Latest Contributions by Suzanne-Boden

Python is one of the most popular languages used to script ArcGIS tasks, maybe because it has a relatively low learning curve and it can do a lot. Python is integrated into ArcGIS Desktop. You can create Python add-ins and toolboxes and in ArcMap you can directly enter Python label and MapTip expressions instead of having to load a script file. It's nice to have these options. Amidst all the Python love floating around, some have said ModelBuilder is the forgotten hero of the ArcGIS automation world. They contend that ModelBuilder deserves a bigger share of the spotlight that's shining on Python. I've always found GIS professionals to be visual thinkers who embrace scripting, so it makes sense they would use both Python and ModelBuilder to do their ArcGIS work (and many of you likely do). But if there is a Python vs. ModelBuilder debate, I'll stay neutral by saying that both are valuable and have their place. When an older child angrily accuses them of favoritism towards the unwanted intruder, wise parents know to respond, "We love your little brother just as much as we love you." And so it is with ModelBuilder and Python. Both are tools to automate GIS analysis, data management, and mapping workflows. Unlike Alex and little Sam, they can even play nicely together (you can add a Python script tool to a model, and you can export a model to a Python script). I've heard ModelBuilder called a "visual programming language." This is an interesting way to think about it as it's true there are many things you can do in ModelBuilder that mirror programming techniques—such as creating variables, iterating (looping) through a set of data, setting preconditions, and nesting submodels inside a larger model (similar to calling another script from inside a script). Let's shine a little light on ModelBuilder just in case it is feeling left out. Why might you want to create a model? Visualizing Workflows A model is extremely useful when designing and sharing a workflow, especially when it's complicated. You can always draw on a whiteboard, but dragging and dropping tools into a model scores efficiency points—you can dynamically build and adjust the workflow as you're thinking it through. You can easily rearrange model tools and elements and add text labels to document the flow and datasets being created. When collaborating with others, communicating about a workflow using a model is more effective than reading and scrolling down through a script. A model is like a map—it's a graphical medium that engages people and enhances understanding. You can even export a model to a graphic or PDF file and bring it along to a meeting as a handout. Exploring Results and Testing Scenarios Because you run a model from inside ArcGIS Desktop (ArcGIS Pro or ArcMap), you can examine the results immediately. In this way, a model is part of the daily fabric of your GIS work. If the model output creates a new question to investigate, all you have to do is change a model parameter (e.g., switch out a tool's input dataset or value) and rerun a process or rerun the entire model, then explore what that yields. With a script, it's certainly doable but not as easy to work iteratively like this. You have to open the script file, find the lines of code that need to be edited, make the edits, save the script, run the script, then possibly open ArcMap and add the output and maybe some other layers for context. To make things easier, you can start by loading the script into the Python window in ArcGIS and go from there. Identifying Problems One of the cool things about a model is that you can eyeball its progress as it runs. As each process executes, the tool flashes red and a drop-shadow displays under its elements when the process completes. You can immediately tell where an error occurred in the model because it stops processing there. Just find the first process that doesn't have a drop-shadow. After correcting the error, you can continue running the model from that point—you don't have to go back to the beginning. Just right-click the tool element in the process that failed and choose Run. Identifying the location of a problem in a script is not always as fast. Applying Skills You Already Have The most obvious reason to create a model over a script is that you lack scripting skills, whether Python or any other language, and you don't want or can't afford to invest the time to learn scripting. Though these days it's fairly easy to acquire basic Python skills. From Esri alone there are free tutorials, presentations, training seminars, and free and low-cost web courses. ArcGIS geoprocessing tools output Python code snippets you can examine to learn from and then modify to suit your needs. Basic Python skills don't cut it, however, if you need to automate an advanced workflow. Models chain together and run geoprocessing tools, so if you are a GIS professional who uses ArcGIS, more than likely you already have the skills required to create a model. Setting up a model correctly requires time and planning, like any other task. You have to understand your data, environment settings, tool parameters, and desired outputs. There are nuances and more advanced capabilities of models that you may need to educate yourself about, but again there are lots of resources to do so (see the list at the bottom of this post). In the end, the choice of whether to automate an ArcGIS workflow using a script or a model comes down to what you feel most comfortable doing. Python is a logic-driven text-based tool and ModelBuilder is a logic-driven visual tool. When feeding your culture appetite, sometimes you want to read a great novel and sometimes you want to look at a beautiful painting. Similarly, there may be times you want to write a script and other times you want to create a model. The beauty of ArcGIS is that you can easily do both, right inside ArcGIS Desktop. Related posts: ModelBuilder 101 for ArcGIS Pro ModelBuilder 101 (ArcMap) Resources to learn more about ModelBuilder: Esri training options ArcGIS Help (ArcGIS Pro, ArcMap) Resources to learn more about Python scripting: Python website Esri training options for Python ... View more

Updated February 13, 2018 A previous post covered converting standard annotation to feature-linked annotation—to recap, it cannot be done directly. A recommended workflow when you have standard annotation that you wish were feature-linked is to create an empty feature-linked annotation class, then append the standard annotation features to it (using the Append Annotation Feature Classes tool). Several readers have wondered, once you have feature-linked annotation in place, what happens when you need to replace the data linked to the annotation? No one wants to repeat the work of setting up annotation if they can avoid it. Can you change which feature class your feature-linked annotation is linked to? The answer is no. Feature-linked annotation can be associated with only one feature class (the one specified when the feature-linked annotation was created). The feature-linked annotation and the feature class participate in a relationship class that you cannot alter. Despite this, when you receive new data, there is a way to preserve the annotation. Consider this scenario: Every month, you receive updates to a geodatabase feature class in shapefile format. Each month, features have been deleted, there