cover dataset demarcates 14 land cover types by area; such as Residential, Commercial, Industrial, Forest, Agriculture, etc. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Geography Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 1994 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: The dataset was created by Chester Environmental through combined image processing and GIS analysis of Landsat TM imagery of October 2, 1992, existing aerial photography, hardcopy and digital mapping sources and Census Bureau demographic data. The original dataset was created in 1993, then updated by Chester in 1994. Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/1VfUflfki42mpLSkr1R-up_OXGD3mHnv8tqeXf6XS9O0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us

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.

Data Download: The Secured Areas 2024 dataset is also available as an ESRI polygon geodatabase dataset.The secured areas dataset shows public and private lands that are permanently secured against conversion to development, GAP 1-3, through fee ownership, easements, or permanent conservation restrictions. It also includes a set of more temporary easement and GAP 4 open space lands not permanently secured for nature conservation. TNC compiled these data from state, federal, and private sources and assigned a GAP Status and other standard attribute fields to the best of our ability. The Secured Areas dataset is a TNC product created primarily for estimating current extent and status of secured lands with a conservation focus, GAP 1-3. The non GAP 1-3 lands are less comprehensively mapped given the lack of their inclusion in some primary source datasets, but they are included as available in our source datasets. Any updates, corrections, or discrepancies with respect to official versions of source federal, state, or local protected areas databases should be viewed as provisional until such time as such changes have been reviewed and accepted by the official data stewards for those other protected areas databases.GAP STATUS GAP status is a classification developed by the US Fish and Wildlife Service, to reflect the intent of the landowner or easement holder. GAP 1 and 2 are commonly thought of as “protected” for nature", while GAP 3 are “multiple-use” lands. Other temporary conservation easement lands and/or protected open space without a conservation value or intent are assigned GAP 4. (Citation: Crist, P.J., B. Thompson, T. C. Edwards, C. G. Homer, S. D. Bassett. 1998. Mapping and Categorizing Land Stewardship. A Handbook for Conducting Gap Analysis.) In addition to GAP 1-3 lands, in our TNC secured areas product we classified six additional classes of open space lands (permanent agricultural easements, temporary conservation easements, temporary agricultural easements, urban parks, state board lands, other GAP 4 lands). The following definitions guided our assignment of lands into the following nine classes:TNC CLASS CODE (fields TNCCLASS, TNCCLASS_D)1 = GAP 1: Permanently Secured for Nature and Natural Processes. Managed for biodiversity with all natural processes, little to no human intervention. Primary intention of the owner or easement holder is for biodiversity, nature protection, natural diversity, and natural processes. Land and water managed through natural processes including disturbances with little or no human intervention.Examples: wilderness area, some national parks2 = GAP 2 = Permanently Secured for Nature with Management: Managed for biodiversity, with hands on management or interventions. Primary intention of the owner or easement holder is for biodiversity conservation, nature protection, and natural diversity. Land and water managed for natural biodiversity conservation, but may include some hands on manipulation or suppression of disturbance and natural processes. Examples: national wildlife refuges, areas of critical environmental concern, inventoried roadless areas, some natural areas and preserves3 = GAP 3: Permanently Secured for Multiple Uses, including nature: Primary intention of the owner or easement holder for multiple uses. Strong focus on recreational use, game species production, timber production, grazing and other uses in additional to these lands providing some biodiversity value. May include extractive uses of a broad, low-intensity type (e.g. some logging. grazing) or of a localized intense type (e.g. mining, military artillery testing area, public access beach area within large natural state park). Examples: recreation focused protected areas such as state parks, state recreation areas, wildlife management areas, gamelands, state and national forests, local conservation lands with primary focus on recreational use.38 = State Board Lands and State Trust Lands: Lands in western and some southern states that are owned by the state and prevented from being developed, but which are managed to produce long term sustained revenue for the state’s educational system. These lands were separated from other protected multiple use lands in GAP 3. Most of these lands are subject to timber extraction and management for profitable forest product production. Some also have agricultural use and revenue generated from grazing and/or agricultural production leasing. These lands are not specifically managed for biodiversity values, and some are occasionally sold in periodic auctions by the state for revenue generation. Note this type of land is most commonly assigned GAP 3 in the PAD-US GAP classification.39 = Permanent Agricultural Easements: Conservation land where the primary intent is the preservation of farmland. Land is in a permanent agricultural easement or an easement to maintain grass cover. The land will not be converted to a built or paved development. Example: vegetable farm with permanent easement to prevent development. Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.4 = GAP 4: Areas with no known mandate for permanent biodiversity protection. Municipal lands and other protected open space (e.g. town commons, historic parks) where the intention in management and the use of the open space is not for permanent biodiversity values. It was beyond our capacity to comprehensively compile these GAP 4 lands, and as such, they are included only where source data made it feasible to easily incorporate them. 5 = Temporary Natural Easements: Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.6 = Temporary Agricultural Easements: Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.9 = Urban Parks: While unlikely to have biodiversity value, urban parks provide important places for recreation and open space for people. We went through and identified parks whose name is recreation based (i.e. Playground, Community garden, Golf, fields, baseball, soccer, Mini, school, elementary, Triangle, Pool, Aquatic, Sports, Pool, Athletic, Pocket, Splash, Skate, Dog, Cemetery, Boat). Note this type of land would be assigned GAP 4 in the PAD-US GAP classification.OWNERSHIP DEFINITIONSThe type of owner or interest holder for each polygon was assigned to a set of simple reporting categories as follows (see fields = Fee_Own_T and InterstH_T )TVA -Tennessee Valley Authority, BLM -Bureau of Land Management, , BOR- Bureau of Reclamation, FWS - U.S. Fish & Wildlife Service, UFS - Forest Service, DOD - Department of Defense, ACE - Army Corps of Engineers , DOE - Department of Energy, NPS - National Park Service, NRC - Natural Resources Conservation Service, FED – Federal Other, TRB - American Indian Lands, SPR - State Park and Recreation , SDC - State Department of Conservation, SLB - State Land Board , SFW - State Fish and Wildlife, SNR - State Department of Natural Resources, STL -State Department of Land, STA - Other or Unknown State Land, REG - Regional Agency Land, LOC – Local Government (City, County), NGO - Non-Governmental Organization, PVT- Private, JNT - Joint , OTH- Other , UNK - UnknownPROTECTION TYPE DEFINITIONS: (see field PRO_TYPE_D)DesignationEasementEasement and DesignationFeeFee and DesignationFee and EasementFee, Easement, and DesignationDATA SOURCES: The 2024 CONUS Secured Areas dataset was compiled by TNC from multiple sources. These include state, federal, and other non-profit and land trust data. The primarily datasets are listed below. 1. U.S. Geological Survey (USGS) Gap Analysis Project (GAP), 2022. Protected Areas Database of the United States (PAD-US) 3.0: U.S. Geological Survey data release, https://doi.org/10.5066/P9Q9LQ4B.) Downloaded 1/10/2024 Note this dataset was used as the primary source outside of the Northeast 13 states. For the Northeast states, please see more detailed source information below.2. National Conservation Easement Database (NCED) https://www.conservationeasement.us/ Downloaded 1/12/2024. Note this dataset was used outside the Northeast 13 states. For Northeast states, please see more detailed source information below. 3. Natural Resources Conservation Service (NRCS) Easements. 2024. Downloaded 1/12/2024 https://datagateway.nrcs.usda.gov/4. Conservation Science Partners, Inc. 2024. Wild and Scenic River corridor areas. Dataset compiled by Conservation Science Partners, Inc. for American Rivers as of 2/14/2024 (per. Communication Lise Comte , Conservation Science Partners, Inc. 2/14/2024)5. The Nature Conservancy. 2024. TNC Lands. Downloaded 3/1/2024.6. The Nature Conservancy Center for Resilient Conservation Science. 2021. Military Lands of the Southeast United States. Extracted from Secured areas spatial database (CONUS) 2021. https://tnc.maps.arcgis.com/home/item.html?id=e033e6bf6069459592903a04797b8b07.7. The Nature Conservancy Center for Resilient Conservation Science. 2022. Northeast States Secured Areas. https://tnc.maps.arcgis.com/home/item.html?id=fb80d71d5aa74a91a25e55b6f1810574

