ArcGIS Knowledge es el nuevo producto de ESRI, el cual permite a los usuarios explorar y analizar datos espaciales, no espaciales, no estructurados y estructurados juntos para acelerar la toma de decisiones a través de un Knowledge Graph o Gráfico de Conocimiento.

Partial experimental results data

GIS in the age of community health (Learn ArcGIS Path). Arm yourself with hands-on skills and knowledge of how GIS tools can analyze health data and better understand diseases.

Create your own initiative by combining existing applications with a custom site. Use this initiative to form teams around a problem and invite your community to participate.

Sum of coins in the cell justified due to general knowledge gap

This seminar is an applied study of deep learning methods for extracting information from geospatial data, such as aerial imagery, multispectral imagery, digital terrain data, and other digital cartographic representations. We first provide an introduction and conceptualization of artificial neural networks (ANNs). Next, we explore appropriate loss and assessment metrics for different use cases followed by the tensor data model, which is central to applying deep learning methods. Convolutional neural networks (CNNs) are then conceptualized with scene classification use cases. Lastly, we explore semantic segmentation, object detection, and instance segmentation. The primary focus of this course is semantic segmenation for pixel-level classification. The associated GitHub repo provides a series of applied examples. We hope to continue to add examples as methods and technologies further develop. These examples make use of a vareity of datasets (e.g., SAT-6, topoDL, Inria, LandCover.ai, vfillDL, and wvlcDL). Please see the repo for links to the data and associated papers. All examples have associated videos that walk through the process, which are also linked to the repo. A variety of deep learning architectures are explored including UNet, UNet++, DeepLabv3+, and Mask R-CNN. Currenlty, two examples use ArcGIS Pro and require no coding. The remaining five examples require coding and make use of PyTorch, Python, and R within the RStudio IDE. It is assumed that you have prior knowledge of coding in the Python and R enviroinments. If you do not have experience coding, please take a look at our Open-Source GIScience and Open-Source Spatial Analytics (R) courses, which explore coding in Python and R, respectively. After completing this seminar you will be able to: explain how ANNs work including weights, bias, activation, and optimization. describe and explain different loss and assessment metrics and determine appropriate use cases. use the tensor data model to represent data as input for deep learning. explain how CNNs work including convolutional operations/layers, kernel size, stride, padding, max pooling, activation, and batch normalization. use PyTorch, Python, and R to prepare data, produce and assess scene classification models, and infer to new data. explain common semantic segmentation architectures and how these methods allow for pixel-level classification and how they are different from traditional CNNs. use PyTorch, Python, and R (or ArcGIS Pro) to prepare data, produce and assess semantic segmentation models, and infer to new data.

This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. Esri created this vector tile basemap from the Daylight map distribution of OSM data, which is supported by Facebook and supplemented with additional data from Microsoft. This version of the map is rendered using OSM cartography. The OSM Daylight map will be updated every month with the latest version of OSM Daylight data.OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site:www.OpenStreetMap.org. Esri is a supporter of the OSM project and is excited to make this enhanced vector basemap available to the ArcGIS user and developer communities.

Mature Support Notice: This item is in mature support as of December 2024. A new version of this item is available for your use. Esri recommends updating your maps and apps to use the new version. See blog for more information.This web map presents a vector basemap of OpenStreetMap (OSM) data hosted by Esri. This version of the map is rendered in a style similar to the Esri Street Map (with Relief). It includes the World Hillshade layer. Created from the sunsetted Daylight map distribution, data updates supporting this layer are no longer available. OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project. Precise Tile Registration: The web map uses the improved tiling scheme “WGS84 Geographic, Version 2” to ensure proper tile positioning at higher resolutions (neighborhood level and beyond). The new tiling scheme is much more precise than tiling schemes of the legacy basemaps Esri released years ago. We recommend that you start using this new basemap for any new web maps in WGS84 that you plan to author. Due to the number of differences between the old and new tiling schemes, some web clients will not be able to overlay tile layers in the old and new tiling schemes in one web map.

