Module 6: Working with Geometries

This week, I worked with shapefile geometries and extracting vertex data using Python scripting. I learned how to use the Search Cursor function on existing shapefile features. In orgder to do this, it was important to understand the structure of geometry objects, containing arrays of points. Features represent geographic objects as rows in the attribute table. Arrays organize the points that make up a feature’s geometry, and points define individual coordinates as vertices. I also used code to write vertex coordinates to a text file in an organized and readable way.

I chose to write ths script in IDLE . I started by importing arcpy, setting the workspace and overwrite environment, and defining the feature class (rivers.shp). Then I created a Search Cursor to access the Object ID, geometry, and name of each feature. I opened a text file for writing and used nested loops to iterate through each feature and its vertices, using the .getPart(0) method. For each vertex, I incremented a vertex ID and wrote a line to the text file with the feature ID, vertex ID, X and Y coordinates, and river name. Adding "\n" at the end of each line made the output readable.

Print statements were included inside the nested loops to display the feature OID, vertex ID, and the X and Y coordinates of each point as they were written to the text file. This helped me verify the script was processing each vertex correctly. Figure 1 is the result of my script.

Figure 1

This exercise improved my understanding of parsing complex geometries and exporting detailed spatial data using Python, which is valuable for automating GIS workflows.

Module 5: Exploring & Manipulating Data

This week’s lab focused on using Python scripting with ArcPy to manage geospatial data more efficiently. The tasks centered around building a full workflow from scratch: creating a geodatabase, copying in feature classes, filtering attributes with a search cursor, and generating a dictionary of population data for county seats. While the end goal was clear, the learning process was where most of the value came from.

I began by setting up the environment and script in IDLE to test and troubleshoot before moving it into ArcGIS Pro. Getting the overwriteOutput setting in place early was helpful as it prevented errors related to existing outputs and let me test sections of the script without starting over. Creating the geodatabase went smoothly, but I ran into a common issue when trying to rerun the script: the geodatabase already existed. I solved this by adding a condition that checks if the file exists before trying to create it again.

Copying the feature classes required using the Describe function to grab the basename, which ended up being essential to avoid issues with file naming inside the geodatabase. The real logic came together when working with the search cursor and dictionary. It was important to structure the WHERE clause correctly, and cleaning up cursor objects after running the loop was a small but necessary detail.

Running the full script and seeing the printed results confirmed it worked. I copied the IDLE script into a ArcGIS project Notebook and the visual map helped validate the data. While the map output was useful, the biggest takeaway from this assignment was writing and debugging a complete Python workflow that successfully managed real data from start to finish.

I just created a map from a functioning script!

Figure 1 shows the printed output from the script (3 clips), and Figure 2 displays the final map in ArcGIS Pro after running the script successfully.

Figure 1: Results from Script

Figure 2: Map from Script

Geoprocessing

This week's lab, we worked with fundamental geoprocessing with Notebooks using ArcPy and ModelBuilder. The main tasks involved taking a feature dataset and transforming it by applying a sequence of tools, including adding spatial coordinates, creating proximity zones, and then consolidating areas of interest (Figure 1).

Figure 1 Results of Script

My script started in Notebooks by importing arcpy (in order to be able to test and debug the code in IDLE at a later step) and setting up my workspace. I also enabled arcpy.env.overwriteOutput = True, which, honestly, was valuable during the debugging process, preventing "file already exists" errors. I learned that lesson the hard way!

The first big geoprocessing task involved enhancing the input data by adding spatial coordinates. Initially, running the tool hit a snag in IDLE because the input file couldn't be found. My "aha!" moment came when I realized my input data wasn't in the specific workspace I'd set for the script. Following some examples, the fix was to first make a copy of the original data into the designated workspace. This ensured the tool had a writable version to operate on without issue.

Next up was creating buffers. I used a geoprocessing tool to define proximity zones around the features, setting a specific distance to define these areas. I made sure to define the parameters carefully to get accurate circular zones.

Then, to bring all those individual zones together, I used a dissolve tool. This allowed me to merge the buffered areas into one single output, consolidating them into a coherent representation of the overall area of interest. Properly setting dissolve parameters was key to connecting everything logically.

Beyond the scripting, this lab module also had me building a ModelBuilder model focused on spatial selection and erasure (Figure 2). This involved chaining several tools like clipping, selecting, and erasing. I definitely had a moment when I messed up a conditional expression for a selection, getting the exact opposite result! It was a good reminder to double-check those conditions in any geoprocessing step.

Figure 2 Soil Erase in Model Builder

 Overall, this assignment was a great exercise in chaining geoprocessing tools, both in code and visually. From figuring out file paths to making precise selections, it showed me how powerful and “picky” these GIS workflows can be.

Module 3: Debugging and Error Handling in Python Scripts

 

This week’s assignment centered around improving our ability to identify and fix errors in Python scripts used with ArcGIS, as well as making our scripts handle exceptions without crashing. The tasks were designed to build both confidence in debugging and a deeper understanding of error management in real-world GIS programming.

In the first part, I worked on a script that printed all the field names from a shapefile’s attribute table. After reviewing the dataset in ArcGIS Pro, I opened the script in IDLE and used the syntax checking tool to quickly find and fix errors such as variable name typos and incorrect capitalization, which are a common pitfall in Python. Once corrected, the script ran smoothly and printed the expected field names (Figure 1). It was a good reminder that even tiny inconsistencies in naming or indentation can completely stop a script.

Figure 1

The second script required listing all layers in an ArcGIS project file. Here, debugging went beyond syntax, several runtime issues appeared, such as incorrect file paths and handling data locked by other applications. I learned that errors like OSError can happen if a project file is open elsewhere, such as ArcGIS Pro. After closing the project, the script ran properly and listed each layer by name (Figure 2). This script helped reinforce how external files can affect script behavior and the importance of verifying that file paths and object names match exactly.

Figure 2

The third script required a different approach. Instead of fixing the error in Part A, we were instructed to use a try-except block to let it fail while still allowing Part B to run. I created a flowchart to illustrate the process (Figure 3). I located the error and wrapped Part A in a try-except structure. By printing a custom message along with the exception itself using Exception as e, I was able to clearly display the error without stopping the script from running. Part B, which prints layer names, data sources, and spatial references, then ran as intended (Figure 4).

Figure 3

Figure 4

Overall, this module strengthened my ability to debug Python scripts, implement error handling effectively, and build scripts that continue running even when encountering issues. These are critical skills for creating reliable GIS tools that can handle real-world data complexities.