Download In State Plane Projection Here. This Trails theme is the result of a collaborative effort by the Lake County Division of Transportation, the Lake County Forest Preserve District, the municipalities of Gurnee, Highland Park, Lake Forest, Libertyville, Lincolnshire, Vernon Hills, and Waukegan among others, and the GIS Division of the Lake County Department of Information Technology. Note that the trails in this theme are actually constructed, open, in-use trails. This theme does not include any future trail plans or trails under new construction. In addition, trail loops not greater than around 1000 ft were not included (i.e. to avoid isolated playgrounds). All the geometry has been verified to match the 2014 aerials as much as possible but some areas have been drawn with older aerials when the 2014 tree cover was too dense. Therefore the spatial reference is now in NAD83(NSRS2007) vs HARN to match the 2014 aerials. The intended usage scale for this theme is 1" = 100' or a scale ratio of 1:1200. This specification derives from the scale of the orthophotography used as a reference for the trail line features, for trails not mapped through field GPS data gathering. The bike, horse, snowmobile, and walking usage types were chosen since we have the most complete information on them. They are blank when information is not available. The surface types are photo interpreted and have not been field verified.Update Frequency:This dataset is updated on a weekly basis.

We analyzed the corpus of three geoscientific journals to investigate if there are enough locational references in research articles to apply a geographical search method, on the example of New Zealand. We counted place name occurrences that match records from the official Land Information New Zealand (LINZ) gazetteer in the titles, abstracts and full texts of freely available papers of the New Zealand Journal of Geology and Geophysics, the New Zealand Journal of Marine and Freshwater Research, and the Journal of Hydrology, New Zealand, for the years 1958 to 2015. We generated ISO standard compliant metadata records for each article including the spatial references and make them available in a public catalogue service.

articles_georef_count_data.xlsx: The counts and evaluation tracking of the place name occurrences in the journal articles.

summary_final.xlsx: Summary statistics for evaluation based on the counts data.

article_template.xml: XML template for ISO 19139 compliant metadata record filled for each article.

full_article.xml: Exemplary fully filled ISO 19139 compliant metadata record.

This Trails theme is the result of a collaborative effort by the Lake County Division of Transportation, the Lake County Forest Preserve District, the municipalities of Gurnee, Highland Park, Lake Forest, Libertyville, Lincolnshire, Vernon Hills, and Waukegan among others, and the GIS Division of the Lake County Department of Information Technology. Note that the trails in this theme are actually constructed, open, in-use trails. This theme does not include any future trail plans or trails under new construction. In addition, trail loops not greater than around 1000 ft were not included (i.e. to avoid isolated playgrounds). All the geometry has been verified to match the 2014 aerials as much as possible but some areas have been drawn with older aerials when the 2014 tree cover was too dense. Therefore the spatial reference is now in NAD83(NSRS2007) vs HARN to match the 2014 aerials. The intended usage scale for this theme is 1" = 100' or a scale ratio of 1:1200. This specification derives from the scale of the orthophotography used as a reference for the trail line features, for trails not mapped through field GPS data gathering. The bike, horse, snowmobile, and walking usage types were chosen since we have the most complete information on them. They are blank when information is not available. The surface types are photo interpreted and have not been field verified.Update Frequency:This dataset is updated on a weekly basis.

Link to the ScienceBase Item Summary page for the item described by this metadata record. Service Protocol: Link to the ScienceBase Item Summary page for the item described by this metadata record. Application Profile: Web Browser. Link Function: information

Description: These Reference grids have been created for the NaturaConnect project and are based on an intersection of the European Coastline delineation and the GADM database. Thee reference grids have been created in a way so that they are fully consistent with the EEA reference grid (https://www.eea.europa.eu/data-and-maps/data/eea-reference-grids-2), meaning that for example two 5km gridded cells fully match a 10km grid cell in width.

Filestructure: ReferenceGrid_Europe_{format}_{grain}

format is either "frac" for fractional data (which has been multiplied with 10000 to save in integer format) or binary (0,1).

grain is provided as layers in 100m, 1000m, 5000m, 10000m, 50000m spatial resolution. Alternative aggregations can be provided on request.

File format: The layers are gridded geoTiff files and can be loaded in any conventional Graphical Information System (GIS) or specific analytical programming languages (e.g. R or python). In addition external pyramids (.tfw) have been precreated to enable faster rendering.

Geographic projection: We use the Lamberts-Equal-Area Projection by default for all layers in NaturaConnect. This is an equal-area (but distorted shape) projection and commonly used by European institution with a focus on the European continent. For global layers the equal-area World Mollweide projection is used.

Sourcecode: The code to reproduce the layers has been made available in the "code" file.

We processed Sentinel-2 image time series from 2017 to 2023 for Switzerland with the Software FORCE (Frantz 2019) on the basis of Sentinel-2 images. The respective parameter files can be found here: Github. All the image time series consist of several TB and therefore access will be granted upon request. The available bands (in spatial reference system EPSG 3035) are the following: Red, Green, Blue, NIR, Red-Edge-1, Red-Edge-2, Red-Edge-3, SWIR-1, SWIR-2 The available indices (in spatial reference system EPSG 3035) are the following: CCI, CIRE, NDWI/NDMI, NDVI, EVI Processing On the basis of processed Sentinel-2 images for the 14 Sentinel-2 tiles covering Switzerland (T31TGN, T32TLT, T32TMT, T32UMU, T32TNT, T32TPT, T31TGM, T32TLS, T32TMS, T32TNS, T32TPS, T32TLR, T32TMR, T32TNR), we processed the image time series further with FORCE v. 3.7.8-12. We generated interpolated Sentinel-2 time series with a 5-day interval, corresponding to the theoretical revisit time of the Sentinel-2 satellites. It's important to note that the 5-day time series consist of interpolated and smoothed composites, not the original images. We used the radial basis convolutional filtering (RBF) available in the FORCE time series analysis (TSA) submodule (Schwieder et al. 2016). The RBF is similar to a spatial moving window average approach over time (Schwieder et al. 2016). We applied kernel width values of 10, 20, 30, and 50 days. We spectrally adjusted all the images to match Sentinel-2A, and we removed curve outliers and pixels that failed the quality checks for clouds and their shadows, snow, saturation, and limited illumination. The processed image time series are available in tiles of 30 by 30 km. Example images Uploaded is an example of the index EVI for one of the generated 30 by 30 km tiles located around the city of Zürich. The values are multiplied by 10.000. The time series spans the month of July from 2018.

