Instructions on how to create a layer containing recent earthquakes from a CSV file downloaded from GNS Sciences GeoNet website to a Web Map.The CSV file must contain latitude and longitude fields for the earthquake location for it to be added to a Web Map as a point layer.Document designed to support the Natural Hazards - Earthquakes story map

Mapping incident locations from a CSV file in a web map (YouTube video).View this short demonstration video to learn how to geocode incident locations from a spreadsheet in ArcGIS Online. In this demonstration, the presenter drags a simple .csv file into a browser-based Web Map and maps the appropriate address fields to display incident points allowing different types of spatial overlays and analysis. _Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This dataset contains a list of places shown on the Beauplan maps, provided in a .csv format for easy access and analysis. It includes geographical data on locations featured in these historical maps, allowing users to explore and analyze the historical layout of places according to the Beauplan maps. This dataset serves as a valuable resource for researchers and historians studying historical geography and cartography.

Folium makes it easy to visualize data that’s been manipulated in Python on an interactive leaflet map. It enables both the binding of data to a map for choropleth visualizations as well as passing rich vector/raster/HTML visualizations as markers on the map. These files can be used to mark the state boundaries on the map of INDIA using folium library and the CSV also contains the state data and how to use it in our notebooks. I have used it in one of my kernels which can be viewed.

The library has a number of built-in tilesets from OpenStreetMap, Mapbox, and Stamen, and supports custom tilesets with Mapbox or Cloudmade API keys. folium supports both Image, Video, GeoJSON, and TopoJSON overlays. Due to extensible functionalities I find folium the best map plotting library in python. Do give it a try and use it in your kernels.

The Residential School Locations Dataset [IRS_Locations.csv] contains the locations (latitude and longitude) of Residential Schools and student hostels operated by the federal government in Canada. All the residential schools and hostels that are listed in the Indian Residential School Settlement Agreement are included in this dataset, as well as several Industrial schools and residential schools that were not part of the IRRSA. This version of the dataset doesn’t include the five schools under the Newfoundland and Labrador Residential Schools Settlement Agreement. The original school location data was created by the Truth and Reconciliation Commission, and was provided to the researcher (Rosa Orlandini) by the National Centre for Truth and Reconciliation in April 2017. The dataset was created by Rosa Orlandini, and builds upon and enhances the previous work of the Truth and Reconcilation Commission, Morgan Hite (creator of the Atlas of Indian Residential Schools in Canada that was produced for the Tk'emlups First Nation and Justice for Day Scholar's Initiative, and Stephanie Pyne (project lead for the Residential Schools Interactive Map). Each individual school location in this dataset is attributed either to RSIM, Morgan Hite, NCTR or Rosa Orlandini. Many schools/hostels had several locations throughout the history of the institution. If the school/hostel moved from its’ original location to another property, then the school is considered to have two unique locations in this dataset,the original location and the new location. For example, Lejac Indian Residential School had two locations while it was operating, Stuart Lake and Fraser Lake. If a new school building was constructed on the same property as the original school building, it isn't considered to be a new location, as is the case of Girouard Indian Residential School.When the precise location is known, the coordinates of the main building are provided, and when the precise location of the building isn’t known, an approximate location is provided. For each residential school institution location, the following information is provided: official names, alternative name, dates of operation, religious affiliation, latitude and longitude coordinates, community location, Indigenous community name, contributor (of the location coordinates), school/institution photo (when available), location point precision, type of school (hostel or residential school) and list of references used to determine the location of the main buildings or sites.

The table MEJ for the state of Colorado csv is part of the dataset Mapping for Environmental Justice's map for the state of Colorado, available at https://redivis.com/datasets/e7qz-a6b024b0q. It contains 1249 rows across 60 variables.

This map shows locations of active Physical Oceanographic Real-Time System (PORTS) stations maintained and operated by the Center for Operational Oceanographic Products and Services (CO-OPS). These stations are operated in partnership with a range of entities to facilitate maritime commerce.PORTS is a decision support tool that improves the safety and efficiency of maritime commerce and coastal resource management through the integration of real-time environmental observations, forecasts and other geospatial information. PORTS provides accurate real-time oceanographic information tailored to the specific needs of the local community. These regional systems allow mariners to maintain an adequate margin of safety for the increasingly large vessels visiting U.S. ports, while allowing port operators to maximize port throughput.

Context

Trail maps for Jefferson County, Colorado, USA

Content

PDF maps, JPEG maps, and .CSV metadata from jeffco.us, cityofgolden.net, and/or https://data-jeffersoncounty.opendata.arcgis.com/. Each individual file description will have a link to the original source where you can confirm the open data license.

