Purpose:This feature layer describes water quality sampling data performed at several operating coal mines in the South Fork of Cherry watershed, West Virginia.Source & Data:Data was downloaded from WV Department of Environmental Protection's ApplicationXtender online database and EPA's ECHO online database between January and April, 2023.There are five data sets here: Surface Water Monitoring Sites, which contains basic information about monitoring sites (name, lat/long, etc.) and NPDES Outlet Monitoring Sites, which contains similar information about outfall discharges surrounding the active mines. Biological Assessment Stations (BAS) contain similar information for pre-project biological sampling. NOV Summary contains locations of Notices of Violation received by South Fork Coal Company from WV Department of Environmental Protection. The Quarterly Monitoring Reports table contains the sampling data for the Surface Water Monitoring Sites, which actually goes as far back as 2018 for some mines. Parameters of concern include iron, aluminum and selenium, among others.A relationship class between Surface Water Monitoring Sites and the Quarterly Monitoring Reports allows access to individual sample results.Processing:Notices of Violation were obtained from the WV DEP AppXtender database for Mining and Reclamation Article 3 (SMCRA) Permitting, and Mining and Reclamation NPDES Permitting. Violation data were entered into Excel and loaded into ArcGIS Pro as a CSV text file with Lat/Long coordinates for each Violation. The CSV file was converted to a point feature class.Water quality data were downloaded in PDF format from the WVDEP AppXtender website. Non-searchable PDFs were converted via Optical Character Recognition, so that data could be copied. Sample results were copied and pasted manually to Notepad++, and several columns were re-ordered. Data was grouped by sample station and sorted chronologically. Sample data, contained in the associated table (SW_QM_Reports) were linked back to the monitoring station locations using the Station_ID text field in a geodatabase relationship class.Water monitoring station locations were taken from published Drainage Maps and from water quality reports. A CSV table was created with station Lat/Long locations and loaded into ArcGIS Pro. It was then converted to a point feature class.Stream Crossings and Road Construction Areas were digitized as polygon feature classes from project Drainage and Progress maps that were converted to TIFF image format from PDF and georeferenced.The ArcGIS Pro map - South Fork Cherry River Water Quality, was published as a service definition to ArcGIS Online.Symbology:NOV Summary - dark blue, solid pointLost Flats Surface Water Monitoring Sites: Data Available - medium blue point, black outlineLost Flats Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineLost Flats NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Surface Water Monitoring Sites: Data Available - medium blue point, black outlineBlue Knob Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineBlue Knob NPDES Outlet Monitoring Sites - orange point, black outlineBlue Knob Biological Assessment Stations: Data Available - medium green point, black outlineBlue Knob Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Surface Water Monitoring Sites: Data Available - medium blue point, black outlineRocky Run Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineRocky Run NPDES Outlet Monitoring Sites - orange point, black outlineRocky Run Biological Assessment Stations: Data Available - medium green point, black outlineRocky Run Biological Assessment Stations: No Data Available - no-fill point, thick medium green outlineRocky Run Stream Crossings: turquoise blue polygon with red outlineRocky Run Haul Road Construction Areas: dark red (40% transparent) polygon with black outlineHaul Road No 2 Surface Water Monitoring Sites: Data Available - medium blue point, black outlineHaul Road No 2 Surface Water Monitoring Sites: No Data Available - no-fill point, thick medium blue outlineHaul Road No 2 NPDES Outlet Monitoring Sites - orange point, black outline

Geostatistics analyzes and predicts the values associated with spatial or spatial-temporal phenomena. It incorporates the spatial (and in some cases temporal) coordinates of the data within the analyses. It is a practical means of describing spatial patterns and interpolating values for locations where samples were not taken (and measures the uncertainty of those values, which is critical to informed decision making). This archive contains results of geostatistical analysis of COVID-19 case counts for all available US counties. Test results were obtained with ArcGIS Pro (ESRI). Sources are state health departments, which are scraped and aggregated by the Johns Hopkins Coronavirus Resource Center and then pre-processed by MappingSupport.com.

This update of the Zenodo dataset (version 6) consists of three compressed archives containing geostatistical analyses of SARS-CoV-2 testing data. This dataset utilizes many of the geostatistical techniques used in previous versions of this Zenodo archive, but has been significantly expanded to include analyses of up-to-date U.S. COVID-19 case data (from March 24th to September 8^th, 2020):

Archive #1: “1.Geostat. Space-Time analysis of SARS-CoV-2 in the US (Mar24-Sept6).zip” – results of a geostatistical analysis of COVID-19 cases incorporating spatially-weighted hotspots that are conserved over one-week timespans. Results are reported starting from when U.S. COVID-19 case data first became available (March 24^th, 2020) for 25 consecutive 1-week intervals (March 24th through to September 6th, 2020). Hotspots, where found, are reported in each individual state, rather than the entire continental United States.

Archive #2: "2.Geostat. Spatial analysis of SARS-CoV-2 in the US (Mar24-Sept8).zip" – the results from geostatistical spatial analyses only of corrected COVID-19 case data for the continental United States, spanning the period from March 24^th through September 8th, 2020. The geostatistical techniques utilized in this archive includes ‘Hot Spot’ analysis and ‘Cluster and Outlier’ analysis.

Archive #3: "3.Kriging and Densification of SARS-CoV-2 in LA and MA.zip" – this dataset provides preliminary kriging and densification analysis of COVID-19 case data for certain dates within the U.S. states of Louisiana and Massachusetts.

These archives consist of map files (as both static images and as animations) and data files (including text files which contain the underlying data of said map files [where applicable]) which were generated when performing the following Geostatistical analyses: Hot Spot analysis (Getis-Ord Gi*) [‘Archive #1’: consecutive weeklong Space-Time Hot Spot analysis; ‘Archive #2’: daily Hot Spot Analysis], Cluster and Outlier analysis (Anselin Local Moran's I) [‘Archive #2’], Spatial Autocorrelation (Global Moran's I) [‘Archive #2’], and point-to-point comparisons with Kriging and Densification analysis [‘Archive #3’].

The Word document provided ("Description-of-Archive.Updated-Geostatistical-Analysis-of-SARS-CoV-2 (version 6).docx") details the contents of each file and folder within these three archives and gives general interpretations of these results.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into tree and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Trees is useful in applications such as high-quality 3D basemap creation, urban planning, forestry workflows, and planning climate change response.Trees could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Tree in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.InputThe model is trained with classified LiDAR that follows the LINZ base specification. The input data should be similar to this specification.Note: The model is dependent on additional attributes such as Intensity, Number of Returns, etc, similar to the LINZ base specification. This model is trained to work on classified and unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: 0 Background 5 Trees / High-vegetationApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Wellington CityTesting dataset - Tawa CityValidation/Evaluation dataset - Christchurch City Dataset City Training Wellington Testing Tawa Validating ChristchurchModel architectureThis model uses the PointCNN model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.991200 0.975404 0.983239 High Vegetation 0.933569 0.975559 0.954102Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 80%, Test: 20%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-121.69 m to 26.84 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-15 to +15 Maximum points per block8192 Block Size20 Meters Class structure[0, 5]Sample resultsModel to classify a dataset with 5pts/m density Christchurch city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

This packaged data collection contains all of the outputs from our primary model, including the following data layers: Habitat Cores (vector polygons) Least-cost Paths (vector lines) Least-cost Corridors (raster) Least-cost Corridors (vector polygon interpretation) Modeling Extent (vector polygon) Please refer to the embedded spatial metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in shapefile (.shp) or raster GeoTIFF (.tif) formats.

