Search

Stream gauging stations are important in hydrology and water science for obtaining water-related information, such as stage and discharge. However, for efficient operation and management, a more accurate grouping method is needed, which should be based on the interrelationships between stream gauging stations. This study presents a grouping method that employs community detection based on complex networks. The proposed grouping method was compared with the cluster analysis approach, which is based on statistics, to verify its adaptability. To achieve this goal, 39 stream gauging stations in the Yeongsan River basin of South Korea were investigated. The numbers of groups (clusters) in the study were two, four, six, and eight, which were determined to be suitable by fusion coefficient analysis. Ward's method was employed for cluster analysis, and multilevel modularity optimization was applied for community detection. A higher level of cohesion between stream gauging stations was observed in the community detection method at the basin scale and the stream link scale within the basin than in the cluster analysis. This suggests that community detection is more effective than cluster analysis in terms of hydrologic similarity, persistence, and connectivity. As such, these findings could be applied to grouping methods for efficient operation and maintenance of stream gauging stations. [ABSTRACT FROM AUTHOR]

The walkability of destinations plays a decisive role in obtaining a sense of place for urban visitors. For an improved walkability-based visit experience, the city structure can be analyzed by dividing it into small units, starting with the destination. However, existing studies focus on the walkability of residents by administrative district and use limited contextual information and structural analysis tools. Therefore, this study aims to structurally understand walkability through network analysis with mobile-based visitor data collected from 822 point-of-interest (POI) in Daejeon. In this study, the actual walking distance between 95,817 cells with visitors and POIs was measured to calculate the edge weight and influence of cells on POI. We analyzed the POI-cell network through edge weight and extracted nine communities to understand the city context through POI analysis and derived the following findings. First, it is possible to determine the potential demand by visitors' walkability-based division. Second, our walkability measurement method follows a bottom-up approach, starting with a small unit cell, understanding the entire city as a community extraction model, and possibly extending to other cities in Korea. Third, POI-centered structural analysis is possible using the number of visitors and distance in the proposed network analysis method. • Data-driven walkability measurement method proposed. • Network analysis on mobile-based visitor data structurally evaluates walkability. • Point-of-interest (POI)-cell network analyzed through edge weight. • Understand city context through POI analysis of nine extracted communities. • Determine potential POI demand by visitor's walkability-based division. [ABSTRACT FROM AUTHOR]

This article demonstrates how large-scale tourist mobility data can be linked with network science approaches to better understand tourism destinations and their interactions. By analyzing a mobile positioning dataset that captures the nationality and movement patterns of foreign tourists to South Korea, we employ a few metrics to quantify the network properties of tourism destinations, aiming to reveal the collective dynamics of tourist movements and key differences across nationalities. According to the results, the number of inbound tourists to destinations follows a log-normal distribution, which indicates a notable heterogeneity of destination attractiveness. Although this finding holds across different nationalities, we find that tourists from different countries tended to visit different places in South Korea. A community detection algorithm partitions South Korea into several tourism regions, each covering a set of destinations that are closely connected by tourist flows. The implications for transportation development and regional tourism planning are discussed. [ABSTRACT FROM AUTHOR]