Description of columns in the ArcGIS point file "Points for Maps" which provides the final statistics used to make the maps of mean daily water levels and maps of the 25th, 50th, and 75th percentiles of daily water levels during 2000–2009 in Miami-Dade County; and maps showing the differences in the statistics of water levels between 1990–1999 and 2000–2009.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually hig ...

Crowther_Nature_Files.zip This description pertains to the original download. Details on revised (newer) versions of the datasets are listed below. When more than one version of a file exists in Figshare, the original DOI will take users to the latest version, though each version technically has its own DOI. -- Two global maps (raster files) of tree density. These maps highlight how the number of trees varies across the world. One map was generated using biome-level models of tree density, and applied at the biome scale. The other map was generated using ecoregion-level models of tree density, and applied at the ecoregion scale. For this reason, transitions between biomes or between ecoregions may be unrealistically harsh, but large-scale estimates are robust (see Crowther et al 2015 and Glick et al 2016). At the outset, this study was intended to generate reliable estimates at broad spatial scales, which inherently comes at the cost of fine-scale precision. For this reason, country-scale (or larger) estimates are generally more robust than individual pixel-level estimates. Additionally, due to data limitations, estimates for Mangroves and Tropical coniferous forest (as identified by WWF and TNC) were generated using models constructed from Topical moist broadleaf forest data and Temperate coniferous forest data, respectively. Because we used ecological analogy, the estimates for these two biomes should be considered less reliable than those of other biomes . These two maps initially appeared in Crowther et al (2015), with the biome map being featured more prominently. Explicit publication of the data is associated with Glick et al (2016). As they are produced, updated versions of these datasets, as well as alternative formats, will be made available under Additional Versions (see below).

Methods: We collected over 420,000 ground-sources estimates of tree density from around the world. We then constructed linear regression models using vegetative, climatic, topographic, and anthropogenic variables to produce forest tree density estimates for all locations globally. All modeling was done in R. Mapping was done using R and ArcGIS 10.1.

Viewing Instructions: Load the files into an appropriate geographic information system (GIS). For the original download (ArcGIS geodatabase files), load the files into ArcGIS to view or export the data to other formats. Because these datasets are large and have a unique coordinate system that is not read by many GIS, we suggest loading them into an ArcGIS dataframe whose coordinate system matches that of the data (see File Format). For GeoTiff files (see Additional Versions), load them into any compatible GIS or image management program.

Comments: The original download provides a zipped folder that contains (1) an ArcGIS File Geodatabase (.gdb) containing one raster file for each of the two global models of tree density – one based on biomes and one based on ecoregions; (2) a layer file (.lyr) for each of the global models with the symbology used for each respective model in Crowther et al (2015); and an ArcGIS Map Document (.mxd) that contains the layers and symbology for each map in the paper. The data is delivered in the Goode homolosine interrupted projected coordinate system that was used to compute biome, ecoregion, and global estimates of the number and density of trees presented in Crowther et al (2015). To obtain maps like those presented in the official publication, raster files will need to be reprojected to the Eckert III projected coordinate system. Details on subsequent revisions and alternative file formats are list below under Additional Versions.----------

Additional Versions: Crowther_Nature_Files_Revision_01.zip contains tree density predictions for small islands that are not included in the data available in the original dataset. These predictions were not taken into consideration in production of maps and figures presented in Crowther et al (2015), with the exception of the values presented in Supplemental Table 2. The file structure follows that of the original data and includes both biome- and ecoregion-level models.

Crowther_Nature_Files_Revision_01_WGS84_GeoTiff.zip contains Revision_01 of the biome-level model, but stored in WGS84 and GeoTiff format. This file was produced by reprojecting the original Goode homolosine files to WGS84 using nearest neighbor resampling in ArcMap. All areal computations presented in the manuscript were computed using the Goode homolosine projection. This means that comparable computations made with projected versions of this WGS84 data are likely to differ (substantially at greater latitudes) as a product of the resampling. Included in this .zip file are the primary .tif and its visualization support files.