The new Oregon Address Geocoder is used to find the location coordinates for street addresses in the State of Oregon. This service is:Free PublicUpdated regularlyOutputs location coordinates in Oregon Lambert, feet (SRID 2992)Uses over 2 million address points and 288,000 streets for referenceIt is an ArcGIS multirole locator with two roles:Point Address - Generally more accurate results from rooftop location points. Includes a Subaddress if a unit number is located. Street Address - Less accurate results from an estimated distance along a street centerline address range if a Point Address was not found.Instructions for using the Geocoder via ArcGIS Pro, ArcGIS Online, and REST Services are below:ArcGIS ProWeb ServicesArcGIS Online

The dataset presented here, a historical coregonine spawning database, or CORHIST for short, is the result of several years of coordinated research in archives, libraries, and field stations, to track down evidence of spawning locations for the Coregoninae sub-family of ciscoes and whitefishes in the Great Lakes of North America and their tributaries. Our objective was to accurately identify _location information to coordinates and add all associated data and metadata to a database built specifically for these types of records (a database capable of storing historical, geospatial, and biological data). Data for a total of 11 accepted species of coregonines are included in this dataset. Spawning or nursery habitat designations were assigned based on a wide-range of evidence from original sources, including descriptions of physiology, ontogeny, and behaviors, interviews, first-hand and Indigenous Ecological Knowledge, and by our own examination of museum specimens. Georeferencing was completed using evidence from original records, including navigational information such as dead reckonings, landmarks like islands, lighthouses, reefs, and river mouths, and by using depth and substrate descriptions. Occasionally, supplemental sources including various historical maps and/or published bathymetry and substrate layers were used to assist in georeferencing points. Data points were summarized and quality-checked using ArcMap 10.8 and ArcGIS Pro (datum: WGS84). Reference tables are also included with this dataset.

Data on aggregated radon test results in residential properties from January 1994 to December 2024 within each Vermont municipality. Radon data can inform public health outreach, educate stakeholders and the public, and encourage testing and mitigation. View this data in the Department of Health's radon risk map.Radon is a naturally occurring radioactive gas that is estimated to kill 50 Vermonters a year due to lung cancer. Radon can only be detected by testing and buildings with elevated radon levels (≥4 pCi/L (picocuries per Liter)) are found throughout the state. The average level of radon in Vermont homes is 2.4 pCi/L compared with the national average of 1.3 pCi/L. The EPA recommends that homes testing at or above 4 pCi/L be fixed, but as there is no known safe level of radon, the EPA suggests that homes testing between 2-4 pCi/L should also be fixed.This data set contains the Environmental Health Radon program’s radon in-air long term test data from 1994-2024, and the Vermont Department of Health Laboratory’s radon in-air short, medium, and long-term test data for 2020-2024. Data have been geocoded and aggregated to the town level to display the number and percent of residences tested by town and the number and percent of residences tested that exceed 4 pCi/L by town.Data SourceSource data for these maps comes from the highest radon test result ever found at a residence (many residences test more than once). Results are provided by the Radon Program long term test data (1994-2024) and the Vermont Department of Health Laboratory, short, medium, and long term test data (2020-2024). Radon results are aggregated by town based on whether the result was elevated (≥4.0 pCi/L) or not elevated (<4.0 pCi/L).Data LimitationsPrison, institutional residence, and nursing home E911 locations are not included in the aggregation of residences by town or geological area. For areas of low population density or low number of tests, data extremes carry more weight and can distort analytic results. Therefore, in the Rates of Radon Testing by Town, data for towns with fewer than 7 tested residences are not displayed; and in Elevated Radon Results, data for towns with fewer than 20 tested residences are not displayed.MethodologyRecord level radon in indoor air test results were extracted from the VDH-EH Radon database by Radon Program staff and from the LIMS system at the VDHL by laboratory staff. The Tracking analyst used SAS version 9.4 and ArcGIS Pro version 2.4.1 to process the data. Geocoded data from the Tracking program were used for the Radon Risk Maps. GIS work to populate the final maps was done collaboratively with partners from the Agency of Digital Services using ArcGIS Pro version 2.4.1.The residential data are from the VT Data – E911 Site Locations (address points) where the following were selected from the SITETYPE variable: mobile home, multi-family dwelling, other residential, single-family dwelling, residential farm, seasonal home, commercial with residence, condominium, and camp. The residential data in these maps is aggregated by town and geological area to provide the denominator for calculations.

This packaged data collection contains all of the outputs from our secondary model, which replaced habitat cores with the boundaries of the four existing coastal marten populations (known as Extant Population Areas, as defined in the USFWS Species Status Assessment v2.0) and used Linkage Mapper to produce corridors connecting these populations. This package includes the following data layers: Coastal Marten Extant Population Areas (EPAs) Least-cost Paths Connecting EPAs Least-cost Corridors Connecting EPAs Please refer to the embedded metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in shapefile (.shp) or raster GeoTIFF (.tif) formats.

