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.

ArcGIS and QGIS map packages, with ESRI shapefiles for the DSM2 Model Grid. These are not finalized products. Locations in these shapefiles are approximate.

Monitoring Stations - shapefile with approximate locations of monitoring stations.

DSM2 Grid 2025-05-28 Historical

FC_2023.01

DSM2 v8.2.0, calibrated version:

• dsm2_8_2_grid_map_calibrated.mpkx - ArcGIS Pro map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_grid_map_calibrated.mpk - ArcGIS Desktop map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_grid_map_qgis.zip - QGIS map package containing all layers and symbology for the calibrated grid map.
• dsm2_8_2_0_calibrated_gridmap_shapefiles.zip - A zip file containing all the shapefiles used in the above map packages:
• dsm2_8_2_0_calibrated_channels_centerlines - channel centerlines, follwing the path of CSDP centerlines
• dsm2_8_2_0_calibrated_network_channels - channels represented by straight line segments which are connected the upstream and downstream nodes
• dsm2_8_2_0_calibrated_nodes - DSM2 nodes
• dsm2_8_2_0_calibrated_dcd_only_nodes - Nodes that are only used by DCD
• dsm2_8_2_0_calibrated_and_dcd_nodes - Nodes that are shared by DSM2 and DCD
• dsm2_8_2_0_calibrated_and_smcd_nodes - Nodes that are shared by DSM2 and SMCD
• dsm2_8_2_0_calibrated_gates_actual_loc - The approximate actual locations of each gate in DSM2
• dsm2_8_2_0_calibrated_gates_grid_loc - The locations of each gate in the DSM2 model grid
• dsm2_8_2_0_calibrated_reservoirs - The approximate locations of the reservoirs in DSM2
• dsm2_8_2_0_calibrated_reservoir_connections - Lines showing connections from reservoirs to nodes in DSM2

DSM2 v8.2.1, historical version:

• DSM2 v8.2.1, historical version grid map release notes (PDF), updated 7/12/2022
• DSM2 v8.2.1, historical version grid map, single zoom level (PDF)
• DSM2 v8.2.1, historical version grid map, multiple zoom levels (PDF) - PDF grid map designed to be printed on 3 foot wide plotter paper.
• DSM2 v8.2.1, historical version map package for ArcGIS Desktop: A map package for ArcGIS Desktop containing the grid map layers with symbology.
• DSM2 v8.2.1, historical version grid map shapefiles (zip): A zip file containing the shapefiles used in the grid map.

Change Log

7/12/2022: The document "DSM2 v8.2.1, historical version grid map release notes (PDF)" was corrected by removing section 4.4, which incorrectly stated that the grid included channels 710-714, representing the Toe Drain, and that the Yolo Flyway restoration area was included.

This data can be imported to GIS software, such as Quantum GIS or ESRI. Guinea, Liberia, Mali and Sierra Leone. OpenStreetMap Ebola Response

Please note: for a correct view and use of this dataset it is advisable to consult it at original page on the Arezzo Portal. At the same address there are also, for the enabled datasets, additional access formats, the preview of the visualization via API call, the consultation of the fields in DCAT-AP IT format, the possibility to express an evaluation and comment on the dataset itself. All resource formats available for this dataset can be downloaded as ZIP packages: inside the package sarà available the resource in the chosen format, complete with all the information on the metadata and the license associated with it. The resource is the logical container of the projects of the cartographic works of the start of the procedure of the variant to the Structural Plan and the new Operational Plan, realized through the desktop application QGIS.

Aerial Imagery/Photography, Scanned Historical Maps, CCT DRAFT Ground Level Map (GLM), Infrared Imagery, Digital Elevation Models (DEM), etc. All Raster Image Services (WMS):Use URL below to add WMS Server Connection in ArcGIS Desktop, ArcPro, QGIS, AutoCAD, etc.https://cityimg.capetown.gov.za/erdas-iws/ogc/wms/GeoSpatial Datasets?service=WMS&request=getcapabilities&All Raster Image Services (REST):https://cityimg.capetown.gov.za/erdas-iws/esri/GeoSpatial%20Datasets/rest/services/Popular Image Services: 2021 Aerial Imagery , 2020 Aerial Imagery , 2019 Aerial Imagery , DRAFT CCT Ground Level Map (GLM) 2019_5m_ DEM

