The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. We converted the photointerpreted data into a format usable in a geographic information system (GIS) by employing three fundamental processes: (1) orthorectify, (2) digitize, and (3) develop the geodatabase. All digital map automation was projected in Universal Transverse Mercator (UTM), Zone 16, using the North American Datum of 1983 (NAD83). Orthorectify: We orthorectified the interpreted overlays by using OrthoMapper, a softcopy photogrammetric software for GIS. One function of OrthoMapper is to create orthorectified imagery from scanned and unrectified imagery (Image Processing Software, Inc., 2002). The software features a method of visual orientation involving a point-and-click operation that uses existing orthorectified horizontal and vertical base maps. Of primary importance to us, OrthoMapper also has the capability to orthorectify the photointerpreted overlays of each photograph based on the reference information provided. Digitize: To produce a polygon vector layer for use in ArcGIS (Environmental Systems Research Institute [ESRI], Redlands, California), we converted each raster-based image mosaic of orthorectified overlays containing the photointerpreted data into a grid format by using ArcGIS. In ArcGIS, we used the ArcScan extension to trace the raster data and produce ESRI shapefiles. We digitally assigned map-attribute codes (both map-class codes and physiognomic modifier codes) to the polygons and checked the digital data against the photointerpreted overlays for line and attribute consistency. Ultimately, we merged the individual layers into a seamless layer. Geodatabase: At this stage, the map layer has only map-attribute codes assigned to each polygon. To assign meaningful information to each polygon (e.g., map-class names, physiognomic definitions, links to NVCS types), we produced a feature-class table, along with other supportive tables and subsequently related them together via an ArcGIS Geodatabase. This geodatabase also links the map to other feature-class layers produced from this project, including vegetation sample plots, accuracy assessment (AA) sites, aerial photo locations, and project boundary extent. A geodatabase provides access to a variety of interlocking data sets, is expandable, and equips resource managers and researchers with a powerful GIS tool.

This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. Data is extracted from the Chicago Police Department's CLEAR (Citizen Law Enforcement Analysis and Reporting) system. In order to protect the privacy of crime victims, addresses are shown at the block level only and specific locations are not identified. Should you have questions about this dataset, you may contact the Research & Development Division of the Chicago Police Department at 312.745.6071 or RandD@chicagopolice.org. Disclaimer: These crimes may be based upon preliminary information supplied to the Police Department by the reporting parties that have not been verified. The preliminary crime classifications may be changed at a later date based upon additional investigation and there is always the possibility of mechanical or human error. Therefore, the Chicago Police Department does not guarantee (either expressed or implied) the accuracy, completeness, timeliness, or correct sequencing of the information and the information should not be used for comparison purposes over time. The Chicago Police Department will not be responsible for any error or omission, or for the use of, or the results obtained from the use of this information. All data visualizations on maps should be considered approximate and attempts to derive specific addresses are strictly prohibited. The Chicago Police Department is not responsible for the content of any off-site pages that are referenced by or that reference this web page other than an official City of Chicago or Chicago Police Department web page. The user specifically acknowledges that the Chicago Police Department is not responsible for any defamatory, offensive, misleading, or illegal conduct of other users, links, or third parties and that the risk of injury from the foregoing rests entirely with the user. The unauthorized use of the words "Chicago Police Department," "Chicago Police," or any colorable imitation of these words or the unauthorized use of the Chicago Police Department logo is unlawful. This web page does not, in any way, authorize such use. Data is updated daily Tuesday through Sunday. The dataset contains more than 65,000 records/rows of data and cannot be viewed in full in Microsoft Excel. Therefore, when downloading the file, select CSV from the Export menu. Open the file in an ASCII text editor, such as Wordpad, to view and search. To access a list of Chicago Police Department - Illinois Uniform Crime Reporting (IUCR) codes, go to http://data.cityofchicago.org/Public-Safety/Chicago-Police-Department-Illinois-Uniform-Crime-R/c7ck-438e

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Using the National Vegetation Classification System (NVCS) developed by Natureserve, with additional classes and modifiers, overstory vegetation communities for each park were interpreted from stereo color infrared aerial photographs using manual interpretation methods. Using a minimum mapping unit of 0.5 hectares (MMU = 0.5 ha), polygons representing areas of relatively uniform vegetation were delineated and annotated on clear plastic overlays registered to the aerial photographs. Polygons were labeled according to the dominant vegetation community. Where the polygons were not uniform, second and third vegetation classes were added. Further, a number of modifier codes were employed to indicate important aspects of the polygon that could be interpreted from the photograph (for example, burn condition). The polygons on the plastic overlays were then corrected using photogrammetric procedures and converted to vector format for use in creating a geographic information system (GIS) database for each park. In addition, high resolution color orthophotographs were created from the original aerial photographs for use in the GIS. Upon completion of the GIS database (including vegetation, orthophotos and updated roads and hydrology layers), both hardcopy and softcopy maps were produced for delivery. Metadata for each database includes a description of the vegetation classification system used for each park, summary statistics and documentation of the sources, procedures and spatial accuracies of the data. At the time of this writing, an accuracy assessment of the vegetation mapping has not been performed for most of these parks.