Rising sea levels (SLR) will cause coastal groundwater to rise in many coastal urban environments. Inundation of contaminated soils by groundwater rise (GWR) will alter the physical, biological, and geochemical conditions that influence the fate and transport of existing contaminants. These transformed products can be more toxic and/or more mobile under future conditions driven by SLR and GWR. We reviewed the vulnerability of contaminated sites to GWR in a US national database and in a case comparison with the San Francisco Bay region to estimate the risk of rising groundwater to human and ecosystem health. The results show that 326 sites in the US Superfund program may be vulnerable to changes in groundwater depth or flow direction as a result of SLR, representing 18.1 million hectares of contaminated land. In the San Francisco Bay Area, we found that GWR is predicted to impact twice as much coastal land area as inundation from SLR alone, and 5,297 state-managed sites of contamination may be vulnerable to inundation from GWR in a 1-meter SLR scenario. Increases of only a few centimeters of elevation can mobilize soil contaminants, alter flow directions in a heterogeneous urban environment with underground pipes and utility trenches, and result in new exposure pathways. Pumping for flood protection will elevate the salt water interface, changing groundwater salinity and mobilizing metals in soil. Socially vulnerable communities are more exposed to this risk at both the national scale and in a regional comparison with the San Francisco Bay Area. Methods Data Dryad This data set includes data from the California State Water Resources Control Board (WRCB), the California Department of Toxic Substances Control (DTSC), the USGS, the US EPA, and the US Census. National Assessment Data Processing: For this portion of the project, ArcGIS Pro and RStudio software applications were used. Data processing for superfund site contaminants in the text and supplementary materials was done in RStudio using R programming language. RStudio and R were also used to clean population data from the American Community Survey. Packages used include: Dplyr, data.table, and tidyverse to clean and organize data from the EPA and ACS. ArcGIS Pro was used to compute spatial data regarding sites in the risk zone and vulnerable populations. DEM data processed for each state removed any elevation data above 10m, keeping anything 10m and below. The Intersection tool was used to identify superfund sites within the 10m sea level rise risk zone. The Calculate Geometry tool was used to calculate the area within each coastal state that was occupied by the 10m SLR zone and used again to calculate the area of each superfund site. Summary Statistics were used to generate the total proportion of superfund site surface area / 10m SLR area for each state. To generate population estimates of socially vulnerable households in proximity to superfund sites, we followed methods similar to that of Carter and Kalman (2020). First, we generated buffers at the 1km, 3km, and 5km distance of superfund sites. Then, using Tabulate Intersection, the estimated population of each census block group within each buffer zone was calculated. Summary Statistics were used to generate total numbers for each state. Bay Area Data Processing: In this regional study, we compared the groundwater elevation projections by Befus et al (2020) to a combined dataset of contaminated sites that we built from two separate databases (Envirostor and GeoTracker) that are maintained by two independent agencies of the State of California (DTSC and WRCB). We used ArcGIS to manage both the groundwater surfaces, as raster files, from Befus et al (2020) and the State’s point datasets of street addresses for contaminated sites. We used SF BCDC (2020) as the source of social vulnerability rankings for census blocks, using block shapefiles from the US Census (ACS) dataset. In addition, we generated isolines that represent the magnitude of change in groundwater elevation in specific sea level rise scenarios. We compared these isolines of change in elevation to the USGS geological map of the San Francisco Bay region and noted that groundwater is predicted to rise farther inland where Holocene paleochannels meet artificial fill near the shoreline. We also used maps of historic baylands (altered by dikes and fill) from the San Francisco Estuary Institute (SFEI) to identify the number of contaminated sites over rising groundwater that are located on former mudflats and tidal marshes. The contaminated sites' data from the California State Water Resources Control Board (WRCB) and the Department of Toxic Substances (DTSC) was clipped to our study area of nine-bay area counties. The study area does not include the ocean shorelines or the north bay delta area because the water system dynamics differ in deltas. The data was cleaned of any duplicates within each dataset using the Find Identical and Delete Identical tools. Then duplicates between the two datasets were removed by running the intersect tool for the DTSC and WRCB point data. We chose this method over searching for duplicates by name because some sites change names when management is transferred from DTSC to WRCB. Lastly, the datasets were sorted into open and closed sites based on the DTSC and WRCB classifications which are shown in a table in the paper's supplemental material. To calculate areas of rising groundwater, we used data from the USGS paper “Projected groundwater head for coastal California using present-day and future sea-level rise scenarios” by Befus, K. M., Barnard, P., Hoover, D. J., & Erikson, L. (2020). We used the hydraulic conductivity of 1 condition (Kh1) to calculate areas of rising groundwater. We used the Raster Calculator to subtract the existing groundwater head from the groundwater head under a 1-meter of sea level rise scenario to find the areas where groundwater is rising. Using the Reclass Raster tool, we reclassified the data to give every cell with a value of 0.1016 meters (4”) or greater a value of 1. We chose 0.1016 because groundwater rise of that little can leach into pipes and infrastructure. We then used the Raster to Poly tool to generate polygons of areas of groundwater rise.

Introduction

Geographical scale, in terms of spatial extent, provide a basis for other branches of science. This dataset contains newly proposed geographical and geological GIS boundaries for the Pan-Tibetan Highlands (new proposed name for the High Mountain Asia), based on geological and geomorphological features. This region comprises the Tibetan Plateau and three adjacent mountain regions: the Himalaya, Hengduan Mountains and Mountains of Central Asia, and boundaries are also given for each subregion individually. The dataset will benefit quantitative spatial analysis by providing a well-defined geographical scale for other branches of research, aiding cross-disciplinary comparisons and synthesis, as well as reproducibility of research results.

The dataset comprises three subsets, and we provide three data formats (.shp, .geojson and .kmz) for each of them. Shapefile format (.shp) was generated in ArcGIS Pro, and the other two were converted from shapefile, the conversion steps refer to 'Data processing' section below. The following is a description of the three subsets:

(1) The GIS boundaries we newly defined of the Pan-Tibetan Highlands and its four constituent sub-regions, i.e. the Tibetan Plateau, Himalaya, Hengduan Mountains and the Mountains of Central Asia. All files are placed in the "Pan-Tibetan Highlands (Liu et al._2022)" folder.

(2) We also provide GIS boundaries that were applied by other studies (cited in Fig. 3 of our work) in the folder "Tibetan Plateau and adjacent mountains (Others’ definitions)". If these data is used, please cite the relevent paper accrodingly. In addition, it is worthy to note that the GIS boundaries of Hengduan Mountains (Li et al. 1987a) and Mountains of Central Asia (Foggin et al. 2021) were newly generated in our study using Georeferencing toolbox in ArcGIS Pro.

(3) Geological assemblages and characters of the Pan-Tibetan Highlands, including Cratons and micro-continental blocks (Fig. S1), plus sutures, faults and thrusts (Fig. 4), are placed in the "Pan-Tibetan Highlands (geological files)" folder.

Note: High Mountain Asia: The name ‘High Mountain Asia’ is the only direct synonym of Pan-Tibetan Highlands, but this term is both grammatically awkward and somewhat misleading, and hence the term ‘Pan-Tibetan Highlands’ is here proposed to replace it. Third Pole: The first use of the term ‘Third Pole’ was in reference to the Himalaya by Kurz & Montandon (1933), but the usage was subsequently broadened to the Tibetan Plateau or the whole of the Pan-Tibetan Highlands. The mainstream scientific literature refer the ‘Third Pole’ to the region encompassing the Tibetan Plateau, Himalaya, Hengduan Mountains, Karakoram, Hindu Kush and Pamir. This definition was surpported by geological strcture (Main Pamir Thrust) in the western part, and generally overlaps with the ‘Tibetan Plateau’ sensu lato defined by some previous studies, but is more specific.

More discussion and reference about names please refer to the paper. The figures (Figs. 3, 4, S1) mentioned above were attached in the end of this document.

Data processing

We provide three data formats. Conversion of shapefile data to kmz format was done in ArcGIS Pro. We used the Layer to KML tool in Conversion Toolbox to convert the shapefile to kmz format. Conversion of shapefile data to geojson format was done in R. We read the data using the shapefile function of the raster package, and wrote it as a geojson file using the geojson_write function in the geojsonio package.

