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This collection consists of geospatial data layers and summary data at the country and country sub-division levels that are part of USAID's Demographic Health Survey Spatial Data Repository. This collection includes geographically-linked health and demographic data from the DHS Program and the U.S. Census Bureau for mapping in a geographic information system (GIS). The data includes indicators related to: fertility, family planning, maternal and child health, gender, HIV/AIDS, literacy, malaria, nutrition, and sanitation. Each set of files is associated with a specific health survey for a given year for over 90 different countries that were part of the following surveys:Demographic Health Survey (DHS)Malaria Indicator Survey (MIS)Service Provisions Assessment (SPA)Other qualitative surveys (OTH)Individual files are named with identifiers that indicate: country, survey year, survey, and in some cases the name of a variable or indicator. A list of the two-letter country codes is included in a CSV file.Datasets are subdivided into the following folders:Survey boundaries: polygon shapefiles of administrative subdivision boundaries for countries used in specific surveys. Indicator data: polygon shapefiles and geodatabases of countries and subdivisions with 25 of the most common health indicators collected in the DHS. Estimates generated from survey data.Modeled surfaces: geospatial raster files that represent gridded population and health indicators generated from survey data, for several countries.Geospatial covariates: CSV files that link survey cluster locations to ancillary data (known as covariates) that contain data on topics including population, climate, and environmental factors.Population estimates: spreadsheets and polygon shapefiles for countries and subdivisions with 5-year age/sex group population estimates and projections for 2000-2020 from the US Census Bureau, for designated countries in the PEPFAR program.Workshop materials: a tutorial with sample data for learning how to map health data using DHS SDR datasets with QGIS. Documentation that is specific to each dataset is included in the subfolders, and a methodological summary for all of the datasets is included in the root folder as an HTML file. File-level metadata is available for most files. Countries for which data included in the repository include: Afghanistan, Albania, Angola, Armenia, Azerbaijan, Bangladesh, Benin, Bolivia, Botswana, Brazil, Burkina Faso, Burundi, Cape Verde, Cambodia, Cameroon, Central African Republic, Chad, Colombia, Comoros, Congo, Congo (Democratic Republic of the), Cote d'Ivoire, Dominican Republic, Ecuador, Egypt, El Salvador, Equatorial Guinea, Eritrea, Eswatini (Swaziland), Ethiopia, Gabon, Gambia, Ghana, Guatemala, Guinea, Guyana, Haiti, Honduras, India, Indonesia, Jordan, Kazakhstan, Kenya, Kyrgyzstan, Lesotho, Liberia, Madagascar, Malawi, Maldives, Mali, Mauritania, Mexico, Moldova, Morocco, Mozambique, Myanmar, Namibia, Nepal, Nicaragua, Niger, Nigeria, Pakistan, Papua New Guinea, Paraguay, Peru, Philippines, Russia, Rwanda, Samoa, Sao Tome and Principe, Senegal, Sierra Leone, South Africa, Sri Lanka, Sudan, Tajikistan, Tanzania, Thailand, Timor-Leste, Togo, Trinidad and Tobago, Tunisia, Turkey, Turkmenistan, Uganda, Ukraine, Uzbekistan, Viet Nam, Yemen, Zambia, Zimbabwe

In this blog I’ll share the workflow and tools used in the GIS part of this analysis. To understand where crashes are occurring, first the dataset had to be mapped. The software of choice in this instance was ArcGIS, though most of the analysis could have been done using QGIS. Heat maps are all the rage, and if you want to make simple heat maps for free and you appreciate good documentation, I recommend the QGIS Heatmap plugin. There are also some great tools in the free open-source program GeoDa for spatial statistics.

Description

This dataset consist of two vector files which show the change in the building stock of the City of DaNang retrieved from satellite image analysis. Buildings were first identified from a Pléiades satellite image from 24.10.2015 and classified into 9 categories in a semi-automatic workflow desribed by Warth et al. (2019) and Vetter-Gindele et al. (2019).

