OVERVIEWThis site is dedicated to raising the level of spatial and data literacy used in public policy. We invite you to explore curated content, training, best practices, and datasets that can provide a baseline for your research, analysis, and policy recommendations. Learn about emerging policy questions and how GIS can be used to help come up with solutions to those questions.EXPLOREGo to your area of interest and explore hundreds of maps about various topics such as social equity, economic opportunity, public safety, and more. Browse and view the maps, or collect them and share via a simple URL. Sharing a collection of maps is an easy way to use maps as a tool for understanding. Help policymakers and stakeholders use data as a driving factor for policy decisions in your area.ISSUESBrowse different categories to find data layers, maps, and tools. Use this set of content as a driving force for your GIS workflows related to policy. RESOURCESTo maximize your experience with the Policy Maps, we’ve assembled education, training, best practices, and industry perspectives that help raise your data literacy, provide you with models, and connect you with the work of your peers.

Esri's Water Resources GIS Platform offers a comprehensive suite of tools and resources designed to modernize water resource management. It emphasizes geospatial solutions for monitoring, analyzing, and modeling water systems, helping decision-makers tackle challenges like drought resilience, flood mitigation, and environmental protection. By leveraging the capabilities of ArcGIS, users can transform raw water data into actionable insights, ensuring more efficient and effective water resource management.A central feature of the platform is Arc Hydro, a specialized data model and toolkit developed for GIS-based water resource analysis. This toolset allows users to integrate, analyze, and visualize water datasets for applications ranging from live stream gauge monitoring to pollution control. Additionally, the platform connects users to the ArcGIS Living Atlas of the World, which offers extensive water-related datasets such as rivers, wetlands, and soils, supporting in-depth analyses of hydrologic conditions. The Hydro Community further enhances collaboration, enabling stakeholders to share expertise, discuss challenges, and build innovative solutions together.Esri’s platform also provides training opportunities and professional services to empower users with technical knowledge and skills. Through instructor-led courses, documentation, and best practices, users gain expertise in using ArcGIS and Arc Hydro for their specific water management needs. The combination of tools, datasets, and community engagement makes Esri's water resources platform a powerful asset for advancing sustainable water management initiatives across public and private sectors.

ArcGIS has many analysis and geoprocessing tools that can help you solve real-world problems with your data. In some cases, you are able to run individual tools to complete an analysis. But sometimes you may require a more comprehensive way to create, share, and document your analysis workflow.In these situations, you can use a built-in application called ModelBuilder to create a workflow that you can reuse, modify, save, and share with others.In this course, you will learn the basics of working with ModelBuilder and creating models. Models contain many different elements, many of which you will learn about. You will also learn how to work with models that others create and share with you. Sharing models is one of the major advantages of working with ModelBuilder and models in general. You will learn how to prepare a model for sharing by setting various model parameters.After completing this course, you will be able to:Identify model elements and states.Describe a prebuilt model's processes and outputs.Create and document models for site selection and network analysis.Define model parameters and prepare a model for sharing.

You have been assigned a new project, which you have researched, and you have identified the data that you need.The next step is to gather, organize, and potentially create the data that you need for your project analysis.In this course, you will learn how to gather and organize data using ArcGIS Pro. You will also create a file geodatabase where you will store the data that you import and create.After completing this course, you will be able to perform the following tasks:Create a geodatabase in ArcGIS Pro.Create feature classes in ArcGIS Pro by exporting and importing data.Create a new, empty feature class in ArcGIS Pro.

Enroll in this plan to get familiar with the user interface, apply commonly used tools, and master the basics of mapping and analyzing data using ArcGIS Pro.Goals Install ArcGIS Pro and efficiently locate tools, options, and user interface elements. Add data to a map, symbolize map features to represent type, categories, or quantities; and optimize map display at various scales. Create a file geodatabase to organize and accurately maintain GIS data over time. Complete common mapping, editing, and analysis workflows.

Coconuts and coconut products are an important commodity in the Tongan economy. Plantations, such as the one in the town of Kolovai, have thousands of trees. Inventorying each of these trees by hand would require lots of time and manpower. Alternatively, tree health and location can be surveyed using remote sensing and deep learning. In this lesson, you'll use the Deep Learning tools in ArcGIS Pro to create training samples and run a deep learning model to identify the trees on the plantation. Then, you'll estimate tree health using a Visible Atmospherically Resistant Index (VARI) calculation to determine which trees may need inspection or maintenance.

To detect palm trees and calculate vegetation health, you only need ArcGIS Pro with the Image Analyst extension. To publish the palm tree health data as a feature service, you need ArcGIS Online and the Spatial Analyst extension.