In this research, the best management practices include vegetative/structural conservation practices (SCP) across crop fields, such as grassed waterways and terraces. This reference dataset includes 500,000 pair patches (false-color image (B1: NIR, B2: Red, B3: Green) and binary label (SCP: yes[1] or no[0]). These training samples were randomly extracted from Iowa BMP project (https://www.gis.iastate.edu/gisf/projects/conservation-practices) and present 90% of patches with SCP areas and 10% of patches non-SCP area. The patch dimension is 256 x 256 pixels at 2-m resolution. Due to the file size, the images were upload in different *.rar files (imagem_0_200k.rar, imagem_200_400k.rar, imagem_400_500k.rar), and the user should download all and merge them in the same folder. The corresponding labels are all in "class_bin.rar" file.

Application: These pair images are useful for conservation practitioners interested in the classification of vegetative/structural SCPs using deep-learning semantic segmentation methods.

Further information will be available in future.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. The products are designed with the goal of facilitating ecologically-based natural resources management and research. The vector (polygon) map is in digital format within a geodatabase structure that allows for complex relationships to be established between spatial and tabular data, and allows much of the data to be accessed concurrently. Each map unit has multiple photo attachments viewable easily from within the geodatabase, linked to their actual location on the ground. The Geographic Information System (GIS) format of the map allows user flexibility and will also enable updates to be made as new information becomes available (such as revised NVC codes or vegetation type names) or in the event of major disturbance events that could impact the vegetation. Unlike previous vegetation maps created by SODN, the map for Saguaro National Park was not created via in-situ mapping. Instead, we employed a remote sensing approach aided by our robust field dataset. The final version of the map was created in summer 2016. The map was created using the image-classification toolbox included in the spatial analyst extension for ArcMap (ESRI 2017). Using these tools, we performed a supervised classification with the maximum-likelihood classifier. This tool uses a set of user-defined training samples (polygons) to classify imagery by placing pixels with the maximum likelihood into each map class. We used a pixel size equivalent to the coarsest raster included in the classification, 30 meters.

This dataset contains model-based county-level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2018 or 2017 county population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2015 county boundary file in a GIS system to produce maps for 27 measures at the county level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

Focus on Geodatabases in ArcGIS Pro introduces readers to the geodatabase, the comprehensive information model for representing and managing geographic information across the ArcGIS platform.Sharing best practices for creating and maintaining data integrity, chapter topics include the careful design of a geodatabase schema, building geodatabases that include data integrity rules, populating geodatabases with existing data, working with topologies, editing data using various techniques, building 3D views, and sharing data on the web. Each chapter includes important concepts with hands-on, step-by-step tutorials, sample projects and datasets, 'Your turn' segments with less instruction, study questions for classroom use, and an independent project. Instructor resources are available by request.AUDIENCEProfessional and scholarly.AUTHOR BIODavid W. Allen has been working in the GIS field for over 35 years, the last 30 with the City of Euless, Texas, and has seen many versions of ArcInfo and ArcGIS come along since he started with version 5. He spent 18 years as an adjunct professor at Tarrant County College in Fort Worth, Texas, and now serves as the State Director of Operations for a volunteer emergency response group developing databases and templates. Mr. Allen is the author of GIS Tutorial 2: Spatial Analysis Workbook (Esri Press, 2016).Pub Date: Print: 6/17/2019 Digital: 4/29/2019 Format: PaperbackISBN: Print: 9781589484450 Digital: 9781589484467 Trim: 7.5 x 9.25 in.Price: Print: $59.99 USD Digital: $59.99 USD Pages: 260