In a second step, these buildings were inspected for changes based on a second Pléiades satellite image acquired on 13.08.2017 based on visual interpretation. Changes were also classified into 5 categories and aggregated by administrative wards (first dataset: adm) and a hexagon grid of 250 meter length (second dataset: hex).

The full workflow of the generation of this dataset, including a detailled description of its contents and a discussion on its potential use is published by Braun et al. 2020: Changes in the building stock of DaNang between 2015 and 2017

Contents

Both datasets (adm and hex) are stored as ESRI shapefiles which can be used in common Geographic Information Systems (GIS) and consist of the following parts:

• shp: polygon geometries (geometries of the administrative boundaries and hexagons)
• dbf: attribute table (containing the number of buildings per class for 2015 and 2017 and the underlying changes (e.g. number of new buildings, number of demolished buildings, ect.)
• shx: index file combining the geometries with the attributes
• cpg: encoding of the attributes (UTF-8)
• prj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for ArcGIS
• qpj: spatial reference of the datasets (UTM zone 49 North, EPSG:32649) for QGIS
• lyr: symbology suggestion for the polygons(predefined is the number of local type shophouses in 2017) for ArcGIS
• qml: symbology suggestion for the polygons (predefined is the number of new buildings between 2015 and 2017) for QGIS

Citation and documentation

To cite this dataset, please refer to the publication

• Braun, A.; Warth, G.; Bachofer, F.; Quynh Bui, T.T.; Tran, H.; Hochschild, V. (2020): Changes in the Building Stock of Da Nang between 2015 and 2017. Data, 5, 42. doi:10.3390/data5020042

This article contains a detailed description of the dataset, the defined building type classes and the types of changes which were analyzed. Furthermore, the article makes recommendations on the use of the datasets and discusses potential error sources.

This dataset contains open vector data for railways, forests and power lines, as well an open digital elevation model (DEM) for a small area around a sample forest range in Europe (Germany, Upper Bavaria, Kochel Forest Range, some 70 km south of München, at the edge of Bavarian Alps). The purpose of this dataset is to provide a documented sample dataset in order to demonstrate geospatial preprocessing at FOSS4G2019 based on open data and software. This sample has been produced based on several existing open data sources (detailed below), therefore documenting the sources for obtaining some data needed for computations related to forest accessibility and wood harvesting. For example, they can be used with the open methodology and QGIS plugin Seilaplan for optimising the geometric layout cable roads or with additional open software for computing the forest accessibility for wood harvesting. The vector data (railways, forests and power lines) was extracted from OpenStreetMap (data copyrighted OpenStreetMap contributors and available from https://www.openstreetmap.org). The railways and forests were downloaded and extracted on 18.05.2019 using the open sources QGIS (https://www.qgis.org) with the QuickOSM plugin, while the power lines were downloaded a couple of days later on 23.05.2019. Additional notes for vector data: Please note that OpenStreeMap data extracts such as forests, roads and railways (except power lines) can also be downloaded in a GIS friendly format (Shapefile) from http://download.geofabrik.de/ or using the QGIS built-in download function for OpenStreetMap data. The most efficient way to retrieve specific OSM tags (such as power=line) is to use the QuickOSM plugin for QGIS (using the Overpass API - https://wiki.openstreetmap.org/wiki/Overpass_API) or directly using overpass turbo (https://overpass-turbo.eu/). Finally, the digitised perimeter of the sample forest range is also made available for reproducibility purposes, although any perimeter or area can be digitised freely using the QGIS editing toolbar. The DEM was originally adapted and modified also with QGIS (https://www.qgis.org) based on the elevation data available from two different sources, by reprojecting and downsampling datasets to 25m then selecting, for each individual raster cell, the elevation value that was closer to the average. These two different elevation sources are: - Copernicus Land Monitoring Service - EU-DEM v.1.1 (TILE ID E40N20, downloaded from https://land.copernicus.eu/imagery-in-situ/eu-dem/eu-dem-v1.1; this original DEM was produced by the Copernicus Land Monitoring Service “with funding by the European Union” based on SRTM and ASTER GDEM) - Digitales Geländemodell 50 m Gitterweite (https://opendata.bayern.de/detailansicht/datensatz/digitales-gelaendemodell-50-m-gitterweite/), produced by the Bayerische Vermessungsverwaltung – www.geodaten.bayern.de –and downloaded from http://www.geodaten.bayern.de/opendata/DGM50/dgm50_epsg4258.tif This methodology was chosen as a way of performing a basic quality check, by comparing the EU-DEM v.1.1 derived from globally available DEM data (such as SRTM) with more authoritative data for the randomly selected region, since using authoritative data is preferred (if open and available). For other sample regions, where authoritative open data is not available, such comparisons cannot longer be performed. Additional notes DEM: a very good DEM open data source for Germany is the open data set collected and resampled by Sonny (sonnyy7@gmail.com) and made available on the Austrian Open Data Portal http://data.opendataportal.at/dataset/dtm-germany. In order to simplify end-to-end reproducibility of the paper planned for FOSS4G2019, we use and distribute an adapted (reprojected and resampled to 25 meters) sample of the above mentioned dataset for the selected forest range. This sample dataset is accompanied by software in Python, as a Jupiter Notebook that generates harmonized output rasters with the same extent from the input data. The extent is given by the polygon vector dataset (Perimeter). These output rasters, such as obstacles, aspect, slope, forest cover, can serve as input data for later computations related to forest accessibility and wood harvesting questions. The obstacles output is obtained by transforming line vector datasets (railway lines, high voltage power lines) to raster. Aspect and slope are both derived from the sample digital elevation model.

