Wading in Quinnipiac Mud Guides Career Path for Beth Owen

Beth Owen is just one of many Yale School of Forestry and Environmental Studies graduate students and alumni to participate in an independent research project through the support of Connecticut Sea Grant. The internships have been as ambitious as they are diverse, and all have given participants a new perspective on the role of research in their future. The program is based at Yale’s Center for Coastal and Watershed Systems. Beth sampled and analyzed sediments for heavy metals from the lower Quinnipiac River.

Fast Algorithms Using Minimal Data Structures for Common Topological Relationships in Large, Irregularly-spaced Topographic Data Sets

Digital terrain models (DTM) typically contain large numbers of postings, from hundreds of thousands to billions. Many algorithms that run on DTMs require topological knowledge of the postings, such as finding nearest neighbors, finding the posting closest to a chosen location, etc. If the postings are arranged irregu- larly, topological information is costly to compute and to store. This paper offers a practical approach to organizing and searching irregularly-space data sets by presenting a collection of efficient algorithms (O(N),O(lgN)) that compute important topological relationships with only a simple supporting data structure. These relationships include finding the postings within a window, locating the posting nearest a point of interest, finding the neighborhood of postings nearest a point of interest, and ordering the neighborhood counter-clockwise. These algorithms depend only on two sorted arrays of two-element tuples, holding a planimetric coordinate and an integer identification number indicating which posting the coordinate belongs to. There is one array for each planimetric coordinate (eastings and northings). These two arrays cost minimal overhead to create and store but permit the data to remain arranged irregularly.