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.

The High Resolution Digital Elevation Model (HRDEM) product is derived from airborne LiDAR data (mainly in the south) and satellite images in the north. The complete coverage of the Canadian territory is gradually being established. It includes a Digital Terrain Model (DTM), a Digital Surface Model (DSM) and other derived data. For DTM datasets, derived data available are slope, aspect, shaded relief, color relief and color shaded relief maps and for DSM datasets, derived data available are shaded relief, color relief and color shaded relief maps. The productive forest line is used to separate the northern and the southern parts of the country. This line is approximate and may change based on requirements. In the southern part of the country (south of the productive forest line), DTM and DSM datasets are generated from airborne LiDAR data. They are offered at a 1 m or 2 m resolution and projected to the UTM NAD83 (CSRS) coordinate system and the corresponding zones. The datasets at a 1 m resolution cover an area of 10 km x 10 km while datasets at a 2 m resolution cover an area of 20 km by 20 km. In the northern part of the country (north of the productive forest line), due to the low density of vegetation and infrastructure, only DSM datasets are generally generated. Most of these datasets have optical digital images as their source data. They are generated at a 2 m resolution using the Polar Stereographic North coordinate system referenced to WGS84 horizontal datum or UTM NAD83 (CSRS) coordinate system. Each dataset covers an area of 50 km by 50 km. For some locations in the north, DSM and DTM datasets can also be generated from airborne LiDAR data. In this case, these products will be generated with the same specifications as those generated from airborne LiDAR in the southern part of the country. The HRDEM product is referenced to the Canadian Geodetic Vertical Datum of 2013 (CGVD2013), which is now the reference standard for heights across Canada. Source data for HRDEM datasets is acquired through multiple projects with different partners. Since data is being acquired by project, there is no integration or edgematching done between projects. The tiles are aligned within each project. The product High Resolution Digital Elevation Model (HRDEM) is part of the CanElevation Series created in support to the National Elevation Data Strategy implemented by NRCan. Collaboration is a key factor to the success of the National Elevation Data Strategy. Refer to the “Supporting Document” section to access the list of the different partners including links to their respective data.

This dataset supports the METRO·MIX research project, which investigates urban proximity and land-use mix as foundational criteria for promoting healthier, more compact metropolitan areas. The data are organized into three main components: General Data, City Data, and 15-Minute City Data, covering the Spanish cities of Barcelona, Madrid, Málaga, and A Coruña between 2021 and 2023. The General Data includes harmonized national-scale information derived from cadastral records and demographic statistics provided by the Spanish Land Registry and the National Institute of Statistics (INE), with variables such as land use categories, building function, population structure, and socioeconomic indicators. The City Data component provides spatially disaggregated information at the census section level for each city, integrating official records with field-collected data on urban functions. This data was processed to derive indices such as the Residential/Non-Residential Balance (RNR Index) and the Land Use Mix Index (LUM Index), facilitating comparative urban analysis. The 15-Minute City Data focuses on neighborhood-scale accessibility and functional diversity, particularly in Barcelona. It incorporates high-resolution, geolocated data on ground-floor commercial activities, categorized and verified through in-situ fieldwork and a custom-built mobile application. All datasets were processed using GIS software (QGIS 3.32) and validated through a multi-step quality control process, including spatial checks, field verification, and harmonization protocols. The dataset is structured in open formats (CSV, GeoJSON, Shapefiles) and intended to support further analysis in urban planning, geography, and public health research. Tablas en formato CSV Los datos utilizados como fuente se encuentra de libre acceso

