Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.

Here you will find an assembly of the open street map in metropolitan france. The geopackage version also contains data from neighbouring countries (border regions except espagne). The the.qgz project allows the geopackage data to be opened with the busy style and hacking depending on the zoom level. video presenting this data gpkg and QGIS: https://www.youtube.com/watch?v=R6O9cMqVVvM&t=6s The version.sql is characterised by an additional attribute for each geometric entity: The INSEE code This data will be updated on a monthly basis.

INSTRUCTIONS FOR DECLARING GPKG DATA: Download all files and rename as follows:

OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.001 OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_002.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.002 OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_003.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.003 OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_004.zip — > OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.004

or if you know the batch back to create a.bat file containing this (or you rename the renowned file. txt as rename.bat):

pushd “% ~ DP0” REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.001 REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_002.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.002 REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_003.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.003 REN OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_004.zip OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214.zip.004

and launch.bat by double clicking on it (the batch must be in the same place as the zip files)

Then right-click on the OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001.zip file and have it extracted to “OSM_QGZ_GPKG_ET_FRONTALIER_PRDG_FXX_ED214_001\” with your pressure relief software. There is no need to click on 002, 003, 004. Opening file.001 opens all other parts of the archive

For version.sql, the procedure is the same: rename OSM_SQL_FXX_PRDG_D000_ED214_001.zip to OSM_SQL_FXX_PRDG_D000_ED214.zip.001 OSM_SQL_FXX_PRDG_D000_ED214_002.zip to OSM_SQL_FXX_PRDG_D000_ED214.zip.002 OSM_SQL_FXX_PRDG_D000_ED214_003.zip to OSM_SQL_FXX_PRDG_D000_ED214.zip.003 Then carry out pressure relief

This folder contains .gpkg format files for book layover and waypoint places, and auto, boat and train routes that can be loaded into GIS applications such as ArcGIS and QGIS.

Sampling design used in the production of the global maps of grassland dynamics 2000–2022 at 30 m spatial resolution in the scope of the Global Pasture Wath initiative. The sampling desing was based in Feature Space Coverage Sampling and resulted in 10,000 sample tiles (1x1 km) distributed across the World, which were visual interpreted in Very-High Resolution imagery thorugh the QGIS plugin QGIS Fast Grid Inspection.

FSCS steps include:

• Short vegetation mask that includes all pixels mapped as mosaic, shrubland, grassland, and sparse vegetation in at least one year from 1993 to 2021 according to ESA/CCI global land cover (gpw_short.veg.mask_esacci.lc_p_1km_s_19920101_20201231_go_epsg.3857_v1.tif),
• 87 input raster layers (including vegetation indices, terrain, land temperature, climate and water variable),
• Principal Components Analysis (PCA) using all input layers,
• Selection of the 10 first components (explaining 75% of variance),
• K-Means with 10,000 clusters (targeted number of samples -
  gpw_grassland_fscs.kmeans.cluster_c_1km_20000101_20221231_go_epsg.3857_v1.tif)
• Calculation of euclidean distance (in the principal component space) of all 1-km pixels to the centre of each cluster,
• Selection of the pixel with the shortest distance for each cluster,
• Conversion of the selected pixels into sample tiles ()

The file gpw_grassland_fscs_tile.samples_1km_20000101_20221231_go_epsg.3857_v1.gpkg provides the sample tiles and include the follow collumns:

• X: Latitude in Web Mercator projection (EPSG:3857),
• Y: Longitude in Web Mercator projection (EPSG:3857),
• cluster_id: K-Means output ranging from 0—9999,
• cluster_distance: Distance from the selected sample to the centre of the cluster,
• cluster_size: Number o 1-km pixels inside the K-Means cluster, estimated using Web Mercator projection (EPSG:3857)
• cluster_size_equal_area: Number o 1-km pixels inside the K-Means cluster, estimated using Goode Homolosine Land projection (ESRI:54052)
• cluster_size_corr: Correction factor to adjust the area distortion due to Web Mercator projection, estimated by the difference in normalized propotional values of cluster_size and cluster_size_equal_area.
• rf_n_pred: Number of pixels predicted by a RF model trained to estimate probability to select the pixel closer to the centre of the KMeans cluster. The RF models were trained individually per each cluster using the 10 first components derived by PCA (gpw_comps_fscs.pca_m_1km_20000101_20221231_go_epsg.3857_v1.tar.gz).
• rf_samp_prob: Sampling probability based on RF model (rf_n_pred / cluster_size)
• rf_samp_wei: Sampling weight estimated in Web Mercator projection.
• rf_samp_wei_coor: Corrected sampling weight estimated in Goode Homolosine Land projection.