are new features to be added, and remaining features have changes. Replacing the data would be faster than editing the existing feature class. However, the feature class has linked annotation. A reader shared a solution for this situation: delete the existing features from the feature class (thus creating an empty feature class), then append the new features to repopulate the feature class. This solution works and it's a good one when you have data that changes a lot and doesn't participate in table joins or relationships (besides feature-linked annotation). This method is not advisable in all situations. Before deleting features, check your workflows to make sure there are no unintended impacts. Depending on how extensive the data changes are, you can replace all features or just a selected set. Below are the steps to do this using ArcMap. Of course, before deleting any data, it's always wise to make a backup copy first. For example, you could create a file geodatabase in an archive workspace and copy the feature class and feature-linked annotation class to it before performing the steps below. To create or edit a feature class that has feature-linked annotation, you need an Standard or Advanced license of ArcGIS Desktop. Delete Existing Features In ArcMap, open a blank map document and add the feature class that needs to be updated and its feature-linked annotation to the Table of Contents. Start an edit session. Select the features you want to delete. You can build a query expression to select a subset or, to delete all features, right-click the feature class layer and click Selection > Select All. Click the Delete button, then save your edits. Stop the edit session. Open the attribute table for the feature-linked annotation and note that its features have been deleted as well. Append Replacement Features Add the dataset that contains the new data to the map document. The Append tool accepts map layers, shapefiles, and geodatabase feature classes (and other data types) as input. You can use its optional Schema Type and Field Map parameters to control how attributes are treated. In the Catalog window, expand Toolboxes > System Toolboxes > Data Management Tools > General (or just use the Search window to search for "append"). Double-click the Append tool to open its dialog box. For Input Dataset, click the drop-down arrow and choose the layer that stores the new data. For Target Dataset, choose the existing feature class, then click OK to run the tool. After the Append tool finishes running, open the attribute tables for the updated feature class and the annotation. Note that the features were added and new annotation features were created for them. Review the features and annotation on the map and reposition individual annotations as needed, based on the new feature locations and shapes. So there you have it: an easy way to mass-update data and associated feature-linked annotation. Thanks to reader Pedonkus for sharing this solution. If you regularly receive new data to replace current data that has feature-linked annotation, you can automate this process using a Python script. The ArcPy site package includes the UpdateCursor function (can use to delete existing features). The Append tool help topic includes example Python code. Related Post: ArcGIS Annotation: Woes and Woohoos ... View more

Updated February 11, 2018 These days, "Is there an app for that?" is a rhetorical question. The answer is assumed to be yes. Surprise (annoyance even) is likely to surface if it turns out an app doesn't exist for a particular need. So it's no surprise there's a continuing conversation about whether GIS professionals—analysts, specialists, technicians, and others—should add programming to their list of must-have skills. And how much weight are organizations giving to programming expertise when evaluating GIS job candidates? The fact is that a lot of GIS professionals have been enthusiastic about programming for a long time. If you attend an Esri conference, you're almost guaranteed to overhear at least one veteran ARC/INFO user fondly reminisce about jamming with AML (ARC Macro Language). If there are former ArcView 3.x users in the room, Avenue talk rules. When ArcGIS came on the scene, many GIS professionals embraced first VBA, then Python. Given this strong tradition, the current conversation should focus not on whether GIS professionals should cultivate a programming language, but which languages and which platforms will help them do their jobs faster and better. The main reason to learn a programming language is to accomplish something, after all. Here are some reasons you may want to consider. Be more productive by automating ArcGIS tasks and workflows. How: You can create scripts to execute time-consuming or repetitive tasks, and schedule them to run after business hours. You can document and easily repeat complicated workflows that some projects require. You can  share your scripted workflows with other ArcGIS users to boost their productivity too. Language: Python. Python is still the replacement for AML, Avenue, and VBA. It's free, cross-platform, and integrated into the ArcGIS platform. If you want to script ArcGIS tasks, learn Python if you haven't already. Tip: Keep an eye on Arcade, a newish scripting language for ArcGIS that may become as popular as Python some day. Training options for Python Extend the value of GIS throughout your organization. How: If there are non-GIS users in your organization who perform GIS-powered tasks, you can help them be more efficient by simplifying things for them. Give them the ArcGIS tools they need without overwhelming them with a lot of other features and functions. Maybe they never even know they're using a GIS application—maybe they're just opening a map, getting information, and printing a report. Language: For desktop environments, add-ins are a relatively easy way to create and deliver a custom ArcGIS experience. The Microsoft .NET Framework (VB.NET and C# languages) is commonly used to create add-ins. Python add-ins are also supported. Training options for add-ins More and more organizations are having non-GIS staff use web apps to do their jobs thanks to their cross-platform accessibility and lower system footprint. One way to quickly create custom web mapping applications is to use the ArcGIS Online configurable apps. With a configurable app,  you don't have to write any code, you just configure the tools and data you need to deliver a friendly experience and enhanced productivity to end users. Training options for configurable apps Add new capabilities to support your organization's unique business workflows. How: If you really want to embrace your inner developer, you can learn how to create custom GIS applications accessible to desktop, online, or mobile workers who create, maintain, manage, or make decisions using your organization's geographic content. Language: This depends on your organization's preferred platform. Custom desktop applications are often developed using ArcGIS Engine and .NET, Java, or C++. ArcGIS Runtime SDKs are a great option for mobile apps and lightweight desktop apps. For web apps, ArcGIS API for JavaScript has many benefits and has a lower learning curve than other APIs. If you already know some HTML, you may actually have fun learning JavaScript. Training options for web and mobile development Bottom line: Do you need to learn a programming language? If it's not a requirement of your current job, then learning how to program is optional. Can programming help you perform your job better? More than likely at least one aspect of your job could be done better if you knew some programming. Can programming knowledge make you more valuable to your employer or potential employers? A non-scientific review of major job posting sites reveals that about half of GIS-related postings list a programming language as either a requirement or a recommended skill. It's a good bet that sharpening your programming skills is worth the effort and will help you grow your career. ... View more

