This is a link to the QGIS website where you can download open-source GIS software for viewing, analyzing and manipulating geodata like our downloadable shapefiles.

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.

PROMICE-2022 Ice Mask QGIS Bundle This dataset contains the PROMICE-2022 Ice Mask packaged as a ready-to-use QGIS project. The zipped bundle includes the QGIS project file and all associated vector/raster layers and styles, so you can open the project directly and view the pre-styled layers in a GIS environment. Contents QGIS project file (.qgz) with preconfigured layouts Raster and vector layers of the PROMICE-2022 ice mask WMS layer for the topographic map of Greenland WMS layer for SPOT 6/7 imagery WMS layers for Sentinel-2 imagery (2022) Layer style files README with usage notes Brief description The PROMICE-2022 Ice Mask provides an updated delineation of ice/non-ice areas of the Greenland Ice Sheet from August 2022. It is intended for visualization, mapping, and as an input layer for further geospatial analysis in QGIS or other GIS software. For detailed methodology, processing steps, validation, and full technical documentation, please consult the primary repository (link below). Citation Please cite this dataset as: Luetzenburg, Gregor; Korsgaard, Niels J.; Deichmann, Anna K.; Socher, Tobias; Gleie, Karin; Scharffenberger, Thomas; Fahrner, Dominik; Nielsen, Eva B.; How, Penelope; Bjørk, Anders A.; Kjeldsen, Kristian K.; Ahlstrøm, Andreas P.; Fausto, Robert S., 2025, PROMICE-2022 Ice Mask, GEUS Dataverse. DOI: https://doi.org/10.22008/FK2/O8CLRE How to use Download and unzip the package. Open the included .qgz QGIS project in QGIS (recommended QGIS 3.## or newer). Related Datasets Google Earth Engine The PROMICE-2022 Ice Mask (file 06) is available as an asset in the Google Earth Engine Feature Collection Access it here: projects/promice-data-ee/assets/ice_masks/PROMICE_2022_ICE_MASK PROMICE-2022 Ice Mask (Master Dataset) This is the dataset containing the updated PROMICE-2022 ice mask. The QGIS bundle provided here includes this dataset but users seeking the raw data files, metadata, and versioning should refer to the master record. Access it here: https://doi.org/10.22008/FK2/O8CLRE PROMICE-2022 Ice Mask Sentinel-2 RGB Mosaic (August 2022) This dataset provides the Greenland-wide Sentinel-2 RGB mosaic used as the primary visual reference during ice mask delineation. In the QGIS project, the Sentinel-2 images from 2022 are included as a WMS layer, but users who need the underlying mosaic files should reference the dedicated dataset. Access it here: https://doi.org/10.22008/FK2/OUKHBW

Data Usage and Deployment This guide intends to describe how the provided datasets can be used within the environment of ArcGIS version 10.4, although the same rules and guidelines apply for usage in the QGIS and ArcGIS Pro environment. In the ZENODO data repository (accessed at https://doi.org/10.5281/zenodo.17579289), end users can find four compressed files. Use Winrar, Winzip or the Windows embedded decompression tools to open the files and decompress the included filed. The file titled “FSim_Dataset_Greece_LYR_files_ARCGIS_QGIS_ARCGISPRO.rar” contains .lyr files that are an ArcGIS file type that is a container that stores the visualization and metadata properties for a single map layer, such as symbology, labeling, and transparency. It does not contain the actual geographic data itself but acts as a "pointer" to the source data (like a shapefile or a raster). These files can be used withing the ArcGIS environment to assign the proper colors and classes, as portrayed on the maps of the paper. Layer files are available only for raster type datasets. In addition, we provide the layer files that hold the symbology and classes at the format of .qml, for use in QGIS, and .lyrx for use in ArcGIS Pro. The file titled “Metadata.rar” contains all the individual .xml files holding information about the metadata of each dataset, created with the ISO 19139 XML format. The Geodatabase_metadata.xml contains the metadata of the File Geodatabase. The FSim_Geodatabase_Schema_Report is the schema report of the File Geodatabase, created with ArcGIS Pro 3.5 in four readable versions (Excel, JSON, PDF, and HTML). They can be directly opened with any relevant software that supports each file type, and users can navigate to the different sections of metadata information for all datasets included in the File Geodatabase. The file titled “FSim_Dataset_Greece_raw_files.rar” contains all datasets in file formats that are not part of a File Geodatabase, like ESRI shapefiles (.shp) and ERDAS IMAGINE raster filed (.img), intended for use in GIS software that are not owned by the ESRI (like the QGIS). Each dataset contained in the File Geodatabase can be found in this compressed file with the same name, as reported in the paper. Below the image shows how the data in folder appear when viewed inside ArcCatalog. The file titled “FSim_Dataset_Greece.gdb.rar” contains the File Geodatabase, created with ArcGIS version 10.4. The File Geodatabase has all the datasets, including their accompanying metadata. This File Geodatabase can be opened with any version of ArcGIS or ArcGIS Pro. To Open ESRI File (gdb) using GDAL (Geospatial Data Abstraction Library) follow these steps: Install GDAL: If you don’t have GDAL installed on your system, download and install it from the official website (https://gdal.org/download.html). Make sure to get the version that supports the OpenFileGDB driver. Open a command prompt or terminal window: Launch the command prompt (Windows) or terminal (macOS/Linux). Use ogrinfo: To list the layers available in the gdb file, use the ogrinfo command followed by the path to the gdb folder. For example: ogrinfo path/to/your/geodatabase.gdb Replace path/to/your/geodatabase.gdb with the actual path to your gdb file. This command will display information about the layers in the geodatabase. Access specific layers: To access a specific layer within the gdb, you can use the ogr2ogr command to convert the layer to another format, such as a shapefile, GeoJSON, or CSV. For example, to convert a layer named “example_layer” to a shapefile, use: ogr2ogr -f "ESRI Shapefile" output_shapefile.shp path/to/your/geodatabase.gdb example_layer Replace output_shapefile.shp with the desired name for the output shapefile, and path/to/your/geodatabase.gdb with the actual path to your gdb file. This command will convert the “example_layer” to a shapefile format. To open the File Geodatabase in QGIS, follow these steps: 1. Open QGIS: Launch your QGIS application to begin the import process. 2. Access Data Source Manager: Click on the Data Source Manager button in the toolbar. 3. Select Directory: In the Data Source Manager window, click on the Directory tab. Choose Open File GDB as the source type. 4. Browse to the .gdb Folder: Click the Browse button and navigate to the unzipped .gdb folder. Select the folder and click Add. 5. Adding Layers: After adding the folder, you will see the layers contained within the geodatabase. Click Close to finish the process. An example of how the Metadata appear when viewed through ArcCatalog is provided in the figure below: Open the datasets in ArcGIS version 10.4 and use the .lyr files First, open the ArcGIS, navigate to the decompress folder of the File Geodatabase after pressing “Add Data”, and select a dataset (in this case, the Burn Probability raster). Then, double click on the Burn_Prob layer in the Table of Contents, move to Symbology, select “Classified” since all layers have a