The Hydrology Feature Dataset contains photogrammetrically compiled water drainage features and structures including rivers, streams, drainage canals, locks, dams, lakes, ponds, reservoirs and mooring cells. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Polygons are created from Stream edges and River Edges. The Ohio River, Monongahela River and Allegheny River are coded as Major River polygons. All other River and Stream polygons are coded as River. A Drainage Canal is a manmade or channelized hydrographic feature. Drainage Canals are differentiated from streams in that drainage canals have had the sides and/or bottom stabilized to prevent erosion for the predominant length of the feature. Streams may have had some stabilization done, but are primarily in a natural state. Lakes are large standing bodies of water greater than five acres in size. Ponds are large standing bodies of water greater than one acre in size and less than five acres in size. Reservoirs are manmade embankments of water. Included in this definition are both covered and uncovered water tanks. Reservoirs that are greater than one acre in size are digitized. Hidden Streams, Hidden Rivers and Hidden Drainage Canal or Culverts are those areas of drainage where the water flows through a manmade facility such as a culvert. Hydrology Annotation is not being updated but will be preserved. If a drainage feature has been removed, as apparent on the aerial photography, the associated drainage name annotation will be removed. A Mooring Cell is a structure to which tows can tie off while awaiting lockage. They are normally constructed of concrete and steel and are anchored to the river bottom by means of gravity or sheet piling. Mooring Cells do not currently exist in the Allegheny County dataset but will be added. Locks are devices that are used to control flow or access to a hydrologic feature. The edges of the Lock are captured. Dams are devices that are used to hold or delay the natural flow of water. The edges of the Dam are shown.

