This guide describes how to create a map layout with the print widget. The digital map that you create can then be printed or saved.

This web map serves as a starter template to allow users to quickly create customized maps by including many commonly used data layers in project specific web maps for North Carolina. Use this web map template by opening and then saving a copy of the map, removing unnecessary layers, and customizing with project specific data. If you have discovered this resource from a data portal, such as NC OneMap, you will need to click on the View Metadata link in the About section to open the map.

To create this app:Make a map of the AfDB projects CSV file in the Training Materials group.Download the CSV file, click Map (at the top of the page), and drag and drop the file onto your mapFrom the layer menu on your Projects layer choose Change Symbols and show the projects using Unique Symbols and the Status of field.Make a second map of the AfDB projects shown using Unique Symbols and the Sector field.HINT: Create a copy of your first map using Save As... and modify the copy.Assemble your story map on the Esri Story Maps websiteGo to storymaps.arcgis.comAt the top of the site, click AppsFind the Story Map Tabbed app and click Build a Tabbed Story MapFollow the instructions in the app builder. Add the maps you made in previous steps and copy the text from this sample app to your app. Explore and experiment with the app configuration settings.=============OPTIONAL - Make a third map of the AFDB projects summarized by country and add it to your story map.Add the World Countries layer to your map (Add > Search for Layers)From the layer menu on your Projects layer choose Perform Analysis > Summarize Data > Aggregate Points and run the tool to summarize the projects in each country.HINT: UNCHECK "Keep areas with no points"Experiment with changing the symbols and settings on your new layer and remove other unnecessary layers.Save AS... a new map.At the top of the site, click My Content.Find your story map application item, open its Details page, and click Configure App.Use the builder to add your third map and a description to the app and save it.

Purpose: This is an ArcGIS Pro template that GIS Specialists can use to identify vulnerable populations and special needs infrastructure most at risk to flooding events.How does it work?Determine and understand the Place Vulnerability (based on Cutter et al. 1997) and the Special Needs Infrastructure for an area of interest based on Special Flood Hazard Zones, Social Vulnerability Index, and the distribution of its Population and Housing units. The final product will be charts of the data distribution and a Hosted Feature Layer. See this Story Map example for a more detailed explanation.This uses the FEMA National Flood Hazard Layer as an input (although you can substitute your own flood hazard data), check availability for your County before beginning the Task: FEMA NFHL ViewerThe solution consists of several tasks that allow you to:Select an area of interest for your Place Vulnerability Analysis. Select a Hazard that may occur within your area of interest.Select the Social Vulnerability Index (SVI) features contained within your area of interest using the CDC’s Social Vulnerability Index (SVI) – 2016 overall SVI layer at the census tract level in the map.Determine and understand the Social Vulnerability Index for the hazard zones identified within you area of interest.Identify the Special Needs Infrastructure features located within the hazard zones identified within you area of interest.Share your data to ArcGIS Online as a Hosted Feature Layer.FIRST STEPS:Create a folder C:\GIS\ if you do not already have this folder created. (This is a suggested step as the ArcGIS Pro Tasks does not appear to keep relative paths)Download the ZIP file.Extract the ZIP file and save it to the C:\GIS\ location on your computer. Open the PlaceVulnerabilityAnalysis.aprx file.Once the Project file (.aprx) opens, we suggest the following setup to easily view the Tasks instructions, the Map and its Contents, and the Databases (.gdb) from the Catalog pane.The following public web map is included as a Template in the ArcGIS Pro solution file: Place Vulnerability Template Web MapNote 1:As this is a beta version, please take note of some pain points:Data input and output locations may need to be manually populated from the related workspaces (.gdb) or the tools may fail to run. Make sure to unzip/extract the file to the C:\GIS\ location on your computer to avoid issues.Switching from one step to the next may not be totally seamless yet.If you are experiencing any issues with the Flood Hazard Zones service provided, or if the data is not available for your area of interest, you can also download your Flood Hazard Zones data from the FEMA Flood Map Service Center. In the search, use the FEMA ID. Once downloaded, save the data in your project folder and use it as an input.Note 2:In this task, the default hazard being used are the National Flood Hazard Zones. If you would like to use a different hazard, you will need to add the new hazard layer to the map and update all query expressions accordingly.For questions, bug reports, or new requirements contact pdoherty@publicsafetygis.org

The Regional Crime Analysis GIS (RCAGIS) is an Environmental Systems Research Institute (ESRI) MapObjects-based system that was developed by the United States Department of Justice Criminal Division Geographic Information Systems (GIS) Staff, in conjunction with the Baltimore County Police Department and the Regional Crime Analysis System (RCAS) group, to facilitate the analysis of crime on a regional basis. The RCAGIS system was designed specifically to assist in the analysis of crime incident data across jurisdictional boundaries. Features of the system include: (1) three modes, each designed for a specific level of analysis (simple queries, crime analysis, or reports), (2) wizard-driven (guided) incident database queries, (3) graphical tools for the creation, saving, and printing of map layout files, (4) an interface with CrimeStat spatial statistics software developed by Ned Levine and Associates for advanced analysis tools such as hot spot surfaces and ellipses, (5) tools for graphically viewing and analyzing historical crime trends in specific areas, and (6) linkage tools for drawing connections between vehicle theft and recovery locations, incident locations and suspects' homes, and between attributes in any two loaded shapefiles. RCAGIS also supports digital imagery, such as orthophotos and other raster data sources, and geographic source data in multiple projections. RCAGIS can be configured to support multiple incident database backends and varying database schemas using a field mapping utility.

