The expanding deployment of sensor systems that capture location, time, and multiple thematic variables is increasing the need for exploratory spatio-temporal data analysis tools. Geographic information systems (GIS) and time series analysis tools support exploration of spatial and temporal patterns respectively and independently, but tools for the exploration of both dimensions within a single system are relatively rare. The contribution of this research is a framework for the visualization and exploration of spatial, temporal, and thematic dimensions of sensor-based data. The unit of analysis is an event, a spatio-temporal data type extracted from sensor data. The conceptual framework suggests an approach for design layout that can be flexibly modified to explore spatial and temporal trends, temporal relationships among events, periodic temporal patterns, the timing of irregularly repeating events, event–event relationships in terms of thematic attributes, and event patterns at different spatial and temporal granularities. Flexible assignment of spatial, temporal, and thematic categories to a set of graphical interface elements that can be easily rearranged provides exploratory power as well as a generalizable design layout structure. The framework is illustrated with events extracted from Gulf of Maine Ocean Observing System data but the approach has broad application to other domains and applications in which time, space, and attributes need to be considered in conjunction.
Visualization encompasses the display of quantities or qualities of visible or invisible phenomena through the combined use of points, lines, a coordinate system, numbers, symbols, words, shading, color, and animation. The objectives of visualization are to provoke insights and expand comprehension of information by revealing complex relationships among data. Geographical information is visualized in the form of maps. Recent concern over the accuracy and reliability of spatial information in geographic information systems has raised an interest in applying visualization tools to comprehend and communicate the reliability of GIS information and products. This paper develops design requirements for visualization of spatial data quality based on characterizations of quality, a range of quality assessment tasks, and different contexts under which data quality might be investigated.
Aquaculture has been responsible for an impressive growth in the global supply of seafood. As of 2016, more than half of all global seafood production comes from aquaculture. To meet future global seafood demands, there is need and opportunity to expand marine aquaculture production in ways that are both socially and ecologically sustainable. This requires integrating biophysical, social, and engineering sciences. Such interdisciplinary research is difficult due to the complexity and multi-scale aspects of marine aquaculture and inherent challenges researchers face working across disciplines. To this end, we developed a framework based on Elinor Ostrom’s social–ecological system framework (SESF) to guide interdisciplinary research on marine aquaculture. We first present the framework and the social–ecological system variables relevant to research on marine aquaculture and then illustrate one application of this framework to interdisciplinary research underway in Maine, the largest producer of marine aquaculture products in the United States. We use the framework to compare oyster aquaculture in two study regions, with a focus on factors influencing the social and biophysical carrying capacity. We conclude that the flexibility provided by the SESF is well suited to inform interdisciplinary research on marine aquaculture, especially comparative, cross-case analysis.
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.
Measures of similarity or differences between data objects are applied frequently in geography, biology, computer science, linguistics, logic, business analytics, and statistics, among other fields. This work focuses on event sequence similarity among event sequences extracted from time series observed at spatially deployed monitoring locations with the aim of enhancing the understanding of process similarity over time and geospatial locations. We present a framework for a novel matrix-based spatiotemporal event sequence representation that unifies punctual and interval-based representation of events. This unified representation of spatiotemporal event sequences (STES) supports different event data types and provides support for data mining and sequence classification and clustering. The similarity measure is based on the Jaccard index with temporal order constraints and accommodates different event data types. The approach is demonstrated through simulated data examples and the performance of the similarity measures is evaluated with a k-nearest neighbor algorithm (k-NN) classification test on synthetic datasets. As a case study, we demonstrate the use of these similarity measures in a spatiotemporal analysis of event sequences extracted from space time series of a water quality monitoring system.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.