(c) ACM, 2012. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in Communications of the ACM , Volume 56, Issue 1, 2013-01-01.
{Characterizing human mobility patterns is critical to a deeper understanding of the effects of people’s travel on society and the environment. Location data from cellular telephone networks can shed light on human movements cheaply, frequently, and on a large scale. We have developed techniques for analyzing anonymized cellphone locations to explore various aspects of human mobility, in particular for hundreds of thousands of people in each of the Los Angeles, San Francisco, and New York metropolitan areas. Our results include measures of how far people travel every day, estimates of carbon footprints due to home-to-work commutes, maps of the residential areas that contribute workers to a city, and relative traffic volumes on commuting routes. We have validated the accuracy of our techniques through comparisons against ground truth provided by volunteers and against independent sources such as the US Census Bureau. Throughout our work, we have taken measures to preserve the privacy of cellphone users. This article presents an overview of our methodologies and findings.}
(c) ACM, 2011. This is the author's version of the work. It is posted here by permission of ACM for your personal use. Not for redistribution. The definitive version was published in 13th ACM International Conference on Ubiquitous Computing , 2011-09-01.
{Understanding utilization of city roads is important for urban planners. In this paper, we show how to use cellular hand- off patterns from cellular phone networks to identify which routes people take through a city. Specifically, this paper makes the following 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 al- gorithms 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 mea- suring relative volumes of traffic on routes leading into and out of a specific city, and validate our methods using statis- tics published by a state transportation authority.}
The definitive version was published in PURBA-2011. , 2011-06-12
{Understanding the mix of different types of people in a city is an important input into urban planning. In this paper we identify distinct sectors of a population by their cellular phone usage. In a study of a small suburban city in New Jersey, we use unsupervised clustering to identify the usage patterns of heavy users . We uncover 7 unique usage patterns. We interpret two of the patterns as belonging to commuters and students, and verify these interpretations with deeper analysis of temporal and spatial patterns. }
The definitive version was published in IEEE Pervasive Computing , 2010-04-01, URL: https://ecopyright.ieee.org/ECTT/login.jsp Username: SCHPCSI-2011-01-0005 Password: 1295115660850
{The rapid growth of modern cities leaves urban planners faced with numerous challenges, such as high congestion and pollution levels. Effectively solving these challenges re- quires a deep understanding of existing city dynamics. In this paper, we describe methodology to study and monitor these dynamics by using Call Detail Records (CDRs), rou- tinely collected by wireless service providers as part of run- ning their networks. Our methodology scales to an entire population, has little additional cost, and can be continually updated. This provides an unprecedented opportunity to study and monitor cities in a way that current practices are not able to do.}
Method And Apparatus For Measuring And Extracting Proximity In Networks,
Tue Nov 09 15:50:43 EST 2010
A method and apparatus for measuring and extracting proximity in networks are disclosed. In one embodiment, the present method receives a network from a user for analysis and extraction of a smaller proximity sub-graph. The method computes a candidate sub-graph and determines at least one Cycle Free Escape Conductivity (CFEC) proximity of at least two nodes in accordance with the candidate sub-graph. The method then extracts and presents a proximity sub-graph that best captures the proximity.
Method and system for squashing a large data set,
Tue Mar 25 18:08:39 EST 2003
Apparatus and method for summarizing an original large data set with a representative data set. The data elements in both the original data set and the representative data set have the same variables, but there are significantly fewer data elements in the representative data set. Each data element in the representative data set has an associated weight, representing the degree of compression. There are three steps for constructing the representative data set. First, the original data elements are partitioned into separate bins. Second, moments of the data elements partitioned in each bin are calculated. Finally, the representative data set is generated by finding data elements and associated weights having substantially the same moments as the original data set.
Awards
AT&T Science and Technology Medal, 2012.
For technical leadership and innovative contributions in statistics for data modeling and analytics.