Like people, training comes in different sizes. Put another way, training suits different purposes—and it scales from individuals to organizations. Individuals use training to gain new knowledge and skills that will help them do their current job faster and smarter, earn more responsibility (and pay), and reinvent themselves professionally. Projects use training to make sure team members acquire the skills they need to complete the project on time, on budget, and on spec. The skills that project team members acquire are almost always transferable to future projects. Departments use training plans to support functional job roles. Training is used to onboard new employees and support  performance objectives for all employees. Training plans are a great tool to document the knowledge and skills needed for each role—they also assist with writing job descriptions used to recruit and evaluate job candidates. Organizations increasingly understand the strategic benefits of workforce development. Workforce development includes training but more fundamentally, it encompasses the organization's belief that investment in their human talent is directly linked to achieving long-term business objectives. When integrated into the fabric of an organization, resources spent on workforce development are validated by the organization's bottom line. Because when individuals are empowered to perform to their potential—and they believe the organization is vested in their success—productivity, loyalty, and innovation have a rich environment in which to thrive. Where are you on the training scale? Esri offers free planning assistance for all sizes.  ... View more

"If you fail to plan, you plan to fail." This old adage has wide applicability—when you want to lose 10 pounds, be picked for a leadership position, land the perfect job, and on and on. So just like everything else in life, when it comes to GIS analysis, planning pays off. To ensure reliable results, here's the tried and true process we recommend: Frame the question. Explore and prepare data. Choose analysis methods and tools. Perform the analysis. Examine and refine results. Step 2 is arguably the most critical as your final results are only as reliable as the data you start with. Read on for a closer look at exploring and preparing data for an analysis project.Exploring Data The first part of step 2 flows directly from the analysis question. Spend time upfront identifying all the data, including attributes, needed to answer the question. Don't get halfway through a project and discover you're missing a key dataset. The point of exploring data is to understand what valid things you can do with it. You should: Examine the metadata. Note the spatial resolution and accuracy, coordinate system, when the data was collected and by whom, data use constraints, and other important information. Explore all the spatial datasets you plan to use in ArcMap. Do the layers align properly together? Are some layers more generalized than others? Do some layers have a larger (or smaller) extent than needed? Explore the attribute table for each layer and note the number of records and the attributes. Sort fields and look at field statistics to understand the values. Note obvious data entry errors and inconsistencies. Preparing Data Preparing data means ensuring datasets can be validly analyzed together and reducing processing time as much as possible. Data preparation tasks often include projecting data, reducing the spatial extent to the area of interest, deleting unneeded attributes, creating new attributes, cleaning up attribute values, and more. Get the most out of ArcGIS geoprocessing tools. Many can be run in batch mode and some have options you can use to combine data preparation tasks. For example, inside the Feature Class to Feature Class tool, you can create a SQL expression to import only features located in your area of interest, you can exclude source attributes you don't need, and you can define new attribute fields that you do need. With one tool, you accomplish three preparation steps. You can also create a model to automate data preparation—just drag the geoprocessing tools you need into a model window and define their parameters. Creating a model is an excellent way to visualize and order tasks as you go. The same data may be used for multiple analyses. It's good practice to make a copy of the original data before you make changes to prepare for a specific project. This way, the original data is preserved for subsequent analyses—and you have something to go back to just in case.Example: Analyze Piracy Incidents Let's look at a simple example to illustrate the importance of exploring and preparing data. The analysis question has been framed as: In the years 2009 through 2011, what types of vessels were the most frequent victims of piracy in and around the Gulf of Aden and Arabian Sea? The U.S. National Geospatial-Intelligence Agency (NGA) distributes Anti-Shipping Activity Messages (ASAM) reports, which include locations and descriptions of hostile acts against ships and mariners worldwide. You can download ASAM data in shapefile format from the NGA website.Explore the Data After downloading the ASAM shapefile, add it to ArcMap. For geographic context, it helps to add a basemap; in this case, the World Topograpic Map from ArcGIS Online works well. The map graphic on the right shows the global extent of the ASAM data (symbolized with red dots). A quick exploration of the attribute table reveals 6,158 records; an attribute that stores a world subregion code; and an attribute that stores the date each incident occurred—the date range is 5/1/1978 through 1/9/2012.  To efficiently answer the analysis question, you need to reduce the data to incidents that occurred between 1/1/2009 and 12/31/2011 in subregion 62 (the NGA website lists subregion codes). You also need to do some other preparation work to resolve issues with this data.Issue 1: The data has no spatial reference. When the ASAM shapefile was added to ArcMap, a message that the data was missing a spatial reference displayed. For shapefiles, spatial reference information is stored in the projection (.PRJ) file and it's not unusual for the .PRJ file to be missing, as it is here. So how do you know which coordinate system to use? A good place to start is the Layer Properties dialog box. In the Source tab, when you look at the extent coordinates, the number of digits to the left of the decimals tells you it's a geographic coordinate system. With geographic coordinate systems, the Left and Right extent values will have one to three digits to the left of the decimal, while the Top and Bottom extent values will have one or two digits to the left of the decimal. Tip: You can learn methods to identify unknown coordinate systems in the Working with Coordinate Systems in ArcGIS web course. Since the data has a global extent, it is reasonable to assign the WGS 1984 geographic coordinate system. If your analysis involved precise measurements, you would want to assign a suitable projected coordinate system for the area of interest as well.Issue 2: The data is more extensive than you need. The ASAM data has a larger spatial extent and a longer temporal extent than you need. SQL queries will resolve these issues. For this analysis, the initial data preparation tasks are: 1. Create a file geodatabase to organize the project data. 2. Import the ASAM shapefile into the file geodatabase. Import only features within subregion 62 that fall within the date range 1/1/2009-12/31/2011. 