In order to use the QGIS plugin ‘Seilaplan’ for digital cable line planning, a digital terrain model (DTM) is required. The plugin ‘Swiss Geo Downloader’, which is available for the open source geoinformation software QGIS, allows freely available Swiss geodata to be downloaded and displayed directly within QGIS. It was developed in 2021 by Patricia Moll in collaboration with the Swiss Federal Institute for Forest, Snow and Landscape Research WSL. In this tutorial we describe how to download the high accuracy elevation model ‘swissALTI3D’ with the help of the ‘Swiss Geo Downloader’ and how to use it for digital planning of a cable line with the plugin ‘Seilaplan’. Please note that the tutorial language is German!

Link to the Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link to Seilaplan website: https://seilaplan.wsl.ch

Für die Verwendung des QGIS Plugins Seilaplan zur digitalen Seillinienplanung ist ein digitales Höhenmodell (DHM) nötig. Das Plugin Swiss Geo Downloader, welches für das Open Source Geoinformationssystem QGIS zur Verfügung steht, ermöglicht frei verfügbare Schweizer Geodaten direkt innerhalb von QGIS herunterzuladen und anzuzeigen. Es wurde 2021 von Patricia Moll in Zusammenarbeit mit der eidgenössischen Forschungsanstalt Wald, Schnee und Landschaft WSL entwickelt. In diesem Tutorial beschreiben wir, wie man mit Hilfe des Swiss Geo Downloaders das hochgenaue Höhenmodell swissALTI3D herunterladen und für die Seillinienplanung mit dem Plugin Seilaplan verwenden kann.

