SEI+II Information Integration Through Events

Many environmental observations are collected at different space and time scales that preclude easy integration of the data and hinder broader understanding of ecosystem dynamics. Ocean Observing Systems provide a specific example of multi-sensor systems observing several variables in different space - time regimes. This project integrates diverse space-time environmental sensor streams based on the conversion of their information content to a common higher-level abstraction: a space-time event data type. The space-time event data type normalizes across the diversity of observation level data to produce a common data type for exploration and analysis. Gulf of Maine Ocean Observing System (GOMOOS) data provide the multivariate time and space-time series from which space-time events are detected and assembled. Event detection employs a combined top down-bottom up approach. The top down component specifies an event ontology while the bottom up component is based on extraction of primitive events (e.g. decreasing, increasing, local maxima and minima sequences) from time and space-time series. Exploration and analysis of the extracted events employs a graphic exploratory environment based on a graphic primitive called an event band and its composition into event band stacks and panels that support investigation of various space-time patterns.The project contributes a new information integration approach based on the concept of an event that can be extended to many domains including socio-economic, financial, legislative, surveillance and health related information. The project will contribute new data mining strategies for event detection in time and space-time series and a set of flexible exploratory tools for examination and development of hypotheses on space-time event patterns and interactions

Digital Government: Knowledge Management Over Time-Varying Geospatial Datasets

Spatially-related data is collected by many government agencies in various formats and for various uses. This project seeks to facilitate the integration of these data, thus providing new uses. This will require the development of a knowledge management framework to provide syntax, context, and semantics, as well as exploring the introduction of time-varying data into the framework. Education and outreach will be part of the project through the development of an on-line short courses related to data integration in the area of geographical information systems. The grantees will be working with government partners (National Imagery and Mapping Agency, the National Agricultural Statistics Service, and the US Army Topographic Engineering Center), as well as an industrial organization, Base Systems, and the non-profit OpenGIS Consortium, which works closely with vendors of GIS products

ITR/IM: Enabling the Creation and Use of GeoGrids for Next Generation Geospatial Information

The objective of this project is to advance science in information management, focusing in particular on geospatial information. It addresses the development of concepts, algorithms, and system architectures to enable users on a grid to query, analyze, and contribute to multivariate, quality-aware geospatial information. The approach consists of three complementary research areas: (1) establishing a statistical framework for assessing geospatial data quality; (2) developing uncertainty-based query processing capabilities; and (3) supporting the development of space- and accuracy-aware adaptive systems for geospatial datasets. The results of this project will support the extension of the concept of the computational grid to facilitate ubiquitous access, interaction, and contributions of quality-aware next generation geospatial information. By developing novel query processes as well as quality and similarity metrics the project aims to enable the integration and use of large collections of disperse information of varying quality and accuracy. This supports the evolution of a novel geocomputational paradigm, moving away from current standards-driven approaches to an inclusive, adaptive system, with example potential applications in mobile computing, bioinformatics, and geographic information systems. This experimental research is linked to educational activities in three different academic programs among the three participating sites. The outreach activities of this project include collaboration with U.S. federal agencies involved in geospatial data collection, an international partner (Brazil\u27s National Institute for Space Research), and the organization of a 2-day workshop with the participation of U.S. and international experts

Application of Spatial Concepts to Genome Data

This project will investigate the application of geographic information science concepts and methods to the modeling and analysis of genome data. The primary objective of the research is to develop a data model for genomes that supports the graphical exploration of the higher order spatial arrangement of genome features through spatial queries and spatial data analysis tools. The spatial genome model formalizes topological and order relationships among genome features (before, after, overlap), uses metric properties to refine spatial topologies, and includes representations of features that have uncertain metric properties. The genome spatial model enhances the integrative and comparative potential of genome data by providing the foundation for more powerful spatial reasoning and inferences than can be achieved by data models that incorporate only a small subset of possible temporal-spatial relationships among genome features (e.g. order and distance). The research represents a logical extension from current feature by feature analytical approaches of genome studies to one that allows biologists to ask questions about the contextual and organizational significance of the spatial arrangement of genome features. These functional capabilities should, in turn, aid in the automation of repetitive analytical tasks associated with the mapping of genome features and drive the discovery of biologically significant aspects of genome organization and function

An Analysis of Spatio-Temporal Landscape Patterns for Protected Areas in Northern New England: 1099-2010

Context: Landscape ecology theory provides insight about how large assemblages of protected areas (PAs) should be configured to protect biodiversity. We adapted these theories to evaluate whether the emergence of decentralized land protection in a largely private landscape followed the principles of reserve design. Objectives: Our objectives were to determine: (1) Are there distinct clusters of PAs in time and space? (2) Are PAs becoming more spatially clustered through time? and (3) Does the resulting PA portfolio have traits characteristic of ideal reserve design? Methods: We developed an historical dataset of the PAs enacted since 1900 in the northern New England region of the US. We conducted spatio-temporal clustering, landscape pattern, and aggregation analyses at both the landscape scale and for specific classes of land ownership, conservation method, and degree of protection. Results: We found the frequency of PAs increased through time, and that area-weighted clusters of PAs were heavily influenced by a few recent large PAs. PA clustering around preexisting PAs was driven primarily by establishment of large PAs focused on natural resource management, rather than strict reserves. Since 1990, the complete portfolio has increased in aggregation, but reserve patches have become less aggregated and smaller, while patches that allow extractive uses have become more aggregated and larger. Conclusions: Our extension of landscape ecology theory to a diverse portfolio of PAs underscores the importance of prioritizing conservation choices in the context of existing PAs, and elucidates the landscape scale effects of individual actions within a portfolio of protected areas