Database of the land cover of the Grand Est (OCS Grand Est) on the department of Aube (10). The nomenclature is composed of 4 levels interlocked with 53 posts describing the cover, a level 5 transversal characterizes the permeability of the soil of artificialized areas. The publication of the third vintage introduces the following elements: - A level 6 associated with military rights-of-way. - Correspondence with the nomenclature of the IGN OCSGE and the classes characterising the cover - Correspondence with the nomenclature of the LCR Act in 10 classes relating to Decree No 2023-1096 of 27 November 2023 on the assessment and monitoring of land take More information The data files can be downloaded via this link. The data is accompanied by documentation, QGIS and ArcMap projects and their ready-to-use style files.

Database of the land cover of the Grand Est (OCS Grand Est) on the department of Meurthe-et-Moselle (54). The nomenclature is composed of 4 levels interlocked with 53 posts describing the cover, a level 5 transversal characterizes the permeability of the soil of artificialized areas. The publication of the third vintage introduces the following elements: - A level 6 associated with military rights-of-way. - Correspondence with the nomenclature of the IGN OCSGE and the classes characterising the cover - Correspondence with the nomenclature of the LCR Act in 10 classes relating to Decree No 2023-1096 of 27 November 2023 on the assessment and monitoring of land take More information The data files can be downloaded via this link. The data is accompanied by documentation, QGIS and ArcMap projects and their ready-to-use style files.

