Data from mobile phone operators: A tool for smarter cities?

Abstract The use of mobile phone data provides new spatio-temporal tools for improving urban planning, and for reducing inefficiencies in present-day urban systems. Data from mobile phones, originally intended as a communication tool, are increasingly used as innovative tools in geography and social sciences research. Empirical studies on complex city systems from human-centred and urban dynamics perspectives provide new insights to develop promising applications for supporting smart city initiatives. This paper provides a comprehensive review and a typology of spatial studies on mobile phone data, and highlights the applicability of such digital data to develop innovative applications for enhanced urban management

Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview

This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented

Capturing time in space : Dynamic analysis of accessibility and mobility to support spatial planning with open data and tools

Understanding the spatial patterns of accessibility and mobility are a key (factor) to comprehend the functioning of our societies. Hence, their analysis has become increasingly important for both scientific research and spatial planning. Spatial accessibility and mobility are closely related concepts, as accessibility describes the potential to move by modeling, whereas spatial mobility describes the realized movements of individuals. While both spatial accessibility and mobility have been widely studied, the understanding of how time and temporal change affects accessibility and mobility has been rather limited this far. In the era of ‘big data’, the wealth of temporally sensitive spatial data has made it possible, better than ever, to capture and understand the temporal realities of spatial accessibility and mobility, and hence start to understand better the dynamics of our societies and complex living environment. In this thesis, I aim to develop novel approaches and methods to study the spatio-temporal realities of our living environments via concepts of accessibility and mobility: How people can access places, how they actually move, and how they use space. I inspect these dynamics on several temporal granularities, covering hourly, daily, monthly, and yearly observations and analyses. With novel big data sources, the methodological development and careful assessment of the information extracted from them is extremely important as they are increasingly used to guide decision-making. Hence, I investigate the opportunities and pitfalls of different data sources and methodological approaches in this work. Contextually, I aim to reveal the role of time and the mode of transportation in relation to spatial accessibility and mobility, in both urban and rural environments, and discuss their role in spatial planning. I base my findings on five scientific articles on studies carried out in: Peruvian Amazonia; national parks of South Africa and Finland; Tallinn, Estonia; and Helsinki metropolitan area, Finland. I use and combine data from various sources to extract knowledge from them, including GPS devices; transportation schedules; mobile phones; social media; statistics; land-use data; and surveys. My results demonstrate that spatial accessibility and mobility are highly dependent on time, having clear diurnal and seasonal changes. Hence, it is important to consider temporality when analyzing accessibility, as people, transport and activities all fluctuate as a function of time that affects e.g. the spatial equality of reaching services. In addition, different transport modes should be considered as there are clear differences between them. Furthermore, I show that, in addition to the observed spatial population dynamics, also nature’s own dynamism affects accessibility and mobility on a regional level due to the seasonal variation in river-levels. Also, the visitation patterns in national parks vary significantly over time, as can be observed from social media. Methodologically, this work demonstrates that with a sophisticated fusion of methods and data, it is possible to assess; enrich; harmonize; and increase the spatial and temporal accuracy of data that can be used to better inform spatial planning and decision-making. Finally, I wish to emphasize the importance of bringing scientific knowledge and tools into practice. Hence, all the tools, analytical workflows, and data are openly available for everyone whenever possible. This approach has helped to bring the knowledge and tools into practice with relevant stakeholders in relation to spatial planning

From MANET to people-centric networking: Milestones and open research challenges

In this paper, we discuss the state of the art of (mobile) multi-hop ad hoc networking with the aim to present the current status of the research activities and identify the consolidated research areas, with limited research opportunities, and the hot and emerging research areas for which further research is required. We start by briefly discussing the MANET paradigm, and why the research on MANET protocols is now a cold research topic. Then we analyze the active research areas. Specifically, after discussing the wireless-network technologies, we analyze four successful ad hoc networking paradigms, mesh networks, opportunistic networks, vehicular networks, and sensor networks that emerged from the MANET world. We also present an emerging research direction in the multi-hop ad hoc networking field: people centric networking, triggered by the increasing penetration of the smartphones in everyday life, which is generating a people-centric revolution in computing and communications

Estimating Trail Use and Visitor Spatial Distribution Using Mobile Device Data: An Example From the Nature Reserve of Orange County, California USA

Monitoring visitor use in parks and protected areas (PPAs) provides essential information for managers of PPAs to evaluate aspects of the visitor experience and balance the ecological disturbance that use creates. Traditional methods for quantifying visitation and spatial use of PPAs are resource intensive and thus are conducted infrequently or at cost-effective intervals which may fail to capture the dynamic nature of modern visitor use trends. This paper provides an addition to a growing literature using mobile-device data to quantify visitation and spatial density of use of urban-proximate PPAs in Orange County, California, USA using the analysis platform Streetlight, Inc. The results of our analysis compared favorably with well-established automatic trail counting and GPS-based monitoring methods, and illustrate several advantages of mobile device data to inform the management of PPAs. Mobile device data provide reliable estimates of visitation and spatial density of use and can augment and compliment existing social and resource monitoring for PPA management and planning

Big Data: The Engine to Future Cities—A Reflective Case Study in Urban Transport

In an era of smart cities, artificial intelligence and machine learning, data is purported to be the ‘new oil’, fuelling increasingly complex analytics and assisting us to craft and invent future cities. This paper outlines the role of what we know today as big data in understanding the city and includes a summary of its evolution. Through a critical reflective case study approach, the research examines the application of urban transport big data for informing planning of the city of Sydney. Specifically, transport smart card data, with its diverse constraints, was used to understand mobility patterns through the lens of the 30 min city concept. The paper concludes by offering reflections on the opportunities and challenges of big data and the promise it holds in supporting data-driven approaches to planning future cities

Who Can Find My Devices? Security and Privacy of Apple's Crowd-Sourced Bluetooth Location Tracking System

Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world's largest crowd-sourced location tracking network called offline finding (OF). OF leverages online finder devices to detect the presence of missing offline devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, untrackability of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user's top locations with an error in the order of 10 meters in urban areas. While we find that OF's design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available.Comment: Accepted at Privacy Enhancing Technologies Symposium (PETS) 202

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices provides an unprecedented opportunity to understand inhabitants' behavior and their interactions with the built environments. Among the widely used data resources, mobile phone data is the one passively collected and has the largest coverage in the population. However, mobile operators cannot pinpoint one user within meters, leading to the difficulties in activity inference. To that end, we propose a data analysis framework to identify user's activity via coupling the mobile phone data with location-based social networks (LBSN) data. The two datasets are integrated into a Bayesian inference module, considering people's circadian rhythms in both time and space. Specifically, the framework considers the pattern of arrival time to each type of facility and the spatial distribution of facilities. The former can be observed from the LBSN Data and the latter is provided by the points of interest (POIs) dataset. Taking Shanghai as an example, we reconstruct the activity chains of 1,000,000 active mobile phone users and analyze the temporal and spatial characteristics of each activity type. We assess the results with some official surveys and a real-world check-in dataset collected in Shanghai, indicating that the proposed method can capture and analyze human activities effectively. Next, we cluster users' inferred activity chains with a topic model to understand the behavior of different groups of users. This data analysis framework provides an example of reconstructing and understanding the activity of the population at an urban scale with big data fusion