An Analysis of Spatio-Temporal Landscape Patterns for Protected Areas in Northern New England: 1099-2010

Context: Landscape ecology theory provides insight about how large assemblages of protected areas (PAs) should be configured to protect biodiversity. We adapted these theories to evaluate whether the emergence of decentralized land protection in a largely private landscape followed the principles of reserve design. Objectives: Our objectives were to determine: (1) Are there distinct clusters of PAs in time and space? (2) Are PAs becoming more spatially clustered through time? and (3) Does the resulting PA portfolio have traits characteristic of ideal reserve design? Methods: We developed an historical dataset of the PAs enacted since 1900 in the northern New England region of the US. We conducted spatio-temporal clustering, landscape pattern, and aggregation analyses at both the landscape scale and for specific classes of land ownership, conservation method, and degree of protection. Results: We found the frequency of PAs increased through time, and that area-weighted clusters of PAs were heavily influenced by a few recent large PAs. PA clustering around preexisting PAs was driven primarily by establishment of large PAs focused on natural resource management, rather than strict reserves. Since 1990, the complete portfolio has increased in aggregation, but reserve patches have become less aggregated and smaller, while patches that allow extractive uses have become more aggregated and larger. Conclusions: Our extension of landscape ecology theory to a diverse portfolio of PAs underscores the importance of prioritizing conservation choices in the context of existing PAs, and elucidates the landscape scale effects of individual actions within a portfolio of protected areas

Biodiversity and Ecosystem Informatics: Event and Process Tagging for Information Integration for the International Gulf of Main Watershed

This incubation proposal addresses the issue of integrating large, diverse, and autonomous collections of scientific data within a complex institutional setting. The goal is to convert these autonomous collections into a shareable repository that supports synthesis of data through new metadata structures based on events and processes. The institutional setting is the data and data-gathering activities of over 80 agencies, NGOS, and academic and research institutions operating within the Gulf of Maine watershed. The metadata development will be coordinated by library and spatial information scientists working jointly with domain scientists. An essential task of this incubation effort will be the development of a shared understanding of environmental processes and events that becomes a shareable ontology.Libraries play a vital role in organizing intellectual access to creative works. Scientific data has tended to be outside this traditional purview and has thus lacked the benefits of cataloging and indexing that promote shared access. We are proposing a new metadata structure that exploits common units of analysis in environmental studies: events and processes. Associating scientific data sets with event and process tags, in addition to other metadata elements, can substantially improve the ability to integrate and synthesize diverse scientific collections. The metadata initiative of this proposal will lay the foundation for specifying events and processes through the collaboration of data collections content specialists and information management specialists

IGERT: Sensor Science, Engineering, and Informatics

This Sensor Science, Engineering and Informatics (SSEI) IGERT program will provide multidisciplinary doctoral training in the area of sensor systems ranging from the science and engineering of new materials and sensing mechanisms to the interpretation of sensor data. The design and management of effective sensor systems requires a holistic understanding of how information is collected, stored, integrated, evaluated, and communicated within sensing systems and to decision makers in diverse application contexts. The SSEI IGERT weaves together three research focus areas: (1) Sensor Materials and Devices, (2) Sensor Systems and Networks, and (3) Sensor Informatics. The intellectual merit of the project includes education and research activities that are designed to ensure a feedback loop so that SSEI IGERT trainees are able to transform new knowledge from sensor-generated data to further development of sensor systems and networks and advances in sensor materials and devices, and vice versa. Innovative components of the program include (1) development and use of a testbed prototype that will require interdisciplinary interaction across the three research areas; (2) a tight integration of the social, legal, ethical, and economic dimensions of sensing environments in both research and training, (3) expanded relationships with companies and federal laboratories engaged in sensor research, (4) international collaborations, and (5) synergistic integration with sensor science and engineering education at the middle, high school, and undergraduate level. The broader impacts of the SSEI IGERT program are a new breed of scientists and engineers who will be versatile in dealing with the diverse technical components that contribute to sensing systems, knowledgeable in the legal, social, and ethical contexts of heavily sensed environments, and aware of the human values that must be preserved, protected and promoted within such systems. IGERT is an NSF-wide program intended to meet the challenges of educating U.S. Ph.D. scientists and engineers with the interdisciplinary background, deep knowledge in a chosen discipline, and the technical, professional, and personal skills needed for the career demands of the future. The program is intended to catalyze a cultural change in graduate education by establishing innovative new models for graduate education and training in a fertile environment for collaborative research that transcends traditional disciplinary boundaries