Version

Version 2022.1.

Acknowledgements

This study was supported by the Strategic Priority Research Program of Chinese Academy of Sciences (XDB31010000), the National Natural Science Foundation of China (41971071), the Key Research Program of Frontier Sciences, CAS (ZDBS-LY-7001). We are grateful to our coauthors insightful discussion and comments. We also want to thank professors Jed Kaplan, Yin An, Dai Erfu, Zhang Guoqing, Peter Cawood, Tobias Bolch and Marc Foggin for suggestions and providing GIS files.

Citation

Liu, J., Milne, R. I., Zhu, G. F., Spicer, R. A., Wambulwa, M. C., Wu, Z. Y., Li, D. Z. (2022). Name and scale matters: Clarifying the geography of Tibetan Plateau and adjacent mountain regions. Global and Planetary Change, In revision

Jie Liu & Guangfu Zhu. (2022). Geographical and geological GIS boundaries of the Tibetan Plateau and adjacent mountain regions (Version 2022.1). https://doi.org/10.5281/zenodo.6432940

Contacts

Dr. Jie LIU: E-mail: liujie@mail.kib.ac.cn;

Mr. Guangfu ZHU: zhuguangfu@mail.kib.ac.cn

Institution: Kunming Institute of Botany, Chinese Academy of Sciences

Address: 132# Lanhei Road, Heilongtan, Kunming 650201, Yunnan, China

Copyright

This dataset is available under the Attribution-ShareAlike 4.0 International (CC BY-SA 4.0).

This packaged data collection contains two sets of two additional model runs that used the same inputs and parameters as our primary model, with the exception being we implemented a "maximum corridor length" constraint that allowed us to identify and visualize the corridors as being well-connected (≤15km) or moderately connected (≤45km). This is based on an assumption that corridors longer than 45km are too long to sufficiently accommodate dispersal. One of these sets is based on a maximum corridor length that uses Euclidean (straight-line) distance, while the other set is based on a maximum corridor length that uses cost-weighted distance. These two sets of corridors can be compared against the full set of corridors from our primary model to identify the remaining corridors, which could be considered poorly connected. This package includes the following data layers: Corridors classified as well connected (≤15km) based on Cost-weighted Distance Corridors classified as moderately connected (≤45km) based on Cost-weighted Distance Corridors classified as well connected (≤15km) based on Euclidean Distance Corridors classified as moderately connected (≤45km) based on Euclidean Distance Please refer to the embedded metadata and the information in our full report for details on the development of these data layers. Packaged data are available in two formats: Geodatabase (.gdb): A related set of file geodatabase rasters and feature classes, packaged in an ESRI file geodatabase. ArcGIS Pro Map Package (.mpkx): The same data included in the geodatabase, presented as fully-symbolized layers in a map. Note that you must have ArcGIS Pro version 2.0 or greater to view. See Cross-References for links to individual datasets, which can be downloaded in raster GeoTIFF (.tif) format.

Please note that this dataset is not an official City of Toronto land use dataset. It was created for personal and academic use using City of Toronto Land Use Maps (2019) found on the City of Toronto Official Plan website at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/official-plan-maps-copy, along with the City of Toronto parcel fabric (Property Boundaries) found at https://open.toronto.ca/dataset/property-boundaries/ and Statistics Canada Census Dissemination Blocks level boundary files (2016). The property boundaries used were dated November 11, 2021. Further detail about the City of Toronto's Official Plan, consolidation of the information presented in its online form, and considerations for its interpretation can be found at https://www.toronto.ca/city-government/planning-development/official-plan-guidelines/official-plan/ Data Creation Documentation and Procedures Software Used The spatial vector data were created using ArcGIS Pro 2.9.0 in December 2021. PDF File Conversions Using Adobe Acrobat Pro DC software, the following downloaded PDF map images were converted to TIF format. 9028-cp-official-plan-Map-14_LandUse_AODA.pdf 9042-cp-official-plan-Map-22_LandUse_AODA.pdf 9070-cp-official-plan-Map-20_LandUse_AODA.pdf 908a-cp-official-plan-Map-13_LandUse_AODA.pdf 978e-cp-official-plan-Map-17_LandUse_AODA.pdf 97cc-cp-official-plan-Map-15_LandUse_AODA.pdf 97d4-cp-official-plan-Map-23_LandUse_AODA.pdf 97f2-cp-official-plan-Map-19_LandUse_AODA.pdf 97fe-cp-official-plan-Map-18_LandUse_AODA.pdf 9811-cp-official-plan-Map-16_LandUse_AODA.pdf 982d-cp-official-plan-Map-21_LandUse_AODA.pdf Georeferencing and Reprojecting Data Files The original projection of the PDF maps is unknown but were most likely published using MTM Zone 10 EPSG 2019 as per many of the City of Toronto's many datasets. They could also have possibly been published in UTM Zone 17 EPSG 26917 The TIF images were georeferenced in ArcGIS Pro using this projection with very good results. The images were matched against the City of Toronto's Centreline dataset found here The resulting TIF files and their supporting spatial files include: TOLandUseMap13.tfwx TOLandUseMap13.tif TOLandUseMap13.tif.aux.xml TOLandUseMap13.tif.ovr TOLandUseMap14.tfwx TOLandUseMap14.tif TOLandUseMap14.tif.aux.xml TOLandUseMap14.tif.ovr TOLandUseMap15.tfwx TOLandUseMap15.tif TOLandUseMap15.tif.aux.xml TOLandUseMap15.tif.ovr TOLandUseMap16.tfwx TOLandUseMap16.tif TOLandUseMap16.tif.aux.xml TOLandUseMap16.tif.ovr TOLandUseMap17.tfwx TOLandUseMap17.tif TOLandUseMap17.tif.aux.xml TOLandUseMap17.tif.ovr TOLandUseMap18.tfwx TOLandUseMap18.tif TOLandUseMap18.tif.aux.xml TOLandUseMap18.tif.ovr TOLandUseMap19.tif TOLandUseMap19.tif.aux.xml TOLandUseMap19.tif.ovr TOLandUseMap20.tfwx TOLandUseMap20.tif TOLandUseMap20.tif.aux.xml TOLandUseMap20.tif.ovr TOLandUseMap21.tfwx TOLandUseMap21.tif TOLandUseMap21.tif.aux.xml TOLandUseMap21.tif.ovr TOLandUseMap22.tfwx TOLandUseMap22.tif TOLandUseMap22.tif.aux.xml TOLandUseMap22.tif.ovr TOLandUseMap23.tfwx TOLandUseMap23.tif TOLandUseMap23.tif.aux.xml TOLandUseMap23.tif.ov Ground control points were saved for all georeferenced images. The files are the following: map13.txt map14.txt map15.txt map16.txt map17.txt map18.txt map19.txt map21.txt map22.txt map23.txt The City of Toronto's Property Boundaries shapefile, "property_bnds_gcc_wgs84.zip" were unzipped and also reprojected to EPSG 26917 (UTM Zone 17) into a new shapefile, "Property_Boundaries_UTM.shp" Mosaicing Images Once georeferenced, all images were then mosaiced into one image file, "LandUseMosaic20211220v01", within the project-generated Geodatabase, "Landuse.gdb" and exported TIF, "LandUseMosaic20211220.tif" Reclassifying Images Because the original images were of low quality and the conversion to TIF made the image colours even more inconsistent, a method was required to reclassify the images so that different land use classes could be identified. Using Deep learning Objects, the images were re-classified into useful consistent colours. Deep Learning Objects and Training The resulting mosaic was then prepared for reclassification using the Label Objects for Deep Learning tool in ArcGIS Pro. A training sample, "LandUseTrainingSamples20211220", was created in the geodatabase for all land use types as follows: Neighbourhoods Insitutional Natural Areas Core Employment Areas Mixed Use Areas Apartment Neighbourhoods Parks Roads Utility Corridors Other Open Spaces General Employment Areas Regeneration Areas Lettering (not a land use type, but an image colour (black), used to label streets). By identifying the letters, it then made the reclassification and vectorization results easier to clean up of unnecessary clutter caused by the labels of streets. Reclassification Once the training samples were created and saved, the raster was then reclassified using the Image Classification Wizard tool in ArcGIS Pro, using the Support...

