Study on the Spatial Distribution Characteristics and Influencing Factors in the Reuse of National Industrial Heritage Sites in China

Abstract

Industrial heritage is a valuable spatial resource for urban stock updates, and its preservation and reuse play an important role in the transmission of urban history and culture. Typological analysis, descriptive statistical analysis, relevant spatial analysis using ArcGIS 10.8, and geographic probes were employed to explore the spatial distribution characteristics and influencing factors regarding the current status of the reuse of 196 heritage sites selected from five batches of China’s National Industrial Heritage (NIH) lists. The results reveal the following: (1) The spatial distribution of China’s NIH sites is uneven and cohesive, forming a dense circle with the Yangtze River Delta region and the Beijing–Tianjin–Hebei region at its core. (2) Three-fourths of the NIH sites have entered the reuse stage, and high-density and relatively high-density clusters have formed in the eastern and central regions. (3) The conservation and reuse directions of China’s NIH sites are mainly divided into publicization and marketization. (4) The spatial distribution differentiation of the reuse of NIH sites is not due to a single cause but, rather, a combination of various contributing factors. Natural geographic and socio-economic factors exert varying degrees of influence on the spatial distribution of reused heritage sites, with tourism resources and government support playing primary roles in shaping this distribution. These findings establish a fundamental database of China’s NIH sites and provide guidance for the current conservation and reuse of industrial heritage.

1. Introduction

Industrial heritage is a vital record and witness of urban development and the progress of human civilization in the past 200 years [1]. As an essential carrier of inherited industrial civilization, industrial heritage witnesses the industrial development process of a country or region, which has immense value in history, technology, socio-culture, and art [2]. In 2003, the International Committee for the Conservation of Industrial Heritage (TICCIH) adopted the renowned “Nizhny Tagil Charter.” Serving as a foundational document in the field of industrial heritage conservation, the charter explicitly states that industrial heritage consists of the remains of industrial culture, which possess historical, technical, social, architectural, or scientific value [3]. In 2012, TICCIH passed the “Taipei Declaration.” Recognizing the distinct industrial development processes in the Asian region compared to the Western world, the definition of industrial heritage in Asia is considered broader, encompassing not only buildings, machinery, workshops, factories, mines, and refined processing sites but also warehouses, storage facilities, energy production, transmission, use, transportation, and all social activity sites related to industry [4]. The varied definitions of industrial heritage in different regions reflect a fundamental consensus in today’s international society. Subsequently, the effective conservation and revitalization of industrial heritage have gradually become a globally shared concern.

Currently, most countries are in the stage of urban stock renewal and development for socio-economic development and extended urban culture, while the drawbacks of the construction and layout of most traditional industrial cities are becoming increasingly prominent [5,6,7,8]. Once-prosperous industrial heritage has become the primary focus of urban renewal [9,10,11,12]. NIH sites should not be indiscriminately demolished simply because their original political, economic, and cultural functions have changed. They carry the essence of urban culture and are an integral part of material cultural heritage [13]. In the post-industrial era, modern means are used to maintain the stability of the spatial distribution of industrial heritage, which is conducive to preserving and transmitting the unique history of different geographical spaces [14]. Meanwhile, the conservation and reuse of industrial heritage [15,16,17,18,19] will be advantageous to the sustainable development of industrial cultural heritage.

The current scholarly inquiry into historical sites and cultural heritage spans micro, meso, and macro scales. Within the context of digital humanities, digitization technologies, such as historical cultural repositories, have been widely applied at the micro- and meso-scales in the realm of heritage conservation [20]. Various aspects have been explored, employing a data visualization perspective for visual element recognition [21,22], capturing multisensory techniques [23], and developing heritage information databases [24]. At the macro level, with the advancement of remote sensing and geographic information systems (GISs), the spatial distribution of historical landmarks has been quantified [25]. In recent years, the geospatial data visualization techniques of GISs have gained extensive traction in the realm of both tangible and intangible cultural heritage. This application spans resource management, spatial distribution, and addressing regional disparities in the context of heritage conservation [26,27,28,29,30]. ArcGIS, serving as a geographic information platform, incorporates a variety of spatial analysis methods. Methods, such as buffer analysis, overlay analysis, and spatial matching, have been widely used to explore the spatial relationships between nonmaterial cultural heritage and various factors, including topography, watersheds, population, roads, and economic development [31,32,33]. Techniques such as kernel density analysis, nearest neighbor index, and spatial autocorrelation analysis offer an intuitive reflection of the geographical distribution of urban industrial heritage, enabling an objective analysis of its spatial characteristics [32,34,35]. The application of GIS technology not only facilitates a comprehensive understanding of the current status of industrial heritage within specific regions but also provides a broader information base and scientifically derived analytical results for urban planning [36,37]. Thus, the application of spatial analysis methods in studying the distribution characteristics of cultural heritage presents a novel perspective for industrial heritage conservation in this study [38].

