The Digital Geomorphic-GIS Map of Gulf Islands National Seashore (5-meter accuracy and 1-foot resolution 2006-2007 mapping), Mississippi and Florida is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (guis_geomorphology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (guis_geomorphology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (guis_geomorphology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (guis_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (guis_geomorphology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (guis_geomorphology_metadata_faq.pdf). Please read the guis_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (guis_geomorphology_metadata.txt or guis_geomorphology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:26,000 and United States National Map Accuracy Standards features are within (horizontally) 13.2 meters or 43.3 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

Simple Map Viewer is a configurable app template with a straightforward and simple user experience for exploring a web map. Use CasesDisplays a map with a legend and description within a sliding drawer pane. This is a good general-purpose map app when simple navigation tools are needed.Configurable OptionsSimple Map Viewer presents content from a web map and can be configured using the following options:Provide a title and description.Choose the color of the theme, text, and legend header.Enable and customize the ability for feature and location search.Enable tools for finding current location and zooming to the default home extentSupported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis application has no data requirements.Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to create a web appOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The Digital Map Ecosystem is poised for substantial growth, projected to reach an estimated market size of $35,000 million by 2025, with a robust Compound Annual Growth Rate (CAGR) of 15% extending through 2033. This expansion is primarily fueled by the escalating demand for advanced navigation solutions, the increasing integration of digital maps in autonomous driving systems, and the burgeoning use of location-based services across diverse sectors. The proliferation of smart devices and the growing adoption of IoT technologies further amplify the need for precise and real-time mapping data, driving innovation in map acquisition and processing. Furthermore, the defense sector's reliance on sophisticated mapping for reconnaissance and strategic planning, alongside the civil sector's demand for efficient logistics, urban planning, and personalized user experiences, are significant growth catalysts. The market is characterized by rapid advancements in 3D mapping, AI-powered data analysis, and the development of highly detailed, dynamic map layers that cater to increasingly complex user requirements. Despite the optimistic outlook, the Digital Map Ecosystem faces certain restraints. High infrastructure costs associated with data acquisition, processing, and maintenance present a significant barrier to entry and scalability for smaller players. Privacy concerns surrounding the collection and usage of location data, coupled with stringent data protection regulations in various regions, necessitate careful compliance and can slow down the pace of innovation and deployment. Additionally, the intense competition among established tech giants and emerging startups can lead to pricing pressures and a constant need for differentiation. However, the market is actively addressing these challenges through strategic partnerships, cloud-based solutions, and a focus on delivering value-added services beyond basic mapping. The segmentation of the market into Civil and Military applications, with further subdivisions into Acquisition, Production, and Release Systems, highlights the specialized needs and technological advancements tailored to each segment, ensuring continued relevance and adoption. This report offers an in-depth analysis of the global Digital Map Ecosystem, forecasting its trajectory from the historical period of 2019-2024, through the base year of 2025, and into the forecast period of 2025-2033. We delve into the intricate workings of this vital sector, providing actionable insights for stakeholders.

TDEC is continuously striving to create better business practices through GIS and one way that we have found to provide information and answer some question is utilizing an interactive map. An interactive map is a display of geospatial data that allows you to manipulate and query the contents to get the information needed using a set of provided tools. Interactive maps are created using GIS software, and then distributed to users, usually over a computer network. The TDEC Land and Water interactive map will allow you to do simple tasks such as pan, zoom, measure and find a lat/long, while also giving you the capability of running simple queries to locate land and waters by name, entity, and number. With the ability to turn off and on back ground images such as aerial imagery (both black and white as well as color), we hope that you can find much utility in the tools provided.