Use the Nearby template to guides your app users to places of interest close to an address. This template helps users find focused types of locations (such as schools) within a search distance of an address, their current location, or other place they specify. They can adjust distance values to change the search radius and get directions to locations they select. For users who are searching, you can set a range for the distance slider so users can define their search buffer or pan the map to see results from the map view. Include directions to help users navigate to locations within a defined search radius. Include the export tool to allow users to capture images of the map along with results from the search. Examples: Create a store locator app that allows customers to input a location, find a nearby store, and navigate to it. Create an app for finding health care facilities within a specified distance of a searched address. Provide users with directions and information for election polling locations. Build an app where users can find nearby trails and view an elevation profile of each result. Data requirements The Nearby template requires a feature layer to take full advantage of its capabilities. Key app capabilities Distance slider - Set a minimum and maximum search radius for finding results. Map extent result - Show all the results in the map view. Panel options - Customize result panel location information with feature attributes from a configured pop-up. Results-focused layout - Keep the map out of the app to maintain focus on the search and results. Attribute filter - Configure map filter options that are available to app users. Export - Print or export the search results or selected features as a .pdf, .jpg, or .png file that includes the pop-up content of returned features and an option to include the map. Alternatively, download the search results as a .csv file. Directions - Provide directions from a searched location to a result location. Elevation profile - Generate an elevation profile graph across an input line feature that can be selected in the scene or from drawing a single or multisegment line using the tool. Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This series of products from MODIS represents the only daily global composites available and is suitable for use at global and regional levels. This True Color band composition (Bands 1 4 3 | Red, Green, Blue) most accurately shows how we see the earth’s surface with our own eyes. It is a natural looking image that is useful for land surface, oceanic and atmospheric analysis. There are four True Color products in total. For each satellite (Aqua and Terra) there is a 250 meter corrected reflectance product and a 500 meter surface reflectance product. Although the resolution is coarser than other satellites, this allows for a global collection of imagery on a daily basis, which is made available in near real-time. In contrast, Landsat needs 16 days to collect a global composite. Besides the maximum resolution difference, the surface and corrected reflectance products also differ in the algorithm used for atmospheric correction.NASA Global Imagery Browse Services (GIBS)This image layer provides access to a subset of the NASA Global Imagery Browse Services (GIBS), which are a set of standard services to deliver global, full-resolution satellite imagery. The GIBS goal is to enable interactive exploration of NASA's Earth imagery for a broad range of users. The purpose of this image layer, and the other GIBS image services hosted by Esri, is to enable convenient access to this beautiful and useful satellite imagery for users of ArcGIS. The source data used by this image layer is a finished image; it is not recommended for quantitative analysis.Several full resolution, global imagery products are built and served by GIBS in near real-time (usually within 3.5 hours of observation). These products are built from NASA Earth Observing System satellites data courtesy of LANCE data providers and other sources. The MODIS instrument aboard Terra and Aqua satellites, the AIRS instrument aboard Aqua, and the OMI instrument aboard Aura are used as sources. Several of the MODIS global products are made available on this Esri hosted service.This image layer hosted by Esri provides direct access to one of the GIBS image products. The Esri servers do not store any of this data itself. Instead, for each received data request, multiple image tiles are retrieved from GIBS, which are then processed and assembled into the proper image for the response. This processing takes place on-the-fly, for each and every request. This ensures that any update to the GIBS data is immediately available in the Esri mosaic service.Note on Time: The image service supporting this map is time enabled, but time has been disabled on this image layer so that the most recent imagery displays by default. If you would like to view imagery over time, you can update the layer properties to enable time animation and configure time settings. The results can be saved in a web map to use later or share with others.

Do not share this map Publicly!This template is for ACTIVE INCIDENTS only. For training, please use the Training template (found here). This workflow uses one template web map and contains all layers of the National Incident Feature Service in a single service (Unlike the standard template which splits features into Edit, View, and Repair services). It is for teams looking for a simple approach to ArcGIS Online implementation. All features are visible; editing is enabled for points, lines, and polygons and disabled for the IR layers [Workflow LINK]; contains the National Incident Feature Service layers: NWCG approved Event schema.This template web map is provided for quick deployment. Listed next are the steps to implement this Standard Workflow:1) Open this web map template in Map Viewer2) Do a Save As (Click Save and select Save As)3) Zoom to your fire area and add bookmarks4) Look for a red triangle polygon with your fire's attributes - do either of these: a. Use this polygon as a start for your incident and modify as needed b. Copy the attributes (most importantly, the IRWIN ID) into a new polygon and delete the triangle (delete in ArcMap or Pro)5) Create a display filter on features to only show features related to your incident (Optional).6) Create a new Photo Point Layer (Content > Create > Feature Layer > From Existing > #TEMPLATE - PhotoPoint). Add this to your web map and remove default PhotoPoint Layer7) Share with your Mobile Editing group8) Add necessary incident personnel to the Mobile Editing group9) Make available for Viewers:a. Save out a second version of this map and disable editing on all the layers except Photo Points.b. Share this version with the Viewing group.10) To track and manage suppression repair needs use the Suppression Repair Add-on

The soil map shows the dominant composition of the soil in the first 2 meters below ground level. In the interests of simplification, a classification in sand, loam or clay per square kilometre has been chosen. This was drawn up on the basis of the soil type map and supplemented with drilling rigs of own drillings and drillings found in the Database Subsurface Flanders. In the context of rainwater policy and the principles of optimal separation with the aim of promoting the natural runoff and infiltration of rainwater, it is important to have an insight into the soil condition and infiltration sensitivity of the upper soil layer in Antwerp. In this way, the integration of water management into urban design can be controlled more effectively and efficiently, as well as the localisation of infiltration-prone areas for future construction projects. For example, city authorities regularly receive questions from contractors, architects and design offices about the possibility of infiltration on a particular plot. This information is also important in the design and implementation of public urban renewal works. The layout of geohydrological maps can contribute to a better alignment between spatial planning, public space design, green management and water management. It is quite possible to save costs by combining multiple management aspects with the construction of green-blue structures: tackling flooding, combating soil desiccation, developing more urban nature and biodiversity. Finally, these data are used as substantiation in the preparation of the rainwater plan; a plan indicating at district level how much infiltration and/or buffer capacity is desirable and in which forms (e.g. collective wadi, canal or pond). The contract concerns the preparation of four geohydrological maps, in particular: a soil map, a groundwater map (meter - ground level) with an annual average depth of the phreatic groundwater table below street level, a groundwater map with annual average levels relative to the topographic reference level (meter -/+ TAW) and an infiltration map of the Antwerp region. The study is part of the characterisation of the subsurface of Antwerp with a view to the localisation of infiltration-prone areas for future construction projects. These maps describe the entire regionthe territory of Antwerp (city of Antwerp with its 9 districts and submunicipalities), with the exception of the Antwerp port area. For the right bank of the Antwerp port area, the possibilities of rainwater infiltration and buffering have already been investigated (What about rainwater in the Antwerp port area?, IMDC iov Port of Antwerp and Alfaport, 2013). In function of the calibration and calculation of groundwater and groundwater data, it was important to integrate the port area into the model area. . The study area is bounded to the north, east, south and west respectively by the national border and municipalities of Berendrecht, Deurne, HobokenStabroek, Kapellen, Brasschaat, Schoten Wijnegem, Wommelgem, Borsbeek, Mortsel, Edegem, Aartselaar, Hemiksem and LinkeroeverZwijndrecht.

