Spatial Fuzziness Research Articles

Digital Mapping of Medieval Cemeteries

It has become almost standard practice that archaeological research on cemeteries is published in a similar fashion, specifically when primary sources supplement the data presented. Aside from the interpretative part, a catalog of all graves, buried individuals, and finds is published along with a map of the site and graphical depictions of the various entities. This is mostly structured within a four-level hierarchy beginning with the cemetery, the contained graves, the burials from each grave, and the finds associated with the burial. Today, even though many publications and their catalogs are based on or derived from digital data and published as open access, the outcome is often printed text such as a pdf file. Digital data that is properly structured and can be used out of the box for further analyses is rarely available. The presented article discusses how to digitize data on burials and how to provide them to the public in sustainable and comprehensible ways. Within previous and ongoing projects, the author and his team have developed a database system (OpenAtlas) that is used for the data acquisition of archaeological and anthropological research data that also maps information directly to the CIDOC CRM. Temporal and spatial fuzziness are dealt with following various concepts such as GeoJSON-T. For providing the data as Linked Open Data, the “linked places” format is used and an API provides a JSON-LD representation of each entity. Due to the “standard” approach implemented when publishing cemeteries, the data acquisition is mostly achieved by manually recording the published information in the database. In the following projects, data from several hundred Early Medieval Austrian and Czech burial sites with several thousand graves and finds have been digitized. To publicize the information, an online web application (https://thanados.net) has been developed to present and disseminate this data.

Applications of FP Growth and Apriori Algorithm for Mining Fuzzified Spatial Dataset

Spatial data, also called geospatial data, is term needed to describe data linked to or containing knowledgeable data about a particular location on Earth’s surface. Spatial data mining's primary goal is to uncover hidden complicated information from spatial & non-spatial information in spite of their enormous quantity and find the spatial relations density. Spatial Data Mining techniques, however, continue to be an expansion of individuals utilized in standard data mining. Spatial Data is an extremely challenging area since enormous quantities of spatial data have been obtained from the remote sensed to the GIS (Geographic Information Systems), ecological estimation, computer cartography, planning and many more. In a given paper, we only focus on an essential type of spatial vagueness termed as spatial fuzziness. Spatial fuzziness intakes the property of several spatial objects in certainty which don’t contain boundaries of sharp type and interiors or whose boundaries as well as interiors can't be determined in precise form. This paper provides the method for finding fuzzy spatial data of association rule. Association rules provided valuable data in the assessment of important correlations observed in big databases. Compared to the previous research work, the current approach for there search highlights the superiority over the same dataset in terms of time taken and generated rules. The rules generated tell about the occurrence of attributes. The results show that the current research is more efficient than that of the previous work and also less time-consuming.

Uncertainty representation of ocean fronts based on fuzzy-rough set theory

Analysis of ocean fronts’ uncertainties indicates that they result from indiscernibility of their spatial position and fuzziness of their intensity. In view of this, a flow hierarchy for uncertainty representation of ocean fronts is proposed on the basis of fuzzy-rough set theory. Firstly, raster scanning and blurring are carried out on an ocean front, and the upper and lower approximate sets, the indiscernible relation in fuzzy-rough theories and related operators in fuzzy set theories are adopted to represent its uncertainties, then they are classified into three sets: with members one hundred percent belonging to the ocean front, belonging to the ocean front’s edge and definitely not belonging to the ocean front. Finally, the approximate precision and roughness degree are utilized to evaluate the ocean front’s degree of uncertainties and the precision of the representation. It has been proven that the method is not only capable of representing ocean fronts’ uncertainties, but also provides a new theory and method for uncertainty representation of other oceanic phenomena.

Centered Composite Analysis of Variations Associated with the Madden–Julian Oscillation

Abstract Centered composite analysis is described and applied to gain a better understanding of the initial phases of the Madden–Julian oscillation (MJO). Centered composite analysis identifies the dates and central locations of key events. The elements of the composite means are centered on these central locations before averages are calculated. In this way much of the spatial fuzziness, which is inherent in traditional composite analysis, is removed. The results for the MJO, based on MJO-filtered outgoing longwave radiation for the reference data and 40-yr ECMWF Re-Analysis (ERA-40) and NCEP–NCAR reanalysis products for the composites, show highly significant composites of unfiltered data for not only zero lag, but also lags back to 20 days before the target events. These composites identify propagating patterns of surface pressure, upper- and lower-troposphere zonal winds, surface temperature, and 850-hPa specific humidity associated with MJO convective events in the Indian Ocean. The propagation characteristics of important features, especially surface pressure, differ substantially for MJO convective anomalies centered over the Indian or western Pacific Oceans. This suggests that distinctly different mechanisms may be dominant in these two regions, and that many earlier analyses may be mixing properties of the two.

Design and implementation of finite resolution crisp and fuzzy spatial objects

Uncertainty management for geometric data is currently an important problem in spatial databases, image databases, and geographic information systems. Spatial entities do not always have homogeneous interiors and sharply defined boundaries but frequently their interiors and boundaries are partially or totally indeterminate and vague. For an important kind of spatial vagueness called spatial fuzziness this paper provides a conceptual and an implementation model of fuzzy spatial objects that also incorporates fuzzy geometric union, intersection, and difference operations as well as fuzzy topological predicates. In particular, this model is not based on Euclidean space and not on an infinite-precision arithmetic which lead to lacking numerical robustness and to topological inconsistency of implementations on a computer; it rests on a finite, discrete geometric domain called grid partition which takes into account finite-precision number systems available in computers. Last but not least, this paper is a contribution to achieve a uniform treatment of vector and raster data.