The repurposing of industrial heritage has been studied for many years, including many countries, such as the United Kingdom and America. They have been made to address urban challenges in the post-industrial era [39,40,41,42]. Europe and America have placed significant emphases on developing evaluation and recognition standards for industrial heritage [43,44] and studying its universal value [45,46,47]. Building upon this foundation, they have classified and protected industrial heritage sites [48]. The trend of reusing industrial heritage was also explored in China [49,50]. Studies on industrial heritage characteristics have primarily focused on the temporal and spatial features of regional [51,52] and individual sites [15,18,52] in China. Meanwhile, the preservation and reuse models of industrial heritage were studied in the late 1980s [16]. These factors have stimulated numerous theoretical and practical studies on industrial heritage preservation and reuse. Various models for preserving and utilizing industrial heritage have been identified, including industrial museums, landscape park transformation, commercial development, and creative park utilization models [53]. Additionally, some specific preservation and utilization models have been proposed, such as the regional protection model, holistic protection model, and partial protection model for physical industrial heritage groups [54]. Although the spatial distribution characteristics and related influencing factors of the first four batches of NIH sites have been analyzed [55], more research is needed on the current status of industrial heritage reuse. This lack of research hinders a comprehensive understanding of the spatial and typological variations in NIH site reuse. Moreover, there is a need for analysis and research on the intensity of factors influencing the spatial differentiation of industrial heritage reuse.

Two main issues can be identified based on the analysis of the current research status of industrial heritage. First, research on China’s NIH sites has primarily focused on the regional and individual levels, and a comprehensive understanding at the national level is needed. Second, there needs to be more exploration of the NIH sites’ spatial structure and geographical distribution patterns, as well as the correlation between the spatial distribution of industrial heritage and their conservation and reuse models in China. This research addresses three key directions. The first direction is to systematically investigate the spatial distribution types of the current five batches of NIH sites in China according to industrial types and geographical locations. The second direction is to investigate the progress of NIH site conservation and reuse, analyze the current distribution characteristics and patterns of reuse, and explore the future direction of industrial heritage conservation and reuse. The third direction involves using geographic probes to quantitatively analyze the influencing factors of spatial differentiation in the reuse of industrial heritage sites. This study can provide a quantitative database, guidance, and direction for NIH conservation and reuse.

2. Materials and Methods

2.1. Data Sources

Since December 2017, the Ministry of Industry and Information Technology of China (https://www.miit.gov.cn, accessed on 9 December 2022) has published 197 industrial heritage sites in five batches of the NIH list (13 in the first batch, 42 in the second batch, 49 in the third batch, 62 in the fourth batch, and 31 in the fifth batch, with no heritage sites selected in Hong Kong, Macao, Taiwan, or Hainan Province for the time being) [56]. The industrial heritage sites distributed across regions are considered to be counted according to different numbers, such as the Hanyeping Company in the first batch, which is regarded as three industrial heritage sites (Anyuan Coal Mine in Jiangxi Province, Daye Iron Works in Hubei Province, and Hanyang Iron Works in Hubei Province). Since the Beijing Satellite Manufacturing Plant in the fourth batch supplements the second batch, it is regarded as one industrial heritage site. Therefore, the actual research object is 196 industrial heritage sites.

The base map for this study was produced according to the standard map of the Ministry of Natural Resources of the People’s Republic of China (Audit No. GS (2023) No. 2763) (http://bzdt.ch.mnr.gov.cn, accessed on 1 September 2023). DEM, annual precipitation, and river spatial data were obtained from the National Basic Geographic Information System database (https://www.gscloud.cn/, accessed on 12 March 2023). Data on A-class scenic spots were obtained from the official website of the Ministry of Culture and Tourism (https://www.mct.gov.cn/, accessed on 15 March 2023). Per capita GDP, industrial production accounting for the total regional output, tourism market scale, tourism development level, cultural and tourism financial expenditure, urban environmental infrastructure, and environmental regulation intensity were obtained from the China Urban Statistical Yearbook, provincial and municipal statistical yearbooks, and statistical bulletins of prefecture-level cities (http://www.stats.gov.cn/, accessed on 18 March 2023).

2.2. Data Processing

The data processing process is as follows:

• Industrial Heritage Data Collection: Information on industrial heritage was gathered based on publicly disclosed catalogs providing names and addresses of heritage sites. Geographic coordinates were obtained by the Baidu Map API coordinate picker.
• Calibration of Heritage Geographic Coordinates Coordinated with Google Earth and Imported into ArcGIS 10.8: The geographic coordinates of heritage sites were calibrated in coordination with Google Earth. The calibrated data were subsequently imported into ArcGIS 10.8. Considering the spatial structure of industrial heritage, especially for area-based heritage or linear heritage, the most representative heritage points were selected as reference points for geographic spatial location calibration (e.g., the Harbin Forestry Bureau was used as a reference point for the Harbin–Hailin Railway).
• Integration of Official Government Websites and Web Content Related to Heritage Reuse: Official government websites and relevant web content on heritage reuse were integrated. Directional categorization and statistical analysis of reuse for different regions and types of heritage sites were conducted. Excel 2016 was utilized for data organization and bar graph creation, employing one-way analysis of variance (ANOVA) to test differences.
• Attribute Identification in ArcGIS Settings: In the ArcGIS settings, clear identification of attributes, including names and types, for each heritage point was provided in the attribute table. This facilitated digitized operations and spatial analysis.

2.3. Methods

First, this study conducted a typological classification based on the current industrial classification standards [57], relevant literature research [58,59], and classification criteria provided by the National Bureau of Statistics of China (http://www.stats.gov.cn/, accessed on 18 March 2023). This classification encompassed three key criteria: industrial type, reuse model, and geographical zoning, as presented in

Table 1. NIH site typology criteria.

