Search

There are several goals of the two-dimensional data analysis: one may be interested in searching for groups of similar objects (clustering), another one may be focused on searching for some dependencies between a specified one and other variables (classification, regression, associate rules induction), and finally, some may be interested in serching for well-defined patterns in the data called biclusters. It was already proved that there exists a mathematically proven symmetry between some patterns in the matrix and implicants of data-defined Boolean function. This paper provides the new look for a specific pattern search—the pattern named the δ -shifting pattern. The shifting pattern is interesting, as it accounts for constant fluctuations in data, i.e., it captures situations in which all the values in the pattern move up or down for one dimension, maintaining the range amplitude for all the dimensions. Such a behavior is very common in real data, e.g., in the analysis of gene expression data. In such a domain, a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. A δ -shifting pattern meets the necessity of shifting pattern induction together with the bias of the real values acquisition where the original shifts may be disturbed with some outer conditions. Experiments with a real dataset show the potential of our approach at finding biclusters with δ -shifting patterns, providing excellent performance. It was possible to find the 12 × 9 pattern in the 112 × 9 input data with M S R = 0.00653 . The experiments also revealed that δ -shifting patterns are quite difficult to be found by some well-known methods of biclustering, as these are not designed to focus on shifting patterns—results comparable due to M S R had much more variability (in terms of δ) than patterns found with Boolean reasoning. [ABSTRACT FROM AUTHOR]

There are several goals of the two-dimensional data analysis: one may be interested in searching for groups of similar objects (clustering), another one may be focused on searching for some dependencies between a specified one and other variables (classification, regression, associate rules induction), and finally, some may be interested in serching for well-defined patterns in the data called biclusters. It was already proved that there exists a mathematically proven symmetry between some patterns in the matrix and implicants of data-defined Boolean function. This paper provides the new look for a specific pattern search—the pattern named the δ-shifting pattern. The shifting pattern is interesting, as it accounts for constant fluctuations in data, i.e., it captures situations in which all the values in the pattern move up or down for one dimension, maintaining the range amplitude for all the dimensions. Such a behavior is very common in real data, e.g., in the analysis of gene expression data. In such a domain, a subset of genes might go up or down for a subset of patients or experimental conditions, identifying functionally coherent categories. A δ-shifting pattern meets the necessity of shifting pattern induction together with the bias of the real values acquisition where the original shifts may be disturbed with some outer conditions. Experiments with a real dataset show the potential of our approach at finding biclusters with δ-shifting patterns, providing excellent performance. It was possible to find the 12×9 pattern in the 112×9 input data with MSR=0.00653. The experiments also revealed that δ-shifting patterns are quite difficult to be found by some well-known methods of biclustering, as these are not designed to focus on shifting patterns—results comparable due to MSR had much more variability (in terms of δ) than patterns found with Boolean reasoning.

A study was conducted to understand if the disaster death in Odisha, India across five categories, viz. tropical cyclone, lightning, heat wave, cold wave and extreme precipitation events underwent any significant change during 2001-14. It was based on timeseries data available at the National Data Portal of India. Results of the study suggest that the number of fatalities from sporadic meso-scale meteorological hazards like cyclones and heavy precipitation have drastically reduced due to better forecasting and effective evacuation strategies adopted by the Government. However, fatalities due to more frequent recurring extreme events, such as lightning and heat stress are on the rise. Male adults and middle-aged people (30-44 and 45-59 years respectively) constituted the most vulnerable groups affected by lightning and heat stress which account for maximum number of deaths in the state. Older population (especially older women) were more vulnerable towards cold wave due to reduced thermoregulatory mechanism. The finding is significant, because often deaths due to lightning injury, heat stress and cold wave either go unnoticed or are under-reported. We expect that the present study which focuses on gender and age disaggregated death would help in adopting more targeted mitigation or adaptation strategies in Odisha. The study also points out the need of a single and detailed spatio-temporal data infrastructure for all kinds of disaster deaths for more in-depth and insightful analysis. [ABSTRACT FROM AUTHOR]

Flooding, including hurricanes and tornadoes, accounts for approximately 40 percent of natural disasters worldwide and kills 100 people on average in the United States each year, which is more than any other single weather hazard. Since flooding is a common hazard in the U.S. and flood-related casualties have been increasing in recent years, it is important to understand the spatial patterns of different vulnerable population groups in the flooding regions. To achieve this objective, spatial scan statistics were used to identify the spatial clusters of different demographic groups (children and elderly, poor, White, African American, and Hispanic) in the 100-year floodplain areas of Birmingham. Using the decennial census data from 1990 to 2015, this research examined whether these vulnerable population groups had aggregated more in the flooding areas or moved away from the flooding areas in the past thirty years. The findings of this research indicate that most of the minorities are increasingly aggregating in the floodplain areas of Village Creek in Birmingham. The findings also suggest that the non-minorities are moving away from the flooding regions in Birmingham, AL. As part of the minorities and non-minorities group, approximately 50 percent of African Americans and 4 percent of White populations aggregated in the Village Creek flooding areas in 2015. Although the percentage of White populations is very low, the findings suggest that they are still exposed to floods. The multi-decadal analysis of flood risk will help the local governments to understand which population groups could be more affected by floods historically and need more attention in future flood hazards. This understanding will help them prepare for future flood hazards by allocating resources efficiently among the different racial and ethnic groups. [ABSTRACT FROM AUTHOR]