Today, deep neural networks are widely used in many computer vision problems, also for geographic information systems (GIS) data. This type of data is commonly used for urban analyzes and spatial planning. We used orthophotographic images of two residential districts from Kielce, Poland for research including urban sprawl automatic analysis with Transformer-based neural network application.Orthophotomaps were obtained from Kielce GIS portal. Then, the map was manually masked into building and building surroundings classes. Finally, the ortophotomap and corresponding classification mask were simultaneously divided into small tiles. This approach is common in image data preprocessing for machine learning algorithms learning phase. Data contains two original orthophotomaps from Wietrznia and Pod Telegrafem residential districts with corresponding masks and also their tiled version, ready to provide as a training data for machine learning models.Transformed-based neural network has undergone a training process on the Wietrznia dataset, targeted for semantic segmentation of the tiles into buildings and surroundings classes. After that, inference of the models was used to test model's generalization ability on the Pod Telegrafem dataset. The efficiency of the model was satisfying, so it can be used in automatic semantic building segmentation. Then, the process of dividing the images can be reversed and complete classification mask retrieved. This mask can be used for area of the buildings calculations and urban sprawl monitoring, if the research would be repeated for GIS data from wider time horizon.Since the dataset was collected from Kielce GIS portal, as the part of the Polish Main Office of Geodesy and Cartography data resource, it may be used only for non-profit and non-commertial purposes, in private or scientific applications, under the law "Ustawa z dnia 4 lutego 1994 r. o prawie autorskim i prawach pokrewnych (Dz.U. z 2006 r. nr 90 poz 631 z późn. zm.)". There are no other legal or ethical considerations in reuse potential.Data information is presented below.wietrznia_2019.jpg - orthophotomap of Wietrznia districtmodel's - used for training, as an explanatory imagewietrznia_2019.png - classification mask of Wietrznia district - used for model's training, as a target imagewietrznia_2019_validation.jpg - one image from Wietrznia district - used for model's validation during training phasepod_telegrafem_2019.jpg - orthophotomap of Pod Telegrafem district - used for model's evaluation after training phasewietrznia_2019 - folder with wietrznia_2019.jpg (image) and wietrznia_2019.png (annotation) images, divided into 810 tiles (512 x 512 pixels each), tiles with no information were manually removed, so the training data would contain only informative tilestiles presented - used for the model during training (images and annotations for fitting the model to the data)wietrznia_2019_vaidation - folder with wietrznia_2019_validation.jpg image divided into 16 tiles (256 x 256 pixels each) - tiles were presented to the model during training (images for validation model's efficiency); it was not the part of the training datapod_telegrafem_2019 - folder with pod_telegrafem.jpg image divided into 196 tiles (256 x 265 pixels each) - tiles were presented to the model during inference (images for evaluation model's robustness)Dataset was created as described below.Firstly, the orthophotomaps were collected from Kielce Geoportal (https://gis.kielce.eu). Kielce Geoportal offers a .pst recent map from April 2019. It is an orthophotomap with a resolution of 5 x 5 pixels, constructed from a plane flight at 700 meters over ground height, taken with a camera for vertical photos. Downloading was done by WMS in open-source QGIS software (https://www.qgis.org), as a 1:500 scale map, then converted to a 1200 dpi PNG image.Secondly, the map from Wietrznia residential district was manually labelled, also in QGIS, in the same scope, as the orthophotomap. Annotation based on land cover map information was also obtained from Kielce Geoportal. There are two classes - residential building and surrounding. Second map, from Pod Telegrafem district was not annotated, since it was used in the testing phase and imitates situation, where there is no annotation for the new data presented to the model.Next, the images was converted to an RGB JPG images, and the annotation map was converted to 8-bit GRAY PNG image.Finally, Wietrznia data files were tiled to 512 x 512 pixels tiles, in Python PIL library. Tiles with no information or a relatively small amount of information (only white background or mostly white background) were manually removed. So, from the 29113 x 15938 pixels orthophotomap, only 810 tiles with corresponding annotations were left, ready to train the machine learning model for the semantic segmentation task. Pod Telegrafem orthophotomap was tiled with no manual removing, so from the 7168 x 7168 pixels ortophotomap were created 197 tiles with 256 x 256 pixels resolution. There was also image of one residential building, used for model's validation during training phase, it was not the part of the training data, but was a part of Wietrznia residential area. It was 2048 x 2048 pixel ortophotomap, tiled to 16 tiles 256 x 265 pixels each.

Database of the land cover of the Grand Est (OCS Grand Est) on the department of Haute-Marne (52). The nomenclature is composed of 4 levels interlocked with 53 posts describing the cover, a level 5 transversal characterizes the permeability of the soil of artificialized areas. The publication of the third vintage introduces the following elements: - A level 6 associated with military rights-of-way. - Correspondence with the nomenclature of the IGN OCSGE and the classes characterising the cover - Correspondence with the nomenclature of the LCR Act in 10 classes relating to Decree No 2023-1096 of 27 November 2023 on the assessment and monitoring of land take More information The data files can be downloaded via this link. The data is accompanied by documentation, QGIS and ArcMap projects and their ready-to-use style files.