Acknowledgements

https://opendata.arcgis.com/datasets/c1ac715614704dbf9361b9877c4ee7ab_1.csv https://opendata.arcgis.com/datasets/05a60a13859747e0bff472fda5f6d7a1_1.csv https://opendata.arcgis.com/datasets/92a0ad2f328d4a289b6504ad057f0b83_1.csv https://www.jeffco.us/DocumentCenter/View/9872/Windy-Saddle-Park-Map?bidId= https://www.jeffco.us/DocumentCenter/View/14152/Beaver-Brook-map https://www.jeffco.us/DocumentCenter/View/9380/North-Table-Mountain-Park-Map?bidId= https://www.jeffco.us/DocumentCenter/View/10275/Peaks-to-Plains-Trail-Map https://www.jeffco.us/DocumentCenter/View/9361/Apex-Park-Map?bidId= https://www.jeffco.us/DocumentCenter/View/16163/Peaks-to-Plains-Mouth-of-the-Canyon-Fact-Sheet https://www.jeffco.us/DocumentCenter/View/9374/Lookout-Mountain-Preserve-Map?bidId= https://www.jeffco.us/ImageRepository/Document?documentId=23051 https://www.jeffco.us/DocumentCenter/View/9383/South-Table-Mountain-Park-Map?bidId= https://www.jeffco.us/DocumentCenter/View/15156/Physical-Map-And-3D-Model https://www.jeffco.us/DocumentCenter/View/16154/Mouth_of_Clear_Creek_Canyon_Map https://www.cityofgolden.net/media/TrailMap.pdf

This dataset contains a mapping between all the bibliographic resources and responsabile agents included in OpenCitation Meta (https://opencitations.net/meta) identified by an OMID and their corresponding PID(s) (e.g., DOI, PMID, ORCID, etc).The dataset was released on March 24, 2025. The repository contains two datasets, one for the bibliographic resources (meta_br) and one for the responsabile agents (meta_ra). Each line of the CSV file maps an OMID to its corresponding PID(s), e.g. "06230199640,pmid:25088780 doi:10.1016/j.ymeth.2014.07.008". This version of the dataset contains 103,808,586 bibliographic resources and 8,987,807 responsabile agents, identified by an OMID value, and their corresponding PID(s). Note: The data provided in this dump is based on the state of OpenCitations Meta at the time this collection was generated.

If you have geographic information stored as a table, ArcGIS Pro can display it on a map and convert it to spatial data. In this tutorial, you'll create spatial data from a table containing the latitude-longitude coordinates of huts in a New Zealand national park. Huts in New Zealand are equivalent to cabins in the United States—they may or may not have sleeping bunks, kitchen facilities, electricity, and running water. The table of hut locations is stored as a comma-separated values (CSV) file. CSV files are a common, nonproprietary file type for tabular data.Estimated time: 45 minutesSoftware requirements: ArcGIS Pro

This data repository is for the "Lane-Level Localization and Map Matching for Advanced Connected and Automated Vehicle (CAV) Applications" project. This project investigated and demonstrated the utility of lane-level map-matching and localization. In this project, data were collected in support of three folders for Tasks 2, 5, and 6.

Task 2: Lane-Level Mapping. Experimental data were acquired to assess the accuracy of the USDOT Mapping Tool. The data analysis used 39 feature points within about 200 meters of the intersection verified point and 55 feature points distributed over longer distances from the verified point (94 points total). Along with the data files, the repository includes a README file and two Matlab scripts that process the data.

Task 5: Demonstration. Experimental data were acquired to assess the probability of correct lane determination. Three road tests were performed. The data for each test is organized into its own subdirectory. The main directory contains a README file that discusses the file contents and how to process them using the included Matlab scripts.

Task 6: Simulation Study. Each simulation run created 4 .csv files: Chicago Intersection Queue information, Cranford Intersection Queue information, Iowa Intersection Queue information, and general vehicle information. Queue information consisted of the estimated queue information and actual queue information for each lane versus time. General vehicle information consisted of simulation time, vehicle id, vehicle speed, vehicle position, perturbed vehicle position, and vehicle direction. Each .csv file has column headers for distinction. In total there were 1200 .csv files: 4 .csv files for each simulation, 10 simulations for each scenario, and for the 30 scenarios described in the Simulation Scenario Section.

