Measuring and mitigating behavioural segregation using Call Detail Records

The overwhelming amounts of data we generate in our daily routine and in social networks has been crucial for the understanding of various social and economic factors. The use of this data represents a low-cost alternative source of information in parallel to census data and surveys. Here, we advocate for such an approach to assess and alleviate the segregation of Syrian refugees in Turkey. Using a large dataset of mobile phone records provided by Turkey's largest mobile phone service operator, Türk Telekom, in the frame of the Data 4 Refugees project, we define, analyse and optimise inter-group integration as it relates to the communication patterns of two segregated populations: refugees living in Turkey and the local Turkish population. Our main hypothesis is that making these two communities more similar (in our case, in terms of behaviour) may increase the level of positive exposure between them, due to the well-known sociological principle of homophily. To achieve this, working from the records of call and SMS origins and destinations between and among both populations, we develop an extensible, statistically-solid, and reliable framework to measure the differences between the communication patterns of two groups. In order to show the applicability of our framework, we assess how house mixing strategies, in combination with public and private investment, may help to overcome segregation. We first identify the districts of the Istanbul province where refugees and local population communication patterns differ in order to then utilise our framework to improve the situation. Our results show potential in this regard, as we observe a significant reduction of segregation while limiting, in turn, the consequences in terms of rent increase

Making big data work: smart, sustainable, and safe cities

The goal of the present thematic series is to showcase some of the most relevant contributions submitted to the ‘Telecom Italia Big Data Challenge 2014’ and to provide a discussion venue about recent advances in the appplication of mobile phone and social media data to the study of individual and collective behaviors. Particular attention is devoted to data-driven studies aimed at understanding city dynamics. These studies include: modeling individual and collective traffic patterns and automatically identifying areas with traffic congestion, creating high-resolution population estimates for Milan inhabitants, clustering urban dynamics of migrants and visitors traveling to a city for business or tourism, and investigating the relationship between urban communication and urban happiness

Can Temperature be Used as a Predictor of Data Traffic? A Real Network Big Data Analysis

The proliferation of mobile devices and big data has made it possible to understand the human movements and forecasts of precise and intelligent short and long-term data consumption of services like call, sms, or internet data which has interesting and promising applications in modern cellular networks. Human nature and moods are known to be synonymous with the physical attributes of mother nature such as temperature. The change in those physical features affects the human routines and activities such as cellular data consumptions. The future of telecommunication lies in the exploration of heap of information and data available to companies and inferring the valuable results through extensive analysis. In this paper, we analyze three main traits of cellular activity: sms, call, and internet. This paper investigates whether the relationship between the temperature and the cellular data consumption exits or not. This work introduces a novel approach to identify the strength of relationship between the temperature and cellular activity (sms, call, internet) and discuss the methods to quantify the relationship using correlation method. The real network CDR big data set - Milano Grid data set is used to analyze the behavior of the cellular activity with respect to temperature

Machine Learning at the Edge: A Data-Driven Architecture with Applications to 5G Cellular Networks

The fifth generation of cellular networks (5G) will rely on edge cloud deployments to satisfy the ultra-low latency demand of future applications. In this paper, we argue that such deployments can also be used to enable advanced data-driven and Machine Learning (ML) applications in mobile networks. We propose an edge-controller-based architecture for cellular networks and evaluate its performance with real data from hundreds of base stations of a major U.S. operator. In this regard, we will provide insights on how to dynamically cluster and associate base stations and controllers, according to the global mobility patterns of the users. Then, we will describe how the controllers can be used to run ML algorithms to predict the number of users in each base station, and a use case in which these predictions are exploited by a higher-layer application to route vehicular traffic according to network Key Performance Indicators (KPIs). We show that the prediction accuracy improves when based on machine learning algorithms that rely on the controllers' view and, consequently, on the spatial correlation introduced by the user mobility, with respect to when the prediction is based only on the local data of each single base station.Comment: 15 pages, 10 figures, 5 tables. IEEE Transactions on Mobile Computin

Passenger-Centric Metrics for Air Transportation Leveraging Mobile Phone and Twitter Data

International audienceThis paper aims at presenting a detailed analysis of domestic air passengers behavior during a major air-traffic disturbance, from two complementary passenger-centric perspective: a passenger mobility perspective and a passenger social media perspective. By leveraging over 5 billion records of mobile phone location data per day from a major carrier in the United States, passenger mobility can be reliably analyzed, no matter which airline the passengers fly on or which airport they fly to and from. Such information is currently unavailable to the major aviation stakeholders at such scale and can be used to establish performance benchmarks from a passenger's perspective. Combining it with a Twitter analysis provides a more detailed and passenger-focused analysis than the traditional flight-centric measurements used to evaluate the overall system performance. More generally, these two passenger-centric analysis could be implemented in real-time for a daily evaluation of the Air Transportation System, enabling a faster analysis of the impact of major disruptions, whether due to meteorological conditions or system failures

A multi-source dataset of urban life in the city of Milan and the Province of Trentino

The study of socio-technical systems has been revolutionized by the unprecedented amount of digital records that are constantly being produced by human activities such as accessing Internet services, using mobile devices, and consuming energy and knowledge. In this paper, we describe the richest open multi-source dataset ever released on two geographical areas. The dataset is composed of telecommunications, weather, news, social networks and electricity data from the city of Milan and the Province of Trentino. The unique multi-source composition of the dataset makes it an ideal testbed for methodologies and approaches aimed at tackling a wide range of problems including energy consumption, mobility planning, tourist and migrant flows, urban structures and interactions, event detection, urban well-being and many others

Fine-Grained Mapping of Migrants in Istanbul Using Satellite Imaging and Mobile Phone Data

This study aims to create a fine grained mapping of the migrant population in Istanbul using land use, nighttime satellite, and extended detail records (xDR) data. We use statistical bias correction methods such as calibration and weighting, spatial scaling methods, and machine learning methods to create the fine granular maps. The use of big data allows for a granular analysis of migrant behavior, contributing to evidence based policies, which can improve the living conditions of migrants. In this study, we use only aggregated data in order to protect personal data. The results demonstrate that satellite and mobile data sources can be used for fine-grained population mapping