This data set is to accompany the version 1.2.0 release of Demeter. Demeter can be accessed on GitHub here: https://github.com/JGCRI/demeter

## Structure
demeter_v1.2.0_data_supplement
├── config_gcam_reference.ini
├── example.py
├── inputs
│  ├── allocation
│  │  ├── gcam_regbasin_modis_v6_type5_5arcmin_constraint_alloc.csv
│  │  ├── gcam_regbasin_modis_v6_type5_5arcmin_kernel_weighting.csv
│  │  ├── gcam_regbasin_modis_v6_type5_5arcmin_observed_alloc.csv
│  │  ├── gcam_regbasin_modis_v6_type5_5arcmin_order_alloc.csv
│  │  ├── gcam_regbasin_modis_v6_type5_5arcmin_projected_alloc.csv
│  │  └── gcam_regbasin_modis_v6_type5_5arcmin_transition_alloc.csv
│  ├── constraints
│  │  ├── 000_nutrientavail_hswd_5arcmin.csv
│  │  └── 001_soilquality_hswd_5arcmin.csv
│  ├── observed
│  │  └── gcam_reg32_basin235_modis_v6_2010_5arcmin_sqdeg_wgs84_11Jul2019.zip
│  └── projected
│    └── gcam_ref_scenario_reg32_basin235_v5p1p3.csv
└── outputs
  └── gcam5p1p3_ref_2021-05-19_10h57m15s
    ├── log_files
    │  └── logfile_gcam5p1p3_ref_2021-05-19_10h57m15s.log
    └── spatial_landcover_tabular
      ├── landcover_2010_timestep.csv
      ├── landcover_2015_timestep.csv
      ├── landcover_2020_timestep.csv
      └── landcover_2025_timestep.csv

## Context
- **demeter_v1.2.0_data_supplement**: root directory
- **config_gcam_reference.ini**: example configuration file for Demeter. The `run_dir` needs to be set to the directory where this data has been downloaded to.
- **example.py**: sample Python script to run Demeter 
- **inputs**: directory containing the model input files 
- **allocation**: directory containing the model allocation input files 
- **gcam_regbasin_modis_v6_type5_5arcmin_constraint_alloc.csv**: allocation file to weight the influence of constraint. See https://github.com/JGCRI/demeter#constraint-weighting
- **gcam_regbasin_modis_v6_type5_5arcmin_kernel_weighting.csv**: allocation file to weight the influence of kernel density on extensification. See https://github.com/JGCRI/demeter#kernel-density-weighting
- **gcam_regbasin_modis_v6_type5_5arcmin_observed_alloc.csv**: allocation file to bin land classes in the observed spaital data input to Demeter's final output land types. See https://github.com/JGCRI/demeter#observational-spatial-data-class-allocation
- **gcam_regbasin_modis_v6_type5_5arcmin_order_alloc.csv**: allocation file having the order in which land classes will be processed. See https://github.com/JGCRI/demeter#treatment-order
- **gcam_regbasin_modis_v6_type5_5arcmin_projected_alloc.csv**: allocation file to bin the land classes in the projected data input to Demeter's final output land classes. See https://github.com/JGCRI/demeter#projected-land-class-allocation
- **gcam_regbasin_modis_v6_type5_5arcmin_transition_alloc.csv**: allocation file that defines the order in which one land class should transition to another. See https://github.com/JGCRI/demeter#transition-priority
- **constraints**: directory containing the model constraints 
- **000_nutrientavail_hswd_5arcmin.csv**: constraint file for nutrient availability from data provided in the Harmonized World Soil Database (HWSD) and described in Le Page et al. 2016 containing a feature id matching that in the observed spatial data and a weight per grid cell that describes how suitable a grid cell is for the target constraint. 
- **001_soilquality_hswd_5arcmin.csv**: constraint file for soil workability from data provided in the Harmonized World Soil Database (HWSD) and described in Le Page et al. 2016 containing a feature id matching that in the observed spatial data and a weight per grid cell that describes how suitable a grid cell is for the target constraint.
- **observed**: directory containing the observed spatial base layer data for Demeter 
- **gcam_reg32_basin235_modis_v6_2010_5arcmin_sqdeg_wgs84_11Jul2019.zip**: compressed CSV file of a 5-arcmin (0.083333-degree) resolution dataset of land cells that have a region/basin designation in GCAM. Values are in units square degrees. The observed data was derived from MODIS v6 Classification Scheme 5 data (Friedl et al. 2010) and binned into Demeter's final land cover types for each 5-arcmin grid cell
- **projected**: directory containing the projected data file
- **gcam_ref_scenario_reg32_basin235_v5p1p3.csv**: projected land allocation data for years 2010 through 2100 derived from a Global Change Analysis Model (GCAM; Calvin et al. 2019) version 5.1.3 output database for a Reference scenario. Demeter can build this file directly from a GCAM output XML database by adding the `gcam_database` option with a path to a target database into the `[PROJECTED]` section of the configuration file if so desired. GCAM output databases can be rather large; therefore, we provide the output of that query as a CSV in this data record.
- **outputs**: directory containing the expected outputs from a Demeter run under the example configuration for years 2010 through 2025 in 5 year timesteps
- **gcam5p1p3_ref_2021-05-19_10h57m15s**: directory from the example Demeter run to compare against. Other runs will generate a new directory with the appropriate time stamp.
- **log_files**: directory containing any log files 
- **logfile_gcam5p1p3_ref_2021-05-19_10h57m15s.log**: log file from the example run 
- **spatial_landcover_tabular**: directory containing the outputs from Demeter 
- **landcover_2010_timestep.csv**: output file from Demeter containing the fraction of land for each 5-arcmin grid cell for each final land type from Demeter for year 2010. Units are in fraction of a grid cell. Spatial reference is EPSG:4326 (WGS84) for the latitude and longitude of each grid cell centroid in degrees.
- **landcover_2015_timestep.csv**: output file from Demeter containing the fraction of land for each 5-arcmin grid cell for each final land type from Demeter for year 2015. Units are in fraction of a grid cell. Spatial reference is EPSG:4326 (WGS84) for the latitude and longitude of each grid cell centroid in degrees.
- **landcover_2020_timestep.csv**: output file from Demeter containing the fraction of land for each 5-arcmin grid cell for each final land type from Demeter for year 2020. Units are in fraction of a grid cell. Spatial reference is EPSG:4326 (WGS84) for the latitude and longitude of each grid cell centroid in degrees.
- **landcover_2025_timestep.csv**: output file from Demeter containing the fraction of land for each 5-arcmin grid cell for each final land type from Demeter for year 2025. Units are in fraction of a grid cell. Spatial reference is EPSG:4326 (WGS84) for the latitude and longitude of each grid cell centroid in degrees.