References:

Crowther, T. W., Glick, H. B., Covey, K. R., Bettigole, C., Maynard, D. S., Thomas, S. M., Smith, J. R., Hintler, G., Duguid, M. C., Amatulli, G., Tuanmu, M. N., Jetz, W., Salas, C., Stam, C., Piotto, D., Tavani, R., Green, S., Bruce, G., Williams, S. J., Wiser, S. K., Huber, M. O., Hengeveld, G. M., Nabuurs, G. J., Tikhonova, E., Borchardt, P., Li, C. F., Powrie, L. W., Fischer, M., Hemp, A., Homeier, J., Cho, P., Vibrans, A. C., Umunay, P. M., Piao, S. L., Rowe, C. W., Ashton, M. S., Crane, P. R., and Bradford, M. A. 2015. Mapping tree density at a global scale. Nature, 525(7568): 201-205. DOI: http://doi.org/10.1038/nature14967Glick, H. B., Bettigole, C. B., Maynard, D. S., Covey, K. R., Smith, J. R., and Crowther, T. W. 2016. Spatially explicit models of global tree density. Scientific Data, 3(160069), doi:10.1038/sdata.2016.69.

The distribution map of Festuca dolichophylla relies on diverse data sources. Geographical coordinates (latitude and longitude) and country initials (countryCode) were extracted from Tropicos, the Gbif repository (up to May 2019), and the iDigBio database (up to July 2021). Additionally, data from other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999), were integrated. The Gbif data points are associated with gbifID numbers for reference. Please note that this compilation provides essential information for understanding the distribution of F. dolichophylla across various regions.

Software

Organized data by geographic coordinates was uploaded to ArcGIS Pro v. 3.2.0 for map production. Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Methods

The dataset for the distribution map of Festuca dolichophylla was meticulously collected from various sources.

Data Collection:

Tropicos: Data were extracted from Tropicos until December 2023.

Gbif Repository: Data was sourced from the Gbif repository until May 2019.

iDigBio Database: Additional data points were retrieved from the iDigBio database up to July 2021.

Other Sources: We also incorporated data from various other sources, including BMAP Peru (2023), Eduardo-Palomino (2022), Ccora et al. (2019), Arana et al. (2013), Castro (2019), Flores (2017), Gonzales (2017), and Martínez y Pérez (1999).

Data Organization and Processing:

All collected data points were meticulously organized by coordinates.

We ensured consistency by cross-referencing and validating the data.

The dataset was then uploaded to ArcGIS Pro v. 3.2.0 for map production.

Geospatial visualization and mapping were carried out using ArcGIS Pro, allowing us to create the distribution map of F. dolichophylla.

