This story map explains how to use two attributes to make a map using both color and size using the smart mapping capability within ArcGIS Online and ArcGIS Enterprise. You can easily select two attributes, and one will be shown in your map using color, while the other will be used to represent size. This mapping technique can help to show relationships you might not have known existed. This method can also help turn multiple maps into a single map to share with others. This story map walks you through multiple examples, which can help get you started with smart mapping color and size.

User guide for the ArcGIS Online Statewide Traffic Count AppThe guide covers essential aspects, including:Map Functions Overview: This section details the basic interactive functions of the map, including zooming, panning, and identifying features. It will explain how to navigate the map interface effectively, find specific locations, and understand the map's overall layout and controls. Turn Layers On and Off: This portion of the guide will teach users how to control the visibility of different data layers within the map. Users will learn how to toggle layers on and off to customize the map display, focusing on specific traffic count data or related information. This allows for a more focused analysis of the data. Attribute Table and Export Data: This section explains how to access and utilize the attribute table associated with the traffic count data. Users will learn how to view detailed information about each traffic count location, including specific count values, dates, and other relevant attributes. Furthermore, this section will instruct how to export the attribute table data into formats like CSV or Excel for further analysis outside of the online application. Downloading Data: This portion of the guide will explain how to download the traffic count data. It will explain what file types are available for download, and any restrictions that are placed on the data.

Contents: This is an ArcGIS Pro zip file that you can download and use for creating map books based on United States National Grid (USNG). It contains a geodatabase, layouts, and tasks designed to teach you how to create a basic map book.Version 1.0.0 Uploaded on May 24th and created with ArcGIS Pro 2.1.3 - Please see the README below before getting started!Updated to 1.1.0 on August 20thUpdated to 1.2.0 on September 7thUpdated to 2.0.0 on October 12thUpdate to 2.1.0 on December 29thBack to 1.2.0 due to breaking changes in the templateBack to 1.0.0 due to breaking changes in the template as of June 11th 2019Updated to 2.1.1 on October 8th 2019Audience: GIS Professionals and new users of ArcGIS Pro who support Public Safety agencies with map books. If you are looking for apps that can be used by any public safety professional, see the USNG Lookup Viewer.Purpose: To teach you how to make a map book with critical infrastructure and a basemap, based on USNG. You NEED to follow the steps in the task and not try to take shortcuts the first time you use this task in order to receive the full benefits. Background: This ArcGIS Pro template is meant to be a starting point for your map book projects and is based on best practices by the USNG National Implementation Center (TUNIC) at Delta State University and is hosted by the NAPSG Foundation. This does not replace previous templates created in ArcMap, but is a new experimental approach to making map books. We will continue to refine this template and work with other organizations to make improvements over time. So please send us your feedback admin@publicsafetygis.org and comments below. Instructions: Download the zip file by clicking on the thumbnail or the Download button.Unzip the file to an appropriate location on your computer (C:\Users\YourUsername\Documents\ArcGIS\Projects is a common location for ArcGIS Pro Projects).Open the USNG Map book Project File (APRX).If the Task is not already open by default, navigate to Catalog > Tasks > and open 'Create a US National Grid Map Book' Follow the instructions! This task will have some automated processes and models that run in the background but you should pay close attention to the instructions so you also learn all of the steps. This will allow you to innovate and customize the template for your own use.FAQsWhat is US National Grid? The US National Grid (USNG) is a point and area reference system that provides for actionable location information in a uniform format. Its use helps achieve consistent situational awareness across all levels of government, disciplines, and threats & hazards – regardless of your role in an incident.One of the key resources NAPSG makes available to support emergency responders is a basic USNG situational awareness application. See the NAPSG Foundation and USNG Center websites for more information.What is an ArcGIS Pro Task? A task is a set of preconfigured steps that guide you and others through a workflow or business process. A task can be used to implement a best-practice workflow, improve the efficiency of a workflow, or create a series of interactive tutorial steps. See "What is a Task?" for more information.Do I need to be proficient in ArcGIS Pro to use this template? We feel that this is a good starting point if you have already taken the ArcGIS Pro QuickStart Tutorials. While the task will automate many steps, you will want to get comfortable with the map layouts and other new features in ArcGIS Pro.Is this template free? This resources is provided at no-cost, but also with no guarantees of quality assurance or support at this time. Can't I just use ArcMap? Ok - here you go. USNG 1:24K Map Template for ArcMapKnown Limitations and BugsZoom To: It appears there may be a bug or limitation with automatically zooming the map to the proper extent, so get comfortable with navigation or zoom to feature via the attribute table.FGDC Compliance: We are seeking feedback from experts in the field to make sure that this meets minimum requirements. At this point in time we do not claim to have any official endorsement of standardization. File Size: Highly detailed basemaps can really add up and contribute to your overall file size, especially over a large area / many pages. Consider making a simple "Basemap" of street centerlines and building footprints.We will do the best we can to address limitations and are very open to feedback!

