I have found the unique musical sounds of a place to be a great indicator of local culture. Inspired by the musical diversity I've experience in my travels, I created LocalSound, a mobile music discovery app for travelers who believe music has a deep connection to place and can give a window into the culture of a population. LocalSound allows a user to enter a city they would like to "hear" and provides them with a playlist of the most popular artists from that city.
LocalSound is an exercise in combining RESTful APIs to create a useful product. I built the program using Python to access artist, song, and geographic origin data from Echo Nest, which then calls Spotify to create a playlist of the most popular artists with the indicated city of origin. HTML and CSS were used to create the interface. Future improvements would include detecting user location for immediate playlist generation upon opening the app in a new city.
You can play with LocalSound here.
Persistence Dash is a Tableau dashboard application that my team developed to assist the Seattle chapter of Summer Search, a national nonprofit that mentors low-income, minority, and first-generation high school students. Summer Search has an incredibly successful mentoring program, but they expressed a need for help identifying colleges that best fit their students' unique individual needs.
Using the National Center for Educational Statistics database, Persistence Dash allows a mentor to select a student's unique demographic profile to filter US colleges that exhibit the highest rates of college persistence among demographically similar students. The financial cost profile of these schools can then be compared to hone in on schools that offer low tuition and/or high financial aid for students in a particular income bracket.
Our team of four (myself, Jess Landquist, Yoanna Dosouto, and Bonny Rivers) employed a user-centric design process, working directly with members of the Summer Search Seattle program team in identifying their pain points. Semi-structured interviews were conducted at various stages throughout the project, and usability tests were conducted to help us iterate on our design.
While each of us collaborated on all aspects of the project, my primary role was as the designer. Lo-fidelity sketches and mockups led to more than a dozen iterations before landing on the final design. Data visualization best practices were adhered to throughout the process, including using appropriate graphical data representations, interactivity, and color palette.
You can visit the Tableau Public page to interact with the Persistence Dash visualization here.
Our academic paper further detailing our process, design choices, and future improvements can be viewed here.
I conducted a user research study into reusable bag usage while grocery shopping to gain insights into the hurdles people may face when adopting consistent use patterns. Anecdotally finding that remembering to bring reusable bags to the grocery store is an issue for many people despite the best of intentions, I chose to dig deeper into the practices of grocery shopping specifically surrounding grocery transportation.
Through observational studies, interviews, and surveys conducted over the course of about eight weeks, I obtained valuable quantitative and qualitative data to drive future design efforts to address low reusable bag adoption and usage rates. The most consistently cited issue throughout the study was convenience of accessibility during storage. With this in mind, I made a design recommendation for a simple reusable bag storage solution to increase usage.
You may read the research report and additional findings here:
User Research Report: Reusable Bags and Grocery Shopping