Funding

Neotropical Grassland Conservancy, Award: Memorial grant 2020

Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. Parcel boundaries in GIS are created and maintained by the Assessor’s Division Mapping section and Information Technology Services. There are approximately 51,300 real property parcels in Napa County. Parcels delineate the approximate boundaries of property ownership as described in Napa County deeds, filed maps, and other source documents. GIS parcel boundaries are maintained by the Information Technology Services GIS team. Assessor Parcel Maps are created and maintained by the Assessor Division Mapping Section. Each parcel has an Assessor Parcel Number (APN) that is its unique identifier. The APN is the link to various Napa County databases containing information such as owner name, situs address, property value, land use, zoning, flood data, and other related information. Data for this map service is sourced from the Napa County Parcels dataset which is updated nightly with any recent changes made by the mapping team. There may at times be a delay between when a document is recorded and when the new parcel boundary configuration and corresponding information is available in the online GIS parcel viewer.From 1850 to early 1900s assessor staff wrote the name of the property owner and the property value on map pages. They began using larger maps, called “tank maps” because of the large steel cabinet they were kept in, organized by school district (before unification) on which names and values were written. In the 1920s, the assessor kept large books of maps by road district on which names were written. In the 1950s, most county assessors contracted with the State Board of Equalization for board staff to draw standardized 11x17 inch maps following the provisions of Assessor Handbook 215. Maps were originally drawn on linen. By the 1980’s Assessor maps were being drawn on mylar rather than linen. In the early 1990s Napa County transitioned from drawing on mylar to creating maps in AutoCAD. When GIS arrived in Napa County in the mid-1990s, the AutoCAD images were copied over into the GIS parcel layer. Sidwell, an independent consultant, was then contracted by the Assessor’s Office to convert these APN files into the current seamless ArcGIS parcel fabric for the entire County. Beginning with the 2024-2025 assessment roll, the maps are being drawn directly in the parcel fabric layer.Parcels in the GIS parcel fabric are drawn according to the legal description using coordinate geometry (COGO) drawing tools and various reference data such as Public Lands Survey section boundaries and road centerlines. The legal descriptions are not defined by the GIS parcel fabric. Any changes made in the GIS parcel fabric via official records, filed maps, and other source documents are uploaded overnight. There is always at least a 6-month delay between when a document is recorded and when the new parcel configuration and corresponding information is available in the online parcel viewer for search or download.Parcel boundary accuracy can vary significantly, with errors ranging from a few feet to several hundred feet. These distortions are caused by several factors such as: the map projection - the error derived when a spherical coordinate system model is projected into a planar coordinate system using the local projected coordinate system; and the ground to grid conversion - the distortion between ground survey measurements and the virtual grid measurements. The aim of the parcel fabric is to construct a visual interpretation that is adequate for basic geographic understanding. This digital data is intended for illustration and demonstration purposes only and is not considered a legal resource, nor legally authoritative.SFAP & CFAP DISCLAIMER: Per the California Code, RTC 606. some legal parcels may have been combined for assessment purposes (CFAP) or separated for assessment purposes (SFAP) into multiple parcels for a variety of tax assessment reasons. SFAP and CFAP parcels are assigned their own APN number and primarily result from a parcel being split by a tax rate area boundary, due to a recorded land use lease, or by request of the property owner. Assessor parcel (APN) maps reflect when parcels have been separated or combined for assessment purposes, and are one legal entity. The goal of the GIS parcel fabric data is to distinguish the SFAP and CFAP parcel configurations from the legal configurations, to convey the legal parcel configurations. This workflow is in progress. Please be advised that while we endeavor to restore SFAP and CFAP parcels back to their legal configurations in the primary parcel fabric layer, SFAP and CFAP parcels may be distributed throughout the dataset. Parcels that have been restored to their legal configurations, do not reflect the SFAP or CFAP parcel configurations that correspond to the current property tax delineations. We intend for parcel reports and parcel data to capture when a parcel has been separated or combined for assessment purposes, however in some cases, information may not be available in GIS for the SFAP/CFAP status of a parcel configuration shown. For help or questions regarding a parcel’s SFAP/CFAP status, or property survey data, please visit Napa County’s Surveying Services or Property Mapping Information. For more information you can visit our website: When a Parcel is Not a Parcel | Napa County, CA

Data last synced 11-07-2025 04:26. Data synced on a Weekly interval.

MCGD_Data_V2.2 contains all the data that we have collected on locations in modern China, plus a number of locations outside of China that we encounter frequently in historical sources on China. All further updates will appear under the name "MCGD_Data" with a time stamp (e.g., MCGD_Data2023-06-21)

You can also have access to this dataset and all the datasets that the ENP-China makes available on GitLab: https://gitlab.com/enpchina/IndexesEnp

Altogether there are 464,970 entries. The data include the name of locations and their variants in Chinese, pinyin, and any recorded transliteration; the name of the province in Chinese and in pinyin; Province ID; the latitude and longitude; the Name ID and Location ID, and NameID_Legacy. The Name IDs all start with H followed by seven digits. This is the internal ID system of MCGD (the NameID_Legacy column records the Name IDs in their original format depending on the source). Locations IDs that start with "DH" are data points extracted from China Historical GIS (Harvard University); those that start with "D" are locations extracted from the data points in Geonames; those that have only digits (8 digits) are data points we have added from various map sources.

One of the main features of the MCGD Main Dataset is the systematic collection and compilation of place names from non-Chinese language historical sources. Locations were designated in transliteration systems that are hardly comprehensible today, which makes it very difficult to find the actual locations they correspond to. This dataset allows for the conversion from these obsolete transliterations to the current names and geocoordinates.

From June 2021 onward, we have adopted a different file naming system to keep track of versions. From MCGD_Data_V1 we have moved to MCGD_Data_V2. In June 2022, we introduced time stamps, which result in the following naming convention: MCGD_Data_YYYY.MM.DD.