The Digital Geologic-GIS Map of Sagamore Hill National Historic Site and Vicinity, New York is composed of GIS data layers and GIS tables, and is available in the following GRI-supported GIS data formats: 1.) a 10.1 file geodatabase (sahi_geology.gdb), a 2.) Open Geospatial Consortium (OGC) geopackage, and 3.) 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. The file geodatabase format is supported with a 1.) ArcGIS Pro map file (.mapx) file (sahi_geology.mapx) and individual Pro layer (.lyrx) files (for each GIS data layer), as well as with a 2.) 10.1 ArcMap (.mxd) map document (sahi_geology.mxd) and individual 10.1 layer (.lyr) files (for each GIS data layer). The OGC geopackage is supported with a QGIS project (.qgz) file. Upon request, the GIS data is also available in ESRI 10.1 shapefile format. Contact Stephanie O'Meara (see contact information below) to acquire the GIS data in these GIS data formats. In addition to the GIS data and supporting GIS files, three additional files comprise a GRI digital geologic-GIS dataset or map: 1.) A GIS readme file (sahi_geology_gis_readme.pdf), 2.) the GRI ancillary map information document (.pdf) file (sahi_geology.pdf) which contains geologic unit descriptions, as well as other ancillary map information and graphics from the source map(s) used by the GRI in the production of the GRI digital geologic-GIS data for the park, and 3.) a user-friendly FAQ PDF version of the metadata (sahi_geology_metadata_faq.pdf). Please read the sahi_geology_gis_readme.pdf for information pertaining to the proper extraction of the GIS data and other map files. Google Earth software is available for free at: https://www.google.com/earth/versions/. QGIS software is available for free at: https://www.qgis.org/en/site/. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). For a complete listing of GRI products visit the GRI publications webpage: For a complete listing of GRI products visit the GRI publications webpage: https://www.nps.gov/subjects/geology/geologic-resources-inventory-products.htm. For more information about the Geologic Resources Inventory Program visit the GRI webpage: https://www.nps.gov/subjects/geology/gri,htm. At the bottom of that webpage is a "Contact Us" link if you need additional information. You may also directly contact the program coordinator, Jason Kenworthy (jason_kenworthy@nps.gov). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: U.S. Geological Survey. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (sahi_geology_metadata.txt or sahi_geology_metadata_faq.pdf). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:62,500 and United States National Map Accuracy Standards features are within (horizontally) 31.8 meters or 104.2 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS, QGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.3. (available at: https://www.nps.gov/articles/gri-geodatabase-model.htm).

The Story Map Basic application is a simple map viewer with a minimalist user interface. Apart from the title bar, an optional legend, and a configurable search box the map fills the screen. Use this app to let your map speak for itself. Your users can click features on the map to get more information in pop-ups. The Story Map Basic application puts all the emphasis on your map, so it works best when your map has great cartography and tells a clear story.You can create a Basic story map by sharing a web map as an application from the map viewer. You can also click the 'Create a Web App' button on this page to create a story map with this application. Optionally, the application source code can be downloaded for further customization and hosted on your own web server.For more information about the Story Map Basic application, a step-by-step tutorial, and a gallery of examples, please see this page on the Esri Story Maps website.

Rockfall Hazard Mapping Software Market Outlook

According to our latest research, the global Rockfall Hazard Mapping Software market size reached USD 1.21 billion in 2024, reflecting a robust demand for advanced geospatial and risk assessment solutions. The market is poised to expand at a CAGR of 11.8% from 2025 to 2033, ultimately reaching an estimated USD 3.42 billion by 2033. This impressive growth is driven by increasing investments in infrastructure protection, heightened awareness of geohazards, and the integration of AI and cloud technologies in hazard mapping solutions, as per our latest research findings.

The growth trajectory of the Rockfall Hazard Mapping Software market is primarily propelled by the surge in global infrastructure development and the corresponding need for sophisticated risk mitigation tools. As governments and private sector entities embark on large-scale projects such as highways, railways, and tunnels, the imperative to ensure slope stability and prevent rockfall incidents has never been more critical. The adoption of advanced software solutions allows for real-time monitoring, predictive analysis, and data-driven decision-making, significantly reducing the risk of catastrophic events and associated financial losses. Furthermore, regulatory bodies across various regions are mandating comprehensive hazard assessments, further catalyzing the uptake of rockfall hazard mapping software among engineering and construction firms.