Extreme value analysis of CCHaPS, the Coupled Coastal Hazard Prediction System, which is a 2D hydrodynamic and wave model (Semi-implicit Cross-scale Hydroscience Integrated System Model and Wind Wave Model III, SCHISM-WWMIII), configured around the Australian coastline including the Great Barrier Reef, and extending out to deep offshore waters or neighbouring landmasses (https://data.csiro.au/collection/csiro:65669).

The model simulates water levels due to astronomical tides, weather, waves and aspects of wave-flow interaction over multiple decades, allowing for full consideration of the dynamics of extreme sea levels and waves in the Australian region, at high spatial resolution from ~7 km offshore down to ~250 m at the coast, and ~100 m in major river mouths. The model is run on an unstructured (triangular) grid comprising over 1.4 million nodes, which extends overland up to a 12 m elevation contour, to enable modelling of inundation events and sea level rise.

The CCHaPS hindcast covers the 40-year period 1981 to 2020.

Extreme value analysis has been performed on 1) annual maximum{yr_max} significant wave heights {hs}, and 2) detrended annual maximum {yr_max-detrended} water levels {zos}, with the linear trend in mean water levels removed.

Three types of extreme value distributions (EVDs) are fitted to the annual maxima: - two-parameter Gumbel - three-parameter Generalised Extreme Value (GEV) - four-parameter mixed Gumbel distribution (https://doi.org/10.1038/s41598-022-08382-y)

Datasets are provided for 1) the commonly used GEV {fgev}, and 2) the best of the three EVD {bestEVD} types (with positive shape parameters), selected using the Akaike Information Criterion (AIC). Lower values of AIC indicate better models, in the sense that the model fit is better relative to the number of model parameters. Note: GEV fits with a negative shape parameter (i.e. bounded upper tail) can yield lower estimated extremes and are therefore not always preferred (e.g. Haigh et al., 2014; https://doi.org/10.1007/s00382-012-1652-1).

The dataset comprises 1, 2, 5, 10, 20, and 63% Annual Exceedance Probabilities (AEPs) with {upper} and {lower} 95% confidence intervals, stored as netCDF data on an unstructured grid, compliant with CF, ACDD and UGRID metadata conventions. Additional processing is also applied to the confidence intervals. A notebook demonstrating how to interact with the data will be attached under Supporting Documentation, and also available via GitHub (see Related Links, LINK TBA).

The netCDF files can be viewed in QGIS by importing them as a mesh through the Data Source Manager. (https://docs.qgis.org/3.40/en/docs/user_manual/working_with_mesh/mesh.html?utm_source=chatgpt.com)

The data is also stored on CSIRO infrastructure in the {ev-acs-wp3-cchaps} volume, and at NCI in /g/data/ia39/WP3/release/CCHaPS.

A manuscript describing this dataset is in preparation and will be linked to this metadata record in due course. Lineage: Extreme value analysis of hourly CCHaPS data (https://data.csiro.au/collection/csiro:65669) was performed using the R CRAN package loopevd (https://doi.org/10.32614/CRAN.package.loopevd), June 2025. File converted to metadata standards compliant netCDF via Python notebook, June 2025. Final dataset copied from NCI to CSIRO Bowen storage in preparation for publication via CSIRO DAP.

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13231) of a 250 × 250 m grid over the populated areas in the Helsinki metropolitan area by walking, cycling, public transportation, and private car. If applicable, the routes have been calculated for different times of the day (rush hour, midday, off-peak), and assuming different physical abilities (such as walking and cycling speeds), see details below.

The grid follows the geometric properties and enumeration of the versatile Yhdyskuntarakenteen seurantajärjestelmä (YKR) grid used in applications across many domains in Finland, and covers the municipalities of Helsinki, Espoo, Kauniainen, and Vantaa in the Finnish capital region.

Data formats

The data is available in multiple different formats that cater to different requirements, such as different software environments. All data formats share a common set of columns (see below), and can be used interchangeably.