Number	Criteria	Types
1	Industry Type	Light Industry
		Heavy Industry
		Municipal Construction
2	Reuse Model	Marketization: Industrialization of Capital
		Publicization: Public Spatialization
3	Geographic Zoning	Eastern
		Central
		Western

. Second, the spatial distribution characteristics of NIH site reuse across China were analyzed in depth, leveraging GIS technology [60,61,62]. Finally, the geographic detector methodology was employed to examine the factors influencing the spatial distribution of reused NIH sites [63].

2.3.1. Nearest Neighbor Index

The nearest neighbor index is a geographic index indicating the degree of proximity of point-like things in geographic space, which can reflect the spatial distribution characteristics of point-like elements well. It was used to identify the spatial distribution of the five batches of NIH sites in China. The calculation formula [64] is as follows:

$$\left\{\begin{array}{ll}{r}_E& =\frac{1}{2\sqrt{n/A}}=\frac{1}{2\sqrt{D}}\\ & R=\frac{\overline{r_1}}{r_E}=2\sqrt{D{r}_1}\end{array}\right.$$

(1)

where n is the number of points; A is the area of the region; D is the density of points; R is the nearest neighbor index; $\overline{r_1}$ is the average of the distance r₁ between the closest points; and r_E is the theoretical nearest neighbor distance. When R < 1, the NIH sites tend to be clustered; when R > 1, the NIH sites are uniformly distributed; and when R = 1, the NIH sites are randomly distributed.

2.3.2. Geographic Concentration Index

The geographic concentration index is an important indicator for studying the degree of concentration of industrial heritage. It was applied to further explore the degree of concentration of five batches of NIH sites in China. The calculation formula [65] is

$$G=100\times \sqrt{{\displaystyle \sum _{i=1}^n{(\frac{x_i}{T})}^2}}$$

(2)

where G indicates the geographic concentration index of the spatial distribution, n indicates the total number of regions in the studied area, X_i indicates the number of NIH sites in region i, T indicates the sum of the number of NIH sites, and the value of G ranges from 0 to 100. A larger value of G indicates a higher concentration of NIH site spatial distribution, and a smaller value of G indicates a smaller concentration of NIH site spatial distribution.

2.3.3. Kernel Density

Kernel density reflects the spatial dispersion and agglomeration characteristics of geographic elements. In this study, the kernel density function was used to analyze the degree of aggregation of Chinese NIH sites in geographic space more intuitively. The calculation formula [66] is

$$R_n\left(X\right)=\frac{1}{n}{\displaystyle \sum _{i=1}^{n}K\left(\frac{X-X_i}{h}\right)}$$

(3)

where R_n(X) indicates the kernel density estimate of the NIH sites, and a larger value of R_n(X) indicates a denser distribution; n indicates the number of NIH sites; (X − X_i) indicates the distance from the estimated point X to the sample X_i; h > 0 indicates the bandwidth; and K is the spatial weight function.

2.3.4. Geographic Detector

The geographic detector method is a statistical method to explore the spatial variations of geographical elements and reveal the underlying driving forces [16]. It detects spatial differentiation phenomena and their driving factors by analyzing the relationship between the variance within a specific attribute layer and the total variance. Its core advantage lies in its ability to investigate the interaction effects of two overlaying factors [63]. Considering the complexity of the influencing factors in the spatial distribution of industrial heritage reuse points, this study opted for a combination of single-factor and multi-factor interaction detection methods for a comprehensive investigation [67]. The expression is as follows:

$$q=1-\frac{1}{{N\delta }^2}{\displaystyle \sum _{i=1}^L{N}_i}{\delta }_i^2$$

(4)

where i = 1, 2, …; L is the strata of the TV or RTCV or the factor X; N is the number of the samples; N_i is the sample number of type i; δ_i^2 is the variance of the affected index of Y of type i; and δ² is the variance of the affected factor. Usually, the value of q lies between 0 and 1. The closer the q-value is to 1, the greater the impact of the factor on the affected index.

3. Results

3.1. Overall Spatial Distribution of NIH Sites

3.1.1. Type of Spatial Distribution

In this study, data related to the year of construction, industrial type, morphological type, preservation status, and reuse pattern of NIH sites in China were integrated. A spatial distribution map of industrial heritage sites is shown in Figure 1. The average nearest distance tool was used to calculate that the actual nearest distance r1 is approximately 58,888.21 m, and the theoretical nearest distance rE of the spatial distribution of NIH sites in the provincial administrative regions of China is approximately 109,951.62 m. According to the formula R = 0.54, the R value is less than one, which indicates that the spatial distribution type of NIH sites in China is clustered.

The geographic concentration index G, which represents the degree of geographic concentration, was used to study the degree of concentration of national industrial cultural heritage sites in the provincial administrative regions of the country. The total number of NIH sites in every provincial administrative region was T = 196, and the number of provincial administrative regions was n = 30 (Hong Kong, Macao, Taiwan, and Hainan Province are not selected for the time being). The geographic concentration index of NIH sites in provincial administrative regions was calculated as G = 21.67. G_e was assumed to be the geographic concentration index of NIH sites in each provincial administrative region. According to the formula, the national industrial cultural heritage in each provincial administrative region was G_e = 6.53. The results showed that G > G_e, which means that the degree of concentration of NIH sites in China was stronger. Therefore, combined with the nearest neighbor index and geographic concentration index, the type of spatial distribution of the NIH sites was judged as clustered. Meanwhile, as shown in Figure 1, the layout of China’s NIH sites was characterized by a spatial structure where the eastern and central regions were mainly concentrated, while the western region was scattered.

