TerraMetrics, Inc., proposes an SBIR Phase I R/R&D program to investigate and develop a key web services architecture that provides data processing, storage and delivery capabilities and enables successful deployment, display and visual interaction of diverse, massive, multi-dimensional science datasets within popular web-based geospatial platforms like Google Earth, Google Maps, NASA's World Wind and others. The proposed innovation exploits the use of a wired and wireless, network-friendly, wavelet-compressed data format and server architecture that extracts and delivers appropriately-sized blocks of multi-resolution geospatial data to client applications on demand and in real time. The resulting format and architecture intelligently delivers client-required data from a server, or multiple distributed servers, to a wide range of networked client applications that can build a composite, user-interactive 3D visualization of fused, disparate, geospatial datasets. The proposed innovation provides a highly scalable approach to data storage and management while offering geospatial data services to client science applications and a wide range of client and connection types from broadband-connected desktop computers to wireless cell phones. TerraMetrics offers to research the feasibility of the proposed innovation and demonstrate it within the context of a live, server-supported, Google Earth-compatible client application with high-density, multi-dimensional NASA science data.

Here are a few use cases for this project:

1. Smart Parking Assistance: Utilize the Tarik-Tyane-annotations model to develop an application that assists drivers in locating available parking spots, including valid and invalid spots like handicapped and fire hydrant zones, in real-time, making the parking experience more convenient and efficient.

2. Traffic Management: Integrate the model into city traffic management systems to monitor and manage parking spaces in high traffic areas. By identifying improper parking, such as in fire hydrant zones, bus stops, or handicapped spots, authorities can enforce parking regulations proactively.

3. Urban Planning and Analysis: Use the Tarik-Tyane-annotations model to analyze public parking data, identifying patterns and trends related to parking spots' utilization. This can help city planners make informed decisions on the allocation and distribution of parking spaces, optimizing infrastructure for future growth.

4. Navigation App Integration: Enhance navigation applications like Google Maps or Waze with the model's parking spot information, allowing users to find not only available parking spaces nearby but also information on rules and restrictions in real-time, avoiding fines or inconveniences.

5. Emergency Response: Equip emergency response vehicles, such as fire trucks and ambulances, with the Tarik-Tyane-annotations model to identify fire hydrant locations or restricted parking areas quickly. This can help emergency services to navigate congested areas and ensure unblocked access to critical infrastructure during emergencies.

The Worldview tool from NASA's Earth Observing System Data and Information System (EOSDIS) provides the capability to interactively browse over 800 global, full-resolution satellite imagery layers and then download the underlying data. Many of the imagery layers are updated within three hours of observation, essentially showing the entire Earth as it looks "right now". This supports time-critical application areas such as wildfire management, air quality measurements, and flood monitoring. View current natural hazards and events using the Events tab which reveals a list of natural events, including wildfires, tropical storms, and volcanic eruptions. Animate the imagery over time. Arctic and Antarctic views of many products are also available for a "full globe" perspective. Browsing on tablet and smartphone devices is generally supported for mobile access to the imagery.Worldview MODIS video: NASA's Worldview: Two decades of Earth Data at your FingertipsWorldview Tutorial - How to View and Share your Planet with WorldviewWorldview Webinar - Explore the Entire Earth, Every Day, with Satellite Imagery from NASA WorldviewPowered by GIBSWorldview uses NASA's Global Imagery Browse Services (GIBS) to rapidly retrieve its imagery for an interactive browsing experience. While Worldview uses OpenLayers as its mapping library, GIBS imagery can also be accessed from Google Earth, NASA WorldWind, and several other clients. We encourage interested developers to build their own clients or integrate NASA imagery into their existing ones using these services.Comments/suggestions/problem reports are welcome via Earthdata Support. View frequently asked questions (FAQ) about Worldview.Source Information Obtained Oct 18, 2018 from https://earthdata.nasa.gov/worldview

Description

Road development in the Congo Basin forest is continuously monitored from 2019 onwards in high spatial and temporal detail. A deep learning method is applied to 10 m scale Sentinel-1 and Sentinel-2 imagery for automated road detections on a monthly basis. This version presents 5 years of road development (46,311 km) from 2019-2023.