• `Helsinki_Travel_Time_Matrix_2023.csv.zst`: comma-separated values (CSV) of all data columns, without geometries. This data set contains all routes in one file, and can be filtered by origin or destination according to the analysis at hand. The data records can also be joined to the geometries as available below. The file is compressed using the Zstandard algorithm, that many data science libraries, for instance, pandas, support transparently, directly, and automatically.
• `Helsinki_Travel_Time_Matrix_2023_travel_times.gpkg.zip`: an OGC GeoPackage standard file containing all data columns and the geometries that relate to the destination grid cell. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Matrix_2023_travel_times.csv.zip`: a set of 13231 comma-separated value files containing the routes to one destination grid cell each. The files contain all data columns, no geometry, and can be joined to the geometries as available below. Filenames of the individual files within the ZIP archive follow the pattern `Helsinki_Travel_Time_Matrix_2023_travel_times_to_5787545.csv` where `5787545` is replaced by the `to_id` by which the rows in the file are grouped. Use the `from_id` column to join with the geometries from one of the files below.

Geometry, only:

• `Helsinki_Travel_Time_Matrix_2023_grid.gpkg.zip`: an OGC GeoPackage standard file containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files. The data set is delivered as a ZIP archive, which many GIS systems and libraries, e.g., GDAL/OGR, QGIS, or geopandas, support natively.
• `Helsinki_Travel_Time_Matrix_2023_grid.shp.zip`: an ESRI Shapefile archive containing the geometries and IDs of the grid used in the analysis. This file can be joined both to the `from_id` and `to_id` columns of the data files.

Table structure

from_id

to_id

walk_avg

walk_slo

bike_avg

bike_fst

bike_slo

pt_r_avg

pt_r_slo

pt_m_avg

pt_m_slo

pt_n_avg

pt_n_slo

car_r

car_m

car_n

walk_d

Data for 2013, 2015, and 2018

At the Digital Geography Lab, we started computing travel time matrices in 2013. Our methodology has changed in between the iterations, and naturally, there are systematic differences between the iterations’ results. Not all input data sets are available to recompute the historical matrices with new methods, however, we were able to repeat the 2018 calculation using the same methods as the 2023 data set, please find the results below, in the same format.

For the travel time matrices for 2013 and 2015, as well as for 2018 using an older methodology, please refer to DOI:10.5281/zenodo.3247563.

Methodology

Computations were carried out for Wednesday, 15 February, 2023, and Monday, 29 January, 2018, respectively. ‘Rush hour’ refers to an 1-hour window between 8 and 9 am, ‘midday’ to 12 noon to 1 pm, and ‘nighttime’ to 2-3 am.

All routes have been calculated using r5py, a Python library making use of the R⁵ engine by Conveyal, with modifications to consider local characteristics of the Helsinki use case and to inform the computation models from local real-world data sets. In particular, we made the following modifications:

Walking

Walking speeds, and in turn walking times, are based on the findings of Willberg et al., 2023, in which we measured walking speeds of people of different age groups in varying road surface conditions in Helsinki. Specifically, we chose to use the average measured walking speed in summer conditions for `walk_avg` (as well as the respective `pt_*_walk_avg`), and the slowest quintile of all measured walker across all conditions for `walk_slo` (and the respective `pt_*_walk_slo`).

Cycling

Cycling speeds are derived from two input data sets. First, we averaged cycling speeds per network segment from Strava data, and computed a ratio between the speed ridden in each segment and the overall average speed. We then use these ratios to compute fast, slow, and average cycling speeds for each segment, based on the mean overall Strava speed, the mean speeds cycled in the Helsinki City Bike bike-share system, and the mean between the two.

Further, in line with the values observed by Jäppinen (2012), we add a flat 30 seconds each for unlocking and locking the bicycle at the origin and destination.

Public Transport

We used public transport schedules in General Transit Feed Specification (GTFS) format published by

Database of the land cover of the Grand Est (OCS Grand Est) on the department of Haut-Rhin (68). The nomenclature is composed of 4 levels interlocked with 53 posts describing the cover, a level 5 transversal characterizes the permeability of the soil of artificialized areas. The publication of the third vintage introduces the following elements: - A level 6 associated with military rights-of-way. - Correspondence with the nomenclature of the IGN OCSGE and the classes characterising the cover - Correspondence with the nomenclature of the LCR Act in 10 classes relating to Decree No 2023-1096 of 27 November 2023 on the assessment and monitoring of land take More information The data files can be downloaded via this link. The data is accompanied by documentation, QGIS and ArcMap projects and their ready-to-use style files.