UPDATES

MCGD_Data2023.12.22 contains all the data that we have collected on locations in China, whatever the period. Altogether there are 465,603 entries (of which 187 place names without geocoordinates, labelled in the Lat Long columns as "Unknown"). The dataset also includes locations outside of China for the purpose of matching such locations to the place names extracted from historical sources. For example, one may need to locate individuals born outside of China. Rather than maintaining two separate files, we made the decision to incorporate all the place names found in historical sources in the gazetteer. Such place names can easily be removed by selecting all the entries where the 'Province' data is missing.

Statistical analyses and maps representing mean, high, and low water-level conditions in the surface water and groundwater of Miami-Dade County were made by the U.S. Geological Survey, in cooperation with the Miami-Dade County Department of Regulatory and Economic Resources, to help inform decisions necessary for urban planning and development. Sixteen maps were created that show contours of (1) the mean of daily water levels at each site during October and May for the 2000-2009 water years; (2) the 25th, 50th, and 75th percentiles of the daily water levels at each site during October and May and for all months during 2000-2009; and (3) the differences between mean October and May water levels, as well as the differences in the percentiles of water levels for all months, between 1990-1999 and 2000-2009. The 80th, 90th, and 96th percentiles of the annual maximums of daily groundwater levels during 1974-2009 (a 35-year period) were computed to provide an indication of unusually high groundwater-level conditions. These maps and statistics provide a generalized understanding of the variations of water levels in the aquifer, rather than a survey of concurrent water levels. Water-level measurements from 473 sites in Miami-Dade County and surrounding counties were analyzed to generate statistical analyses. The monitored water levels included surface-water levels in canals and wetland areas and groundwater levels in the Biscayne aquifer. Maps were created by importing site coordinates, summary water-level statistics, and completeness of record statistics into a geographic information system, and by interpolating between water levels at monitoring sites in the canals and water levels along the coastline. Raster surfaces were created from these data by using the triangular irregular network interpolation method. The raster surfaces were contoured by using geographic information system software. These contours were imprecise in some areas because the software could not fully evaluate the hydrology given available information; therefore, contours were manually modified where necessary. The ability to evaluate differences in water levels between 1990-1999 and 2000-2009 is limited in some areas because most of the monitoring sites did not have 80 percent complete records for one or both of these periods. The quality of the analyses was limited by (1) deficiencies in spatial coverage; (2) the combination of pre- and post-construction water levels in areas where canals, levees, retention basins, detention basins, or water-control structures were installed or removed; (3) an inability to address the potential effects of the vertical hydraulic head gradient on water levels in wells of different depths; and (4) an inability to correct for the differences between daily water-level statistics. Contours are dashed in areas where the locations of contours have been approximated because of the uncertainty caused by these limitations. Although the ability of the maps to depict differences in water levels between 1990-1999 and 2000-2009 was limited by missing data, results indicate that near the coast water levels were generally higher in May during 2000-2009 than during 1990-1999; and that inland water levels were generally lower during 2000-2009 than during 1990-1999. Generally, the 25th, 50th, and 75th percentiles of water levels from all months were also higher near the coast and lower inland during 2000–2009 than during 1990-1999. Mean October water levels during 2000-2009 were generally higher than during 1990-1999 in much of western Miami-Dade County, but were lower in a large part of eastern Miami-Dade County.

The Address Points dataset shows Utah address points for all twenty-nine Utah counties. An address point represents a geographic location that has been assigned a US Postal Service (USPS) address by the local address authority (i.e., county or municipality) but does not necessarily receive mail. Address points may include several pieces of information about the structure or location that’s being mapped, such as:the full address (i.e., the USPS mailing address, if the address is for a physical location [rather than a PO box]);the landmark name; whether the location is a building;the type of unit;the city and ZIP code; unique code identifiers of the specific geographic location, including the Federal Information Processing Standard Publication (FIPS) county code and the US National Grid (USNG) spatial address;the address source; andthe date that the address point was loaded into the map layer.This dataset is mapping grade; it is a framework layer that receives regular updates. As with all our datasets, the Utah Geospatial Resource Center (UGRC) works to ensure the quality and accuracy of our data to the best of our abilities. Maintaining the dataset is now an ongoing effort between UGRC, counties, and municipalities. Specifically, UGRC works with each county or municipality’s Master Address List (MAL) authority to continually improve the address point data. Counties have been placed on an update schedule depending on the rate of new development and change within them. Populous counties, such as Weber, Davis, Salt Lake, Utah, and Washington, are more complete and are updated monthly, while rural or less populous counties may be updated quarterly or every six months.The information in the Address Points dataset was originally compiled by Utah counties and municipalities and was aggregated by UGRC for the MAL grant initiative in 2012. The purpose of this initiative was to make sure that all state entities were using the same verified, accurate county and municipal address information. Since 2012, more data has been added to the Address Points GIS data and is used for geocoding, 911 response, and analysis and planning purposes. The Address Point data is also used as reference data for the api.mapserv.utah.gov geocoding endpoint, and you can find the address points in many web mapping applications. This dataset is updated monthly and can also be found at: https://gis.utah.gov/data/location/address-data/.

