Demand Evaluation of Urban Underground Space through Geospatial Big Data

Abstract

The development of urban underground space (UUS) is an important approach to the sustainable development of modern cities. This means that assessing potential demand differences in undeveloped UUS can inform the areas that need to be prioritized for development in urban planning and development. The recent emergence of geospatial big data provides fine-grained support for capturing changes in the development of various urban subjects. However, to date, research applying geospatial big data to UUS assessment has been limited. In this study, UUS evaluation indicators were constructed using geospatial Big Data, and the weights of the indicators driving the existing UUS demand were determined based on the Geodetector model, avoiding the drawbacks of human subjective influence and the lack of reference of general methods. It also combined the linear weighting method to quantify the UUS demand in undeveloped areas within the grid based on the attribute values and weights of each indicator and used the magnitude of the resulting demand values to provide a grid-scale judgment of priority areas for future underground space development. Taking the Qiantang District of Hangzhou, China, as an example, it was verified that the high-demand areas are consistent with the existing UUS distribution, indicating that the UUS demand evaluation model based on geospatial big data established in this study is feasible and accurate, and in this way, new high-demand areas were circled in the undeveloped areas as a direction for future urban underground development.