Latest Contributions by Suzanne-Boden

Organizations all over the world are using focused web apps to share authoritative 2D and 3D content that's been published to their ArcGIS organizational site or ArcGIS Enterprise portal. App users are visualizing data, performing analysis, finding the shortest route between two points, and completing many other tasks powered by GIS—in most cases, without even realizing they’re using GIS. Web AppBuilder for ArcGIS makes it easy to create feature-rich web apps without writing code. The user interface (UI) is friendly and makes quick work of creating apps that meet your functional requirements. The basic workflow is only three steps: Share Configure Launch Let's walk through the steps.   Share Sharing is the fastest step. On your ArcGIS  organizational site or ArcGIS Enterprise portal, select a web map or 3D web scene and choose to “Create a web app using the Web AppBuilder.” That's it! Configure Now you’re in the AppBuilder facing an “app shell.” In this step, you assign a theme to define the layout and color scheme and if desired add branding elements, such as your organization’s logo. You can also add other content layers.   The most important part of this step is selecting the widgets that will provide end users with the specific functionality you want them to have. There are lots of widgets to choose from but, as a general rule, don’t overwhelm end users with too many widgets—four or five is plenty for a focused web app. Launch After configuring the theme and functionality, launch the web app to preview and evaluate your design. Previewing the app is essential to test both the app’s appearance and functionality—and make sure it will accomplish your intended purpose. If necessary, make changes, then launch again (as many times as needed) to finalize the app design. When you’re confident the app will accomplish your intended purpose, deploy it to your organization’s users—or to the world!   Want to learn more? If you'd like to learn time-saving tips and best practices for working with Web AppBuilder for ArcGIS, check out these training options: Build Your Own Web Apps (training seminar) Introduction to Web AppBuilder for ArcGIS Developer Edition (training seminar) Do-It-Yourself Geo Apps (MOOC) Configuring Web Apps Using Web AppBuilder for ArcGIS (instructor-led class)    This post was contributed by Esri education specialist Jamie Powell. Based in Olympia, Washington, Jamie is a course developer with extensive experience in the IT and GIS industries. ... View more

This post shows how to apply a five-step process to complete an analysis project using ArcMap (the same analysis could be performed just as easily in ArcGIS Pro). Suppose you want to analyze access to health care services in Riverside and San Bernardino counties in southern California. The five steps in the analysis process are: Frame the question Explore and prepare data Choose analysis methods and tools Perform the analysis Examine and refine results   Step 1. Frame the Question This step seems straightforward because typically you're assigned a project to obtain specific information. Some projects involve answering several questions derived from a high-level question. How you frame the questions helps determine which GIS tools and methods you use for the analysis. In this example, you might frame a preliminary high-level question: Is the distribution of health care facilities consistent with the population distribution in Riverside-San Bernardino, CA? This question could be broken down into the following sub-questions: Where are facilities that provide health care services located? What is the population distribution within the study area? Do areas with the highest population density have the greatest number of facilities? Within the study area, are there areas with high population density but no health care facilities?   Step 2. Explore and Prepare Data This step can be the most time-consuming. If you don't have all the data needed for an analysis project, you must collect it. The ArcGIS Living Atlas of the World is an excellent source of high-quality spatial data. In the U.S., the Census Bureau has a multitude of spatial, population, and demographic data. State data clearinghouses are another useful resource.     Step 2a: Explore Data For each dataset, explore the feature geography, attributes, and metadata to determine whether the data will be useful for your analysis and what kind of preparation, if any, may be required. Questions to ask about the data include: What is the data format? When was the data collected (how current is it)? How detailed is the data—at what scale was it collected? What coordinate system does the data use? Is the data projected? Best practice is to project all datasets into a common coordinate system before doing analysis. Does the feature geometry (i.e., point, line, polygon) work for the analysis? Does the data have the attributes you need? Does the data have any access or use constraints? For this example, the following datasets were used. All use the WGS 1984 geographic coordinate system. Hospitals — this data includes "traditional" hospitals as well as other medical facilities. ZIP Codes — this data includes population attributes. Counties — this data provides the geography for the area of interest (Riverside and San Bernardino counties). States — this data provides additional geographic reference for the area of interest (California).   Step 2b: Prepare Data To start, you need to decide what data format to use. Project data doesn't have to be all in the same format, but it can make things easier. The important thing is to verify that the analysis tools you need to use accept your data format; also consider whether you will be distributing the data created by the analysis. You can use the geoprocessing tools in the ArcToolbox Conversion Tools toolbox to quickly convert data to another format. If you have access to the ArcGIS Data Interoperability extension, you can directly work with many data formats.   Organizing data into a project folder helps simplify analysis tasks (you can specify a default input workspace for all the geoprocessing tools). For this project, a file folder was created to organize the shapefiles. If you are working with feature classes stored in different geodatabases, you could copy or import them into a single file-based project geodatabase. You might also want to create separate folders or geodatabases to store intermediate (temporary) data output from analysis operations as well as final data.   