The data is composed of line features distributed in .shp and .geojson formats. The following attributes are stored for the line features:

• NetworkID: A unique ID for each connected road network.
• SegLenM: The length of the road segment (in meters).
• NetLenM: The length of the connected road network (in meters).
• Month: The road segment opening month.
• Year: The road segment opening year.
• MonthNum: The road segment opening month, depicted as a continuing count since the start of monitoring (e.g. 13 = January 2020). This attribute can be used for smooth and continuous temporal analyses or visualizations.

Additional information

• More information about the forest road mapping project can be found at: https://wur.eu/forest-roads
• Continuously updated road maps can be interactively viewed at: https://nrtwur.users.earthengine.app/view/forest-roads
• The dataset can be accessed in Google Earth Engine at: ee.FeatureCollection('projects/wurnrt-loggingroads/assets/distribution/forestroads_afr_2019-01_2023-12')
• The scientific paper (Slagter et al., 2024) describing the methods to produce this dataset can be found at: https://doi.org/10.1016/j.rse.2024.114380" target="_blank" rel="noopener">https://doi.org/10.1016/j.rse.2024.114380

Citation

Please cite the following when referring to this dataset:

This data is part of the Monthly aggregated Water Vapor MODIS MCD19A2 (1 km) dataset. Check the related identifiers section on the Zenodo side panel to access other parts of the dataset.

General Description

The monthly aggregated water vapor dataset is derived from MCD19A2 v061. The Water Vapor data measures the column above ground retrieved from MODIS near-IR bands at 0.94μm. The dataset time spans from 2000 to 2022 and provides data that covers the entire globe. The dataset can be used in many applications like water cycle modeling, vegetation mapping, and soil mapping. This dataset includes:

• Monthly time-series:
  Derived from MCD19A2 v061, this data provides a monthly aggregated mean and standard deviation of daily water vapor time-series data from 2000 to 2022. Only positive non-cloudy pixels were considered valid observations to derive the mean and the standard deviation. The remaining no-data values were filled using the TMWM algorithm. This dataset also includes smoothed mean and standard deviation values using the Whittaker method. The quality assessment layers and the number of valid observations for each month can provide an indication of the reliability of the monthly mean and standard deviation values.
• Yearly time-series:
  Derived from monthly time-series, this data provides a yearly time-series aggregated statistics of the monthly time-series data.
• Long-term data (2000-2022):
  Derived from monthly time-series, this data provides long-term aggregated statistics for the whole series of monthly observations.

Data Details

• Time period: 2021–2022
• Type of data: Water vapor column above the ground (0.001cm)
• How the data was collected or derived: Derived from MCD19A2 v061 using Google Earth Engine. Cloudy pixels were removed and only positive values of water vapor were considered to compute the statistics. The time-series gap-filling and time-series smoothing were computed using the Scikit-map Python package.
• Statistical methods used: Four statistics were derived: mean, standard deviation, smoothed mean, smoothed standard deviation.
• Limitations or exclusions in the data: The dataset does not include data for Antarctica.
• Coordinate reference system: EPSG:4326
• Bounding box (Xmin, Ymin, Xmax, Ymax): (-180.00000, -62.00081, 179.99994, 87.37000)
• Spatial resolution: 1/120 d.d. = 0.008333333 (1km)
• Image size: 43,200 x 17,924
• File format: Cloud Optimized Geotiff (COG) format.

