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Adopting a User Modeling Approach to Quantify the City

Università degli Studi di Torino
Università degli Studi di Torino
Università degli Studi di TorinoPh.D Student at Università degli Studi di Torino

Quantified Self movement allows to collect a lot of personal data which can be used to nurture the model of the users. Evenly, when aggregated, these personal data become a picture of the people of a space in a City Model. This model can be fed also by data coming from crowdsensing. The resulting City Model can be used to provide personalized services to citizen, and to increase people awareness about their behaviour that can help in promoting collective behavioural change. The paper

Adopting a User Modeling Approach to Quantify the City

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ADOPTING A USER MODELING APPROACH TO
QUANTIFY THE CITY
Assunta MatassaFederica Cena
Department of Computer Science - University of Torino
BACKGROUND/1
QS.
Quantified Self (QS) helps people to acquire
personal data on different aspects of their daily
lives, like the activities performed, the space
visited, people encountered, physiological and
psychological states.


Department of Computer Science - University ofTorino
1
BACKGROUND/2
USER MODEL
All these data are gathered by means of Personal
Informatics tools and represent an opportunity for
the User Model, a repository of user personal
information that can be used to provide
personalization.


Department of Computer Science - University ofTorino
2
BACKGROUND/3
CROWDSENSING
individuals with sensing and computing devices collectively share data
and extract information to measure and map phenomena of common interest.
It requires the active involvement of individuals to contribute sensor data
(e.g. taking a picture, reporting a road closure) related to a large-scale
phenomena.
Department of Computer Science - University ofTorino
3
RESEARCH QUESTION
What if we are able to apply the model of the QS to the
development of our cities?
It is a question that appears to be gaining steam.

Department of Computer Science - University ofTorino
4
If the city can be defined as a composite individual, its data
can be managed as the composition of the User Models of all its
citizens.
Department of Computer Science - University ofTorino
5
OUR PROPOSAL

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Adopting a User Modeling Approach to Quantify the City

  • 1. ADOPTING A USER MODELING APPROACH TO QUANTIFY THE CITY Assunta MatassaFederica Cena Department of Computer Science - University of Torino
  • 2. BACKGROUND/1 QS. Quantified Self (QS) helps people to acquire personal data on different aspects of their daily lives, like the activities performed, the space visited, people encountered, physiological and psychological states. 
 Department of Computer Science - University ofTorino 1
  • 3. BACKGROUND/2 USER MODEL All these data are gathered by means of Personal Informatics tools and represent an opportunity for the User Model, a repository of user personal information that can be used to provide personalization. 
 Department of Computer Science - University ofTorino 2
  • 4. BACKGROUND/3 CROWDSENSING individuals with sensing and computing devices collectively share data and extract information to measure and map phenomena of common interest. It requires the active involvement of individuals to contribute sensor data (e.g. taking a picture, reporting a road closure) related to a large-scale phenomena. Department of Computer Science - University ofTorino 3
  • 5. RESEARCH QUESTION What if we are able to apply the model of the QS to the development of our cities? It is a question that appears to be gaining steam.
 Department of Computer Science - University ofTorino 4
  • 6. If the city can be defined as a composite individual, its data can be managed as the composition of the User Models of all its citizens. Department of Computer Science - University ofTorino 5 OUR PROPOSAL
  • 7. QS provides a complete picture of user with her habits, behaviour and activities in the User Model, then the aggregation of User Models can provide a complete picture of a city.
 All these data can be used to build a City Model, to provide services "adapted" to collective people and space features. Department of Computer Science - University ofTorino 5
  • 8. Department of Computer Science - University ofTorino 5 The cooperation among mobile devices leveraging on the multi- sensing capabilities, can help to create a cyber-sensing-system for the smart city when many devices work together such as a “swarm”. Smartphones can also be used as mobile sensors to measure the quality of the environment in which we live.Allowing them to gather some information and share it, in a completely safe and anonymous way, we could form a dynamic map of the city.
  • 9. GOALS Department of Computer Science - University ofTorino 6 A. make individual aware of collective behaviour and foster in that way an individual behaviour change; B. enable citizens to make better decisions; C. allow citizens to monitor the performance and spending of public services; D. allow stakeholders to make more informant decision regarding the collective space. 

  • 10. Our idea is to combine a User Model with a Crowdsensing approach for collecting and analysing data. Department of Computer Science - University ofTorino 8 NOVELTY OFTHE APPROACH
  • 11. 4 STEP APPROACH Department of Computer Science - University ofTorino 10 1. exploit User and Group Modeling techniques in order to create the City Model from the individual User Models 2.exploit crowdsensing approach to fill the City Model 3.exploit machine learning and data mining algorithms in order to aggregate and analyse the data in the City Model and find behavioural patterns and interesting correlations 4.provide meaningful visualisation of the data in order to make easier to understand complex collective phenomena.
  • 12. 1° STEP: CITY MODELING Traditionally, User Modeling is the process of creating and maintaining a model of the user, with information about its preferences, interest, etc. Moreover, there is a long tradition in aggregating single user models in Group Model. Group Modeling can be seen as the process of modeling the group member in order to find the optimal solution for every ones This approach can be used to create the City Model. Department of Computer Science - University ofTorino 11
  • 13. 2° STEP: CROWDSENSING The involvement of citizens in collecting data in order to monitor some large-scale phenomena that cannot be easily measured by a single individual. It requires a minimal effort from the users, in fact the information can derive from the study of movements of crowd in the city monitoring by mobile devices and information voluntarily provided by users. Providing real time information about the space, it opens new perspectives for cost-effective ways of making local communities and cities more sustainable. Department of Computer Science - University ofTorino 12
  • 14. 3° STEP:ANALYTICS The analysis phase of the data is one of the most important since it allows to find patterns, co-occurences and new aspects within of the data. Standard statistics and data mining techniques can be applied to the data (clustering, decision tree) in order to find new knowledge and insight on the single user or on the city at a whole. For example, we can correlate users activity level with city traffic level to see if these two facts are somehow correlated. Department of Computer Science - University ofTorino 13
  • 15. 4° STEP:VISUALISATION A meaningful visualisation of these collected data should be presented for the users instead of a classical one, in order to enhance their understanding about data. We support the adoption of a storytelling approach as a meaningful and effective way to convey data. Indeed, a hypothetical solution could be presenting a story focusing on the values of parameter which is more relevant for user. Department of Computer Science - University ofTorino 14
  • 16. EXPECTED RESULTS We aim to create a City Model by means of: A. data explicitly declared by users, exploiting crowdsensing B. implicitly collected personal data, exploiting QS tools to gather data and data mining techniques to infer data from behaviour C. aggregating data in order to create a collective picture D. exploiting Group Modeling techniques to creating Group Models. Department of Computer Science - University ofTorino 15
  • 17. NEXT STEPS collect data exploiting crowdsensing regarding data about the comfort on different space to fill the City Model. Real example would be using data coming from our existent project, ComfortSense. Department of Computer Science - University ofTorino 16
  • 18. Assunta Matassa University of Torino matassa@di.unito.it Thank you for the attention! Q&A