Extracting data to have the same extent as the study area helps speed up processing time and enhances data visualization in ArcMap. In this example, the project datasets cover the entire U.S. Clipping the hospitals and ZIP Codes to the extent of the two counties will be part of data preparation. In order to clip the data, you can create a selection layer of just Riverside and San Bernardino counties, or just select the two counties on the map. If you plan to use the same study area for multiple analysis projects, it's a good idea to export selected features and selection layers to their own shapefile or geodatabase feature class. That way, you have your study area feature data ready to go. For this example, we will simply select the two counties of interest.  Here's are the data preparation tasks for this project: Start ArcMap, add the project data, and zoom to the study area. Using the Select Features tool, select Riverside and San Bernardino counties. Now you will clip the U.S. ZIP Codes to the extent of the two counties. Open ArcToolbox, expand Analysis Tools, expand Extract, and double-click Clip to open the tool dialog box. For Input Features, choose U.S. ZIP Codes. For Clip Features, choose Counties. When the clip layer has selected features, only the selected features will be used to clip the input features. Accept or change the output location and name, then click OK to run the tool. The clipped layer that contains only ZIP Codes in Riverside and San Bernardino counties is added to the Table of Contents. Repeat the steps to clip the hospitals. Double-click the Clip tool to open its dialog box. For Input Features, choose Hospitals. For Clip Features, choose Counties. For Output Feature Class, accept or change the default output location and name, then click OK. When the clip operation completes, a layer representing hospitals within the study area is added to the Table of Contents. Change the default symbol as desired and remove the U.S. hospitals and ZIP Code layers (right-click each layer in the Table of Contents and choose Remove). The data preparation tasks are now complete.     Step 3. Choose Analysis Methods and Tools To choose the appropriate methods and tools for an analysis project, consider the questions framed in step 1 and document the methods and tools that will answer each one.   Question Methods and Tools   Where are facilities that provide health care services located? Examine distribution of hospitals on the map. What is the population distribution within the study area? Symbolize ZIP Codes layer based on population density using graduated colors. Do areas with the highest population density have the greatest number of facilities? First, do a visual analysis of the map to get a general idea, then do a spatial join operation between the Hospitals and ZIP Codes. The output of the spatial join will be one record for each hospital and the ZIP Code attributes. Within the study area, are there areas with high population but no health care facilities? Summarize the ZIP field in the table output from the spatial join. The summary table will include a count of hospitals in each ZIP code that contains a hospital, plus population data for each ZIP Code. It's helpful at this step to diagram the analysis. The diagram doesn't have to be anything fancy (although it can be if you like that sort of thing). An easy thing is to quickly draw on paper or a whiteboard like the example below.   Step 4. Perform the Analysis If you've diagrammed the process in step 3, then in this step, you simply follow the diagram, completing each task in sequence. For complicated analyses, you may want to create a model in ModelBuilder to automate the process. A model also allows you to quickly change a parameter and run the model again to explore different scenarios. Examine the distribution of the hospital features on the map. Zoom and pan around as needed. Symbolize ZIP Codes with graduated colors based on the POP07_SQMI (2007 population density) attribute. A visual analysis of the data shows the greatest number of hospitals and the most densely populated ZIP Codes (in darker shades of green on the map below) are in the southwestern part of the study area. You can get more information by doing a spatial join between the Hospitals and ZIP Codes layers. Right-click Hospitals and click Joins and Relates > Join. In the dialog box, choose to join data from another layer based on spatial location. Choose ZIP Codes in the drop-down list of layers, specify the output feature class name and location, and click OK. The output of the spatial join is a new point layer that contains all the hospital features plus the attributes of the ZIP Code each facility falls within. The ZIP field contains the five-digit ZIP Code in which the hospital is located, and the PO_NAME field contains the post office name (corresponds to the city name) for that ZIP Code. The POP07_SQMI field shows the population density associated with each hospital's ZIP Code.   Sorting the PO_NAME field reveals that multiple hospitals are located in some ZIP Codes.   The last step is to summarize the ZIP field. This operation will output a table that contains one record for each ZIP Code that contains a hospital, plus a field containing the count of hospitals within each ZIP Code. You can also choose to output statistics for numeric fields (such as POP07_SQMI). In the joined table, right-click the ZIP field and choose Summarize. For summary statistics, check First and Last for NAME (this is the hospital name) and check Average for both POP2007 (total population) and POP07_SQMI. Specify an output location and name, then click OK. Choose to add the result table to the map and open it.   Step 5. Examine and Refine Results So what information does the summary table provide? The Count_ZIP field tells you the number of hospitals in each ZIP Code that contains a hospital.   Sorting the POP07_SQMI field reveals that all the ZIP Codes that have more than 2,000 people per square mile have at least one health care facility.   The analysis shows that the distribution of health care services is generally consistent with the distribution of the population within the study area—that is the most facilities are located where the population is most dense. You could refine this analysis by considering the number of patients each facility can serve and other variables of interest. You could also extend the project to analyze whether access to health care services in the low-population areas is adequate. The current map indicates that residents of ZIP Codes with a low population density may have to travel a great distance to reach a hospital.   Want to learn more about performing analysis in ArcGIS? Check out these training options: Getting Started with Spatial Analysis (for ArcMap or ArcGIS Pro) Going Places with Spatial Analysis (for ArcGIS Online) ArcGIS 3: Performing Analysis (for ArcMap) Spatial Analysis with ArcGIS Pro ... View more

