Spatiotemporal Information Mining of Direct Drinking Water Supply Hotline Data

The direct drinking water supply hotline serves as a critical component in the construction of a smart water system. Effectively mining the spatiotemporal information of hotline data and optimizing service efficiency are key challenges faced by direct drinking water supply enterprises. Taking the hotline data of Direct Drinking Water Company K in Baotou City, Inner Mongolia, as the research object, this study investigates the effects of factors such as temperature, holidays, and seasons on hotline volume from both temporal and spatial dimensions using a multivariate linear model. Furthermore, the significance between various urban points of interest (POIs) and hotspot issues is analyzed through the chi-square test method. The results indicate that seasonal variations significantly impact hotline issues, whereas holidays have no significant effect. Hotspot issues frequently occur in areas of intensive economic activities and high population density, with regional differences in their distribution across the city. The spatiotemporal analysis of hotline data provides accurate information for predicting and locating accident causes, offering practical value for enhancing refined management and advancing smart water system development.