The National Bridge Inventory Elements dataset is as of June 27, 2024 from the Federal Highway Administration (FHWA) and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The data describes more than 615,000 of the Nation's bridges located on public roads, including Interstate Highways, U.S. highways, State and county roads, as well as publicly-accessible bridges on Federal and Tribal lands. The inventory data present a complete picture of the location, description, classification, and general condition data for each bridge. The element data present a breakdown of the condition of each structural and bridge management element for each bridge on the National Highway System (NHS). The Specification for the National Bridge Inventory Bridge Elements contains a detailed description of each data element including coding instructions and attribute definitions. The Coding Guide is available at: https://doi.org/10.21949/1519106.

The Harmonized World Soil Database version 2.0 (HWSD v2.0) is a unique global soil inventory providing information on the morphological, chemical and physical properties of soils at approximately 1 km resolution. Its main objective is to serve as a basis for prospective studies on agro-ecological zoning, food security and climate change. The Harmonized World Soil Database (HWSD) was established in 2008 by the International Institute for Applied Systems Analysis (IIASA) and FAO, and in partnership with International Soil Reference and Information Centre (ISRIC), the European Soil Bureau Network (ESBN) and the Institute for Soil Sciences Chinese Academy of Sciences (CAS). The data entry and harmonization within a Geographic Information System (GIS) was carried out at IIASA, with verification of the database undertaken by all partners. HWSD was then updated in 2013 (HWSD v1.2) and in 2023 (HWSD v2.0). This updated version (HWSD v2.0) is built on the previous versions of HWSD with several improvements on (i) the data source that now includes several national soil databases, (ii) an enhanced number of soil attributes available for seven soil depth layers, instead of two in HWSD v1.2, and (iii) a common soil reference for all soil units (FAO1990 and the World Reference Base for Soil Resources). This contributes to a further harmonization of the database. The GIS raster image file is linked to the soil attribute database. The HWSD v2.0 soil attribute database provides information on the soil unit composition for each of the near 30 000 soil association mapping units. The HWSD v2.0 Viewer, provided with the database, creates this link automatically and provides direct access to the soil attribute data and the soil association information. Note: - A tutorial for accessing HWSD ver. 2.0 using R (prepared by David Rossiter, June 2023) has been added as an 'associated resource' (NOTE: Needs the SQLite version of HWSD v2 as provided below). - Soil property estimates in HWSDv2 were derived from Batjes (2016), Geoderma (https://doi.org/10.1016/j.geoderma.2016.01.034).