This dataset originates from the research activities (WP2-WP3) carried out during the H2020- MSCA-IF 2019 EU-funded project "Materializing Modernity - Socialist and Post-socialist Rural Legacy in Contemporary Albania (MaMo)", GA. no. 896925, implemented by Federica Pompejano (MSCA-IF Researcher) at the Instituti i Antropologjise Kulturore dhe i Studimit te Artit (Akademia e Studimeve Albanologjike), Tirana, Albania. This dataset contains five .qgz files and a set of georeferenced raw data (JPG pictures and GPS tracks) collected during the MaMo fieldwork research activities (WP3). This dataset has been curated by Dr Federica Pompejano. The mapping of buildings and landscapes carried out during the fieldwork activities aimed at providing extensive visual documentation of the rural landscape of the five representative macro-areas selected for the MaMo research project. The mapping activity was carried out by means of the QFIELD App and then synchronized in QGIS Desktop 3.18.2. The QGIS Projects (WP2) have been created by Dr Federica Pompejano (IAKSA-ASA) in collaboration and with the support of Prof. Bianca Federici (Università di Genova - UNIGE) and Dr Ilaria Ferrando (Università di Genova - UNIGE). Unless otherwise specified, the data contained in this dataset are open for public disposal under the terms and conditions described in the CC BY-NC-SA 4.0 license. A copy of each QGIS project is deposited at the Scientific Archive of Ethnography and Folklore (IAKSA-ASA) where all MaMo materials are stored, preserved, and are available for consultation under the Archive's terms and conditions. The Scientific Archive of Ethnography and Folklore (IAKSA-ASA) is in Rruga Naim Frasheri 1, Tirana (Albania). E-mail contact is: iaksa@asa.edu.al. ACKNOWLEDGMENTS: This dataset and all its content are part of a project that has received funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No. 896925. NOTE: Due to the early termination of the GA. no. 896925, data are updated to March 30th, 2022. DISCLAIMER: Materials and outputs reflect only the author's view. The REA and the EU Commission are not responsible for any use that may be made of the information they contain.

Water resources management is of primary importance for better understanding the impact on scenarios of climate change. The mean monthly runoff, soil moisture and aquifer recharge long-run forecast can support decisions to manage water demand, to recover degraded areas, water security, irrigation, electrical energy generation and urban water supply. The integrative and comprehensive analysis considering the spatial and temporal representation of hydrological process such as the distribution of rainfall, land cover and land use, ground elevation is a challenge. Therefore, these input data are important to modeling the water balance. We present the Rainfall-Runoff Balance Enhanced Model (RUBEM) as a grided hydrological model capable to represent the canopy interception, runoff, soil moisture on the non-saturated soil layer, baseflow and aquifer recharge. The RUBEM includes evapotranspiration and the interception based on the leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR) and normalized difference vegetation index (NVDI). The land use and land cover are updated during the simulations. The RUBEM was tested for tree tropical watersheds in Brazil with different hydrological and soil properties zones. The Piracicaba River has 10,701 km² (latitude 22.7o S), Ipojuca River has 3,471 km² (latitude 8.3o S) and Alto Iguaçu River with 2,696 km² (latitude 25.6o S). The input data from 2000 to 2010 was used to calibrate the runoff and the Nash-Sutcliffe indicator (NSI) results in 0.63, 0.48 and 0.60, respectively. The data input from 2011 to 2018 was the validation model period and NSI results in 0.66, 0.43 and 0.77. According to the NSI results, the model had a suitable calibration and validation in different hydrological zones and soils constitutions. The RUBEM is an important grided hydrological model with capabilities to support researchers, policymakers, and decision-makers under spatial and temporal water balance analysis to water managements plans, recovery degradation areas and long-run forecast.

