Context

I created a dataset to help people create choropleth maps of United States states.

Content

One geojson to plot the countries borders, and one csv from the Census Bureau for the us population per state.

Inspiration

I think the best way to use this dataset is in joining it with other data. For example, I used this dataset to plot police killings using the data from https://www.kaggle.com/jpmiller/police-violence-in-the-us

Shape of regions and departments of France regions (metropolitan and overseas) in GEOJSON format.

Original and additional data can be found on the GitHub repository of Gregoire David the original author of this dataset. - https://github.com/gregoiredavid/france-geojson - https://france-geojson.gregoiredavid.fr/

Data last uploaded 4 years ago.

The dataset is subject to Open License (detail here)

Route and fare information of different means of public transport

Geospatial Data Pack for Visualization 🗺️

Learn Geographic Mapping with Altair, Vega-Lite and Vega using Curated Datasets

Complete geographic and geophysical data collection for mapping and visualization. This consolidation includes 18 complementary datasets used by 31+ Vega, Vega-Lite, and Altair examples 📊. Perfect for learning geographic visualization techniques including projections, choropleths, point maps, vector fields, and interactive displays.

Source data lives on GitHub and can also be accessed via CDN. The vega-datasets project serves as a common repository for example datasets used across these visualization libraries and related projects.

Why Use This Dataset? 🤔

• Comprehensive Geospatial Types: Explore a variety of core geospatial data models:
  • Vector Data: Includes points (like airports.csv), lines (like londonTubeLines.json), and polygons (like us-10m.json).
  • Raster-like Data: Work with gridded datasets (like windvectors.csv, annual-precip.json).
• Diverse Formats: Gain experience with standard and efficient geospatial formats like GeoJSON (see Table 1, 2, 4), compressed TopoJSON (see Table 1), and plain CSV/TSV (see Table 2, 3, 4) for point data and attribute tables ready for joining.
• Multi-Scale Coverage: Practice visualization across different geographic scales, from global and national (Table 1, 4) down to the city level (Table 1).
• Rich Thematic Mapping: Includes multiple datasets (Table 3) specifically designed for joining attributes to geographic boundaries (like states or counties from Table 1) to create insightful choropleth maps.
• Ready-to-Use & Example-Driven: Cleaned datasets tightly integrated with 31+ official examples (see Appendix) from Altair, Vega-Lite, and Vega, allowing you to immediately practice techniques like projections, point maps, network maps, and interactive displays.
• Python Friendly: Works seamlessly with essential Python libraries like Altair (which can directly read TopoJSON/GeoJSON), Pandas, and GeoPandas, fitting perfectly into the Kaggle notebook environment.

Table of Contents

• Why Use This Dataset?
• Dataset Inventory
• Usage Guide
• Appendix: Geospatial Examples by Technique
• Licenses
• Sources
• Learning Resources

Dataset Inventory 🗂️

This pack includes 18 datasets covering base maps, reference points, statistical data for choropleths, and geophysical data.

1. BASE MAP BOUNDARIES (Topological Data)

2. GEOGRAPHIC REFERENCE POINTS (Point Data) 📍

City features collection A collection of features for ~700 European cities, for the reference year 2018. ## Features The features are divided in three main thematic areas: land, climate and socioeconomic characteristics. Find more information about the features in the codebook cities_features_collection_codebook.csv. Codelists for categorical features are in the same folder codelist_<feature>.csv. ## Cities City selection (and outline polygon) is taken from the Eurostat Urban Atlas. More information here. The original list of cities with geometries can be downloaded at these links: - EPSG:4326 (WGS84) https://gisco-services.ec.europa.eu/distribution/v2/urau/geojson/URAU_RG_01M_2018_4326_CITIES.geojson - EPSG:3035 https://gisco-services.ec.europa.eu/distribution/v2/urau/geojson/URAU_RG_01M_2018_3035_CITIES.geojson Note: the dataset city_features_collection.geojson only contains the city outline in CRS EPSG:4326. ## Example usage Clustering analysis of European cities: check out this interactive demo notebook: notebooks\demo\cities_clustering_interactive_demo.ipynb.