Support

If you discover a bug, artifact, or inconsistency, or if you have a question please use some of the following channels:

• Technical issues and questions about the code: GitLab Issues
• General questions and comments: LandGIS Forum

Name convention

To ensure consistency and ease of use across and within the projects, we follow the standard Open-Earth-Monitor file-naming convention. The convention works with 10 fields that describes important properties of the data. In this way users can search files, prepare data analysis etc, without needing to open files. The fields are:

1. generic variable name: wv = Water vapor
2. variable procedure combination: mcd19a2v061.seasconv = MCD19A2 v061 with gap-filling algorithm
3. Position in the probability distribution / variable type: m = mean | sd = standard deviation | n = number of observations | qa = quality assessment
4. Spatial support: 1km
5. Depth reference: s = surface
6. Time reference begin time: 20210101 = 2021-01-01
7. Time reference end time: 20221231 = 2022-12-31
8. Bounding box: go = global (without Antarctica)
9. EPSG code: epsg.4326 = EPSG:4326
10. Version code: v20230619 = 2023-06-19 (creation date)

Here are a few use cases for this project:

1. Automatic Driving Assistance: This model could be implemented in cars to assist with automatic driving features. The system would detect and decipher road signs to guide decisions such as stopping, altering speed, or obeying specified restrictions.

2. Road Maintenance Monitoring: Government or local authorities can use this model to automatically survey road signs, identify those that are worn out, damaged, blocked, or missing, helping to prioritize maintenance tasks.

3. Interactive Maps and Navigation Apps: This model could be used to update and enhance detailing on interactive maps such as Google Street View or GPS navigation apps by detecting and labeling road signs.

4. Road Safety Education: The model could be integrated into driving training and testing applications to educate new drivers about different road signs and what they represent in a controlled and repeatable digital environment.

5. Traffic Flow Analysis: The model can be used in conjunction with CCTV or drone footage to monitor and analyze traffic compliance with specific road signs, contributing to traffic management and city planning strategies.

DNRGPS is an update to the popular DNRGarmin application. DNRGPS and its predecessor were built to transfer data between Garmin handheld GPS receivers and GIS software.

DNRGPS was released as Open Source software with the intention that the GPS user community will become stewards of the application, initiating future modifications and enhancements.

DNRGPS does not require installation. Simply run the application .exe

See the DNRGPS application documentation for more details.

Compatible with: Windows (XP, 7, 8, 10, and 11), ArcGIS shapefiles and file geodatabases, Google Earth, most hand-held Garmin GPSs, and other NMEA output GPSs

Limited Compatibility: Interactions with ArcMap layer files and ArcMap graphics are no longer supported. Instead use shapefile or geodatabase.

Prerequisite: .NET 4 Framework

DNR Data and Software License Agreement

Subscribe to the DNRGPS announcement list to be notified of upgrades or updates.

Geoscience Australia has been updating its collection of navigation for marine seismic surveys in Australia. These include original navigation files, the 2003 SNIP navigation files and digitised survey track maps. The result will be an updated cleansed navigation collection.

The collection is based on the SNIP format P190 navigation file which follows the UKOOA standard. Industry standard metadata associated with a seismic survey is preserved.

To assist industry, Geoscience Australia is making available its updated version of cleansed navigation. Although the process of updating the navigation data is ongoing and there is still legacy data to check, the navigation data is at a point where a significant improvement has been achieved and it is now usable. Users should be aware that this navigation is not final and there may be errors. Geoscience Australia (email - AusGeodata@ga.gov.au) appreciates being notified of any errors found.

The data is available in both KML and Shape file formats.

The KML file can be viewed using a range of applications including Google Earth, NASA WorldWind, ESRI ArcGIS Explorer, Adobe PhotoShop, AutoCAD3D or any other earth browser (geobrowser) that accepts KML formatted data.

Alternatively the Shape files can be downloaded and viewed using any application that supports shape files.

Disclaimer: Geoscience Australia gives no warranty regarding the data downloads provided herein nor the data's accuracy, completeness, currency or suitability for any particular purpose. Geoscience Australia disclaims all other liability for all loss, damages, expense and costs incurred by any person as a result of relying on the information in the data downloads.