Another significant driver is the rapid technological evolution within the geospatial analytics sector. The integration of artificial intelligence, machine learning, and cloud computing capabilities into rockfall hazard mapping software has revolutionized the industry. These advancements enable more accurate and dynamic hazard models, automate the identification of high-risk zones, and facilitate seamless data sharing among stakeholders. The ability to process vast datasets from remote sensing technologies such as LiDAR and UAVs has enhanced the precision and efficiency of mapping, making these solutions indispensable for both public and private sector clients. As technology continues to advance, the market is expected to witness further innovation, driving adoption across new application areas and industries.

The growing frequency and intensity of natural disasters, exacerbated by climate change, have heightened the urgency for robust geohazard management systems. Rockfalls, landslides, and slope failures pose significant threats to human life, critical infrastructure, and economic stability, particularly in mountainous and seismically active regions. The Rockfall Hazard Mapping Software market is responding to this challenge by offering comprehensive risk assessment tools that not only predict potential hazards but also support emergency response planning and long-term land-use strategies. As awareness of these risks increases, demand for specialized software is anticipated to rise among government agencies, mining companies, transportation authorities, and construction firms worldwide.

Regionally, North America and Europe currently dominate the Rockfall Hazard Mapping Software market, owing to their advanced infrastructure, stringent regulatory frameworks, and early adoption of geospatial technologies. However, the Asia Pacific region is expected to register the fastest growth during the forecast period, driven by massive infrastructure investments, rapid urbanization, and heightened vulnerability to geohazards. Countries such as China, India, and Japan are increasingly investing in state-of-the-art hazard mapping solutions to safeguard their transportation networks, mining operations, and urban developments. The Middle East & Africa and Latin America are also witnessing steady growth, supported by government initiatives and international collaborations aimed at enhancing disaster resilience.

Component Analysis

The Component segment of the Rockfall Hazard Mapping Software market is bifurcated into software and services, each playing a pivotal role in the industry’s value chain. The software sub-segment encompasses a wide range of solutions, from basic hazard mapping tools to comprehensive platforms integrating AI, machine learning, and geospatial analytics. These solutions enable users to model rockfall scenarios, assess slope stability, and generate actionable insights for risk mitigation. As the complexity and scale of infrastructure projects grow, demand for advanced software with real-time m