Local relief is the amount of elevation change (in meters) within a local area. This layer shows local relief within a 6-km neighborhood. Local relief is a useful component for many environmental assessment models, including terrain analysis, because it gives insight into local variation of soil and vegetation characteristics. This local relief layer provides the amount of elevation change (in meters) within a 6-km neighborhood.Dataset SummaryThis layer provides relief values calculated from GMTED elevation data (250-meter resolution). To produce this layer, the GMTED elevation data was projected to World Equidistant Cylindrical. For each cell in that raster, a neighborhood analysis summarized the elevation range in a 6-km circle. Each cell was then assigned a local relief class based on the difference between the highest and lowest elevation values within a 6-km neighborhood. The cells in this layer are not clipped to the coastlines because local relief is measured to the extent of the neighborhood, which allows for analysis of relief along coasts.This layer is provided using the World Web Mercator (Auxiliary Sphere) coordinate system, and the underlying data was projected from World Equidistant Cylindrical to WGS_1984. The latter coordinate system most easily and correctly supports re-projection into any relevant coordinate system needed for analysis, with the least amount of data loss.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. 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.

Updated 10/6/2022: In the Time/Distance analysis process, points that were found to have been included initially, but with no significant or year-round population were removed. The layer of removed points is also available for viewing. MCNA - Removed Population PointsThe Network Adequacy Standards Representative Population Points feature layer contains 97,694 points spread across California that were created from USPS postal delivery route data and US Census data. Each population point also contains the variables for Time and Distance Standards for the County that the point is within. These standards differ by County due to the County "type" which is based on the population density of the county. There are 5 county categories within California: Rural (<50 people/sq mile), Small (51-200 people/sq mile), Medium (201-599 people/sq mile), and Dense (>600 people/sq mile). The Time and Distance data is divided out by Provider Type, Adult and Pediatric separately, so that the Time or Distance analysis can be performed with greater detail. HospitalsOB/GYN SpecialtyAdult Cardiology/Interventional CardiologyAdult DermatologyAdult EndocrinologyAdult ENT/OtolaryngologyAdult GastroenterologyAdult General SurgeryAdult HematologyAdult HIV/AIDS/Infectious DiseaseAdult Mental Health Outpatient ServicesAdult NephrologyAdult NeurologyAdult OncologyAdult OphthalmologyAdult Orthopedic SurgeryAdult PCPAdult Physical Medicine and RehabilitationAdult PsychiatryAdult PulmonologyPediatric Cardiology/Interventional CardiologyPediatric DermatologyPediatric EndocrinologyPediatric ENT/OtolaryngologyPediatric GastroenterologyPediatric General SurgeryPediatric HematologyPediatric HIV/AIDS/Infectious DiseasePediatric Mental Health Outpatient ServicesPediatric NephrologyPediatric NeurologyPediatric OncologyPediatric OphthalmologyPediatric Orthopedic SurgeryPediatric PCPPediatric Physical Medicine and RehabilitationPediatric PsychiatryPediatric Pulmonology

Summary:

The files contained herein represent green roof footprints in NYC visible in 2016 high-resolution orthoimagery of NYC (described at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_AerialImagery.md). Previously documented green roofs were aggregated in 2016 from multiple data sources including from NYC Department of Parks and Recreation and the NYC Department of Environmental Protection, greenroofs.com, and greenhomenyc.org. Footprints of the green roof surfaces were manually digitized based on the 2016 imagery, and a sample of other roof types were digitized to create a set of training data for classification of the imagery. A Mahalanobis distance classifier was employed in Google Earth Engine, and results were manually corrected, removing non-green roofs that were classified and adjusting shape/outlines of the classified green roofs to remove significant errors based on visual inspection with imagery across multiple time points. Ultimately, these initial data represent an estimate of where green roofs existed as of the imagery used, in 2016.