Link zum Swiss Geo Downloader: https://pimoll.github.io/swissgeodownloader

Link zur Seilaplan-Webseite: https://seilaplan.wsl.ch

The Los Angeles County Storm Drain System is a geometric network model representing the storm drain infrastructure within Los Angeles County. The long term goal of this network is to seamlessly integrate the countywide drainage infrastructure, regardless of ownership or jurisdiction. Current uses by the Department of Public Works (DPW) include asset inventory, operational maintenance, and compliance with environmental regulations.GIS DATA DOWNLOADS: (More information is in the table below)File geodatabase: A limited set of feature classes comprise the majority of this geometric network. These nine feature classes are available in one file geodatabase (.gdb). ArcMap versions compatible with the .gdb are 10.1 and later. Read-only access is provided by the open-source software QGIS. Instructions on opening a .gdb file are available here, and a QGIS plugin can be downloaded here.Acronyms and Definitions (pdf) are provided to better understand terms used. ONLINE VIEWING: Use your PC’s browser to search for drains by street address or drain name and download engineering drawings. The Web Viewer link is: https://pw.lacounty.gov/mpm/gis/fcd/More About these Downloads All data added or updated by Public Works is contained in nine feature classes, with definitions listed below. The file geodatabase (.gdb) download contains these eleven feature classes without network connectivity. Feature classes include attributes with unabbreviated field names and domains.ArcMap versions compatible with the .gdb are 10.1 and later. Feature ClassDownloadDescriptionCatchBasinIn .gdbCatch basins collect urban runoff from guttersCulvertIn .gdbA relatively short conduit that conveys storm water runoff underneath a road or embankment. Typical materials include reinforced concrete pipe (RCP) and corrugated metal pipe (CMP). Typical shapes are circular, rectangular, elliptical, or arched.ForceMainIn .gdbForce mains carry stormwater uphill from pump stations into gravity mains and open channels.GravityMainIn .gdbUnderground pipes and channels.LateralLineIn .gdbLaterals connect catch basins to underground gravity mains or open channels.MaintenanceHoleIn .gdbThe top opening to an underground gravity main used for inspection and maintenance.NaturalDrainageIn .gdbStreams and rivers that flow through natural creek bedsOpenChannelIn .gdbConcrete lined stormwater channels.PumpStationIn .gdbWhere terrain causes accumulation, lift stations are used to pump stormwater to where it can once again flow towards the oceanData Field DescriptionsMost of the feature classes in this storm drain geometric network share the same GIS table schema. Only the most critical attributes are listed here per LACFCD operations. AttributeDescriptionASBDATEThe date the design plans were approved “as-built” or accepted as “final records”.CROSS_SECTIN_SHAPEThe cross-sectional shape of the pipe or channel. Examples include round, square, trapezoidal, arch, etc.DIAMETER_HEIGHTThe diameter of a round pipe or the height of an underground box or open channel.DWGNODrain Plan Drawing Number per LACFCD NomenclatureEQNUMAsset No. assigned by the Department of Public Works’ (in Maximo Database).MAINTAINED_BYIdentifies, to the best of LAFCD’s knowledge, the agency responsible for maintaining the structure.MOD_DATEDate the GIS features were last modified.NAMEName of the individual drainage infrastructure.OWNERAgency that owns the drainage infrastructure in question.Q_DESIGNThe peak storm water runoff used for the design of the drainage infrastructure.SOFT_BOTTOMFor open channels, indicates whether the channel invert is in its natural state (not lined).SUBTYPEMost feature classes in this drainage geometric nature contain multiple subtypes.UPDATED_BYThe person who last updated the GIS feature.WIDTHWidth of a channel in feet.Contact email: mapping@dpw.lacounty.gov

Developed by SOLARGIS and provided by the Global Solar Atlas (GSA), this data resource contains photovoltaic power potential (PVOUT) in kWh/kWp covering the globe. Data is provided in a geographic spatial reference (EPSG:4326). The resolution (pixel size) of solar resource data (GHI, DIF, GTI, DNI) is 9 arcsec (nominally 250 m), PVOUT and TEMP 30 arcsec (nominally 1 km) and OPTA 2 arcmin (nominally 4 km). The data is hyperlinked under 'resources' with the following characteristics: PVOUT LTAy_AvgDailyTotals (GeoTIFF) Data format: GEOTIFF File size : 3.6 GB There are two temporal representation of solar resource and PVOUT data available: • Longterm yearly/monthly average of daily totals (LTAym_AvgDailyTotals) • Longterm average of yearly/monthly totals (LTAym_YearlyMonthlyTotals) Both type of data are equivalent, you can select the summarization of your preference. The relation between datasets is described by simple equations: • LTAy_YearlyTotals = LTAy_DailyTotals * 365.25 • LTAy_MonthlyTotals = LTAy_DailyTotals * Number_of_Days_In_The_Month For individual country or regional data downloads please see: https://globalsolaratlas.info/download (use the drop-down menu to select country or region of interest) For data provided in AAIGrid please see: https://globalsolaratlas.info/download/world. For more information and terms of use, please, read metadata, provided in PDF and XML format for each data layer in a download file. For other data formats, resolution or time aggregation, please, visit Solargis website. Data can be used for visualization, further processing, and geo-analysis in all mainstream GIS software with raster data processing capabilities (such as open source QGIS, commercial ESRI ArcGIS products and others).

This .zip file contains pre-configured files for members of the public to interact with Kendall County's public GIS layers in a desktop environment. Included are:An ArcGIS Pro PackageA QGIS Project FIleArcGIS Pro requires an ESRI license to use. See the ArcGIS Pro product page for more information.QGIS is free, open-source software that is available for a variety of computing environments. See the QGIS Downloads page to select the appropriate installation method.With the appropriate software installed, users can simply open the corresponding file. It may take a minute or two to load, due to the number of layers that need to load. Once loaded, users will have read-only access to all of the major public layers, and can adjust how they are displayed. In a desktop environment, users can also create and interact with other data sources, such as private site plans, annotations, and other public data layers from non-County entities.Please note that the layers included in these packages are the same live data sources found in the web maps. An internet connection is required for these files to function properly.