The establishment of a BES Multi-User Geodatabase (BES-MUG) allows for the storage, management, and distribution of geospatial data associated with the Baltimore Ecosystem Study. At present, BES data is distributed over the internet via the BES website. While having geospatial data available for download is a vast improvement over having the data housed at individual research institutions, it still suffers from some limitations. BES-MUG overcomes these limitations; improving the quality of the geospatial data available to BES researches, thereby leading to more informed decision-making. BES-MUG builds on Environmental Systems Research Institute's (ESRI) ArcGIS and ArcSDE technology. ESRI was selected because its geospatial software offers robust capabilities. ArcGIS is implemented agency-wide within the USDA and is the predominant geospatial software package used by collaborating institutions. Commercially available enterprise database packages (DB2, Oracle, SQL) provide an efficient means to store, manage, and share large datasets. However, standard database capabilities are limited with respect to geographic datasets because they lack the ability to deal with complex spatial relationships. By using ESRI's ArcSDE (Spatial Database Engine) in conjunction with database software, geospatial data can be handled much more effectively through the implementation of the Geodatabase model. Through ArcSDE and the Geodatabase model the database's capabilities are expanded, allowing for multiuser editing, intelligent feature types, and the establishment of rules and relationships. ArcSDE also allows users to connect to the database using ArcGIS software without being burdened by the intricacies of the database itself. For an example of how BES-MUG will help improve the quality and timeless of BES geospatial data consider a census block group layer that is in need of updating. Rather than the researcher downloading the dataset, editing it, and resubmitting to through ORS, access rules will allow the authorized user to edit the dataset over the network. Established rules will ensure that the attribute and topological integrity is maintained, so that key fields are not left blank and that the block group boundaries stay within tract boundaries. Metadata will automatically be updated showing who edited the dataset and when they did in the event any questions arise. Currently, a functioning prototype Multi-User Database has been developed for BES at the University of Vermont Spatial Analysis Lab, using Arc SDE and IBM's DB2 Enterprise Database as a back end architecture. This database, which is currently only accessible to those on the UVM campus network, will shortly be migrated to a Linux server where it will be accessible for database connections over the Internet. Passwords can then be handed out to all interested researchers on the project, who will be able to make a database connection through the Geographic Information Systems software interface on their desktop computer. This database will include a very large number of thematic layers. Those layers are currently divided into biophysical, socio-economic and imagery categories. Biophysical includes data on topography, soils, forest cover, habitat areas, hydrology and toxics. Socio-economics includes political and administrative boundaries, transportation and infrastructure networks, property data, census data, household survey data, parks, protected areas, land use/land cover, zoning, public health and historic land use change. Imagery includes a variety of aerial and satellite imagery. See the readme: http://96.56.36.108/geodatabase_SAL/readme.txt See the file listing: http://96.56.36.108/geodatabase_SAL/diroutput.txt

The research focus in the field of remotely sensed imagery has shifted from collection and warehousing of data ' tasks for which a mature technology already exists, to auto-extraction of information and knowledge discovery from this valuable resource ' tasks for which technology is still under active development. In particular, intelligent algorithms for analysis of very large rasters, either high resolutions images or medium resolution global datasets, that are becoming more and more prevalent, are lacking. We propose to develop the Geospatial Pattern Analysis Toolbox (GeoPAT) a computationally efficient, scalable, and robust suite of algorithms that supports GIS processes such as segmentation, unsupervised/supervised classification of segments, query and retrieval, and change detection in giga-pixel and larger rasters. At the core of the technology that underpins GeoPAT is the novel concept of pattern-based image analysis. Unlike pixel-based or object-based (OBIA) image analysis, GeoPAT partitions an image into overlapping square scenes containing 1,000'100,000 pixels and performs further processing on those scenes using pattern signature and pattern similarity ' concepts first developed in the field of Content-Based Image Retrieval. This fusion of methods from two different areas of research results in orders of magnitude performance boost in application to very large images without sacrificing quality of the output.

GeoPAT v.1.0 already exists as the GRASS GIS add-on that has been developed and tested on medium resolution continental-scale datasets including the National Land Cover Dataset and the National Elevation Dataset. Proposed project will develop GeoPAT v.2.0 ' much improved and extended version of the present software. We estimate an overall entry TRL for GeoPAT v.1.0 to be 3-4 and the planned exit TRL for GeoPAT v.2.0 to be 5-6. Moreover, several new important functionalities will be added. Proposed improvements includes conversion of GeoPAT from being the GRASS add-on to stand-alone software capable of being integrated with other systems, full implementation of web-based interface, writing new modules to extent it applicability to high resolution images/rasters and medium resolution climate data, extension to spatio-temporal domain, enabling hierarchical search and segmentation, development of improved pattern signature and their similarity measures, parallelization of the code, implementation of divide and conquer strategy to speed up selected modules.

The proposed technology will contribute to a wide range of Earth Science investigations and missions through enabling extraction of information from diverse types of very large datasets. Analyzing the entire dataset without the need of sub-dividing it due to software limitations offers important advantage of uniformity and consistency. We propose to demonstrate the utilization of GeoPAT technology on two specific applications. The first application is a web-based, real time, visual search engine for local physiography utilizing query-by-example on the entire, global-extent SRTM 90 m resolution dataset. User selects region where process of interest is known to occur and the search engine identifies other areas around the world with similar physiographic character and thus potential for similar process. The second application is monitoring urban areas in their entirety at the high resolution including mapping of impervious surface and identifying settlements for improved disaggregation of census data.