Introduction

This travel time matrix records travel times and travel distances for routes between all centroids (N = 13132) 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 13132 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: ID number of the origin grid cell
to_id: ID number of the destination grid cell
walk_avg: Travel time in minutes from origin to destination by walking at an average speed
walk_slo: Travel time in minutes from origin to destination by walking slowly
bike_avg: Travel time in minutes from origin to destination by cycling at an average speed
bike_fst: Travel time in minutes from origin to destination by cycling fast
bike_slo: Travel time in minutes from origin to destination by cycling slowly
pt_r_avg: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at an average speed
pt_r_slo: Travel time in minutes from origin to destination by public transportation in rush hour traffic, walking at a slower speed
pt_m_avg: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at an average speed
pt_m_slo: Travel time in minutes from origin to destination by public transportation in midday traffic, walking at a slower speed
pt_n_avg: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at an average speed
pt_n_slo: Travel time in minutes from origin to destination by public transportation in nighttime traffic, walking at a lower speed
car_r: Travel time in minutes from origin to destination by private car in rush hour traffic
car_m: Travel time in minutes from origin to destination by private car in midday traffic
car_n: Travel time in minutes from origin to destination by private car in nighttime traffic
walk_d: Distance from origin to destination, in meters, on foot

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 the Helsinki Regional Transport Authority, and adjusted the walking speeds (for connections between vehicles, as well as for access and egress to and from public transport stops) using the same methods as described above for walking.

Private motorcar

To represent road speeds actually driven in the Helsinki metropolitan region, we used floating car data of a representative sample of the roads in the region to derive the differences between the speed limit and the driven speed on different road classes, and by speed limit, see Perola (2023) for a detailed description of the methodology. Because these per-segment speeds factor in potential waiting times at road crossings, we eliminated turn penalties from R5.

Our modifications were carried out in two ways: some changes can

Datasets associated to the report titled 'Shore Channel Sedimentary Processes, Passability by Migrating Fish and Habitat Suitability'.Summary:The current study consists of three parts (1) an analysis of the sedimentary processes in the shore channels along the longitudinal dams in the River Waal (LTDs), (2) an assessment of the upstream passability of the shore channel inflows by migratory fish species, and (3) an analysis of the habitat suitability of the shore channels for fish, macroinvertebrates and macrophytes. For the first analysis, light detection and ranging (LiDAR), multibeam echosounder (MBES), and aerial photographs datasets were used to examine geomorphological processes (erosion and deposition), calculate the retreat rate of eroding banklines, and analyze the development of shoreline length over time in the mesohabitats of shore channels and reference study areas. The second part of the analysis focused on the use of acoustic doppler current profiler (ADCP) datasets to produce 3D lattices of flow velocity in the inflow openings of shore channels at high river discharge. This was combined with data and linear relations from scientific literature on the swimming performance of relevant migratory fish species in the Rhine. The third part consisted in assessing the habitat suitability of the shore channel with the data on substrate, water depth and flow velocity collected in 2020. This was done using the species sensitivity distributions (SSDs) available in the scientific literature for fish, macroinvertebrates and macrophytes occurring in the Rhine. The produced substrate maps of May 2020 were compared with substrate maps of April 2019. The main conclusions of the studies are:1. The shore channels of the LTDs showed a pattern of aggradation of the bed towards the dams and degradation towards the bank. From 2015 to 2019 there was net sediment loss in all three shore channels with Wamel having the least and Dreumel themost. Compared to the groyne field areas the Wamel shore channel is almost stable. The eroding banklines had a retreat rate of 1.6 m/y and the sand dominated mesohabitats in the shore channels had longer shorelines.2. Larger juveniles (TL = 70 mm) of fish species occurring in the Rhine passed some of the study sites during high discharge conditions and performed better during average discharge conditions. Adult fish had no problems passing the inflow opening, with the exception of Gasterosteus aculeatus aculeatus. Fish species were able to pass all 10 3D lattices produced once they reached a minimum TL of about 165 mm. The inflow of the Ophemert shore channel was the least passable of the LTDs.3. The habitats in the center and bank lines of the shore channels were most suitable for all species groups studied because of substrate heterogeneity, shallow water and relatively low flow velocities. All three shore channels had more mixed substrate typein 2020 than in 2019.The dataset includes:1. Sedimentary Processes.zip includes:a. ErosionDeposition.zip: file with the GeoTIFF files of the erosion and deposition analysis results for all of the periods (files name: SubtractionYear1_Year2Location_Channel or vegetated bank(VegBank).tif; coordinate system: Amersfoort/RD New; opens with ArcGIS or QGIS).b. ErodedBankline.zip: file with the manually digitized bankline shapefiles per year (file name: Location_Year.shp; coordinate system: Amersfoort/RD New; opens with ArcGIS or QGIS).c. ShorelineLength.zip: file with all of the shoreline length analysis shapefiles (file name: MonthYear_Mesohabitats_NoStony if boulder areas are not included. shp; coordinate system: Amersfoort/RD New; opens with ArcGIS or QGIS).d. DTMs.zip: file with the GeoTIFF files (files name: Location_rYear_NN (gridding method Natural Neighbor).tif; coordinate system: Amersfoort/RD New; opens with ArcGIS or QGIS) of the combined LiDAR and MBES DTMs produced.2. Passability.zip includes:a. Passability_Lattices_FishSpecies.zip: file containing the images (.png; opens with Photos) of the 3D lattices of the final swimming speed for all of the fish species assessed.b. FlowVelocity.zip: file containing the images (.png; opens with Photos) of the 3D lattices of the flow velocities per year and location assessed.3. FINAL_Rasters.zip: file containing the GeoTIFF files (.tif; reference coordinate system: WGS84; opens with ArcGIS or QGIS) for the water depth (Depth_Clipped_WGS84_March2020 folder), flow velocity (Flow Velocity_Clipped_WGS84) and substrate types for allof the study sites. Substrate files have the Potentially Occurring Fraction of EPT macroinvertebrates (Substrate_EPTs_WGS84 folder) or mussels (Substrate_Mussels_WGS84 folder) as cell values.4. SubstrateClassification.zip: file with all of the substrate classification polygon shapefiles (reference coordinate system: WGS84; opens with ArcGIS or QGIS).