3.1.2. Spatial Distribution Density

A kernel density analysis tool was used to generate a kernel density map to show the aggregation status of NIH sites nationwide. As shown in Figure 2, three core areas of the NIH sites were formed in China: the Yangtze River Delta, the Beijing–Tianjin–Hebei region, and Sichuan Province. There were a large number of NIH sites in the density circle. In addition, there were several medium-density circles of China’s NIH sites distributed in other provincial administrative regions, such as Shandong Province, Liaoning Province, and Anhui Province.

The hierarchical symbol can be used to analyze the concentration of NIH sites in every administrative region in China, which was employed to visualize the spatial distribution in the provincial administrative regions. According to Figure 3, five color blocks were obtained in the map of the amount of China’s NIH sites distributed in each administrative region. The deeper the color is, the more NIH sites there are, while the lighter the color is, the fewer NIH sites there are. Inner Mongolia and Ningxia Province have the lowest number of NIH sites, with only one site per province. In contrast, Shandong and Sichuan Provinces had the highest number of NIH sites, with 14 and 19 sites, respectively. The number of sites in other provinces ranges from 2 to 13 industrial heritage sites. As illustrated in Figure 3, provinces with a relatively higher number of NIH sites were concentrated in the eastern region, such as Jiangxi, Jiangsu, and Liaoning.

3.2. Spatial Distribution Characteristics of the Number of Reused NIH Sites

3.2.1. NIH Reuse in Different Regions

Based on publicly available information on the Internet, four main ways were summarized to address the conservation and reuse of NIH plans: manufacturing, idle, demolished, and preserved or reused. Those NIH sites still in production, usage or idle, and demolished states were classified as not reused, and industrialized or publicized NIH sites were classified as reused. As shown in

Table 2. The number of reused NIH projects in China’s different administrative regions.

Eastern	Sites	Reuse	Central	Sites	Reuse	Western	Sites	Reuse
Shandong	14	13	Jiangxi	12	10	Sichuan	19	16
Liaoning	12	8	Hubei	10	9	Shanxi	9	8
Jiangsu	11	10	Anhui	9	7	Yunnan	6	5
Beijing	9	5	Heilongjiang	8	3	Guizhou	6	4
Hebei	9	7	Shanxi	6	6	Chongqing	5	4
Zhejiang	7	7	Hunan	6	3	Gansu	4	2
Fujian	5	4	Henan	5	3	Xinjiang	3	3
Shanghai	4	3	Jilin	2	1	Tibet	3	0
Guangdong	3	2				Qinghai	2	2
Tianjin	3	3				Guangxi	2	2
						Inner Mongolia	1	0
						Ningxia	1	0
Total	77	62	Total	58	42	Total	61	46
Reuse rate	0.81		Reuse rate	0.72		Reuse rate	0.75

, the number of reused industrial heritage sites in each administrative region was compiled according to the current distribution of reused items of NIH in China. The reuse rate of Tibet, Inner Mongolia, and Ningxia Province was 0%, while the other provinces exhibited high rates. Across China’s three major economic regions, reuse rates show minimal disparity: eastern (0.81) > western (0.75) > central (0.72).

As shown in Figure 4a, the reused portion of NIH sites accounted for 76.53%, indicating that most of the NIH sites in China have entered the reuse stage at present. Based on statistical data concerning the number of NIH sites and the reuse of these sites, variations in reuse rates are evident across different regions in China. However, the differences in reuse rates among the three major economic zones are relatively minor, as depicted in Figure 4b.

The spatial distribution of not-reused and reused NIH sites is depicted in Figure 5a, with GIS spatial analysis employed to generate respective kernel density plots (Figure 5b,c). Figure 5b shows a higher density circle of unused NIH sites formed in the eastern region (mainly in Beijing and Hebei Province). Most factories are still being used according to their original industrial types, so there are few cases of renovation and reuse. Figure 5c shows that high- and higher-density circles of reused NIH sites were formed in the eastern and central regions. The radiation of the high-density circle of reused NIH sites in China was mainly in Sichuan Province, Shandong Province, and Liaoning Province, with the largest number of reused NIH sites in Sichuan Province.

3.2.2. NIH Reuse of Different Types

The type and number of NIH sites in China were counted and divided into three types: light industry, heavy industry, and municipal construction [58]. According to

Table 3. Number of NIH sites in China by industry type.