These data are associated with an existing GitHub Repository, https://github.com/tnc-ny-science/NYC_GreenRoofMapping, and as needed and appropriate pending future work, versioned updates will be released here.

Terms of Use:

The Nature Conservancy and co-authors of this work shall not be held liable for improper or incorrect use of the data described and/or contained herein. Any sale, distribution, loan, or offering for use of these digital data, in whole or in part, is prohibited without the approval of The Nature Conservancy and co-authors. The use of these data to produce other GIS products and services with the intent to sell for a profit is prohibited without the written consent of The Nature Conservancy and co-authors. All parties receiving these data must be informed of these restrictions. Authors of this work shall be acknowledged as data contributors to any reports or other products derived from these data.

Associated Files:

As of this release, the specific files included here are:

• GreenRoofData2016_20180917.geojson is in the human-readable, GeoJSON format, in geographic coordinates (Lat/Long, WGS84; EPSG 4263).
• GreenRoofData2016_20180917.gpkg is in the GeoPackage format, which is an Open Standard readable by most GIS software including Esri products (tested on ArcMap 10.3.1 and multiple versions of QGIS). This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917_Shapefile.zip is a zipped folder containing a Shapefile and associated files. Please note that some field names were truncated due to limitations of Shapefiles, but columns are in the same order as for other files and in the same order as listed below. This dataset is in the New York State Plan Coordinate System (units in feet) for the Long Island Zone, North American Datum 1983, EPSG 2263.
• GreenRoofData2016_20180917.csv is a comma-separated values file (CSV) with coordinates for centroids for the green roofs stored in the table itself. This allows for easily opening the data in a tool like spreadsheet software (e.g., Microsoft Excel) or a text editor.

Column Information for the datasets:

Some, but not all fields were joined to the green roof footprint data based on building footprint and tax lot data; those datasets are embedded as hyperlinks below.

• fid - Unique identifier
• bin - NYC Building ID Number based on overlap between green roof areas and a building footprint dataset for NYC from August, 2017. (Newer building footprint datasets do not have linkages to the tax lot identifier (bbl), thus this older dataset was used). The most current building footprint dataset should be available at: https://data.cityofnewyork.us/Housing-Development/Building-Footprints/nqwf-w8eh. Associated metadata for fields from that dataset are available at https://github.com/CityOfNewYork/nyc-geo-metadata/blob/master/Metadata/Metadata_BuildingFootprints.md.
• bbl - Boro Block and Lot number as a single string. This field is a tax lot identifier for NYC, which can be tied to the Digital Tax Map (http://gis.nyc.gov/taxmap/map.htm) and PLUTO/MapPLUTO (https://www1.nyc.gov/site/planning/data-maps/open-data/dwn-pluto-mappluto.page). Metadata for fields pulled from PLUTO/MapPLUTO can be found in the PLUTO Data Dictionary found on the aforementioned page. All joins to this bbl were based on MapPLUTO version 18v1.
• gr_area - Total area of the footprint of the green roof as per this data layer, in square feet, calculated using the projected coordinate system (EPSG 2263).
• bldg_area - Total area of the footprint of the associated building, in square feet, calculated using the projected coordinate system (EPSG 2263).
• prop_gr - Proportion of the building covered by green roof according to this layer (gr_area/bldg_area).
• cnstrct_yr - Year the building was constructed, pulled from the Building Footprint data.
• doitt_id - An identifier for the building assigned by the NYC Dept. of Information Technology and Telecommunications, pulled from the Building Footprint Data.
• heightroof - Height of the roof of the associated building, pulled from the Building Footprint Data.
• feat_code - Code describing the type of building, pulled from the Building Footprint Data.
• groundelev - Lowest elevation at the building level, pulled from the Building Footprint Data.
• qa - Flag indicating a positive QA/QC check (using multiple types of imagery); all data in this dataset should have 'Good'
• notes - Any notes about the green roof taken during visual inspection of imagery; for example, it was noted if the green roof appeared to be missing in newer imagery, or if there were parts of the roof for which it was unclear whether there was green roof area or potted plants.
• classified - Flag indicating whether the green roof was detected image classification. (1 for yes, 0 for no)
• digitized - Flag indicating whether the green roof was digitized prior to image classification and used as training data. (1 for yes, 0 for no)
• newlyadded - Flag indicating whether the green roof was detected solely by visual inspection after the image classification and added. (1 for yes, 0 for no)
• original_source - Indication of what the original data source was, whether a specific website, agency such as NYC Dept. of Parks and Recreation (DPR), or NYC Dept. of Environmental Protection (DEP). Multiple sources are separated by a slash.
• address - Address based on MapPLUTO, joined to the dataset based on bbl.
• borough - Borough abbreviation pulled from MapPLUTO.
• ownertype - Owner type field pulled from MapPLUTO.
• zonedist1 - Zoning District 1 type pulled from MapPLUTO.
• spdist1 - Special District 1 pulled from MapPLUTO.
• bbl_fixed - Flag to indicate whether bbl was manually fixed. Since tax lot data may have changed slightly since the release of the building footprint data used in this work, a small percentage of bbl codes had to be manually updated based on overlay between the green roof footprint and the MapPLUTO data, when no join was feasible based on the bbl code from the building footprint data. (1 for yes, 0 for no)

