A palaeomagnetic study of fracture fills in the Holy Cross Mountains of Central Poland and its application in dating tectonic processes

Calcite and sedimentary fills in fractures cutting the Upper Devonian carbonates in the Holy Cross Mountains (HCM) were dated palaeomagnetically by comparison with the apparent polar wander path (APWP). Haematite-bearing calcite possessed well-defined components of natural remanent magnetization (NRM), which were preserved under thermal demagnetization to temperatures of approximately 500 °C, when specimens disintegrated. Although not completely demagnetized, some specimens revealed a stable NRM component before destruction, thus making a component analysis possible. Five components were determined using density point distribution and cluster analysis. One has a mean that is similar to the present-day local geomagnetic vector. The remaining four components yielded palaeomagnetic poles located at: A (70.3°S, 5.5°E), B (71.3°S, 31.2°E), C (48.7°S, 351.0°E, virtual geomagnetic pole), and D (11.6°S, 312.3°E). Antipodal polarities found in the fracture fills, together with dissimilarities in magnetization found in calcite and hosting carbonates, indicate the lack of simultaneous remagnetization, and different times of remanence acquisition for the rocks under comparison. Taking both palaeomagnetically inferred palaeolatitudes and regional tectonics into consideration, a Mesozoic (Cretaceous?) age is estimated for palaeopoles A and B, a Permian age for pole C, and a Carboniferous age for pole D. These age determinations are in line with the calcite ages estimated from isotopic studies. A comparative palaeomagnetic study performed on a well-dated Upper Devonian neptunian dyke of limestone and a Lower Triassic clastic vein yielded virtual geomagnetic poles (VGPs) close to the APWP for Baltica. Generally, the remanence from fracture fills may be useful for dating related tectonics, karst phenomena and mineralization processes.     

An efficient measure of compactness for two-dimensional shapes and its application in regionalization problems

A measure of shape compactness is a numerical quantity representing the degree to which a shape is compact. Ways to provide an accurate measure have been given great attention due to its application in a broad range of GIS problems, such as detecting clustering patterns from remote-sensing images, understanding urban sprawl, and redrawing electoral districts to avoid gerrymandering. In this article, we propose an effective and efficient approach to computing shape compactness based on the moment of inertia (MI), a well-known concept in physics. The mathematical framework and the computer implementation for both raster and vector models are discussed in detail. In addition to computing compactness for a single shape, we propose a computational method that is capable of calculating the variations in compactness as a shape grows or shrinks, which is a typical application found in regionalization problems. We conducted a number of experiments that demonstrate the superiority of the MI over the popular isoperimetric quotient approach in terms of (1) computational efficiency; (2) tolerance of positional uncertainty and irregular boundaries; (3) ability to handle shapes with holes and multiple parts; and (4) applicability and efficacy in districting/zonation/regionalization problems.     

An algorithm for converting weather radar data into GIS polygons and its application in severe weather warning systems

An algorithm was developed for converting radar data from matrix format into polygons that can be easily visualized, processed, and analyzed using Geographic Information Systems. Spatial operators can be used to overlap radar polygons with land surface features represented by points, lines, and polygons to meet the demands of severe weather identification and tracking and risk recognition. Application and testing of the algorithm demonstrate that the converted radar polygons are suitable for use in weather modification, risk assessments of flash floods in urban areas, and the identification of lightning activity for lightning risk recognition, all of which are essential in real-time severe weather monitoring and warning.