Related resources

• Maps of dominant grassland:
  2000-2002 2003-2005 2006-2008 2009-2011 2012-2014 2015-2017 2018-2020 2021-2022
• Probability maps of cultivated grassland:
  2000-2022 (All URLs)
• Probability maps of natural/semi-natural grassland:
  2000-2022 (All URLs)
• Grassland reference samples based on VHR imagery (2000–2022):
  GeoPackage files
• Global machine learning models (Random Forest):
  Parquet and joblib python files
• Reference sampling design derived by FSCV:
  GeoPackage and raster files
• Harmonized reference samples based on existing LULC dataset:
  GeoPackage and raster files
• Source code for reproducibility:
  GitHub release
  
• Mapping feedback tool:
  GeoWiki
• Data catalogues:
  OpenLandMap STAC Google Earth Engine

Support

For questions of bugs/inconsistencies related to the dataset raise a GitHub issue in https://github.com/wri/global-pasture-watch

Click on the title for more details and to download the file. (File Size - 92 MB)

QDGC tables delivered in geopackage file - - - - - - - - - - - - - - - - - - - - - - QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth. Within each geopackage file you will find a number of tables with these names: -tbl_qdgc_01 -tbl_qdgc_02 -tbl_qdgc_03 -tbl_qdgc_04 -tbl_qdgc_05 -etc The attributes for each table are: qdgc Unique Quarter Degree Grid Cell reference string level_qdgc QDGC level cellsize degrees decimal degree for the longitudal and latitudal length of the cell lon_center Longitude center of the cell lat_center Latitudal center of the cell area_km2 Calculated area for the cell geom Geometry Metadata -------- Geodata GCS_WGS_1984 Datum: D_WGS_1984 Prime Meridian: 0 Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this: - st_area(st_transform(geom, 102022))/1000000) Conditions ---------- Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc Thankyou! -------- The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them: - TAWIRI (http://tawiri.or.tz/) - Dept of Biology, NTNU, Norway - Norwegian Environment Agency - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey References ---------- * http://en.wikipedia.org/wiki/QDGC * http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/ * http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection * http://www.safe.com

QDGC tables delivered in geopackage file

QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.

Within each geopackage file you will find a number of tables with these names:

-tbl_qdgc_01 -tbl_qdgc_02 -tbl_qdgc_03 -tbl_qdgc_04 -tbl_qdgc_05 -etc

The attributes for each table are:

qdgc Unique Quarter Degree Grid Cell reference string area_reference Country level_qdgc QDGC level cellsize degrees decimal degree for the longitudal and latitudal length of the cell lon_center Longitude center of the cell lat_center Latitudal center of the cell area_km2 Calculated area for the cell geom Geometry

Metadata

Geodata GCS_WGS_1984 Datum: D_WGS_1984 Prime Meridian: 0

Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this: - st_area(st_transform(geom, 102022))/1000000)

Licensing

Creative Commons Attribution 4.0 International

Conditions

Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc

Thankyou

The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them: - Tanzania Wildlife Research Institute - Dept of Biology, NTNU, Norway - Norwegian Environment Agency - Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey

References

• http://en.wikipedia.org/wiki/QDGC
• http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
• http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
• http://www.safe.com

Ragnvald Larsen Trondheim 23rd of January, 2021

ragnvald@mindland.com www.mindland.com

Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.

