Redai dili (Dec 2023)
Data-Assisted Smart Territorial Spatial Planning Practice: A Case Study of Guangzhou
Abstract
In recent years, the state has promulgated a series of policies aimed at establishing a national territorial spatial planning system and advocating for the creation of an integrated "multi-regulation in one" system. The issuance of these policies marked the formal commencement of constructing the territorial spatial planning system. Territorial space is a type of environment, and the description of its functions essentially elucidates the relationship between humans and land. This relationship represents a complex interplay of static and dynamic interactions among elements such as stakeholders, the environment, and activities within a defined spatiotemporal framework of the system. In the new era, territorial spatial planning is required to perceive, analyze, evaluate, and decide upon various resource elements and the spatiotemporal information of diverse activities of people within a national territory. Multi-source urban spatiotemporal data empowers the intelligent processes of perception, analysis, evaluation, and decision-making regarding these resources and activities, propelling the evolution from traditional to smart territorial spatial planning. This study aimed to construct a comprehensive framework for smart territorial spatial planning and multi-source urban spatiotemporal data application to promote the modernization of the territorial governance system and its capabilities. This was done by adhering to the fundamental principles of "ecological priority" and "human-centricity." Taking Guangzhou as an example, this study investigated how multi-source urban spatiotemporal data empowers the drafting, approval, and supervisory execution of smart territorial spatial planning. Multi-source urban spatiotemporal data support territorial spatial planning in four dimensions: sustainable development, high-quality growth, high-quality living, and high-level governance. For sustainable development, spatiotemporal data obtained from land surveys, ground/subterranean observations, and spatial planning outcomes, bolster the assessment of land resource carrying capacity and suitability evaluation for territorial development. This approach fosters optimized layouts for ecological, agricultural, and urban spaces and propel the construction of beautiful territorial spaces. Regarding high-quality growth, spatiotemporal data provide a robust foundation for data support and decision-making in national territorial space governance. In urban villages, multi-source data aid in enhancing the quality of high-density mixed-use spaces. In wholesale markets, data analyses assist in the optimal allocation of spatial resources, promoting orderly governance. In village-level industrial parks, spatiotemporal data underpin classified policymaking, refining industrial layouts. In terms of high-quality living, multi-source urban spatiotemporal data support the construction of diverse human-centric cities by precisely quantifying the level of street greening and estimating the demand for various public services. Regarding high-level governance, multi-source data facilitate the monitoring, assessment, and early warning of territorial space resources as well as the scientific adjustment and decision-making of related management measures. This data-driven planning approach provides scientific decision-making support for high-level urban governance, marking a transition toward more intelligent and refined territorial spatial planning. However, practicing smart territorial spatial planning in Guangzhou not only validates the empowering role of multi-source urban spatiotemporal data but also exposes the challenges in its application. The acquisition of multi-source urban spatiotemporal data is difficult and costly, and the absence of unified application guidelines presents challenges in data acquisition and comparison of analytical results. Consequently, the future research on smart territorial spatial planning should focus on establishing standardized data channels and application norms, enhancing the multi-source data integration and the construction of a "one map" platform, and fully incorporating interdisciplinary theories and technologies.
Keywords