## References
Calvin, K., Patel, P., Clarke, L., Asrar, G., Bond-Lamberty, B., Cui, R. Y., Di Vittorio, A., Dorheim, K., Edmonds, J., Hartin, C., Hejazi, M., Horowitz, R., Iyer, G., Kyle, P., Kim, S., Link, R., McJeon, H., Smith, S. J., Snyder, A., Waldhoff, S., and Wise, M.: GCAM v5.1: representing the linkages between energy, water, land, climate, and economic systems, Geosci. Model Dev., 12, 677–698, https://doi.org/10.5194/gmd-12-677-2019, 2019.

Friedl, M. A., Sulla-Menashe, D., Tan, B., Schneider, A., Ramankutty, N., Sibley, A., andHuang, X. (2010). MODIS Collection 5 global land cover: Algorithm refinements and characterization of new datasets. Remote Sensing of Environment, 114, 168–182

Le Page, Y, West, T, Link, R and Patel, P 2016 Downscaling land use and land cover from the Global Change Assessment Model for coupling with Earth system models. Geosci. Model Dev., 9: 3055–3069. DOI: https://doi.org/10.5194/gmd-9-3055-2016

The service allows the visualization of match model analysis of air quality and of the atmospheric composition on Europe. Analyzed parameter : concentration of species at surface level. Unit : kg/m3. Analysis from step -24H to step -1H, i.e from validity time D-1 00 UTC to validity time D-1 23 UTC (D 00 UTC reference time), with a time step of 1 hour. D is the day of the reference time. Spatial resolution on a regular latitude x longitude grid of 0.1x0.1 degree. GRIB2 format. More information can be found in the technical guide available on the CAMS regional website in the documentation category.

CartoSat-1 (also known as IRS-P5) archive products are available as PAN-Aft (backward), PAN-Fore (forward) and Stereo (PAN-Aft and PAN-Fore). - Sensor: PAN - Products: PAN-Aft (backward), PAN-Fore (forward), Stereo (PAN-Aft+PAN-Fore) - Type: Panchromatic - Resolution (m): 2.5 - Coverage (km x km): 27 x 27 - System or radiometrically corrected - Ortho corrected (DN) - Neustralitz archive: 2007 - 2016 - Global archive: 2005 - 2019 Note: - Resolution 2.5 m. - Coverage 27 km x 27 km. - System or radiometrically corrected. For Ortho corrected products: If unavailable, user has to supply ground control information and DEM in suitable quality, - For Stereo ortho corrected: only one of the datasets will be ortho corrected. Euro-Maps 3D is a homogeneous, 5 m spaced digital surface model (DSM) semi-automatically derived from 2.5 m in-flight stereo data provided by IRS-P5 CartoSat-1 and developed in cooperation with the German Aerospace Center, DLR. The very detailed and accurate representation of the surface is achieved by using a sophisticated and well adapted algorithm implemented on the basis of the Semi-Global Matching approach. In addition, the final product includes detailed flanking information consisting of several pixel-based quality and traceability layers also including an ortho layer. Product Overview: - Post spacing: 5m - Spatial reference system: DD, UTM or other projections on WGS84 - Height reference system: EGM96 - Absolute vertical accuracy: LE90 5-10 m - Absolute Horizontal Accuracy: CE90 5-10 m - Relative vertical accuracy: LE90 2.5 m - File format: GeoTIFF, 16 bit - Tiling: 0.5° x 0.5° - Ortho Layer Pixel Size: 2.5 m The CartoSat-1 products and Euro-Maps 3D are available as part of the GAF Imagery products from the Indian missions: IRS-1C, IRS-1D, CartoSat-1 (IRS-P5), ResourceSat-1 (IRS-P6) and ResourceSat-2 (IRS-R2) missions. ‘Cartosat-1 archive’ collection has worldwide coverage: for data acquired over Neustrelitz footprint, the users can browse the EOWEB GeoPortal catalogue (http://www.euromap.de/products/serv_003.html) to search archived products; worldwide data (out the Neustrelitz footprint) as well as Euro-Maps 3D DSM products can be requested by contacting GAF user support to check the readiness since no catalogue is available. All details about the data provision, data access conditions and quota assignment procedure are described into the Terms of Applicability available in Resources section.

