A comparison of network model creation algorithms based on the quality of wayfinding results

Various network model creation algorithms have been introduced to demonstrate a better approximation of the actual walking pattern and to provide a better wayfinding guide. However, it is under‐investigated which algorithm creates the most appropriate indoor navigation network model in the context of wayfinding applications. Due to the lack of discussion, some studies unconsciously extended an algorithm designed for creating an outdoor navigation network model to indoor space applications. This is problematic because indoor space has different spatial contexts from outdoor space, such as non‐linear space and no‐designated walking space. Our solution is to select five well‐known algorithms that have been introduced, to reproduce the algorithm for the automated construction of the indoor navigation network model, and to evaluate the applicability of algorithms for indoor wayfinding applications. This article compares the quality of wayfinding results from the output of the indoor navigation network model against two criteria: route efficiency (i.e., length) and route simplicity (i.e., number of directions). Our statistical analysis illustrates that the visibility graph algorithm is the most appropriate for indoor wayfinding applications.     

A cyberGIS approach to exploring neighborhood-level social vulnerability for disaster risk management

Timely identification of disaster-prone neighborhoods and examination of disparity in disaster exposure are critical for policymakers to plan efficient disaster management strategies. Many studies have investigated racial, ethnic, and geographic disparities and populations most vulnerable to disasters. However, little attention has been paid to the development of easily accessible and reusable tools to enable: (1) the prompt identification of vulnerable neighborhoods; and (2) the examination of social disparity in disaster impact. In this research, we have developed a visual analytics tool that allows users to: (1) delineate neighborhoods based on their selection of variables; and (2) explore which neighborhoods are susceptible to the impacts of disasters based on specific socioeconomic and demographic characteristics. Through an exploration of COVID-19 data in the case study, we revealed that the tool can provide new insights into the identification of vulnerable neighborhoods that need immediate attention for disaster control, management, and relief.     

Analyzing the triggering factors of glacial lake outburst floods with SAR and optical images: a case study in Jinweng Co,Tibet, China

Landslides - On June 25, 2020, Jinweng Co in Yiga, Tibet, experienced an outburst flood that resulted in catastrophic damage to farmland and roads. The complex causal factors for glacial lake...     

Dissolved Pb Concentrations and Stable Pb Isotope Ratios in the Ulleung Basin,East/Japan Sea

Ocean Science Journal - The spatial distributions of dissolved lead (Pb) concentrations and stable Pb isotope ratios in the Ulleung Basin, East/Japan Sea, were investigated to identify the Pb...