User location is of critical importance to cellular network operators. It is often used for network capacity planning and to aid in the analysis of service and network diagnostics. However, existing localization techniques rely on user-provided information (e.g., Angle-of-Arrival), which are not available to the operator, and often require a significant effort to collect training data. Our main contribution is the design and evaluation of the Network-Based Localization (NBL) System for localizing a user in a 4G LTE network. The NBL System consists of 2 stages. In an offline stage, we develop RF coverage maps based on a large-scale crowd-sourced channel measurement campaign. Then, in an online stage, we present a localization algorithm to quickly match RF measurements (which are already collected as part of normal network operation) to coverage map locations. The system is more practical than related works, as it does not make any assumptions about user mobility, nor does it require expensive manual training measurements. Despite the realistic assumptions, our extensive evaluations in a national 4G LTE network show that the NBL System achieves a localization accuracy which is comparable to related works (i.e., a median accuracy of 5% of the cell's coverage region).

Models of human mobility have broad applicability in fields such as mobile computing, urban planning, and ecology. This paper proposes and evaluates WHERE, a novel approach to modeling how large populations move within different metropolitan areas. WHERE takes as input spatial and temporal probability distributions drawn from empirical data, such as Call Detail Records (CDRs) from a cellular telephone network, and produces synthetic CDRs for a synthetic population. We have validated WHERE against billions of anonymous location samples for hundreds of thousands of phones in the New York and Los Angeles metropolitan areas. We found that WHERE offers significantly higher fidelity than other modeling approaches. For example, daily range of travel statistics fall within one mile of their true values, an improvement of more than 14 times over a Weighted Random Waypoint model. Our modeling techniques and synthetic CDRs can be applied to a wide range of problems while avoiding many of the privacy concerns surrounding real CDRs.

Understanding utilization of city roads is important for urban planners. In this paper, we show how to use handoff patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes three contributions. First, we show that cellular handoff patterns on a given route are stable across a range of conditions and propose a way to measure stability within and between routes using a variant of Earth Mover's Distance. Second, we present two accurate classification algorithms for matching cellular handoff patterns to routes: one requires test drives on the routes while the other uses signal strength data collected by high-resolution scanners. Finally, we present an application of our algorithms for measuring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statistics published by a state transportation authority.

Networks are critical to modern society, and a thorough understanding of how they behave is crucial to their efficient operation. Fortunately, data on networks is plentiful; by visualizing this data, it is possible to greatly improve our understanding. Our focus is on visualizing the data associated with a network and not on simply visualizing the structure of the network itself. We begin with three static network displays; two of these use geographical relationships, while the third is a matrix arrangement that gives equal emphasis to all network links. Static displays can be swamped with large amounts of data; hence we introduce direct manipulation techniques that permit the graphs to continue to reveal relationships in the context of much more data. In effect, the static displays are parameterized so that interesting views may easily be discovered interactively. The software to carry out this network visualization is called SeeNet.>

The authors describe several dynamic graphics tools for visualizing network data involving statistics associated with the nodes or links in a network. The authors suggest a number of ideas for the static display of network data, while motivating the need for interaction through dynamic graphics. A brief discussion of dynamic graphics in general is presented. The authors specialize this to the case of network data. An example is presented.>

'S' is an interactive language and system developed at Bell Laboratories for statistical computing, graphics, and data management. The language provides a combination of simplicity with power and extensibility, and applies a number of recent ideas in computer science to statistical systems for the first time. This paper describes some of the main design features of the system and outlines the implementation strategy.