Identification of Industrial Land Parcels and Its Implications for Environmental Risk Management in the Beijing–Tianjin–Hebei Urban Agglomeration

Abstract

Due to rapid, sprawling urban and industrial development, urbanization in China has led to serious environmental pollution with subsequent risks to human well-being. Landscapes comprised of intermingled residential and industrial areas are common across China, which is a large challenge for effective urban planning and environmental protection. Being able to identify industrial land across the urban landscape is critical for understanding patterns of urban design and subsequent consequences for the environment. Here, we describe a method to quickly identify industrial parcels using points of interest (POIs) and large-scale spatial data. We used the Beijing–Tianjin–Hebei urban agglomeration as a case study and identified 8325 square kilometers of industrial land, accounting for 30.7% of the total built land. Based on ground-truth randomly-sampled sites, the accuracy, precision, and recall of identified industrial areas were 87.1%, 66.4%, and 68.1%, respectively. Furthermore, we found that over 350 km2 of the industrial parcels were high human settlement risks and mainly were distributed in Tianjin and Tangshan city. Over 28.8% of the identified industrial land parcels might be at the risk of potential soil contamination. The results can be helpful in future urban planning and for identifying urban areas that are targets for implementing environmental risk management and remediation.

Spatial distribution characteristics and analysis of influencing factors on different manufacturing types in Shandong Province

“…It is an effective data source for the distribution analysis of spatial elements on a microscale, where corresponding statistical data is difficult to obtain. It is also widely used in industrial spatiotemporal development research [ 2 , 10 ]. Unlike previous studies, this study aimed to analyze the spatial distribution characteristics of different industry types and their influencing factors.…”

“…China’s industry has been developing rapidly since the reform and opening up, and most cities are completely industrialized [ 1 ]. However, the development process is characterized by high resource consumption and substantial environmental pollution [ 2 , 3 ]. To improve the development process, the government has implemented a number of policies to guide industrial transformation and upgrades [ 4 , 5 ].…”

“…Previous microscale studies relied primarily on historical planning maps or enterprise network data containing land use area, position, and management conditions [ 6 , 11 ]. With the development of big data in recent years, point of interest (POI) data have been widely used in microscale enterprise studies, and it is expected that this trend will continue in the future [ 2 , 12 , 13 ].…”

Exploring Relationships between Spatial Pattern Change in Steel Plants and Land Cover Change in Tangshan City

“…Sustainability 2023, 15, x FOR PEER REVIEW 3 of 25 many scholars have used street network data provided by OSM (hereinafter referred to as OSM data) to segment urban parcels and used the parcels as the basic spatial units of urban research [23][24][25]. In addition, Wang et al applied the OSM data to the identification of industrial land in the Beijing-Tianjin-Hebei region with satisfactory results [26]. Therefore, this study attempts to introduce the OSM data into the segmentation of steel plants.…”