Exploring the spatial distribution characteristics of regional cultural and tourism resources (CTRs) is crucial to the sustainable development of cultural and tourism industries. Based on 651 CTRs obtained from the latest round of field surveys in the Xihu District of Hangzhou, this article analyzed the spatial distribution of these CTRs from an overall, categorical, and hierarchical multiple perspective using the average nearest neighbor index and kernel density analysis and explored the reasons for the differences in the spatial distribution of the above different types of CTRs using multiple linear regression and Geodetector. The results indicate that the CTRs present a spatially clustered state, and the distribution of CTRs in different categories and grades has diverse characteristics, as natural resources have three high-density areas, humanistic resources have one high-density area, and both general and superior resources have only one high-density area, but the low-density areas are different. In addition, the spatial distribution of the overall, different categories, and different grades of CTRs in this region is influenced by several factors, with the West Lake generating the greatest impact. Additionally, interactive factors can have a greater impact than single factors. These results enrich the research content of Hangzhou’s cultural and tourism industries and provide theoretical support for the high-quality development of Xihu District’s cultural and tourism industries.

Urban land use is constantly changing via human activities. These changes are recorded by imprint data. Traditionally, urban dynamics studies focus on two-dimensional spatiotemporal analysis. Based on our best knowledge, there is no study in the literature that uses imprint data for better understanding urban dynamics. In this research, we propose a spatial–temporal–semantic triple analytical framework to better understand urban dynamics by making full use of the imprint data, toponyms. The framework includes a text classification method and geographical analysis methods to understand urban dynamics in depth. Based on the inherent temporal and spatial information, we enrich semantic information with street names to explain urban dynamics in multiple dimensions. Taking Hangzhou city as an example, we used street names to reproduce the city changes over the past century. The results obtained through analysis of street names may accurately reflect the real development process of Hangzhou. This research demonstrates that imprint data left by urban development may play a pivotal role in better understanding urban dynamics.