This data was prepared as input for the Selkie GIS-TE tool. This GIS tool aids site selection, logistics optimization and financial analysis of wave or tidal farms in the Irish and Welsh maritime areas. Read more here: https://www.selkie-project.eu/selkie-tools-gis-technoeconomic-model/

This research was funded by the Science Foundation Ireland (SFI) through MaREI, the SFI Research Centre for Energy, Climate and the Marine and by the Sustainable Energy Authority of Ireland (SEAI). Support was also received from the European Union's European Regional Development Fund through the Ireland Wales Cooperation Programme as part of the Selkie project.

File Formats

Results are presented in three file formats:

tif Can be imported into a GIS software (such as ARC GIS) csv Human-readable text format, which can also be opened in Excel png Image files that can be viewed in standard desktop software and give a spatial view of results

Input Data

All calculations use open-source data from the Copernicus store and the open-source software Python. The Python xarray library is used to read the data.

Hourly Data from 2000 to 2019

• Wind - Copernicus ERA5 dataset 17 by 27.5 km grid
  10m wind speed

• Wave - Copernicus Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis dataset 3 by 5 km grid

Accessibility

The maximum limits for Hs and wind speed are applied when mapping the accessibility of a site.
The Accessibility layer shows the percentage of time the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5) are below these limits for the month.

Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined by checking if
the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total number of hours for the month.

Environmental data is from the Copernicus data store (https://cds.climate.copernicus.eu/). Wave hourly data is from the 'Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis' dataset.
Wind hourly data is from the ERA 5 dataset.

Availability

A device's availability to produce electricity depends on the device's reliability and the time to repair any failures. The repair time depends on weather
windows and other logistical factors (for example, the availability of repair vessels and personnel.). A 2013 study by O'Connor et al. determined the
relationship between the accessibility and availability of a wave energy device. The resulting graph (see Fig. 1 of their paper) shows the correlation between accessibility at Hs of 2m and wind speed of 15.0m/s and availability. This graph is used to calculate the availability layer from the accessibility layer.

The input value, accessibility, measures how accessible a site is for installation or operation and maintenance activities. It is the percentage time the
environmental conditions, i.e. the Hs (Atlantic -Iberian Biscay Irish - Ocean Wave Reanalysis) and wind speed (ERA5), are below operational limits.
Input data is 20 years of hourly wave and wind data from 2000 to 2019, partitioned by month. At each timestep, the accessibility of the site was determined
by checking if the Hs and wind speed were below their respective limits. The percentage accessibility is the number of hours within limits divided by the total
number of hours for the month. Once the accessibility was known, the percentage availability was calculated using the O'Connor et al. graph of the relationship between the two. A mature technology reliability was assumed.

Weather Window

The weather window availability is the percentage of possible x-duration windows where weather conditions (Hs, wind speed) are below maximum limits for the
given duration for the month.

The resolution of the wave dataset (0.05° × 0.05°) is higher than that of the wind dataset
(0.25° x 0.25°), so the nearest wind value is used for each wave data point. The weather window layer is at the resolution of the wave layer.

The first step in calculating the weather window for a particular set of inputs (Hs, wind speed and duration) is to calculate the accessibility at each timestep.
The accessibility is based on a simple boolean evaluation: are the wave and wind conditions within the required limits at the given timestep?

Once the time series of accessibility is calculated, the next step is to look for periods of sustained favourable environmental conditions, i.e. the weather
windows. Here all possible operating periods with a duration matching the required weather-window value are assessed to see if the weather conditions remain
suitable for the entire period. The percentage availability of the weather window is calculated based on the percentage of x-duration windows with suitable
weather conditions for their entire duration.The weather window availability can be considered as the probability of having the required weather window available
at any given point in the month.

Extreme Wind and Wave

The Extreme wave layers show the highest significant wave height expected to occur during the given return period. The Extreme wind layers show the highest wind speed expected to occur during the given return period.

To predict extreme values, we use Extreme Value Analysis (EVA). EVA focuses on the extreme part of the data and seeks to determine a model to fit this reduced
portion accurately. EVA consists of three main stages. The first stage is the selection of extreme values from a time series. The next step is to fit a model
that best approximates the selected extremes by determining the shape parameters for a suitable probability distribution. The model then predicts extreme values
for the selected return period. All calculations use the python pyextremes library. Two methods are used - Block Maxima and Peaks over threshold.

The Block Maxima methods selects the annual maxima and fits a GEVD probability distribution.