3. Assign the WGS 1984 coordinate system to the geodatabase feature class. The model graphic on the right shows the geoprocessing workflow. A SQL expression was defined in the Feature Class to Feature Class tool dialog so only the incidents meeting the analysis criteria will be imported. Tip: If you need help creating a SQL expression, use the SQL reference in the ArcGIS Help. After running the model, you have a new feature class with 643 features. Now that the data is a more manageable size, before moving on to step 3 of the analysis process, you need to verify that all 643 features represent piracy incidents that occurred during the analysis timeframe. In ASAM data, two fields contain date information (Reference and DateOfOcc). DateOfOcc stores mm/dd/yyyy, while Reference includes the year followed by a unique ID number. The Reference field was used in the Feature Class to Feature Class dialog's SQL expression for convenience, but it means there's cleanup to be done now. Sorting the DateOfOcc field reveals that four records have 2009 in the Reference field (reflecting the year the report was submitted), but their DateOfOcc values tell you the incidents actually occurred during the last week of 2008. These records can be deleted. Now there are 639 records to analyze.Issue 3: Attribute values are inconsistent. Next, you need to make sure all records are incidents of piracy. The Aggressor field stores this information. Sorting the Aggressor field reveals multiple values, the vast majority of which contain some variation of "pirate."  Tip: A quick visual method to understand how the Aggressor values vary is to open the Layer Properties dialog box and symbolize the layer by Unique Values based on the Aggressor field. Each unique aggressor value displays and the Count field tells you how many of each. After exploring the Aggressor values, you can create an attribute query to select all records that have one of the "pirate" variations in the Aggressor field, then switch the selection to see how many records don't directly reference pirate aggressors. In this case, only 16 incidents have Aggressor values that don't reference pirates. You need to explore the incident descriptions to determine whether any of these 16 likely were attacks by pirates. If they clearly did not involve pirate aggressors, delete the records. In this case, the incident descriptions don't clearly rule out pirate aggressors, so you choose to keep the records but categorize them to account for the ambiguity. You use the Field Calculator to change those 16 Aggressor values to Possible Pirates, and you clean up the remaining records so they all have an Aggressor value of Pirates.Issue 4: The table has duplicate records. With the DateOfOcc field sorted, a look at the Reference field shows something alarming. There seem to be many duplicate records—records with the same Reference value and identical data in the other attribute fields. How could this be? Did something go wrong when you converted the shapefile to a geodatabase feature class?  This is the time to go back to the original data and determine if the duplicates existed there. Because of the number of features in the shapefile, it's efficient to summarize the Reference field, which outputs a table with a count of each unique reference value in the shapefile. After creating the summary table and sorting the Count_Reference field, you see that many Reference numbers have a duplicate. To find out exactly how many, select the records whose Count_Reference value is equal to 2. It turns out that 493 Reference numbers have a duplicate in the source data you started with. Determining how many of those 493 are in your geodatabase feature class requires more work. Just like you did for the shapefile, in the Incidents to Analyze layer, summarize the Reference field to output a table with a count of reference values, then select the records whose Count_Reference value is equal to 2. It turns out that 170 records have a duplicate. Is there a way to easily remove 170 duplicate records? Yes, fairly easily. Here's how: Join the summary table of reference values to the Incidents to Analyze table. Select records whose Count_Reference value is equal to 2—as expected, there are 340 selected records. Start an edit session, then use the Delete Identical tool to delete the selected records that have the same Reference value as another record (note that before running the Delete Identical tool, you must remove the table join—the selected records remain selected after the join is removed). Final result: 469 incidents ready to be used as input in step 3 of the GIS analysis process. All this data exploring and preparing really only required a couple of hours of focused work. Of course, the incident victim values likely need cleanup as well—but we'll end the example here as the boundary line between steps 2 and 3 of the GIS analysis process is not a solid fence.Takeaway: When preparing data for analysis, you have to make decisions and live with some uncertainty. Your specific analysis criteria and how well you know the topic determine how far you go to clean up the data. To avoid creating or propagating errors, carefully explore the data and document any data preparation you do and why you chose to do it. Remember that a model is a valuable tool to document your workflow and help others better understand your analysis results. The data you use for a GIS analysis project may not be perfect and it may not fit your needs exactly. But planning and preparation go a long way toward making sure the data generates reliable results you can confidently share with others. Want to learn more best practices for GIS analysis? Here are courses that can help. Building Geoprocessing Models Using ArcGIS Pro ArcGIS 3: Performing Analysis Quality Control Using ArcGIS Data Reviewer ... View more