Dataset for the textbook Computational Methods and GIS Applications in Social Science (3rd Edition), 2023 Fahui Wang, Lingbo Liu Main Book Citation: Wang, F., & Liu, L. (2023). Computational Methods and GIS Applications in Social Science (3rd ed.). CRC Press. https://doi.org/10.1201/9781003292302 KNIME Lab Manual Citation: Liu, L., & Wang, F. (2023). Computational Methods and GIS Applications in Social Science - Lab Manual. CRC Press. https://doi.org/10.1201/9781003304357 KNIME Hub Dataset and Workflow for Computational Methods and GIS Applications in Social Science-Lab Manual Update Log If Python package not found in Package Management, use ArcGIS Pro's Python Command Prompt to install them, e.g., conda install -c conda-forge python-igraph leidenalg NetworkCommDetPro in CMGIS-V3-Tools was updated on July 10,2024 Add spatial adjacency table into Florida on June 29,2024 The dataset and tool for ABM Crime Simulation were updated on August 3, 2023, The toolkits in CMGIS-V3-Tools was updated on August 3rd,2023. Report Issues on GitHub https://github.com/UrbanGISer/Computational-Methods-and-GIS-Applications-in-Social-Science Following the website of Fahui Wang : http://faculty.lsu.edu/fahui Contents Chapter 1. Getting Started with ArcGIS: Data Management and Basic Spatial Analysis Tools Case Study 1: Mapping and Analyzing Population Density Pattern in Baton Rouge, Louisiana Chapter 2. Measuring Distance and Travel Time and Analyzing Distance Decay Behavior Case Study 2A: Estimating Drive Time and Transit Time in Baton Rouge, Louisiana Case Study 2B: Analyzing Distance Decay Behavior for Hospitalization in Florida Chapter 3. Spatial Smoothing and Spatial Interpolation Case Study 3A: Mapping Place Names in Guangxi, China Case Study 3B: Area-Based Interpolations of Population in Baton Rouge, Louisiana Case Study 3C: Detecting Spatiotemporal Crime Hotspots in Baton Rouge, Louisiana Chapter 4. Delineating Functional Regions and Applications in Health Geography Case Study 4A: Defining Service Areas of Acute Hospitals in Baton Rouge, Louisiana Case Study 4B: Automated Delineation of Hospital Service Areas in Florida Chapter 5. GIS-Based Measures of Spatial Accessibility and Application in Examining Healthcare Disparity Case Study 5: Measuring Accessibility of Primary Care Physicians in Baton Rouge Chapter 6. Function Fittings by Regressions and Application in Analyzing Urban Density Patterns Case Study 6: Analyzing Population Density Patterns in Chicago Urban Area >Chapter 7. Principal Components, Factor and Cluster Analyses and Application in Social Area Analysis Case Study 7: Social Area Analysis in Beijing Chapter 8. Spatial Statistics and Applications in Cultural and Crime Geography Case Study 8A: Spatial Distribution and Clusters of Place Names in Yunnan, China Case Study 8B: Detecting Colocation Between Crime Incidents and Facilities Case Study 8C: Spatial Cluster and Regression Analyses of Homicide Patterns in Chicago Chapter 9. Regionalization Methods and Application in Analysis of Cancer Data Case Study 9: Constructing Geographical Areas for Mapping Cancer Rates in Louisiana Chapter 10. System of Linear Equations and Application of Garin-Lowry in Simulating Urban Population and Employment Patterns Case Study 10: Simulating Population and Service Employment Distributions in a Hypothetical City Chapter 11. Linear and Quadratic Programming and Applications in Examining Wasteful Commuting and Allocating Healthcare Providers Case Study 11A: Measuring Wasteful Commuting in Columbus, Ohio Case Study 11B: Location-Allocation Analysis of Hospitals in Rural China Chapter 12. Monte Carlo Method and Applications in Urban Population and Traffic Simulations Case Study 12A. Examining Zonal Effect on Urban Population Density Functions in Chicago by Monte Carlo Simulation Case Study 12B: Monte Carlo-Based Traffic Simulation in Baton Rouge, Louisiana Chapter 13. Agent-Based Model and Application in Crime Simulation Case Study 13: Agent-Based Crime Simulation in Baton Rouge, Louisiana Chapter 14. Spatiotemporal Big Data Analytics and Application in Urban Studies Case Study 14A: Exploring Taxi Trajectory in ArcGIS Case Study 14B: Identifying High Traffic Corridors and Destinations in Shanghai Dataset File Structure 1 BatonRouge Census.gdb BR.gdb 2A BatonRouge BR_Road.gdb Hosp_Address.csv TransitNetworkTemplate.xml BR_GTFS Google API Pro.tbx 2B Florida FL_HSA.gdb R_ArcGIS_Tools.tbx (RegressionR) 3A China_GX GX.gdb 3B BatonRouge BR.gdb 3C BatonRouge BRcrime R_ArcGIS_Tools.tbx (STKDE) 4A BatonRouge BRRoad.gdb 4B Florida FL_HSA.gdb HSA Delineation Pro.tbx Huff Model Pro.tbx FLplgnAdjAppend.csv 5 BRMSA BRMSA.gdb Accessibility Pro.tbx 6 Chicago ChiUrArea.gdb R_ArcGIS_Tools.tbx (RegressionR) 7 Beijing BJSA.gdb bjattr.csv R_ArcGIS_Tools.tbx (PCAandFA, BasicClustering) 8A Yunnan YN.gdb R_ArcGIS_Tools.tbx (SaTScanR) 8B Jiangsu JS.gdb 8C Chicago ChiCity.gdb cityattr.csv ...