Notice: this is not the latest Heat Island Severity image service. For 2023 data, visit https://tpl.maps.arcgis.com/home/item.html?id=db5bdb0f0c8c4b85b8270ec67448a0b6. This layer contains the relative heat severity for every pixel for every city in the contiguous United States. This 30-meter raster was derived from Landsat 8 imagery band 10 (ground-level thermal sensor) from the summer of 2021, patched with data from 2020 where necessary.Federal statistics over a 30-year period show extreme heat is the leading cause of weather-related deaths in the United States. Extreme heat exacerbated by urban heat islands can lead to increased respiratory difficulties, heat exhaustion, and heat stroke. These heat impacts significantly affect the most vulnerable—children, the elderly, and those with preexisting conditions.The purpose of this layer is to show where certain areas of cities are hotter than the average temperature for that same city as a whole. Severity is measured on a scale of 1 to 5, with 1 being a relatively mild heat area (slightly above the mean for the city), and 5 being a severe heat area (significantly above the mean for the city). The absolute heat above mean values are classified into these 5 classes using the Jenks Natural Breaks classification method, which seeks to reduce the variance within classes and maximize the variance between classes. Knowing where areas of high heat are located can help a city government plan for mitigation strategies.This dataset represents a snapshot in time. It will be updated yearly, but is static between updates. It does not take into account changes in heat during a single day, for example, from building shadows moving. The thermal readings detected by the Landsat 8 sensor are surface-level, whether that surface is the ground or the top of a building. Although there is strong correlation between surface temperature and air temperature, they are not the same. We believe that this is useful at the national level, and for cities that don’t have the ability to conduct their own hyper local temperature survey. Where local data is available, it may be more accurate than this dataset. Dataset SummaryThis dataset was developed using proprietary Python code developed at The Trust for Public Land, running on the Descartes Labs platform through the Descartes Labs API for Python. The Descartes Labs platform allows for extremely fast retrieval and processing of imagery, which makes it possible to produce heat island data for all cities in the United States in a relatively short amount of time.What can you do with this layer?This layer has query, identify, and export image services available. Since it is served as an image service, it is not necessary to download the data; the service itself is data that can be used directly in any Esri geoprocessing tool that accepts raster data as input.In order to click on the image service and see the raw pixel values in a map viewer, you must be signed in to ArcGIS Online, then Enable Pop-Ups and Configure Pop-Ups.Using the Urban Heat Island (UHI) Image ServicesThe data is made available as an image service. There is a processing template applied that supplies the yellow-to-red or blue-to-red color ramp, but once this processing template is removed (you can do this in ArcGIS Pro or ArcGIS Desktop, or in QGIS), the actual data values come through the service and can be used directly in a geoprocessing tool (for example, to extract an area of interest). Following are instructions for doing this in Pro.In ArcGIS Pro, in a Map view, in the Catalog window, click on Portal. In the Portal window, click on the far-right icon representing Living Atlas. Search on the acronyms “tpl” and “uhi”. The results returned will be the UHI image services. Right click on a result and select “Add to current map” from the context menu. When the image service is added to the map, right-click on it in the map view, and select Properties. In the Properties window, select Processing Templates. On the drop-down menu at the top of the window, the default Processing Template is either a yellow-to-red ramp or a blue-to-red ramp. Click the drop-down, and select “None”, then “OK”. Now you will have the actual pixel values displayed in the map, and available to any geoprocessing tool that takes a raster as input. Below is a screenshot of ArcGIS Pro with a UHI image service loaded, color ramp removed, and symbology changed back to a yellow-to-red ramp (a classified renderer can also be used): Other Sources of Heat Island InformationPlease see these websites for valuable information on heat islands and to learn about exciting new heat island research being led by scientists across the country:EPA’s Heat Island Resource CenterDr. Ladd Keith, University of ArizonaDr. Ben McMahan, University of Arizona Dr. Jeremy Hoffman, Science Museum of Virginia Dr. Hunter Jones, NOAA Daphne Lundi, Senior Policy Advisor, NYC Mayor's Office of Recovery and ResiliencyDisclaimer/FeedbackWith nearly 14,000 cities represented, checking each city's heat island raster for quality assurance would be prohibitively time-consuming, so The Trust for Public Land checked a statistically significant sample size for data quality. The sample passed all quality checks, with about 98.5% of the output cities error-free, but there could be instances where the user finds errors in the data. These errors will most likely take the form of a line of discontinuity where there is no city boundary; this type of error is caused by large temperature differences in two adjacent Landsat scenes, so the discontinuity occurs along scene boundaries (see figure below). The Trust for Public Land would appreciate feedback on these errors so that version 2 of the national UHI dataset can be improved. Contact Dale.Watt@tpl.org with feedback.