Database of the land cover of the Grand Est (OCS Grand Est) on the department of Vosges (88). The nomenclature is composed of 4 levels interlocked with 53 posts describing the cover, a level 5 transversal characterizes the permeability of the soil of artificialized areas. The publication of the third vintage introduces the following elements: - A level 6 associated with military rights-of-way. - Correspondence with the nomenclature of the IGN OCSGE and the classes characterising the cover - Correspondence with the nomenclature of the LCR Act in 10 classes relating to Decree No 2023-1096 of 27 November 2023 on the assessment and monitoring of land take More information The data files can be downloaded via this link. The data is accompanied by documentation, QGIS and ArcMap projects and their ready-to-use style files.

Database of the land cover of the Grand Est (OCS Grand Est) on the department of the Ardennes (08). The nomenclature is composed of 4 levels interlocked with 53 posts describing the cover, a level 5 transversal characterizes the permeability of the soil of artificialized areas. The publication of the third vintage introduces the following elements: - A level 6 associated with military rights-of-way. - Correspondence with the nomenclature of the IGN OCSGE and the classes characterising the cover - Correspondence with the nomenclature of the LCR Act in 10 classes relating to Decree No 2023-1096 of 27 November 2023 on the assessment and monitoring of land take More information The data files can be downloaded via this link. The data is accompanied by documentation, QGIS and ArcMap projects and their ready-to-use style files.

The Plan de Ville Communal et Intercommunal (PVCI) of Rennes Métropole is the official map plan of the community. It consists of some fifty layers of geographic data managed by the Geographic Information Service and various other services of the community but also by other partners (OSM, IGNF). You will find at the address below, several map productions in PDF format generated from these data: ‘HTTPS://PUBLIC.SIG.RENNESMETROPOLE.FR/HEADER/CARTES’. These data are available as a GeoPackage (*.gpkg) for use under QGIS software. This GeoPackage is accompanied by a pvci_list_layches.ods file that lists and describes each data layer (if applicable, a link to the metadata is available). The data is updated approximately every 2 months. Click here to download this dataset (zip format of 250 MB). It is also possible to connect to the data streams (OCC web services) of the public server of Rennes Métropole. Below you will find documentation to connect to these streams under QGIS: Click here to access PVCI in data stream mode

Base de données de l'occupation du sol du Grand Est (OCS Grand Est) sur le département du Haut-Rhin (68). La nomenclature est composée de 4 niveaux emboités de 53 postes décrivant le couvert, un niveau 5 transversal caractérise la perméabilité du sol des zones artificialisées. La publication du 3e millésime introduit les éléments suivants :

• Un niveau 6 associé aux emprises militaires.
• Une correspondance avec la nomenclature de l’OCSGE IGN et les classes caractérisant le couvert
• Une correspondance avec la nomenclature de la loi LCR en 10 classes relative au Décret no 2023-1096 du 27 novembre 2023 relatif à l’évaluation et au suivi de l’artificialisation des sols Plus d'information

Les fichiers de données sont accessibles en téléchargement via ce lien. Les données sont accompagnées d'une documentation, de projets QGIS et ArcMap et de leurs fichiers de style prêts à l'emploi.