AbstractThe H1B is an employment-based visa category for temporary foreign workers in the United States. Every year, the US immigration department receives over 200,000 petitions and selects 85,000 applications through a random process and the U.S. employer must submit a petition for an H1B visa to the US immigration department. This is the most common visa status applied to international students once they complete college or higher education and begin working in a full-time position. The project provides essential information on job titles, preferred regions of settlement, foreign applicants and employers' trends for H1B visa application. According to locations, employers, job titles and salary range make up most of the H1B petitions, so different visualization utilizing tools will be used in order to analyze and interpreted in relation to the trends of the H1B visa to provide a recommendation to the applicant. This report is the base of the project for Visualization of Complex Data class at the George Washington University, some examples in this project has an analysis for the different relevant variables (Case Status, Employer Name, SOC name, Job Title, Prevailing Wage, Worksite, and Latitude and Longitude information) from Kaggle and Office of Foreign Labor Certification(OFLC) in order to see the H1B visa changes in the past several decades. Keywords: H1B visa, Data Analysis, Visualization of Complex Data, HTML, JavaScript, CSS, Tableau, D3.jsDatasetThe dataset contains 10 columns and covers a total of 3 million records spanning from 2011-2016. The relevant columns in the dataset include case status, employer name, SOC name, jobe title, full time position, prevailing wage, year, worksite, and latitude and longitude information.Link to dataset: https://www.kaggle.com/nsharan/h-1b-visaLink to dataset(FY2017): https://www.foreignlaborcert.doleta.gov/performancedata.cfmRunning the codeOpen Index.htmlData ProcessingDoing some data preprocessing to transform the raw data into an understandable format.Find and combine any other external datasets to enrich the analysis such as dataset of FY2017.To make appropriated Visualizations, variables should be Developed and compiled into visualization programs.Draw a geo map and scatter plot to compare the fastest growth in fixed value and in percentages.Extract some aspects and analyze the changes in employers’ preference as well as forecasts for the future trends.VisualizationsCombo chart: this chart shows the overall volume of receipts and approvals rate.Scatter plot: scatter plot shows the beneficiary country of birth.Geo map: this map shows All States of H1B petitions filed.Line chart: this chart shows top10 states of H1B petitions filed. Pie chart: this chart shows comparison of Education level and occupations for petitions FY2011 vs FY2017.Tree map: tree map shows overall top employers who submit the greatest number of applications.Side-by-side bar chart: this chart shows overall comparison of Data Scientist and Data Analyst.Highlight table: this table shows mean wage of a Data Scientist and Data Analyst with case status certified.Bubble chart: this chart shows top10 companies for Data Scientist and Data Analyst.Related ResearchThe H-1B Visa Debate, Explained - Harvard Business Reviewhttps://hbr.org/2017/05/the-h-1b-visa-debate-explainedForeign Labor Certification Data Centerhttps://www.foreignlaborcert.doleta.govKey facts about the U.S. H-1B visa programhttp://www.pewresearch.org/fact-tank/2017/04/27/key-facts-about-the-u-s-h-1b-visa-program/H1B visa News and Updates from The Economic Timeshttps://economictimes.indiatimes.com/topic/H1B-visa/newsH-1B visa - Wikipediahttps://en.wikipedia.org/wiki/H-1B_visaKey FindingsFrom the analysis, the government is cutting down the number of approvals for H1B on 2017.In the past decade, due to the nature of demand for high-skilled workers, visa holders have clustered in STEM fields and come mostly from countries in Asia such as China and India.Technical Jobs fill up the majority of Top 10 Jobs among foreign workers such as Computer Systems Analyst and Software Developers.The employers located in the metro areas thrive to find foreign workforce who can fill the technical position that they have in their organization.States like California, New York, Washington, New Jersey, Massachusetts, Illinois, and Texas are the prime location for foreign workers and provide many job opportunities. Top Companies such Infosys, Tata, IBM India that submit most H1B Visa Applications are companies based in India associated with software and IT services.Data Scientist position has experienced an exponential growth in terms of H1B visa applications and jobs are clustered in West region with the highest number.Visualization utilizing programsHTML, JavaScript, CSS, D3.js, Google API, Python, R, and Tableau