Extraction Examples Dataset

This dataset contains 17 examples for testing extraction workflows.

  Dataset Structure

Each example includes:

PDF file: Original document map_info.json: Map extraction metadata direction.json: Direction information
GeoJSON files: Polygon geometries Area JSON files: Area definitions

  File Organization

files/ ├── example1/ │ ├── document.pdf │ ├── map_info.json │ ├── direction.json │ ├── polygon1.geojson │ └── area1.json… See the full description on the dataset page: https://huggingface.co/datasets/alexdzm/extraction-examples.

This dataset belongs to the Taiwan-building-footprints project. It contains a example of the visualization code and the data needed to run the code. More code and information can be found on the Github Repo and Juputer Book.

The ZIP file contains 80 images showcasing the result various visualization options, with 4 images for each county. These images are the same to those showed in the Jupyter Book, but this Zip file contains the original .png files without compression.

Content

The file has been fetched from: https://gadm.org/maps.html And then, simplified and converted to geojson format using: https://mapshaper.org/

License

These data were extracted from the GADM database (www.gadm.org), version 3.4, April 2018. They can be used for non-commercial purposes only. It is not allowed to redistribute these data, or use them for commercial purposes, without prior consent. See the website (www.gadm.org) for more information.

Overview

Orthophoto (Orthofoto) and LiDAR (Laser) data, which are organized into folders named after the area and year they are from.

  Dataset Structure

Geodata Kristiansand.zip (example) fgb Vann_22.fgb...

geojson Vann_22.geojson...

Ortofoto Agder_og_Telemark_2021.zip (example) Agder_og_Telemark_2021.zip_mosaic_cog.tif

Laser Bergen_2pkt_2010 Bergen_2pkt_2010_mosaic.laz

  Usage

Currently only been used in QGIS to display Ortofoto, Laser Data and… See the full description on the dataset page: https://huggingface.co/datasets/kartai/DX_datasett.

Because I had problems in a project with GeoJson files created by the Argentine government, which did not seem to have the format I needed for FOLIUM, I decided to create my own GeoJson file using the website https://geojson.io/, which I recommend. I drew the polygons for each of the provinces and configured the names of each one.

This is my first contribution to the community, and since it was something I struggled with for weeks, I am sure that the same thing can happen to other users and I would like to make life easier for them. There is a lot on the edges of each polygon to improve, this is a first version to see if it worked with Folium and it did!

Example of the GEOJSON File working:

https://i.ibb.co/5xct4jh/Example1.png" alt="Folium map usage example">

#Données of the 2022 Hydroclimatic Atlas ## #Description The Hydroclimatic Atlas describes the current and future water regime of southern Quebec in order to support the implementation of water management practices that are resilient to climate change. These data are from the most recent version of the Hydroclimatic Atlas. ## #Nouveautés * Improvement of the spatial resolution of the hydrographic network; * Greater spatial coverage; * Addition of the CliMEX and CORDEX-NA sets, in addition to the scenarios in the CMIP5 set; * Use of six hydrological platforms; * * Addition of indicators, especially annual ones. * Etc. ## #Liste data available * Link to the new Hydroclimatic Atlas website. * Map of the 24,604 river sections of the Hydroclimatic Atlas with their attributes, available in GeoJSON and shapefile format. To facilitate download and display, the map is divided into 11 GeoJSON files: ABIT (Abitibi and Lac Abitibi region), CND west (North Shore A and B regions), CND east (North Shore regions C, D and E), GASP (North Shore regions C, D and E), GASP (Gaspésie), MONT (Gaspesie), MONT (Montégérie), OUTM (Outaouais Upstream), OUTV (Outaouais Downstream), OUTV (Outaouais Downstream), SAGU (Saguenay), SLNO (St-Laurent Nord-Ouest), SLSO (St-Laurent Sud-Ouest), and VAUD (Vaudreuil). * The CSV tables (“Magnitude...”) for each of the 76 hydrological indicators describing the amplement, the direction and the dispersion for RCP 4.5 and RCP8.5, for the three future horizons (see the documentation for details). * The CSV tables (“Projected indicator...”) for each of the 76 hydrological indicators detailing the flow values with their uncertainty for the historical period and the three future horizons (RCP4.5 and 8.5). See the documentation for more details. * A PDF with the metadata and a more detailed description of the data. ## #Note The 2018 version data is archived on Data Quebec for reference, for example for old reports or analyses referring to this version of the data. Any new study or analysis should use the most recent data available below or on the Atlas website.**This third party metadata element was translated using an automated translation tool (Amazon Translate).**