Updated February 13, 2018 Labeling features can be a time-consuming part of creating a map. When you're dealing with many features, it may seem downright onerous. But there are ways to make the job easier. Consider this illustrative tale. Week 1: James—smart, ambitious, new on the job—is designated the department map maker. Sam, data technician by day, ink artist by night, tells James about a new dataset with a thousand or so point features that will be used as an operational layer in several high-profile maps produced by the department. The data will be updated weekly and the maps need to be in sync (Sam also suggests a warrior armband to command respect from Marc, the alpha analyst in the group). In ArcMap, James adds the point layer and turns on dynamic labels, spends time creating label classes and setting scale ranges and formatting the labels in each class appropriately for the map products. He converts the labels to a standard annotation feature class stored in the same geodatabase as the point feature class so the annotation can be reused easily on multiple maps. He then spends several hours painstakingly positioning the annotation until he's satisfied the map text looks perfect. Marc will be impressed, he thinks (and mulls whether to go for a drink after work to discuss those Aztec jaguar symbols Sam just texted). Week 2: James receives a message that the point feature class has been updated. Ignoring Sam's question about flame throwers, he adds the point and annotation feature classes to a map document. Uh oh. Orphan text and unlabeled points galore. His heart sinks as he realizes how much work needs to be done to fix this. The thought occurs to him—is he going to have to redo this work every single week? Searching the ArcGIS help, James discovers he should have created feature-linked annotation when he converted the labels. If he had, he wouldn't have to edit the annotation feature class every time a point feature is added or deleted. If he had, then the annotation (and the maps that reference the annotation feature class) would automatically sync up with the point data—when a feature gets deleted, its annotation would also get deleted and when new point features get added, annotation would be created for them. If a feature got moved, the annotation would...well, Marc was going to have a field day with this. James wonders if there's a way to convert his standard annotation class to feature-linked annotation. Taking a deep breath, he dives back into the help... Week 3: Just as James is sitting down with his mocha java lite to look over the latest sketches from Sam, a soft ding heralds a message that the points have been updated. He shakes off a little fritter of nervous anxiety. Again he adds the point and annotation feature classes to a map document. Sweet relief, he is a warrior! The annotation for the features displays as it should, where it should. The maps are good to go. Yes, Sam, the eagle sun god eating a snake is just what he needs! Are you wondering how James' story ended so happily? Create Feature-Linked Annotation from Standard Annotation You can't convert standard annotation to feature-linked annotation, but you can add standard annotation features to a feature-linked annotation class. Creating feature-linked annotation requires an ArcGIS Desktop Standard or Advanced license. The feature class and the feature-linked annotation must be stored in the same geodatabase, or in the same feature dataset if the feature class is inside a feature dataset. Here are the high-level steps to do it. In the ArcMap Catalog window or ArcCatalog, create a new feature-linked annotation class in a geodatabase (right-click the geodatabase, click New > Feature Class and work through the New Feature Class wizard). Be sure to check the option to link the annotation to a feature class and specify that feature class. You can import the coordinate system information from the feature class you are linking to or specify it yourself. Set the reference scale to the appropriate one for your mapping needs. After creating the feature-linked annotation class, in the Catalog tree, expand System Toolboxes > Data Management Tools > Feature Class and double-click the Append Annotation Feature Classes tool. For input features, specify the existing standard annotation class. For output feature class, browse to the empty feature-linked annotation class. Specify the reference scale, check the options to create a single annotation class and to create annotation when new features are added. Click OK. Voila, the annotation features are now feature-linked and will update as edits are made to the linked features. You can delete the standard annotation class or perhaps archive it. Related Post: Lose the Features, Keep the Annotation Properties Want to learn more about working with annotation? Building Geodatabases Designing Maps with ArcGIS ... View more

One of the features of ArcGIS Desktop 10.x is the ability to "time-enable" your data. Visualizing how data changes over time provides opportunities for powerful, more in-depth analysis. The example below uses ArcMap to show how to visualize piracy-related incidents that occurred between March, 2007 and February, 2009 in and around the Gulf of Aden.  The map document shown on the right includes the World Topographic Map basemap for geographic context, and an Incidents point layer that represents locations where pirate attacks occurred. The Incidents data is a set of Anti-Shipping Activity Messages that was downloaded as a shapefile from the National Geospatial-Intelligence Agency (NGA) Maritime Safety Information portal and loaded into a file geodatabase feature class. The points on the map reveal the number of attacks that occurred during the time period, and you can see the incident distribution. By time-enabling the incidents, however, you can visualize the progression of the attacks and find out whether there were "clusters within the cluster." The Incidents layer contains a DateOfOcc attribute, which stores the date (mmddyyyy) that each attack occurred. In order to time-enable a layer, you must have one (or more) fields that stores time data (such as dates). A best practice is to store time data in a date field, but numeric or text fields will also work. Set Time Properties The ArcMap Layer Properties dialog box has a Time tab. So the first step to visualizing the time component of a layer is to simply open the dialog box and click the Time tab. Then follow the steps below: Check the box in the upper-left to "Enable time on this layer." In the Layer Time drop-down list, choose "Each feature has a single time field." For this example, this is the appropriate setting because the attribute table has only one time field. Sometimes, there are two fields that store time data; for example, StartDate and EndDate. For Time Field, choose the field in the layer that contains your time data. In this case, it's DateOfOcc. For Time Step Interval, you can manually set the interval or you can let the software calculate an interval based on your data. In this example, 2 days was specified. At the bottom of the dialog box, check the box to "Display data cumulatively." These settings will draw incidents for each two-day increment and keep earlier incidents displayed as succeeding incidents draw—allowing you to see the pattern of occurrence over the two years spanned by the data. Time-Enable the Map Click OK to apply the layer's time properties, then click the button on the Tools toolbar to open the Time Slider window. Click the "Enable time on map" button in the upper-left corner of the Time Slider window. After clicking this button, only the data associated with the start time displays on the map. Click the Play button to see the incidents display in order based on their date of occurrence. This time visualization reveals that in 2007 there were fewer incidents of piracy, they were much more spread out, and they did not appear to be consistently in close proximity to land. By early 2009, there were significantly more incidents, and they appear to be far more concentrated in the Gulf of Aden, specifically on the Yemen side of the gulf. The change in incident distribution could be due to an attempt by ships to avoid the Somali Coast. NGA Special Warning Number 123 advises mariners to remain at least 200 nautical miles distant from the Somali Coast. Sharing Time Visualizations The time properties are saved with the map document. You can share the time visualization by sharing the map document. But what if the people you want to share with don't have ArcMap? From the Time Slider window, you can quickly export a time-enabled map to an AVI file, which can be viewed using Windows Media Player. Knowing where things occur is the cornerstone of GIS analysis. You have to know where before you can hypothesize why. Another dimension to GIS analysis is when. Knowing where and when things occur means you can not only visualize patterns, but how patterns have evolved over time. With this information, you gain a deeper understanding of your data and can better model and predict future patterns. Related post: Visualizing Time in ArcGIS: Prepare Data in ArcGIS Pro ... View more