Spatial dataset derived from many different open data repositories and cropped on the Omo-Turkana Basin boundary, used to create a base-map describing the components of Water-Energy-Food nexus in the case study.

omo-turkana.gpkg: vector dataset including the following layers, together with the related map style for QGIS Desktop used in the DAFNE Geoportal basemap:

• basin: Hydrological basin of the Omo-Turkana river (case study boundary)
• subbasin: Basins of the main tributaries
• waterbodies: Natural lakes and reservoirs boundaries
• rivers: River network
• dams: Existing dams
• protected_areas: Protected areas
• aei_pct_cells: Area equipped for irrigation, expressed as percentage of total area.
• roads: Main roads network
• cities: Main cities in the riparian countries
• countries: Administrative borders of riparian countries
• markers: DAFNE model components location, with existing and planned dams and power plants, irrigation schemes, environmental target areas.

zambezi_raster.zip: raster dataset including the following layers:

• srtm_90m: Digital Elevation Model
• Global Surface Water:
  • change: Occurrence Change Intensity map provides information on where surface water occurrence increased, decreased or remained the same between 1984-1999 and 2000-2015
  • extent: Maximum Water Extent shows all the locations ever detected as water over period 1984-2015
  • occurence: Occurrence shows where surface water occurred between 1984 and 2015 and provides information concerning overall water dynamics.
  • recurrence: Recurrence provides information concerning the inter-annual behaviour of water surfaces and captures the frequency with which water returns from year to year.
  • seasonality: Seasonality map provides information concerning the intra-annual behaviour of water surfaces for a single year (2015) and shows permanent and seasonal water and the number of months water was present.
  • transitions: Transitions map provides information on the change in surface water seasonality between the first and last years (between 1984 and 2015) and captures changes between the three classes of not water, seasonal water and permanent water.

Original data sources include:

• AQUASTAT, the FAO global information system on water resources and agricultural water management;

• Natural Earth, a public domain map dataset available at different scales;

• Protected Planet, the most up to date and complete source of data on protected areas and other effective area-based conservation measures, maintained by UNEP-WCMC and IUCN;

• OpenStreetMap, a collaborative project to create a free editable map of the world;

• NASA's Shuttle Radar Topography Mission (SRTM) Digital Elevation Database;

• Global Water Surface, a virtual time machine that maps the location and temporal distribution of water surfaces at the global scale.