The Bedford County VA Composite Geocoding service is a composite locator built from geospatial data from NG911 Site Address Points, Road Centerlines, Common Places, Parcels (RPCs, PINs, Owners), Subdivisions, and Populated Places.

This composite locator is made up of three locators: a primary base locator, and two ancillary locators which provide results if supplying Parcel PINs (e.g., 214 A 197 T) and Parcel Owners.

Users can locate using the following search criteria:

• Address Point:
  • (e.g., ‘122 E Main St’)
• Interpolated Street Address:
  • (e.g., ‘125 E Main St’ — Doesn't exist, but the locator estimates where this address would exist)
• Street Name:
  • (e.g., ‘E Main St’)
• Street Intersections (uses ‘ & ’ operator):
  • (e.g., ‘E Main St & N Bridge St’)
• Common Places (i.e., Businesses or other colloquial names for a location):
  • (e.g., ‘Bedford County Animal Shelter’)
• Parcels - RPC:
  • (e.g., ‘80500241’)
• Parcels - PIN:
  • (e.g., ‘214 A 197 T’)
• Parcels - Owner
  • (e.g., ‘COUNTY OF BEDFORD VIRGINIA’, ‘[LAST NAME] [FIRST NAME]’)
• Subdivisions:
  • (e.g., ‘LAKEPOINTE’)
• Populated Places:
  • (e.g., ‘Bedford’, ‘Forest’)

The service can be consumed in ArcGIS Pro, ArcGIS Online, or other GIS applications.

Data is mostly limited to within Bedford County, Virginia, but some common places and populated places in close vicinity to the administrative boundary are still included.

Underlying data is updated regularly, typically weekly, by Bedford County GIS staff.

Understanding bird migration at low altitudes is critical to evaluating risk of collision with obstacles. Recent advances in satellite tracking technologies allow quantifying use of low altitudes by small migrating birds with a high level of precision, allowing species-level inference into potential collision risk based on flight altitude. The American Woodcock (Scolopax minor) is suspected to be a low altitude migrant due to its frequent collisions with buildings, and subsequent mortality during migration may be contributing to population declines. We investigated migratory flight altitudes using satellite transmitters deployed on woodcock in 2020–2024 and examined how flight altitudes compare to the heights of common airspace obstacles. Each transmitter recorded a nocturnal GPS location with an altitude reading every 1–3 days during fall and spring migrations. We implemented a Bayesian hierarchical mixture model to identify whether locations were recorded on the ground or during flight, isolate measurement error, and describe the distribution of flight altitudes. We found that migrating woodcock fly at mean altitudes of 379 m above ground level, flying higher during spring (mean: 444 m, 95% credible interval: 333–578 m) than fall (338 m, 95% CRI: 267–423 m). Woodcock flight altitudes were frequently lower than could be observed using weather radar (27% of observations), and 57% of observations fell within the altitude range of ≥1 airspace obstacle. Our results suggest that woodcock fly at altitudes lower than reported for most nocturnal migrants, which likely contributes to their vulnerability to obstacle collisions. Woodcock provide an example of how vulnerability to obstacle collisions during nocturnal migratory flights are often species-specific, and mitigation efforts should incorporate measures aimed at reducing collisions during both diurnal stopovers and nocturnal migratory flights to effectively reduce bird collision mortality. Methods Data Collection and Preprocessing We collected woodcock locations with altitude readings from 2020 to 2024 using GPS transmitters as a part of a larger collaborative effort by the Eastern Woodcock Migration Research Cooperative (Blomberg et al. 2023, Clements et al. 2024, Fish et al. 2024). We captured woodcock at 100 sites across the eastern portion of their range, including Alabama, Florida, Georgia, Louisiana, Maine, Maryland, New Jersey, New York, North Carolina, Nova Scotia, Ontario, Pennsylvania, Québec, Rhode Island, South Carolina, Vermont, Virginia, West Virginia, and Wisconsin. We caught woodcock using a combination of spotlighting and mist netting (McAuley et al. 1993). We aged and sexed birds upon capture, where we classified birds undertaking their first fall and spring migrations as juveniles, and all other birds as adults. We then attached 4–7 g PinPoint transmitters (Lotek Wireless Inc., Newmarket, Ontario, CA) using a rump-mounted leg loop harness (Fish et al. 2024). We programmed transmitters to collect locations every 1–3 days during migration, with locations alternating between diurnal (1300–1500 hours Eastern Time) and nocturnal (0000–0100 hours) times. Transmitters recorded time, latitude, longitude, and GPS-derived altitude above the WGS84 ellipsoid, and transmitted data back to the ARGOS satellite constellation after every third location. We subset these locations to include only those within the migratory classification dataset produced by Berigan (2024). This dataset classified individual locations as migratory or non-migratory based on the assumption that migration starts after the first ≥16.1 km movement and ends after the final ≥16.1 km movement of the season. We used ArcGIS Pro 3.2.1 (ESRI 2024a) to calculate the difference between the altitude and orthometric elevation recorded for each location (ESRI composite elevation layer; ESRI 2024b), providing a measurement of altitude above ground level for each point. We classified data for our models based on prior descriptions of woodcock activity patterns. Woodcock are ground-feeding birds that rarely fly outside of crepuscular hours (Rabe et al. 1983). When rare diurnal flights do occur, these are generally brief, comprising 1–3% of diurnal time budgets, and close to the ground (McAuley et al. 2020). We therefore made a modeling assumption that all diurnal locations could be treated as though they were known to be recorded on the ground (hereinafter “known ground locations”). As woodcock are nocturnal migrants, we define potential flight locations as all points that were nocturnal, occurred during migration based on the classification in Berigan (2024), and were preceded and followed by >6.68 km steps (defined as lines connecting consecutive locations). The 6.68 km threshold was based on the 99th percentile of step lengths recorded within a stopover site (Berigan 2024). Ensuring that the preceding and following steps were >6.68 km increased the likelihood that the bird had moved away from a stopover site before the point was recorded. References Berigan, L.A. (2024). Full annual cycle analysis of American Woodcock (Scolopax minor) distribution, habitat use, and migration ecology. PhD dissertation. University of Maine, Orono, Maine. Blomberg, E. J., A. C. Fish, L. A. Berigan, A. M. Roth, R. Rau, S. J. Clements, G. Balkcom, B. Carpenter, G. Costanzo, J. Duguay, C. L. Graham, et al. (2023). The American Woodcock Singing Ground Survey largely conforms to the phenology of male woodcock migration. Journal of Wildlife Management 87:e22488. Clements, S. J., L. A. Berigan, A. C. Fish, R. L. Darling, A. M. Roth, G. Balkcom, B. Carpenter, G. Costanzo, J. Duguay, and K. Filkins (2024). Satellite tracking of American Woodcock reveals a gradient of migration strategies. Ornithology141:ukae008. ESRI (2024a). ArcGIS Pro. Redlands, CA, USA. ESRI (2024b). Terrain. [Online.] Available at https://www.arcgis.com/home/item.html?id=58a541efc59545e6b7137f961d7de883. Fish, A. C., A. M. Roth, G. Balkcom, L. Berigan, K. Brunette, S. Clements, G. Costanzo, C. L. Graham, W. F. Harvey, M. Hook, D. L. Howell, et al. (2024). American woodcock migration phenology in eastern North America: implications for hunting season timing. Journal of Wildlife Management 88:e22565. McAuley, D. G., D. M. Keppie, and R. M. Whiting Jr. (2020). American Woodcock (Scolopax minor), version 1.0. In Birds of the World (A. F. Poole, Editor). Cornell Lab of Ornithology, Ithaca, NY, USA. McAuley, D. G., J. R. Longcore, and G. F. Sepik (1993). Techniques for Research into Woodcocks: Experiences and Recommendations. Proceedings of the eighth American woodcock symposium. U.S. Fish and Wildlife Service, p. 5. Rabe, D. L., H. H. Prince, and E. D. Goodman (1983). The effect of weather on bioenergetics of breeding American woodcock. Journal of Wildlife Management 47:762–771.