The NRCS National Water and Climate Center's Interactive Map displays both current and historic hydrometeorological data in an easy-to-use, visual interface. The information on the map comes from many sources. Natural Resources Conservation Service snowpack and precipitation data are derived from manually-collected snow courses and automated Snow Telemetry (SNOTEL) and Soil Climate Analysis Network (SCAN) stations. Other data sources include precipitation, streamflow, and reservoir data from the U.S. Bureau of Reclamation (BoR), the Applied Climate Information System (ACIS), the U.S. Geological Survey (USGS), and other hydrometeorological monitoring entities. The Interactive Map has two regions: the map display itself, and the map controls which determine both the display mode and the types of data and stations to show on the map: Display Modes; Map Components; Station Conditions Controls; Basin Conditions Controls; Station Inventory Controls. Resources in this dataset:Resource Title: Interactive Map home. File Name: Web Page, url: https://www.nrcs.usda.gov/wps/portal/wcc/home/quicklinks/predefinedMaps/ The Interactive Map provides spatial visualization of current and historic hydrometeorological data collected by the Natural Resources Conservation Service and other monitoring agencies. The map also provides station inventories based on sensor and geographic filters. This page has links to pre-defined maps organized by data type. After opening a map, users can zoom to area of interest, customize the map, and then bookmark the URL to save the settings.

This dataset is the first 1: 100,000 desert spatial database in China based on the graphic data of desert thematic maps. It mainly reflects the geographical distribution, area size, and mobility of sand dunes in China. According to the system design requirements and relevant standards, the input data is standardized and uniformly converted into a standard format for various types of data input. Build a library to run the delivery system. This project uses the TM image in 2000 as the information source, and interprets, extracts, and edits the coverage of the national land use map and TM digital image information in 2000. It uses remote sensing and geographic information system technology to 1: 100,000 Thematic mapping requirements for scale bar maps were made on the desert, sandy land and gravel Gobi in China. The 1: 100,000 desert map across the country can save users a lot of data entry and editing work when they are engaged in research on resources and the environment. Digital maps can be easily converted into layout maps The dataset properties are as follows: Divided into two folders e00 and shp: Desert map name and province comparison table in each folder 01 Ahsm Anhui 02 Bjsm Beijing 03 Fjsm Fujian 04 Gdsm Guangdong 05 Gssm Gansu 06 Gxsm Guangxi Zhuang Autonomous Region 07 Gzsm Guizhou 08 Hebsm Hebei 09 Hensm Henan 10 Hljsm Heilongjiang 11 Hndsm Hainan 12 Hubsm Hubei 13 Jlsm Jilin Province 14 Jssm Jiangsu 15 Jxsm Jiangxi 16 Lnsm Liaoning 17 Nmsm Inner Mongolia Gu Autonomous Region 18 Nxsm Ningxia Hui Autonomous Region 19 Qhsm Qinghai 20 Scsm Sichuan 21 Sdsm Shandong 22 Sxsm Shaanxi Province 23 Tjsm Tianjin 24 Twsm Taiwan Province 25 Xjsm Xinjiang Uygur Autonomous Region 26 Xzsm Tibet Autonomous Region 27 Zjsm Zhejiang 28 Shxsm Shanxi 1. Data projection: Projection: Albers False_Easting: 0.000000 False_Northing: 0.000000 Central_Meridian: 105.000000 Standard_Parallel_1: 25.000000 Standard_Parallel_2: 47.000000 Latitude_Of_Origin: 0.000000 Linear Unit: Meter (1.000000) 2. Data attribute table: area (area) perimeter ashm_ (sequence code) class (desert encoding) ashm_id (desert encoding) 3. Desert coding: mobile sandy land 2341010 Semi-mobile sandy land Semi-fixed sandy land 2341030 Gobi 2342000 Saline land 2343000 4: File format: National, sub-provincial and county-level desert map data types are vector shapefiles and E00 5: File naming: Data organization based on the National Basic Resources and Environmental Remote Sensing Dynamic Information Service System is performed on the file management layer of Windows NT. The file and directory names are compound names of English characters and numbers. Pinyin + SM composition, such as the desert map of Gansu Province is GSSM. The flag and county desert map is the pinyin + xxxx of the province name, and xxxx is the last four digits of the flag and county code. The division of provinces, districts, flags and counties is based on the administrative division data files in the national basic resources and environmental remote sensing dynamic information service operation system.

