Estimating Movement from Mobile Telephony Data

Mobile enabled devices are ubiquitous in modern society. The information gathered by their normal service operations has become one of the primary data sources used in the understanding of human mobility, social connection and information transfer. This thesis investigates techniques that can extract useful information from anonymised call detail records (CDR). CDR consist of mobile subscriber data related to people in connection with the network operators, the nature of their communication activity (voice, SMS, data, etc.), duration of the activity and starting time of the activity and servicing cell identification numbers of both the sender and the receiver when available. The main contributions of the research are a methodology for distance measurements which enables the identification of mobile subscriber travel paths and a methodology for population density estimation based on significant mobile subscriber regions of interest. In addition, insights are given into how a mobile network operator may use geographically located subscriber data to create new revenue streams and improved network performance. A range of novel algorithms and techniques underpin the development of these methodologies. These include, among others, techniques for CDR feature extraction, data visualisation and CDR data cleansing. The primary data source used in this body of work was the CDR of Meteor, a mobile network operator in the Republic of Ireland. The Meteor network under investigation has just over 1 million customers, which represents approximately a quarter of the country’s 4.6 million inhabitants, and operates using both 2G and 3G cellular telephony technologies. Results show that the steady state vector analysis of modified Markov chain mobility models can return population density estimates comparable to population estimates obtained through a census. Evaluated using a test dataset, results of travel path identification showed that developed distance measurements achieved greater accuracy when classifying the routes CDR journey trajectories took compared to traditional trajectory distance measurements. Results from subscriber segmentation indicate that subscribers who have perceived similar relationships to geographical features can be grouped based on weighted steady state mobility vectors. Overall, this thesis proposes novel algorithms and techniques for the estimation of movement from mobile telephony data addressing practical issues related to sampling, privacy and spatial uncertainty

Measuring Large-Scale Social Networks with High Resolution

This paper describes the deployment of a large-scale study designed to measure human interactions across a variety of communication channels, with high temporal resolution and spanning multiple years-the Copenhagen Networks Study. Specifically, we collect data on face-to-face interactions, telecommunication, social networks, location, and background information (personality, demographics, health, politics) for a densely connected population of 1 000 individuals, using state-of-the-art smartphones as social sensors. Here we provide an overview of the related work and describe the motivation and research agenda driving the study. Additionally, the paper details the data-types measured, and the technical infrastructure in terms of both backend and phone software, as well as an outline of the deployment procedures. We document the participant privacy procedures and their underlying principles. The paper is concluded with early results from data analysis, illustrating the importance of multi-channel high-resolution approach to data collection

Challenges When Identifying Migration from Geo-Located Twitter Data

Given the challenges in collecting up-to-date, comparable data on migrant populations the potential of digital trace data to study migration and migrants has sparked considerable interest among researchers and policy makers. In this paper we assess the reliability of one such data source that is heavily used within the research community: geolocated tweets. We assess strategies used in previous work to identify migrants based on their geolocation histories. We apply these approaches to infer the travel history of a set of Twitter users who regularly posted geolocated tweets between July 2012 and June 2015. In a second step we hand-code the entire tweet histories of a subset of the accounts identified as migrants by these methods. Upon close inspection very few of the accounts that are classified as migrants appear to be migrants in any conventional sense or international students. Rather we find these approaches identify other highly mobile populations such as frequent business or leisure travellers, or people who might best be described as “transnationals”. For demographic research that draws on this kind of data to generate estimates of migration flows this high mis-classification rate implies that findings are likely sensitive to the adjustment model used. For most research trying to use these data to study migrant populations, the data will be of limited utility. We suspect that increasing the correct classification rate substantially will not be easy and may introduce other biases

Supersampling and network reconstruction of urban mobility

Understanding human mobility is of vital importance for urban planning, epidemiology, and many other fields that draw policies from the activities of humans in space. Despite the recent availability of large-scale data sets of GPS traces or mobile phone records capturing human mobility, typically only a subsample of the population of interest is represented, giving a possibly incomplete picture of the entire system under study. Methods to reliably extract mobility information from such reduced data and to assess their sampling biases are lacking. To that end, we analyzed a data set of millions of taxi movements in New York City. We first show that, once they are appropriately transformed, mobility patterns are highly stable over long time scales. Based on this observation, we develop a supersampling methodology to reliably extrapolate mobility records from a reduced sample based on an entropy maximization procedure, and we propose a number of network-based metrics to assess the accuracy of the predicted vehicle flows. Our approach provides a well founded way to exploit temporal patterns to save effort in recording mobility data, and opens the possibility to scale up data from limited records when information on the full system is required

