Sample data in GPKG - GeoPackage format available for download for testing purposes.

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.

This geopackage (gpkg) file, which is an essential data repository, houses comprehensive information pertaining to Ireland's official counties as of 2021, encompassing critical data such as population statistics, surface area measurements, official region names, associated codes, and intricate geographical geometries that collectively provide a detailed and up-to-date snapshot of Ireland's regional landscape.

PLEASE USE THE LATEST VERSION INSTEAD OF THIS ONE

This dataset provides a gridded subnational datasets for

• Income inequality (Gini coefficient) at admin 1 level
• Gross national income (GNI) per capita PPP at admin 1 level

The datasets are based on reported subnational admin data and spans three decades from 1990 to 2021.

The datasets are presented in details in the following publication. Please cite this paper when using data.

Chrisendo D, Niva V, Hoffman R, Sayyar SM, Rocha J, Sandström V, Solt F, Kummu M. 2024. Income inequality has increased for over two-thirds of the global population. Preprint. doi: https://doi.org/10.21203/rs.3.rs-5548291/v1

Code is available at following repositories:

• Gini coefficient data creation: https://github.com/mattikummu/subnatGini
• GNI per capita data creation: https://github.com/mattikummu/subnatGNI
• analyses for the article: https://github.com/mattikummu/gini_gni_analyses

The following data is given (formats in brackets)

• Income inequality (Gini coefficient) at admin 0 level (national) (GeoTIFF, gpkg, csv)
• Income inequality (Gini coefficient) at admin 1 level (subnational) (GeoTIFF, gpkg, csv)
• Gross national income (GNI) per capita PPP at admin 0 level (national) (GeoTIFF, gpkg, csv)
• Gross national income (GNI) per capita PPP at admin 1 level (subnational) (GeoTIFF, gpkg, csv)
• Slope for Gini coefficient at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)
• Slope for GNI per capita (log10) at admin 1 level (GeoTIFF; slope is given also in gpk and csv files)
• Input data for the script that was used to generate the Gini coefficient (input_data_gini.zip)
• Input data for the script that was used to generate the GNI per capita PPP (input_data_GNI.zip)

Files are named as follows
Format: raster data (GeoTIFF) starts with rast_*, polygon data (gpkg) with polyg_*, and tabulated with tabulated_*.
Admin levels: adm0 for admin 0 level, adm1 for admin 1 level
Product type:

• _gini_disp_ for gini coefficient based on disposable income
• _gni_perCapita_ for GNI per capita PPP

Metadata

Grids

Resolution: 5 arc-min (0.083333333 degrees)

Spatial extent: Lon: -180, 180; -90, 90 (xmin, xmax, ymin, ymax)

Coordinate ref system: EPSG:4326 - WGS 84

Format: Multiband geotiff; one band for each year over 1990-2021

Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita

Geospatial polygon (gpkg) files:

Spatial extent: -180, 180; -90, 83.67 (xmin, xmax, ymin, ymax)

Temporal extent: annual over 1990-2021

Coordinate ref system: EPSG:4326 - WGS 84

Format: gkpk

Unit: no unit for Gini coefficient and PPP USD in 2017 international dollars for GNI per capita

Version 2 changes (20.03.2025)

- admin area names added to tabulated and gpkg files

- slope in tabulated data rounded correctly

- GNI per capita data for Nauru updated (scaled based on Tuvalu)

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 geopackage (gpkg) file, which is an essential data repository, houses comprehensive information pertaining to Switzerland's official kantons as of 2021, encompassing critical data such as population statistics, surface area measurements, official region names, associated codes, and intricate geographical geometries that collectively provide a detailed and up-to-date snapshot of Portugal's regional landscape.

These are 2 files with Geographic vector layers with Continent Boundaries. You can use this data to create Choropleth maps or for any map visualization that requires vectors at the Continent level.

Citation of the data source:

Shepherd, Stephanie (2020). Continent Polygons. figshare. Dataset. https://doi.org/10.6084/m9.figshare.12555170.v3

I transformed the data lightly to make a GPKG file and to add the documents to Kaggle.

The original data is similar to the file continent_boundaries_8.gpkg, which includes the following continents:

• Africa
• Antarctica
• Asia
• Australia
• Europe
• North America
• Oceania
• South America

I also created a file called continent_boundaries_7.gpkg that merges Australia and Oceania as a single continent.

You can find the transformations in the following Kaggle Notebook: https://www.kaggle.com/code/ericnarro/create-continents-geodataframe-and-file/notebook

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.

We provide the entire dataset of the paper "Dataset of seismic ambient vibrations from the Quaternary Norcia basin (central Italy)" submitted to "Data in Brief" journal, including geophysical and geospatial data.

The dataset was used and analysed in the article:

Di Giulio, G., Ercoli, M., Vassallo, M., Porreca, M. (2020). Investigation of the Norcia basin (Central Italy) through ambient vibration measurements and geological surveys, Engineering Geology, 267, 105501, https://doi.org/10.1016/j.enggeo.2020.105501

The geophysical dataset was collected in the Norcia basin in Central Italy, area struck by a long earthquake sequence during the 2016-2017, including five main-shocks with Mw>5.0.

The Mw 6.5 mainshock occurred on 30 October 2016 close to the town of Norcia. Different degrees of damages were observed during this seismic crisis, with a variable seismic shaking controlled, among many factors, by important 1D and 2D variation of Quaternary fluvio-lacustrine sediments infilling the basin.