Cette carte de l'eau souterraine montre la position/hauteur de l'eau souterraine phréatique et la direction d'écoulement prédominante par rapport au deuxième passage général de l'eau (TAW), le plan topographique de référence en Flandre. Celle-ci a été établie sur la base du réseau d'écartement existant de la ville d'Anvers. Dans les endroits où il manquait des données sur les eaux souterraines, des tubes de surveillance supplémentaires ont été installés dans le cadre de cette étude. Comme pour la carte des eaux souterraines (m-mv), des données supplémentaires provenant de sondes de la base de données du sous-sol de la Flandre ont été utilisées à cette fin. Dans le contexte de la politique des eaux de pluie et des principes de séparation optimale dans le but de promouvoir le ruissellement naturel et l'infiltration des eaux de pluie, il est important d'avoir un aperçu de l'état du sol et de la sensibilité à l'infiltration de la couche supérieure du sol à Anvers. De cette façon, l'intégration de la gestion de l'eau dans la conception urbaine peut être contrôlée de manière plus efficace et efficiente, ainsi que la localisation des zones sujettes aux infiltrations pour les futurs projets de construction. Par exemple, les autorités municipales reçoivent régulièrement des questions des entrepreneurs, des architectes et des bureaux d'études sur la possibilité d'infiltration sur une parcelle particulière. Ces informations sont également importantes dans la conception et la mise en œuvre des travaux publics de rénovation urbaine. La mise en page des cartes géohydrologiques peut contribuer à un meilleur alignement entre l'aménagement du territoire, la conception de l'espace public, la gestion verte et la gestion de l'eau. Il est tout à fait possible de réduire les coûts en combinant de multiples aspects de gestion avec la construction de structures vertes-bleues: la lutte contre les inondations, la lutte contre la dessiccation des sols, le développement de la nature urbaine et de la biodiversité. Enfin, ces données sont utilisées pour étayer l’élaboration du plan relatif aux eaux de pluie; un plan indiquant au niveau du district la quantité d’infiltration et/ou la capacité tampon souhaitable et sous quelles formes (par exemple, oued collectif, canal ou étang). Le marché porte sur l'élaboration de quatre cartes géohydrologiques, notamment: une carte du sol, une carte des eaux souterraines (mètre - niveau du sol) avec une profondeur moyenne annuelle de la nappe phréatique sous le niveau de la rue, une carte des eaux souterraines avec des niveaux moyens annuels par rapport au niveau de référence topographique (mètre -/+ TAW) et une carte des infiltrations de la région d’Anvers. L'étude s'inscrit dans la caractérisation du sous-sol d'Anvers en vue de la localisation de zones sujettes aux infiltrations pour de futurs projets de construction. Ces cartes décrivent l'ensemble du territoire d'Anvers (la ville d'Anvers avec ses 9 districts et sous-municipalités), à l'exception de la zone portuaire d'Anvers. Pour la rive droite de la zone portuaire d'Anvers, les possibilités d'infiltration et de tamponnage des eaux de pluie ont déjà été étudiées (Qu'en est-il des eaux de pluie dans la zone portuaire d'Anvers?, IMDC iov Port of Antwerp and Alfaport, 2013). En fonction de l'étalonnage et du calcul des données sur les eaux souterraines et les eaux souterraines, il était important d'intégrer la zone portuaire dans la zone modèle. . La zone d'étude est délimitée au nord, à l'est, au sud et à l'ouest respectivement par la frontière nationale et les municipalités de Berendrecht, Deurne, HobokenStabroek, Kapellen, Brasschaat, Schoten Wijnegem, Wommelgem, Borsbeek, Mortsel, Edegem, Aartselaar, Hemiksem et LinkeroeverZwijndrecht.

Minimalist showcases your map with the option to include a legend, item description, and popup info in side panel. This app maximizes the view of your map in a simplified layout. Add a custom header to your app to match the theme of your organization. Include Bookmarks to guide viewers to points of interest on your map. Enable the Screenshot tool to allow viewers to capture images of your map.Examples:Maximize screen real estate of your map and allow it to speak for itself Create an app that viewers with any level of experience can navigate with easePresent a map with feature layers, imagery, or feature collectionsData RequirementsThis app has no data requirements.Key App CapabilitiesMap details - Share details from an already populated map description in the side panel of the appPop-up panel - Present pop-up content in the side panel of the app rather than in the mapScreenshot - Capture an image of the map to exportHover pop-ups - Provide viewers quick access to attribute information that appears when hovering on a featureNavigation boundary - Keep the area in the map in focus by using a navigation boundary or disabling the ability to scrollBasemap toggle - Provide an option to change the look of the map by toggling to a different basemapHome, Zoom Controls, Legend, Layer List, SearchSupportabilityThis web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This map shows the extent to which rainwater can be infiltrated into the top layer of the soil. Based on the soil map and the groundwater map (m-mv) with depth of the groundwater table, the infiltration map was drawn up. This combines the soil type data, with their estimated infiltration capacity, and the groundwater levels to give an estimate of the infiltration sensitivity. This infiltration sensitivity is visually displayed on the infiltration card. In the context of rainwater policy and the principles of optimal separation with the aim of promoting the natural runoff and infiltration of rainwater, it is important to have an insight into the soil condition and infiltration sensitivity of the upper soil layer in Antwerp. In this way, the integration of water management into urban design can be controlled more effectively and efficiently, as well as the localisation of infiltration-prone areas for future construction projects. For example, city authorities regularly receive questions from contractors, architects and design offices about the possibility of infiltration on a particular plot. This information is also important in the design and implementation of public urban renewal works. The layout of geohydrological maps can contribute to a better alignment between spatial planning, public space design, green management and water management. It is quite possible to save costs by combining multiple management aspects with the construction of green-blue structures: tackling flooding, combating soil desiccation, developing more urban nature and biodiversity. Finally, these data are used as substantiation in the preparation of the rainwater plan; a plan indicating at district level how much infiltration and/or buffer capacity is desirable and in which forms (e.g. collective wadi, canal or pond). The contract concerns the preparation of four geohydrological maps, in particular: a soil map, a groundwater map (meter - ground level) with an annual average depth of the phreatic groundwater table below street level, a groundwater map with annual average levels relative to the topographic reference level (meter -/+ TAW) and an infiltration map of the Antwerp region. The study is part of the characterisation of the subsurface of Antwerp with a view to the localisation of infiltration-prone areas for future construction projects. These maps describe the entire regionthe territory of Antwerp (city of Antwerp with its 9 districts and submunicipalities), with the exception of the Antwerp port area. For the right bank of the Antwerp port area, the possibilities of rainwater infiltration and buffering have already been investigated (What about rainwater in the Antwerp port area?, IMDC iov Port of Antwerp and Alfaport, 2013). In function of the calibration and calculation of groundwater and groundwater data, it was important to integrate the port area into the model area. . The study area is bounded to the north, east, south and west respectively by the national border and municipalities of Berendrecht, Deurne, HobokenStabroek, Kapellen, Brasschaat, Schoten Wijnegem, Wommelgem, Borsbeek, Mortsel, Edegem, Aartselaar, Hemiksem and LinkeroeverZwijndrecht.

