  Location and mobility patterns of individuals are important to environmental
planning, societal resilience, public health, and a host of commercial
applications. Mining telecommunication traffic and transactions data for such
purposes is controversial, in particular raising issues of privacy. However,
our hypothesis is that privacy-sensitive uses are possible and often beneficial
enough to warrant considerable research and development efforts. Our work
contends that peoples behavior can yield patterns of both significant
commercial, and research, value. For such purposes, methods and algorithms for
mining telecommunication data to extract commonly used routes and locations,
articulated through time-geographical constructs, are described in a case study
within the area of transportation planning and analysis. From the outset, these
were designed to balance the privacy of subscribers and the added value of
mobility patterns derived from their mobile communication traffic and
transactions data. Our work directly contrasts the current, commonly held
notion that value can only be added to services by directly monitoring the
behavior of individuals, such as in current attempts at location-based
services. We position our work within relevant legal frameworks for privacy and
data protection, and show that our methods comply with such requirements and
also follow best-practices
