Article

Innovative framework for identification and spatiotemporal dynamics analysis of industrial land at parcel scale with multidimensional attributes

Author

Zhang Zhengfeng, Professor, School of Public Administration and Policy, RUC

Guo Shan, Associate Professor, School of Public Administration and Policy, RUC

Abstract

Understanding the spatiotemporal dynamics of industrial land is crucial for sustainable land use and industrial transformation. However, data and methodological limitations hinder large-scale, long-term studies of industrial land from a high-granularity, multi-dimensional perspective. This study develops a methodological framework using multi-source data to identify industrial land parcels, enabling a systematic analysis of both external attributes (including quantity scale and spatial pattern) and internal attributes (including functional structure and utilization intensity). The validity of the framework is demonstrated through a case study of the Southern Jiangsu Urban Agglomeration in China from 1990 to 2020. The overall accuracy of industrial land identification reaches 94.70 %, with detailed representations of parcel morphology. Based on this data, the study reveals the nonlinearity of industrial land evolution, as well as the interrelationships and asynchrony among various attributes. Spatial patterns at the parcel level are uncovered, including four types of spatial evolution. Finally, the study identifies potential dimensions and areas for industrial land upgrading and provides specific recommendations for addressing compatibility between different attributes in planning and correcting land misallocation in land supply. The study contributes a new analytical method for advancing industrial land theory and offers support for refined management practices.

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