Emotions are an essential part of a person's life and mental well-being. Sometimes, it is difficult for a person, especially if unfamiliar with the city, to find places where they can experience certain emotions or, on the contrary, to identify places associated with triggering emotions. The main aim of our map is to help people solve this problem by developing a guide for emotional navigation in the city. In addition, the map can be interesting for people familiar with Munich as an overview of the emotional side of different places. In this project, we considered six basic emotions: happiness, sadness, surprise, disgust, anger, and fear.
"Do you want to explore Munich emotionally? Try our map!"
What it is about
How we built it
Challenges we ran into
Emotions are subjective and vary among individuals and over time. Linking abstract emotions to physical places is challenging due to limited existing research. We had difficulties in designing the survey, receiving just 28 valid responses. Creating an interactive map also had challenges, from layout selection to writing JS functions from scratch. In addition, unintentionally, the appearance of the map varies across browsers, so we recommend using Google Chrome.
What we're proud of
Mapping mental emotions is challenging; we created a whole new algorithm to calculate the relevance between emotions and places. We're also proud that we were able to build the whole map from scratch and create 2 JS filters, which was a completely new task for us.
What we learned
While processing the spatial datasets in ArcGIS Pro, we explored many practical tools and methods with this powerful application. We also discovered many things about web map development, such as how many CSS layouts exist (and how hard it is to create a responsive design layout) and how many powerful functions Mapbox JS GL has (and how hard it is to combine them across different functions).
We experimented with mapping the emotions of places based on our survey. For further development, expanding the survey audience to get more responses and automating the analysis of survey results and spatial data processing would be excellent. We also want to improve the map design, optimise the code to work better with different browsers, try other visualisations to make the map more user-friendly, and improve the process of colour selection to better represent specific emotions .
 Kushkin, A., Giordano, A., Griffin, A., & Savelyev, A. (2023). Cognitively Congruent Color Palettes for Mapping Spatial Emotional Data. Matching Colors to Emotions. Cartographic Perspectives. https://doi.org/10.14714/CP102.1821