The In-Service catalog provides a list of courses offered by the LMPD Academy Staff. In 2017 all sworn personnel were required to take the mandated in-service course based on recommendations from the Policing in the 21st Century report and current events that have been occurring nationwide.

GIS project files and imagery data required to complete the Introduction to Planetary Image Analysis and Geologic Mapping in ArcGIS Pro tutorial. These data cover the area in and around Jezero crater, Mars.

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.

Seattle Parks and Recreation Golf Course locations. SPR Golf Courses are managed by contractors.Refresh Cycle: WeeklyFeature Class: DPR.GolfCourse

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 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...

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.

Übersicht Haltestellen DB Station&Service AG. Dieser Datenbestand kann Fehler enthalten und/oder unvollständig sein. DB Station&Service AG übernimmt keine Haftung und leistet keinerlei Gewähr.Quelle: Open Data der Deutsche Bahn AG.Verarbeitungsprozesse: CSV Datei wurde in ArcGIS Pro importiert, georeferenziert, nach WebMercator projiziert und als Feature Service in ArcGIS Online veröffentlicht.

This layer shows the location of Families Clinics and Education and Training Centre in Family Medicine in Hong Kong. It is a subset of the data made available by the Department of Health under the Government of Hong Kong Special Administrative Region (the “Government”) at https://DATA.GOV.HK/ (“DATA.GOV.HK”). The source data is in XLSX format and has been processed and converted into Esri File Geodatabase format and then uploaded to Esri’s ArcGIS Online platform for sharing and reference purpose. The objectives are to facilitate our Hong Kong ArcGIS Online users to use the data in a spatial ready format and save their data conversion effort.For details about the data, source format and terms of conditions of usage, please refer to the website of DATA.GOV.HK at https://data.gov.hk.

Note: updates to this beta layer are currently paused while we sync new versions of the OSM layers for Europe.This feature layer provides access to OpenStreetMap (OSM) address point data for Europe, which is updated every 5 minutes with the latest edits. This hosted feature layer view is referencing a hosted feature layer of OSM point (node) data in ArcGIS Online that is updated with minutely diffs from the OSM planet file. This feature layer view includes amenities, shops and other point features that have detailed address data, defined as a query against the hosted feature layer (i.e. addr:housenumber and addr:street is not blank).Zoom in to large scales (e.g. Neighborhood level or 1:20k scale) to see the address features display. You can click on a feature to get the name of the feature (if available). The name of the feature will display by default at large scales (e.g. Street level of 1:5k scale). Labels can be turned off in your map if you prefer.

This New Zealand Point Cloud Classification Deep Learning Package will classify point clouds into building and background classes. This model is optimized to work with New Zealand aerial LiDAR data.The classification of point cloud datasets to identify Building is useful in applications such as high-quality 3D basemap creation, urban planning, and planning climate change response.Building could have a complex irregular geometrical structure that is hard to capture using traditional means. Deep learning models are highly capable of learning these complex structures and giving superior results.This model is designed to extract Building in both urban and rural area in New Zealand.The Training/Testing/Validation dataset are taken within New Zealand resulting of a high reliability to recognize the pattern of NZ common building architecture.Licensing requirementsArcGIS Desktop - ArcGIS 3D Analyst extension for ArcGIS ProUsing the modelThe model can be used in ArcGIS Pro's Classify Point Cloud Using Trained Model tool. Before using this model, ensure that the supported deep learning frameworks libraries are installed. For more details, check Deep Learning Libraries Installer for ArcGIS.Note: Deep learning is computationally intensive, and a powerful GPU is recommended to process large datasets.The model is trained with classified LiDAR that follows the 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: 0 Background 6 BuildingApplicable geographiesThe model is expected to work well in the New Zealand. It's seen to produce favorable results as shown in many regions. However, results can vary for datasets that are statistically dissimilar to training data.Training dataset - Auckland, Christchurch, Kapiti, Wellington Testing dataset - Auckland, WellingtonValidation/Evaluation dataset - Hutt City Dataset City Training Auckland, Christchurch, Kapiti, Wellington Testing Auckland, Wellington Validating HuttModel architectureThis model uses the SemanticQueryNetwork model architecture implemented in ArcGIS Pro.Accuracy metricsThe table below summarizes the accuracy of the predictions on the validation dataset. - Precision Recall F1-score Never Classified 0.984921 0.975853 0.979762 Building 0.951285 0.967563 0.9584Training dataThis model is trained on classified dataset originally provided by Open TopoGraphy with < 1% of manual labelling and correction.Train-Test split percentage {Train: 75~%, Test: 25~%} Chosen this ratio based on the analysis from previous epoch statistics which appears to have a descent improvementThe training data used has the following characteristics: X, Y, and Z linear unitMeter Z range-137.74 m to 410.50 m Number of Returns1 to 5 Intensity16 to 65520 Point spacing0.2 ± 0.1 Scan angle-17 to +17 Maximum points per block8192 Block Size50 Meters Class structure[0, 6]Sample resultsModel to classify a dataset with 23pts/m density Wellington city dataset. The model's performance are directly proportional to the dataset point density and noise exlcuded point clouds.To learn how to use this model, see this story