Use the Interactive Legend template to allow users to filter layers in your map by toggling the visibility of features based categories and ranges in the legend. Choose from paired feature-specific effects, such as bloom and blur, to distinguish between selected items in the legend and the remaining data. Choose from several options to emphasize selected items in the legend while other items remain on the map in muted colors. Examples: Form a better understanding of the spatial relationship between map features by changing the visibility of the content. Present economic data relevant to numerical range values of interest during a seminar. Analyze crime data to facilitate decision making of law enforcement distribution pertaining to specific crime categories. Data requirements The Interactive Legend template requires a feature layer to use all of its capabilities. The following drawing styles are supported: Location (Single Symbol) Types (Unique symbols) Counts and amounts (Size) - Classify Data Checked Counts and Amounts (Color) - Classify Data Checked Relationship Relationship and Size (Partially Interactive) Predominant Category Predominant Category and Size (Partially interactive) Types and Size (Partially interactive) Key app capabilities Layer effects - Use layer effects to differentiate between features included and excluded in a filter, and specify how features are emphasized and de-emphasized when a filter is applied using the legend. Zoom to button - Allow users to zoom to features selected in the legend. Feature count - Include a feature count for items that are selected in the legend Export - Capture an image (PDF, JPG, or PNG) from the app that a user can save. Time filter - Filter features in the map using time enabled layers Language switcher - Provide translations for custom text and create a multilingual app. Home, Zoom controls, Legend, Layer List, Search Supportability This web app is designed responsively to be used in browsers on desktops, mobile phones, and tablets. We are committed to ongoing efforts towards making our apps as accessible as possible. Please feel free to leave a comment on how we can improve the accessibility of our apps for those who use assistive technologies.

This dataset contains both large (A0) printable maps of the Torres Strait broken into six overlapping regions, based on a clear sky, clear water composite Sentinel 2 composite imagery and the imagery used to create these maps. These maps show satellite imagery of the region, overlaid with reef and island boundaries and names. Not all features are named, just the more prominent features. This also includes a vector map of Ashmore Reef and Boot Reef in Coral Sea as these were used in the same discussions that these maps were developed for. The map of Ashmore Reef includes the atoll platform, reef boundaries and depth polygons for 5 m and 10 m.

This dataset contains all working files used in the development of these maps. This includes all a copy of all the source datasets and all derived satellite image tiles and QGIS files used to create the maps. This includes cloud free Sentinel 2 composite imagery of the Torres Strait region with alpha blended edges to allow the creation of a smooth high resolution basemap of the region.

The base imagery is similar to the older base imagery dataset: Torres Strait clear sky, clear water Landsat 5 satellite composite (NERP TE 13.1 eAtlas, AIMS, source: NASA).

Most of the imagery in the composite imagery from 2017 - 2021.


Method:
The Sentinel 2 basemap was produced by processing imagery from the World_AIMS_Marine-satellite-imagery dataset (01-data/World_AIMS_Marine-satellite-imagery in the data download) for the Torres Strait region. The TrueColour imagery for the scenes covering the mapped area were downloaded. Both the reference 1 imagery (R1) and reference 2 imagery (R2) was copied for processing. R1 imagery contains the lowest noise, most cloud free imagery, while R2 contains the next best set of imagery. Both R1 and R2 are typically composite images from multiple dates.

The R2 images were selectively blended using manually created masks with the R1 images. This was done to get the best combination of both images and typically resulted in a reduction in some of the cloud artefacts in the R1 images. The mask creation and previewing of the blending was performed in Photoshop. The created masks were saved in 01-data/R2-R1-masks. To help with the blending of neighbouring images a feathered alpha channel was added to the imagery. The processing of the merging (using the masks) and the creation of the feathered borders on the images was performed using a Python script (src/local/03-merge-R2-R1-images.py) using the Pillow library and GDAL. The neighbouring image blending mask was created by applying a blurring of the original hard image mask. This allowed neighbouring image tiles to merge together.

The imagery and reference datasets (reef boundaries, EEZ) were loaded into QGIS for the creation of the printable maps.

To optimise the matching of the resulting map slight brightness adjustments were applied to each scene tile to match its neighbours. This was done in the setup of each image in QGIS. This adjustment was imperfect as each tile was made from a different combinations of days (to remove clouds) resulting in each scene having a different tonal gradients across the scene then its neighbours. Additionally Sentinel 2 has slight stripes (at 13 degrees off the vertical) due to the swath of each sensor having a slight sensitivity difference. This effect was uncorrected in this imagery.


Single merged composite GeoTiff:
The image tiles with alpha blended edges work well in QGIS, but not in ArcGIS Pro. To allow this imagery to be used across tools that don't support the alpha blending we merged and flattened the tiles into a single large GeoTiff with no alpha channel. This was done by rendering the map created in QGIS into a single large image. This was done in multiple steps to make the process manageable.

The rendered map was cut into twenty 1 x 1 degree georeferenced PNG images using the Atlas feature of QGIS. This process baked in the alpha blending across neighbouring Sentinel 2 scenes. The PNG images were then merged back into a large GeoTiff image using GDAL (via QGIS), removing the alpha channel. The brightness of the image was adjusted so that the darkest pixels in the image were 1, saving the value 0 for nodata masking and the boundary was clipped, using a polygon boundary, to trim off the outer feathering. The image was then optimised for performance by using internal tiling and adding overviews. A full breakdown of these steps is provided in the README.md in the 'Browse and download all data files' link.