The statewide roads dataset is maintained by AGRC in partnership with local government, the Utah 911 Committee, and UDOT.Questions can be directed to AGRC.Usage:Transportation.Roads utilizes the Statewide Roads Data Model Standard, formally known as the Utah Transportation Data Model. The UGIC Road Centerline Sub-committee has been in the process of revising the content and structure of the Utah Transportation Data Model. The final draft of what is now being referred to as the â Statewide Roads Data Model Standardâ is pending approval by the UGIC Standards Committee.View the data model attribute descriptions and definitions: https://docs.google.com/document/d/1ojjqCa1Z6IG6Wj0oAbZatoYsmbKzO9XwdD88-kqm-zQ/edit?usp=sharingView the data model schema diagram: https://docs.google.com/open?id=0Bz18jufMWioiU25icDNoQWlJa2MThe Statewide Roads Data Model Standard is divided into two â tierâ levels:Tier Level 1 fields pertain to address location, cartography, and routing. Stewards are encouraged to maintain data that will map to the Tier Level 1 fields. The field named DOT_* are an exception, as UDOT will maintain these themselves.Examples of the CARTOCODE (formally CFCC) field descriptions:1 â Interstates2 â US Highway, Separated4 â Major State Highway, Separated7 â Ramps, Collectors9 â Major Local Roads, PavedTier Level 2 fields pertain to local agency maintenance and inventory and are utilized and shared if the local steward chooses to do so.Changes to the data model include a more logical approach to the ordering of fields and their names, in addition to the removal of multiple redundant fields. Proposals by the UGIC Standards Committee are meant as suggested â best practicesâ and are primarily designed for data transfer and sharing. It is not the intent that all stewards will change their current data management process to match this standard, but rather that stewards will evealuate their current practices to see how well their data â mapsâ to this schema for data sharing purposes. Questions can be directed to AGRC.The native spatial reference for this dataset is UTM Zone 12N, NAD83 (0.01 meter coordinate precision).

The four-digit postal codes refer to the postal codes indicated in the official list of localities: https://www.cadastre.ch/de/services/service/plz.html Institutional postal codes, which are not specified in the locality directory, have been assigned to a postal code with a spatial reference (e.g. 3000 Bern 65 to 3014 Bern). An assignment to the municipalities is possible using the GWR correspondence table between the postal code and the municipality of the Federal Statistical Office: https://www.bfs.admin.ch/bfs/de/home/grundlagen/agvch/gwr-korrespondenztabelle.htmlLetzter Update March 2021The four-digit postal codes refer to the postal codes listed in the official register of localities: https://www.cadastre.ch/de/services/service/plz.html Institutional postal codes that are not listed in the list of localities have been assigned to a postal code with spatial reference (e.g. 3000 Bern 65 to 3014 Bern). An assignment to the municipalities is possible using the GWR correspondence table between postal code and municipality of the Federal Statistical Office: https://www.bfs.admin.ch/bfs/de/home/grundlagen/agvch/gwr-korrespondenztabelle.htmlLast Update march with data 2021 Year_An_Anno Year of the Cut-off date of the data extract. The data is extracted in week 51 of the Reference year. POSTCODE_NPA Four-digit Postcode according to the locality directory of cadastre.ch. GA_AG Number General subscriptions in circulation per cut-off date. GA_AG_flag If 1: For data protection reasons, the mean of the number is given General subscriptions for all postal code circles in the first postcode number match and list fewer than 20 subscriptions. HTA_ADT_meta-prezzo Number Half-fare subscriptions in circulation per cut-off date. HTA_ADT_meta-prezzo_flag If 2: For data protection reasons, the mean of the number is given Half-fare subscriptions for all postal code circles that have less than 20 subscriptions expulsion.

Recent reports show that focusing attention on the location where pain is expected can enhance its perception. Moreover, crossing the hands over the body’s midline is known to impair the ability to localise stimuli and decrease tactile and pain sensations in healthy participants. The present study investigated the role of transient spatial attention on the perception of painful and non-painful electrical stimuli in conditions in which a match or a mismatch was induced between skin-based and external frames of reference (uncrossed and crossed hands positions, respectively). We measured the subjective experience (Numerical Rating Scale scores) and the electrophysiological response elicited by brief electric stimuli by analysing the P3 component of Event-Related Potentials (ERPs). Twenty-two participants underwent eight painful and eight non-painful stimulus blocks. The electrical stimuli were applied to either the left or the right hand, held in either a crossed or uncrossed position. Each stimulus was preceded by a direction cue (leftward or rightward arrow). In 80% of the trials, the arrow correctly pointed to the spatial regions where the stimulus would appear (congruent cueing). Our results indicated that congruent cues resulted in increased pain NRS scores compared to incongruent ones. For non-painful stimuli such an effect was observed only in the uncrossed hands position. For both non-painful and painful stimuli the P3 peak amplitudes were higher and occurred later for incongruently cued stimuli compared to congruent ones. However, we found that crossing the hands substantially reduced the cueing effect of the P3 peak amplitudes elicited by painful stimuli. Taken together, our results showed a strong influence of transient attention manipulations on the NRS ratings and on the brain activity. Our results also suggest that hand position may modulate the strength of the cueing effect, although differences between painful and non-painful stimuli exist.