The dataset includes: - a bricked raster file with the yearly greenest pixel composites covering the city of Berlin between 1984 and 2021 (each band corresponds to one year). The composites were created in Google Earth Engine using the median value from Landsat collections 4, 5, 7, and 8, after applying the corrections suggested by Zulian et al. (2022) and implemented in their code here: https://code.earthengine.google.com/6f77dca2e8a17407db20ecf9b9fc83a4. - a raster file with the administrative area of Berlin. available for download from the portal FIS-Broker. Both raster maps are in .tif format, 30 m resolution, EPSG:3035.

The application is described in the article: Cortinovis C., Haase D., Geneletti D. (2023). Gradual or abrupt? An algorithm to monitor urban vegetation dynamics in support of greening policies. Urban Forestry & Urban Greening. https://doi.org/10.1016/j.ufug.2023.128030 To replicate it, use this dataset to run the algorithm available at https://doi.org/10.6084/m9.figshare.23668941.v1 (just download the dataset and adjust the paths).

This data collection is associated with the publications: Merdith, A. S., Landgrebe, T. C. W., Dutkiewicz, A., & Müller, R. D. (2013). Towards a predictive model for opal exploration using a spatio-temporal data mining approach. Australian Journal of Earth Sciences, 60(2), 217-229. doi: 10.1080/08120099.2012.754793

and

Landgrebe, T. C. W., Merdith, A., Dutkiewicz, A., & Müller, R. D. (2013). Relationships between palaeogeography and opal occurrence in Australia: A data-mining approach. Computers & Geosciences, 56(0), 76-82. doi: 10.1016/j.cageo.2013.02.002

Publication Abstract - Merdith et al. (2013)

Opal is Australia's national gemstone, however most significant opal discoveries were made in the early 1900's - more than 100 years ago - until recently. Currently there is no formal exploration model for opal, meaning there are no widely accepted concepts or methodologies available to suggest where new opal fields may be found. As a consequence opal mining in Australia is a cottage industry with the majority of opal exploration focused around old opal fields. The EarthByte Group has developed a new opal exploration methodology for the Great Artesian Basin. The work is based on the concept of applying “big data mining” approaches to data sets relevant for identifying regions that are prospective for opal. The group combined a multitude of geological and geophysical data sets that were jointly analysed to establish associations between particular features in the data with known opal mining sites. A “training set” of known opal localities (1036 opal mines) was assembled, using those localities, which were featured in published reports and on maps. The data used include rock types, soil type, regolith type, topography, radiometric data and a stack of digital palaeogeographic maps. The different data layers were analysed via spatio-temporal data mining combining the GPlates PaleoGIS software (www.gplates.org) with the Orange data mining software (orange.biolab.si) to produce the first opal prospectivity map for the Great Artesian Basin. One of the main results of the study is that the geological conditions favourable for opal were found to be related to a particular sequence of surface environments over geological time. These conditions involved alternating shallow seas and river systems followed by uplift and erosion. The approach reduces the entire area of the Great Artesian Basin to a mere 6% that is deemed to be prospective for opal exploration. The work is described in two companion papers in the Australian Journal of Earth Sciences and Computers and Geosciences.

Publication Abstract - Landgrebe et al. (2013)

