Mining sensor datasets with spatiotemporal neighborhoods
نویسندگان
چکیده
Many spatiotemporal data mining methods are dependent on how relationships between a spatiotemporal unit and its neighbors are defined. These relationships are often termed the neighborhood of a spatiotemporal object. The focus of this paper is the discovery of spatiotemporal neighborhoods to find automatically spatiotemporal sub-regions in a sensor dataset. This research is motivated by the need to characterize large sensor datasets like those found in oceanographic and meteorological research. The approach presented in this paper finds spatiotemporal neighborhoods in sensor datasets by combining an agglomerative method to create temporal intervals and a graph-based method to find spatial neighborhoods within each temporal interval. These methods were tested on realworld datasets including (a) sea surface temperature data from the Tropical Atmospheric Ocean Project (TAO) array in the Equatorial Pacific Ocean and (b) NEXRAD precipitation data from the Hydro-NEXRAD system. The results were evaluated based on known patterns of the phenomenon being measured. Furthermore, the results were quantified by performing hypothesis testing to establish the statistical significance using Monte Carlo simulations. The approach was also compared with existing approaches using validation metrics namely spatial autocorrelation and temporal interval dissimilarity. The results of these experiments show that our approach indeed identifies highly refined spatiotemporal neighborhoods.
منابع مشابه
Mining Volunteered Geographic Information datasets with heterogeneous spatial reference
When the information created online by users has a spatial reference, it is known as Volunteered Geographic Information (VGI). The increased availability of spatiotemporal data collected from satellite imagery and other remote sensors provides opportunities for enhanced analysis of Spatiotemporal Patterns. This area can be defined as efficiently discovering interesting patterns from large data ...
متن کاملEfficient Mining of Spatiotemporal Patterns
The problem of mining spatiotemporal patterns is finding sequences of events that occur frequently in spatiotemporal datasets. Spatiotemporal datasets store the evolution of objects over time. Examples include sequences of sensor images of a geographical region, data that describes the location and movement of individual objects over time, or data that describes the evolution of natural phenome...
متن کاملERMO-DG: Evolving Region Moving Object Dataset Generator
It is often essential to create datasets with foreseeable characteristics. For the design and testing of advanced spatiotemporal pattern mining algorithms, adaptable and large datasets are needed. In this paper, we present a synthetic dataset generator, ERMO-DG, that is intended for creating spatiotemporal patterns. Generated datasets consist of spatiotemporal object instances of different feat...
متن کاملGranularity Analysis for Spatio-Temporal Web Sensors
In recent years, many researches to mine the exploding Web world, especially User Generated Content (UGC) such as weblogs, for knowledge about various phenomena and events in the physical world have been done actively, and also Web services with the Web-mined knowledge have begun to be developed for the public. However, there are few detailed investigations on how accurately Web-mined data refl...
متن کاملSpatial and Spatiotemporal Data Mining
The significant growth of spatial and spatiotemporal data collection as well as the emergence of new technologies have heightened the need for automated discovery of spatiotemporal knowledge. Spatial and spatiotemporal data mining techniques are crucial to organizations which make decisions based on large spatial and spatiotemporal datasets. The interdisciplinary nature of spatial and spatiotem...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید
ثبت ناماگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید
ورودعنوان ژورنال:
- J. Spatial Information Science
دوره 6 شماره
صفحات -
تاریخ انتشار 2013