The merged final image is available in export\TS_AIMS_Torres Strait-Sentinel-2_Composite.tif.


Source datasets:
Complete Great Barrier Reef (GBR) Island and Reef Feature boundaries including Torres Strait Version 1b (NESP TWQ 3.13, AIMS, TSRA, GBRMPA), https://eatlas.org.au/data/uuid/d2396b2c-68d4-4f4b-aab0-52f7bc4a81f5

Geoscience Australia (2014b), Seas and Submerged Lands Act 1973 - Australian Maritime Boundaries 2014a - Geodatabase [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, https://dx.doi.org/10.4225/25/5539DFE87D895

Basemap/AU_GA_AMB_2014a/Exclusive_Economic_Zone_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014a). The Geodatabase was loaded in ArcMap. The Exclusive_Economic_Zone_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

Geoscience Australia (2014a), Treaties - Australian Maritime Boundaries (AMB) 2014a [Dataset]. Canberra, Australia: Author. https://creativecommons.org/licenses/by/4.0/ [license]. Sourced on 12 July 2017, http://dx.doi.org/10.4225/25/5539E01878302
Basemap/AU_GA_Treaties-AMB_2014a/Papua_New_Guinea_TSPZ_AMB2014a_Limit.shp
The original data was obtained from GA (Geoscience Australia, 2014b). The Geodatabase was loaded in ArcMap. The Papua_New_Guinea_TSPZ_AMB2014a_Limit layer was loaded and exported as a shapefile. Since this file was small no clipping was applied to the data.

AIMS Coral Sea Features (2022) - DRAFT
This is a draft version of this dataset. The region for Ashmore and Boot reef was checked. The attributes in these datasets haven't been cleaned up. Note these files should not be considered finalised and are only suitable for maps around Ashmore Reef. Please source an updated version of this dataset for any other purpose.
CS_AIMS_Coral-Sea-Features/CS_Names/Names.shp
CS_AIMS_Coral-Sea-Features/CS_Platform_adj/CS_Platform.shp
CS_AIMS_Coral-Sea-Features/CS_Reef_Boundaries_adj/CS_Reef_Boundaries.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth5m_Coral-Sea.shp
CS_AIMS_Coral-Sea-Features/CS_Depth/CS_AIMS_Coral-Sea-Features_Img_S2_R1_Depth10m_Coral-Sea.shp

Murray Island 20 Sept 2011 15cm SISP aerial imagery, Queensland Spatial Imagery Services Program, Department of Resources, Queensland
This is the high resolution imagery used to create the map of Mer.

World_AIMS_Marine-satellite-imagery
The base image composites used in this dataset were based on an early version of Lawrey, E., Hammerton, M. (2024). Marine satellite imagery test collections (AIMS) [Data set]. eAtlas. https://doi.org/10.26274/zq26-a956. A snapshot of the code at the time this dataset was developed is made available in the 01-data/World_AIMS_Marine-satellite-imagery folder of the download of this dataset.


Data Location:
This dataset is filed in the eAtlas enduring data repository at: data\custodian\2020-2029-AIMS\TS_AIMS_Torres-Strait-Sentinel-2-regional-maps. On the eAtlas server it is stored at eAtlas GeoServer\data\2020-2029-AIMS.


Change Log:
2025-05-12: Eric Lawrey
Added Torres-Strait-Region-Map-Masig-Ugar-Erub-45k-A0 and Torres-Strait-Eastern-Region-Map-Landscape-A0. These maps have a brighten satellite imagery to allow easier reading of writing on the maps. They also include markers for geo-referencing the maps for digitisation.

2025-02-04: Eric Lawrey
Fixed up the reference to the World_AIMS_Marine-satellite-imagery dataset, clarifying where the source that was used in this dataset. Added ORCID and RORs to the record.

2023-11-22: Eric Lawrey
Added the data and maps for close up of Mer.
- 01-data/TS_DNRM_Mer-aerial-imagery/
- preview/Torres-Strait-Mer-Map-Landscape-A0.jpeg
- exports/Torres-Strait-Mer-Map-Landscape-A0.pdf
Updated 02-Torres-Strait-regional-maps.qgz to include the layout for the new map.

2023-03-02: Eric Lawrey
Created a merged version of the satellite imagery, with no alpha blending so that it can be used in ArcGIS Pro. It is now a single large GeoTiff image. The Google Earth Engine source code for the World_AIMS_Marine-satellite-imagery was included to improve the reproducibility and provenance of the dataset, along with a calculation of the distribution of image dates that went into the final composite image. A WMS service for the imagery was also setup and linked to from the metadata. A cross reference to the older Torres Strait clear sky clear water Landsat composite imagery was also added to the record.

This dataset shows the results of mapping the connectivity of key values (natural heritage, indigenous heritage, social and historic and economic) of the Great Barrier Reef with its neighbouring regions (Torres Strait, Coral Sea and Great Sandy Strait). The purpose of this mapping process was to identify values that need joint management across multiple regions. It contains a spreadsheet containing the connection information obtained from expert elicitation, all maps derived from this information and all GIS files needed to recreate these maps. This dataset contains the connection strength for 59 attributes of the values between 7 regions (GBR Far Northern, GBR Cairns-Cooktown, GBR Whitsunday-Townsville, GBR Mackay-Capricorn, Torres Strait, Coral Sea and Great Sandy Strait) based on expert opinion. Each connection is assessed based on its strength, mechanism and confidence. Where a connection was known to not exist between two regions then this was also explicitly recorded. A video tutorial on this dataset and its maps is available from https://vimeo.com/335053846.