This is a gridded dataset of monthly industrial water withdrawal (IWW) for China, namely, the China industrial water withdrawal dataset (CIWW). The dataset begins in January 1965 and is ongoing (currently up to December 2020) with a temporal resolution of a month and a spatial resolution of 0.1°/0.25°. The CIWW dataset, together with its auxiliary data, will be useful for water resource management and hydrological models.Version history:V1.1_20240403Update the seasonal variability.Compared to version 1.0, we estimated the seasonality of the subsector (Electricity and Heating Power Production and Supply,) based on spatial classification and then recreated the CIWW data with the updated seasonal variability. More details are described in Hou et al. (2023). The seasonal variation in the updated version is less different from the previous one.V1.0_20230209Using notes：Updated notes about opening the data with ArcGIS and other software (Jan 13, 2025)When opening the CIWW dataset (NetCDF format) in ArcGIS, the following issues may appear, as reported by users:1) The file cannot be successfully opened in ArcGIS.2) The time dimension value could not be properly displayed (e.g., time fixed to January 1, 1965).a) For ArcGIS users, it is recommended to utilize the Multidimension Tools in the toolbox and select the Make NetCDFRaster Layer tool. During the import process:Choose iww_layer as the variable.Select time as the third dimension in addition to longitude and latitude.After importing, open the Properties of the layer and navigate to the Symbology tab. You will see 672 different bands, representing the monthly data from January 1965 to December 2020.If the dataset is directly dragged into ArcGIS, the variable cell_area will be opened by default. This variable represents the area of each grid cell at a resolution of 0.25°/0.1° within the longitude and latitude range of China. The industrial water withdrawal is provided in units of mm/month. If needed, you can convert this to m³/month by multiplying the values by the corresponding grid cell area. For detailed variable descriptions, refer to the readme.txt file.b) The CIWW dataset can be opened using QGIS. Users can select the relevant dimensions and drag the dataset directly into QGIS. The time dimension includes 672 bands, with each band representing the number of days since January 1, 1965.c) The NetCDF format CIWW dataset can be easily opened by any programing language with NetCDF capabilities, for example, the xarry package in Python, Matlab, R, and others).Authors: Chengcheng Hou (cch@mail.bnu.edu.cn), Yan Li (yanli@bnu.edu.cn).Reference: Hou, C., Li, Y., Sang, S., Zhao, X., Liu, Y., Liu, Y., and Zhao, F.: High-resolution mapping of monthly industrial water withdrawal in China from 1965 to 2020, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2023-66, in review, 2023.

