Parental Criminal Justice Involvement and Children's Involvement With Child Protective Services: Do Adult Drug Treatment Courts Prevent Child Maltreatment?

BACKGROUND: In light of evidence showing reduced criminal recidivism and cost savings, adult drug treatment courts have grown in popularity. However, the potential spillover benefits to family members are understudied. OBJECTIVES: To examine: (1) the overlap between parents who were convicted of a substance-related offense and their children's involvement with child protective services (CPS); and (2) whether parental participation in an adult drug treatment court program reduces children's risk for CPS involvement. METHODS: Administrative data from North Carolina courts, birth records, and social services were linked at the child level. First, children of parents convicted of a substance-related offense were matched to (a) children of parents convicted of a nonsubstance-related offense and (b) those not convicted of any offense. Second, we compared children of parents who completed a DTC program with children of parents who were referred but did not enroll, who enrolled for <90 days but did not complete, and who enrolled for 90+ days but did not complete. Multivariate logistic regression was used to model group differences in the odds of being reported to CPS in the 1 to 3 years following parental criminal conviction or, alternatively, being referred to a DTC program. RESULTS: Children of parents convicted of a substance-related offense were at greater risk of CPS involvement than children whose parents were not convicted of any charge, but DTC participation did not mitigate this risk. Conclusion/Importance: The role of specialty courts as a strategy for reducing children's risk of maltreatment should be further explored.

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.