Projected changes in habitat suitability for 33 marine species on the Northeast US shelf. Changes in habitat suitability are calculated based on species distribution models fit to bottom trawl survey data from the NOAA Northeast Fisheries Science Center. Positive values indicate an increase in habitat suitability by 2040-2050 relative to historical (1963-2005). The spatial resolution of projections is 0.25 x 0.25 degrees. access_formats=.htmlTable,.csv,.json,.mat,.nc,.tsv,.esriCsv,.geoJson acquisition_description=The following methods are excerpted from Rogers et al. (in press):
Bottom trawl data from the NOAA Northeast Fisheries Science Center (NEFSC) fall (1963-2014) surveys were used to characterize the realized thermal niches of species. At each survey station, fish of each species were counted and weighed, and surface and bottom temperature measurements were taken. Correction factors were applied to standardize catch rates for changes in vessel and gear type. A total of 33 species were selected based on their near continuous presence in the survey as well as relative importance to commercial fisheries. For 4 species, data from 1972 onwards were used because observations were irregular prior to that year.

Generalized Additive Models were used to estimate the realized thermal niches of species. We restricted k (number of knots) to 4 or 6 for each of our covariates to ensure biologically meaningful responses. Our response variable was probability of occurrence in a trawl haul, and we used a binomial response with logit transform:

p(occur\u1d67,\u2c7c) ~ logit-1 (s(ST\u1d67,\u2c7c)+s(BT\u1d67,\u2c7c)+s(meanbiomass\u1d67)+s(rugosity\u2c7c))

where ST\u1d67,\u2c7c and BT\u1d67,\u2c7c are sea surface temperature and bottom temperature measured at each haul location j in year y, and meanbiomass\u1d67 is the average annual catch across all hauls to account for interannual changes in abundance due to, e.g., fishing. Rugosity\u1d67 is a measure of benthic habitat roughness, measured as the Terrain Ruggedness Index, using the GEBCO 2014 30-arcsecond bathymetry data (downloaded 4 Feb 2015 from http://www.gebco.net/). The resulting estimated smooth functions describing the relationship between probability of occurrence and temperature can be interpreted as realized thermal niches.

For each species, the change in predicted probability of occurrence under future (2040-2050) projected climate conditions was compared to historical (1963-2005) conditions for each cell within a 0.25\u00b0x0.25\u00b0 spatial grid. Because the modeled probability of occurrence included a component of catchability, values for each species were scaled by dividing by the maximum observed or predicted probability of occurrence across the study area. Positive values for a grid square indicated a projected increase in probability of occurrence, whereas negative values indicated a projected decrease in probability of occurrence.