Mosaics are published as ArcGIS image serviceswhich circumvent the need to download or order data. GEO-IDS image services are different from standard web services as they provide access to the raw imagery data. This enhances user experiences by allowing for user driven dynamic area of interest image display enhancement, raw data querying through tools such as the ArcPro information tool, full geospatial analysis, and automation through scripting tools such as ArcPy. Image services are best accessed through the ArcGIS REST APIand REST endpoints (URL's). You can copy the OPS ArcGIS REST API link below into a web browser to gain access to a directory containing all OPS image services. Individual services can be added into ArcPro for display and analysis by using Add Data -> Add Data From Path and copying one of the image service ArcGIS REST endpoint below into the resultant text box. They can also be accessed by setting up an ArcGIS server connectionin ESRI software using the ArcGIS Image Server REST endpoint/URL. Services can also be accessed in open-source software. For example, in QGIS you can right click on the type of service you want to add in the browser pane (e.g., ArcGIS REST Server, WCS, WMS/WMTS) and copy and paste the appropriate URL below into the resultant popup window. All services are in Web Mercator projection. For more information on what functionality is available and how to work with the service, read the Ontario Web Raster Services User Guide. If you have questions about how to use the service, email Geospatial Ontario (GEO) at geospatial@ontario.ca Available Products: ArcGIS REST APIhttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/ Image Service ArcGIS REST endpoint / URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer https://ws.geoservices.lrc.gov.on.ca/arcgis5/rest/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServerWeb Coverage Services (WCS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WCSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WCSServer/Web Mapping Service (WMS) URL'shttps://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2013to2017/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2018to2022/ImageServer/WMSServer/https://ws.geoservices.lrc.gov.on.ca/arcgis5/services/AerialImagery/GEO_Imagery_Data_Service_2023to2027/ImageServer/WMSServer/ Metadata for all imagery products available in GEO-IDS can be accessed at the links below:South Central Ontario Orthophotography Project (SCOOP) 2023North-Western Ontario Orthophotography Project (NWOOP) 2022 Central Ontario Orthophotography Project (COOP) 2021 South-Western Ontario Orthophotography Project (SWOOP) 2020 Digital Raster Acquisition Project Eastern Ontario (DRAPE) 2019-2020 South Central Ontario Orthophotography Project (SCOOP) 2018 North-Western Ontario Orthophotography Project (NWOOP) 2017 Central Ontario Orthophotography Project (COOP) 2016 South-Western Ontario Orthophotography Project (SWOOP) 2015 Algonquin Orthophotography Project (2015) Additional Documentation: Ontario Web Raster Services User Guide (Word) Status:Completed: Production of the data has been completed Maintenance and Update Frequency:Annually: Data is updated every year Contact:Geospatial Ontario (GEO), geospatial@ontario.ca

Introduction

We are enclosing the database used in our research titled "Concentration and Geospatial Modelling of Health Development Offices' Accessibility for the Total and Elderly Populations in Hungary", along with our statistical calculations. For the sake of reproducibility, further information can be found in the file Short_Description_of_Data_Analysis.pdf and Statistical_formulas.pdf

The sharing of data is part of our aim to strengthen the base of our scientific research. As of March 7, 2024, the detailed submission and analysis of our research findings to a scientific journal has not yet been completed.

The dataset was expanded on 23rd September 2024 to include SPSS statistical analysis data, a heatmap, and buffer zone analysis around the Health Development Offices (HDOs) created in QGIS software.

Short Description of Data Analysis and Attached Files (datasets):

Our research utilised data from 2022, serving as the basis for statistical standardisation. The 2022 Hungarian census provided an objective basis for our analysis, with age group data available at the county level from the Hungarian Central Statistical Office (KSH) website. The 2022 demographic data provided an accurate picture compared to the data available from the 2023 microcensus. The used calculation is based on our standardisation of the 2022 data. For xlsx files, we used MS Excel 2019 (version: 1808, build: 10406.20006) with the SOLVER add-in.

Hungarian Central Statistical Office served as the data source for population by age group, county, and regions: https://www.ksh.hu/stadat_files/nep/hu/nep0035.html, (accessed 04 Jan. 2024.) with data recorded in MS Excel in the Data_of_demography.xlsx file.

In 2022, 108 Health Development Offices (HDOs) were operational, and it's noteworthy that no developments have occurred in this area since 2022. The availability of these offices and the demographic data from the Central Statistical Office in Hungary are considered public interest data, freely usable for research purposes without requiring permission.

The contact details for the Health Development Offices were sourced from the following page (Hungarian National Population Centre (NNK)): https://www.nnk.gov.hu/index.php/efi (n=107). The Semmelweis University Health Development Centre was not listed by NNK, hence it was separately recorded as the 108th HDO. More information about the office can be found here: https://semmelweis.hu/egeszsegfejlesztes/en/ (n=1). (accessed 05 Dec. 2023.)

Geocoordinates were determined using Google Maps (N=108): https://www.google.com/maps. (accessed 02 Jan. 2024.) Recording of geocoordinates (latitude and longitude according to WGS 84 standard), address data (postal code, town name, street, and house number), and the name of each HDO was carried out in the: Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file.

The foundational software for geospatial modelling and display (QGIS 3.34), an open-source software, can be downloaded from:

https://qgis.org/en/site/forusers/download.html. (accessed 04 Jan. 2024.)