This service displays a licensed dataset from DigitalGlobe, Inc. USDA-NRCS-National Geospatial Center of Excellence acquired this dataset from the NOAA-Pacific Services Center. NOAA has purchased a Enterprise Premium license for this Orthoimagery dataset from DigitalGlobe, Inc. Any government, education, not-for-profit agency and public/individuals not engaged in using the "Product for Commercial Exploitation or Commercial Purposes" can use this licensed data. Use of this product for Commercial Purposes by a person/company/organization for a profit or fee is strictly prohibited. This dataset cannot be re-sold. Please refer to the separately attached license from DigitalGlobe, Inc. for additional information. Digital orthoimagery combines the image characteristics of a digital image with the geometric qualities of a map. The primary dynamic digital orthophoto is a 50 centimeter ground resolution, image cast to the customer specified projection and datum defined in the Spatial Reference Information section of this metadata document. The overedge is included to facilitate tonal matching for mosaicking and ensure full coverage if the imagery is reprojected. The normal orientation of data is by lines (rows) and samples (columns). Each line contains a series of pixels ordered from west to east with the order of the lines from north to south. DigitalGlobe WorldView-2 Satellite Orthoimagery was delivered to NOAA in GeoTIFF file format. This specific dataset was delivered to NOAA orthorectified to scale 1:12,000. This GeoTIFF composite contains the following four Multi-Spectral bands: Band 1: Blue (450 - 510 nm), Band 2: Green (510 - 580 nm), Band 3: Red (625 - 690 nm) and (585 - 625 nm) and Band 4: Color Infrared 1/NIR 1 (770 - 895 nm).

NYS Building Footprints - metadata info:The New York State building footprints service contains building footprints with address information. The footprints have address point information folded in from the Streets and Address Matching (SAM - https://gis.ny.gov/streets/) address point file. The building footprints have a field called “Address Range”, this field shows (where available) either a single address or an address range, depending on the address points that fall within the footprint. Ex: 3860 Atlantic Avenue or Ex: 32 - 34 Wheatfield Circle Building footprints in New York State are from four different sources: Microsoft, Open Data, New York State Energy Research and Development Authority (NYSERDA), and Geospatial Services. The majority of the footprints are from NYSERDA, except in NYC where the primary source was Open Data. Microsoft footprints were added where the other 2 sources were missing polygons. Field Descriptions: NYSGeo Source : tells the end user if the source is NYSERDA, Microsoft, NYC Open Data, and could expand from here in the futureAddress Point Count: the number of address points that fall within that building footprintAddress Range : If an address point falls within a footprint it lists the range of those address points. Ex: if a building is on a corner of South Pearl and Beaver Street, 40 points fall on the building, and 35 are South Pearl Street it would give the range of addresses for South Pearl. We also removed sub addresses from this range, primarily apartment related. For example, in above example, it would not list 30 South Pearl, Apartment 5A, it would list 30 South Pearl.Most Common Street : the street name of the largest number of address points. In the above example, it would list “South Pearl” as the most common street since the majority of address points list it as the street. Other Streets: the list of other streets that fall within the building footprint, if any. In the above example, “Beaver Street” would be listed since address points for Beaver Street fall on the footprint but are not in the majority.County Name : County name populated from CIESINs. If not populated from CIESINs, identified by the GSMunicipality Name : Municipality name populated from CIESINs. If not populated from CIESINs, identified by the GSSource: Source where the data came from. If NYSGeo Source = NYSERDA, the data would typically list orthoimagery, LIDAR, county data, etc.Source ID: if NYSGeo Source = NYSERDA, Source ID would typically list an orthoimage or LIDAR tileSource Date: Date the footprint was created. If the source image was from 2016 orthoimagery, 2016 would be the Source Date. Description of each footprint source:NYSERDA Building footprints that were created as part of the New York State Flood Impact Decision Support Systems https://fidss.ciesin.columbia.edu/home Footprints vary in age from county to county.Microsoft Building Footprints released 6/28/2018 - vintage unknown/varies. More info on this dataset can be found at https://blogs.bing.com/maps/2018-06/microsoft-releases-125-million-building-footprints-in-the-us-as-open-data.NYC Open Data - Building Footprints of New York City as a polygon feature class. Last updated 7/30/2018, downloaded on 8/6/2018. Feature Class of footprint outlines of buildings in New York City. Please see the following link for additional documentation- https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.mdSpatial Reference of Source Data: UTM Zone 18, meters, NAD 83. Spatial Reference of Web Service: Spatial Reference of Web Service: WGS 1984 Web Mercator Auxiliary Sphere.

Last Revised: February 2016

Map Information

This nowCOAST™ time-offsets map service provides maps depicting the NWS significant wave height forecasts from the National Digital Forecast Database (NDFD) at 6-hr increments out to 3 days. (NDFD has forecasts of waves out to 6 days which are available via the nowCOAST™ time-enabled map service for NDFD elements.) The wave heights are displayed in units of feet. The wave heights are indicated by different colors at 2 feet increments up to 20 feet and then at 5 feet increments up to 40 feet. The forecasts are updated in the nowCOAST™ map service four times per day.
For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

Background Information

The significant wave height is defined as the average height (trough to crest) of the highest one third of waves.

The NDFD is a seamless composite or mosaic of gridded forecasts from individual NWS Weather Forecast Offices (WFOs) from around the U.S. as well as the NCEP Ocean Prediction Center and National Hurricane Center/TAFB. NDFD has a spatial resolution of 2.5 km (1.6 miles). The time resolution of forecast projections varies by variable (element) based on user needs, forecast skill, and forecaster workload. Each WFO prepares gridded NDFD forecasts for their specific geographic area of responsibility. When these locally generated forecasts are merged into a national mosaic, occasionally areas of discontinuity will be evident. Staff at NWS forecast offices attempt to resolve discontinuities along the boundaries of the forecasts by coordinating with forecasters at surrounding WFOs and using workstation forecast tools that identify and resolve some of these differences. The NWS is making progress in this area, and recognizes that this is a significant issue in which improvements are still needed. The NDFD was developed by NWS Meteorological Development Laboratory.

