Mining significant local spatial association rules for multi-category point data.

Spatial association rule mining can reveal the inherent laws of spatial object interdependence and is an important part of spatial data mining. Most of the existing algorithms for mining local spatial association rules are oriented towards the spatial association between two categories of points and cannot fully reflect the spatial heterogeneity of complex spatial relations among multiple categories of points. In addition, the interactions between points in different categories are often asymmetrical. However, the existing algorithms ignore this asymmetry. To address the above problems, an algorithm for mining local spatial association rules for point data of multiple categories based on position quotients is proposed. First, the proximity relationship between points is determined by an adaptive filter, and the spatial weight value is given according to Gaussian kernel function. Then, the multivariate local colocation quotient of each point is calculated to measure the strength of the local regional spatial association rule. Finally, the Monte Carlo simulation function is used to generate a random sample distribution to test the significance of the results. The algorithm is verified on artificial simulation data and real Point of Interest (POI) data. The experimental results show that the algorithm can identify significant association regions of different spatial association rules for point sets.

Information Seeking and Processing during the Outbreak of COVID-19 in Taiwan: Examining the Effects of Emotions and Informational Subjective Norms.

Adopting the model of risk information seeking and processing (RISP) as a theoretical framework, the objective of this study was to investigate the factors that prompted individuals' information-seeking and -processing behaviors during the COVID-19 pandemic in Taiwan. There were two unique aspects in this study: one was to adopt specific emotions to investigate the impact of negative emotions, and the other was to examine the effect of informational subjective norms (ISNs) on information-seeking and -processing behavior. An online survey was conducted by a professional polling company, and a stratified random sampling method was employed, using gender, age, education, personal income, and residential areas as strata to select participants. This study obtained 1100 valid questionnaires. The results showed that (1) risk perception did not exert any significant impacts on respondents' perceived information insufficiency; (2) risk perception exerted a powerful impact on respondents' ISNs, which, in turn, positively affected their information insufficiency; (3) the respondents who experienced fear were found to have a high probability of using a systematic-processing mode, while the respondents who experienced anger were more likely to adopt a heuristic-processing mode to process information; and (4) the use of a systematic-processing mode was positively associated, while the use of a heuristic-processing mode was negatively associated, with information-seeking behavior.