The primary objective of this study was to predict the existing geographic range of Liquidambar orientalis, commonly known as the oriental sweetgum. To gain insights into the potential effects of climate change on the oriental sweetgum, the study employed species distribution models to project the model to future periods. Considering two Shared Socioeconomic Pathways (SSP1-2.6 and SSP5-8.5), the ensemble modeling approach utilized the biomod2 package in the R programming language to analyze the alterations in the spatial distribution of the species in forthcoming periods (namely, for the years 2035s, 2055s, and 2070s). , 1. Occurrence data 81 occurrence data were obtained from two reputable sources: the Global Biodiversity Information Facility (GBIF 2023, www.gbif.org) and the European Forest Genetic Resources Program (EUFORGEN 2023). In the dataset obtained from the Global Biodiversity Information Facility (GBIF), erroneous and redundant records were removed. 2. Environmental data The dataset used in this study consisted of nineteen bioclimatic variables (BIO1 to BIO19) obtained from the CHELSA version 2.1 (https://chelsa-climate.org/). These variables as .tiff format represented climatic and environmental factors and were downloaded at a spatial resolution of 30-arc seconds. The dataset covered four temporal ranges: 1981-2010, 2011-2040, 2041-2070, and 2071-2100. The bioclimatic variable values of the grid cell were obtained using QGIS 3.18.2. The data utilized in this study were obtained from two Global Circulation Models (GCMs), namely the Max Planck Institute Earth System Model (MPI-ESM1-2-HR) and ..., , # The dataset of Liquidambar orientalis for species distribution models

https://doi.org/10.5061/dryad.1ns1rn914 The dataset includes 1. Occurrence points of Liquidambar orientalis obtained from GBIF and EUFORGEN\, 2. Bioclimate data of the occurrences obtained from CHELSA\, 3. Variance Inflation Factors (VIF) results of four variables (bio1\, bio2\, bio13\, and bio18)\, 4. The Area Under the Curve (AUC) of the Receiving Operator Characteristics (ROC) and the True Skill Statistic (TSS) results of the modeling\, 5. Bioclimate variables' importance scores after modeling 6. The ROC graph (including each algorithm's total scores) 7. "Suitability.xls" contains the area calculation of suitable habitats.

Description of the data and file structure

1. "occurence_points.xls" contains 81 geographical coordinates (latitude and longitude) of Liquidambar orientalis in Anatolia and Rhodos Island. 2. "bioclim_variables.xls" c...

WORKING VERSION. All layers are visible in this linked webgis app along with estimated error. The layers available in this dataset are in a WGS84 geographic coordinate reference system (EPSG:4326) where latitude and longitude coordinates at 0.0008983 degrees ground sampling distance per cell, which corresponds to about 1 ha, i.e. ~100 m x ~100 m at the equator, but decreases in area with increasing latitude as the coordinate system is not equal-area, e.g. ~70 m at 45° latitude and ~50 m at 60° latitude. Aspect.tif, slope.tif and elevation.tif represent Earth surface morphology biomass2020fireres.tif - Biomass values at year 2020 Mg/ha CanopyBulkDensity.tif - Amount of canopy biomass per volume of canopy (kg/m3) CanopyBaseHeight.tif - Height of lower canopy from the ground (m) CanopyHeight.tif - Total height of canopy from the ground (m) Fuel Model FuelModelClasses_ScottBurgan.tif - the category of Fuel Model according to Scott&Burgan 2005 FuelModelClasses_Aragonese.tif - the category of Fuel Model according to Aragonese et al. 2023 DOI: 10.5194/essd-15-1287-2023 - values are from 1 to 24, with a Look Up Table for correspondence (values are ordered matching the order in table 1 of the article) . FuelModelClasses_ScottBurgan.clr/qml CLR/QML - style file for QGIS FuelModelClasses_Aragonese.clr/qml CLR/QML - style file for QGIS FuelModelPercent - the percent of fuel model category belonging to that pixel, between 0 and 100 FuelModelAllPerc - multi-band raster with percent of each fuel model category to belong to each pixel.