NOTE: An updated Introduction to ArcGIS GeoEvent Server Tutorial is available here. It is recommended you use the new tutorial for getting started with GeoEvent Server. The old Introduction Tutorial available on this page is relevant for 10.8.x and earlier and will not be updated.The Introduction to GeoEvent Server Tutorial (10.8.x and earlier) introduces you to the Real-Time Visualization and Analytic capabilities of ArcGIS GeoEvent Server. GeoEvent Server allows you to:

Incorporate real-time data feeds in your existing GIS data and IT infrastructure. Perform continuous processing and analysis on streaming data, as it is received. Produce new streams of data that can be leveraged across the ArcGIS system.

Once you have completed the exercises in this tutorial you should be able to:

Use ArcGIS GeoEvent Manager to monitor and perform administrative tasks. Create and maintain GeoEvent Service elements such as inputs, outputs, and processors. Use GeoEvent Simulator to simulate event data into GeoEvent Server. Configure GeoEvent Services to append and update features in a published feature service. Work with processors and filters to enhance and direct GeoEvents from event data.

The knowledge gained from this tutorial will prepare you for other GeoEvent Server tutorials available in the ArcGIS GeoEvent Server Gallery.

Releases

Each release contains a tutorial compatible with the version of GeoEvent Server listed. The release of the component you deploy does not have to match your version of ArcGIS GeoEvent Server, so long as the release of the component is compatible with the version of GeoEvent Server you are using. For example, if the release contains a tutorial for version 10.6; this tutorial is compatible with ArcGIS GeoEvent Server 10.6 and later. Each release contains a Release History document with a compatibility table that illustrates which versions of ArcGIS GeoEvent Server the component is compatible with.

NOTE: The release strategy for ArcGIS GeoEvent Server components delivered in the ArcGIS GeoEvent Server Gallery has been updated. Going forward, a new release will only be created when

  a component has an issue,
  is being enhanced with new capabilities,
  or is not compatible with newer versions of ArcGIS GeoEvent Server.

This strategy makes upgrades of these custom
components easier since you will not have to
upgrade them for every version of ArcGIS GeoEvent Server
unless there is a new release of
the component. The documentation for the
latest release has been
updated and includes instructions for updating
your configuration to align with this strategy.

Latest

Release 7 - March 30, 2018 - Compatible with ArcGIS GeoEvent Server 10.6 and later.

Previous

Release 6 - January 12, 2018 - Compatible with ArcGIS GeoEvent Server 10.5 thru 10.8.

Release 5 - July 30, 2016 - Compatible with ArcGIS GeoEvent Server 10.4 thru 10.8.

Release 4 - July 30, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x.

Release 3 - April 24, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 2 - January 22, 2015 - Compatible with ArcGIS GeoEvent Server 10.3.x. Not available.

Release 1 - April 11, 2014 - Compatible with ArcGIS GeoEvent Server 10.2.x.

Data was hand drawn on USGS Topographic quads by foresters of the Vermont Department of Forests, Parks, & Recreation using orthophotos, survey data, and personal knowledge of the area as references.

Pacific lamprey (Entosphenus tridentata) are native fish to the Columbia River Basin. Over the past 60 years, anthropogenic disturbances have contributed to a 95% decline of historical population numbers. Member-tribes of the Columbia River Inter-Tribal Fish Commission have acknowledged the importance of Pacific lamprey to the Columbia River ecosystem and expressed concern about the loss of an essential tribal cultural resource. As a result, the Columbia River Inter-Tribal Fish Commission created the Tribal Pacific Lamprey Restoration Plan to halt their decline, re-establish the species, and restore the population to sustainable, harvestable levels throughout their historical range.

Limited knowledge about the movement and preferred habitat of larval Pacific lamprey, such as optimal habitat conditions, demographic information, and species resilience, results in challenges to monitor and protect the species. Pacific lamprey is known to use the mainstem Columbia River to migrate between their spawning grounds and the Pacific Ocean. However, dams, levees, and culverts within the Columbia River Estuary and adjacent tributaries have restricted the lamprey's access to spawning grounds and other upstream habitats. These restrictions have prompted conservation and restoration efforts to better understand how Pacific lamprey utilizes the Columbia River Estuary.