Classification	Type	Total Number	Reuse	Proportion
Light Industry	Brewing	21	18	0.86
	Textile	11	10	0.91
	Ceramics	8	8	1.00
	Food	7	5	0.71
	Tea Making	6	3	0.50
	Stationery Tools	4	3	0.75
	Tobacco	2	1	0.50
	Flour	2	2	1.00
	Home Appliances	2	2	1.00
	Culture	2	2	1.00
	Printing	1	1	1.00
	Subtotal	66	55	0.83
Heavy Industry	Mining and Metallurgy	28	23	0.82
	Machinery	16	11	0.69
	Building Materials	15	12	0.80
	Kernel Industry	9	6	0.67
	Oil	8	8	1.00
	Chemical	8	3	0.38
	Aerospace	8	5	0.63
	Soldier Industry	5	5	1.00
	Shipbuilding	4	2	0.50
	Minting	2	2	1.00
	Subtotal	103	77	0.75
Municipal Construction	Power	13	8	0.62
	Telecommunications	9	5	0.56
	Transportation	2	1	0.50
	Water	1	0	0.00
	Railroad	1	1	1.00
	Postal	1	1	1.00
	Subtotal	27	16	0.59

, the industrial system was diversified, the types of industrial heritage were abundant, and the industrial structure was disproportionate. There were more heavy industrial types than light industrial and municipal construction types. The reuse of heritage varies by industrial type; the light industry type has the largest proportion of industrial heritage reuse, and the municipal construction type has the smallest proportion of industrial heritage reuse. Sixty-six NIH sites belong to light industry; the number of industrial heritage sites of brewing, textile, and ceramic types is relatively high, and the number of reuses of these types is also relatively high. Heavy industry types were mainly mining and metallurgy, and building material categories occupied a high percentage of reuse, with 103 sites in the mining and metallurgy and machinery and building materials categories. The fewest types were municipal construction, with 27 sites. Among them, electric power- and telecommunication-type industrial heritage accounts for approximately 80% of the total, and the reuse rate is low.

As shown in Figure 6a, the overall spatial distribution map illustrates the unbalanced spatial distribution of light industry, heavy industry, and municipal construction during China’s industrialization process. Subsequently, kernel density analysis was employed to generate kernel density maps for light industry types, heavy industry types, and municipal construction types (Figure 6b–d), which visualize the degree of aggregation of these types in China. The light industry type’s kernel density forms a high-density circle in the Yangtze River Delta region (Figure 6b). Heavy industry types were primarily concentrated in the northeastern and central regions (Figure 6c). Municipal construction types were predominantly distributed in the eastern region, especially in the Beijing, Tianjin, Hebei, Sichuan, and Chongqing regions (Figure 6d). Although regional differences in industrial levels and economic development contribute to varying reuse strategies for industrial heritage in each area, the specific industrial types significantly impact the reuse scenarios.

3.3. Spatial Distribution Characteristics of the Direction of Reused NIH Sites

3.3.1. Classification of Reuse Direction

Against the backdrop of urban culture and the economy, the evolution of industrial heritage functions has facilitated spatial reconstruction [68] and injected vitality into urban environments. The conservation and reutilization of industrial heritage are highlighted as one of the focal points [54]. In this domain, there exist numerous successful cases where numerous industrial heritage sites have been repurposed as vehicles for cultural or economic development [69,70]. However, certain industrial heritage sites lack a market base and remain idle for various reasons [71]. Based on the compiled information regarding the current situation, this study categorized the directions of industrial heritage conservation and reuse into two primary categories: the marketization of industrial heritage and the publicization of industrial heritage. Additionally, the direction of public cultural space was subdivided into the public cultural facility model and public recreational space model (

Table 4. Classification of NIH reuse models in China.

Reuse Direction	Main Modes	Main Content
Marketization: Industrialization of Capital	Creative Park Model	Combining industrial heritage with cultural and creative parks
	Cultural Tourism Model	Cultural tourism with a commercial dimension
	Culture and Leisure Mode	A commercial center for leisure, entertainment, and shopping
	Other Social Use Patterns	Become a space carrier for the development of other industries
Publicization: Public Spatialization	Public Facility Model	Whole or part of it is built as a museum, etc.
	Public Open Space Model	Public open space for daily recreation of the public

).

3.3.2. Regional Differentiation Characteristics

• Reuse in the direction of industrialization

The main models that exist for the industrialized development of NIH sites in China are as follows: the creative park model, cultural tourism model, cultural recreation model, and comprehensive development model. In general, the creative park model combines the cultural tourism model with public cultural facilities and the cultural tourism model with public cultural facilities and recreation space [72,73]. Statistics on different types of industrialized capital development of NIH sites in different regions of China are shown in

Table 5. Statistics of different types of industrialized capital development of China’s NIH sites.

Mode	Acronym	East	Middle	West	Total
Creative Park Model	CPM	14	12	8	34
Creative Park Model +	CPM+	6	7	1	14
Cultural Tourism Model	CTM	13	9	13	35
Cultural Tourism Model +	CTM+	5	6	5	16
Culture, Entertainment, and Leisure Model	CEALN	1	0	0	1
Culture, Entertainment, and Leisure Model +	CEALM+	1	0	0	1
Total (excluding Compound Development Model)		28	21	21	70

. In general, the eastern region exhibits greater diversity and quantity in terms of creative parks and cultural tourism, while the central region lags behind in these aspects. In contrast, the western region is on par with the eastern region in terms of cultural tourism. In the eastern region, there are 18 cultural tourism models, while the central region has 15, and the western region has 18. The number of creative park models in the eastern and central regions is quite similar, primarily located in Beijing, Jiangsu, Shandong, Hebei, and other areas. Although the western region has fewer creative park models, it boasts a higher number of cultural tourism models, mainly distributed in the Sichuan, Guizhou, Qinghai, Shaanxi, and Gansu provinces. The cultural entertainment and leisure models are predominantly concentrated in Shanghai.

