General Spatial Pattern and Meta-Pattern Model for Problems That Need Analytical Approach in Complex Spatial Systems

Spatial Pattern (SP) Description, Identification, and Application Methodology (SPDIAM) was developed for describing and detecting spatial economic, social, and environmental phenomena and providing basic information technology (IT) artefacts that can be used for the spatial analysis development using GIS technologies. SPDIAM allows urban planning and design practitioners to describe SP in a computerized manner, identify SP automatically, and apply them in the spatial planning and design domain. In this article, we explain the general SP and spatial meta-pattern model, used in SPDIAM, that is based on the theory of Complex Spatial System (CSS), spatial configuration, and spatial capital concepts and is presented using UML diagrams as standard used for visualization of project models from structure and behavior points of views. The practical experiment of describing and identifying 6 basic spatial meta-pattern values is conducted using the new algorithm that combines Space Syntax method, Visibility Graph Analysis (VGA), and VGA measures to create a computer model of space and to quantify its configuration, which can then be used to handle geographic and geometric data associated with attribute information, to perform spatial, mathematical, and statistical calculations and to visualize SP. The results of the experiment show that the model and the algorithm are appropriate for spatial meta-patterns identification, and the best results can be achieved using VGA measure Isovist Compactness. In the future, general SP and the spatial meta-pattern model can be used to describe and identify complex SP and to solve problems in CSS with the help of the spatial meta-pattern values described in this article.

Exploratory Graph Analysis for Factor Retention: Simulation Results for Continuous and Binary Data

Exploratory graph analysis (EGA) is a commonly applied technique intended to help social scientists discover latent variables. Yet, the results can be influenced by the methodological decisions the researcher makes along the way. In this article, we focus on the choice regarding the number of factors to retain: We compare the performance of the recently developed EGA with various traditional factor retention criteria. We use both continuous and binary data, as evidence regarding the accuracy of such criteria in the latter case is scarce. Simulation results, based on scenarios resulting from varying sample size, communalities from major factors, interfactor correlations, skewness, and correlation measure, show that EGA outperforms the traditional factor retention criteria considered in most cases in terms of bias and accuracy. In addition, we show that factor retention decisions for binary data are preferably made using Pearson, instead of tetrachoric, correlations, which is contradictory to popular belief.

The network of the subjective experience in embodiment phenomena

Body illusions are designed to temporarily alter body representation by embodying fake bodies or part of them. Despite their large use, the embodiment questionnaires have been validated only for the embodiment of fake hands in the Rubber Hand Illusion (RHI). With the current study, we aimed at (i) extending the validation of embodiment questionnaires to a different illusory situation (e.g., the Full-Body Illusion - FBI); (ii) comparing two methods to explore the questionnaires structures: a classic Exploratory Factor Analysis (EFA) and a modern Exploratory Graph Analysis (EGA). 118 healthy participants completed an FBI procedure where the subjective experience of embodiment was measured with a standard questionnaire. The EFA results in two-factor structures. However, the Confirmatory Factor Analysis (CFA) fit indices do not show a good fit with the data. Conversely, the EGA identified four communities: ownership, agency, co-location and disembodiment; the solution was confirmed by a CFA. Overall, the EGA seems to be the best fitting method for the present data. Our results confirm the EGA as a suitable substitute for a more classical EFA. Moreover, the emerged structure suggests that the FBI induces similar effects to the RHI, implying that the embodiment sensations are common to different illusory methods. Public Significance Statement: The study indicates that the Exploratory Graph Analysis is a convenient substitute for a more classical Exploratory Factor Analysis. Moreover, the present paper suggests that different illusory methods induce similar embodiment effects.

Scientometric Analysis of Disaster Risk Perception: 2000–2020

This paper used 1526 works from the literature on disaster risk perception from 2000 to 2020 in the Web of Science core collection database as the research subject. The CiteSpace knowledge graph analysis tool was used to visual analyze the country, author, institution, discipline distribution, keywords, and keyword clustering mapping. The paper drew the following conclusions. Firstly, disaster risk perception research has experienced three stages of steady development, undulating growth, and rapid growth. Secondly, the field of disaster risk perception was mainly concentrated in the disciplines of engineering, natural science, and management science. Thirdly, meteorological disasters, earthquakes, nuclear radiation, and epidemics were the main disasters in the field of disaster risk perception. Residents and adolescents were the main subjects of research in the field of disaster risk perception. Fourthly, research on human risk behavior and risk psychology and research on disaster risk control and emergency management were two major research hotspots in the field of disaster risk perception. Finally, the research field of disaster risk perception is constantly expanding. There is a trend from theory to application and multi-perspective combination, and future research on disaster risk perception will be presented more systematically. The conclusion can provide a reference for disaster risk perception research, as well as directions for future research.