Here, we address these knowledge gaps in an effort to aid restoration initiatives by completing a Habitat Suitability Analysis to determine where optimal larval Pacific lamprey habitat may exist in the Columbia River Estuary. The project identified the spatial and temporal distribution of suitable habitat for larval Pacific lamprey and generated recommendations to address habitat-related knowledge gaps and further evaluate anthropogenic threats to their recovery. The results of the Habitat Suitability Analysis suggest that habitat conditions in the Columbia River itself are unable to support larval lamprey year-round, but may provide suitable habitat on a seasonal basis due to spatial and temporal limitations. However, we stress that our analyses were necessarily limited to aquatic conditions and that the temperature of the water column used in our analyses may differ from the temperature within fine sediments, where larval lamprey burrow. Our results imply that suitable lamprey habitat is present at times throughout the year in the Columbia River Estuary, and these locations can be used to support habitat restoration and conservation strategies for improving the species' recovery.

Anthropogenic threats to the Columbia River continue to alter habitat conditions, including average water temperature, salinity, and sedimentation. Laboratory experiments have provided insight into the potential impacts of changing temperature and salinity on larval Pacific lamprey, where elevated water temperatures can affect their development and elevated salinity levels can result in larval mortality. In addition, anthropogenic disturbances such as dams, levees, and culverts have cut off the Columbia River Estuary's floodplain habitats from the mainstem Columbia River, decreased sedimentation rates, and separated adult lamprey from the floodplains and tributaries that they use to spawn. The presence of these barriers in the region can inhibit the distribution of fine sediments in the river, limiting where larval lamprey burrow and develop. The burrowing behavior of larval lamprey has yet to fully be investigated in the Columbia River Estuary. Limited research may be due to the lack of resources for studying Pacific lamprey's life cycle, habitat, and population dynamics since they are not federally designated as an endangered species, like resident salmonid species. This has further added to the challenge of understanding the species and restoring its population to sustainable numbers.

To the best of our knowledge, this project is the first to explore spatial and temporal trends of suitable larval Pacific lamprey habitat conditions in the Columbia River Estuary. The Habitat Suitability Analysis provides technical information about the presence and distribution of suitable conditions to address habitat-related uncertainties. The member-tribes of the Columbia River Inter-Tribal Fish Commission and their collaborators can incorporate the information into current and future Pacific lamprey restoration, conservation, and education programs to enhance general understanding of lamprey populations throughout the Columbia River Basin. Key recommendations are provided to address additional knowledge gaps and prioritize future restoration projects in the Columbia River Basin including the refinement of the Habitat Suitability Analysis, evaluation of barrier effects on Pacific lamprey passage, and assessment of climate change scenarios on larval lamprey habitat.

LfULG Sachsen has developed a method for recording medium soil sealing for the entire state area from existing data sets. Information from the ATKIS base DLM (Land Survey, as of 2018) is used, and the mean soil sealing is assigned to the respective legend units. For the use of the information in the 3 planning areas of regional, regional and municipal planning, the information is classified into 3 different grids with the following cell sizes: 1000 × 1000 meters: Country planning; 100 × 100 meters: Regional planning; 25 × 25 meters: Local planning. Each cell carries the mean degree of sealing of the soil as surface information. The information will be gradually updated over the next few years and new levels of knowledge will be incorporated. This information will be published with updates.

Open-Source GIS plays a pivotal role in advancing GIS education, fostering research collaboration, and supporting global sustainability by enabling the sharing of data, models, and knowledge. However, the integration of big data, deep learning methods, and artificial intelligence deep learning in geospatial research presents significant challenges for GIS education. These include increasing software learning costs, higher computational power demand, and the management of fragmented information in the Web 2.0 context. Addressing these challenges while integrating emerging GIS innovations and restructuring GIS knowledge systems is crucial for the evolution of GIS Education 3.0. This study introduces a Visual Programming-based Geospatial Cyberinfrastructure (V-GCI) framework, integrated with the replicable and reproducible (R&R) framework, to enhance GIS function compatibility, learning scalability, and web GIS application interoperability. Through a case study on spatial accessibility using the generalized two-step floating catchment area method (G2SFCA), this paper demonstrates how V-GCI can reshape the GIS knowledge tree and its potential to enhance replicability and reproducibility within open-source GIS Education 3.0.