The HDOs_GeoCoordinates.gpkg QGIS project file contains Hungary's administrative map and the recorded addresses of the HDOs from the

Geo_coordinates_and_names_of_Hungarian_Health_Development_Offices.csv file,

imported via .csv file.

The OpenStreetMap tileset is directly accessible from www.openstreetmap.org in QGIS. (accessed 04 Jan. 2024.)

The Hungarian county administrative boundaries were downloaded from the following website: https://data2.openstreetmap.hu/hatarok/index.php?admin=6 (accessed 04 Jan. 2024.)

HDO_Buffers.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding buffer zones with a radius of 7.5 km.

Heatmap.gpkg is a QGIS project file that includes the administrative map of Hungary, the county boundaries, as well as the HDO offices and their corresponding heatmap (Kernel Density Estimation).

A brief description of the statistical formulas applied is included in the Statistical_formulas.pdf.

Recording of our base data for statistical concentration and diversification measurement was done using MS Excel 2019 (version: 1808, build: 10406.20006) in .xlsx format.

• Aggregated number of HDOs by county: Number_of_HDOs.xlsx
• Standardised data (Number of HDOs per 100,000 residents): Standardized_data.xlsx
• Calculation of the Lorenz curve: Lorenz_curve.xlsx
• Calculation of the Gini index: Gini_Index.xlsx
• Calculation of the LQ index: LQ_Index.xlsx
• Calculation of the Herfindahl-Hirschman Index: Herfindahl_Hirschman_Index.xlsx
• Calculation of the Entropy index: Entropy_Index.xlsx
• Regression and correlation analysis calculation: Regression_correlation.xlsx

Using the SPSS 29.0.1.0 program, we performed the following statistical calculations with the databases Data_HDOs_population_without_outliers.sav and Data_HDOs_population.sav:

• Regression curve estimation with elderly population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_elderly_without_outlier.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county, excluding outlier values such as Budapest and Pest County: Pearson_Correlation_populations_HDOs_number_without_outliers.spv.
• Dot diagram including total population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_total_population_without_outliers.spv.
• Dot diagram including elderly (64<) population and number of HDOs per county, excluding outlier values such as Budapest and Pest Counties: Dot_HDO_elderly_population_without_outliers.spv
• Regression curve estimation with total population and number of HDOs, excluding outlier values (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_without_outlier.spv
• Dot diagram including elderly (64<) population and number of HDOs per county: Dot_HDO_elderly_population.spv
• Dot diagram including total population and number of HDOs per county: Dot_HDO_total_population.spv
• Pearson correlation table between the total population, elderly population, and number of HDOs per county: Pearson_Correlation_populations_HDOs_number.spv
• Regression curve estimation with total population and number of HDOs, (Types of analyzed equations: Linear, Logarithmic, Inverse, Quadratic, Cubic, Compound, Power, S, Growth, Exponential, Logistic, with summary and ANOVA analysis table): Curve_estimation_total_population.spv

For easier readability, the files have been provided in both SPV and PDF formats.

The translation of these supplementary files into English was completed on 23rd Sept. 2024.

If you have any further questions regarding the dataset, please contact the corresponding author: domjan.peter@phd.semmelweis.hu

The dataset contains a map of the main classes of agricultural land use (dominant crop types and other land use types) in Germany for the year 2022. It complements a series of maps that are produced annually at the Thünen Institute beginning with the year 2017 on the basis of satellite data. The maps cover the entire open landscape, i.e., the agriculturally used area (UAA) and e.g., uncultivated areas. The map was derived from time series of Sentinel-1, Sentinel-2, Landsat 8 and additional environmental data. Map production is based on the methods described in Blickensdörfer et al. (2022). All optical satellite data were managed, pre-processed and structured in an analysis-ready data (ARD) cube using the open-source software FORCE - Framework for Operational Radiometric Correction for Environmental monitoring (Frantz, D., 2019), in which SAR and environmental data were integrated. The map extent covers all areas in Germany that are defined as agricultural land, grassland, small woody features, heathland, peatland or unvegetated areas according to ATKIS Basis-DLM (Geobasisdaten: © GeoBasis-DE / BKG, 2020). Version v201: Post-processing of the maps included a sieve filter as well as a ruleset for the reduction of non-plausible areas using the Basis-DLM and the digital terrain model of Germany (Geobasisdaten: © GeoBasis-DE / BKG, 2015). The final post-processing step comprises the aggregation of the gridded data to homogeneous objects (fields) based on the approach that is described in Tetteh et al. (2021) and Tetteh et al. (2023). The maps are available in FlatGeobuf format, which makes downloading the full dataset optional. All data can directly be accessed in QGIS, R, Python or any supported software of your choice using the URL to the datasets that will be provided on request. By doing so the entire map area or only the regions of interest can be accessed. QGIS legend files for data visualization can be downloaded separately. Class-specific accuracies for each year are proveded in the respective tables. We provide this dataset "as is" without any warranty regarding the accuracy or completeness and exclude all liability. References: Blickensdörfer, L., Schwieder, M., Pflugmacher, D., Nendel, C., Erasmi, S., & Hostert, P. (2022). Mapping of crop types and crop sequences with combined time series of Sentinel-1, Sentinel-2 and Landsat 8 data for Germany. Remote Sensing of Environment, 269, 112831. BKG, Bundesamt für Kartographie und Geodäsie (2015). Digitales Geländemodell Gitterweite 10 m. DGM10. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/dgm10.pdf (last accessed: 28. April 2022). BKG, Bundesamt für Kartographie und Geodäsie (2020). Digitales Basis-Landschaftsmodell. https://sg.geodatenzentrum.de/web_public/gdz/dokumentation/deu/basis-dlm.pdf (last accessed: 28. April 2022). Frantz, D. (2019). FORCE—Landsat + Sentinel-2 Analysis Ready Data and Beyond. Remote Sensing, 11, 1124. Tetteh, G.O., Gocht, A., Erasmi, S., Schwieder, M., & Conrad, C. (2021). Evaluation of Sentinel-1 and Sentinel-2 Feature Sets for Delineating Agricultural Fields in Heterogeneous Landscapes. IEEE Access, 9, 116702-116719. Tetteh, G.O., Schwieder, M., Erasmi, S., Conrad, C., & Gocht, A. (2023). Comparison of an Optimised Multiresolution Segmentation Approach with Deep Neural Networks for Delineating Agricultural Fields from Sentinel-2 Images. PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science