See related dataset for\u00a0NEFSC bottom trawl data:\u00a0"%5C%22https://www.bco-%0Admo.org/dataset/753142%5C%22">https://www.bco- dmo.org/dataset/753142\u00a0(doi:\u00a010.1575/1912/bco-dmo.753142.1) awards_0_award_nid=559955 awards_0_award_number=OCE-1426891 awards_0_data_url=http://www.nsf.gov/awardsearch/showAward.do?AwardNumber=1426891 awards_0_funder_name=NSF Division of Ocean Sciences awards_0_funding_acronym=NSF OCE awards_0_funding_source_nid=355 awards_0_program_manager=Michael E. Sieracki awards_0_program_manager_nid=50446 cdm_data_type=Other comment=Projected changes in habitat suitability PIs: Lauren Rogers & Malin Pinsky Version date: 22-April-2019 Conventions=COARDS, CF-1.6, ACDD-1.3 data_source=extract_data_as_tsv version 2.3 19 Dec 2019 defaultDataQuery=&time<now doi=10.1575/1912/bco-dmo.765386.1 Easternmost_Easting=-64.875 geospatial_lat_max=44.875 geospatial_lat_min=33.625 geospatial_lat_units=degrees_north geospatial_lon_max=-64.875 geospatial_lon_min=-76.875 geospatial_lon_units=degrees_east infoUrl=https://www.bco-dmo.org/dataset/765386 institution=BCO-DMO keywords_vocabulary=GCMD Science Keywords metadata_source=https://www.bco-dmo.org/api/dataset/765386 Northernmost_Northing=44.875 param_mapping={'765386': {'lat': 'master - latitude', 'lon': 'master - longitude'}} parameter_source=https://www.bco-dmo.org/mapserver/dataset/765386/parameters people_0_affiliation=Rutgers University people_0_person_name=Malin Pinsky people_0_person_nid=554708 people_0_role=Principal Investigator people_0_role_type=originator people_1_affiliation=Stanford University people_1_person_name=Lauren Rogers people_1_person_nid=765425 people_1_role=Principal Investigator people_1_role_type=originator people_2_affiliation=Stanford University people_2_person_name=Robert Griffin people_2_person_nid=768380 people_2_role=Co-Principal Investigator people_2_role_type=originator people_3_affiliation=Rutgers University people_3_person_name=Kevin St. Martin people_3_person_nid=559961 people_3_role=Co-Principal Investigator people_3_role_type=originator people_4_affiliation=Princeton University people_4_person_name=Emma Fuller people_4_person_nid=748888 people_4_role=Scientist people_4_role_type=originator people_5_affiliation=Rutgers University people_5_person_name=Talia Young people_5_person_nid=752628 people_5_role=Scientist people_5_role_type=originator people_6_affiliation=National Oceanic and Atmospheric Administration - Alaska Fisheries Science Center people_6_affiliation_acronym=NOAA-AFSC people_6_person_name=Lauren Rogers people_6_person_nid=765425 people_6_role=Contact people_6_role_type=related people_7_affiliation=Woods Hole Oceanographic Institution people_7_affiliation_acronym=WHOI BCO-DMO people_7_person_name=Shannon Rauch people_7_person_nid=51498 people_7_role=BCO-DMO Data Manager people_7_role_type=related project=CC Fishery Adaptations projects_0_acronym=CC Fishery Adaptations projects_0_description=Description from NSF award abstract: Climate change presents a profound challenge to the sustainability of coastal systems. Most research has overlooked the important coupling between human responses to climate effects and the cumulative impacts of these responses on ecosystems. Fisheries are a prime example of this feedback: climate changes cause shifts in species distributions and abundances, and fisheries adapt to these shifts. However, changes in the location and intensity of fishing also have major ecosystem impacts. This project's goal is to understand how climate and fishing interact to affect the long-term sustainability of marine populations and the ecosystem services they support. In addition, the project will explore how to design fisheries management and other institutions that are robust to climate-driven shifts in species distributions. The project focuses on fisheries for summer flounder and hake on the northeast U.S. continental shelf, which target some of the most rapidly shifting species in North America. By focusing on factors affecting the adaptation of fish, fisheries, fishing communities, and management institutions to the impacts of climate change, this project will have direct application to coastal sustainability. The project involves close collaboration with the National Oceanic and Atmospheric Administration, and researchers will conduct regular presentations for and maintain frequent dialogue with the Mid-Atlantic and New England Fisheries Management Councils in charge of the summer flounder and hake fisheries. To enhance undergraduate education, project participants will design a new online laboratory investigation to explore the impacts of climate change on fisheries, complete with visualization tools that allow students to explore inquiry-driven problems and that highlight the benefits of teaching with authentic data. This project is supported as part of the National Science Foundation's Coastal Science, Engineering, and Education for Sustainability program - Coastal SEES. The project will address three questions: 1) How do the interacting impacts of fishing and climate change affect the persistence, abundance, and distribution of marine fishes? 2) How do fishers and fishing communities adapt to species range shifts and related changes in abundance? and 3) Which institutions create incentives that sustain or maximize the value of natural capital and comprehensive social wealth in the face of rapid climate change? An interdisciplinary team of scientists will use dynamic range and statistical models with four decades of geo-referenced data on fisheries catch and fish biogeography to determine how fish populations are affected by the cumulative impacts of fishing, climate, and changing species interactions. The group will then use comprehensive information on changes in fisher behavior to understand how fishers respond to changes in species distribution and abundance. Interviews will explore the social, regulatory, and economic factors that shape these strategies. Finally, a bioeconomic model for summer flounder and hake fisheries will examine how spatial distribution of regulatory authority, social feedbacks within human communities, and uncertainty affect society's ability to maintain natural and social capital. projects_0_end_date=2018-08 projects_0_geolocation=Northeast US Continental Shelf Large Marine Ecosystem projects_0_name=Adaptations of fish and fishing communities to rapid climate change projects_0_project_nid=559948 projects_0_start_date=2014-09 sourceUrl=(local files) Southernmost_Northing=33.625 standard_name_vocabulary=CF Standard Name Table v55 version=1 Westernmost_Easting=-76.875 xml_source=osprey2erddap.update_xml() v1.3