ssurgoOnDemandThe purpose of these tools are to give users the ability to get Soil Survey Geographic Database (SSURGO) properties and interpretations in an efficient manner. They are very similiar to the United States Department of Agriculture - Natural Resource Conservation Service's distributed Soil Data Viewer (SDV), although there are distinct differences. The most important difference is the data collected with the SSURGO On-Demand (SOD) tools are collected in real-time via web requests to Soil Data Access (https://sdmdataaccess.nrcs.usda.gov/). SOD tools do not require users to have the data found in a traditional SSURGO download from the NRCS's official repository, Web Soil Survey (https://websoilsurvey.sc.egov.usda.gov/App/HomePage.htm). The main intent of both SOD and SDV are to hide the complex relationships of the SSURGO tables and allow the users to focus on asking the question they need to get the information they want. This is accomplished in the user interface of the tools and the subsequent SQL is built and executed for the user. Currently, the tools packaged here are designed to run within the ESRI ArcGIS Desktop Application - ArcMap, version 10.1 or greater. However, much of the Python code is recyclable and could run within a Python intepreter or other GIS applications such as Quantum GIS with some modification.NOTE: The queries in these tools only consider the major components of soil map units.Within the SOD tools are 2 primary toolsets, descibed as follows:<1. AreasymbolThe Areasymbol tools collect SSURGO properties and interpretations based on a user supplied list of Soil Survey areasymbols (e.g. NC123). After the areasymbols have been collected, an aggregation method (see below) is selected . Tee aggregation method has no affect on interpretations other than how the SSURGO data aggregated. For soil properties, the aggregation method drives what properties can be run. For example, you can't run the weighted average aggregation method on Taxonomic Order. Similarly, for the same soil property, you wouldn't specify a depth range. The point here is the aggregation method affects what parameters need to be supplied for the SQL generation. It is important to note the user can specify any number of areasymbols and any number of interpretations. This is another distinct advantage of these tools. You could collect all of the SSURGO interpretations for every soil survey area (areasymbol) by executing the tool 1 time. This also demonstrates the flexibility SOD has in defining the geographic extent over which information is collected. The only constraint is the extent of soil survey areas selected to run (and these can be discontinuous).As SOD Areasymbol tools execute, 2 lists are collected from the tool dialog, a list of interpretations/properties and a list of areasymbols. As each interpretation/property is run, every areasymbol is run against the interpretation/property requested. For instance, suppose you wanted to collect the weighted average of sand, silt and clay for 5 soil survey areas. The sand property would run for all 5 soil survey areas and built into a table. Next the silt would run for all 5 soil survey areas and built into a table, and so on. In this example a total of 15 web request would have been sent and 3 tables are built. Two VERY IMPORTANT things here...A. All the areasymbol tools do is generate tables. They are not collecting spatial data.B. They are collecting stored information. They are not making calculations(with the exception of the weighted average aggregation method).<2. ExpressThe Express toolset is nearly identical to the Areasymbol toolset, with 2 exceptions.A. The area to collect SSURGO information over is defined by the user. The user digitizes coordinates into a 'feature set' after the tool is open. The points in the feature set are closed (first point is also the last) into a polygon. The polygon is sent to Soil Data Access and the features set points (polygon) are used to clip SSURGO spatial data. The geomotries of the clip operation are returned, along with the mapunit keys (unique identifier). It is best to keep the points in the feature set simple and beware of self intersections as they are fatal.B. Instead of running on a list of areasymbols, the SQL queries on a list of mapunit keys.The properties and interpretations options are identical to what was discussed for the Areasymbol toolset.The Express tools present the user the option of creating layer files (.lyr) where the the resultant interpretation/property are joined to the geometry and saved to disk as a virtual join. Additionally, for soil properties, an option exists to append all of the selected soil properties to a single table. In this case, if the user ran sand, silt, and clay properties, instead of 3 output tables, there is only 1 table with a sand column, a silt column, and a clay column.<Supplemental Information<sAggregation MethodAggregation is the process by which a set of component attribute values is reduced to a single value to represent the map unit as a whole.A map unit is typically composed of one or more "components". A component is either some type of soil or some nonsoil entity, e.g., rock outcrop. The components in the map unit name represent the major soils within a map unit delineation. Minor components make up the balance of the map unit. Great differences in soil properties can occur between map unit components and within short distances. Minor components may be very different from the major components. Such differences could significantly affect use and management of the map unit. Minor components may or may not be documented in the database. The results of aggregation do not reflect the presence or absence of limitations of the components which are not listed in the database. An on-site investigation is required to identify the location of individual map unit components. For queries of soil properties, only major components are considered for Dominant Component (numeric) and Weighted Average aggregation methods (see below). Additionally, the aggregation method selected drives the available properties to be queried. For queries of soil interpretations, all components are condisered.For each of a map unit's components, a corresponding percent composition is recorded. A percent composition of 60 indicates that the corresponding component typically makes up approximately 60% of the map unit. Percent composition is a critical factor in some, but not all, aggregation methods.For the attribute being aggregated, the first step of the aggregation process is to derive one attribute value for each of a map unit's components. From this set of component attributes, the next step of the aggregation process derives a single value that represents the map unit as a whole. Once a single value for each map unit is derived, a thematic map for soil map units can be generated. Aggregation must be done because, on any soil map, map units are delineated but components are not.The aggregation method "Dominant Component" returns the attribute value associated with the component with the highest percent composition in the map unit. If more than one component shares the highest percent composition, the value of the first named component is returned.The aggregation method "Dominant Condition" first groups like attribute values for the components in a map unit. For each group, percent composition is set to the sum of the percent composition of all components participating in that group. These groups now represent "conditions" rather than components. The attribute value associated with the group with the highest cumulative percent composition is returned. If more than one group shares the highest cumulative percent composition, the value of the group having the first named component of the mapunit is returned.The aggregation method "Weighted Average" computes a weighted average value for all components in the map unit. Percent composition is the weighting factor. The result returned by this aggregation method represents a weighted average value of the corresponding attribute throughout the map unit.The aggregation method "Minimum or Maximum" returns either the lowest or highest attribute value among all components of the map unit, depending on the corresponding "tie-break" rule. In this case, the "tie-break" rule indicates whether the lowest or highest value among all components should be returned. For this aggregation method, percent composition ties cannot occur. The result may correspond to a map unit component of very minor extent. This aggregation method is appropriate for either numeric attributes or attributes with a ranked or logically ordered domain.