The Behavioral Ecology of the Tibetan Macaque

This open access book summarizes the multi-disciplinary results of one of China’s main primatological research projects on the endemic Tibetan macaque (Macaca thibetana), which had continued for over 30 years, but which had never been reported on systematically. Dedicated to this exceptional Old World monkey, this book makes the work of Chinese primatologists on the social behavior, cooperation, culture, cognition, group dynamics, and emerging technologies in primate research accessible to the international scientific community

Historical social research: the use of historical and process-produced data

Die Entwicklung einer quantitativen Sozialgeschichtsschreibung verstärkt die interdisziplinären Beziehungen zwischen Geschichte, Soziologie, Politikwissenschaft und anderen Sozialwissenschaften. Diese verstärkte Kooperation und methodische Diskussion findet in dem Sammelband ihren Niederschlag. Behandelt werden (1) theoretische Überlegungen zum Problem einer quantifizierenden Geschichtswissenschaft; (2) Analysen von Volkszählungsdaten; (3) Analysen von kollektiven und individuellen Biographien; (4) Gehaltsanalysen von Dokumenten; (5) Periodisierungsprobleme; (6) Analysen des sozialen Netzwerks; (7) Probleme der offiziellen statistischen Daten; (8) Probleme der Datenorganisation; (9) neue Datenbanken und Projekte. (BG

Modelling land use using demographic forecasting and local optimisation: A case study of general education provision in Riyadh, Saudi Arabia

Globally accepted guidelines for land use allocation in Riyadh, Saudi Arabia have been based on an outmoded practice that was created over a century ago. This approach is based on a mix of predetermined population densities, walking distances, and per person area ratios. The latter criterion is essentially based on a worldwide average for facility areas and user numbers. The fundamental criticism levelled at such practices is their insensitivity to population trends and limited land resources. In this context, this research is aimed at updating common practice in the light of population growth and residential mobility projections at the city and district levels. The models introduced aim to provide comprehensive and adaptable simulation tools for optimising any type of land use provision standard over a specified time period. The simulation environment makes use of an agent-based framework that adapts and integrates a number of well-known methodologies, including Cohort Component Modelling (CCM) for population projection, Spatial Interaction (SI) modelling for residential mobility, and AutoRegressive Integrated Moving Average (ARIMA) for various ratio extrapolation. Additionally, new hybrid concepts and approaches have been evaluated, including a household based CCM and the use of Neural Network algorithms (NN) to forecast residential mobility. The case study focuses on Saudi populations in Riyadh, Saudi Arabia where the three general education stages at elementary, middle, and secondary levels were optimised for both genders. Moreover, the optimisation time horizon spans 50 years, from 2020 to 2070 while the focus of research at the city level optimises the conventional ratio of area per student based on the present stock of education allocated land and a land consumption ratio defined for every five years. The district level optimisation, on the other hand, balances the demand and supply of education over 50 years by utilising the Ministry of Education's (MOE) predesigned school prototypes. The research findings demonstrate the feasibility of developing a tool for optimising land use guidelines that is capable of producing acceptable outcomes while being sensitive to demographic change and land resource availability

Modeling, Predicting and Capturing Human Mobility

Realistic models of human mobility are critical for modern day applications, specifically for recommendation systems, resource planning and process optimization domains. Given the rapid proliferation of mobile devices equipped with Internet connectivity and GPS functionality today, aggregating large sums of individual geolocation data is feasible. The thesis focuses on methodologies to facilitate data-driven mobility modeling by drawing parallels between the inherent nature of mobility trajectories, statistical physics and information theory. On the applied side, the thesis contributions lie in leveraging the formulated mobility models to construct prediction workflows by adopting a privacy-by-design perspective. This enables end users to derive utility from location-based services while preserving their location privacy. Finally, the thesis presents several approaches to generate large-scale synthetic mobility datasets by applying machine learning approaches to facilitate experimental reproducibility