The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.

The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.

You can choose between the dynamic or the manual version of the tool.

The main objective of the mapping tool is to provide a simple and useable platform for MSs to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the DCF.

The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.

You can choose between the dynamic or the manual version of the tool.

TIGERweb allows the viewing of TIGER spatial data online and for TIGER data to be streamed to your mapping application. TIGERweb consists of a web mapping service and a REST service.

   Thew web mapping service is an Open Geospatial Consortium (OGC) service that allows users to visualize our TIGER
   (Topologically Integrated Geographic Encoding and Referencing database) data. This service consists of two
   applications and eight services. The applications allow users to select features and view their attributes, to search
   for features by name or geocode, and to identify features by selecting them from a map. The TIGERweb applications are a
   simple way to view our TIGER data without having to download the data. The web Mapping services provide a simple HTTP
   interface for requesting geo-registered map images from our geospatial database. It allows users to
   produce maps containing TIGERweb layers with layers from other servers. TIGERweb consists of
   the following two applications and eight services: 
   Applications: TIGERweb, TIGERweb Decennial
   Services: Current, ACS16, ACS15, ACS14, ACS13, Econ12, Census 2010 (for the TIGERweb application), Physical Features (for the TIGERweb application),
   Census 2010 (for the TIGERweb Decennial application), Census 2000 and Physical Features (for the TIGERweb Decennial application)

   The REST service is a way for Web clients to communicate with geographic information system (GIS) servers through Representational
   State Transfer (REST) technology. It allows users to interface with the REST server with structured URLs using a computer language like PYTHON or JAVA. The
   server responds with map images, text-based geographic information, or other resources that satisfy the request. There are three groups of services: 
   TIGERweb, TIGERweb Generalized and TIGERweb Decennial. TIGERweb consists of boundaries as of January 1, 2016 while TIGERweb Decennial consists of boundaries
   as they were of January 1, 2010. TIGERweb Generalized is specifically designed for small-scale thematic mapping.

   The following REST services are offered for both groups:
   American Indian, Alaska Native, and Native Hawaiian Areas
   Census Regions and Divisions
   Census Tracts and Blocks
   Legislative Areas 
   Metropolitan and Micropolitan Statistical Areas and Related Statistical Areas
   Places and County Subdivisions
   PUMAs, UGAs and ZCTAs
   School Districts
   States and Counties
   Urban Areas 

   The following services are only offered in TIGERweb and TIGERweb Decennial:
    Hydrography 
    Labels
    Military and Other Special Land Use Areas
    Transportation (Roads and Railroads)
    Tribal Census Tracts and Block Groups

   The following services is only offered in TIGERweb Generalized: 
    Places and County Subdivisions (Economic Places)

Create a basic Story Map: Disease investigations (Learn ArcGIS PDF Lesson). This lesson will show you how to prepare a story map explaining John Snow’s famous investigation of the 1854 cholera outbreak in London._Communities around the world are taking strides in mitigating the threat that COVID-19 (coronavirus) poses. Geography and location analysis have a crucial role in better understanding this evolving pandemic.When you need help quickly, Esri can provide data, software, configurable applications, and technical support for your emergency GIS operations. Use GIS to rapidly access and visualize mission-critical information. Get the information you need quickly, in a way that’s easy to understand, to make better decisions during a crisis.Esri’s Disaster Response Program (DRP) assists with disasters worldwide as part of our corporate citizenship. We support response and relief efforts with GIS technology and expertise.More information...