We got a lot of requests on our Esri Training survey for more courses on scripting, particularly with Python, the preferred scripting environment for ArcGIS Desktop. Currently, we have an instructor-led course and a variety of e-Learning resources that cover Python scripting. So why the big interest in scripting? The main reason is that scripts allow you to automate GIS workflows that would be time-consuming to complete one task at a time. For example, suppose you needed to update attributes for thousands of features on a daily basis. You can write a script to update the attributes, and have the script run at a specific time each day (or at night after working hours). The steps below show a quick way to create a Python script using what I call the F-C-P-M-T method (that's Find, Copy, Paste, Modify, Test). Like developers who find sample code and modify it for their own purposes, you can find example Python script code in the ArcGIS Desktop Help and modify it for your needs. To work through the steps, download and unzip the data. The steps assume you have ArcGIS 9.3 or 9.3.1, Python 2.5, and PythonWin installed on your computer or a network location you can access and write data to. Note: If you are going to write Python scripts, you'll want to have PythonWin, an application that provides a friendly script-creation, testing, and debugging environment. PythonWin 2.5.1 is included on the ArcGIS 9.3 and 9.3.1 installation media, but it isn't installed by default. You can also get PythonWin version 2.5.4 from http://www.python.org/download/releases/2.5.4/. The Scenario Suppose you work in public safety for the city of San Diego and your task is to identify trolley stops that may need to be evacuated and closed in the event of an emergency on a nearby freeway. You have feature classes representing the San Diego freeways, trolley lines, and trolley stops. You want to write a script to buffer the freeways, then overlay the trolley stops and the buffers to output all the trolley stops that may be impacted in an emergency to a new feature class. Why bother to write a script to do this fairly simple GIS operation? Reusability—you can quickly change the buffer distance if you need to just by editing the script, then run the script to create new buffers. You can share the script file with colleagues, who may want to use the script for their own purposes. They could modify the buffer distance and specify different feature classes to create data for their own projects, all without ever opening ArcMap or ArcCatalog. You can also save the script as a custom tool and include it in a geoprocessing model. Step 1: Find Geoprocessing Tool Script Code Start ArcMap, open a new empty map document, and display the ArcToolbox window. Expand the Analysis Tools toolbox and the Proximity toolset. Right-click the Buffer tool and choose Help. In the help window, scroll down until you see the Scripting syntax section. Below the syntax is a script example. Expand the example to see the code. Step 2: Copy/Paste Example Code into a New Script Click Start > All Programs > Python 2.5 > PythonWin. From the File menu, choose New > Python Script and click OK. With the Script1 window active, from the File menu, choose Save As and save the script with the name BufferIntersect.py in a local folder. Go back to the ArcGIS Desktop Help window, select all the code in the script example, right-click the selection, and choose Copy. In PythonWin, right-click inside the BufferIntersect.py window and choose Paste. Close the ArcGIS Desktop Help window and navigate to the Analysis Tools toolbox > Overlay toolset > Intersect tool. Right-click the Intersect tool and choose Help. Again, scroll down to the script example, select all the code, right-click the selection, and choose Copy. In PythonWin, press Enter to move your cursor to a new line, then right-click and choose Paste. Close the ArcGIS Desktop Help window and minimize ArcMap. At this point, you have created a script that contains example code that will run the two geoprocessing tools you need for your project. Because you have combined the examples, your script contains some extra code you don't need, plus of course the example code refers to data you don't have. The first thing to do is examine the code and remove the duplicate lines.Step 3: Modify the Code to Meet Your Needs Python scripts that incorporate ArcGIS geoprocessing functionality start with these two lines of code: In the script window, you see this code at the very top of the first example and again in the second example, which starts with a comment that explains the code's purpose. Including comments in a script is good practice so that others can understand what the code is doing. In Python, comments display in green italic text and are always preceded by a pound sign (#). When the script is run, comments are ignored. Cut the Purpose and "Create the Geoprocessor object" comment lines and paste them at the top of the script. Edit the purpose text to reflect this project's purpose and change the second comment text to: Import ArcGIS scripting module, create the Geoprocessor object, and specify the workspace and toolbox. Add a Date Created comment as the first line in the script. In the gp.workspace line, change the path to reflect the path where you saved the San Diego geodatabase. Note that Python is case-sensitive. The gp.toolbox = "analysis" line of code means that the geoprocessing tools referenced in the script are located in the Analysis toolbox. This is true for your project, so you don't need to change it. Select and delete the second (duplicate) lines that import the ArcGIS scripting module and create the geoprocessor object. Step 3a: Modify the Code for the Buffer Tool The example code for the buffer tool includes all seven parameters accepted by the tool. Only the first three parameters are required (input feature class, output feature class, and buffer distance). To learn more about the parameters, open the ArcGIS Desktop Help for the tool and look at the scripting syntax. For this project, you only need the required parameters. Notice that parameters are separated by a comma in Python. Edit the comment text about what is being buffered to reflect this project. Change the input and output parameters as follows: Input feature class: freeways (remember, Python is case-sensitive) Output feature class: Freeway_Buffers In