A GIS-based study on the spatial distribution and revitalization patterns of industrial heritage in northeast China

Introduction: Industrial heritage revitalization is a key driver of urban renewal and industrial transformation in old industrial bases. However, there is a lack of systematic quantitative evidence at the regional scale regarding the spatial clustering of industrial heritage and the differentiated revitalization patterns across different urban typologies in Northeast China.

Methods: This study integrates national and provincial industrial heritage lists for Northeast China. It constructs five revitalization patterns—cultural exhibition, commercial development, landscape -park reuse, in-production activation, and static conservation—and applies GIS-based spatial statistical methods to analyze the spatial clustering characteristics and revitalization differentiation.

Results: The study reveals a “local clustering and overall dispersion” pattern, with high-density clusters around major urban agglomerations and industrial corridors. The urban typologies follow a policy-based classification into core cities, nodal cities, and resource-based cities. A clear “space–function coupling” differentiation is observed: core cities have the most diverse revitalization patterns, mainly dominated by cultural exhibition and commercial development; nodal cities show corridor-like distributions along transportation and industrial corridors, with in-production activation and exhibition functions interwoven; resource-based cities are primarily dominated by static conservation and landscape–park reuse.

Discussion: Further analysis suggests that population size and economic strength (GRP) influence revitalization choices and functional diversity. Based on the findings, the study proposes a “core–radiation–infill” regional activation pathway, offering quantitative evidence and differentiated policy implications for industrial heritage conservation, urban renewal, and industrial transformation in Northeast China.

1 Introduction

Industrial heritage, as a key physical witness to modern industrialization, reflects technological achievements, industrial structures, and social transformations of specific historical periods (Hain and Ganobjak, 2017; Copic et al., 2014). With the global industrial landscape shifting and the rise of the “post-industrial era,” many traditional industrial facilities have ceased production. How to achieve their revitalization and sustainable transformation has gradually become an important topic in urban planning and sustainable development research (Loures and Panagopoulos, 2007).

Industrial heritage conservation has evolved from a narrow focus on physical remains to a comprehensive perspective that incorporates social, cultural, and ecological values. Since the 1950s, Western countries have taken the lead in conducting industrial heritage surveys and conservation practices. By improving legal frameworks, establishing professional management institutions, and introducing community participation mechanisms, they have built a complete chain covering heritage identification, value assessment, conservation management, and adaptive reuse (Zhang et al., 2022a; Nguyen et al., 2025). The formation of the International Committee for the Conservation of the Industrial Heritage (TICCIH) in 1973 marked a key step in institutionalizing industrial heritage conservation. The Burra Charter (1999), Nizhny Tagil Charter (2003), and Dublin Principles (2011) issued by ICOMOS–TICCIH further clarified conservation principles, emphasizing authenticity, integrity, social value, and the relationship with the surrounding landscape. Iconic examples like the Ironbridge Gorge in the UK and the Ruhr Industrial Region in Germany demonstrate that industrial heritage conservation is not just about physical restoration but also drives social–cultural renewal and regional regeneration (Cho and Shin, 2014; Nocca, 2017).

The formation of this system is closely tied to “de-industrialization” in Western countries since the 1970s (Rowthorn and Ramaswamy, 1997). With the decline of old industrial regions, conservation and reuse efforts increasingly involved multiple actors—community residents, labor organizations, and heritage experts—guided by government policies. This process showed strong grassroots mobilization, emphasizing preserving and reinterpreting industrial memory to sustain place identity, mitigate community disintegration, and highlight heritage’s emotional value (Rezende, 2025; Chatzi Rodopoulou, 2019). Western industrial heritage conservation is marked by: (1) a mature conservation system with long-term evolution, (2) deep integration into urban transformation and regional regeneration strategies, and (3) strengthened community participation under government guidance, with continuous reshaping of heritage values through multi-actor collaboration.

In contrast, industrial heritage conservation and research in China are characterized by contemporaneity and institutional specificity, rooted in state-led industrialization and rapid urbanization (Gan et al., 2024; Zhang et al., 2022b; Qian, 2023). China has undergone three main stages: (1) the industrial accumulation period (1950s–1970s), when a large-scale, multi-type industrial system was established, laying the foundation for industrial heritage; (2) from the 1990s, industrial restructuring and state-owned enterprise reforms led to the release of industrial land, resulting in redevelopment pressures on heritage sites during urban renewal; and (3) the gradual institutionalization of industrial heritage conservation in the 21st century. The “Wuxi Recommendations” (State Administration of Cultural Heritage, 2006a) first proposed national-level industrial heritage conservation, and the “Notice on Strengthening the Protection of Industrial Heritage” (State Administration of Cultural Heritage, 2006b) formally included it within the national cultural heritage system. The establishment of the national industrial heritage list system in 2017 unified industrial heritage management under the national framework, followed by provincial lists and conservation guidelines, which fostered rapid and standardized development (Xu and Aoki, 2018). Notable cases, such as Beijing’s 798 Art District, Shanghai’s Shipyard 1862, and Wuhan’s Hanyangzao, have transformed industrial heritage from “relics” into “resources,” with reuse strategies emphasizing land value, city branding, and industrial restructuring (Zhang, 2024).