The Travel Monitoring Analysis System (TMAS) - Volume dataset was compiled on December 31, 2023 and was published on July 16, 2024 from the Federal Highway Administration (FHWA), and is part of the U.S. Department of Transportation (USDOT)/Bureau of Transportation Statistics (BTS) National Transportation Atlas Database (NTAD). The TMAS data included in this table have been collected by the FHWA from State DOTs through (temporal data representing each time period) permanent count data. DOTs determine what volume data is reported for any given month or day within the month. Each record in the volume data for the reported site, direction or lane is for the given day of record (it contains all 24 hours of data). The attributes are used by FHWA for its Travel Monitoring Analysis System and external agencies and have been intentionally limited to location referencing attributes since the core station description attribute data are contained within TMAS. The attributes in the Volume data correspond with the Volume file format found in Chapter 6 of the 2001 Traffic Monitoring Guide (https://doi.org/10.21949/1519109).

Pop-ups allow you to highlight attribute information in a web map. In this tutorial, you will learn how to create a web map and configure pop-ups in ArcGIS Online.

This quick guide introduces how to make a map that visualizes the relationship between two numeric attributes in point, line, or polygon feature data. Most maps of numeric data focus on a single attribute. Though, often we need to understand our data in relation to other attributes to explain the patterns we're seeing. For example, a map showing the relationship between a hurricane’s wind speed and its barometric pressure help better communicate where hurricanes tend to intensify (over warm water) and weaken (overland). In order to really understand our data, context and data relationships need to be considered.This is part of the Smart Mapping Styles in Map Viewer collection of tutorials.

Uses the Soil Data Development Toolbox for Gridded National Soil Survey Geographic Database (gNATSGO). Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptionsNotes:.A GeoTIFF version of the gNATSGO CONUS raster is availableThe USDA-NRCS-SPSD refreshes all published soil databases annually. gNATSGO will be included in the refresh cycle, which will provide a new up-to-date version of the database each year.gNATSGO is an ESRI file geodatabase.In the state and island territory databases, the soil map units are delivered only as a 10-meter raster version.In the CONUS database, the raster is 30-meter.No vectorized version of the soil map units is included in gNATSGO.The soil map units are uniquely identified by the mukey, which is included in the attribute table.The database has 70 tables that contain soil attributes, and relationship classes are built into the database to define relationships among tables.The raster can be joined to the Mapunit and Muaggatt tables in the MUKEY field.The database contains a feature class called SAPOLYGON. The “source” field in this feature class indicates whether the data was derived from SSURGO, STATSGO2, or an RSS.If you encounter an ArcMap error when working with a gNATSGO dataset that reads “The number of unique values exceeds the limit” try increasing the maximum number of unique values to render in your Raster ArcMap Options. Specific instructions can be obtained here: https://support.esri.com/en/technical-article/000010117

Gridded SSURGO (gSSURGO) is similar to the standard product from the United States Department of Agriculture (USDA) Natural Resources Conservation Service (NRCS) Soil Survey Geographic (SSURGO) Database, but is in the Environmental Systems Research Institute, Inc. (ESRI®) file geodatabase format. A file geodatabase has the capacity to store significantly more data and thus greater spatial extents than the traditional SSURGO product. This allows for statewide or even Conterminous United States (CONUS) tiling of data. gSSURGO contains all of the original soil attribute tables in SSURGO. All spatial data are stored within the geodatabase instead of externally as separate shape files. Both SSURGO and gSSURGO are considered products of the National Cooperative Soil Survey (NCSS). An important addition to the new format is a 10-meter raster (MapunitRaster_10m) of the map unit soil polygons feature class, which provides statewide coverage in a single layer. The CONUS database includes a 30-meter raster because of size constraints. This new addition provides greater performance and important analysis capabilities to users of soils data. Statewide tiles consist of soil survey areas needed to provide full coverage for a given State. In order to create a true statewide soils layer, some clipping of excess soil survey area gSSURGO data may be required. The new format also includes a national Value Added Look Up (valu) Table that has several new “ready to map” attributes.Other Documents to Reference:gSSURGO FactsheetgSSURGO User Guide ArcMap version 2.4Soil Data Development Toolbox User Guide v5 for ArcMapgSSURGO Mapping Detailed GuidegSSURGO Valu1 table column descriptions

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.

