Role: UX Researcher
Intent: Thought Exercise
Client: The Wranglers
Timeline: Summer 2019

Step 1: Identifying the Problem

Since this was a summer job, that meant little consistency in roles. New horse wranglers would come every summer and the most recent existing wrangler guide was full of outdated information. Additionally, the camp rented 20 horses to fill out their herd in the summers. These horses came with no information and the new wranglers had to quickly determine which horses were best for younger children. Having ridden horses for over 10 years and helped out at riding programs for five, if I know anything, it’s that you need to be sure your horses temperament is well suited for the chaos of little kids. If not, then there’s going to be a lot of problems, and with a thousand pound animal, you really don’t want to have problems. So the question is: how do I make sure what I had to go through doesn’t happen with the next wranglers? How do I pass down all the useful information on horses? Well, I thought an app might do the trick. A horse-child matching app.

Step 2: Collect all Relevant Information

So the next step is what information do I need to collect to input into this app to get a desired outcome? Well, turns out, there’s a lot.

For Horses:

Each horse would get their own profile with a picture and their name and the wranglers would be asked to fill out a form with questions based on the Criteria below and the option to rate the horse in each category on a scale from 1 to 5.

For the Children:

Each child would also get their own profile with their name and answers to the following criteria on a scale from 1 to 5 similarly to the horses. The Wranglers would probably need to get most of this information from the counselors beforehand.

Extraneous Factors

1. Weather for the day

2. Total number of children for program

3. Type of ride planned

After completing these forms, the algorithm will assign the horse a number based on overall usability in the program. The children will similarly be assigned a number based on the difficulty they might pose in the program.

Step 3: Rules for Matching Horses and Children

Overall: The calmer, better beginner horses, should have low scores, while the high-strung and dangerous horses should have higher scores. Ideally, horses with too high of a score would get returned or replaced in the program. For the kids, the lower the score, the worse rider the kid is probably going to be. The higher the score, the better rider. Thus, the horses with low scores should be ideally placed with the low score children and vice a versa. We want the kids who are likely to not listen or are nervous riders to be on the bomb-proof horses that aren’t likely to do much more than follow the trail.

Step 4: The Algorithm

This is where the magical algorithm will come into play; i.e. time to hand this information over to a developer. Here is where all the above information is inputted and produces the desired outcome; a horse to child match. Ideally, each child for the program would be matched with a suitable horse(s).

Step 4(b) : Additional Features

The main part of this app would be the horse-child matching system. However, there would be some other important features that I wish I had been given. These fall under nice-to-have features category on the app.

• One subsection of this app would be a bridle-saddle-horse matching system, where all the tack would be labeled based on size and then matched to horses that they fit on. This wouldn’t have to be a one-to-one match, but rather a list of compatible horses for each saddle.

• Another subsection would be a map feature with all the trails clearly marked from start to finish with the difficulty level. I’d rather not get lost in the woods on a horse again thank you very much.

• Pictures of Horses and Children

• Finally, sort of an addition to the main matching algorithm, a cumulative, historical database of horses and children would be helpful. There would be an option to select the “herd” for the summer and save it as a subsection of all of the horses and only run the algorithm through that. This would allow information to be passed along from summer to summer as horses may come back for another summer and the information will already be there. Same with children, though both may need to be updated.

Step 5: Implementation

This would all be put into an app that could be easily accessed on cellphones as there were no laptops available by the horses. At the beginning of the summer, the wranglers would spend most of the time imputing information on the herd, figuring out which horses were usable, and which were not. The app would then build profiles for each horse with a number based on difficulty. Ideally, during this time, horses with high numbers would be returned to the rental place and replaced. Each horse will also be able to be listed as Active or Inactive depending on injuries or sores. Then, when the kids arrive, the wranglers would input all the information available about each kid for that day (or week). Then, poof, magic! The app would match the best horse to each child from the whole herd. If there are enough horses to choose from, the app would even give multiple options of horses for each kid. The wrangler can then hand pick from their own personal experience and produce a list of horses to use for each section of the program.

1. Project Overview

Between 2009 and 2011, Dr. M conducted research on political motivations and violence, collecting data through surveys that combined questionnaires with participant-drawn maps. These maps offered unique insights into individuals' “psycho-geographies”—how different groups perceive and navigate shared physical spaces based on political or social affiliations.

While the research culminated in a published paper, all the data existed only on paper maps, making it difficult to compare, analyze, or build upon. Dr. M approached our team to help digitize this data and create a system for visualizing and comparing the maps more interactively.

Concept Statement:
- No existing system currently allows for qualitative map comparison tied to participant affiliations. Our solution explores how digital tools can reveal patterns in how different groups perceive space.

Goal Statement:
- Design a system that allows Dr. M to digitize and compare participant-generated maps, visualize affiliation-based patterns, and provide contextual background on the original research—enabling deeper, more scalable analysis.