#Data-driven #DataVisualization #Mapping #NLP #Emotion

Urban Emotion

; The interrogation of social media and its implications within an urban context

Harvard Graduate School of Design | 2019
Instructor: Jose Luis Garcia del Castillo Lopez
Team: David Rosenwasser
Duration: 1 months
Role: Ideation, research, data collection, NLP, data visualization
Mapbox API(JavaScript), Python, Watson Tone Analyzer

Data: Instagram posts with geo-location data

Conference Paper published (eCAADe 2020)

> How might we analyze how citizens perceive the public spaces?

| Problem | 
Designers and planners find it challenging to constantly connect with their users and receive feedback from the spaces they created.

| Goal |
1) Help decision-makers and designers easily understand how public spaces are utilized and perceived by users.
2) Create a feedback loop that decision-makers find whether the places are utilized as they intended.

>> Social media as an analytical tool, helping to transform public policy-making
by detecting emotions using Natural Language Processing

How it works

Feedback Loop for decision-makers and the public space users

| Data Collection |

10,000 instagram Boston-geographically-related posts were collected. 

  1. Posts contained a geotagged location.
  2. Data was derived from the 1000 most posted about locations in order to limit the scope to places that had larger numbers of posts for the analysis.
  3. The search filtered out posts with the locations “Boston, Massachusetts,” “Cambridge,” “South Boston,” and “East Boston” due to the broad nature of these locations. 
  4. All posts were geotagged within a 2 mile radius of coordinate 42.361139, -71.058254, the centermost part of Boston, where Boston City Hall is located.

Example of data found within CSV file. Each instagram post is broken down by row

| Natural Language Processing |

The emotion and score of each post was extracted from its content, using IBM Watson Tone Analyzer API.

Sample data of GeoJSON as a result of emotion analysis


| EmojiScape |

Qualitative representation of the inhabitants’ perception of the urban fabric

| TrendScape |

Temporal dimension of emotions and the understanding of their evolving quality

       L: Highlighting activity by date and visualizing these changes over time.
R: Showcasing a changing visualization of activity during differing blocks of hours during the day.


  User demonstrations and activity from exhibition

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copyright. Eunsu Kim | 2022