The Digital Geologic-GIS Map of Yellowstone National Park and Vicinity, Wyoming, Montana, and Idaho is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) an ESRI file geodatabase (yell_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro 3.X map file (.mapx) file (yell_geology.mapx) and individual Pro 3.X layer (.lyrx) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) a readme file (yell_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (yell_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (yell_geology_metadata_faq.pdf). Also included is a zip containing a Montana State University Master's thesis and supporting documents and data. The thesis focuses on addressing map boundary inconsistencies and remapping portions of the park. Data and documents supporting the thesis are 1.) a geodatabase containing field data points, 2.) a collection of documents describing field sites, 3.) spreadsheets containing geochemical analysis results, and 4.) photographs taken during field work. Please read the yell_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri.htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey, Montana Bureau of Mines and Geology and Montana State University. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (yell_geology_metadata.txt or yell_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:125,000 and United States National Map Accuracy Standards features are within (horizontally) 63.5 meters or 208.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS Pro, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Notice: this is not the latest Heat Island Anomalies image service.This layer contains the relative degrees Fahrenheit difference between any given pixel and the mean heat value for the city in which it is located, for every city in the contiguous United States, Alaska, Hawaii, and Puerto Rico. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2022, with patching from summer of 2021 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter or cooler than the average temperature for that same city as a whole. This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): A typical operation at this point is to clip out your area of interest. To do this, add your polygon shapefile or feature class to the map view, and use the Clip Raster tool to export your area of interest as a geoTIFF raster (file extension ".tif"). In the environments tab for the Clip Raster tool, click the dropdown for "Extent" and select "Same as Layer:", and select the name of your polygon. If you then need to convert the output raster to a polygon shapefile or feature class, run the Raster to Polygon tool, and select "Value" as the field.Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Measure and Map Access to Grocery StoresFrom the perspective of the people living in each neighborhoodHow do people in your city get to the grocery store? The answer to that question depends on the person and where they live. This web map helps answer the question in this app.Some live in cities and stop by a grocery store within a short walk or bike ride of home or work. Others live in areas where car ownership is more prevalent, and so they drive to a store. Some do not own a vehicle, and rely on a friend or public transit. Others rely on grocery delivery for their needs. And, many live in rural areas far from town, so a trip to a grocery store is an infrequent event involving a long drive.This map from Esri shows which areas are within a ten minute walk or ten minute drive of a grocery store in the United States and Puerto Rico. Darker color indicates access to more stores. The chart shows how many people can walk to a grocery store if they wanted to or needed to.It is estimated that 20% of U.S. population live within a 10 minute walk of a grocery store, and 92% of the population live within a 10 minute drive of a grocery store.Look up your city to see how the numbers change as you move around the map. Or, draw a neighborhood boundary on the map to get numbers for that area.Every census block is scored with a count of walkable and drivable stores nearby, making this a map suitable for a dashboard for any city, or any of the 50 states, DC and Puerto Rico. Two colorful layers visualize this definition of access, one for walkable access (suitable for looking at a city neighborhood by neighborhood) and one for drivable access (suitable for looking across a city, county, region or state).On the walkable layer, shades of green define areas within a ten minute walk of one or more grocery stores. The colors become more intense and trend to a blue-green color for the busiest neighborhoods, such as downtown San Francisco. As you zoom in, a layer of Census block points visualizes the local population with or without walkable access.As you zoom out to see the entire city, the map adds a light blue - to dark blue layer, showing which parts of the region fall within ten minutes' drive of one or more grocery stores. As a result, the map is useful at all scales, from national to regional, state and local levels. It becomes easier to spot grocery stores that sit within a highly populated area, and grocery stores that sit in a shopping center far away from populated areas. This view of a city begins to hint at the question: how many people have each type of access to grocery stores? And, what if they are unable to walk a mile regularly, or don't own a car?How to Use This MapUse this map to introduce the concepts of access to grocery stores in your city or town. This is the kind of map where people will want to look up their home or work address to validate what the map is saying.The map was built with that use in mind. Many maps of access use straight-line, as-the-crow-flies distance, which ignores real-world barriers to walkability like rivers, lakes, interstates and other characteristics of the built environment. Block analysis using a network data set and Origin-Destination analysis factors these barriers in, resulting in a more realistic depiction of access.There is data behind the map, which can be summarized to show how many people have walkable access to local grocery stores. The map includes a feature layer of population in Census block points, which are visible when you zoom in far enough. This feature layer can be plugged into an app like this one that summarizes the population with/without walkable or drivable access.Lastly, this map can serve as backdrop to other community resources, like food banks, farmers markets (example), and transit (example). Add a transit layer to immediately gauge its impact on the population's grocery access. You can also use this map to see how it relates to communities of concern. Add a layer of any block group or tract demographics, such as Percent Senior Population (examples), or Percent of Households with Access to 0 Vehicles (examples).The map is a useful visual and analytic resource for helping community leaders, business and government leaders see their town from the perspective of its residents, and begin asking questions about how their community could be improved.Data sourcesPopulation data is from the 2010 U.S. Census blocks. Each census block has a count of stores within a 10 minute walk, and a count of stores within a ten minute drive. Census blocks known to be unpopulated are given a score of 0. The layer is available as a hosted feature layer.Grocery store locations are from SafeGraph, reflecting what was in the data as of October 2020. Access to the layer was obtained from the SafeGraph offering in ArcGIS Marketplace. For this project, ArcGIS StreetMap Premium was used for the street network in the origin-destination analysis work, because it already has the necessary attributes on each street segment to identify which streets are considered walkable, and supports a wide variety of driving parameters.The walkable access layer and drivable access layers are rasters, whose colors were chosen to allow the drivable access layer to serve as backdrop to the walkable access layer. Alternative versions of these layers are available. These pairs use different colors but are otherwise identical in content.Data PreparationArcGIS Network Analyst was used to set up a network street layer for analysis. ArcGIS StreetMap Premium was installed to a local hard drive and selected in the Origin-Destination workflow as the network data source. This allows the origins (Census block centroids) and destinations (SafeGraph grocery stores) to be connected to that network, to allow origin-destination analysis.The Census blocks layer contains the centroid of each Census block. The data allows a simple popup to be created. This layer's block figures can be summarized further, to tract, county and state levels.The SafeGraph grocery store locations were created by querying the SafeGraph source layer based on primary NAICS code. After connecting to the layer in ArcGIS Pro, a definition query was set to only show records with NAICS code 445110 as an initial screening. The layer was exported to a local disk drive for further definition query refinement, to eliminate any records that were obviously not grocery stores. The final layer used in the analysis had approximately 53,600 records. In this map, this layer is included as a vector tile layer.MethodologyEvery census block in the U.S. was assigned two access scores, whose numbers are simply how many grocery stores are within a 10 minute walk and a 10 minute drive of that census block. Every census block has a score of 0 (no stores), 1, 2 or more stores. The count of accessible stores was determined using Origin-Destination Analysis in ArcGIS Network Analyst, in ArcGIS Pro. A set of Tools in this ArcGIS Pro package allow a similar analysis to be conducted for any city or other area. The Tools step through the data prep and analysis steps. Download the Pro package, open it and substitute your own layers for Origins and Destinations. Parcel centroids are a suggested option for Origins, for example. Origin-Destination analysis was configured, using ArcGIS StreetMap Premium as the network data source. Census block centroids with population greater than zero were used as the Origins, and grocery store locations were used as the Destinations. A cutoff of 10 minutes was used with the Walk Time option. Only one restriction was applied to the street network: Walkable, which means Interstates and other non-walkable street segments were treated appropriately. You see the results in the map: wherever freeway overpasses and underpasses are present near a grocery store, the walkable area extends across/through that pass, but not along the freeway.A cutoff of 10 minutes was used with the Drive Time option. The default restrictions were applied to the street network, which means a typical vehicle's access to all types of roads was factored in.The results for each analysis were captured in the Lines layer, which shows which origins are within the cutoff of each destination over the street network, given the assumptions about that network (walking, or driving a vehicle).The Lines layer was then summarized by census block ID to capture the Maximum value of the Destination_Rank field. A census block within 10 minutes of 3 stores would have 3 records in the Lines layer, but only one value in the summarized table, with a MAX_Destination_Rank field value of 3. This is the number of stores accessible to that census block in the 10 minutes measured, for walking and driving. These data were joined to the block centroids layer and given unique names. At this point, all blocks with zero population or null values in the MAX_Destination_Rank fields were given a store count of 0, to help the next step.Walkable and Drivable areas are calculated into a raster layer, using Nearest Neighbor geoprocessing tool on the count of stores within a 10 minute walk, and a count of stores within a ten minute drive, respectively. This tool uses a 200 meter grid and interpolates the values between each census block. A census tracts layer containing all water polygons "erased" from the census tract boundaries was used as an environment setting, to help constrain interpolation into/across bodies of water. The same layer use used to "shoreline" the Nearest Neighbor results, to eliminate any interpolation into the ocean or Great Lakes. This helped but was not perfect.Notes and LimitationsThe map provides a baseline for discussing access to grocery stores in a city. It does not presume local population has the desire or means to walk or drive to obtain groceries. It does not take elevation gain or loss into account. It does not factor time of day nor weather, seasons, or other variables that affect a