The peaks_over_threshold method has two variable calculation parameters. The first is the percentile above which values must be to be selected as extreme (0.9 or 0.998). The second input is the time difference between extreme values for them to be considered independent (3 days). A Generalised Pareto Distribution is fitted to the selected
extremes and used to calculate the extreme value for the selected return period.

This spatial dataset is quantifying soil organic carbon (SOC) and total nitrogen (TN) storage with their carbon to nitrogen ratio (C/N) in soils of the northern circumpolar permafrost region (17.9 × 10⁶ km²) based on ESA’s Climate Change Initiative (CCI) Global Land Cover dataset at 300 m pixel resolution.

The dataset contains GIS grids of the northern circumpolar permafrost region for SOC, TN and C/N ratio for the following depth increments (0⁠ – ⁠30, 0⁠ – ⁠50, 0⁠ – ⁠100, 100⁠ – ⁠200, 200⁠ – ⁠300, 0⁠ – ⁠300 cm). These GIS grids are based on 651 soil pedons encompassing more than 6500 samples from 16 different study areas across the northern permafrost region.

Additional metadata with the actual pedon and profile data is available as a companion dataset.

Citation

Juri Palmtag, Jaroslav Obu, Peter Kuhry, Matthias Siewert, Niels Weiss, Gustaf Hugelius (2022) A high spatial resolution soil carbon and nitrogen dataset for the northern permafrost region. Dataset version 1. Bolin Centre Database. https://doi.org/10.17043/palmtag-2022-spatial-1

Data description

The dataset contains 18 GIS grids of the northern circumpolar permafrost region; one grid for each of the three variables SOC, TN and C/N ratio for each of the following six depth increments (0⁠ – ⁠30, 0⁠ – ⁠50, 0⁠ – ⁠100, 100⁠ – ⁠200, 200⁠ – ⁠300, 0⁠ – ⁠300 cm).

There GIS grids are provided as tif, tfw and xml files. Total uncompressed file size: 10 GB. A compressed (zip) file is available for download. Compressed file size: 141.5 MB.

Comments

The used permafrost region dataset stretches over 17.9 × 10⁶ km² of the Northern Hemisphere, and is based on equilibrium state model for the temperature at the top of the permafrost (TTOP) for the 2000⁠ – ⁠2016 period (Obu et al. 2019).

Detailed pedon data on soil carbon and nitrogen for the northern permafrost region, based on 6529 analyzed samples from 651 soil pedons in 16 different sampling locations, is available as a companion dataset.

The GIS dataset is based on the companion dataset and is part of a publication.

All profiles were assigned to land cover class based on field descriptions. The main aim of the field studies compiled in the companion dataset was to perform SOC/TN pool inventories of each study area considering different land cover types, geomorphological landforms and soil properties.

For the upscaling, we used the land cover map from the Global ESA Land cover Climate Change Initiative (CCI) project at 300 m spatial resolution, retrieved from the ESA Climate Change Initiative Land Cover visualization interface.

We thank the ESA CCI Land Cover project for providing their data, which was used for upscaling our product to circumpolar scale.

This dataset contains model-based ZIP Code tabulation Areas (ZCTA) level estimates for the PLACES project 2020 release in GIS-friendly format. The PLACES project is the expansion of the original 500 Cities project and covers the entire United States—50 states and the District of Columbia (DC)—at county, place, census tract, and ZIP Code tabulation Areas (ZCTA) levels. It represents a first-of-its kind effort to release information uniformly on this large scale for local areas at 4 geographic levels. Estimates were provided by the Centers for Disease Control and Prevention (CDC), Division of Population Health, Epidemiology and Surveillance Branch. The project was funded by the Robert Wood Johnson Foundation (RWJF) in conjunction with the CDC Foundation. Data sources used to generate these model-based estimates include Behavioral Risk Factor Surveillance System (BRFSS) 2018 or 2017 data, Census Bureau 2010 population estimates, and American Community Survey (ACS) 2014-2018 or 2013-2017 estimates. The 2020 release uses 2018 BRFSS data for 23 measures and 2017 BRFSS data for 4 measures (high blood pressure, taking high blood pressure medication, high cholesterol, and cholesterol screening). Four measures are based on the 2017 BRFSS data because the relevant questions are only asked every other year in the BRFSS. These data can be joined with the census 2010 ZCTA boundary file in a GIS system to produce maps for 27 measures at the ZCTA level. An ArcGIS Online feature service is also available at https://www.arcgis.com/home/item.html?id=8eca985039464f4d83467b8f6aeb1320 for users to make maps online or to add data to desktop GIS software.