Geologisk kart over Svalbard (1:250 000).

https://api.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/_file/daf3eeae9d3aeb5bdf9a2b9f86ba8bab?key=8ee185b7c7f70470041e8801b3451517+Uyhjrqc9jddVIG52JAZO6t00BYN7eakD" alt="Mobilkart i felt">

Sammendrag (for English see below)

Dette geologiske kartet fra Norsk Polarinstitutt har blitt produsert med tanke på å brukes på smart-telefon, nettbrett eller PC uten nett-tilkobling, for eksempel til feltarbeid eller som et hendig oppslags-kart. Kartet består av 5 raster-filer i GIS-formatet JPEG2000 og er tilgjengelig som nedlasting fra datasenteret til Norsk Polarinstitutt

Informasjon om de geologiske enhetene er plassert som tekst-merkelapper direkte i kartbildet, i motsetning til en vanlig tegnforklaring. Ved å zoome inn på kartet finnes informasjon om geologiske enheter, vist med blå tekst (alder i parentes). I tillegg er hvert enhet (farge) merket med en tilsvarende 4-sifret kode i blå skrift.

I felten kan mobile dingser med GPS vise brukeren sin posisjon på kartet. Avhengig av skjermoppløsning er full detaljgrad i kartet synlig på ca. 1:30 000-skala, men kartet kan også vises på mye større skala for å se f.eks. regionale geologiske trekk.

Kartet kan vises på Android eller iOS-enheter med appen "Geoviewer" fra Extensis (tidligere Lizardtech). På datamaskin fungerer QGIS eller ArcMap bra for å vise kartet. Se forklaring på hvordan overføre kartet til din smart-telefon eller nettbrett lenger nede på sida.

Data

Kartet er laget ved å bruke data fra Norsk Polarinstitutt 1:250 000-skala geologiske kart for Svalbard, opprinnelig publisert i "Geoscience Atlas of Svalbard" av Dallmann (ed.) 2015. Dette kartet er generalisert fra 1:100 000-skala kart-data i hovedkartserien til Norsk Polarinstitutt, og er publisert i Geoscience Atlas of Svalbard (Dallmann 2015).

Til å produsere dette kartet er topografiske data fra S100 (topografi, vann) og S250 (kystlinje)-datasettene fra Norsk Polarinstitutt brukt. Fjellskygge er konstruert med S0 Terrengmodell med 20 meter pr. pixel oppløsning. Bre og snøflekk-områder er vist med datasettet for 2001-2010 av König mfl. (2013), som gir et mer oppdatert bilde av blotning-situasjonen nær breer og snøflekker. Områder der geologiske polygoner ikke er justert til nye blotninger er vist i brunt. Kystlinjen er i noen tilfeller endret for å tilpasses bre-fronter som ender i sjøen.

Forbehold om datakvalitet Dette er et nytt geologisk kartprodukt, og det kan forekomme feil. Spesielt tegnforklaring, som er skrevet direkte på geologiske enheter, kan være problematisk i noen områder. Vi er interessert i tilbakemelding på mulige forbedringer av kartet. Send gjerne tilbakemeldinger på e-post til Geokart@npolar.no.

Dette er et geologisk kart ment for å formidle vitenskapelige data, og er ikke egnet for navigasjon. Noen områder av Svalbard er ennå ikke kartlagt i detalj, og en del av dataene er av eldre dato, så datakvaliteten for dette kartet er varierende. Kartet kan inneholde feil i grunnlagsdata, kartpresentasjon, kartografi og tekst-beskrivelser. For en stor del er geologien kartlagt for en mindre detaljert skala enn den det er mulig å oppnå med dette kartproduktet, så geologiske trekk og enheter vil i ulik grad fremstå feilplassert ved bruk av god GPS-posisjon og detaljert zoom-nivå. Breer og spesielt bre-fronter er i konstant forandring, og selv om ganske oppdaterte data er brukt for å lage kartet, vil det være feil i en del bre-posisjoner. Vær oppmerksom på at det topografiske grunnlaget som er brukt her i mange tilfeller er av nyere dato enn det som opprinnelig var brukt under kartleggingen i felt. Dette kan også føre til feil i kartet.