The service displays data in the form of an indicator for point emissions from production plants and landfills of the Regional Inventory of Atmospheric Emissions (IREA).The estimates made are calculated according to the INEMAR system (air emissions inventory) based on the EMEP — CORINAIR methodology and relate to sources classified according to the SNAP nomenclature (Selected Nomenclature for Air Pollution).These are classified according to the following parameters: reference year, province and municipality, reference activities according to the SNAP methodology (macrosector, sector and emissive activity), fuel used and pollutant emitted. The main pollutants exposed are: CH4 (t/year); Co (t/year); CO2 (kt/year); N2O (t/year); NH3 (t/year); NMVOC (t/year); NOx (t/year); PM10 (t/year); PM2.5 (t/year); PTS (t/year); SO2 (t/year). The data shall be rounded to the fourth decimal place. The service exposes the data in four different spatial resolutions: Municipalities, Provinces, Region, Air Quality Zones.Through a specific function in Environmental Knowledge System, you can view the thematic inventory data based on three different types of statistical classification (Jenks, Equal Interval, Quantum).The WFS service can also be used in any GIS desktop (e.g. QGIS).

Nota bene: per una corretta visualizzazione e fruizione di questo dataset si consiglia di consultarlo alla pagina originale sul Portale del Arezzo. Allo stesso indirizzo sono inoltre presenti, per i dataset abilitati, ulteriori formati di accesso, l'anteprima della visualizzazione tramite chiamata API, la consultazione dei campi in formato DCAT-AP IT, la possibilità di esprimere una valutazione e commentare il dataset stesso.

Tutti i formati delle risorse disponibili per questo dataset sono scaricabili come pacchetti ZIP: all'interno del pacchetto sarà disponibile la risorsa nel formato scelto, completa di tutte le informazioni sulla metadatazione e sulla licenza ad essa associata.

La risorsa e' il contenitore logico dei progetti degli elaborati cartografici dell'avvio del procedimento della variante al Piano Strutturale e del nuovo Piano Operativo, realizzati mediante l'applicativo desktop QGIS.

La risorsa e' il contenitore logico dei progetti degli elaborati cartografici dell'avvio del procedimento della variante al Piano Strutturale e del nuovo Piano Operativo, realizzati mediante l'applicativo desktop QGIS.

Progetto di cooperazione Cacao Correcto Ecuador: Versione spagnola del manuale del corso di introduzione al programma GIS desktop "QGIS".

Proyecto de Cooperación Cacao Correcto Ecuador: Versión en español del manual para la introducción al programa de escritorio SIG "QGIS".

This dataset contains supporting information files for the manuscript entitled:“Combining 3-D Deep Electrical Resistivity Tomography with magnetic surveys to investigate complex tectonic basins: a case study from the central Apennines seismic belt (Italy)” by Sapia, V., Villani, F., Improta, L, Fischanger, F., De Martini, P.M., Romano, V., Materni, V., Baccheschi, P., Smedile, A., Nicolosi, I., Minelli, L., D’Ajello Caracciolo, F., Carluccio, R., Di Giulio, G., Ruggiero, L., Sciarra, A., Pantosti, D., Pischiutta, M., Ricci, T., Civico, R., Vassallo, M., Brunori, C.A., Lupi, M. which is currently undergoing revision on Tectonics. The files are embargoed and will be made public as soon as the paper is accepted. Please cite properly this paper when using this dataset.The details of the FullWaver technology and UAV magnetic survey, as well as the methodology applied for data collection and processing are fully described in the companion paper to which the reader is referred.List of files:CampoFelice_magnetic_anomaly_1600asl_RP.grd: magnetic anomaly map reduced to the pole (UTM coordinates in meters, WGS84 datum, zone 33N).ERT*grd: 2-D resistivity models in grd format.FW_slice_***m_cut.grd: horizontal slices of the FullWaver 3-D resistivity model at different elevations above sea level (from 1475 to 950 m, as indicated by the file name) cut according to the maximum sensitivity (UTM coordinates in meters, WGS84 datum, zone 33N).FW_CampoFelice_3D_rho_clip1475m.vtk: 3-D resistivity model obtained through 3-D Delaunay interpolation of grid points (UTM coordinates in meters, WGS84 datum, zone 33N). The modl top surface is cu at 1475 m above sea level.Notes:.grd files can be opened with Golden Software Surfer© (https://www.goldensoftware.com/products/surfer/) or any GIS tool like QGIS (https://qgis.org/project/overview/) or ArcMap© (https://www.esri.com/it-it/arcgis/products/arcgis-desktop/resources).vtk file is in binary format and can be opened and viewed in 3-D with Paraview© (https://www.paraview.org/)For any query, please contact the corresponding author: Dr. Fabio Villani, Istituto Nazionale di Geofisica e Vulcanologia, Via di Vigna Murata 605 - 0014 Rome (Italy); e-mail: fabio.villani@ingv.it