Splitgraph serves as an HTTP API that lets you run SQL queries directly on this data to power Web applications. For example:

See the Splitgraph documentation for more information.

The files linked to this reference are the geospatial data created as part of the completion of the baseline vegetation inventory project for the NPS park unit. Current format is ArcGIS file geodatabase but older formats may exist as shapefiles. Our final map product is a geographic information system (GIS) database of vegetation structure and composition across the Crater Lake National Park terrestrial landscape, including wetlands. The database includes photos we took at all relevé, validation, and accuracy assessment plots, as well as the plots that were done in the previous wetlands inventory. We conducted an accuracy assessment of the map by evaluating 698 stratified random accuracy assessment plots throughout the project area. We intersected these field data with the vegetation map, resulting in an overall thematic accuracy of 86.2 %. The accuracy of the Cliff, Scree & Rock Vegetation map unit was difficult to assess, as only 9% of this vegetation type was available for sampling due to lack of access. In addition, fires that occurred during the 2017 accuracy assessment field season affected our sample design and may have had a small influence on the accuracy. Our geodatabase contains the locations where particular associations are found at 600 relevé plots, 698 accuracy assessment plots, and 803 validation plots.

The Millennium Coral Reef Mapping Project provides thematic maps of coral reefs worldwide at geomorphological scale. Maps were created by photo-interpretation of Landsat 7 and Landsat 8 satellite images. Maps are provided as standard Shapefiles usable in GIS software. The geomorphological classification scheme is hierarchical and includes 5 levels. The GIS products include for each polygon a number of attributes. The 5 level geomorphological attributes are provided (numerical codes or text). The Level 1 corresponds to the differentiation between oceanic and continental reefs. Then from Levels 2 to 5, the higher the level, the more detailed the thematic classification is. Other binary attributes specify for each polygon if it belongs to terrestrial area (LAND attribute), and sedimentary or hard-bottom reef areas (REEF attribute). Examples and more details on the attributes are provided in the references cited. The products distributed here were created by IRD, in their last version. Shapefiles for 102 atolls of France (in the Pacific and Indian Oceans) as mapped by the Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). The data set provides one zip file per region of interest. Global coral reef mapping project at geomorphological scale using LANDSAT satellite data (L7 and L8). Funded by National Aeronautics and Space Administration, NASA grants NAG5-10908 (University of South Florida, PIs: Franck Muller-Karger and Serge Andréfouët) and CARBON-0000-0257 (NASA, PI: Julie Robinson) from 2001 to 2007. Funded by IRD since 2003 (in kind, PI: Serge Andréfouët).

These data show locations of samples and research areas at Konza that do not fit under our standard classifications. Record type 1 (GIS 600) contains the locations of the Hulbert plots on Konza Prairie. Record type 6 (GIS605) contains locations for rainfall shelters, ramps, experimental streams, restoration plots, the weather station, grasshopper cages, the climate extremes project. Currently no associated LTER datasets exist for these locations. Record type 11 (GIS 610) provides a record of the historic Konza gridded location system. Older datasets may reference these locations with a column letter and row number. Record type 16 (GIS615) contains the location for the Clean Air Status and Trends Network (CASTNET) site on Konza Prairie. For more information, visit the following link: http://www.epa.gov/castnet/javaweb/site_pages/KNZ184.html. Record type 21 (GIS620) contains the location for the USGS gauging station. These data may be used in conjunction with the Stream Discharge for Kings Creek Measured at USGS Gauging Station (ASD01) dataset. For more information, visit the following link: http://waterdata.usgs.gov/nwis/nwisman/?site_no=06879650. Record types 31 (GIS630) and 36 (GIS635) contain the location and treatment information for two bison grant grazing experiments. Currently, no associated LTER datasets exist for these data.

The GIS data maintained by HPPM includes information on buildings and grounds related to Harvard University. Our "standard" base layers are available to Harvard affiliates and their service providers (for example, architects) working on Harvard projects in AutoCAD DWG, ESRI SHP or File Geodatabase format. Additional datasets are sometimes available by special arrangement. http://home.hppm.harvard.edu/pages/gis-data-layers