This set of maps accompanies my related publication, entitled 'A Land Cover Atlas of the United Kingdom', https://doi.org/10.15131/shef.data.5266495, which was published at the same time.Some of the maps in this set feature in the Atlas, but at a lower resolution. I have deposited them here as high resolution images (300dpi PNG files) so that interested users can access and download them.These maps were created using open data and open source software (QGIS) and you are free to use them as you wish.There is one map for each of the 391 Local Authority areas of the United Kingdom.These maps are open data, but the provider of the Corine Land Cover data featured here requires the following statement to be cited when using it:“Copyright rests with the European Commission; Acknowledgement: Produced by the University of Leicester, The Centre for Landscape and Climate Research and Specto Natura and supported by Defra and the European Environment Agency under Grant Agreement 3541/B2012/R0-GIO/EEA.55055 with funding by the European Union. If you reuse the underlying data, you should cite: Cole, B., King, S., Ogutu, B., Palmer, D., Smith, G., Balzter, H. (2015). Corine Land Cover 2012 for the UK, Jersey and Guernsey. NERC Environmental Information Data Centre https://doi.org/10.5285/32533dd6-7c1b-43e1-b892-e80d61a5ea1dThis resource is made available under the terms of the Open Government Licence (http://eidc.ceh.ac.uk/administration-folder/tools/ceh-standard-licence-texts/open-government-licence-corine/plain)”.

Facilities and features in Chicago parks. For more information, visit http://www.chicagoparkdistrict.com/facilities/search/. To view or use these shapefiles, compression software and special GIS software, such as ESRI ArcGIS or QGIS, is required. To download this file, right-click the "Download" link above and choose "Save link as."

Er zijn een aantal lagen met de voorzieningen in Rotterdam beschikbaar als webservice volgens de standaarden WMS/WFS. De informatie wordt rechtstreeks uit de database getoond.

Het webadres van de service voor gebruik in Gis-programma's is:

http://ows.gis.rotterdam.nl/cgi-bin/mapserv.exe?map=d:\gwr\webdata\mapserver\map\vzg_pub.map

(Let op: deze link kun je dus alleen in Gis-programma's toepassen. Als je hem in je browser aanklikt lijkt het alsof hij niet werkt, maar hij werkt dus wel).

Meer informatie over het gebruik van geoservices

Voorbeelden van (gratis) software waarmee je de webservice kunt benaderen zijn: - QGIS - gvSIG - uDig

Handleiding voor het gebruik van Open Geodata (.doc)

1 Vraag informatie over de webservice:

http://ows.gis.rotterdam.nl/cgi-bin/mapserv.exe?map=d:%5Cgwr%5Cwebdata%5Cmapserver%5Cmap%5Cvzg_pub.map&service=wms&request=getcapabilities

2 Haal kaart op:

http://ows.gis.rotterdam.nl/cgi-bin/mapserv.exe?map=d:%5Cgwr%5Cwebdata%5Cmapserver%5Cmap%5Cvzg_pub.map&LAYERS=sdo_rdam_vzg_11686&STYLES=&VERSION=1.3.0&REQUEST=GetMap&CRS=EPSG:28992&BBOX=91187.177790573,435905.88627779,93735.113914687,437929.94768479&WIDTH=963&HEIGHT=765&FORMAT=image/png&TRANSPARENT=FALSE&BGCOLOR=0xFFFFFF&SERVICE=WMS