Time Information

This nowCOAST™ map service is not time-enabled, although it does contain time-varying data. Instead of supporting the time dimension through use of a time parameter in each map request, each individual map layer contains data valid for a different "time offset", or forecast projection, from the dataset's reference time.

Due to software limitations, the full temporal resolution (i.e. maximum forecast horizon and/or all forecast projections) of the data is not provided by this service. Instead, a corresponding time-enabled service containing the full temporal resolution is available for this dataset, and users are highly encouraged to use that service instead, if possible.

This time-offsets map service is provided as a convenience for users who are not yet capable of interacting directly with the time dimension, especially users of legacy nowCOAST™ version 4 map services who wish to access the same data using the new nowCOAST™ version 5 map services. However, this service may be terminated with little advance notice at a later date.

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

In order to determine the latest time information about the data included in this map service, users have two options:

  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 reffered 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

NWS, 2007: National Digital Forecast Database (NDFD) Experimental Gridded Data, Product Description Document, NWS, Silver Spring, MD (Available at http://products.weather.gov/PDD/NDFDGrids.pdf). NWS, 2013: Experimental Gridded Marine Offshore and High Seas Forecasts in the National Digital Forecast Database (NDFD) Product Description Document, NWS, Silver Spring, MD. NWS, 2013: National Digital Forecast Database Element Definition, NWS, Silver Spring, MD (Available at http://www.nws.noaa.gov/ndfd/definitions.htm). NWS, 2014: NDFD Spatial Reference System. NOAA/NWS Meteorological Development Laboratory. (Available at http://graphical.weather.gov/docs/ndfdSRS.htm)

Abstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed …Show full descriptionAbstract This dataset and its metadata statement were supplied to the Bioregional Assessment Programme by a third party and are presented here as originally supplied. This shapefile was constructed by combining crown TSR spatial data, information gathered from Rural Lands Protection Board (RLPB) rangers, and surveyed Conservation and Biodiversity data to compile a layer within 30 RLPB districts in NSW. The layer attempts to spatially reflect current TSRs as accurately as possible with conservation attributes for each one. Dataset History The initial process in production involved using the most up to date extract of TSR from the crown spatial layer as a base map, as this layer should reasonably accurately spatially reflect the location, size, and attributes of TSR in NSW. This crown spatial layer from which the TSR were extracted is maintained by the NSW Department of Lands. The TSR extract is comprised of approximately 25,000 polygons in the study area. These polygons were then attributed with names, IDs and other attributes from the Long Paddock (LP) points layer produced by the RLPB State Council, which contains approximately 4000 named reserves throughout the study area. This layer reflects the names and ID number by which the reserves were or are currently managed by the RLPB's. This layer was spatially joined with the TSR polygon layer by proximity to produce a polygon layer attributed with RLPB reserve names and ID numbers. This process was repeated for other small datasets in order to link data with the polygon layer and LP reserve names. The next and by far the most time consuming and laborious process in the project was transferring the data gathered from surveys undertaken with RLPB rangers about each reserve (location, spatial extent, name, currency conservation value and biodiversity). This spatial information was annotated on hard copy maps and referenced against the spatial join making manual edits where necessary. Edits were conducted manually as the reference information was only on hard copy paper maps. Any corrections were made to the merged layer to produce an accurate spatial reflection of the RLPB reserves by name and ID. This manual editing process composed the bulk of the time for layer production as all reserves in each RLPB district in the study area had to be checked manually. Any necessary changes had to then be made to correct the spatial location of the reserve and ensure the correct ID was assigned for attributing the conservation data. In approximately 80% of cases the spatial join was correct, although this figure would be less where long chains of TSR polygons exist. The majority of time was devoted to making the numerous additions that needed to be incorporated. A spreadsheet based on the LP point layer was attributed with the LP point [OBJECTID] in order to produce a unique reference for each reserve so that conservation and biodiversity value data could be attributed against each reserve in the spatial layer being produced. Any new reserves were allocated [OBJECTID] number both in the GIS and the spreadsheet in order to create this link. All relevant data was entered into the spreadsheet and then edited to a suitable level to be attached as an attribute table. Field names were chosen and appropriate an interpretable data formats each field. The completed spreadsheet was then linked to the shapefile to produce a polygon TSR spatial layer containing all available conservation and biodiversity information. Any additional attribute were either entered manually or obtained by merging with other layers. Attributes for the final layer were selected for usability by those wishing to query valuable Conservation Value (CV) data for each reserve, along with a number of administrative attributes for locating and querying certain aspects of each parcel. Constant error checking was conducted throughout the process to ensure minimal error being transferred to the production. This was done manually, and also by running numerous spatial and attribute based queries to identify potential errors in the spatial layer being produced. Follow up phone calls were made to the rangers to identify exact localities of reserves where polygons could not be allocated due to missing or ambiguous information. If precise location data was provided, polygons could be added in, either from other crown spatial layers or from cadastre. These polygons were also attributed with the lowest confindex rating, as their status as crown land is unknown or doubtful. In some cases existing GIS layers had been created for certain areas. Murray RLPB has data where 400+ polygons do not exist in the current crown TSR extract. According to the rangers interviewed it was determined the majority of these TSR exist. This data was incorporated in the TSR polygon by merging the two layers and then assigning attributes in the normal way, ie by being given a LP Name and ID and then updated from the marked up hard copy maps. In the confidence index these are given a rating of 1 (see confindex matrix) due to the unknown source of the data and no match with any other crown spatial data. A confidence index matrix (confindex) was produced in order to give the end user of the GIS product an idea as to how the data for each reserve was obtained, its purpose, and an indication to whether it is likely to be a current TSR. The higher the confindex, the more secure the user can be in the data. (See Confidence Index Matrix) This was necessary due to conflicting information from a number of datasets, usually the RLPB ranger (mark up on hard copy map) conflicting with the crown spatial data. If these conflicting reserves were to be deleted, this would lead to a large amount of information loss during the project. If additions were made without sufficient data to determine its crown status, currency, location, etc (which was not available in all cases) the end user may rely on data that has a low level of accuracy. The confindex was produced by determining the value of information and scoring it accordingly, compounding its value if data sources showed a correlation. Where an RLPB LP Name and ID point was not assigned to a polygon due to other points being in closer proximity these names and ID are effectively deleted from the polygon layer. In a number of cases this was correct due to land being revoked, relinquished and/or now freehold. In a number of cases where the TSR is thought to exist and a polygon could not be assigned due to no info available (Lot/DP, close proximity to a crown reserve, further ranger interview provided no info, etc etc). For these cases to ensure no information loss a points layer was compiled from the LP points layer with further info from the marked up hard copy maps to place the point in the most accurate approximate location to where the reserve is though to exist and then all CV data attached to the point. In many of these cases some further investigation could provide an exact location and inclusion in the TSR poly layer. The accuracy of the point is mentioned in the metadata, so that the location is not taken as an absolute location and is only to be used as a guide for the approximate location of the reserve. Topology checks were conducted to eliminate slivers in the layer and to remove duplicate polygons. Where two crown reserves existed on the same land parcel, the duplicate polygon was deleted and unique attributes (Crown Reserve Number, Type, and Purpose) were transferred. Once the polygon layer was satisfactorily completed, a list of the LP points not allocated to polygons was compiled. Any points (reserves) that were said to have been revoked or relinquished were then removed from this list to provide a list of those that are said to be current. An extract of the LP points layer was then produced with only the aforementioned points. These points were then attributed with the same conservation and biodiversity data as the polygon layer, in an attempt to minimise the amount of information loss. Dataset Citation "NSW Department of Environment, Climate Change and Water" (2010) Travelling Stock Route Conservation Values. Bioregional Assessment Source Dataset. Viewed 13 March 2019, http://data.bioregionalassessments.gov.au/dataset/198900d5-0d06-4bd0-832b-e30a7c4e8873.

