#12 # When Quantified Self Meet Smart Cities

Quantified self and Smart Cities initiatives have been developed for more than two decades, what if we connect the personal data collected from a ‘quantified selfer’ to the data collected by the millions of sensors and devices deployed in the ‘Smart City’?

Below is a matrix I made through a brainstorm. The columns are the data we have from the city perspective, and the rows lists the personal data that can be collected with the current tracking hardware and software. We can easily observe some interesting crossing of the two that will bring new useful knowledge to the user. Just to point a few:  house price along your current commute route, urban events push based on your interest detected by speech recognition, noise an CO2 monitoring and alerting while hard working on your computer, etc.. Notably, some of the features on the crossing are implemented or being developed by private and public stakeholders. Google, for example, has done serious work with the Google Now app, exploiting the public transit data, news and health for its users.

The matrix reveals the huge potential of combining the personal scale and city scale data. Even if we can’t scale up on the quantified selfer participants to impact the decisions for the city, the quantified selfer will benefit from the personal tailored Smart Cities data to their body, location, health, moody and so on.

  Public transit Air Quality Noise Urban events Public/Green space House price Urban news Weather Crime City Social media POIs Smart home sensors data Health care
Location Real time traffic info nearby AQ report

Air quality map

 local noise level monitoring

Noise map

 Events recommendation from Timeout etc.  Public/Green space opening and closing time Rent/house price compared to user’s  borough news

Community news

 local weather  Local police

Crime warning

Instagram – Nice photo’s of the city

Local tweets


Places worth going to, based on detection of busy/free status Indoor ambient Local health care information
Heart rate Noise monitor and warning Nearby health care

Nutrient/food/restaurant suggetsion

Disability Step-free access POIs with step-free access
Sleep  Late night noise level in the area CO2 concentration level

Fluidity of air

Workout  Air quality level push during workout  Marathon, skateboard, cycling event push based on users’ interest Running route suggestion  Weather forecast for outdoor workouts Gym recommendation CO2 push for workout
Movement Time to go reminder  suggestion for movement modes based on AQ  Noise level along the route Great events along the route at the right time  a greener route for cyclist and walkers  commuter rental cost-effective comparer  Road information Route suggestion  Road information
Social media  matching urban news and personal interest – sports, music, etc in town  filter relevant tweets from city account for the user
Mind activities  Public transit before and after work based on MA detections Ambient detection – air, noise for a cozy work environment
Hobbies  events matching with hobbies detected from typing New push to the user based on hobbies/keywords from speech Hobbies/keyword detection with Voice Assistant
Expenses Cheaper fare recommendation  Free urban events push  rent/other expense ratio  discount information Cheaper fare information Shops recommendation – location based
Favourite music  Musical festival push Musical venue to go
Genetics  Alert – avoid going to certain places for people with bad resistance to air pollutant Personal health care based on Genetics result
Food Diet

Nutrition Restaurant recommendation

Disease Bad air quality warning Disease profiles created by user to the public health care
Weight, height Time to go to nearest sport facilities Open space for workout with nice photos Gym recommendations Personal health  profile
Mood Events suggestion based on users mood POI recommendation according to user’s mood Mood detection from facial expression sensors Connecting depressed people to proper health care

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