the example code, the buffer distance is a string (surrounded by quotes). For this project, you are going to enter an explicit numeric distance. Select and delete "Distance" and replace it with 1320. The distance is specified in feet because the freeway data units are feet. Delete the last four optional parameters from the example code. Step 3b: Modify the Code for the Intersect Tool The example code for the intersect tool includes error handling. The try and except statements are used to handle unexpected errors during the code execution. For this simple project, you won't include error handling. Additionally, the example includes multiple geoprocessing operations and optional parameters for the intersect operation. For this example, you only need the intersect code (gp.Intersect_analysis) and you will use only required intersect parameters (the inputs and output). Delete the try: statement and the except: statement. Delete the comment about setting the workspace and the line of code that specifies the workspace—you have already taken care of this at the top of the script. Edit the comment for the intersect code (starts with # Process) to describe the purpose of this intersect operation. Use the Backspace or Delete key to move the comment and intersect code up to be just below and aligned with the buffer code (remove the indentation from the intersect section). Modify the intersect code as follows: Inputs: trolleystop and Freeway_Buffers Notice that the input feature classes are separated by a semicolon and that quotes are used only before the first input and after the last input. Be sure to keep this formatting. Output: StopsNearFreeways Delete the last three optional parameters. Delete the comments and lines of code that buffer, clip, and generate statistics and delete the print statement at the bottom. The code for the geoprocessing operations now meets the project needs. When the script is run, you want confirmation that the code is executing properly. You can add print statements to display a message in the Interactive Window after each tool runs. Place your cursor at the end of the buffer line of code and press Enter. Type print "The freeway buffers have been created." Place your cursor below the intersect line of code and type print "The intersect was completed." Save the script. That's it, you're done. The script is ready to test.Step 4: Run the Script Click the Run button , then click OK. If you can't see the Interactive Window, from the Window menu, choose Interactive Window. (If you don't see it on the Window menu, use the View menu.) After a moment, in the Interactive Window, you should see the print statements you entered. If your script does not run correctly, carefully compare your code to the code shown in the last graphic of step 3, make any fixes, save and run the script again. Note, however, that if the buffer portion ran correctly the first time but the intersect did not, and you re-run the script, you will get an error because the buffer output will already exist in the geodatabase. Solution? Use ArcCatalog to delete the buffer output (Freeways_Buffer), then run the script again. If you like, you can verify the data was created using ArcMap. In ArcMap, click the Add Data button, navigate to the location where the data was output, and add the Freeway_Buffers and StopsNearFreeways feature classes. Explore the data as desired. When you're finished, exit ArcMap and PythonWin. Want to Learn More? This was obviously a very simple scripting exercise intended to show how you can find example Python code and combine it to create a script that does something you need to do. To create robust scripts, however, you need to invest some time learning Python concepts and syntax. The training options linked to above will get you well on your way to being a script master. A couple of recommended books for beginners are Learning Python, 3rd Edition (by Mark Lutz) and Core Python Programming, 2nd Edition (by Wesley Chun). Many books have been written, however. One of them is sure to appeal to you. ... View more

Updated February 7, 2022 It seems like there's always been confusion around the terms "relate" and "relationship class." In this post, we'll attempt to clear it up. The example screenshots are from ArcMap but the concepts apply to ArcGIS Pro as well.   The Basics A relate (also called a table relate) is a property of a layer in ArcGIS Pro or ArcMap. You create a table relate so that you can query and select features in one layer and see all the related features in another layer or table. A table relate exists only in a map or layer file (.lyr). A relationship class is an object in a geodatabase that stores information about a relationship between two feature classes, between a feature class and a nonspatial table, or between two nonspatial tables. Both participants in a relationship class must be stored in the same geodatabase. Cardinality controls how relates and relationship classes are set up. Cardinality is not a measure of a St. Louis baseball fan's fervor, it is a description of how records in two different tables are related to one another. Cardinality may be one-to-one, one-to-many, many-to-one, or many-to-many. Table relates are created between tables that have a one-to-many or many-to-one cardinality. Relationship classes support all cardinalities. When you create a table relate or a relationship class, you specify the field in each table that the relationship will be based on. The fields must be the same data type (i.e., text, short integer, long integer, object ID, etc.). Relates can be created and edited with a Basic, Standard, or Advanced license. Relationship classes can be created and edited with a Standard or Advanced license. They are read-only with a Basic license. All clear now? No? OK, let's dig in more.   The Example     In the Table of Contents in the map above, you see a layer of city fire stations and a nonspatial table that stores data about the city's fire department personnel. A table relate has been created between the layer and the nonspatial table based on a short integer field in each table that stores a fire station ID number.   What happens when a station is selected on the map?   Below, you see the attribute table for the fire station layer and the fire personnel table. The option to show only selected features is being used for both tables. Notice that while only one fire station record is selected (the Washington station), six records in the fire personnel table are selected.     This tells you that the Fire Stations layer has a one-to-many cardinality with the fire personnel table. For each fire station, multiple fire personnel are associated. In this case, six firefighters are assigned to the Washington station. Because the tables are related, when you select a station you can easily find out who works at the station. Table relates are bi-directional, which means you can also select a record in the fire personnel table and access the name of the station to which the firefighter is assigned.   You don't even need to select a feature or open the tables; you can quickly view the information by using the Identify tool. The name of the related table displays under the feature name on the left side of the Identify window. Expanding the related table reveals the related records. Clicking a related record displays the data stored in the related table.   