Age-coded multi-layered geological datasets are becoming increasingly prevalent with the surge in open-access geodata, yet there are few methodologies for extracting geological information and knowledge from these data. We present a novel methodology, based on the open-source GPlates software in which age-coded digital palaeogeographic maps are used to “data-mine” spatio-temporal patterns related to the occurrence of Australian opal. Our aim is to test the concept that only a particular sequence of depositional/erosional environments may lead to conditions suitable for the formation of gem quality sedimentary opal. Time-varying geographic environment properties are extracted from a digital palaeogeographic dataset of the eastern Australian Great Artesian Basin (GAB) at 1036 opal localities. We obtain a total of 52 independent ordinal sequences sampling 19 time slices from the Early Cretaceous to the present-day. We find that 95% of the known opal deposits are tied to only 27 sequences all comprising fluvial and shallow marine depositional sequences followed by a prolonged phase of erosion. We then map the total area of the GAB that matches these 27 opal-specific sequences, resulting in an opal-prospective region of only about 10% of the total area of the basin. The key patterns underlying this association involve only a small number of key environmental transitions. We demonstrate that these key associations are generally absent at arbitrary locations in the basin. This new methodology allows for the simplification of a complex time-varying geological dataset into a single map view, enabling straightforward application for opal exploration and for future co-assessment with other datasets/geological criteria. This approach may help unravel the poorly understood opal formation process using an empirical spatio-temporal data-mining methodology and readily available datasets to aid hypothesis testing.

Authors and Institutions

Andrew Merdith - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia. ORCID: 0000-0002-7564-8149

Thomas Landgrebe - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia

Adriana Dutkiewicz - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia

R. Dietmar Müller - EarthByte Research Group, School of Geosciences, The University of Sydney, Australia. ORCID: 0000-0002-3334-5764

Overview of Resources Contained

This collection contains geological data from Australia used for data mining in the publications Merdith et al. (2013) and Landgrebe et al. (2013). The resulting maps of opal prospectivity are also included.

List of Resources

Note: For details on the files included in this data collection, see “Description_of_Resources.txt”.

Note: For information on file formats and what programs to use to interact with various file formats, see “File_Formats_and_Recommended_Programs.txt”.

• Map of Barfield region, Australia (.jpg, 270 KB)
• Map overviewing the Great Artesian basins and main opal mining camps (.png, 82 KB)
• Maps showing opal prospectivity data mining results for different geological datasets (.tif, 23.1 MB)
• Map of opal prospectivity from palaeogeography data mining (.pdf, 2.6 MB)
• Raster of palaeogeography target regions for viewing in Google Earth (.jpg, 418 KB)
• Opal mine locations (.gpml, .txt, .kmz, .shp, total 15.6 MB)
• Map of opal prospectivity from all data mining results as a Google Earth overlay (.kmz, 12 KB)
• Map of probability of opal occurrence in prospective regions from all data mining results (.tif, 5.9 MB)
• Paleogeography of Australia (.gpml, .txt, .shp, total 114.2 MB)
• Radiometric data showing potassium concentration contrasts (.tif, .kmz, total 311.3 MB)
• Regolith data (.gpml, .txt, .kml, .shp, total 7.1 MB)
• Soil type data (.gpml, .txt, .kml, .shp, total 7.1 MB)

For more information on this data collection, and links to other datasets from the EarthByte Research Group please visit EarthByte

For more information about using GPlates, including tutorials and a user manual please visit GPlates or EarthByte

This Pre-Clearing map represents the pre-clearing extent of the State Vegetation Type Map (SVTM). Both SVTM and SVTM (Pre-Clearing) map each Plant Community Type, Vegetation Class and Vegetation Formation at a regional scale across all tenures in NSW. Pre-clearing PCT mapping is available for both eastern NSW and Far Western NSW. Coverage of Central NSW is a work in progress.

Pre-clearing extent of PCTs was developed using a combination of aerial photographic interpretation, environmental layers and historical documents. This map is updated periodically as part of the Integrated BioNet Vegetation Data program to improve quality and alignment to the NSW vegetation classification hierarchy.

Further information and technical documents about the SVTM is available from the State Vegetation Type Mapping Program Page

Current Release C2.0.M2.1 (November2024)

This release includes revisions, using the most recent NSW PCT Classification Masterlist (represented by “C2.0” in the version release number). PCT spatial distributions were manually edited based on user and community feedback since the previous C2.0.M2.0 release.

Detailed technical information is available here.