A. Takhtajan defined 35 floristic regions in the world (Takhtajan 1986). The delineation of such floristic regions has been manually georeferenced and is provided here as a spatial vectorial data file (geopackage), suitable to be used in any GIS or mapping software (coordinate reference system: EPSG 4326).

If using this dataset, please cite both Takhtajan's book as well as this data source:

Takhtajan, A. 1986. Floristic Regions of the World. Berkeley: University of California Press.

Rodríguez-Sánchez, Francisco. 2023. Takhtajan's floristic regions of the world (geopackage). https://doi.org/10.5281/zenodo.8206377

Funding: Fondo Europeo de Desarrollo Regional (FEDER) and Consejería de Transformación Económica, Industria, Conocimiento y Universidades of Junta de Andalucía (proyecto US-1381388, Universidad de Sevilla).

QDGC tables delivered in geopackage file
- - - - - - - - - - - - - - - - - - - - - -
QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.


Within each geopackage file you will find a number of tables with these names:


-tbl_qdgc_01
-tbl_qdgc_02
-tbl_qdgc_03
-tbl_qdgc_04
-tbl_qdgc_05
-etc


The attributes for each table are:


qdgc Unique Quarter Degree Grid Cell reference string
area_reference Country
level_qdgc QDGC level
cellsize degrees decimal degree for the longitudal and latitudal length of the cell
lon_center Longitude center of the cell
lat_center Latitudal center of the cell
area_km2 Calculated area for the cell
geom Geometry


Metadata
--------
Geodata GCS_WGS_1984
Datum: D_WGS_1984
Prime Meridian: 0


Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:
- st_area(st_transform(geom, 102022))/1000000)


Licensing
---------
Creative Commons Attribution 4.0 International


Conditions
----------
Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it.


Thankyou
--------
The work has over the years been supported and receicved advice and moral support from many organisations and stakeholders. Here are some of them:
- Tanzania Wildlife Research Institute
- Dept of Biology, NTNU, Norway
- Norwegian Environment Agency
- Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey


References
----------
* http://en.wikipedia.org/wiki/QDGC
* http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
* http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
* http://www.safe.com




Ragnvald Larsen
Trondheim 20th of January, 2021


ragnvald@mindland.com
www.mindland.com

QDGC tables delivered in geopackage file
- - - - - - - - - - - - - - - - - - - - - -
QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.


Within each geopackage file you will find a number of tables with these names:
-tbl_qdgc_01
-tbl_qdgc_02
-tbl_qdgc_03
-tbl_qdgc_04
-tbl_qdgc_05
-etc


The attributes for each table are:
qdgc Unique Quarter Degree Grid Cell reference string
level_qdgc QDGC level
cellsize degrees decimal degree for the longitudal and latitudal length of the cell
lon_center Longitude center of the cell
lat_center Latitudal center of the cell
area_km2 Calculated area for the cell
geom Geometry


Metadata
--------
Geodata GCS_WGS_1984
Datum: D_WGS_1984
Prime Meridian: 0


Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:
- st_area(st_transform(geom, 102022))/1000000)


Conditions
----------
Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc


Thankyou!
--------
The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:
- TAWIRI (http://tawiri.or.tz/)
- Dept of Biology, NTNU, Norway
- Norwegian Environment Agency
- Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey


References
----------
* http://en.wikipedia.org/wiki/QDGC
* http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
* http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
* http://www.safe.com

QDGC tables delivered in geopackage file
- - - - - - - - - - - - - - - - - - - - - -
QDGC represents a way of making (almost) equal area squares covering a specific area to represent specific qualities of the area covered. The squares themselves are based on the degree squares covering earth. Around the equator we have 360 longitudinal lines , and from the north to the south pole we have 180 latitudinal lines. Together this gives us 64800 segments or tiles covering earth.