被災された方々に、心よりお見舞い申し上げます。 / ###静岡県が公開している50cmDEMデータから作成した赤色立体地図です。【リソース】利用規約 / 無償赤色立体図データの使用許諾です。 / 災害前（XYZ） / ###災害前の赤色立体地図です。 / ####GISのXYZのURLに、上記URLを設定いただければご覧いただけます。 / 災害前（WMTS） / ###災害前の赤色立体地図です。 / ####GISのWMTSのURLに、上記URLを設定いただければご覧いただけます。 / wmtsデータの参照方法について / ###「WMTSの参照方法がわからない！」とのお問い合わせを多くいただきましたので、簡単な説明書を作成しました。 / ###以下のGISでの参照方法です。 / ####・ArcGIS Desktop Arcmap / ####・ArcGIS Pro / ####・GlobalMapper / ####・QGIS / 災害前（JPEG） / GISに搭載した際の画面キャプチャですので、位置座標はありません。【キーワード】土石流 / 熱海市 / 赤色立体地図

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

The 5m DEM is derived from the LiDAR2019B dataset (consisting of the 2018, 2019A and 2019B datasets). The 5m DEM has a vertical accuracy of 30cm. The height reference used is the SA Land Levelling Datum and the SAGEOID2010 was employed.The City of Cape Town Ground Level Map 2019 is defined in the City of Cape Town Municipal Planning Amendment By-law, 2019 as: “‘City of Cape Town Ground Level Map’ means a map approved in terms of the development management scheme, indicating the existing ground level based on floating point raster’s and a contour dataset from LiDAR information available to the City”. The Ground Level Map was approved by the City Council on the 27th July 2023.All Raster Image Services (REST):https://cityimg.capetown.gov.za/erdas-iws/esri/GeoSpatial%20Datasets/rest/services/All Raster Image Services (WMS):Use URL below to add WMS Server Connection in ArcGIS Desktop, ArcPro, QGIS, AutoCAD, etc.https://cityimg.capetown.gov.za/erdas-iws/ogc/wms/GeoSpatial Datasets?service=WMS&request=getcapabilities&For a copy or subset of this dataset, please contact the City Maps Office: city.maps@capetown.gov.zaCCT Ground Level Map: ‘How to Access’ Guide – External Users: CCT Ground Level Map: ‘How to Access’ Guide – External Users | Open Data Portal (arcgis.com)Geomatics Ground Level Map Explainer: Geomatics Ground Level Map Explainer | Open Data Portal (arcgis.com)Land Use Management Ground Level Map Explainer: Land Use Management Ground Level Map Explainer | Open Data Portal (arcgis.com)

Karttjenesten presenterer utvalgte tematiske visninger av nøkkelinnholdet i NGUs nasjonale berggrunnsdatabase. Den består av berggrunnskart på nasjonal (1:1 350 000), regional (1:250 000) og lokal (1:50 000) målestokk og inneholder flatelag, linjelag og punktlag. Flatelagene viser berggrunnens utstrekning i form av hovedbergart og bergartsenhet (hvor tilgjengelig), samt dannelsesalder, tektonisk hovedinndeling, dekningsoversikt (kun lokalt nivå), manuskart (kun lokalt nivå) og løsmasseoverdekning (kun lokalt og regionalt nivå). Linjelagene representer grenser mellom ulike bergartsenheter, bergartslinjer representerer ganger og tynne sedimentære/vulkanske lag, og lineærstrukturer viser forkastninger og skjærsoner. Strukturmålepunkter (kun lokalt og regionalt nivå) er punktlag inndelt etter linjestrukturer som lineasjon og foldeakser, og planstrukturer som foliasjon og lagdeling. Kartlagene i karttjenesten er gruppert etter målestokk og geometritype. En sammenstilt målestokksvisning kombinerer data fra de tre målestokkene og viser de best egnete kartdataene i forhold til zoom-nivå. OBS! BerggrunnWMS3 benytter nyere kode enn den som er støttet av den utgående programvaren ArcGIS Desktop/ArcMap. Det er dermed problemer med å bruke "feature info". En fiks er på vei, men brukerne rådes til å heller benytte ArcGIS Pro, QGIS eller kartinnsynet på nett - eller laste ned data selv.