This collection grew out of a prototype case tracking and crime mapping application that was developed for the United States Attorney's Office (USAO), Southern District of New York (SDNY). The purpose of creating the application was to move from the traditionally episodic way of handling cases to a comprehensive and strategic method of collecting case information and linking it to specific geographic locations, and collecting information either not handled at all or not handled with sufficient enough detail by SDNY's existing case management system. The result was an end-user application designed to be run largely by SDNY's nontechnical staff. It consisted of two components, a database to capture case tracking information and a mapping component to link case and geographic data. The case tracking data were contained in a Microsoft Access database and the client application contained all of the forms, queries, reports, macros, table links, and code necessary to enter, navigate through, and query the data. The mapping application was developed using Environmental Systems Research Institute's (ESRI) ArcView 3.0a GIS. This collection shows how the user-interface of the database and the mapping component were customized to allow the staff to perform spatial queries without having to be geographic information systems (GIS) experts. Part 1 of this collection contains the Visual Basic script used to customize the user-interface of the Microsoft Access database. Part 2 contains the Avenue script used to customize ArcView to link the data maintained in the server databases, to automate the office's most common queries, and to run simple analyses.

Basic Viewer is a configurable app template that can be used as a general purpose app for displaying a web map and configuring a variety of tools. This app offers a clean, simple interface that accentuates the web map and includes a toolbar and floating panel.Use CasesDisplays a set of commonly used tools within a floating pane. This is a good choice for balancing the need for a collection of tools while still maximizing the amount of screen real estate dedicated to the map. The app includes the ability to toggle layer visibility, print a map, and show pop-ups in the floating pane.Provides editing capabilities in the context of a general-purpose mapping app. This is a good choice when your audience needs additional tools or information about the map to support their editing activities.Configurable OptionsUse Basic Viewer to present content from a web map and configure it using the following options:Choose a title, sub title, logo, description, and color scheme.Configure a custom splash screen that will display when the app loads.Use custom CSS to customize the look and feel of the app.Enable tools on a toolbar including a basemap gallery, bookmarks, layer list, opacity slider, legend, measure, overview map, etc.Enable an editor tool and an editor toolbar giving users editing capabilities on editable feature layers.Configure a printing tool that can utilize all available print layouts configured in the hosting organization.Configure the ability for feature and location search.Set up custom URL parameters that define how the app and web map appear on load.Supported DevicesThis application is responsively designed to support use in browsers on desktops, mobile phones, and tablets.Data RequirementsThis web app includes the capability to edit a hosted feature service or an ArcGIS Server feature service. Creating hosted feature services requires an ArcGIS Online organizational subscription or an ArcGIS Developer account. Get Started This application can be created in the following ways:Click the Create a Web App button on this pageShare a map and choose to Create a Web AppOn the Content page, click Create - App - From Template Click the Download button to access the source code. Do this if you want to host the app on your own server and optionally customize it to add features or change styling.

The main objective of the mapping tool is to provide a simple and useable platform for Member states and other reporting countriesto map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF).

The catalogues and the specific hierarchy of each data model (AMR, ESBL, PRV, FBO, POP and DST) are already inserted into each of the specific mapping tool. Specific Excel mapping tools correspond to each of the six data models are available.

You can choose between the dynamic or the manual version of the tool.

In May 2014, staff at the San Bernardino National Wildlife Refuge (SBNWR) requested the production of a vegetation map to document the ongoing restoration of the refuge. Utilizing object-based image analysis (OBIA) a 9 class vegetation map was produced. This was a piloted effort to develop a simple, repeatable and low-cost land cover mapping framework that could be carried out on other refuges. Thus, iterative steps were taken and refined as part of the mapping process. This document has a Digital Object Identifier: http://dx.doi.org/10.7944/W3WC7M

The main objective of the mapping tools is to provide a simple and useable platform for Member States and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of antimicrobial resistance data via the Data Collection Framework (DCF).