Within each geopackage file you will find a number of tables with these names:
-tbl_qdgc_01
-tbl_qdgc_02
-tbl_qdgc_03
-tbl_qdgc_04
-tbl_qdgc_05
-etc


The attributes for each table are:
qdgc Unique Quarter Degree Grid Cell reference string
level_qdgc QDGC level
cellsize degrees decimal degree for the longitudal and latitudal length of the cell
lon_center Longitude center of the cell
lat_center Latitudal center of the cell
area_km2 Calculated area for the cell
geom Geometry


Metadata
--------
Geodata GCS_WGS_1984
Datum: D_WGS_1984
Prime Meridian: 0


Areas are calculated with different versions of Albers Equal Area Conic using the PostGIS function st_area. For the African continent I have used Africa Albers Equal Area Conic which will look like this:
- st_area(st_transform(geom, 102022))/1000000)


Conditions
----------
Delivered to the user as-is. No guarantees. If you find errors, please tell me and I will try to fix it. Suggestions for improvements can be addressed to the github repository: https://github.com/ragnvald/qdgc


Thankyou!
--------
The work has over the years been supported and received advice and moral support from many organisations and stakeholders. Here are some of them:
- TAWIRI (http://tawiri.or.tz/)
- Dept of Biology, NTNU, Norway
- Norwegian Environment Agency
- Eivin Røskaft, Steven Prager, Howard Frederick, Julian Blanc, Honori Maliti, Paul Ramsey


References
----------
* http://en.wikipedia.org/wiki/QDGC
* http://www.mindland.com/wp/projects/quarter-degree-grid-cells/about-qdgc/
* http://en.wikipedia.org/wiki/Lambert_azimuthal_equal-area_projection
* http://www.safe.com

Overview:This document describes the 2021 accessibility data released by the Accessibility Observatory at the University of Minnesota. The data are included in the National Accessibility Evaluation Project for 2021, and this information can be accessed for each state in the U.S. at https://access.umn.edu/research/america. The following sections describe the format, naming, and content of the data files.Data Formats: The data files are provided in a Geopackage format. Geopackage (.gpkg) files are an open-source, geospatial filetype that can contain multiple layers of data in a single file, and can be opened with most GIS software, including both ArcGIS and QGIS.Within this zipfile, there are six geopackage files (.gpkg) structured as follows. Each of them contains the blocks shapes layer, results at the block level for all LEHD variables (jobs and workers), with a layer of results for each travel time (5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55, 60 minutes). {MPO ID}_tr_2021_0700-0859-avg.gpkg = Average Transit Access Departing Every Minute 7am-9am{MPO ID}_au_2021_08.gpkg = Average Auto Access Departing 8am{MPO ID}_bi_2021_1200_lts1.gpkg = Average Bike Access on LTS1 Network{MPO ID}_bi_2021_1200_lts2.gpkg = Average Bike Access on LTS2 Network{MPO ID}_bi_2021_1200_lts3.gpkg = Average Bike Access on LTS3 Network{MPO ID}_bi_2021_1200_lts4.gpkg = Average Bike Access on LTS4 NetworkFor mapping and geospatial analysis, the blocks shape layer within each geopackage can be joined to the blockid of the access attribute data. Opening and Using Geopackages in ArcGIS:Unzip the zip archiveUse the "Add Data" function in Arc to select the .gpkg fileSelect which layer(s) are needed — always select "main.blocks" as this layer contains the Census block shapes; select any other attribute data layers as well.There are three types of layers in the geopackage file — the "main.blocks" layer is the spatial features layer, and all other layers are either numerical attribute data tables, or the "fieldname_descriptions" metadata layer. The numerical attribute layers are named with the following format:[mode]_[threshold]_minutes[mode] is a two-character code indicating the transport mode used[threshold] is an integer indicating the travel time threshold used for this data layerTo use the data spatially, perform a join between the "main.blocks" layer and the desired numerical data layer, using either the numerical "id" fields, or 15-digit "blockid" fields as join fields.

UK air pollution removal A GeoPackage (see https://www.geopackage.org/) that contains the spatial data used in this article:https://www.ons.gov.uk/economy/environmentalaccounts/articles/ukairpollutionremovalhowmuchpollutiondoesvegetationremoveinyourarea/2018-07-30The methodology used to develop estimates for the valuation of air pollution in ecosystem accounts can be found here:https://www.ons.gov.uk/economy/environmentalaccounts/articles/developingestimatesforthevaluationofairpollutioninecosystemaccounts/2017-07-25Download file size: 110 MB

This zip file contains the main dataset and consists of a single GeoPackage file. As described in the data record section of the article, the GeoPackage contains the geospatial features associated with each project as well as related information.