Related article: Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39.

In this dataset:

We present temporally dynamic population distribution data from the Helsinki Metropolitan Area, Finland, at the level of 250 m by 250 m statistical grid cells. Three hourly population distribution datasets are provided for regular workdays (Mon – Thu), Saturdays and Sundays. The data are based on aggregated mobile phone data collected by the biggest mobile network operator in Finland. Mobile phone data are assigned to statistical grid cells using an advanced dasymetric interpolation method based on ancillary data about land cover, buildings and a time use survey. The data were validated by comparing population register data from Statistics Finland for night-time hours and a daytime workplace registry. The resulting 24-hour population data can be used to reveal the temporal dynamics of the city and examine population variations relevant to for instance spatial accessibility analyses, crisis management and planning.

Please cite this dataset as:

Bergroth, C., Järv, O., Tenkanen, H., Manninen, M., Toivonen, T., 2022. A 24-hour population distribution dataset based on mobile phone data from Helsinki Metropolitan Area, Finland. Scientific Data 9, 39. https://doi.org/10.1038/s41597-021-01113-4

Organization of data

The dataset is packaged into a single Zipfile Helsinki_dynpop_matrix.zip which contains following files:

HMA_Dynamic_population_24H_workdays.csv represents the dynamic population for average workday in the study area.

HMA_Dynamic_population_24H_sat.csv represents the dynamic population for average saturday in the study area.

HMA_Dynamic_population_24H_sun.csv represents the dynamic population for average sunday in the study area.

target_zones_grid250m_EPSG3067.geojson represents the statistical grid in ETRS89/ETRS-TM35FIN projection that can be used to visualize the data on a map using e.g. QGIS.

Column names

YKR_ID : a unique identifier for each statistical grid cell (n=13,231). The identifier is compatible with the statistical YKR grid cell data by Statistics Finland and Finnish Environment Institute.

H0, H1 ... H23 : Each field represents the proportional distribution of the total population in the study area between grid cells during a one-hour period. In total, 24 fields are formatted as “Hx”, where x stands for the hour of the day (values ranging from 0-23). For example, H0 stands for the first hour of the day: 00:00 - 00:59. The sum of all cell values for each field equals to 100 (i.e. 100% of total population for each one-hour period)

In order to visualize the data on a map, the result tables can be joined with the target_zones_grid250m_EPSG3067.geojson data. The data can be joined by using the field YKR_ID as a common key between the datasets.

License Creative Commons Attribution 4.0 International.

