LUN Misto — City comfort map design for the social project
LUN Misto is a social project that explores urban space, analyzes open data and forms a request for a good quality of life in Kyiv. They have created an interactive map where each person could find researches regarding city (such as noise levels, quality of air or queues at the kindergartens), but this solution was difficult to scale.
Our task was to redesign the city comfort map, visualize data in a convenient way and help people find the best place to live, work and relax in Kyiv. This task was done during Product Interface Design course by Projector.
After the kickoff meeting with stakeholders we defined following main questions to answer during this project:
- What do people mean under the word "comfort"? How do they understand whether a place is comfortable or not? What criteria of comfort are important and how are they defined?
- How to show a lot of heterogeneous information on the map at the same time? The previous solution works well for one or two criteria but turned into a mess if many filters were applied.
The whole process of design during this project was divided into four stages according to the Double Diamond methodology.
The main user of the city comfort map is a person who plans to buy or rent an apartment in the nearest future. Such people try to find as much information as possible regarding neighborhood or house they consider to move into and the map could be one of such sources.
We conducted 25 user interviews to validate our hypothesis, understand how people define, prioritize and compare different criteria of comfort.
“For me, comfort is a combination of historical and modern buildings. It is a place near the park with water, where you can run, ride a skateboard or a bike.”
“The most important thing for me is logistics. I looked for an apartment near transport interchange and renovated metro station.”
Value proposition canvas
Based on information collected during interviews we mapped customers' pains, gains, and jobs on the Value proposition canvas and started to brainstorm ideas. After that, we prioritized a list of ideas based on usefulness for customers and technical feasibility.
Prototype & test
To quickly test our solutions, we created lo-fi prototypes. Together with users we organize criteria of comfort into categories such as infrastructure, transportation, ecology, education, etc that make sense to them. When we had confidence in the concepts, we began creating a visual design.
Each person has his criteria of comfort when choosing a place to live, work or relax. For example, some people want to live as close as possible to the center of the city, while others don't like crowds of people. The initial experience with the comfort map starts with onboarding, where the user can select the most important criteria for him. A quick tutorial following afterward shows the main features of the comfort map.
We figured out a system that allows showing many layers of information simultaneously. According to it, all territory of Kyiv should be divided into small hexagons. Each hex has color from spectrum between green and red depending on indicators in the area (from 0 to 10, where 0 is red, 10 is green). For example, if a neighborhood has many parks within walking distance, then the hex of this part of Kyiv will be green. If any, then its color will be red. If the user adds several filters to the comfort map, the cells will be painted in the color of the arithmetic mean number of all criteria.
When the user hovers over a hex, a tooltip with detailed information will appear telling the customer if this neighborhood is comfortable based on applied filters.
According to analytics, 65% of LUN Misto traffic comes from mobile so we designed mobile version as well.
Presented solution was highly appreciated by the LUN Misto team for scalability (we developed the system that could be easily used to create comfort maps for other cities of Ukraine) as well as awesome UI. Results of our work were included in the roadmap and will be implemented in the nearest future.
Co-designed with Anna Kozlova, Oleksandr Hlushchenko, Olesia Kaita, Taisia Shkovyra and Tanya Mytrovska