The World Soil Information Service (WoSIS) provides quality-assessed and standardized soil profile data to support digital soil mapping and environmental applications at broad scale levels. Since the release of the ‘WoSIS snapshot 2019’ many new soil data were shared with us, registered in the ISRIC data repository, and subsequently standardized in accordance with the licenses specified by the data providers. The source data were contributed by a wide range of data providers, therefore special attention was paid to the standardization of soil property definitions, soil analytical procedures and soil property values (and units of measurement).

We presently consider the following soil chemical properties (organic carbon, total carbon, total carbonate equivalent, total Nitrogen, Phosphorus (extractable-P, total-P, and P-retention), soil pH, cation exchange capacity, and electrical conductivity) and physical properties (soil texture (sand, silt, and clay), bulk density, coarse fragments, and water retention), grouped according to analytical procedures (aggregates) that are operationally comparable.

For each profile we provide the original soil classification (FAO, WRB, USDA, and version) and horizon designations as far as these have been specified in the source databases.

Three measures for 'fitness-for-intended-use' are provided: positional uncertainty (for site locations), time of sampling/description, and a first approximation for the uncertainty associated with the operationally defined analytical methods. These measures should be considered during digital soil mapping and subsequent earth system modelling that use the present set of soil data.

The current dataset comprises 228k profiles from 217k geo-referenced sites that originate from 174 countries. The profiles represent over 900k soil layers (or horizons) and over 6 million records. The actual number of measurements for each property varies (greatly) between profiles and with depth, this generally depending on the objectives of the initial soil sampling programmes.

The data are provided in TSV (tab separated values) format and as GeoPackage. The zip-file (446 Mb) contains the following files: - Readme_woSIS_202312.pdf - wosis_202312.gpkg (GeoPackage file)
- wosis_202312_observations.tsv - wosis_202312_sites.tsv - wosis_2023112_profiles - wosis_202312_layers - wosis_202312_xxxx.tsv (e.g. wosis_202311_bdfiod.tsv, one for each observation)

For additional information see: https://www.isric.org/explore/wosis/faq-wosis.

Citation: Batjes N.H., Calisto, L. and de Sousa L.M., 2023. Providing quality-assessed and standardised soil data to support global mapping and modelling (WoSIS snapshot 2023). Earth System Science Data (Discussions; https://doi.org/10.5194/essd-2024-14).

Dataset for: Regional Correlations in the layered deposits of Arabia Terra, Mars

Overview:

This repository contains the map-projected HiRISE Digital Elevation Models (DEMs) and the map-projected HiRISE image for each DEM and for each site in the study. Also contained in the repository is a GeoPackage file (beds_2019_08_28_09_29.gpkg) that contains the dip corrected bed thickness measurements, longitude and latitude positions, and error information for each bed measured in the study. GeoPackage files supersede shapefiles as a standard geospatial data format and can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS. For more information about GeoPackage files, please use https://www.geopackage.org/ as a resource. A more detailed description of columns in the beds_2019_08_28_09_29.gpkg file is described below in a dedicated section. Table S1 from the supplementary is also included as an excel spreadsheet file (table_s1.xlsx).