Abstract This paper analyzes, from a semantic typological perspective, the spatial subdomain of angular stasis or frames of reference in Toba of Western Formosa (Guaicuruan, Argentina). The corpus is composed of data obtained, through the visual stimulus ‘Men and Tree Photo Matching’ (Levinson et al., 1992), at Vaca Perdida (Formosa, Argentina). The frames of references found are absolute, object-centered, and direct. While absolute and object-centered frames of reference encode positional and orientational information, the direct one only expresses the orientation of the man from the deictic center. The absolute coordinate system, and the encoding of the orientation of the man are prominent in the corpus. The use of the same visual stimuli to study frames of reference among other languages of the South American Gran Chaco region will provide regionally comparable data and contribute to the knowledge of the structure of the spatial semantic domain.

Last Updated: January 2015

Map Information This nowCOAST time-offsets map service provides maps depicting the NWS daily maximum surface air temperature forecasts from the National Digital Forecast Database (NDFD) out to 3-4 days. (NDFD has forecasts out to 7 days which are available via the nowCOAST time-enabled map service for NDFD elements.) The maximum temperature forecast is valid for the daytime period of the day listed. The temperature forecast is the temperature expected at 2 m (6.6 ft) above ground level. The units are in degrees Fahrenheit. The temperature is indicated on the map by different colors at 2 degree F increments from -30 to 130 degrees F in order to use same color legend throughout the year for the United States. The forecasts are updated in the nowCOAST map service four times per day. For more detailed information about the update schedule, see: http://new.nowcoast.noaa.gov/help/#section=updateschedule

Background Information The maximum surface air temperature is the maximum temperature during the daytime hours.

The NDFD is a seamless composite or mosaic of gridded forecasts from individual NWS Weather Forecast Offices (WFOs) from around the U.S. as well as the NCEP Ocean Prediction Center and National Hurricane Center/TAFB in certain marine weather variables. NDFD has a spatial resolution of 2.5 km (1.6 miles). The time resolution of forecast projections varies by variable (element) based on user needs, forecast skill, and forecaster workload. Each WFO prepares gridded NDFD forecasts for their specific geographic area of responsibility. When these locally generated forecasts are merged into a national mosaic, occasionally areas of discontinuity will be evident. Staff at NWS forecast offices attempt to resolve discontinuities along the boundaries of the forecasts by coordinating with forecasters at surrounding WFOs and using workstation forecast tools that identify and resolve some of these differences. The NWS is making progress in this area, and recognizes that this is a significant issue in which improvements are still needed. The NDFD was developed by NWS Meteorological Development Laboratory.

Time Information

This nowCOAST map service is not time-enabled, although it does contain time-varying data. Instead of supporting the time dimension through use of a time parameter in each map request, each individual map layer contains data valid for a different "time offset", or forecast projection, from the dataset's reference time.

Due to software limitations, the full temporal resolution (i.e. maximum forecast horizon and/or all forecast projections) of the data is not provided by this service. Instead, a corresponding time-enabled service containing the full temporal resolution is available for this dataset, and users are highly encouraged to use that service instead, if possible.