Introduction and Rationale: Due to our increasing understanding of the role the surrounding landscape plays in ecological processes, a detailed characterization of land cover, including both agricultural and natural habitats, is ever more important for both researchers and conservation practitioners. Unfortunately, in the United States, different types of land cover data are split across thematic datasets that emphasize agricultural or natural vegetation, but not both. To address this data gap and reduce duplicative efforts in geospatial processing, we merged two major datasets, the LANDFIRE National Vegetation Classification (NVC) and USDA-NASS Cropland Data Layer (CDL), to produce an integrated land cover map. Our workflow leveraged strengths of the NVC and the CDL to produce detailed rasters comprising both agricultural and natural land-cover classes. We generated these maps for each year from 2012-2021 for the conterminous United States, quantified agreement between input layers and accuracy of our merged product, and published the complete workflow necessary to update these data. In our validation analyses, we found that approximately 5.5% of NVC agricultural pixels conflicted with the CDL, but we resolved a majority of these conflicts based on surrounding agricultural land, leaving only 0.6% of agricultural pixels unresolved in our merged product. Contents: Spatial data Attribute table for merged rasters Technical validation data Number and proportion of mismatched pixels Number and proportion of unresolved pixels Producer's and User's accuracy values and coverage of reference data Resources in this dataset:Resource Title: Attribute table for merged rasters. File Name: CombinedRasterAttributeTable_CDLNVC.csvResource Description: Raster attribute table for merged raster product. Class names and recommended color map were taken from USDA-NASS Cropland Data Layer and LANDFIRE National Vegetation Classification. Class values are also identical to source data, except classes from the CDL are now negative values to avoid overlapping NVC values. Resource Title: Number and proportion of mismatched pixels. File Name: pixel_mismatch_byyear_bycounty.csvResource Description: Number and proportion of pixels that were mismatched between the Cropland Data Layer and National Vegetation Classification, per year from 2012-2021, per county in the conterminous United States.Resource Title: Number and proportion of unresolved pixels. File Name: unresolved_conflict_byyear_bycounty.csvResource Description: Number and proportion of unresolved pixels in the final merged rasters, per year from 2012-2021, per county in the conterminous United States. Unresolved pixels are a result of mismatched pixels that we could not resolve based on surrounding agricultural land (no agriculture with 90m radius).Resource Title: Producer's and User's accuracy values and coverage of reference data. File Name: accuracy_datacoverage_byyear_bycounty.csvResource Description: Producer's and User's accuracy values and coverage of reference data, per year from 2012-2021, per county in the conterminous United States. We defined coverage of reference data as the proportional area of land cover classes that were included in the reference data published by USDA-NASS and LANDFIRE for the Cropland Data Layer and National Vegetation Classification, respectively. CDL and NVC classes with reference data also had published accuracy statistics. Resource Title: Data Dictionary. File Name: Data_Dictionary_RasterMerge.csv

The .csv table is part of a dataset package that was compiled for use as mineral assessment guidance in the Sagebrush Mineral-Resource Assessment project (SaMiRA). Mineral potential maps from previous mineral-resource assessments which included areas of the SaMiRA project areas were georeferenced. The images were clipped to the extent of the map and all explanatory text, gathered from map explanations or report text, was recorded into the All_georef_images_descriptive_information_table.csv table. This table lists and describes the column headings in the All_georef_images_descriptive_information_table.csv table.

This submission contains an update to the previous Exploration Gap Assessment funded in 2012, which identify high potential hydrothermal areas where critical data are needed (gap analysis on exploration data).

The uploaded data are contained in two data files for each data category: A shape (SHP) file containing the grid, and a data file (CSV) containing the individual layers that intersected with the grid. This CSV can be joined with the map to retrieve a list of datasets that are available at any given site. A grid of the contiguous U.S. was created with 88,000 10-km by 10-km grid cells, and each cell was populated with the status of data availability corresponding to five data types:

1. well data
2. geologic maps
3. fault maps
4. geochemistry data
5. geophysical data The raw table of intersected services for the structural maps gap assessment.

The attributes in the CSV include:

1. grid_id : The id of the grid cell that the data intersects with
2. title: This represents the name of the WFS service that intersected with this grid cell
3. abstract: This represents the description of the WFS service that intersected with this grid cell
4. gap_type: This represents the category of data availability that these data fall within. As the current processing is pulling data from NGDS, this category universally represents data that are available in the NGDS and are ready for acquisition for analytic purposes.
5. proprietary_type: Whether the data are considered proprietary
6. service_type: The type of service
7. base_url: The service URL

This zip folder contains three main folders. The contents of each folder is described as follows: Pre-Analysis Data Contains four csv files in the parent level corresponding to the frequency and confidence data for two- and four-coder groups. It also contains two folders housing 44 csv files a piece for the Gwet's AC1 data. Post-Analysis Data Contains three csv files summarizing the Gwet's AC1 results (AC1_Clean.xlsx), the prevalence and split results (Frequency_All.xlsx), and depicting the data underlying the matrix (Matrix_Updated.xlsx) Scripts Contains two scripts: First, frequency_calculation.py that reorganizes and cleans the raw data to come up with prevalence and split values. Second, Gwet.R that calculates the Gwet's AC1 scores and cumulative probabilities for the values.