Data Access

Map data may be downloaded, viewed within the SEED Map Viewer, or accessed via the underlying ArcGIS REST Services or WMS for integration in GIS or business applications.

The Trees Near Me NSW app provides quick access to view the map using a mobile device or desktop. Download the app from Google Play or the App Store, or access the web site at https://treesnearme.app.

Map Data Type

The map is supplied as ESRI Feature Class (Quickview) and 5m GeoTiff Raster, and can be viewed and analysed in most commercial and open-source spatial software packages. If you prefer to use the download package, we supply an ArcGIS v10.6 mxd and/or a layer file for suggested symbology. The raster attributes contain PCT, Vegetation Class and Vegetation Formation.

Feedback and Support

We welcome your feedback to assist us in continuously improving our products. To help us track and process your feedback, please use the SEED Data Feedback tool available via the SEED map viewer or the Feedback function in Trees Near Me NSW.

For further support, contact the BioNet Team at bionet@environment.nsw.gov.au.

Useful Related Data

NSW State Vegetation Type Map: regional scale map of extant NSW Plant Community Types, Vegetation classes and Vegetation Formations.

NSW BioNet Flora Survey Plots – PCT Reference Sites: full floristic plots used in the development of the quantitative Plant Community Type (PCT) classification. Currently available for eastern NSW PCTs version C2.0.

NSW State Vegetation Type Map - technical notes

Eastern NSW - percentage cleared technical notes.

[Map, App, or Layer Name]Burhans, Molly A., Cheney, David M., Gerlt, R.. . “[Name]”. Scale not given. Version 1.2. MO and CT, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2019.Derived from:Global Diocesan Boundaries:Burhans, M., Bell, J., Burhans, D., Carmichael, R., Cheney, D., Deaton, M., Emge, T. Gerlt, B., Grayson, J., Herries, J., Keegan, H., Skinner, A., Smith, M., Sousa, C., Trubetskoy, S. “Diocesean Boundaries of the Catholic Church” [Feature Layer]. Scale not given. Version 1.2. Redlands, CA, USA: GoodLands Inc., Environmental Systems Research Institute, Inc., 2016.Using: ArcGIS. 10.4. Version 10.0. Redlands, CA: Environmental Systems Research Institute, Inc., 2016.Boundary ProvenanceStatistics and Leadership DataCheney, D.M. “Catholic Hierarchy of the World” [Database]. Date Updated: August 2019. Catholic Hierarchy. Using: Paradox. Retrieved from Original Source.Catholic HierarchyAnnuario Pontificio per l’Anno .. Città del Vaticano :Tipografia Poliglotta Vaticana, Multiple Years.The data for these maps was extracted from the gold standard of Church data, the Annuario Pontificio, published yearly by the Vatican. The collection and data development of the Vatican Statistics Office are unknown. GoodLands is not responsible for errors within this data. We encourage people to document and report errant information to us at data@good-lands.org or directly to the Vatican.Additional information about regular changes in bishops and sees comes from a variety of public diocesan and news announcements.GoodLands’ polygon data layers, version 2.0 for global ecclesiastical boundaries of the Roman Catholic Church:Although care has been taken to ensure the accuracy, completeness and reliability of the information provided, due to this being the first developed dataset of global ecclesiastical boundaries curated from many sources it may have a higher margin of error than established geopolitical administrative boundary maps. Boundaries need to be verified with appropriate Ecclesiastical Leadership. The current information is subject to change without notice. No parties involved with the creation of this data are liable for indirect, special or incidental damage resulting from, arising out of or in connection with the use of the information. We referenced 1960 sources to build our global datasets of ecclesiastical jurisdictions. Often, they were isolated images of dioceses, historical documents and information about parishes that were cross checked. These sources can be viewed here:https://docs.google.com/spreadsheets/d/11ANlH1S_aYJOyz4TtG0HHgz0OLxnOvXLHMt4FVOS85Q/edit#gid=0To learn more or contact us please visit: https://good-lands.org/