Le Règlement communautaire (CE) n°1593/2000 a institué l'obligation, dans tous les Etats Membres, de localiser et d'identifier les parcelles agricoles.Pour répondre à cette exigence, la France a mis en place le Registre Parcellaire Graphique (RPG) qui est un système d'information géographique permettant l'identification des parcelles agricoles. Ainsi, chaque année, les agriculteurs adressent à l'administration un dossier de déclaration de surfaces qui comprend notamment le dessin des îlots de culture qu'ils exploitent et les cultures qui y sont pratiquées.La localisation des îlots se fait à l'échelle du 1:5000 sur le fond photographique de la BD Ortho (IGN) et leur mise à jour est annuelle. Cette base de données constitue donc une description à grande échelle et régulièrement mise à jour de la majorité des terres agricoles.Les données disponibles sont les contours des îlots « anonymisés » du RPG et leur occupation culturale représentée par le groupe de cultures majoritaire de l'îlot.Les données ont été nettoyées: - Ajout des types de cultures en fonction des codes (Merge des tables)- Ajout des colonnes commune, code postaux, code Insee en fonction des parcelles (analyse spatiale sous QGIS)- Un zoom sur le pays basque a été réaliséPour avoir accès aux données non travaillés se rendre sur le site de data.gouv:https://www.data.gouv.fr/fr/datasets/registre-parcellaire-graphique-rpg-contours-des-parcelles-et-ilots-culturaux-et-leur-groupe-de-cultures-majoritaire/Documentation:https://geoservices.ign.fr/ressources_documentaires/Espace_documentaire/BASES_VECTORIELLES/RPG/DC_DL_RPG_2-0.pdfDescription Table:

ID_PARCEL Identifiant de la parcell CULTURE D1 culture intercalée entre 2 moissons de culture principale CULTURE D2 culture intercalée entre 2 moissons de culture principale SURF PARCELLE Surface en hectares CODE CULTURE de 1 à 28

Base de données de l'occupation du sol du Grand Est (OCS Grand Est) sur le département de la Marne (51). La nomenclature est composée de 4 niveaux emboités de 53 postes décrivant le couvert, un niveau 5 transversal caractérise la perméabilité du sol des zones artificialisées. La publication du 3e millésime introduit les éléments suivants :

• Un niveau 6 associé aux emprises militaires.
• Une correspondance avec la nomenclature de l’OCSGE IGN et les classes caractérisant le couvert
• Une correspondance avec la nomenclature de la loi LCR en 10 classes relative au Décret no 2023-1096 du 27 novembre 2023 relatif à l’évaluation et au suivi de l’artificialisation des sols Plus d'information

Les fichiers de données sont accessibles en téléchargement via ce lien. Les données sont accompagnées d'une documentation, de projets QGIS et ArcMap et de leurs fichiers de style prêts à l'emploi.

■ 상품 설명 및 특징 - 공간 범위 : 전국 (2019년 기준 데이터 제공) - 파일형식 : ALL.shp(shp, shx, dbf, prj) - 제공파일형식 : zip파일 - 파일타입 : point - 좌표계 : 단일평면직각좌표계 UTM-K (100000, 200000), EPSG:5179 * 사방댐은 토사석력의 이동이 현저한 황폐한 계천의 종침식 및 횡침식을 방지하여 계상물매의 완화, 유출 토석류의 저류 및 조절, 계상을 높여 산각을 고정하고 난류구역에서의 유로 정리 등을 목적으로 함 * 산사태나 땅밀림 등으로 인한 토석류 재해를 저지하여 하류지역을 보전하기 위하여 황폐한 계천을 횡단하여 구축하는 사방공작물 ■ 컬럼 정보 - 사방댐관리번호 : 사방댐의 관리번호 - 사방댐국가지점번호 : 사방댐이 위치한 국가지점번호(예:라바51179677) - 산사태관리기관코드 : 산사태 관리기관코드 - 사방댐지역코드 : 법정동 코드 - 사방댐관리상세주소 : 지번 및 상세주소 - 사방댐위치위도값 : 사방댐 위도값(Y) - 사방댐위치경도값 : 사방댐 경도값(X) ※ 해당파일(SHP)을 확인하시려면 QGIS프로그램을 설치하셔야 확인가능하며. 아래의 링크로 들어가 QGIS 무료오픈소스 프로그램을 다운받으시면 됩니다. https://www.qgis.org/ko/site/ → 무료 QGIS프로그램 다운로드 https://docs.qgis.org/3.10/ko/docs/user_manual/ → QGIS 사용자 지침서 확인 원본 데이터는 로그인 후 구매하여 다운로드 하십시오.