The tools can be used to report antimicrobial resistance data within the framework of Directive 2003/99/EC and Decision 2020/1729/EU.

The catalogues and the specific hierarchy of each data model (AMR and ESBL) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the two data models are available.

Dynamic or manual version of the tool can be chosen for each data models.

The main objective of the mapping tools is to provide a simple and useable platform for Member states and other reporting countries to map their country-specific standard terminology to that used by EFSA and to enable the production of an XML file for the submission of sample or aggregated-based zoonoses monitoring data via the Data Collection Framework (DCF).

The catalogues and the specific hierarchy of each data model (PRV, FBO, AP, DS and SSD2 for Echinococcus multilocularis) are already inserted into each of the specific mapping tool. Specific Excel mapping tools corresponding to each of the five data models are available.

Dynamic or manual version of the tool can be chosen for the first four data models.

The Emulti_SSD2_tool can be used to report sample-based results of Echinococcus multilocularis under the Commission Delegated Regulation (EU) 2018/772.

This is a collection of all GPS- and computer-generated geospatial data specific to the Alpine Treeline Warming Experiment (ATWE), located on Niwot Ridge, Colorado, USA. The experiment ran between 2008 and 2016, and consisted of three sites spread across an elevation gradient. Geospatial data for all three experimental sites and cone/seed collection locations are included in this package. ––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– Geospatial files include cone collection, experimental site, seed trap, and other GPS location/terrain data. File types include ESRI shapefiles, ESRI grid files or Arc/Info binary grids, TIFFs (.tif), and keyhole markup language (.kml) files. Trimble-imported data include plain text files (.txt), Trimble COR (CorelDRAW) files, and Trimble SSF (Standard Storage Format) files. Microsoft Excel (.xlsx) and comma-separated values (.csv) files corresponding to the attribute tables of many files within this package are also included. A complete list of files can be found in this document in the “Data File Organization” section in the included Data User's Guide. Maps are also included in this data package for reference and use. These maps are separated into two categories, 2021 maps and legacy maps, which were made in 2010. Each 2021 map has one copy in portable network graphics (.png) format, and the other in .pdf format. All legacy maps are in .pdf format. .png image files can be opened with any compatible programs, such as Preview (Mac OS) and Photos (Windows). All GIS files were imported into geopackages (.gpkg) using QGIS, and double-checked for compatibility and data/attribute integrity using ESRI ArcGIS Pro. Note that files packaged within geopackages will open in ArcGIS Pro with “main.” preceding each file name, and an extra column named “geom” defining geometry type in the attribute table. The contents of each geospatial file remain intact, unless otherwise stated in “niwot_geospatial_data_list_07012021.pdf/.xlsx”. This list of files can be found as an .xlsx and a .pdf in this archive. As an open-source file format, files within gpkgs (TIFF, shapefiles, ESRI grid or “Arc/Info Binary”) can be read using both QGIS and ArcGIS Pro, and any other geospatial softwares. Text and .csv files can be read using TextEdit/Notepad/any simple text-editing software; .csv’s can also be opened using Microsoft Excel and R. .kml files can be opened using Google Maps or Google Earth, and Trimble files are most compatible with Trimble’s GPS Pathfinder Office software. .xlsx files can be opened using Microsoft Excel. PDFs can be opened using Adobe Acrobat Reader, and any other compatible programs. A selection of original shapefiles within this archive were generated using ArcMap with associated FGDC-standardized metadata (xml file format). We are including these original files because they contain metadata only accessible using ESRI programs at this time, and so that the relationship between shapefiles and xml files is maintained. Individual xml files can be opened (without a GIS-specific program) using TextEdit or Notepad. Since ESRI’s compatibility with FGDC metadata has changed since the generation of these files, many shapefiles will require upgrading to be compatible with ESRI’s latest versions of geospatial software. These details are also noted in the “niwot_geospatial_data_list_07012021” file.