Mapping the Sensorial Layers of a City
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Rossano Schifanella gave a presentation on mapping the sensory layers of cities using digital data. He discussed how data from social media, sensors, and other sources can be used to map elements like smells, sounds, and perceptions of beauty in cities. This can provide insights into how the physical context affects human experiences and well-being. Schifanella presented several projects that mapped elements like smells, sounds, and perceptions of places in London using different data sources. The goal is to better understand urban phenomena at large scales and help cities become more efficient, sustainable, and supportive of human needs.
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Mapping the Sensorial Layers of a City
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- 9. Eureka Presentation SHAKESPEARE, CORIOLANUS “ WHAT IS A CITY BUT PEOPLE? ”
- 11. HOW DIGITAL DATA CAN BE USED TO STUDY URBAN PHENOMENA AT SCALE
- 12. Eureka Presentation URBAN SENSING CULTURALSOCIAL ECONOMIC POLICY MAKERSAPPS DEV URBAN DESIGNERS
- 13. HOW THE PHYSICAL CONTEXT AFFECTS ACTIONS AND, ULTIMATELY, WELL BEING.
- 14. Eureka Presentation SMELLY MAPS How does a city smell? CHATTY MAPS How does a city sound? HAPPY MAPS How visually pleasant is a city? 1 2 3
- 15. HAPPY MAPS GET THE SHORT AND PLEASANT ROUTE HyperText 2014
- 17. Eureka Presentation HOW? URBANGEMS.ORG: online web game collect a ground truth on collective perception of quiet, beautiful and happy urban sceneries. COLLECT URBAN PERCEPTIONS IN LONDON
- 19. Eureka Presentation HOW? COLLECT URBAN PERCEPTIONS IN LONDON Paths are weighted not only on distance but also on how pleasant they are. GENERATE EMOTIONALLY-AWARE ROUTES -Path from Euston Square and Tate Modern -3 situations (happy, quiet, beauty scenarios) -4 paths to vote on a Likert scale (paths are unlabeled) SURVEY IN LONDON -Peaceful, historical, and distinctive are good urban qualities -Busy is the most frequently mentioned negative quality. -No consensus on contrasting qualities (e.g., historical/charming vs. busy) or experience drastic changes over time (e.g., busy during the week, and lovely in the weekend). TAKEAWAYS URBANGEMS.ORG: online web game collect a ground truth on collective perception of quiet, beautiful and happy urban sceneries.
- 21. coordinates WHERE WHEN timestamp WHAT text+visual+audio WHO author WHY intent, context (lon, lat, t) TREE PEOPLE GRASS STREET TRASH BIN STREET LIGHT Wonderful day at the park #nature
- 22. Eureka Presentation HOW? Can we predict beauty scores out of social media? GENERALIZATION USING SOCIAL MEDIA -For each street segment we extract: -Number of pictures (density), number of views, of favorites, of comments, and tags -Tags (LIWC dictionary, 72 categories) -Extract features that are significantly correlated with beauty scores -Example: density, ’posemo’, ‘negemo’, ‘swear’, ‘anx’ (anxiety), ‘sad’, and ‘anger’ LIWC categories -Build a linear model to predict beauty METHOD 7M geolocated photos in London DATA COLLECT URBAN PERCEPTIONS IN LONDON Paths are weighted not only on distance but also on how pleasant they are. GENERATE EMOTIONALLY-AWARE ROUTES URBANGEMS.ORG: online web game collect a ground truth on collective perception of quiet, beautiful and happy urban sceneries.
- 23. Eureka Presentation IS WALKABILITY QUANTIFIABLE? WWW 2015
- 24. HOW DOES A CITY SMELL? SMELLY MAPS ICWSM 2015
- 25. Eureka Presentation 25 Humans discriminates up to 1 Trillion odors Science 2014
- 26. Eureka Presentation 26 Yet, city planning can discriminate only a few bad odors Why this negative perspective?
- 28. Eureka Presentation 28 Smell Walks Amsterdam, Pamplona, Glasgow, Edinburgh, Newport, Paris, New York, Singapore
- 34. Eureka Presentation 34 Victoria Henshaw URBAN SMELLSCAPE AROMA WHEEL Positive + Negative smells
- 39. Eureka Presentation 39 Presence of nature, food, etc. tags OPEN STREET MAPS Air quality indicators CITY OFFICIALS VALIDATION Do the smellscape powered by social media is consistent with offline and online external data?
- 40. 40 AIR POLLUTION vs SMELL in LONDON -0,50 -0,38 -0,25 -0,13 0,00 0,13 0,25 0,38 0,50 NO2 PM10 PM2,5 -0,13 -0,08 -0,33 0,17 0,35 0,47 Emissions Nature CORRELATION Results are consistent across platforms and cities
- 41. 41 OSM NATURAL TAGS -0,23 -0,15 -0,08 0,00 0,08 0,15 0,23 0,30 0 5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100 Emissions smell Nature smell # OF TAGS FOR STREET SEGMENT CORRELATION
- 42. 42 OSM FOOD TAGS -0,10 0,00 0,10 0,20 0,30 0 10 20 30 40 50 60 70 80 90 100 Food smell # OF TAGS FOR STREET SEGMENT CORRELATION
- 43. Eureka Presentation 43 HOW DOES THE URBAN SMELLSCAPE CHANGE THROUGH TIME AND SPACE? ICWSM 2016
- 44. CHATTY MAPS HOW DOES A CITY SOUND? RSOS 2016
- 45. Eureka Presentation 45 URBAN SOUNDSCAPE WHEEL Beyond noise
- 47. Eureka Presentation 47 EMOTIONS To model sentiment we adopt the EmoLex lexicon that follows the 8 primary emotions from Plutchik’s psychoevolutionary theory.
- 48. Eureka Presentation EMISSIONS WASTE CORRELATION BETWEEN EMOTIONS AND SMELLS FOOD NATURE
- 50. Eureka Presentation MUSIC Music triggers both joy and sadness
- 53. ECONOMIC CAPITAL CULTURAL CAPITAL SOCIAL CAPITAL MAPPING CULTURAL EXPRESSIONS AT NEIGHBORHOOD LEVEL AND CORRELATING IT WITH MEASURES OF ECONOMIC DEVELOPMENT THERE IS MUCH MORE MAPPING INNOVATION, WEALTH, AND ECONOMIC ACTIVITIES AT DIFFERENT GEOGRAPHICAL GRANULARITIES MAPPING SOCIAL INTERACTIONS AND SOCIETAL PHENOMENA, ENVIRONMENTAL ISSUES, INDOOR/OUTDOOR ACTIVITIES