In this lesson you will build skills in these areas:

• Creating training schema
• Digitizing training samples
• Using deep learning tools in ArcGIS Pro
• Calculating VARI
• Extracting data to points

Learn ArcGIS is a hands-on, problem-based learning website using real-world scenarios. Our mission is to encourage critical thinking, and to develop resources that support STEM education.

Attend this session to find out how teachers are using GIS to engage students in hands-on learning.Engaging Secondary Students with Spatial Community Based ProjectsCory Munro, Saugeen District Secondary School, Bluewater District School BoardStudents become engaged when they collect and analyze data for projects that produce meaningful results. This session will briefly highlight the work of several student and class projects at the local and international level. Forming community partnerships in recent years has provided excellent opportunities for students to build their spatial analysis skills using ArcMap, ArcGIS Online, Survey123, Story Maps, and Collector for ArcGIS. Projects to be highlighted include mapping safe routes to school based on local infrastructure and student surveys, tracking school graduates and their post-secondary destinations, fire safety in Saugeen Shores, and more.

This dataset represents public schools composed of all public elementary and secondary education in Denver, Colorado. For each field, the Not Available and NULL designations are used to indicate that the data for the particular record and field is unavailable. This dataset was derived from data provided by the National Center for Education Statistics (Institute of Education Sciences), US Department of Education. The data and related materials are made available through Esri (http://www.esri.com) and are intended for educational purposes only (see Access and Use Constraints section).

The classification of point cloud datasets to identify distribution wires is useful for identifying vegetation encroachment around power lines. Such workflows are important for preventing fires and power outages and are typically manual, recurring, and labor-intensive. This model is designed to extract distribution wires at the street level. Its predictions for high-tension transmission wires are less consistent with changes in geography as compared to street-level distribution wires. In the case of high-tension transmission wires, a lower ‘recall’ value is observed as compared to the value observed for low-lying street wires and poles.Using the modelFollow the guide to use the model. The model can be used with ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.InputThe model accepts unclassified point clouds with point geometry (X, Y and Z values). Note: The model is not dependent on any additional attributes such as Intensity, Number of Returns, etc. This model is trained to work on unclassified point clouds that are in a projected coordinate system, in which the units of X, Y and Z are based on the metric system of measurement. If the dataset is in degrees or feet, it needs to be re-projected accordingly. The model was trained using a training dataset with the full set of points. Therefore, it is important to make the full set of points available to the neural network while predicting - allowing it to better discriminate points of 'class of interest' versus background points. It is recommended to use 'selective/target classification' and 'class preservation' functionalities during prediction to have better control over the classification and scenarios with false positives.The model was trained on airborne lidar datasets and is expected to perform best with similar datasets. Classification of terrestrial point cloud datasets may work but has not been validated. For such cases, this pre-trained model may be fine-tuned to save on cost, time, and compute resources while improving accuracy. Another example where fine-tuning this model can be useful is when the object of interest is tram wires, railway wires, etc. which are geometrically similar to electricity wires. When fine-tuning this model, the target training data characteristics such as class structure, maximum number of points per block and extra attributes should match those of the data originally used for training this model (see Training data section below).OutputThe model will classify the point cloud into the following classes with their meaning as defined by the American Society for Photogrammetry and Remote Sensing (ASPRS) described below: Classcode Class Description 0 Background Class 14 Distribution Wires 15 Distribution Tower/PolesApplicable geographiesThe model is expected to work within any geography. It's seen to produce favorable results as shown here in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Model architectureThis model uses the RandLANet model architecture implemented in ArcGIS API for Python.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Background (0) 0.999679 0.999876 0.999778 Distribution Wires (14) 0.955085 0.936825 0.945867 Distribution Poles (15) 0.707983 0.553888 0.621527Training dataThis model is trained on manually classified training dataset provided to Esri by AAM group. The training data used has the following characteristics: X, Y, and Z linear unitmeter Z range-240.34 m to 731.17 m Number of Returns1 to 5 Intensity1 to 4095 Point spacing0.2 ± 0.1 Scan angle-42 to +35 Maximum points per block20000 Extra attributesNone Class structure[0, 14, 15]Sample resultsHere are a few results from the model.

ArcGIS Dashboards Training Videos for COVID-19With the current COVID-19 situation across the world, there’s been a proliferation of corona virus themed dashboards emerging over the last few weeks in ArcGIS Online. Many of these were created with ArcGIS Dashboards, which enables users to convey information by presenting location-based analytics using intuitive and interactive data visualizations on a single screen._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 dataset should be used as reference to locate parks, golf courses, and recreation centers managed by the Department of Parks and Recreation in the City and County of Denver. Data is based on parcel ownership and does not include other areas maintained by the department such as medians and parkways.The data should be used for planning and design purposes and cartographic purposes only.