This data release includes GIS datasets supporting the Colorado Legacy Mine Lands Watershed Delineation and Scoring tool (WaDeS), a web mapping application available at https://geonarrative.usgs.gov/colmlwades/. Water chemistry data were compiled from the U.S. Geological Survey (USGS) National Water Information System (NWIS), U.S. Environmental Protection Agency (EPA) STORET database, and the USGS Central Colorado Assessment Project (CCAP) (Church and others, 2009). The CCAP study area was used for this application. Samples were summarized at each monitoring station and hardness-dependent chronic and acute toxicity thresholds for aquatic life protections under Colorado Regulation No. 31 (CDPHE, 5 CCR 1002-31) for cadmium, copper, lead, and/or zinc were calculated. Samples were scored according to how metal concentrations compared with acute and chronic toxicity thresholds. The results were used in combination with remote sensing derived hydrothermal alteration (Rockwell and Bonham, 2017) and mine-related features (Horton and San Juan, 2016) to identify potential mine remediation sites within the headwaters of the central Colorado mineral belt. Headwaters were defined by watersheds delineated from a 10-meter digital elevation dataset (DEM), ranging in 5-35 square kilometers in size. Python and R scripts used to derive these products are included with this data release as documentation of the processing steps and to enable users to adapt the methods for their own applications. References Church, S.E., San Juan, C.A., Fey, D.L., Schmidt, T.S., Klein, T.L. DeWitt, E.H., Wanty, R.B., Verplanck, P.L., Mitchell, K.A., Adams, M.G., Choate, L.M., Todorov, T.I., Rockwell, B.W., McEachron, Luke, and Anthony, M.W., 2012, Geospatial database for regional environmental assessment of central Colorado: U.S. Geological Survey Data Series 614, 76 p., https://doi.org/10.3133/ds614. Colorado Department of Public Health and Environment (CDPHE), Water Quality Control Commission 5 CCR 1002-31. Regulation No. 31 The Basic Standards and Methodologies for Surface Water. Effective 12/31/2021, accessed on July 28, 2023 at https://cdphe.colorado.gov/water-quality-control-commission-regulations. Horton, J.D., and San Juan, C.A., 2022, Prospect- and mine-related features from U.S. Geological Survey 7.5- and 15-minute topographic quadrangle maps of the United States (ver. 8.0, September 2022): U.S. Geological Survey data release, https://doi.org/10.5066/F78W3CHG. Rockwell, B.W. and Bonham, L.C., 2017, Digital maps of hydrothermal alteration type, key mineral groups, and green vegetation of the western United States derived from automated analysis of ASTER satellite data: U.S. Geological Survey data release, https://doi.org/10.5066/F7CR5RK7.

The "Restoration Projects" feature layer is a component of the "Pollinator Restoration 2022" map which is itself a component of the "USFWS Pollinator Restoration Projects Mapper" which is a dashboard showing management projects that benefit pollinators across the Western U.S. See below for a description of the "USFWS Pollinator Restoration Projects Mapper."The "USFWS Pollinator Restoration Projects Mapper" is under development by the Region 1 (Pacific Northwest) USFWS Science Applications program. Completion is anticipated by Winter 2023. Contact: Alan Yanahan (alan_yanahan@fws.gov).The purpose of the "USFWS Pollinator Restoration Projects Mapper" is to inform future pollinator conservation efforts by providing a way to identify geographic areas where additional pollinator conservation may be needed.The "USFWS Pollinator Restoration Projects Mapper" maps the locations of where on-the-ground projects that are beneficial to pollinators have taken place. Its primary focus is projects on public lands. The majority of records included in this tool come from internal databases for the USFWS, US Forest Service, and the Bureau of Land Management, which were queried for relevant projects. The tool is not intended as a database for reporting projects to. Rather, the tool synthesizes records from existing databases.The geographic scope of the tool includes the western states of Arizona, California, Idaho, Nevada, Oregon, Utah, and Washington.When possible, the tool includes projects from 2014 to the present. This timespan was chosen because it matches the timespan of the USFWS Monarch Conservation Database For consistency, the tool groups pollinator beneficial projects into the following four activity types:Restoration: Actions taken after a disturbance, such as planting native forbs after a wildfireMaintenance: Actions taken outside the growing season that maintain habitat quality through regular disturbance using manual or chemical means. Examples: mowing, spraying weeds, prescribed fireConservation: Acquiring land or creating easements that are managed for biodiversityEnhancement: Actions that increase forb diversity and nectar resources, such as planting native milkweedThe tool includes a map that aggregates project point locations within 49 square mile sized hexagon grid cells. Users can click on individual grid cells to activate a pop-up menu to cycle through the projects that occurred within that grid cell. Information for each project include, but are not limited to, acreage, type of activity (i.e., restoration, maintenance, conservation, enhancement), data source, and lead organization.The tool also includes a dashboard to view bar graphs and pie charts that display project acreages and project number based on location (i.e., state), project activity type (i.e., restoration, maintenance, conservation, enhancement), data source, and management type. Data can be filtered by data source, activity type, and year. Data filtering will update the map, bar graphs, and pie charts.