For GreenRoofData2016_20180917.csv there are two additional columns, representing the coordinates of centroids in geographic coordinates (Lat/Long, WGS84; EPSG 4263):

• xcoord - Longitude in decimal degrees.
• ycoord - Latitude in decimal degrees.

Acknowledgements:

This work was primarily supported through funding from the J.M. Kaplan Fund, awarded to the New York City Program of The Nature Conservancy, with additional support from the New York Community Trust, through New York City Audubon and the Green Roof Researchers Alliance.

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

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.

Unlock precise, high-quality Map data covering 164M+ verified locations across 220+ countries. With 50+ enriched attributes including coordinates, building structures, and spatial geometry our dataset provides the granularity and accuracy needed for in-depth spatial analysis. Powered by AI-driven enrichment and deduplication, and backed by 30+ years of expertise, our GIS solutions support industries ranging from mapping and navigation to urban planning and market analysis, helping businesses and organizations make smarter, data-driven decisions.

Key use cases of GIS Data helping our customers :

1. Optimize Mapping & Spatial Analysis : Use GIS data to analyse landscapes, urban infrastructure, and competitor locations, ensuring data-driven planning and decision-making.
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This feature class is updated every business day using Python scripts and the Permit database. Please disregard the "Date Updated" field as it does not keep in sync with DWR's internal enterprise geodatabase updates. This dataset contains the points of diversion (POD) for water rights based on the coordinate location (XY) provided in the NDWR’s Permit Database. Since there can be multiple permits on the same POD site, this dataset contains duplicate point features where several permits may be stacked on top of each other spatially. The advantage to using this dataset is that all permits in NDWR’s Permit database are available. Use a filter or definition query to restrict the permits needed.Background:NDWR’s Permit Database was created in 1992. Water Right applications are entered into the database with the Township Range and Section (TRS) of the proposed place of use). The Permit Database was designed to automatically create the point of diversion (POD) based on the centroid of the TRS provided.Starting in 2007, the Hydrology section began mapping PODs by the permit application description. Water rights points of diversion are mapped that contain one of the following: coordinate location (XY), bearing/distance based on a monument tie, application map that can be georeferenced, parcel number, or location description that can be identified on a topo map. The workflow for mapping PODs includes updating the auto-generated POD in the Permit Database to the location coordinates derived from mapping the application description. Some older water rights including Vested or Decreed Water Rights may not be mapped due to lack of sufficient location information.The Water Rights Section of NDWR is responsible for reviewing and approving water rights applications, for new appropriations and for changes to existing water rights, as well as evaluating and responding to protests of applications, approving subdivision dedications for water quantity, evaluating domestic well credits and relinquishments, issuing certificates for permitted water rights, conducting field investigations, and processing requests for extensions of time for filing proofs of completion and proofs of beneficial use.Please note that this POD feature class may not contain all water right information on a site or permit. The GIS datasets do not replace the need to review the Permit database and hard copy permit files and are intended