Have you ever assessed the quality of your data? Just as you would run spell check before publishing an important document, it is also beneficial to perform a quality control (QC) review before delivering data or map products. This course gives you the opportunity to learn how you can use ArcGIS Data Reviewer to manage and automate the quality control review process. While exploring the fundamental concepts of QC, you will gain hands-on experience configuring and running automated data checks. You will also practice organizing data review and building a comprehensive quality control model. You can easily modify and reuse this QC model over time as your organizational requirements change.After completing this course, you will be able to:Explain the importance of data quality.Select data checks to find specific errors.Apply a workflow to run individual data checks.Build a batch job to run cumulative data checks.

Der Datensatz beinhaltet die geografische Verteilung der gewerbetreibenden Unternehmen des Landes Berlin, nach Branche, Beschäftigtenklasse, Alter des Unternehmens und Rechtsform. Die Daten stellen die von der IHK erfassten, aktiven Gewerbetreibenden Berlins (Hauptsitze und Filialen) dar. Übersicht der Wirtschaftszweige und zugehöriger WahlgruppenQuelle: Berlin Open DataVerarbeitungsprozess: CSV-Tabelle wurde in ArcGIS Pro anhand der XY-Werte nach als Punkte-Feature-Klasse transformiert, nach Web Mercator projiziert und als Web Layer in ArcGIS Online veröffentlicht.

This scene highlights layers for Berlin, Germany available in ArcGIS to support your work in 3D. Use these layers in conjunction with your own layers to create new scenes focused on a specific topic or area of interest to you.What's in this scene? Terrain: Includes a global 3D terrain layer to provide elevation context. Your layers are placed in relationship to this terrainBasemap: Includes one of the ArcGIS Basemaps regularly used in in your mapping workScene Layers: Includes a layer of 3D buildings to help understand your data within the context of the built environment. The layer is a file type optimized for rendering in 3D.Create your own sceneOpen this item using the Open in Scene Viewer buttonChoose basemap: Select one of the ArcGIS basemaps from the Basemap GalleryAdd your own unique layersCreate slides to direct users to interesting places in your scene - See MoreSave and share the results of your work with others in your organization and the publicFor more see these helpful videosMashup 3D Content Using ArcGIS OnlineAuthor Web Scenes Using ArcGIS Online

Imagery is processed and used for a wide variety of geospatial applications, including geographic context, visualization, and analysis. You may want to apply processing techniques on image data, visually interpret the data, use it as a background to aid interpretation of other data, or use it for analysis. In this course, you will use tools in ArcGIS to perform basic image processing. You will learn how to dynamically modify properties that enhance image display, visualize surface features, and create multiple products.After completing this course, you will be able to:Describe common types of image processing used for analysis.Relate the access of imagery to decisions in processing.Apply on-the-fly display techniques to enhance imagery.Use image-processing functions to modify images for analysis.

The Python language offers an efficient way to automate and extend geoprocessing and mapping functionality. In ArcGIS 10, Python was fully integrated into ArcGIS Desktop with the addition of the Python window and the ArcPy site package. This course introduces Python scripting within ArcGIS Desktop to automate geoprocessing workflows. These skills are needed by GIS analysts to work efficiently and productively with ArcGIS for Desktop.After completing this course, you will be able to:Create geoprocessing scripts using the ArcPy site package.Identify common scripting workflows.Write Python scripts that create and update data.Create a script tool using built-in validation.

This map includes data that represents parks, golf courses, and recreation centers within the boundaries of the City and County of Denver. This data was derived from data in the City of Denver Open Data Catalog (http://data.denvergov.org). The data and related materials are made available through Esri (http://www.esri.com) and are intended for educational purposes only (see Access and Use Constraints section).

This data represents the location of volcanoes around the world. The data and related materials are made available through Esri (http://www.esri.com) and are intended for educational purposes only (see Access and Use Constraints).

Daten des amtlichen Liegenschaftskatsterinformationssystems (ALKIS) - Ortsteile von Berlin.Quelle: Geoportal BerlinVerarbeitungsprozesse: WFS wurde in ArcGIS Pro als Feature Layer importiert, nach Web Mercator projiziert und als Web Layer in ArcGIS Online veröffentlicht.

Students will learn about the key fronts of World War I and the impact of the United States’ involvement. The activity uses a web-based map and is tied to the C3 Framework.

Learning outcomes:

Students will be able to identify the Allied Powers, Central Powers, Western Front, and Eastern Front.Students will be able to identify and explain how U.S. involvement changed the course of World War I.

Find more US History GeoInquiries here or explore all GeoInquiries at https://www.esri.com/geoinquiries