Methods:

The information for the connectivity maps was gathered from experts (~30) during a 3-day workshop in August 2017. Experts were provided with a template containing a map of Queensland and the neighbouring seas, with an overlay of the regions of interest to assess the connectivity. These were Torres Strait, GBR:Far North Queensland, GBR:Cairns to Cooktown, GBC: Townsville to Whitsundays, GBR: Mackay to Capricorn Bunkers and Great Sandy Strait (which includes Hervey bay). A range of reference maps showing locations of the values were provided, where this information could be obtained. As well as the map the template provided 7x7 table for filling in the connectivity strength and connection type between all combinations of these regions. The experts self-organised into groups to discuss and complete the template for each attribute to be mapped. Each expert was asked to estimate the strength of connection between each region as well as the connection mechanism and their confidence in the information. Due to the limited workshop time the experts were asked to focus on initially recording the connections between the GBR and its neighbouring regions and not to worry about the internal connections in the GBR, or long-distance connections along the Queensland coast. In the second half of the workshop the experts were asked to review the maps created and expand on the connections to include those internal to the GBR. After the workshop an initial set of maps were produced and reviewed by the project team and a range of issues were identified and resolved. Additional connectivity maps for some attributes were prepared after the workshop by the subject experts within the project team. The data gathered from these templates was translated into a spreadsheet, then processing into the graphic maps using QGIS to present the connectivity information. The following are the value attributes where their connectivity was mapped: Seagrass meadows: pan-regional species (e.g. Halophila spp. and Halodule spp.) Seagrass meadows: tropical/sub-tropical (Cymodocea serrulata, Syringodium isoetifolium) Seagrass meadows: tropical (Thalassia, Cymodocea, Thalassodendron, Enhalus, Rotundata) Seagrass meadows: Zostera muelleri Mangroves & saltmarsh Hard corals Crustose coralline algae Macroalgae Crown of thorns starfish larval flow Acropora larval flow Casuarina equisetifolia & Pandanus tectorius Argusia argentia Pisonia grandis: cay vegetation Inter-reef gardens (sponges + gorgonians) (Incomplete) Halimeda Upwellings Pelagic foraging seabirds Inshore and offshore foraging seabirds Migratory shorebirds Ornate rock lobster Yellowfin tuna Black marlin Spanish mackerel Tiger shark Grey nurse shark Humpback whales Dugongs Green turtles Hawksbill turtles Loggerhead turtles Flatback turtles Longfin & Shortfin Eels Red-spot king prawn Brown tiger prawn Eastern king prawns Great White Shark Sandfish (H. scabra) Black teatfish (H. whitmaei) Location of sea country Tangible cultural resources Location of place attachment Location of historic shipwrecks Location of places of social significance Location of commercial fishing activity Location of recreational use Location of tourism destinations Australian blacktip shark (C. tilstoni) Barramundi Common black tip shark (C. limbatus) Dogtooth tuna Grey mackerel Mud crab Coral trout (Plectropomus laevis) Coral trout (Plectropomus leopardus) Red throat emperor Reef manta Saucer scallop (Ylistrum balloti) Bull shark Grey reef shark

Limitations of the data:

The connectivity information in this dataset is only rough in nature, capturing the interconnections between 7 regions. The connectivity data is based on expert elicitation and so is limited by the knowledge of the experts that were available for the workshop. In most cases the experts had sufficient knowledge to create robust maps. There were however some cases where the knowledge of the participants was limited, or the available scientific knowledge on the topic was limited (particularly for the ‘inter-reefal gardens’ attribute) or the exact meaning of the value attribute was poorly understood or could not be agreed up on (particularly for the social and indigenous heritage maps). This information was noted with the maps. These connectivity maps should be considered as an initial assessment of the connections between each of the regions and should not be used as authoritative maps without consulting with additional sources of information. Each of the connectivity links between regions was recorded with a level of confidence, however these were self-reported, and each assessment was performed relatively quickly, with little time for reflection or review of all the available evidence. It is likely that in many cases the experts tended to have a bias to mark links with strong confidence. During subsequent revisions of some maps there were substantial corrections and adjustments even for connections with a strong confidence, indicating that there could be significant errors in the maps where the experts were not available for subsequent revisions. Each of the maps were reviewed by several project team members with broad general knowledge. Not all connection combinations were captured in this process due to the limited expert time available. A focus was made on capturing the connections between the GBR and its neighbouring regions. Where additional time was available the connections within 4 regions in the GBR was also captured. The connectivity maps only show connections between immediately neighbouring regions, not far connections such as between Torres Strait and Great Sandy Strait. In some cases the connection information for longer distances was recorded from the experts but not used in the mapping process. The coastline polygon and the region boundaries in the maps are not spatially accurate. They were simplified to make the maps more diagrammatic. This was done to reduce the chance of misinterpreting the connection arrows on the map as being spatially explicit.

Format:

This dataset is made up of a spreadsheet that contains all the connectivity information recorded from the expert elicitation and all the GIS files needed to recreate the generated maps.

original/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_Master_v2018-09-05.xlsx: ‘Values connectivity’: This sheet contains the raw connectivity codes transcribed from the templates produced prepared by the subject experts. This is the master copy of the connection information. Subsequent sheets in the spreadsheet are derived using formulas from this table. 1-Vertical-data: This is a transformation of the ‘Values connectivity’ sheet so that each source and destination connection is represented as a single row. This also has the connection mechanism codes split into individual columns to allow easier processing in the map generation. This sheet pulls in the spatial information for the arrows on the maps (‘LinkGeom’ attribute) or crosses that represent no connections (‘NoLinkGeom’) using lookup tables from the ‘Arrow-Geom-LUT’ and ‘NoConnection-Geom-LUT’ sheets. 2.Point-extract: This contains all the ‘no connection’ points from the ‘Values connectivity’ dataset. This was saved as working/ GBR_NESP-TWQ-3-3-3_Seascape-connectivity_no-con-pt.csv and used by the QGIS maps to draw all the crosses on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘NoLinkGeom’ attribute is used to filter out all line features, by unchecking blank rows in the ‘NoLinkGeom’ filter. 2.Line-extract: This contains all the ‘connections’ between regions from the ‘Values connectivity’ dataset. This was saved as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_arrows.csv and used by the QGIS maps to draw all the arrows on the maps. This table is created by copy and pasting (values only) the ‘1-Vertical-data’ sheet when the ‘LinkGeom’ attribute is used to filter out all point features, by unchecking blank rows in the ‘LinkGeom’ filter. Map-Atlas-Settings: This contains the metadata for each of the maps generated by QGIS. This sheet was exported as working/GBR_NESP-TWQ-3-3-3_Seascape-connectivity_map-atlas-settings.csv and used by QGIS to drive its Atlas feature to generate one map per row of this table. The AttribID is used to enable and disable the appropriate connections on the map being generated. The WKT attribute (Well Known Text) determines the bounding box of the map to be generated and the other attributes are used to display text on the map. map-image-metadata: This table contains metadata descriptions for each of the value attribute maps. This metadata was exported as a CSV and saved into the final generated JPEG maps using the eAtlas Image Metadata Editor Application