Specifically, the industrialized development of the NIH sites in the eastern region was concentrated in coastal riverside provinces and cities, such as Zhejiang Province, Jiangsu Province, Fujian Province, Guangdong Province, Shandong Province, Beijing, and Shanghai. As shown in Figure 7, the largest numbers of NIH sites were cultural tourism models, mainly distributed in the eastern, western, and central regions, followed by creative park models, mainly distributed in the eastern, central, and western regions. The fewest were cultural entertainment and leisure models.

• Reuse in the direction of public spatialization

According to

Table 6. Statistics of different types of public space-based conservation and reuse of China’s NIH sites.

Type	East	Middle	West	Total
Public Cultural Facilities	32	20	17	69
Public Open Space	5	7	8	20
Total	37	27	25	89

, the basic numbers for the type of conservation and reuse of public spaces of the NIH sites were distributed in the eastern, central, and western regions. In terms of types, the reuse category has more public cultural facility models than public recreation space models. In terms of quantity, the eastern region has more NIH public space-based conservation and reuse than the central and western regions. (Some of the NIH conservation and reuse modes have public cultural facilities and public open space functions, and this study repeats the statistics of this type in collating data.)

Public cultural facilities are public welfare nonprofit service facilities, including libraries, museums, cultural centers, memorials, science and technology centers, etc. This study refers to four main types of NIH sites transformed into public cultural facilities in China: museums, exhibition halls, exhibition halls, and memorial halls. Figure 8 shows an imbalance in the number of public cultural facility types in the eastern, central, and western regions. Museums are the main type of public cultural facility for conservation and reuse in these three regions, with 25 in the eastern region, 18 in the central region, and 12 in the western region (

Table 7. Statistics of the main types of public cultural facilities of China’s NIH sites.

Type	East	Middle	West	Total
Museum	25	18	12	55
Exhibition Hall	2	2	3	7
Memorial Hall	1	1	1	3
Showroom	1	0	1	2
Total	29	21	17	67

). The urban public open space converted by the NIH referred to free outdoor open space that could meet the needs of people for recreation, communication, entertainment, leisure, etc. The research objects were mainly urban parks developed by China’s NIH and open NIH scenic spots [74].

4. Factors Influencing the Spatial Distribution of Reuse Heritage Sites

4.1. Influencing Factors

The distribution of reused heritage sites in China is intricate and influenced by multiple factors. In existing studies, the influencing factors of the spatial distribution of cultural heritage sites have been systematically reviewed [75,76]. Combining the typicality of relevant factors and data availability, this study preliminarily analyzes multiple secondary factors selected from four dimensions: natural geography, socio-economic factors, tourism resources, and government support. Each indicator is classified into five categories (enhanced from one to five in sequence) using the natural breakpoint method [77]. Subsequently, employing geographic detectors, the study calculates the impact of secondary factors from different dimensions on the spatial distribution of reused heritage sites, obtaining Q values to filter out 12 secondary factors with significant data correlations (

Table 8. Factors influencing the spatial distribution of reused heritage sites in China.

Indicator Dimensions	Detection Factors	Data Source	Q-Value
Natural geography	Elevation (X1)	Official statistics	0.17
	Distance from river (X2)	ArcGIS processing data	0.06
	Annual precipitation (X3)	Official statistics	0.15
Socioeconomics	GDP per capita (X4)	Official statistics	0.08
	Distance from central city (X5)	ArcGIS processing data	0.04
	Industry accounts for regional GDP (X6)	Official statistics	0.06
Tourism resources	Number of A-level and above scenic spots (X7)	Official statistics	0.05
	Tourism market scale (X8)	Official statistics	0.13
	Tourism development level (X9)	Official statistics	0.24
Government support	Cultural tourism expenditure (X10)	Official statistics	0.14
	Urban environmental infrastructure expenditure (X11)	Official statistics	0.30
	Industrial pollution control intensity (X12)	Official statistics	0.86

). Next, the spatial coupling relationship with the heritage of reused heritage sites was explored through interaction analysis, and the spatial superposition method was employed to analyze the combined influence of multiple factors from each dimension (Figure 9). Finally, the above results were compiled to investigate the influencing factors behind the spatial differentiation in the NIH reuse situation.

4.2. Analysis of Influencing Factors

The results showed variability in the magnitude of the explanatory power of 12 factors of different dimensions on the spatial differentiation of reused heritage sites. Based on the single-factor analysis, environmental regulation intensity (Q = 0.86), urban environmental infrastructure expenditure (Q = 0.30), and tourism development level (Q = 0.24) are vital influencing factors. The distance from the central city (Q = 0.04) was the weakest. In view of the results of the multifactor interaction, the most significant enhancement effects of the interaction factors are X4∩X12 and X1∩X12, with values of 0.94 and 0.94, respectively. This indicates that governmental management of the industrial environment plays a prominent role in the spatial distribution pattern of industrial heritage reuse sites. The spatial distribution of these industrial heritage sites deepens with the degree of influence of GDP per capita and geographical elevation. In addition, the value of the X8∩X10 interaction was significantly higher than the Q value of its single factor. The effect of the interaction between tourism resources and government financial support on the spatial distribution pattern of reused industrial heritage sites was significantly greater than that of the individual factors. A specific analysis of each dimension is shown below.

