Understanding the City Behaviour through Data Analysis: A Case Study of Barcelona

Incentivizing the usage of public transportation services among city residents to improve the air quality of the city has been the major target of city officials all around the world. In this paper, we perform an exploratory data analysis on the daily usage and patterns of bicying service usage (Barcelona's bicycling system and a part of public transport system) and measured air quality values for the city of Barcelona in Spain. Our data analysis results yields three main observations (i) Regarding bicying service, the highest usage of bikes is observed to be during morning and evening rush hours as well as weekdays which shows that bicying service is mostly used by work commuters. (ii) Regarding air quality measurements, the analysis results indicate that Observatori Fabra has the highest air quality whereas the district of Eixample has the poorest air quality among the considered districts of Barcelona and high air pollution are observed during weekdays due to heavy morning rush traffic. (iii) Regarding the relation between bikes service usage and air quality, the correlation between bicycing usage and air quality levels is observed to be low.

I. Introduction

Intelligent systems powered by data analytics can help to address the contemporary challenges experienced by cities around the world including climate change, transportation problems. To accomplish this, real time data generated by advanced measurement devices such as sensors, actuators and controllers that are available on stations dispersed throughout of the city can be used to analyze and describe the ongoing events and situations in real time. This large amount of data can help city officials to make proactive and reactive decisions based on the level of significance of the events. Moreover, intelligent analytics can help to unravel the city dynamics to improve both the service quality and life quality of residences.