This group layer provides a template for showing a thematic pattern based on where humans live. This is done using three things:The WorldPop Populated Footprint layer from ArcGIS Living Atlas. The legend and pop-up are disabled so that your thematic data is still the focus of the mapThe Destination In Blend Mode applied to the World Pop layerWhatever thematic layer (or layers) you want to apply the pattern to, underneath the WorldPop layer within the Table of ContentsThis combination of data sources provides a visualization that helps you see beyond administrative boundaries, and lets you see the pattern based on human settlement. To use this group layer template, add it to your map and replace the County layer with whatever layer (or layers) of thematic data you want. Make sure to keep your thematic data below the Populated Footprint layer in the Table of Contents. To do more with this, try these cartographic techniques:On the group layer itself (not the data layers), apply a Dropshadow Effect. This will make the human settlement pop. If it is too much, try applying 50% opacity to the dropshadow, or change the dropshadow color to something lighter. Duplicate your thematic layer(s) and pull the duplicate layer out of the group layer and underneath it within the Table of Contents. Then apply a 50-60% transparency to the layer. This will allow your data pattern to appear with the traditional boundaries behind the scenes, simpy as context. This blog shows a similar approach.

GPM_3IMERGHHE Early Precipitation Rate L3 V07 (GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 (GPM_3IMERGHHE 07)) is an image service derived from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM) Early dataset. The image service shows precipitation rate (mm/hr), approximately four hours after observation. The image service provides global coverage with a temporal span from 06/01/2000 0:00 UTC to present at 30-minute intervals. The service is updated every three hours to incorporate the new granules. To access the REST endpoint for the service, input the URL into a browser or select View just above the URL.IMERG is an algorithm that estimates precipitation rate from multiple passive microwave sensors in the GPM constellation, the GPM Dual-Frequency Radar, and infrared (IR) sensors mounted on geostationary satellites. Currently, the near-real-time Early Run estimates have no concluding calibration. Briefly describing the Early Run, the input precipitation estimates computed from the various satellite passive microwave sensors are intercalibrated to the Combined Radar-Radiometer Algorithm (CORRA) product (because it is presumed to be the best snapshot Tropical Rainfall Measuring Mission (TRMM)/GPM estimate after adjustment to the monthly Global Precipitation Climatology Project Satellite-Gauge (GPCP SG)), then "forward morphed" and combined with microwave precipitation-calibrated geo-IR fields to provide half-hourly precipitation estimates on a 0.1°x0.1° (roughly 10x10 km) grid over the globe. Precipitation phase is computed using analyses of surface temperature, humidity, and pressure. Dataset at a glance Shortname: GPM_3IMERGHHEDOI: 10.5067/GPM/IMERG/3B-HH-E/07Version: 07Coverage: -180.0,-90.0,180.0,90.0Temporal Coverage: 2000-06-01 to PresentData ResolutionSpatial: 0.1 ° x 0.1 °Temporal: 30 minutes SymbologyThe default symbology in the Map Viewer may be changed to accommodate other color schemes using the settings in the Image Display panel from the layer settings menu. NoData values, and values less than 0.03 mm/hr (the current threshold value for the IMERG algorithm) have been removed. Ensure that pop-ups are enabled to view pixel values (select Modify Map first). Temporal CoverageThe source dataset is in UTC time but the service is displayed in the Map Viewer in local time. The data is available in 30-minute intervals, and the map visualization may be modified by opening the Time Slider Settings menu from the icon on the time slider bar. The total temporal coverage may be limited to the desired range and the time interval may also be changed. The options in the time interval units are based on the total time range input, so a shorter time range will enable shorter time units to be selected from the time interval drop-down menu. If the time settings are set to more than 30-minute intervals, the first time slice in the time interval is visible. Portal Options Select Modify Map to customize the layer visualization. More information about the image service capabilities may be found in the REST endpoint. In the portal, the basemap may be changed by selecting the desired option from the Basemap menu. Further instructions on using the image service may be found at [GES DISC How-To's: How to access the GES DISC IMERG ArcGIS Image Service using the ArcGIS Enterprise Map Viewer (nasa.gov)].

The Gorges du Verdon were ranked in 1990. They have an estimated annual attendance of 700,000 visitors and have an international reputation. Faced with the management problems caused by this attendance, the communities and the State wanted to set up a**Operation Grand Site** approach, led by the Verdon Regional Natural Park since 2002. The framework agreement formalising the commitment of each of the partners (State, Regional Council, Alpes de Haute-Provence and Var departments, 7 municipalities of the Grand Site and Parc du Verdon) was signed in 2010. The goal is to obtain the label Grand Site de France in 2020. The project of Operation Grand Site of the Gorges du Verdon provides for the realisation of several actions to promote, preserve, improve public reception and in particular: * the layout of the gazebos of the Gorges and in particular of certain belvedere interpreters; * the valorisation of the identity heritage of the Gorges; * improving information; * the evolution and deployment of interpretation venues on the Grand Site. The Interpretation Scheme, supported and animated by the Parc du Verdon, should make it possible to define a global strategy of interpretation of the Gorges du Verdon. This action is also part of the Espace Valléen Programme (2015-2020). --- Data are from the final report — interpretation scenario carried out by Cairn Interpretation.