This dataset represents police districts 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).

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.

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.

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

Das Basis-DLM beschreibt die Landschaft in Form von topographischen Objekten und stellt einen präsentationsneutralen, objektbasierten Vektordatenbestand dar.Quelle: Geobasis NRWVerarbeitungsprozesse: SHP Dateien wurden in ArcGIS Pro nach WebMercator projiziert und als Feature Service in ArcGIS Online veröffentlicht.

Der Adress-Datensatz enthält eine Auswahl von Sachattributen, welche in der Regel Referenzen auf andere Raumbezüge in Form von Schlüsselwerten enthalten. Diese sind:AdressID (AdressID, INT)Hausnummer ohne Zusatz (HNR, INT)Hausnummernzusatz (HNR_Zusatz, Text, 1 Zeichen)Straßenschlüssel (STR_NR Text, 5 Ziffern))Straßenname (STR_Name, Text, 100 Zeichen))Postleitzahl (PLZ, Text, 5 Ziffern)Bezirksname (BEZ_Name, Text, 50 Zeichen)Bezirksschlüssel ( BEZ_Nr, Text, 2 Ziffern)Ortsteilname (ORT_Name, Text, 50 Zeichen)Ortsteilschlüssel (ORT_Nr, Text, 4 Ziffern)LOR-Name (PLR_Name, Text, 50 Zeichen)LOR-Schlüssel ( PLR_Nr, Text, 8 Ziffern)1 - 2 = Bezirksschlüssel3 - 4 = Schlüssel Prognoseraum5 - 6 = Schlüssel Bezirksregion7 - 8 = Schlüssel PlanungsraumSchlüssel Statistischer Block (BLK, Text, 6 Ziffern), die ersten 3 Ziffern entsprechen dem Schlüssel des statistischen GebietsAufnahmedatum der Adresse (ADR_Datum, date)Aufnahmedatum der Straße (STR_Datum, date)Qualität der Adresse (Qualitaet, Text, 25 Zeichen)Qualität A = Hauskoordinate im Gebäudeumring (1000)Qualität B = Hauskoordinate innerhalb Flurstück (2000)RBS = Datensatz im Regionalen BezugssystemAdresstyp (Typ, Text, 25 Zeichen)AdressePlatz/Straße ohne HNR – Mittelpunkt von Straße oder Platz, denen keine Adresse zugeordnet istDatensatznummer HKO (gml_id, Text, 100 Zeichen)Für Straßen und Plätze ohne Hausnummer wurde der Mittelpunkt des Verkehrsobjektes generiert. Es handelt sich um keine Adresse. Bitte beachten Sie, dass sich die Regionalisierung (Postleitzahlengebiet, Bezirk etc.) ausschließlich auf den generierten Mittelpunkt bezieht. Die Zuordnungen geben keine Auskunft darüber, ob sich das Verkehrsobjekt auch zusätzlich über andere Gebiete erstreckt.Quelle: Geoportal BerlinVerarbeitungsprozesse: WFS "Adressen Berlin" wurde in ArcGIS Pro importiert, nach Web Mercator projiziert und als Web Layer in ArcGIS Online veröffentlicht.

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).

GK1000_Linien

Daten des amtlichen Liegenschaftskatsterinformationssystems (ALKIS) - Die Bezirksgrenzen der 12 Berliner Bezirke.Quelle: Geoportal BerlinVerarbeitungsprozesse: WFS "ALKIS Bezirke" wurde in ArcGIS Pro importiert, nach Web Mercator projiziert und als Web Layer in ArcGIS Online veröffentlicht.