Geologiske kart-data vil kontinuerlig være gjenstand for re-tolkning og endring. For en full beskrivelse av kartleggingsprogrammet ved Norsk Polarinstitutt, geologiske kart-data presentert her og referanser, se Dallmann (ed.) 2015, eller besøk npolar.no

Hvordan overføre kartet til mobilenheter

Direkte nedlasting Kartet kan nå lastes ned direkte til mobilenheten via lenker øverst. Det er 5 linker, en for hvert område. Enten lagres filene på enheten, eller du vil få et valg om å åpne fila direkte i Geoviewer. NB: Sørg for at det er nok ledig lagringsplass på mobilenheten og vær oppmerksom på fil-størrelsen (550 MB), spesielt hvis det er et betalt internett-abonnenement.

Via PC, kabel eller Dropbox:

NP_S250_Geologi_mobilkart kan brukes direkte i GIS-systemer på PC, mens for bruk på nettbrett og mobil anbefales gratis-appen Geoviewer fra Lizardtech.

Etter å ha lastet ned til PC og pakket opp ZIP-filene, kan kartene for Android-enheter eksempelvis overføres til ønsket plassering på enheten via USB-kabel. For iOS-enheter kan en bruke f.eks. nettjenesten Dropbox som kanal fra PC til enhet. Når kartene er lagret på enheten, kan en legge til de kartrutene en ønsker fra menyen i Geoviewer.

Referanser Kartdata Svalbard 1:100 000 (S100 Kartdata) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/645336c7-adfe-4d5a-978d-9426fe788ee3

M König, J Kohler, C Nuth (2013). Glacier Area Outlines - Svalbard. Norwegian Polar Institute https://data.npolar.no/dataset/89f430f8-862f-11e2-8036-005056ad0004

Dallmann, W.K., (ed.) (2015). Geoscience Atlas of Svalbard, Norsk Polarinstitutt Rapportserie nr. 148

Terrengmodell Svalbard (S0 Terrengmodell) (2014). Norwegian Polar Institute (Tromsø, Norway): https://data.npolar.no/dataset/dce53a47-c726-4845-85c3-a65b46fe2fea

English

Geological map of Svalbard (1:250 000).

Abstract This geological map from the Norwegian Polar Institute has been prepared to be used offline on a smartphone, tablet or computer, for example for field work or a handy reference. It consists of 5 raster-files in the JPEG2000 GIS-format, available to download from the Norwegian Polar Institute data centre data.npolar.no via https://data.npolar.no/dataset/eafafbb7-b3df-4c71-a2df-316e80a7992e/.

Information about the geological units has been placed as text labels (in blue typescript) directly on the map, as opposed to a regular legend. By zooming in, information about each geological unit on the map can be found, shown in blue text (age in parentheses). In addition, each unit is labelled with a corresponding 4-digit code also in blue typescript.

In the field, GPS-enabled devices can show the user's location on the map. Depending on screen resolution, full detail of the map (including text labels) is best viewed at ca. 1:30 000 scale, but the map can also be viewed at much larger scales to see e.g. regional geological features.

For mobile use, the app "Geoviewer" from Extensis (formerly Lizardtech) can be used. On a computer, QGIS works well to view these maps. See an explanation below on how to transfer the map to your tablet or smartphone.

Data

The map is made using data from the Norwegian Polar Institute 1:250 000-scale geological map for Svalbard, originally published in Dallmann (ed.) 2015. This geological map has been generalised from the 1:100 000-scale main map series published by the Norwegian Polar Institute, and is published in Geoscience Atlas of Svalbard (Dallmann 2015).