4.2.1. Natural Geography

The natural environment serves as the geographical foundation of human history, the stage for world history, and a significant influencing factor in the development of human society [78]. Among the natural geographic factors, the Q value of the river system factor is less than 0.1, signifying its insignificant influence on the spatial distribution of reused heritage sites in each municipality. In contrast, the Q values for elevation and average annual precipitation factors exceed 0.1, with elevation having the highest Q value. This suggests that elevation is one of the dominant factors.

4.2.2. Socioeconomics

Socioeconomics can provide a fundamental guarantee for the preservation and inheritance of industrial heritage, which is important for developing and reusing industrial heritage. To investigate the influence of socioeconomics on the spatial distribution of reused industrial heritage sites, the GDP per capita of each region was selected as a quantitative index to measure economic factors. Then, the distance of reused heritage sites from the central city and the industrial share of regional GDP were used to determine the spatial equilibrium and congruence between reuse heritage sites and socioeconomics. After that, based on the geographic probe, the Q values of GDP per capita and the industry share of regional GDP were higher than the Q values of distance from the central city factor. Above all, the spatial distribution of reused industrial heritage sites has a higher correlation with the economy of each region and a lesser correlation with the distance from the central city. This means that regions with higher economic levels have greater financial strength to renovate and reuse industrial heritage.

4.2.3. Tourism Resources

Tourism resources can promote the development of tourism, while the reuse of industrial heritage is an important resource for developing industrial tourism [30]. The number of scenic spots above A grade in each region was used to measure the overall level of tourism resources, the total number of domestic tourists received by travel agencies in each administrative region was used to measure the size of the tourism market, and culture and tourism business expenses were used to measure the level of tourism development. As shown in Figure 9, the level of tourism development has the highest Q value (0.24). In general, regional tourism resources have a coupling effect on the distribution of NIH reuse sites. Therefore, high-quality tourist attractions will promote the reuse construction of industrial heritage [74].

4.2.4. Government Support

The policy system plays an important role in guiding the conservation and reuse of NIH sites. Three factors were selected, government expenditure on cultural tourism, urban environmental infrastructure expenditure, and industrial pollution control intensity, to calculate each factor using a geographic detector. The Q values of cultural tourism expenditure (0.15) and urban environmental infrastructure expenditure (0.30) were more significant than 0.1, and the highest Q value (0.86) was for industrial pollution control intensity. These results indicated a strong correlation between the spatial distribution of reused heritage sites and relevant government policies or financial expenditures. Therefore, at different stages of industrial heritage reuse development (emergence, development, maturity), the government leads industrial heritage reuse to adapt to the laws of different development stages [79].

4.3. Integrated Impact Mechanism

The reuse of NIH sites is a complete result of the interaction between humans and the land. The spatial pattern of reused industrial heritage sites is driven by physical geography, socioeconomics, tourism resources, and policies and institutions at different levels. Based on the above calculations and analysis results, the spatial formation and distribution mechanisms of NIH site reuse are summarized in Figure 10. First, the natural environment plays a fundamental role. Natural geography is closely related to the formation and development of industrial sites, and geographical factors determine the location and layout of various industries, such as elevation, distance from rivers, and precipitation. Second, economic, social, and tourism resources play a driving role. Variations in economic levels lead to differing demands among citizens for their quality of life. Socioeconomic conditions provide the primary prerequisites for urban development, while financial support facilitates specialized conservation and reuse of industrial heritage [80]. Meanwhile, the development of tourism also provides a new avenue for the transformation of industrial heritage, exploring the tourism and cultural values of industrial heritage. Finally, government support and policy guidance play a leading role. The government acts as a promoter in the initial stage of industrial heritage reuse, a manager in the development stage, and a regulator in the mature stage. In each phase, government policy releases and financial support serve as compasses and support for the protection and reuse of NIH sites.

5. Discussion

The protection and reuse of NIH sites vary according to different regions. By clearly understanding the overall distribution pattern of industrial heritage and the preference patterns for reuse in different regions, as well as exploring the underlying factors contributing to the current pattern, better sustainable protection and reuse of heritage sites can be achieved. In this study, three significant findings were obtained.

First, an analysis of the overall spatial distribution pattern of NIH sites was conducted. China’s NIH sites exhibit a clustered distribution with a high degree of spatial imbalance. The overall quantity of NIH sites decreases from the eastern coastal areas to the western inland areas, showing a decreasing trend from east to west. The diverse requirements of various industrial productions lead to regional disparities in the distribution of industrial activities, giving rise to distinctive regional characteristics in industrial development. This is consistent with previous research findings [55]. This analysis covered the distribution of reuse projects for NIH sites and summarized the current status of industrial heritage reuse using an established database. Previous studies primarily focused on theoretical exploration [18,53] or industrial heritage in specific regions [43,51], lacking comprehensive statistical analysis of the current status of site reuse.