Shortest Route Analysis Of Dhaka City Roads Using Various Gis Techniques (Dataset And Sample Outputs)
This dataset falls under the category Public Transport Transport Network Geometries (Geodata).
It contains the following data: This repository is the dataset of the related paper "Shortest Route Analysis of Dhaka City Roads Using Various GIS Techniques".The data presented here are collected and gathered together from several separate locations. All the probable original sources of the dataset are open-source or free to distribute licensed. The dataset has the following items: 1. Road network of Dhaka city. 2. Bus Route network of Dhaka city. 3. Future metro Route network of Dhaka city. 4. All the bus stands in Bangladesh. 5. All planned metro station in Dhaka city. 6. The output of some sample random two points shortest or cheapest path from the related paper.
This dataset was scouted on 2022-02-23 as part of a data sourcing project conducted by TUMI. License information might be outdated: Check original source for current licensing. The data can be accessed using the following URL / API Endpoint: https://data.mendeley.com/datasets/j5b93k2xhk/1\ Please note: This link leads to an external resource. If you experience any issues with its availability, please try again later.

The USGS Protected Areas Database of the United States (PAD-US) is the nation's inventory of protected areas, including public open space and voluntarily provided, private protected areas, identified as an A-16 National Geospatial Data Asset in the Cadastral Theme (http://www.fgdc.gov/ngda-reports/NGDA_Datasets.html). PAD-US is an ongoing project with several published versions of a spatial database of areas dedicated to the preservation of biological diversity, and other natural, recreational or cultural uses, managed for these purposes through legal or other effective means. The geodatabase maps and describes public open space and other protected areas. Most areas are public lands owned in fee; however, long-term easements, leases, and agreements or administrative designations documented in agency management plans may be included. The PAD-US database strives to be a complete “best available” inventory of protected areas (lands and waters) including data provided by managing agencies and organizations. The dataset is built in collaboration with several partners and data providers (http://gapanalysis.usgs.gov/padus/stewards/). See Supplemental Information Section of this metadata record for more information on partnerships and links to major partner organizations. As this dataset is a compilation of many data sets; data completeness, accuracy, and scale may vary. Federal and state data are generally complete, while local government and private protected area coverage is about 50% complete, and depends on data management capacity in the state. For completeness estimates by state: http://www.protectedlands.net/partners. As the federal and state data are reasonably complete; focus is shifting to completing the inventory of local gov and voluntarily provided, private protected areas. The PAD-US geodatabase contains over twenty-five attributes and four feature classes to support data management, queries, web mapping services and analyses: Marine Protected Areas (MPA), Fee, Easements and Combined. The data contained in the MPA Feature class are provided directly by the National Oceanic and Atmospheric Administration (NOAA) Marine Protected Areas Center (MPA, http://marineprotectedareas.noaa.gov ) tracking the National Marine Protected Areas System. The Easements feature class contains data provided directly from the National Conservation Easement Database (NCED, http://conservationeasement.us ) The MPA and Easement feature classes contain some attributes unique to the sole source databases tracking them (e.g. Easement Holder Name from NCED, Protection Level from NOAA MPA Inventory). The "Combined" feature class integrates all fee, easement and MPA features as the best available national inventory of protected areas in the standard PAD-US framework. In addition to geographic boundaries, PAD-US describes the protection mechanism category (e.g. fee, easement, designation, other), owner and managing agency, designation type, unit name, area, public access and state name in a suite of standardized fields. An informative set of references (i.e. Aggregator Source, GIS Source, GIS Source Date) and "local" or source data fields provide a transparent link between standardized PAD-US fields and information from authoritative data sources. The areas in PAD-US are also assigned conservation measures that assess management intent to permanently protect biological diversity: the nationally relevant "GAP Status Code" and global "IUCN Category" standard. A wealth of attributes facilitates a wide variety of data analyses and creates a context for data to be used at local, regional, state, national and international scales. More information about specific updates and changes to this PAD-US version can be found in the Data Quality Information section of this metadata record as well as on the PAD-US website, http://gapanalysis.usgs.gov/padus/data/history/.) Due to the completeness and complexity of these data, it is highly recommended to review the Supplemental Information Section of the metadata record as well as the Data Use Constraints, to better understand data partnerships as well as see tips and ideas of appropriate uses of the data and how to parse out the data that you are looking for. For more information regarding the PAD-US dataset please visit, http://gapanalysis.usgs.gov/padus/. To find more data resources as well as view example analysis performed using PAD-US data visit, http://gapanalysis.usgs.gov/padus/resources/. The PAD-US dataset and data standard are compiled and maintained by the USGS Gap Analysis Program, http://gapanalysis.usgs.gov/ . For more information about data standards and how the data are aggregated please review the “Standards and Methods Manual for PAD-US,” http://gapanalysis.usgs.gov/padus/data/standards/ .