If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below.

Category: Environment

Organization: Allegheny County

Department: Geographic Information Systems Group; Department of Administrative Services

Temporal Coverage: 2006

Data Notes:

Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot

Development Notes: Original Lakes and Drainage datasets combined to create this layer. Data was updated as a result of a flyover in the spring of 2004. A database field has been defined for all map features named "Update Year". This database field will define which dataset provided each map feature. Map features from the current map will be set to "2004". The earlier dataset map features the earlier dataset map features used to supplement the area near the county boundary will be set to "1993". All new or modified map data will have the value for "Update Year" set to "2004".

Other: none

Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/16BWrRkoPtq2ANRkrbG7CrfQk2dUsWRiaS2Ee1mTn7l0/edit?usp=sharing)

Frequency - Data Change: As needed

Frequency - Publishing: As needed

Data Steward Name: Eli Thomas

Data Steward Email: gishelp@alleghenycounty.us

The Hydrology Feature Dataset contains photogrammetrically compiled water drainage features and structures including rivers, streams, drainage canals, locks, dams, lakes, ponds, reservoirs and mooring cells. Rivers, Lakes, Ponds, Reservoirs, Hidden Lakes, Reservoirs or Ponds: If greater than 25 feet and less than 30 feet wide, is captured as a double line stream. If greater than 30 feet wide it is captured as a river. Lakes are large standing bodies of water greater than 5 acres in size. Ponds are large standing bodies of water greater than 1 acre and less than 5 acres in size. Polygons are created from Stream edges and River Edges. The Ohio River, Monongahela River and Allegheny River are coded as Major Rivers. All other River and Stream polygons are coded as River. If a stream is less than 25 feet wide it is placed as a single line and coded as a Stream. Both sides of the stream are digitized and coded as a Stream for Streams whose width is greater than 25 feet. River edges are digitized and coded as River. A Drainage Canal is a manmade or channelized hydrographic feature. Drainage Canals are differentiated from streams in that drainage canals have had the sides and/or bottom stabilized to prevent erosion for the predominant length of the feature. Streams may have had some stabilization done, but are primarily in a natural state. Lakes are large standing bodies of water greater than five acres in size. Ponds are large standing bodies of water greater than one acre in size and less than five acres in size. Reservoirs are manmade embankments of water. Included in this definition are both covered and uncovered water tanks. Reservoirs that are greater than one acre in size are digitized. Hidden Streams, Hidden Rivers and Hidden Drainage Canal or Culverts are those areas of drainage where the water flows through a manmade facility such as a culvert. Hydrology Annotation is not being updated but will be preserved. If a drainage feature has been removed, as apparent on the aerial photography, the associated drainage name annotation will be removed. A Mooring Cell is a structure to which tows can tie off while awaiting lockage. They are normally constructed of concrete and steel and are anchored to the river bottom by means of gravity or sheet piling. Mooring Cells do not currently exist in the Allegheny County dataset but will be added. Locks are devices that are used to control flow or access to a hydrologic feature. The edges of the Lock are captured. Dams are devices that are used to hold or delay the natural flow of water. The edges of the Dam are shown. If viewing this description on the Western Pennsylvania Regional Data Center’s open data portal (http://www.wprdc.org), this dataset is harvested on a weekly basis from Allegheny County’s GIS data portal (http://openac.alcogis.opendata.arcgis.com/). The full metadata record for this dataset can also be found on Allegheny County’s GIS portal. You can access the metadata record and other resources on the GIS portal by clicking on the “Explore” button (and choosing the “Go to resource” option) to the right of the “ArcGIS Open Dataset” text below. Category: Environment Organization: Allegheny County Department: Geographic Information Systems Group; Department of Administrative Services Temporal Coverage: 2006 Data Notes: Coordinate System: Pennsylvania State Plane South Zone 3702; U.S. Survey Foot Development Notes: Original Lakes and Drainage datasets combined to create this layer. Data was updated as a result of a flyover in the spring of 2004. A database field has been defined for all map features named "Update Year". This database field will define which dataset provided each map feature. Map features from the current map will be set to "2004". The earlier dataset map features the earlier dataset map features used to supplement the area near the county boundary will be set to "1993". All new or modified map data will have the value for "Update Year" set to "2004". Other: none Related Document(s): Data Dictionary (https://docs.google.com/spreadsheets/d/16BWrRkoPtq2ANRkrbG7CrfQk2dUsWRiaS2Ee1mTn7l0/edit?usp=sharing) Frequency - Data Change: As needed Frequency - Publishing: As needed Data Steward Name: Eli Thomas Data Steward Email: gishelp@alleghenycounty.us