New Date: The seminar formerly scheduled for October 25 is now scheduled for November 15.    Join us for a free live training seminar to hear all about the latest capabilities of Collector for ArcGIS—direct from the product experts. Three one-hour live sessions will be broadcast throughout the day. Each session includes Q&A time with the presenters.   Seminar Details: Name: Field Data Workflows with Collector for ArcGIS Date: November 15, 2018 Time: 9 a.m., 11:00 a.m. and 3 p.m. Pacific Location:  at the Esri Training website. Add a seminar session to your calendar at https://go.esri.com/collector-lts   ... View more

Updated February 7, 2022 The difference between relates (often called table relates) and relationship classes is a source of much confusion, especially for new ArcGIS users. Though they sound similar, the terms refer to different things. Both have benefits and there are reasons to use each one. Here are the main things to know. A relate exists in a map or layer file. A relationship class is an object in a geodatabase. Relates can be created and edited with an ArcGIS Pro or ArcGIS Desktop Basic, Standard, or Advanced license. Relationship classes can be created and edited with an ArcGIS Pro or ArcGIS Desktop Standard or Advanced license. They are read-only with a Basic license. All clear now? No? Let's continue then. Deconstructing the Terminology Relates are great because they allow you to select features in a layer, then easily see related features in a different layer or related records in a nonspatial table. Relationship classes are great because they enable "smart behavior." You can set up rules for how the participating feature classes or tables behave when something happens. For example, with a relationship class in place, if a feature is deleted, then its associated record in the other feature class or table can be automatically deleted as well.  Both relates and relationship classes rely on cardinality, which describes how records in two different tables are related to one another—cardinality can be one-to-one, one-to-many, many-to-one, or many-to-many. One-to-one: Every feature has exactly one related record in the other table. One-to-many: Features in one table may have more than one related record in the other table. Many-to-one: Multiple features in one table have one related record in the other table.  Many-to-many: Multiple features in one table have multiple records in the other table. Relates support one-to-many and many-to-one cardinalities, while relationship classes support all cardinalities. Feature classes and tables that participate in a relate or a relationship class must have a field of the same data type (text, short integer, long integer, object ID, etc.). That field will be the "connection point" (AKA key field) between the two. The Relate Example The map below contains a layer of fire stations and a nonspatial table that stores data about the city's fire department personnel. A relate was created between the layer and the nonspatial table, which have a one-to-many cardinality (every fire station has multiple personnel). The relate is based on a short integer field in both tables that stores a fire station ID number. The fields have different names, but that doesn't matter at all.       Thanks to the relate, it's easy to find out which personnel are assigned to any given fire station. Just use the Identify tool and click a fire station on the map. In the Identify window, the related table name displays below the fire station feature name. Expanding the table shows the records associated with that station (the Washington station in this example, which has six assigned personnel).     Suppose Brian Butler is transferred to the Adams station. His record in the FirePersonnel table is edited to replace the Washington station number (2) with the Adams station number (202). When the edit is saved, the data shown in the Identify window will reflect his new assignment. Washington now has only five assigned personnel...     ...while the Adams station personnel list now includes Brian.     The Relationship Class Example Table relates are super-useful to quickly view feature data stored in separate tables (for efficient data management purposes). Relationship classes give you the ability to do more than easily view data, however. With a relationship class, you can set rules and properties that control what happens when data in either table is edited. You can also ensure that only valid edits are made. Using the example above, suppose a relationship class named StationsPersonnel has been created between the Fire Stations feature class and Fire Personnel table. Also suppose the city requires that all stations have a minimum of five assigned firefighters and a maximum of 15 assigned firefighters. A rule has been created in the relationship class to enforce this requirement.     With Brian Butler's transfer to the Adams station, Washington is left with five assigned firefighters. Jean Fiorini, however, has requested a transfer, and her request was approved. A GIS technician responsible for maintaining the fire department's GIS data updates Jean's record in the personnel table with the new station number. She gets a message warning her that the edit has broken a rule.  Note: Depending on which version of ArcGIS you are using and how the relationship class was configured, edits that conflict with a relationship class rule may not be accepted. The database knows that without Jean, Washington will have fewer than five assigned personnel. To comply with the relationship class rule, the technician should first add a firefighter to the Washington station, then edit Jean's record to reflect her new station assignment. A relationship class is intended to ensure that all data edits are valid and that an organization's GIS database accurately reflects and supports real-world needs. Suppose the person who approved Jean's transfer didn't realize that Washington would be left with only four firefighters. The relationship class rule surfaced that piece of key information, and we will assume the GIS technician communicates the issue to prevent loss of property or lives down the line due to insufficient staffing.  Want to learn more about relates and relationship classes? Check out these help topics: ArcMap ArcGIS Pro For detailed training and hands-on practice with relates, relationship classes, and other geodatabase capabilities that enforce data integrity, take our Managing Geospatial Data in ArcGIS class. ... View more