for convenience in sharing information on a map, finding a location, seeing spatial patterns, and planning.Code Descriptions:app_status app_status_nameABN ABANDONED (inactive)ABR ABROGATED (inactive)APP APPLICATION (pending)CAN CANCELLED (inactive)CER CERTIFICATE (active)CUR CURTAILED (inactive)DEC DECREED (active)DEN DENIED (inactive)EXP EXPIRED (inactive)FOR FORFEITED (inactive)PER PERMIT (active)REJ REJECTED (inactive)REL RELINQUISHED (inactive)RES RESERVED (pending)RFA READY FOR ACTION (pending)RFP READY FOR ACTION PROTESTED (pending)RLP RELINQUISH A PORTION (active)RSC RESCINDED (inactive)RVK REVOKED (inactive)RVP REVOCABLE PERMIT (active)SUP SUPERSEDED (inactive)SUS SUSPENDED (inactive)VST VESTED RIGHT (pending)WDR WITHDRAWN (inactive)manner of use (mou) use_nameCOM COMMERCIALCON CONSTRUCTIONDEC AS DECREEDDOM DOMESTICDWR DEWATERINGENV ENVIRONMENTALIND INDUSTRIALIRC IRRIGATION-CAREY ACTIRD IRRIGATION-DLEIRR IRRIGATIONMM MINING AND MILLINGMUN MUNICIPALOTH OTHERPWR POWERQM QUASI-MUNICIPALREC RECREATIONALSTK STOCKWATERINGSTO STORAGEUKN UNKNOWNWLD WILDLIFEMMD MINING, MILLING AND DEWATERINGEVP EVAPORATIONsource source_nameEFF EFFLUENTGEO GEOTHERMALLAK LAKEOGW OTHER GROUND WATEROSW OTHER SURFACE WATERRES RESERVOIRSPR SPRINGSTO STORAGESTR STREAMUG UNDERGROUNDDate Field Descriptions:Permit Date—Date the permit was issued.File Date—Date application was filed at the Division.Sent for Publication—Date the notice that the application was filed was sent to the newspaper of record for publication.Last Publication—The last date of publication of said notice in the paper; 30 days from this date is the last day for filing a protest to an application.POC Filed Date—When a Proof of Completion of Work is accepted by this office, it becomes “filed” rather than just received. The filed date is the same as the received date.

Vegetation surveys included in this dataset have been collected through various vegetation classification and mapping projects, and some were also collected independently from these kinds of projects. Reports for these projects can be found in CDFWs document library see https://nrm.dfg.ca.gov/documents/ContextDocs.aspx?cat=VegCAMP. Links to specific projects are provided with project descriptions below. The surveys are of various types, but many follow the rapid assessment/releve protocol which can be seen here: https://wildlife.ca.gov/Data/VegCAMP/Publications-and-ProtocolsThere are associated disturbance and other environmental data taken for many of these surveys that are not included in this dataset. There are also associated photos. These are available upon request.

Download In State Plane Projection Here. ** The Street Centerline feature class now follows the NG911/State of Illinois data specifications including a StreetNameAlias table. The download hyperlink above also contains a full network topology for use with the Esri Network Analyst extension ** These street centerlines were developed for a myriad of uses including E-911, as a cartographic base, and for use in spatial analysis. This coverage should include all public and selected private roads within Lake County, Illinois. Roads are initially entered using recorded documents and then later adjusted using current aerial photography. This dataset should satisfy National Map Accuracy Standards for a 1:1200 product. These centerlines have been provided to the United States Census Bureau and were used to conflate the TIGER road features for Lake County. The Census Bureau evaluated these centerlines and, based on field survey of 109 intersections, determined that there is a 95% confidence level that the coordinate positions in the centerline dataset fall within 1.9 meters of their true ground position. The fields PRE_DIR, ST_NAME, ST_TYPE and SUF_DIR are formatted according to United States Postal Service standards. Update Frequency: This dataset is updated on a weekly basis.