Algorithms for Geometric Matching, Clustering, and Covering

With the popularization of GPS-enabled devices such as mobile phones, location data are becoming available at an unprecedented scale. The locations may be collected from many different sources such as vehicles moving around a city, user check-ins in social networks, and geo-tagged micro-blogging photos or messages. Besides the longitude and latitude, each location record may also have a timestamp and additional information such as the name of the location. Time-ordered sequences of these locations form trajectories, which together contain useful high-level information about people's movement patterns.

The first part of this thesis focuses on a few geometric problems motivated by the matching and clustering of trajectories. We first give a new algorithm for computing a matching between a pair of curves under existing models such as dynamic time warping (DTW). The algorithm is more efficient than standard dynamic programming algorithms both theoretically and practically. We then propose a new matching model for trajectories that avoids the drawbacks of existing models. For trajectory clustering, we present an algorithm that computes clusters of subtrajectories, which correspond to common movement patterns. We also consider trajectories of check-ins, and propose a statistical generative model, which identifies check-in clusters as well as the transition patterns between the clusters.

The second part of the thesis considers the problem of covering shortest paths in a road network, motivated by an EV charging station placement problem. More specifically, a subset of vertices in the road network are selected to place charging stations so that every shortest path contains enough charging stations and can be traveled by an EV without draining the battery. We first introduce a general technique for the geometric set cover problem. This technique leads to near-linear-time approximation algorithms, which are the state-of-the-art algorithms for this problem in either running time or approximation ratio. We then use this technique to develop a near-linear-time algorithm for this

shortest-path cover problem.

Musculoskeletal symptoms among female garment factory workers in Sri Lanka.

OBJECTIVES: To assess the prevalence of musculoskeletal symptoms and their association with sociodemographic risk factors among female garment factory workers in Sri Lanka. METHODS: 1058 randomly selected female garment factory workers employed in the free trade zone of Kogalla, Sri Lanka were recruited to complete two interviewer-administered questionnaires assessing musculoskeletal symptoms and health behaviors. DISCUSSION: Musculoskeletal complaints among female garment workers in the FTZ of Kogalla are less common than expected. Sociocultural factors may have resulted in underreporting and similarly contribute to the low rates of healthcare utilization by these women. RESULTS: 164 (15.5%) of workers reported musculoskeletal symptoms occurring more than 3 times or lasting a week or more during the previous 12-month period. Back (57.3%) and knee (31.7%) were the most common sites of pain. Although most symptomatic women reported that their problems interfered with work and leisure activities, very few missed work as a result of their pain. Prevalence correlated positively with increased age and industry tenure of less than 12 months. Job type, body mass index, and education were not significant predictors of musculoskeletal symptoms.

Mainland size variation informs predictive models of exceptional insular body size change in rodents.

The tendency for island populations of mammalian taxa to diverge in body size from their mainland counterparts consistently in particular directions is both impressive for its regularity and, especially among rodents, troublesome for its exceptions. However, previous studies have largely ignored mainland body size variation, treating size differences of any magnitude as equally noteworthy. Here, we use distributions of mainland population body sizes to identify island populations as 'extremely' big or small, and we compare traits of extreme populations and their islands with those of island populations more typical in body size. We find that although insular rodents vary in the directions of body size change, 'extreme' populations tend towards gigantism. With classification tree methods, we develop a predictive model, which points to resource limitations as major drivers in the few cases of insular dwarfism. Highly successful in classifying our dataset, our model also successfully predicts change in untested cases.

Big or fast: two strategies in the developmental control of body size.

Adult body size is controlled by the mechanisms that stop growth when a species-characteristic size has been reached. The mechanisms by which size is sensed and by which this information is transduced to the growth regulating system are beginning to be understood in a few species of insects. Two rather different strategies for control have been discovered; one favors large body size and the other favors rapid development.