This dataset shows points of interest around Wicklow Mountains National Park, which have been included in an online mapping application - Wicklow Mountains Story Map Tour. CSV file contains points of interest in Wicklow Mountains National Park, along with descriptions and coordinates (Irish Transverse Mercator, Irish Grid and WGS84). Zip folder contains the images used in the Story Map.

List of BEAD-eligible locations in Colorado with associated Project Area. This version of the list does not include served or funded locations (locations where classification = 2). For a full list of locations in Colorado with the BEAD eligibility classification, please see: Final BEAD-eligible location list - APPROVEDFields: location_id: Location ID from the Broadband Serviceable Location Fabric version 3.2 classification: 0 = unserved, 1= underservedPA_ID: Project Area ID

The Traveling Salesperson Problem (TSP) is a class problem of computer science that seeks to find the shortest route between a group of cities. It is an NP-hard problem in combinatorial optimization, important in theoretical computer science and operations research.

https://data.heatonresearch.com/images/wustl/kaggle/tsp/world-tsp.png" alt="World Map">

In this Kaggle competition, your goal is not to find the shortest route among cities. Rather, you must attempt to determine the route labeled on a map.

Calculating Line Distances

The data for this competition is not made up of real-world maps, but rather randomly generated maps of varying attributes of size, city count, and optimality of the routes. The following image demonstrates a relatively small map, with few cities, and an optimal route.

https://data.heatonresearch.com/images/wustl/kaggle/tsp/1.jpg" alt="Small Map">

Not all maps are this small, or contain this optimal a route. Consider the following map, which is much larger.

https://data.heatonresearch.com/images/wustl/kaggle/tsp/6.jpg" alt="Larger Map">

The following attributes were randomly selected to generate each image.

• Height
• Width
• City count
• Cycles of Simulated Annealing optimization of initial random path

The path distance is based on the sum of the Euclidean distance of all segments in the path. The distance units are in pixels.

Dataset Challenges

This is a regression problem, you are to estimate the total path length. Several challenges to consider.

• If you indiscriminately scale the maps, you will lose size information.
• Paths might overlap, causing the ration of total pixels to total length to become misleading.
• As paths overlap bot other path segments and cities, the resulting color becomes brighter.

The following picture shows a section from one map zoomed to the pixel-level:

https://data.heatonresearch.com/images/wustl/kaggle/tsp/tsp_zoom.jpg" alt="TSP Zoom">

CSV Files

The following CSV files are provided, in addition to the images.

• train.csv - Training data, with distance labels.
• test.csv - Test data without distance labels.
• tsp-all.csv - Training and test data combined with complete labels and additional information about each generated map.

CSV File Format

The tsp-all.csv file contains the following data.

id,filename,distance,key
0,0.jpg,83110,503x673-270-83110.jpg
1,1.jpg,1035,906x222-10-1035.jpg
2,2.jpg,20756,810x999-299-20756.jpg
3,3.jpg,13286,781x717-272-13286.jpg
4,4.jpg,13924,609x884-312-13924.jpg

The columns:

• id - A unique ID that allows linking across all three CSV files.
• filename - The name of each map's image file.
• distance - The total distance through the cities, this is the y/label.
• key - The generator filename, provides the dimensions, city count, & distance.

In 2008, as a collaborative effort between Woods Hole Oceanographic Institution and the U.S. Geological Survey, 20 giant gravity cores were collected from areas surrounding Puerto Rico and the U.S. Virgin Islands. The regions sampled have had many large earthquake and landslide events, some of which are believed to have triggered tsunamis. The objective of this coring cruise, carried out aboard the National Oceanic and Atmospheric Administration research vessel Seward Johnson, was to determine the age of several substantial slope failures and seismite layers near Puerto Rico in an effort to map their temporal distribution. Data gathered from the cores collected in 2008 and 11 archive cores from the Lamont-Doherty Earth Observatory are included in this report. These data include lithologic logs, core summary sheets, x-ray fluorescence, wet-bulk density, magnetic susceptibility, grain-size analyses, radiographs, and radiocarbon age dates.