Antibiotics in the Global River System Arising from Human ConsumptionData repositoryLast updated: April 2025prepared byHeloisa Ehalt Macedo (heloisa.ehaltmacedo@mail.mcgill.ca) and Bernhard Lehner (bernhard.lehner@mcgill.ca)1. Overview and backgroundThis repository contains the Python code, input, and output data for the research article: Ehalt Macedo, H., Lehner, B., Nicell, J., Khan, U., Klein, E. (2025). Antibiotics in the global river system arising from human consumption. PNAS Nexus. Further information and description of the model can be found in the same publication.The data repository includes 3 datasets:1. Python code: python project repository including the structure necessary for the model to run.2. Input data:a. A table containing information on all river segments associated with geometric attributes from RiverATLAS (Linke et al., 2019) and HydroROUT (Lehner and Grill, 2013), and attributes used in the HydroFATE model (Ehalt Macedo et al., 2024) based on underlying data such as HydroLAKES (Messager et al., 2016), and HydroWASTE (Ehalt Macedo et al., 2022).b. A table of parameters for the model run. The literature sources of the parameters for all substances and the scenarios are described in the research paper.c. A table of country-level consumption per capita of all contaminants being analyzed, as provided by Klein et al. (2018).3. Output data:a. A table including the unique river reach identifier associated with the river network, the resulting concentration and risk quotient for each contaminant, and totals using HydroFATE as described in the research paper.2. Repository contentThe data repository has the following structure:HydroFATE_v1_1.zip/: repository containing:|---------Main_script/:|---------------------Input/: empty folder to add input data from “Input_data.gdb.zip”|---------------------Output/: empty folder where results will be saved after model run|---------------------HydroFATE_v1_1.py : python code with HydroFATE model version 1.1|---------------------config.py: config file with model parameters|---------LICENSE: license file for python code|---------README.md: readme file for code description and compilation instructions|---------Technical_documentation_Antibiotics: technical documentation for the code and datasetsInput_data.gdb.zip/: file geodatabase in ESRI® geodatabase format containing 3 feature classes (zipped):|---------streams: table including global river network attributes.|---------parameters: table including parameters and configuration settings.|---------consumption: table including country-level consumption per capita of all substances.Output.gdb.zip/: file geodatabase in ESRI® geodatabase format containing 1 feature class (zipped):|---------Total_results: table with model predictions for every river reach of the global river network.3. Data format and projectionA license for the software ArcGIS Pro is required to run the provided scripts. These datasets are available electronically in compressed zip file format. To use the data files, the zip files must first be decompressed. All data layers are provided in geographic (latitude/longitude) projection, referenced to datum WGS84. In ESRI® software this projection is defined by the geographic coordinate system GCS_WGS_1984 and datum D_WGS_1984 (EPSG: 4326). Full descriptions of dataset attributes can be found in the Technical documentation in this repository.4. License and citations4.1 License AgreementThis documentation and datasets are licensed under a Creative Commons Attribution-ShareAlike 4.0 International License (CC-BY-4.0 License). For all regulations regarding license grants, copyright, redistribution restrictions, required attributions, disclaimer of warranty, indemnification, liability, waiver of damages, and a precise definition of licensed materials, please refer to the License Agreement (https://creativecommons.org/licenses/by/4.0/legalcode). For a human-readable summary of the license, please see https://creativecommons.org/licenses/by/4.0/.4.2 Citations and Acknowledgements.Citations and acknowledgements of this dataset should be made as follows:Ehalt Macedo, H., Lehner, B., Nicell, J., Khan, U., Klein, E. (2025). Antibiotics in the global river system arising from human consumption. PNAS Nexus.We kindly ask users to cite this study in any published material produced using it. Onlineclass links to this repository (https://doi.org/10.6084/m9.figshare.25829464) should also be provided.5. ReferencesEhalt Macedo, H., Lehner, B., Nicell, J. & Grill, G. HydroFATE (v1): a high-resolution contaminant fate model for the global river system. Geosci. Model Dev. 17, 2877-2899, doi:10.5194/gmd-17-2877-2024 (2024).Ehalt Macedo, H., Lehner, B., Nicell, J., Grill, G., Li, J., Limtong, A., and Shakya, R.: Distribution and characteristics of wastewater treatment plants within the global river network, Earth Syst. Sci. Data, 14, 559-577, doi: 10.5194/essd-14-559-2022, 2022.Klein, E. Y., Boeckel, T. P. V., Martinez, E. M., Pant, S., Gandra, S., Levin, S. A., Goossens, H., and Laxminarayan, R.: Global increase and geographic convergence in antibiotic consumption between 2000 and 2015, Proceedings of the National Academy of Sciences, 115, E3463-E3470, doi: 10.1073/pnas.1717295115, 2018.Lehner, B. and Grill, G.: Global river hydrography and network routing: baseline data and new approaches to study the world's large river systems, Hydrol Process, 27, 2171-2186, doi: 10.1002/hyp.9740, 2013.Linke, S., Lehner, B., Ouellet Dallaire, C., Ariwi, J., Grill, G., Anand, M., Beames, P., Burchard-Levine, V., Maxwell, S., Moidu, H., Tan, F., and Thieme, M.: Global hydro-environmental sub-basin and river reach characteristics at high spatial resolution, Scientific Data, 6, 283, doi: 10.1038/s41597-019-0300-6, 2019.Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.: Estimating the volume and age of water stored in global lakes using a geo-statistical approach, Nature Communications, 7, 13603, doi: 10.1038/ncomms13603, 2016.

