The identification and efficiency evaluation of industrial parcels by integrating multi-source spatial data in Shenzhen, South China

Figures & data

Figure 1. Geographical location and map of Shenzhen in Guangdong Province, South China; (a) Location of Shenzhen in China; (b) Location of Shenzhen in Guangdong province; (c) Districts in Shenzhen.

Table 1. Datasets used in this study.

Purpose	Dataset	Acquisition time	Format	Source
Generating urban parcels	Shenzhen boundary maps	2020	Polygon features	Administration of Surveying, Mapping and Geoinformation of China
	OSM road network data	2020	Line features	OpenStreetMap Foundation (openstreetmap.org)
Identifying IPs	Industrial POIs	2020	Point feature	Amap Services, China (https://ditu.amap.com/)
	Landsat 8 imagery	2020	30m grid	National Aeronautics and Space Administration (nasa.gov)
Evaluating industrial efficiency	LJ1–01 nightlight data	2018	130m grid	Nightlight Data Portal
	WorldPop mainland China dataset	2020	100m grid	WorldPop Mainland China dataset for 2020
	POIs data (infrastructure)	2020	Point feature	Amap Services, China
	OSM road network data	2020	Line features	Geofabrik Download Server (download.geofabrik.de/asia/china.html)
	ChinaHighPM2.5 dataset	2020	1km grid	https://weijing-rs.github.io/product.html (Wei et al. 2021)
	other statistical data	2020	Table	National Bureau of Statistics of China (stats.gov.cn)
Construct a timeline of IPs	China Land Use/Land Cover Change (CNLUCC) (Xu et al. 2020)	2020	30m grid	Data Center for Resources and Environmental Sciences, Chinese Academy of Sciences (resdc.cn) DOI: 10.12078/2018070201

Table 2. Descriptions and counts of POIs categories for Shenzhen, 2020.

Type	Description	Count
Industrial enterprises	Factories and offices dedicated to manufacturing, assembly, and other production activities.	23,217
Food & Beverages	Restaurants located within proximity to industrial enterprises to serve the needs of workers.	56,658
Hotel & House	Residential areas situated near industrial enterprises to accommodate the housing needs of workers.	3,414
Shopping	Shopping centers located within the vicinity of industrial enterprises to cater to the workers’ needs.	1,075

Figure 2. The framework for identifying industrial parcels and evaluating their efficiencies.

Figure 3. Spectral reflectance curves of different surface objects.

Figure 4. The subdivision of three types of industrial parcels.

Table 3. The typical features and image references of three types of industrial parcels.

Category	Characteristics	Interpretation reference
OIPs	OIPs are meticulously designed to support a broad spectrum of industrial POIs focused on office-based activities. These parcels are distinguished by the presence of high-density office buildings, standalone research facilities, and an array of supportive amenities. It is important to note that the utilization of CSTs is relatively rare in these areas, aligning with their office-centric functions.
MIPs	MIPs are versatile, catering to a wide array of functions such as manufacturing, warehousing, logistics, and wholesaling. These parcels boast a diverse architectural landscape, featuring buildings of varying heights and extensive areas designated for stockpiles and storage sheds, often constructed with CSTs to support their operational needs. Accompanying these are various supporting facilities integral to their multifunctional roles.
CIPs	CIPs serve as hubs for a mixture of industrial activities, including but not limited to manufacturing, storage, and office tasks. Characterized by their large scale and complex operations, these parcels necessitate an advanced infrastructure setup. A significant feature of CIPs is their industrial clustering effect, which is observable through the dense aggregation of industrial POIs and the strategic placement of CSTs within or near these complexes, highlighting their integrated industrial capabilities.

Table 4. The evaluation index system of industrial efficiency.

Index	Indicator	Description of Indicator	Effect
Economic Efficiency	IAVI	IAVI is the economic embodiment of the profitability and development level of industrial enterprises in a region (J. Zhang et al. 2018). It quantifies the effectiveness of industrial activities in generating added value relative to the land area, thereby highlighting the economic productivity of the given space.	+
	IAD	IAD reflects the degree of clustering of industries, crucial for fostering the growth of enterprises of all sizes by providing a supportive organizational structure (J. Liu, Cheng, and Zhang 2017; Otsuka, Goto, and Sueyoshi 2010).	+
	NPI	NPI captures the continuous utilization of industrial resources during night-time hours. This indicator acknowledges the operational advantages gained from uninterrupted activities around the clock (Y. Liu et al. 2018).	+
Social Efficiency	LAD	LAD fosters advantageous synergies for industrial enterprises during nighttime, subsequently magnetizing more businesses and labor to converge in that locale. LAD emphasizes the efficient concentration of the labor force within industrial zones (Borjas and Van Ours 2010).	+
	TA	TA examines the connectivity of transportation networks to and from the industrial area, playing a pivotal role in the efficiency of industrial land use (C. Li et al. 2019).	+
	SFC	SFC gauges the availability and quality of essential amenities and infrastructure within the industrial area. The presence of comprehensive facilities creates an environment that nurtures productive and sustainable industrial operations (Dang and Pheng 2015).	+
Ecological Efficiency	PME	PME serves as an ecological efficiency indicator, addressing fine particulate matter emissions. Lower PME values align with environmentally responsible practices, contributing to sustainability and operational efficiency (Pui, Chen, and Zuo 2014).	-
	IWCPUG	This indicator reflects the impact of industrial activity on water resources, emphasizing the importance of efficient water use in relation to economic output and sustainability (Hoekstra and Chapagain 2007; Qin et al. 2015).	-
	IECPUG	IECPUG measures energy efficiency, with lower consumption levels signaling reduced carbon emissions and a commitment to environmentally-friendly industrial practices (Bilgen 2014; Pérez-Lombard, Ortiz, and Pout 2008).	-

Figure 5. The spatial distribution of industrial parcels in Shenzhen. (a). Industrial parcels at parcel scale; (b). Industrial parcel numbers with districts as the statistical unit.

Figure 6. The Four-quadrant evaluation of different types of industrial parcels. (OIPs, CIPs and MIPs represent office, comprehensive and manufacturing IPs, respectively. LH, LG, GM, DP, FT, PS, BA, YT, LHu and NS represent Longhua district, Longgang district, Guangming district, Dapeng district, Futian district, Pingshan district, Baoan district, Yantian district, Longhu district, and Nanshan district).

Figure 7. The spatial distribution of three different types of industrial parcels. (a) shown as point, (b) shown as parcel, (c) shown as district (parcel numbers), and (d) shown as district (parcel areas).

Figure 8. Industrial parcels efficiency evaluation and cluster analysis results. (a) Shown as point, (b) Shown as parcel, (c) Shown as district (parcel number).

Figure 9. Evolution characteristics of four efficiency types among four levels.

Figure 10. The alluvial diagram of the efficiency hierarchy of various types of industrial parcels.

Figure 11. The establishment years of industrial parcels of four efficiency levels.

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Data availability statement

The data that support the findings of this study are freely available in the OpenStreetMap Foundation (https://download.geofabrik.de/asia/china.html), Amap Services (https://www.amap.com/), National Aeronautics and Space Administration (https://search.earthdata.nasa.gov/), WorldPop Mainland China dataset (https://www.worldpop.org/), ChinaHighPM2.5 dataset (https://weijing-rs.github.io/product.html), National Bureau of Statistics of China (http://www.stats.gov.cn/), China Land Use/Land Cover Change (CNLUCC) (http://www.resdc.cn/DOI, 2018. DOI:10.12078/2018070201).