High-resolution maps of the Köppen-Geiger climate classification for historical and future climate conditions (Beck et al., 2023).IMPORTANT: Most users only need the "koppen_geiger_tif.zip" archive (125 MB). It includes GeoTIFFs of Köppen-Geiger maps in varying resolutions for multiple periods and future socio-economic scenarios.For example, the file "2071_2099\ssp585\koppen_geiger_0p00833333.tif" in the archive provides the Köppen-Geiger map for the period 2071–2099 under the SSP5-8.5 scenario with a resolution of 0.00833333° (approximately 1 km). GeoTIFF files are easily viewed using Geographic Information System (GIS) software such as QGIS.The "koppen_geiger_tif.zip" archive also contains a legend file, "legend.txt", which links the numeric values in the maps to the Köppen-Geiger climate symbols and provides the color scheme used for displaying the maps.The other archives contain underlying climate data in different resolutions. For details on these archives, please refer to the 'Data Records' section in Beck et al. (2023).Please cite Beck et al. (2023) when using the maps in any publication:Beck, H. E., T. R. McVicar, N. Vergopolan, A. Berg, N. J. Lutsko, A. Dufour, Z. Zeng, X. Jiang, A. I. J. M. van Dijk, and D. G. Miralles. High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections. Scientific Data 10, 724 (2023).Note: This dataset has been updated in January 2026 to correct a small calculation error. Full details are available in the Correction associated with the aforementioned Scientific Data article.

Ecco un'immagine dell'aliquota fiscale comunale complessiva (foncier bati + abitazione, per comuni e intercomuni). http://physaphae.noip.me/Img/2015_Rate_54" alt="Tasso d'imposta locale 54 del 2015" title="Tasso d'imposta locale 54 del 2015">

Dato che è alla maglia dipartimentale, non è utile includere la tariffa dipartimentale e la tariffa nazionale. Questo non farebbe parte del paragone.

Per farlo di nuovo da soli avrete bisogno di: - Software QQGIS (gratuito: https://www.qgis.org/it/site/forusers/download.html), - un file qgs del vostro dipartimento (http://www.actualitix.com/shapefiles-des-departements-de-france.html) - un'esportazione di aliquote d'imposta (https://www.data.gouv.fr/en/datasets/local taxes/)

Procedura: Installare QGIS Apri il .qgs del tuo dipartimento

Aggiungi colonne - Proprietà con il tasto destro del mouse sul livello principale - Vai al menu dei campi (a sinistra) - Aggiungere (tramite la matita) le colonne desiderate (qui aliquota fiscale comunale, terreni edificati intercomunitari e abitazioni) - Questi sono reali di una precisione 2, e una lunghezza 4 - Registrazione

Inserire i dati: - Fare clic con il pulsante destro del mouse sul livello "Apri tabella degli attributi" - Seleziona tutto - Copia - Incollare in excel (o openOffice calcs) - Mettere le formule ad hoc in excel (SUM.SI.ENS per recuperare il tasso) - Salvare la scheda desiderata in CSV DOS con i nuovi valori - In QGIS > Menu > Livello > Aggiungi un livello di testo delimitato - Importare il CSV

Presentare i dati: - Per semplificare vi consiglio di fare un livello per tasso, e i livelli sono. Così si marcisce in tre clic tirare fuori l'immagine della velocità desiderata - Per ogni strato (o tasso) - Proprietà del clic destro sul livello csv - Etichette per aggiungere il nome della città e la tariffa desiderata - Stile per la colorazione in fct di un campo csv

Stampa i dati in pdf: - Per stampare, è necessario definire un modello di stampa - Nel menu scegliere il nuovo dialler di stampa - scegliere il formato (un dipartimento in A0 è piuttosto leggibile) - Aggiungi vas legend, ladder e altro - Stampa e voilà...

Aqui está uma imagem da alíquota global do imposto municipal (foncier bati + habitação, para municípios e inter-municípios). http://physaphae.noip.me/Img/2015_Rate_54%20%C2%ABTaxa%20de%20imposto%20local%2054%20de%202015%C2%BB" alt="Taxa de imposto local 54 de 2015">

Dado que está na malha departamental, não é útil incluir a taxa departamental e nacional... Isto não faria parte da comparação.

Para voltar a fazê-lo sozinho, precisará de: - Software QQGIS (Gratuito: https://www.qgis.org/en/site/forusers/download.html), - um ficheiro qgs do seu departamento (http://www.actualitix.com/shapefiles-des-departements-de-france.html) - uma exportação de taxas de imposto (https://www.data.gouv.fr/en/datasets/local taxes/)

Tramitação processual: Instalar o QGIS Abra o .qgs do seu departamento

Acrescentar colunas - Propriedade com o botão direito do rato na camada principal - Vá para o menu de campos (à esquerda) - Adicione (através do lápis) as colunas desejadas (aqui taxa de imposto municipal, terrenos intercomunitários construídos e habitação) - Estes são reais de uma precisão 2, e um comprimento 4 - Registe-se

Inserir dados: - Clique com o botão direito do rato na camada "Abrir tabela de atributos" - Selecionar todos - Cópia - Colar em excel (ou openOffice calcs) - Coloque as fórmulas ad hoc em Excel (SUM.SI.ENS para recuperar a taxa) - Salve a guia desejada no CSV DOS com os novos valores - No QGIS > Menu > Camada > Adicionar uma camada de texto delimitada - Importar o CSV

Apresentar os dados: - Para simplificar, aconselho-o a fazer uma camada por taxa, e as camadas são. Assim, apodrece-o em três cliques tirar a imagem da taxa desejada - Para cada camada (ou taxa) - Propriedades do botão direito do rato na camada csv - Etiquetas para adicionar o nome da cidade e a tarifa desejada - Estilo para colorir no fct de um campo do csv

Imprimir os dados em pdf: - Para imprimir, é necessário definir um modelo de impressão - No menu, escolha um novo marcador de impressão - escolher o formato (um departamento em A0 é bastante legível) - Adicione a lenda do vas, a escada, e o outro - Imprimir e voilá...