This layer estimates the mean of the most likely interpolated values of carbon content for every from soilgrids.org SOC layer to a depth of 30cm. This layer averages most likely interpolated carbon mass from soilgrids.org to estimate a value for the top 30cm of soil. Soilgrids.org soil organic carbon datasets are expressed as mass in tenths of a gram (decigrams) per kilogram of soil overall. This has been converted to grams per kilogram for the Living Atlas. For example, in this layer, a value of 100 in the dataset means there is 100g of carbon every kilogram of soil in the 250m pixel to a depth of 30cm, or 10% carbon by mass.A 0 to 200cm depth version of this layer is also available.Variable mapped: grams of carbon per kilogram of soil (g/kg)Data Projection: Goode's Homolosine (land) WKID 54052Mosaic Projection: Goode's Homolosine (land) WKID 54052Extent: World, except AntarcticaCell Size: 250 mSource Type: ScientificVisible Scale: All scales are visibleSource: SoilGrids.orgPublication Date: June 14, 2021All analysis was done in soilgrids.org own Goode's Homolosine projection (land) in ArcGIS Pro. The data were served in this same projection in ArcGIS Image for ArcGIS Online.To determine the soil organic carbon to 30cm depth, we used a weighted average using the top three mean layers of soil organic carbon, weighted as follows:((0-5cm * 1) + (5-15cm * 2) + (15-30cm * 3))/ 6The output is actually converted from decigrams per kilogram (integers) to grams per kilogram (32 bit floating point) so the following formula was used:(0-5cm + (5-15cm * 2) + (15-30cm * 3)) / 60

Datasets supporting the publication: Analyzing satellite-derived 3D building inventories and quantifying urban growth towards active faults: a case study of Bishkek, Kyrgyzstan. https://doi.org/10.3390/rs14225790

-Please refer to the publication for details on the production of each dataset. -Datasets are ordered following the publication figures. -Please cite the publication and this dataset repository when using the data.

Structure: File ID

-[fields:] description

KH9_1979_builtup.shp -KH9 1979 built-up area classification

S2_2021_builtup.tif -Sentinel-2 2021 built-up area classification.

S2_2021_corine_land_cover_class.tif -Sentinel-2 2021 land cover classification in Corine 2018 land-cover classes.

S2_KH9_DN_change_aggregated.shp -Proportional DN change aggregated to a 1 km^2 grid for areas ≥50% built-up.

building_characteristics.shp -build_count: building count in 500 m square grid cell. -mean_area: mean building size (m^2) in 500 m square grid cell. -median_area: median building size(m^2) in 500 m square grid cell. -cell_coverage: %building coverage of 500 m square grid cell.

pleiades_buildings_all.shp -All building detections from Pleiades data. Confidence values are output from the deep learning model.

pleiades_buildings_heights.shp -Building detections from the Pleiades data that were allocated heights (m). -Zmean, Zmedian,... refer to heights (m)

wv2_buildings_all.shp -All building detections from WorldView-2 data. Confidence values are output from the deep learning model.

wv2_buildings_heights.shp -Building detections from the WorldView-2 data that were allocated heights (m). -Zmean, Zmedian,... refer to heights (m)

trained_rcnn.zip -ArcGIS Pro deep learning model (DLPK) used to extract building footprints.