HiRISE DEMs and Images:

Each HiRISE DEM, and corresponding map-projected image used in the study are included in this repository as GeoTiff files (ending with .tif). The file names correspond to the combination of the HiRISE Image IDs listed in Table 1 that were used to produce the DEM for the site, with the image with the smallest emission angle (most-nadir) listed first. Files ending with “_align_1-DEM-adj.tif” are the DEM files containing the 1 meter per pixel elevation values, and files ending with “_align_1-DRG.tif” are the corresponding map-projected HiRISE (left) image. Table 1 Image Pairs correspond to filenames in this repository in the following way: In Table 1, Sera Crater corresponds to HiRISE Image Pair: PSP_001902_1890/PSP_002047_1890, which corresponds to files: “PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif” for the DEM file and “PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif” for the map-projected image file. Each site is listed below with the DEM and map-projected image filenames that correspond to the site as listed in Table 1. The DEM and Image files can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

· Sera

o DEM: PSP_001902_1890_PSP_002047_1890_align_1-DEM-adj.tif

o Image: PSP_001902_1890_PSP_002047_1890_align_1-DRG.tif

· Banes

o DEM: ESP_013611_1910_ESP_014033_1910_align_1-DEM-adj.tif

o Image: ESP_013611_1910_ESP_014033_1910_align_1-DRG.tif

· Wulai 1

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Wulai 2

o DEM: ESP_028129_1905_ESP_028195_1905_align_1-DEM-adj.tif

o Image: ESP_028129_1905_ESP_028195_1905_align_1-DRG.tif

· Jiji

o DEM: ESP_016657_1890_ESP_017013_1890_align_1-DEM-adj.tif

o Image: ESP_016657_1890_ESP_017013_1890_align_1-DRG.tif

· Alofi

o DEM: ESP_051825_1900_ESP_051970_1900_align_1-DEM-adj.tif

o Image: ESP_051825_1900_ESP_051970_1900_align_1-DRG.tif

· Yelapa

o DEM: ESP_015958_1835_ESP_016235_1835_align_1-DEM-adj.tif

o Image: ESP_015958_1835_ESP_016235_1835_align_1-DRG.tif

· Danielson 1

o DEM: PSP_002733_1880_PSP_002878_1880_align_1-DEM-adj.tif

o Image: PSP_002733_1880_PSP_002878_1880_align_1-DRG.tif

· Danielson 2

o DEM: PSP_008205_1880_PSP_008930_1880_align_1-DEM-adj.tif

o Image: PSP_008205_1880_PSP_008930_1880_align_1-DRG.tif

· Firsoff

o DEM: ESP_047184_1820_ESP_039404_1820_align_1-DEM-adj.tif

o Image: ESP_047184_1820_ESP_039404_1820_align_1-DRG.tif

· Kaporo

o DEM: PSP_002363_1800_PSP_002508_1800_align_1-DEM-adj.tif

o Image: PSP_002363_1800_PSP_002508_1800_align_1-DRG.tif

Description of beds_2019_08_28_09_29.gpkg:

The GeoPackage file “beds_2019_08_28_09_29.gpkg” contains the dip corrected bed thickness measurements among other columns described below. The file can be opened in a variety of open source tools including QGIS, and proprietary tools such as recent versions of ArcGIS.

(Column_Name: Description)

sitewkn: Site name corresponding to the bed (i.e. Danielson 1)

section: Section ID of the bed (sections contain multiple beds)

meansl: The mean slope (dip) in degrees for the section

meanaz: The mean azimuth (dip-direction) in degrees for the section

ang_error: Angular error for a section derived from individual azimuths in the section

B_1: Plane coefficient 1 for the section

B_2: Plane coefficient 2 for the section

lon: Longitude of the centroid of the Bed

lat: Latitude of the centroid of the Bed

thickness: Thickness of the bed BEFORE dip correction

dipcor_thick: Dip-corrected bed thickness

lon1: Longitude of the centroid of the lower layer for the bed (each bed has a lower and upper layer)

lon2: Longitude of the centroid of the upper layer for the bed

lat1: Latitude of the centroid of the lower layer for the bed

lat2: Latitude of the centroid of the upper layer for the bed

meanc1: Mean stratigraphic position of the lower layer for the bed

meanc2: Mean stratigraphic position of the upper layer for the bed

uuid1: Universally unique identifier of the lower layer for the bed

uuid2: Universally unique identifier of the upper layer for the bed

stdc1: Standard deviation of the stratigraphic position of the lower layer for the bed

stdc2: Standard deviation of the stratigraphic position of the upper layer for the bed