Second, the quantity and directions of reused sites were visualized and analyzed spatially. Regarding regional differences, the eastern region, compared to the central and western regions, has a higher number of NIH public cultural facility protection and reuse projects, with museums being predominant. The industrialization development of NIH sites is mainly concentrated in the eastern region, with cultural tourism and creative park models as the main development modes. Overall, the level of protection and reuse of NIH sites in the eastern region is higher than that in the central and western regions, especially in terms of capital industrialization. Moreover, the industrialization of NIH sites in the eastern region focuses on cultural and creative industries, with significant involvement in cultural tourism. This aligns with some research, indicating that the eastern region, being economically developed with high population density and flow, provides market advantages for NIH sites, resulting in a higher degree of industrialization compared to the central and western regions [78]. Additionally, the levels of public cultural spatial protection and reuse of NIH sites decrease from east to west in the eastern, central, and western regions. Economic disparities between regions inevitably lead to differences in the level of public cultural services. The eastern region, being economically developed with a better urban development environment, exhibits the highest quantity and most diverse types of public cultural spatial protection facilities, emphasizing urban leisure, with a greater number of public recreation spaces.

Last, the geographic detector model indicates that natural geography, socio-economic factors, tourism resources, and government support play crucial roles in determining the spatial differentiation of reused sites for NIH. Government support and policy guidance play a dominant role, while natural geographic factors determine the layout of industrial site selection. Economic conditions and tourism development drive the transformation of industrial heritage, with the government playing a crucial role in promotion, management, and regulation. This aligns with similar conclusions from studies on influencing factors of geographic spatial distribution. When selecting factors influencing spatial distribution for reused industrial heritage sites, it is essential to consider historical and cultural value, geographic location and environmental conditions, urban development planning, social needs and market potential, technological and economic feasibility, cultural protection policies, and societal awareness [2,5,81]. These factors will impact the spatial layout and functional positioning of reuse projects to ensure a balance between historical preservation and urban development.

This study holds significance for research on the regeneration and reuse of industrial heritage sites in two aspects. First, the current research on industrial heritage mainly focuses on aspects such as heritage preservation and utilization, often conducted at the level of individual heritage sites. There is a lack of visualized analysis based on a large amount of data regarding the reuse conditions and geographical spatial attributes of industrial heritage sites. Therefore, this study, by understanding the patterns of industrial heritage reuse across different categories, provides evidence for overall regeneration and utilization planning. Second, by employing a geographic detector to investigate the role and distribution mechanisms of NIH reuse spatial formation, this research contributes to the analysis of NIH utilization potential from a spatial perspective. The framework for assessing the influencing factors constructed from different dimensions can also be applied to the study of reuse potential in other tangible (such as ancient buildings, cave temples, stone carvings, and murals) and intangible cultural heritage (oral traditions, folk literature, folk activities, performing arts, etc.). This can aid in establishing a more comprehensive framework and indicator system for assessing reuse potential.

However, this study has some limitations. First, when investigating the patterns of industrial heritage reuse at the national level in China, the classification of reuse directions may not be entirely accurate and comprehensive. Particularly in recent years, with the advent of big data and the Internet era, the direction of heritage transformation has become more digitized and intelligent. In future research, more studies are needed to conduct a more detailed classification of the patterns of industrial heritage reuse. Second, the study did not delve into the specific characteristics and attributes of the current reuse status of each site, which could impact the interpretative power of influencing factors. Future research should encompass the historical culture, structure and mechanism, architectural features, folk culture, and more related industrial sites to better explore the spatial distribution mechanism of their reuse.

The following aspects should be considered in subsequent research. This study explores the spatial distribution and determinants of NIH site reuse at the macro scale. As industrial heritage projects continue to be enriched, investigations could further explore the potential reuse of sites at micro scales, such as at the city and county levels. With technological advancements, diverse consumer patterns, and the introduction of new policies, more diverse and scientifically grounded factors should be selected for the study of reuse driving forces in future research. Specifically, GIS spatial analysis and visualization tools could be collaboratively employed for the digital collection, storage, management, display, and dissemination of heritage textual, visual, auditory, and video data. Establishing a dynamic resource database and a modernized management service platform is crucial. Subsequently, based on the database, constructing a multidimensional indicator system can be undertaken to explore the potential and direction of reuse. On this basis, targeted strategies for heritage reuse development can be proposed, aiming to provide a reference for the sustainable construction of cultural heritage.

6. Conclusions

Based on the data collected from five batches of NIH lists in China, this study investigated the spatial distribution and current status of conservation and reuse in four dimensions. This research aims to enhance our understanding of the reutilization of NIH sites, which is intertwined with aspects of physical geography, socio-economics, tourism resources, and policies and institutions at various levels in China. The primary conclusions are as follows.

• China’s NIH sites exhibit an uneven yet cohesive spatial distribution. They form a densely populated circle, with the core regions being the Yangtze River Delta and the Beijing–Tianjin–Hebei region.
• Approximately three-quarters of China’s NIH sites are already in the reuse stage, with high-density clusters in the eastern and central regions. The heritage types are relatively diverse, with heavy industrial types outnumbering light industrial and municipal construction types. Light industrial types represent the largest proportion of reuse, followed by heavy industrial and municipal construction types.
• The conservation and reuse of NIH sites can be categorized into two main approaches: public- and market-oriented. Regarding regional differences, there is a greater prevalence of public cultural facility-based conservation and reuse of NIH sites in the eastern region than in the central and western regions. Among public cultural facility-based types, museums occupy a dominant position.
• The spatial differentiation of reused NIH sites results from multiple factors. Both physical geography and socioeconomics exert varying degrees of influence on the spatial distribution of reused heritage sites. Additionally, policy regimes and tourism resources play pivotal roles in shaping this spatial differentiation.