Related datasets

Järv, Olle; Tenkanen, Henrikki & Toivonen, Tuuli. (2017). Multi-temporal function-based dasymetric interpolation tool for mobile phone data. Zenodo. https://doi.org/10.5281/zenodo.252612

Tenkanen, Henrikki, & Toivonen, Tuuli. (2019). Helsinki Region Travel Time Matrix [Data set]. Zenodo. http://doi.org/10.5281/zenodo.3247564

This layer is the example dataset provided in the original GitHub Repository for Action 2017.2 on INSPIRE Alternative Encodings from the INSPIRE JRC MIG-T Action 2017.2. It is provided herein as Alternative Encodings Draft GeoJSON imported into ArcGIS Online; this hosted Feature Layer was created from the GeoJSON at the time of import. This layer demonstrates the simplified/flattened address schema developed under MIG-T Action 2017.2 following the guidance provided for community implementations. The remainder of the ArcGIS INSPIRE Open Data streamlined fGDB templates in this collection follow the guidance and document templates laid out by Action 2017.2.Note: This Address point dataset contains only one point as provided through the GitHub Repository.

About

World Country and State coordinate for plotting geospatial maps.

Source

Files source:

1. Folium GitHub Repository:

   • https://github.com/python-visualization/folium

   • https://github.com/python-visualization/folium/tree/master/examples/data

2. World Geo Repository

   • https://github.com/johan/world.geo.json

This dataset lists the intersections between named or numbered channels in OpenStreetMap data covering the French territory. The data is generated in the json streamed format expected by the addok geocoder as well as in the geojson format. Example: {“type”:“inter”,“name”:“Chemin de la carronnière/Route de Thoissey D64”,“context”:“L’Abergement-Clémenciat, Ain”,“citycode”:“01001”,“depcode”:“01”,“lon”:4.906772,“lat”:46.163201} name: contains the name and/or route number (e.g. D40, A6) of each track separated by “/” context: contains the name of the municipality, followed by the name of the department citycode: contains the INSEE code of the municipality depcode: contains the INSEE code of the department The code used to generate the file is available at https://github.com/cquest/osm-intersections

LRVTwin: Measuring Tram 2.0 - Statistics on measuring journeys The measuring tram 2.0, which is equipped as part of the mFUND joint project LRVTwin, continuously collects data in the route network of the Leipziger Verkehrsbetriebe. The data set contains, for example, statistics on individual measurement trips, as well as the summary of a measurement day. Time-recorded measurement data are located in the route network and evaluated on their dwell time in track sections. As an example, the data set contains information on average driving speed, as well as values derived from acceleration sensors for longitudinal and lateral acceleration. The route network reference is available in geojson format. The segmented route network is divided into 25m sections. The network topology is mapped via a turnout table, which contains information on adjacent trunk (mainSegID) and branch tracks (branchSegID). Name: RideXXX.geojson -- single rides (terminal stop-terminal stop) Daily summaryXXX.geojson -- Statistics on multiple crossings of a measurement day XXXYYYYSegmentabschnitte.geojson -- Track network reference Track sections XXXYYYYWeichen.geojson -- Route network reference turnout topology LRVTwin: © 2024 by Fabian Wendrock, Leonhard Heindel, TU Dresden is licensed under CC BY-NC 4.0

The MCCN project is to deliver tools to assist the agricultural sector to understand crop-environment relationships, specifically by facilitating generation of data cubes for spatiotemporal data. This repository contains Jupyter notebooks to demonstrate the functionality of the MCCN data cube components.

The dataset contains input files for the case study (source_data), RO-Crate metadata (ro-crate-metadata.json), results from the case study (results), and Jupyter Notebook (MCCN-CASE 1.ipynb)

Research Activity Identifier (RAiD)

RAiD: https://doi.org/10.26292/8679d473

Case Studies

This repository contains code and sample data for the following case studies. Note that the analyses here are to demonstrate the software and result should not be considered scientifically or statistically meaningful. No effort has been made to address bias in samples, and sample data may not be available at sufficient density to warrant analysis. All case studies end with generation of an RO-Crate data package including the source data, the notebook and generated outputs, including netcdf exports of the datacubes themselves.