Spatial Data Modeller, SDM, is a collection of tools for use with GIS software for adding categorical maps with interval, ordinal, or ratio scale maps to produce a predictive map of where something of interest is likely to occur. The tools include the data-driven methods of Weights of Evidence, Logistic Regression, and two supervised and one unsupervised neural network methods, and categorical tools for a knowledge-driven method Fuzzy Logic. All of the tools have help files that include references to publications discussing the applications of the methods implemented in the tool. Several of the tools create output rasters, tables, or files that require the user to enter a name. Default values are provided in most cases to serve as suggestions of the style of naming that has been found useful. These names, following ArcGIS conventions, can be changed to meet the user’s needs. To make all of the features of SDM work properly it is required that several Environment parameters are set. See the discussion of Environment Settings below for the details. The Weights of Evidence, WofE, and Logistic Regression, LR, tools addresses area as the count of unit cells. It is assumed in the WofE and LR tools that the data has spatial units of meters. If your data has other spatial units, these WofE and LR tools may not work properly.

Website:

http://www.ige.unicamp.br/sdm/

Mature Support Notice: This item is in mature support as of December 2024. See blog for more information.This 3D scene layer presents OpenStreetMap (OSM) trees data hosted by Esri. Esri created buildings and trees scene layers from the OSM Daylight map distribution, which is supported by Facebook and others. The Daylight map distribution has been sunsetted and data updates supporting this layer are no longer available. You can visit openstreetmap.maps.arcgis.com to explore a collection of maps, scenes, and layers featuring OpenStreetMap data in ArcGIS. You can review the 3D Scene Layers Documentation to learn more about how the building and tree features in OSM are modeled and rendered in the 3D scene layers, and see tagging recommendations to get the best results. OpenStreetMap is an open collaborative project to create a free editable map of the world. Volunteers gather location data using GPS, local knowledge, and other free sources of information and upload it. The resulting free map can be viewed and downloaded from the OpenStreetMap site: www.OpenStreetMap.org. Esri is a supporter of the OSM project.Note: This layer is supported in Scene Viewer and ArcGIS Pro 3.0 or higher.

This data set contains basic observation information on Western Grebes in California and is provided by the Avian Knowledge Network (AKN). AKN is a joint venture including sponsors National Biological Information Infrastructure and National Science Foundation, and collaborators Cornell Lab of Ornithology, PRBO Conservations Science, Redwood Sciences Laboratory, Rocky Mountain Bird Observatory, and Bird Studies Canada. AKN collects, organizes, archives, and distributes avian data for various analytical purposes. This large (almost 20,000,000 observations to date) and comprehensive database provides a unified structure where data from multiple sources can be used together to produce more meaningful products. AKN also has developed applications to visualize temporal and spatial patterns of bird movements and numbers. These bird observations were made by professional birding consultants, ornithologists, researchers, and knowledgeable volunteers over the last few years. Most of their observations came from point counts from job sites, back yards, or from sites established to assess bird populations in specific locations or habitats throughout California. Protocols for the three programs that produce the most observations are on the web at: eBird - http://www.ebird.org/content/ Great Backyard Bird Count - http://www.birdsource.org/gbbc/ Project FeederWatch - http://www.birds.cornell.edu/pfw/ The dataset is limited in that the there is no observer provided for each record, the location is only accurate to a circular area equivalent to about one-sixteenth of a square mile, and a number of fields have only code values and the code definitions are not known.

Statistics Canada Census Data from 2021. This dataset includes the knowledge of languages data provided by Statistics Canada joined with the census tracts. Each topic covered by the census was exported as a separate table. Each table contains the total, male, and female characteristics as fields for each census tract. Topics range from population, age and sex, immigration, language, family and households, income, education, and labour. For more information on definitions of terms used in the tables and other notes, refer to Statistics Canada's 2021 Census.

Great Basin LCC S-TEK Project Boundaries

Statistics Canada Census Data from 2021. This dataset includes the knowledge of official languages data provided by Statistics Canada joined with the census tracts. Each topic covered by the census was exported as a separate table. Each table contains the total, male, and female characteristics as fields for each census tract. Topics range from population, age and sex, immigration, language, family and households, income, education, and labour. For more information on definitions of terms used in the tables and other notes, refer to Statistics Canada's 2021 Census.