In 2010, the Alaska Regional Director and Alaska Leadership Council directed all parks to complete a geospatial layer of installations and keep it up to date for use in cumulative impacts analysis. The AKR Installations Geodatabase is a centralized data repository that supports a regionally consistent approach useful for NPS permitting and resource management. By supporting the 2010 initiatives for strengthening NPS wilderness stewardship, this system can inform NEPA analyses, help parks track accountability for maintaining and removing installations, and provide critical information for wilderness character monitoring. This AKR Installations Geodatabase, developed and stewarded by the AKR GIS Team, is available for upkeep by each park through its designated Installations Point-of-Contact (POC) and supporting GIS POC. The Installations POC is generally the park coordinator for wilderness, compliance, and/or permitting while the GIS POC is someone who can assist with organizing and preparing the geospatial data for inclusion in the AKR Installations Geodatabase. The AKR Installations Geodatabase contains four point‐type feature classes: Instrumentation Installations (INSTRUMENTATION), Marker Installations (MARKER), Communication Installations (COMMUNICATION), and Generic Installation Point Installations (GENERIC INSTALLATION POINT). Each feature class in the geodatabase is designed to store a specific type of installation data and represent that installation data as a point feature in ArcGIS. To facilitate data entry, viewing, and analysis the non‐spatial attribute fields contained in each feature class have been standardized. The Installations User Guide was developed as a reference to introduce the NPS Alaska Region Installation geodatabase and provide guidance for installation data entry and record management in this system. It is recommended that the Installations User Guide be reviewed prior to populating the NPS Alaska Region Installations geodatabase.The COMMUNICATION feature class is designed to contain records for installations of stand‐alone communication relay equipment (e.g. radio repeaters, cell phone towers). Installations in the COMMUNICATION feature class differ from those in the INSTRUMENTATION feature class as they serve to transmit data, not collect or record data for scientific purposes. Communication‐related equipment associated with a station‐type installation managed in the INSTRUMENTATION feature class (i.e. continuous GPS station, seismic station, precipitation station, SNOTEL station, snow course station, weather station) are not considered to be stand‐alone. Please review the COMMUNICATION feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 10 and the domain values assigned in the data model’s Installation_Subtype field may further clarify the types of items relevant to this feature class.The GENERIC INSTALLATION POINT feature class is designed to contain records for installations the user deems relevant to the cumulative effects analysis process, but are not appropriate for inclusion in the INSTRUMENTATION, MARKER, or COMMUNICATION features classes at present time. The GENERIC INSTALLATION POINT feature class is intended to serve the purpose of a temporary placeholder for installation data while NPS data standards and the NPS Alaska Region Installations geodatabase evolve. Please review the GENERIC INSTALLATION POINT feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 11 may further clarify the types of items relevant to this feature class. Installation data outside of Table 11 items may also be included, but please remember that all records contained in this feature class will be represented as points in ArcGIS. OHV trails and maintained foot trails are better represented as line features in ArcGIS and have therefore not been marked for inclusion in the GENERIC INSTALLATION POINT feature class.The INSTRUMENTATION feature class is designed to contain records for installations of equipment that collect or record data for scientific purposes. Please review the INSTRUMENTATION feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 2 and the domain values assigned in the data model’s Installation_Subtype field may further clarify the types of items relevant to this feature class.The MARKER feature class is designed to contain records for installations that serve to mark a location of interest. Please review the MARKER feature class data model (Section 12) of the Installations User Guide to gain a general understanding of its structure and data requirements. Table 7 and the domain values assigned in the data model’s the Installation_Subtype field may further clarify the types of items relevant to this feature class. The MARKER feature class in the NPS Alaska Region Installations geodatabase was developed to manage installation data relevant to the cumulative effects analysis process and is not intended to replace the NPS Survey Monumentation Data Standard for the distribution of survey monumentation data to enterprise GIS systems.The corresponding NPS DataStore on Integrated Resource Management Applications (IRMA) reference is:AKR Science in Wilderness Installations PUBLIC