Das Digitale Geländemodell (DGM) ist ein Folgeprodukt aus den 3D-Messdaten. Es beschreibt die Geländeoberfläche, das Relief der Erde, durch die räumlichen Koordinaten einer repräsentativen Menge von Geländepunkten zum Erfassungszeitraum. OGC GeodatendiensteWMShttps://opendata.lgln.niedersachsen.de/doorman/noauth/dgm_wms Anleitung STAC-APIKatalog URL:https://dgm.stac.lgln.niedersachsen.deOpenAPI Service Beschreibung:https://dgm.stac.lgln.niedersachsen.de/api.html MassendownloadGeoJSON Anleitung KoordinatenreferenzsystemLage: EPSG 25832 (ETRS89/UTM 32N)Höhe: EPSG 7837 (DHHN2016 mit Normalhöhen-Null (NHN)) Metadatenhttps://ni-harvest-prod.geocat.live DatenformatCloud-Optimized GeoTiff (COG) Anleitung Konvertierung in XYZ Anleitung Konvertierung in Punkt-Shape-Datei Aktualität Übersicht Dateigröße<5 MB je Kachel Auflösung1 m Kachelgröße1 x 1 km Produkt- und FormatbeschreibungGemäß Produkt- und Qualitätsstandard der Arbeitsgemeinschaft der Vermessungsverwaltungen der Länder der Bundesrepublik Deutschland (https://www.adv-online.de/AdV-Produkte/Standards-und-Produktblaetter/Standards-der-Geotopographie/) Softwareempfehlungkostenfrei:QGISkommerziell:FMEesri ArcGIS BeschreibungDas Digitale Geländemodell (DGM) ist ein Folgeprodukt aus den 3D-Messdaten. Es beschreibt die Geländeoberfläche, das Relief der Erde, durch die räumlichen Koordinaten einer repräsentativen Menge von Geländepunkten zum Erfassungszeitraum. Höheninformationen werden maßstabsunabhängig und datenverarbeitungsgerecht vorgehalten.Auf Grundlage der seit 2019 niedersachsenweit verfügbaren Laserscan-Punktwolken aus Airborne Laserscaning (ALS), die eine geometrische Auflösung von mindestens 4 Punkten/m² aufweisen, wird ein hochgenaues DGM in 1 x 1 km Kacheln bereitgestellt. Die Rasterweite beträgt 1m (DGM1) und die Rasterelementposition liegt im Zentrum auf 0,5 m Positionen (= Pixelmitte). Die Höhengenauigkeit des DGM1 beträgt für flaches bis wenig geneigtes, offenes Gelände ≤15 cm und bei stark geneigtem Gelände mit dichter Vegetation ≤ 30 cm. Diese wurden über eine Delaunay-Triangulation aus der klassifizierten ALS-Punktwolke bestimmt. Das so entstandene COG ist in 32 Bit mit Float-Werten codiert und wurde über das Verfahren LZW komprimiert. Leere Pixel (NoData) enthalten den Wert -9999.EinsatzmöglichkeitenDas Digitale Geländemodell (DGM1) ist u. a. verwendbar fürFachinformationssystemenSimulation von Hochwasser und WindeinflüssenBodenkundlichen ReliefanalysenSchummerungs- und HöhenliniendarstellungenTrassenplanungen, Profildarstellungen und VolumenberechnungenEmissions- und Immissionsberechnungen, FunknetzplanungenForschung und Lehre Ausführliche Produktbeschreibung Brauchen Sie Unterstützung?Für fachliche Fragestellungen zu dem Produkt, sehen Sie bitte in das FAQ.Hierfinden Sie eine Anleitung, wie Sie einen WebMapService (WMS) in eine Software (hier in QGIS) einbinden und nutzen können.In unseren Anleitungen finden Sie weitere Informationen, wie eine STAC-API verwendet werden kann. Für eine schnelle visuelle Darstellung des STAC kann der Radiant Earth STAC-Viewer verwendet werdenFür eine Nutzung der STAC-API in QGIS können Sie das QGIS-Plugin "QGIS STAC API-Browser" verwenden.In ArcGIS Pro können Sie ab der Version 3.2 STAC API Verbindungen herstellen. Hierfinden Sie eine Anleitung für den Massendownload. Sind die Daten für Sie hilfreich?Feedback zum Produkt