This time-offsets map service is provided as a convenience for users who are not yet capable of interacting directly with the time dimension, especially users of legacy nowCOAST version 4 map services who wish to access the same data using the new nowCOAST version 5 map services. However, this service may be terminated with little advance notice at a later date.

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

In order to determine the latest time information about the data included in this map service, users have two options:

  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 reffered 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 help documentation at:
  http://new.nowcoast.noaa.gov/help/#section=layerinfo

References

NWS, 2007: National Digital Forecast Database (NDFD) Experimental Gridded Data, Product Description Document, NWS, Silver Spring, MD (Available at http://products.weather.gov/PDD/NDFDGrids.pdf). NWS, 2014: NDFD Spatial Reference System. NOAA/NWS Meteorological Development Laboratory. (Available at http://graphical.weather.gov/docs/ndfdSRS.htm)

Abstract

This dataset was derived by the Bioregional Assessment Programme. You can find a link to the parent datasets in the Lineage Field in this metadata statement. The History Field in this metadata statement describes how this dataset was derived.

This dataset converts the original Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828) shapefile into the Australian soil classification, as per data from Conversion of the Atlas of Australian Soils to the Australian Soil Classification (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662). A Layer file is also supplied using the RGB colour reference table, also found in the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset.

Purpose

Provides a spatial and cartographic representation of the Digital Atlas of Australian Soils shapefile into the new Australian soil classification.

Dataset History

From the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) the file asclut.txt was converted to .csv format and field headings added (MAP_UNIT, SOIL_CODE, SOIL_SYMBOL, SOIL).

This csv file (asclut.csv) was joined to the Digital Atlas of Australian Soils (GUID: 9e7d2f5b-ff51-4f0f-898a-a55be8837828), soilAtlas2M shapefile on the common 'MAP_UNIT' field. The resulting join was saved as 'soilAtlas2M_ASC_Conversion.shp'

The symbology of this shapefile was updated by matching the RGB values provided in the 'asc_colours.xls' spreadsheet from the Conversion of the Atlas of Australian Soils to the Australian Soil Classification dataset (GUID: 295707d5-2774-4ca5-a539-6c0426bbd662) to the 'SOIL' field. A Layer File was created 'soilAtlas2M_ASC_Conversion.lyr'

Dataset Citation

Bioregional Assessment Programme (2015) Spatial Data Conversion of the Atlas of Australian Soils to the Australian Soil Classification v01. Bioregional Assessment Derived Dataset. Viewed 29 September 2017, http://data.bioregionalassessments.gov.au/dataset/6f804e8b-2de9-4c88-adfa-918ec327c32f.

Dataset Ancestors

• Derived From Digital Atlas of Australian Soils

• Derived From Conversion of the Atlas of Australian Soils to the Australian Soil Classification

Last Revised: February 2016

Map Information

This nowCOAST™ time-offsets map service provides maps depicting the NWS 6-hr quantitative precipitation [amount] forecasts (QPF) from the National Digital Forecast Database (NDFD) ending at specified forecast projection hours out to 2-3 days (NDFD has forecasts out to 7 days which are available via the nowCOAST™ time-enabled map service for NDFD elements). The 6-hr QPF describes the amount of precipitation (rainfall, melted snow and/or sleet) occurring at a location within the specified 6-hour time period. The forecasts are updated in the nowCOAST™ map service four times per day. For more detailed information about layer update frequency and timing, please reference the
nowCOAST™ Dataset Update Schedule.

The 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.

Background Information

The 6-hr quantitative precipitation forecast (QPF) describes the amount of precipitation (rainfall, melted snow and/or sleet) occurring at a location within a specified 6-hour time period.

The NDFD is a seamless composite or mosaic of gridded forecasts from individual NWS Weather Forecast Offices (WFOs) from around the U.S. as well as the NCEP Ocean Prediction Center and National Hurricane Center/TAFB for certain marine weather variables. NDFD has a spatial resolution of 2.5 km (1.6 miles). The time resolution of forecast projections varies by variable (element) based on user needs, forecast skill, and forecaster workload. Each WFO prepares gridded NDFD forecasts for their specific geographic area of responsibility. When these locally generated forecasts are merged into a national mosaic, occasionally areas of discontinuity will be evident. Staff at NWS forecast offices attempt to resolve discontinuities along the boundaries of the forecasts by coordinating with forecasters at surrounding WFOs and using workstation forecast tools that identify and resolve some of these differences. The NWS is making progress in this area, and recognizes that this is a significant issue in which improvements are still needed. The NDFD was developed by NWS Meteorological Development Laboratory.

Time Information

This nowCOAST™ map service is not time-enabled, although it does contain time-varying data. Instead of supporting the time dimension through use of a time parameter in each map request, each individual map layer contains data valid for a different "time offset", or forecast projection, from the dataset's reference time.

Due to software limitations, the full temporal resolution (i.e. maximum forecast horizon and/or all forecast projections) of the data is not provided by this service. Instead, a corresponding time-enabled service containing the full temporal resolution is available for this dataset, and users are highly encouraged to use that service instead, if possible.

This time-offsets map service is provided as a convenience for users who are not yet capable of interacting directly with the time dimension, especially users of legacy nowCOAST™ version 4 map services who wish to access the same data using the new nowCOAST™ version 5 map services. However, this service may be terminated with little advance notice at a later date.

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

In order to determine the latest time information about the data included in this map service, users have two options:

  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 reffered 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

NWS, 2007: National Digital Forecast Database (NDFD) Experimental Gridded Data, Product Description Document, NWS, Silver Spring, MD (Available at http://products.weather.gov/PDD/NDFDGrids.pdf). NWS, 2014: NDFD Spatial Reference System. NOAA/NWS Meteorological Development Laboratory. (Available at http://graphical.weather.gov/docs/ndfdSRS.htm)