Predictive Measurement of the Structure of Land Use in an Urban Agglomeration Space

The scientific measurement of land use in space is an essential task in urban agglomeration studies, and the fractal feature is one of the most powerful tools for describing the phenomenon of space. However, previous research on the fractal feature of land use has mostly been conducted in urban space, and examines the fractal feature of different land use types, respectively; thus, the measurement of the relationship between different land use types was not realized. Meanwhile, previous prediction methods used for spatial land use mostly relied on subjective abstraction of the evolution, theoretically, regardless of whether they were calibrated, so that complete coverage of all the mechanisms could not be guaranteed. Based on this, here, we treat the land use structure in urban agglomeration space as the research object, and attempt to establish a fractal measure method for the relationship between different land use types in the space of urban agglomeration. At the same time, we use the allometric relationship between “entirety” and “local” to establish an objective forecast model for the land use structure in urban agglomeration space based on gray prediction theory, to achieve a predictive measurement of the structure of land use in urban agglomeration space. Finally, this study applied the methods on the Beijing–Tianjin–Hebei urban agglomeration to analyze the evolution of the stability of the structure of land use and achieve predictive measurement of the structure of land use. The results of the case study show that the methods proposed in this study can obtain the measurement of the relationship between different land use types and the land use prediction that does not depend on the subjective exploration of the evolution law. Compared with the measurement methods that analyzed the fractal feature of different land types, respectively, and the prediction methods that rely on subjective choice, the methods presented in this study recaps some innovations and reference values for relevant future research