Suppose Brian Butler is transferring to the Adams station. You are going to edit the FirePersonnel table to reflect this. Here are the steps for accomplishing this using our example. Start an edit session. In the Fire Personnel table, select Brian Butler's record. In the Number_ field, change the value to 1714 (the ID number for the Adams station). Close the table and save your edits. Click the Adams station with the Identify tool. Brian Butler now appears in the list of personnel associated with the Adams station.   Click the Washington station with the Identify tool. Brian Butler's record no longer displays.     Why Use a Relationship Class?   Table relates are useful for quickly accessing and viewing information about the real world that is actually stored in two different tables. Relationship classes offer more powerful capabilities, however.   In addition to selecting records in one table and seeing related records in the other, with a relationship class you can set rules and properties that control what happens when data in either table is edited, as well as ensure that only valid edits are made. You can set up a relationship class so that editing a record in one table automatically updates related records in the other table.   Using the example above, suppose a relationship class is created between the Fire Stations layer and the fire personnel table. The city has a requirement that all stations have a minimum of five firefighters and a maximum of 15 firefighters. A rule has been created for the relationship class that reflects this requirement.   At the end of the table relate example, the Washington station had five assigned firefighters. Now, with the relationship class in place, if a staffer tries to assign a different station number to one of the Washington station firefighters, an error message will display and they will understand that a rule exists that requires all stations to have at least five firefighters assigned. Before editing an existing firefighter's station number, they will have to assign a new firefighter to the Washington station. After that is done, they can change the station number for the existing Washington station firefighter to reflect a transfer.   The relationship class ensures that data edits are valid and supports the fire department's goal that personnel assignments comply with the city's requirement.   Remember Table relates exist only in a map or layer file. Relationship classes exist as discrete objects in a geodatabase. If one of the participating tables is deleted from the geodatabase, the relationship class is deleted as well. Both table relates and relationship classes allow users to access and view information for related features from either table. Relationship classes are often used to ensure data integrity. They help make your geodatabase smart. A lot more could be said about relates and relationship classes, but in the interest of simplicity we will end the post now. Hopefully, some of the confusion has been dispelled. For more information on this topic, check out this help topic. ... 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New ArcGIS users often want to symbolize certain features in a layer so that those features stand out on the map, but they aren't sure how best to go about it, especially if they don't want to make a copy of their data. There's an easy way to accomplish this, and this post describes how. A fast way to get the desired result is to create a unique values legend. When you use a unique values legend, features with the same specified attribute value are given the same symbol. However, with unique values it's easy to assign all the features in a layer the same symbol, then interactively select features of interest and symbolize them differently. Step-by-Step Example For this example, suppose you are researching factors that impact the water quality of Lake Okeechobee in Florida. Your study area is the counties that border the lake. You have a layer representing the lake and a layer of all Florida counties. You want to create a simple map that shows the study area, then export the map as a graphic to include in your report. Add the data to a new map document. Double-click the county layer name in the Table of Contents to open its Layer Properties dialog box. In the Symbology tab's Show area on the left side of the dialog box, click Categories. Unique values is selected by default. On the right choose the Value Field you want to symbolize on in the drop-down list. In this example, we choose the field that stores the county names. In the symbol area, there is one default symbol with "all other values" displayed next to it. Click the Add Values button. In the Add Values dialog box, choose the counties that border the lake. You can hold down the Ctrl key to select multiple counties in the list. Click OK. Back in the symbol area of the Layer Properties dialog box, the symbols for the counties you just added are all unique (a different color). You want them to have the same symbol. Hold down your Shift key and click the top county name symbol (not the "all other values" symbol), then click the bottom symbol so that all the unique county symbols are selected. Right-click the selection and choose Properties for Selected Symbols. In the Symbol Editor, set the desired symbol properties (color, size, etc.), then click OK. We chose a light red fill and a darker red outline with a width of 1.5. Double-click the symbol for "all other values," use the Symbol Editor to set its properties, then click OK. Click in the Label column for the "all other values" symbol and type in the text that you want to display next to that symbol in the Table of Contents. You can also edit or delete the default heading text that will display over the unique county symbols if you want. Note: Sometimes the outline of the symbols for the features of interest will be obscured by the symbols for the other features. To make sure the unique symbols are most prominent, you can set the symbol levels for the layer. Below the symbol area in the Layer Properties dialog box, click Advanced > Symbol Levels. In the dialog box, select the symbol you want to be least prominent and click the Move to Bottom arrow button to move it to the bottom of the list. Click OK, then click OK again to close the Layer Properties dialog box and examine the map. For this example, you would want to zoom into the study area counties and display their labels (use the Label Manager to create a label class that labels only the counties of interest—see our previous post about creating label classes). You could export the zoomed-in map to a graphic file (File menu > Export Map), then zoom back to the previous map extent and export the map showing the location of the study area within the state. Both map graphics could be inserted into your report to provide visual context. That's it. Using basic functionality available through the Layer Properties dialog box, you can quickly create symbology to enhance a simple mapping project. To assign a different symbol to one or a few features in a layer, you could also: Make a copy of the layer and do a definition query on the copy to display only the features of interest. In the original layer, select the features of interest and then create a selection layer of them. These methods have their advantages. But for a quick job, the unique values approach works well. ... View more