POI Data–Driven Identification and Representation of Production–Living–Ecological Spaces at the Urban and Peri–Urban Scale: A Case Study of the Hohhot–Baotou–Ordos–Yulin Urban Agglomeration

The identification of the multifunctional combination of production–living–ecological spaces (PLES) in urban agglomerations, particularly in urban cores and peri–urban areas, is a critical issue in the urbanization process. This study, using the Hohhot–Baotou–Ordos–Yulin (HBOY) urban agglomeration, a key node in China’s “Two Horizontals and Three Verticals” urbanization strategy, proposes a hexagonal grid–based PLES quantification framework using POI data. A three–level POI classification system was developed, with functional element weights determined via the Analytic Hierarchy Process and public perception surveys. The framework quantifies PLES within hexagonal grids and analyzes its patterns and functional coupling mechanisms using spatial overlay, Average Nearest Neighbor Index (ANNI), kernel density analysis, and spatial autocorrelation analysis. The following results were obtained. (1) PLES classification accuracy reached 90.83%, confirming the reliability of the method. (2) The HBOY urban agglomeration exhibits a dominant production space (40.84%), balanced living and ecological spaces (29.37% and 29.36%, respectively), and a severe shortage of mixed spaces (0.43%). (3) Production and living spaces show significant clustering (ANNI ≤ 0.581), mixed spaces follow (ANNI = 0.660), and ecological spaces are relatively evenly distributed (ANNI = 0.870). (4) The spatial distribution patterns show that production and living spaces exhibit “core concentration with peripheral dispersion”, ecological spaces show “block concentration with point–like distribution”, and mixed spaces show “point–like dispersion”. (5) Production and living spaces exhibit strong spatial autocorrelation (Moran’s I > 0.7) and the highest spatial correlation (Bivariate Moran’s I = 0.692), while the spatial correlation with ecological spaces is weakest (Bivariate Moran’s I = 0.150). The proposed PLES identification framework, with its efficiency and dynamic updating potential, provides an innovative approach to urban spatial governance under the global Sustainable Development Goals. The findings offer integrated decision–making support for spatial diagnosis and functional regulation in the ecologically vulnerable areas of northwest China’s new urbanization.