sl1: Individual Slope (dip) of the lower layer for the bed

sl2: Individual Slope (dip) of the upper layer for the bed

az1: Individual Azimuth (dip-direction) of the lower layer for the bed

az2: Individual Azimuth (dip-direction) of the upper layer for the bed

meanz: Mean elevation of the bed

meanz1: Mean elevation of the lower layer for the bed

meanz2: Mean elevation of the upper layer for the bed

rperr1: Regression error for the plane fit of the lower layer for the bed

rperr2: Regression error for the plane fit of the upper layer for the bed

rpstdr1: Standard deviation of the residuals for the plane fit of the lower layer for the bed

rpstdr2: Standard deviation of the residuals for the plane fit of the upper layer for the bed

This deposit offers a comprehensive collection of geospatial and metadata files that constitute the Seatizen Atlas dataset, facilitating the management and analysis of spatial information. To navigate through the data, you can use an interface available at seatizenmonitoring.ifremer.re, which provides a condensed CSV file tailored to your choice of metadata and the selected area.To retrieve the associated images, you will need to use a script that extracts the relevant frames. A brief tutorial is available here: Tutorial.All the scripts for processing sessions, creating the geopackage, and generating files can be found here: SeatizenDOI github repository.The repository includes:

seatizen_atlas_db.gpkg: geopackage file that stores extensive geospatial data, allowing for efficient management and analysis of spatial information.
session_doi.csv: a CSV file listing all sessions published on Zenodo. This file contains the following columns:

session_name: identifies the session.
session_doi: indicates the URL of the session.
place: indicates the location of the session.
date: indicates the date of the session.
raw_data: indicates whether the session contains raw data or not.
processed_data: indicates whether the session contains processed data.
metadata_images.csv: a CSV file describing all metadata for each image published in open access. This file contains the following columns:

OriginalFileName: indicates the original name of the photo.
FileName: indicates the name of the photo adapted to the naming convention adopted by the Seatizen team (i.e., YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number_originalimagename).
relative_file_path: indicates the path of the image in the deposit.
frames_doi: indicates the DOI of the version where the image is located.
GPSLatitude: indicates the latitude of the image (if available).
GPSLongitude: indicates the longitude of the image (if available).
GPSAltitude: indicates the depth of the frame (if available).
GPSRoll: indicates the roll of the image (if available).
GPSPitch: indicates the pitch of the image (if available).
GPSTrack: indicates the track of the image (if available).
GPSDatetime: indicates when frames was take (if available).
GPSFix: indicates GNSS quality levels (if available).
metadata_multilabel_predictions.csv: a CSV file describing all predictions from last multilabel model with georeferenced data.

FileName: indicates the name of the photo adapted to the naming convention adopted by the Seatizen team (i.e., YYYYMMDD_COUNTRYCODE-optionalplace_device_session-number_originalimagename).
frames_doi: indicates the DOI of the version where the image is located.
GPSLatitude: indicates the latitude of the image (if available).
GPSLongitude: indicates the longitude of the image (if available).
GPSAltitude: indicates the depth of the frame (if available).
GPSRoll: indicates the roll of the image (if available).
GPSPitch: indicates the pitch of the image (if available).
GPSTrack: indicates the track of the image (if available).
GPSFix: indicates GNSS quality levels (if available).
prediction_doi: refers to a specific AI model prediction on the current image (if available).
A column for each class predicted by the AI model.
metadata_multilabel_annotation.csv: a CSV file listing the subset of all the images that are annotated, along with their annotations. This file contains the following columns:

FileName: indicates the name of the photo.
frame_doi: indicates the DOI of the version where the image is located.
relative_file_path: indicates the path of the image in the deposit.
annotation_date: indicates the date when the image was annotated.
A column for each class with values:

1: if the class is present.
0: if the class is absent.
-1: if the class was not annotated.
seatizen_atlas.qgz: a qgis project which formats and highlights the geopackage file to facilitate data visualization.
darwincore_multilabel_annotations.zip: a Darwin Core Archive (DwC-A) file listing the subset of all the images that are annotated, along with their annotations.