Computational Tools for Population Biology Tanya Berger-Wolf, Computer Science, UIC; Daniel Rubenstein, Ecology and Evolutionary Biology, Princeton; Jared Saia, Computer Science, U New Mexico Supported by NSF Technical Approach Collect explicitly dynamic social data: sensor collars on animals, disease logs, synthetic population simulations, cellphone and communications Represent a time series of observation snapshots as a layered graph. Questions about persistence and strength of social connections and about criticality of individuals and times can be answered using standard and novel graph connectivity algorithms Validate theoretical predictions derived from the abstract graph representation by simulations on collected data and controlled experiments on real populations Key Achievements and Future Goals A formal computational framework for analysis of dynamic social interactions Valid and tested computational criteria for identifying Individuals critical for spreading processes in a population Times of social and behavioral transition Implicit communities of individuals Preliminary results on Grevy’s zebra and wild donkeys data show that addressing dynamics of the population produces more accurate conclusions Extend and test our framework and computational tools to other problems and other data Problem Statement and Motivation Of the three existing species of zebra, one, the Grevy's zebra, is endangered while another, the plains zebra, is extremely abundant. The two species are similar in almost all but one key characteristic: their social organization. Finding patterns of social interaction within a population has applications from epidemiology and marketing to conservation biology and behavioral ecology. One of the intrinsic characteristics of societies is their continual change. Yet, there are few analysis methods that are explicitly dynamic. Our goal is to develop a novel conceptual and computational framework to accurately describe the social context of an individual at time scales matching changes in individual and group activity. Zebra with a sensor collar A snapshot of zebra population and the corresponding abstract representation

Collaborative Research: Information Integration for Locating and Querying Geospatial Data Lead PI: Isabel F. Cruz (Computer Science). In collaboration with Nancy Wiegand (U. Wisconsin-Madison) Prime Grant Support: NSF Technical Approach Geospatial data are complex and highly heterogeneous, having been developed independently by various levels of government and the private sector Portals created by the geospatial community disseminate data but lack the capability to support complex queries on heterogeneous data Complex queries on heterogeneous data will support information discovery, decision, or emergency response Data integration using ontologies Ontology representation Algorithms for the alignment and merging of ontologies Semantic operators and indexing for geospatial queries User interfaces for Ontology alignment Display of geospatial data Create a geospatial cyberinfrastructure for the web to Automatically locate data Match data semantically to other relevant data sources using automatic methods Provide an environment for exploring, and querying heterogeneous data for emergency managers and government officials Develop a robust and scalable framework that encompasses techniques and algorithms for integrating heterogeneous data sources using an ontology-based approach Problem Statement and Motivation Key Achievements and Future Goals