Following this seismic sequence, we registered seismic vibration measurements, mainly single-seismic station noise data. We aimed to determine the distribution of resonant frequency (f0) of the basin and, though a join analysis with the available geological information, to infer the subsurface basin architecture.

A total of 60 sites were measured to cover the entire extension in the basin. We deployed seismometers along three transects of a total length of 21 km, mostly along the main structural directions of the basin (i.e. NNW-SSE and NE-SW).

Two 2D arrays of seismic stations with a elicoidal-shaped geometry, and a set of MASW active data were also acquired in the northern sector of the basin, in order to better constrain the seismic velocity of the sedimentary infilling.

In comparison to the data used in the paper Di Giulio et al. (2020), seven additional records have been here recovered across the basin (i.e. N54-N60).

We also provide geospatial ancillary data, both as a complete open-source Geographical Information Systems (GIS) project and as a set of single GeoPackage (.gpkg) and Keyhole Markup Language (.kml) files.

The dataset can be used for different purposes: specific researches on the Norcia basin, comparative studies on similar areas around the world, development of new data modeling/analysis software.

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.

This dataset contains information on population in entire USA.

Note that the file type is gpkg, not csv, therefore the preview is not supported on Kaggle.

However, I created a table and included it here and in the "About this file" section, so you can see what is inside.

Gpkg stands for GeoPackage and contains geospatial information. This kind of information can be used for creating maps with data (like on the image of this dataset).

There are 3 variables and 4.268.708 observations.

https://www.googleapis.com/download/storage/v1/b/kaggle-user-content/o/inbox%2F11745954%2F1ad5a1af3cf5156087317f2ce047be8e%2FScreenshot%202023-01-10%20at%2010.33.11.png?generation=1673343424696942&alt=media" alt="">

To read this file you will need in R:

library(sd) 
st_read() 

or in Python:

import fiona
with fiona.open("path/to/file.gpkg", "r")

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.

Support material 1 : supplementary methods, figures, and tables (pdf file).

Support material 2: effect, and characterization factors at the basin scale and aggregated values (excel file).

basins_5min_pcrglobwb.gpkg : geopackage containing the river basin delineation (n=20317) at 5 arcmin spatial resolution.

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).

Cadaster data from PDOK used to illustrate the use of geopandas and shapely, geospatial python packages for manipulating vector data. The brpgewaspercelen_definitief_2020.gpkg file has been subsetted in order to make the download manageable for workshops. Other datasets are copies of those available from PDOK.

Summary:

The files available here include a spatial data layer that represents the output of an analysis of opportunity for additional tree canopy in New York City (NYC) that considers factors that influence where trees can be planted and where canopy can grow, 'practical canopy,' as well as summaries of this data layer by NYC Borough, Community District, City Council District, and Neighborhood Tabulation Area. Links to a preprint and peer-reviewed paper describing the methods and context for this work are available below. The practical canopy layer is the result of a spatial model, and is thus an approximation based on available data and assumptions. See the associated materials for full discussion of limits and potential uses of this work.

If you do not find what you are looking for here, contact Michael Treglia, Lead Scientist with The Nature Conservancy in New York, Cities Program, at michael.treglia@tnc.org.

Terms of Use

© The Nature Conservancy. This material is provided as-is, without warranty under a Creative Commons Attribution-NonCommercial-ShareAlike License as set forth in our Conservation Gateway Terms of Use (available at: http://conservationgateway.org/Pages/Terms-of-Use.aspx)

If using these data, please cite the both the peer-reviewed paper and this set of data, based on the following recommended citations:

Treglia, M. L., Piland, N. C., Leu, K., Van Slooten, A., & Maxwell, E. N. (2022). Understanding opportunities for urban forest expansion to inform goals: working toward a virtuous cycle in New York City. Frontiers in Sustainable Cities. 4:944823. doi: 10.3389/frsc.2022.944823

Treglia, M. L., Piland, N. C., Leu, K., Van Slooten, A., & Maxwell, E. N. (2022). Practical Canopy for New York City—Data Layer and Summarized Results [Data set]. Zenodo. doi: 10.5281/zenodo.6547492

The manuscript for this work is also available in a preprint, with the following citation:

Treglia, M. L., Piland, N. C., Leu, K., Van Slooten, A., & Maxwell, E. N. (2022). Understanding opportunities for urban forest expansion to inform goals: working toward a virtuous cycle in New York City. Preprints. 2022060106. doi: 10.20944/preprints202206.0106.v1

Contents

nyc_practicalcanopy_datalayer.zip - Zipped folder with the practical canopy data layer that resulted from the work described in the associated preprint, as both GeoPackage (.gpkg) and Esri File Geodatabase (.gdb) files, with Data Dictionary files in .docx and .html formats. Both the .gpkg and .gdb files are zipped within the .zip file to save space, such that users may uncompress the format they prefer to use. The uncompressed .gdb file is nearly 3 gb; the uncompressed .gpkg file is about 10 gb.

nyc_practicalcanopy_summary_results.zip - Zipped folder summarized results of the practical canopy analysis by NYC Borough, Community District, City Council District, and Neighborhood Tabulation Area. Data are available as non-spatial .csv files and as both GeoPackage (.gpkg) and Esri File Geodatabase (.gdb) files; Data Dictionaries are included in both .docx and .html formats.

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.

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

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