Case Study 1 - Evaluate impact from environmental events/pressures

Description

Aggregate observations of Caladenia orchids in the ACT so I can analyse the relationship between records and the protection status and vegetation cover of the locations of each species.

This is an example of combining suites of spatial variables at different scales across multiple sites to analyse the factors correlated with a variable of interest. Comparable cases could include:

• Aggregate spatial data for frost and other extreme weather events associated with chickpeas and wheat yields to analyse the effects of such events on different varieties at different stages and advise growers on the best choices
• Aggregate pest data for the same pest across multiple sites and locations to analyse the relationship between population levels and environmental context at the time and over the previous month.

Data sources

• vegetation_cover_northern.tif - GA Landsat Vegetation Cover GeoTIFF at 25m2 for the northern two thirds of the ACT and adjacent NSW in 2020: https://explorer.dea.ga.gov.au/products/ga_ls_landcover_class_cyear_2/datasets/67bb9d38-00c7-46ba-a5e9-b892d9f9ad42 (values defined here: https://knowledge.dea.ga.gov.au/data/product/dea-land-cover-landsat/?tab=details)
• vegetation_cover_southern.tif - GA Landsat Vegetation Cover GeoTIFF at 25m2 for the southern third of the ACT and adjacent NSW in 2020: https://explorer.dea.ga.gov.au/products/ga_ls_landcover_class_cyear_2/datasets/464fd7e2-0554-4095-80e1-42e00f087831
• boundary_act.geojson - 2023 boundary for the Australian Capital Territory from the ACT Government in GeoJSON format: https://actmapi-actgov.opendata.arcgis.com/datasets/ACTGOV::actgov-border/explore
• capad_act.geojson - Protected Area data for the Australian Capital Territory in 2022 from the CAPAD dataset in GeoJSON format: https://fed.dcceew.gov.au/datasets/ec356a872d8048459fe78fc80213dc70_0/explore?filters=eyJTVEFURSI6WyJBQ1QiXX0=&location=-35.437128,149.203518,11.00
• caladenia_act.csv - Distribution records for orchids in the genus Caladenia between 1990 and present from the ALA in CSV format: https://doi.org/10.26197/ala.1e501311-7077-403b-a743-59e096068fa0

Dependencies

• This notebook requires Python 3.10 or higher
• Install relevant Python libraries with: pip install mccn-engine rocrate
• Installing mccn-engine will install other dependencies

Overview

1. Group orchid species records by species
2. Prepare STAC metadata records for each data source (separate records for the distribution data for each orchid species)
3. Load data cube
4. Mask orchid distribution records to boundaries of ACT
5. Calculate the proportion of distribution records for each species occurring inside and outside protected areas
6. Calculate the proportion of distribution records for each species occurring in areas with each class of vegetation cover
7. Report the apparent affinity between each species and protected areas and between each species and different classes of vegetation cover

Notes

• No attempt is made here to compensate for underlying bias in the areas where observers have spent time recording orchids. The analysis should only be considered indicative of relative tendencies.

View on bioimage.io # HPA Cell Image Segmentation Dataset

This dataset includes annotated cell images obtained from the Human Protein Atlas (http://www.proteinatlas.org), each image contains 4 channels (Microtubules, ER, Nuclei and Protein of Interest). The cells in each image are annotated with polygons and saved into GeoJSON format produced with Kaibu(https://kaibu.org) annotation tool.

hpa_cell_segmentation_dataset_v2_512x512_4train_159test.zip is an example dataset for running a deep learning-based interactive annotation tools in ImJoy (https://github.com/imjoy-team/imjoy-interactive-segmentation).

hpa_dataset_v2.zip is a full annotate image segmentation dataset

Utility functions in Python for reading the GeoJSON annotation can be found here: https://github.com/imjoy-team/kaibu-utils/blob/main/kaibu_utils/init.py

A boundary within which National Forest System land parcels have management or use limits placed on them by the Forest Service. Examples include: Archaeological Area, Research Natural Area, and Scenic Area.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService OGC WMS CSV Shapefile GeoJSON KML For complete information, please visit https://data.gov.