For the purpose of this map product, topographic data from the Norwegian Polar Institute S100 Map (topography, water) and S250 (coastline) data sets have been used. Hill shade was created using the NPI S0 Terrengmodell at 20 meters/pixel resolution. Glacier and snow patch outlines are shown using the 2001-2010 dataset of glacier area outlines for Svalbard by König et al. (2013), which gives a more up to date picture of the outcrop situation near glaciers or snow patches. Areas where geology polygons have not been re-adjusted to the new outcrops are shown in brown. The coast line-data has been adjusted in some cases to adapt to glacier fronts ending in the sea.

Disclaimer This is a new geological map product, and errors may occur. In particular the legend, which have been printed directly on the geological units, can be problematic in places. We appreciate feedback on the map that can be used to improve the map in future versions. Please email feedback to Geokart@npolar.no.

This is a geological map meant to convey scientific data, and is not suited for navigation. This map product may contain errors in base data, map presentation, cartography and text descriptions. Much of the geology was originally mapped for a less detailed scale than what is possible to obtain with this map, so geological features will to varying degrees appear out-of place when a good GPS-position and detailed zoom level is used. Glaciers and in particular glaciers fronts are dynamic features, and although using fairly up-to-date data, this map does contain errors in glacier front positions. Note that the topographic base data used here in many cases is of a newer vintage than the data originally used for geological mapping in the field. This may cause some errors in the map. Some areas of Svalbard have not yet been mapped in detail and some of the data are of older origin, so the data quality presented on this map is variable.

Geological map data will be subject to continual re-interpretation and editing. For a full description of the bedrock mapping programme at the Norwegian Polar Institute, the geological map data presented here and

Kongsfjorden Vessel presence derived from AIS data 2017-2018

Quality

Data from the Automatic Identification System (AIS) for vessels was obtained from The Norwegian Coastal Administration. Three different distance buffers (10, 25 and 50 km) were plotted around Kongsfjorden AURAL location. AIS location points contained information such as the Maritime Mobile Service Identity (MMSI, conveying the ship ID), position and time. These were combined with the distance buffers, and the points falling on land, related to buoys or not relatable to vessels, were eliminated (QGIS 3.16 Hannover). Then, csv files from the resulting interceptions were exported and analyzed in R (R version 4.0.5) to calculate the number of vessels per day in each area.

Variables: Date: date Vessel_10: Number of vessels within 10 km radius from the recorder per day Vessel_25: Number of vessels within 25 km radius from the recorder per day Vessel_50: Number of vessels within 50 km radius from the recorder per day V10: Number of vessels within 10 km radius from the recorder per day V25: Number of vessels between 10 to 25 km radius from the recorder per day V50: Number of vessels between 25 to 50 km radius from the recorder per day Acoustic: Number of acoustic detections of vessels per day

Questo dataset contiene le aree allagate corredate della stima di metri quadrati e metri cubi di acqua caduti (derivati ​​da DTM) determinati sulla base dei rilievi effettuati dai volontari aderenti all'iniziative di citizen science. Il rilievo avviene utilizzando l'app QField da dispositivo mobile dopo eventi atmosferici particolarmente importanti. Il dataset è generato attraverso uno script Python ( https://github.com/USAGEHub/CS_floods) eseguito all'interno del software QGis partendo da elementi lineari (linestring) semplificati. Ciascuna area viene creata utilizzando l'operazione buffer (sulla base della larghezza indicata dall'utente). Gli elementi rilevati nello stesso giorno e che presentano una sovrapposizione spaziale vengono accorpati. - Il dataset è generato attraverso uno script Python ( https://github.com/USAGEHub/CS_floods) eseguito all'interno del software QGis partendo da elementi linestring semplificati. Ciascuna area viene creata utilizzando l'operazione buffer (sulla base della larghezza indicata dall'utente). Gli elementi rilevati nello stesso giorno e che presentano una sovrapposizione spaziale vengono accorpati. Il dataset contiene geometrie poligonali s con la seguente struttura dati: Giorno: giorno del rilievo; Area_m2: superficie allagata; Q_min: quota minima DTM della superficie allagata misurata in metri; Q_max: quota massima del DTM della superficie allagata misurata in m; Diff_q la profondità stimata del volume allagato in cm; Stima_v: volume d'acqua stimato in metri cubi