The main purpose of this paper is to use regression models to explore the factors affecting housing prices as well as apply spatial aggregation to explore the changes of urban space prices. This study collected data in Taitung City from the year 2013 to 2017, including 3533 real estate transaction price records. The hedonic price method, spatial lag model and spatial error model were used to conduct global spatial self-correlation tests to explore the performance of house price variables and space price aggregation. We compare the three models by R² and Akaike Information Criterion (AIC) to determine the spatial self-correlation ability performance, and explore the spatial distribution of prices and the changes of price regions from the regional local indicators of spatial association spatial distribution map. Actual analysis results show an improvement in the ability to interpret real estate prices through the feature price mode from the R² value assessment, the spatial delay model and the spatial error model. Performance from the AIC values show that the difference of the spatial delay model is smaller than that of the feature price model and the spatial model, demonstrating a better performance from the space delay model and the spatial error model compared to the feature price model; improving upon the estimation bias caused by spatial self-correlation. For variables affecting house pricing, research results show that Moran’s I is more than 0 in real estate price analysis over the years, all of which show spatial positive correlation. From the LISA analysis of the spatial aggregation phenomenon, we see real estate prices rise in spaces surrounded by high-priced real estate contrast with the scope of space surrounded by low-cost real estate shifting in boundary over the years due to changes in the location and attributes of real estate trading transactions. Through the analysis of space price aggregation characteristics, we are able to observe the trajectory of urban development.

In this study, we examined the risk of offshore travel from the dimensions of tourists and purveyors in the tourism industry. A questionnaire survey was administered for data collection. A factor analysis was performed to determine respondents’ perceptions, evaluations and responses, and demands and intentions concerning travel risk, as well as the degree of hazard impact. The analysis results were then used to investigate the similarities and difference of travelers’ and tourism purveyors’ travel demands. Survey analysis results indicated partial significant differences between travel behaviors and travel risk awareness and travel risk evaluations and responses. In addition, travel risk awareness was partially correlated to travel risk evaluations and responses, travel risk demand and intentions, and degree of hazard impact. Respondents with higher travel risk awareness were more careful in evaluating hazard risk, consequently influencing their tourism and travel behaviors. Applying the analysis results, we addressed traveler-related, operator-related, and environment-related travel risk factors proposed a response strategy for minimizing travel risk, helping parties in the tourism industry cope with hazards and minimizing the risk and losses associated with hazards.

Risk management encompasses all measures taken before the occurrence of risk events to mitigate potential future losses. Although volunteer firefighters serve in a voluntary capacity, their disaster response duties expose them to various risks similar to those faced by professional firefighters. Furthermore, due to differences in training, auxiliary methods, and physical conditions, they may encounter distinct risk factors and potentially higher disaster risks. This paper explores risk identification, control, and reduction for volunteer firefighters through organizational responsibilities and risk management perspectives, employing a group decision making model. Utilizing the Operational Risk Management Integration Tools (ORMIT) and the modified Delphi method, the analysis identifies 54 major risk factors for Taitung County's volunteer firefighters during disaster response duties. These risk factors are categorized according to the 5M model (Man, Machine, Media, Management, Mission), prioritized and addressed using the Main Operational Risk Management List (MOL) and the Risk Control Option Matrix (COM). After proposing corresponding risk control measures, the Average Risk Index (ARI) for the "Potential Risks in Volunteer Firefighters' Disaster Response Duties" decreased from 14.31 to 5.06. The Average Risk Rating (ARR) improved from a high-risk level (H-7) to a low-risk level (L-16). The results demonstrate that through risk identification, assessment, and control procedures, the potential risks faced by volunteer firefighters in disaster response can be significantly reduced.