Simon Moritz

Towards a Comparative Science of Cities: Using Mobile Traffic Records in New York, London, and Hong Kong

This chapter examines the possibility to analyze and compare human activities in an urban environment based on the detection of mobile phone usage patterns. Thanks to an unprecedented collection of counter data recording the number of calls, SMS, and data transfers resolved both in time and space, we confirm the connection between temporal activity profile and land usage in three global cities: New York, London, and Hong Kong. By comparing whole cities’ typical patterns, we provide insights on how cultural, technological, and economical factors shape human dynamics. At a more local scale, we use clustering analysis to identify locations with similar patterns within a city. Our research reveals a universal structure of cities, with core financial centers all sharing similar activity patterns and commercial or residential areas with more city-specific patterns. These findings hint that as the economy becomes more global, common patterns emerge in business areas of different cities across the globe, while the impact of local conditions still remains recognizable on the level of routine people activity.

Utilizing implicit feedback and context to recommend mobile applications from first use

Most mobile platforms of today enable the users to install third-party applications through application portals or stores. As the number of applications available increases, the users of mobile devices find it challenging to find new and relevant applications. The fact that these applications usually are browsed and downloaded from a mobile device, which has a smaller screen compared to desktop computers, makes this information overload even more intense. Recommender systems aid users in finding relevant applications. A challenge with such systems is that they traditionally need a user profile in order to produce recommendations, known as the new user problem. In this paper we present a context-aware recommender system for mobile applications which produces recommendations from the first use. This paper introduces context-based recommender concepts and presents a prototype implementation of said concepts.

Origin/Destination-estimation Using Cellular Network Data

Today there are more than 600 billion geo special transactions every day in the US alone [1], and most of this data is passing through carriers networks. Hence, the carriers are sitting on a huge pile of potential knowledge which they could make more use of. Earlier attempt on this data have been very ambitious and often failed due to the nature of the data. In this paper present an innovative method that does enough, not too much with the data. Our method addresses how to handle the big data challenge as well as how one could plan to secure the privacy of the end users. The latter being one of the main reasons why carriers have not deployed anything similar yet. They have simply been too afraid of what would happen if the data would be mistreated or perceived as misused.

Large Scale Geospatial Analysis on Mobile Application Usage

The recent exponential growth of mobile application instances in combination with the availability of more advanced networks have led to a significant increase of the usage of mobile devices and applications. Several studies regard application usage location and time as strong contextual characteristics and infer that user mobile usage habits can be affected by the users location, such as rural areas and points of interest (schools, airports). In this work we consider a novel approach of collecting usage information from mobile devices, correlating it with other data sources such as Open Street Map and integrating it in spatiotemporal data warehouse. From there we can identify signatures of such usages and the impact of usage time and location. This experimental study includes an analysis of usage logs gathered from thousands of users spread over 127 different countries in a yearly span and presents several extracted correlations and trends such as we detect usage pattern and traffic trends during holiday periods such as increased usages in villages and lower ones in big cities.

Population mobility reductions associated with travel restrictions during the Ebola epidemic in Sierra Leone: use of mobile phone data

Abstract Background Travel restrictions were implemented on an unprecedented scale in 2015 in Sierra Leone to contain and eliminate Ebola virus disease. However, the impact of epidemic travel restrictions on mobility itself remains difficult to measure with traditional methods. New ‘big data’ approaches using mobile phone data can provide, in near real-time, the type of information needed to guide and evaluate control measures. Methods We analysed anonymous mobile phone call detail records (CDRs) from a leading operator in Sierra Leone between 20 March and 1 July in 2015. We used an anomaly detection algorithm to assess changes in travel during a national ‘stay at home’ lockdown from 27 to 29 March. To measure the magnitude of these changes and to assess effect modification by region and historical Ebola burden, we performed a time series analysis and a crossover analysis. Results Routinely collected mobile phone data revealed a dramatic reduction in human mobility during a 3-day lockdown in Sierra Leone. The number of individuals relocating between chiefdoms decreased by 31% within 15 km, by 46% for 15–30 km and by 76% for distances greater than 30 km. This effect was highly heterogeneous in space, with higher impact in regions with higher Ebola incidence. Travel quickly returned to normal patterns after the restrictions were lifted. Conclusions The effects of travel restrictions on mobility can be large, targeted and measurable in near real-time. With appropriate anonymization protocols, mobile phone data should play a central role in guiding and monitoring interventions for epidemic containment.