From a spatial and driving mechanism perspective, industrial heritage conservation in China contrasts with Western approaches. In the West, conservation emerged in the context of long-term de-industrialization, driven by communities, labor groups, and professional institutions, focusing on preserving cultural memory. By contrast, in China, industrial heritage is deeply linked to state-driven industrial restructuring. State-owned enterprise reform and policies like “retreating secondary industry, advancing tertiary industry” have led to the rapid and concentrated withdrawal of industrial facilities, producing a large and visible stock of industrial heritage (Tan and Altrock, 2024; Zhang et al., 2020). Their reuse is now embedded in government-led urban renewal, land redevelopment, and industrial upgrading, with industrial heritage entangled in national strategies and regional development policies (Zeng et al., 2022).

Northeast China provides a particularly representative arena for this inquiry. As one of the earliest industrialized regions in the country, the Northeast has developed a heavy industrial system centered on steel, equipment manufacturing, petrochemicals, energy, and transportation, giving rise to a large number and wide variety of industrial heritage sites. These sites are not only important material carriers of China’s industrial civilization, but also key clues for understanding the formation of northeastern cities, the trajectories of industrial evolution, and the restructuring of urban spatial patterns (Li et al., 2016; Zhang, 2008). Existing studies show that the Northeast is among the regions with the highest concentration of heavy industrial heritage in China, and that its industrial heritage displays pronounced differences in industrial structure, cultural value, and potential for sustainable reuse. Industrial sectors, historical context, and transport accessibility are recognized as important factors influencing the conservation and development of industrial heritage (Liu et al., 2016; Liu et al., 2023). From a national perspective, China’s industrial heritage exhibits strong path dependence rooted in industrial geography, while the strategic position of Northeast China in the national heavy industrial layout makes it an important sample for observing the spatial patterns and revitalization mechanisms of industrial heritage (Zhang et al., 2023).

A substantial body of research has emerged around these industrial heritage sites at regional and urban scales, ranging from resource inventories, value assessment, and historical evolution, to conservation strategies and reuse models, gradually building a fundamental understanding of old industrial cities in the Northeast. For example, The History of Industrial Heritage in China–Liaoning Volume systematically catalogs the types and spatial distribution of industrial heritage in Liaoning Province (Ha, 2021); Liu et al. (2025) construct the spatial structure and value system of Harbin’s industrial heritage; Jin and Chen (2010) under the framework of “multi-plan integration”, propose systematic classification and conservation strategies for industrial heritage in Shenyang; Sun and Ikebe (2023) further reveal the coupling between industrial heritage and urban spatial evolution in Shenyang; and studies on Changchun focus on the integration of culture and tourism, micro-scale urban renewal, and the regeneration effects of representative projects (Zhu and Han, 2022; Han and Zhang, 2025; Han, 2023). Overall, these works have preliminarily constructed a core knowledge base on industrial heritage in Northeast China at three levels—regional patterns, urban characteristics, and project-level cases—and consistently underscore the strategic significance of industrial heritage for revitalizing old industrial bases and promoting urban transformation (Zhao and Li, 2022).

Meanwhile, advances in spatial information technologies, including GIS and spatial statistical methods, have been critical in studying industrial heritage at the regional scale. GIS has supported the identification of distribution patterns, spatial evolution analysis, and regional characteristic modeling for cultural heritage (Liu and Kang, 2023; Fagúndez and Izco, 2016). Tools like kernel density estimation, standard deviational ellipses, spatial autocorrelation, and nearest-neighbor indices are essential for uncovering spatial clustering, structural morphology, and the relationship between heritage and urban systems (Tong et al., 2022; Zhu et al., 2019; Hashimoto et al., 2015). GIS’s visualization and decision support capabilities are vital for heritage surveys, graded conservation, and urban planning (Yao et al., 2021; Sun, 2022). Multi-scale spatial analysis methods enable comprehensive identification of industrial heritage’s spatial configuration, providing a scientific basis for differentiated conservation and regeneration strategies.

However, existing research has notable limitations: (1) most studies focus on a single city or type of heritage, lacking systematic cross-city and cross-sector comparisons; (2) the spatial differentiation of sectors and revitalization patterns at the regional scale has not been sufficiently explored with quantitative methods; and (3) the impact of urban attributes—such as population size, economic strength, and functional positioning—on revitalization diversity remains underexplored. To address these gaps, an integrated analytical framework is needed to reveal the coupling between industrial heritage spatial patterns, urban typologies, and revitalization functions.

This study takes Northeast China as a case study and aims to achieve the following objectives:

1. to build a regional-scale database of industrial heritage in Northeast China and identify its overall spatial structure and clustering characteristics;

2. to analyze differences in revitalization patterns across different city types, based on the official city classification system in national planning;

3. to integrate five revitalization modes with multi-scale spatial statistical methods and construct a coupled analytical framework linking “spatial patterns–city types–revitalization patterns”;

4. to explore scenario-specific revitalization strategies under the broader context of regional development and the revitalization of old industrial bases, providing methodological support and planning guidance for industrial heritage conservation, urban renewal, and industrial restructuring.

Starting from a regional perspective, this study develops an integrated analytical framework that spans multiple cities and revitalization modes, while incorporating key urban attributes—such as population size, economic strength, and functional positioning. It aims to deepen understanding of the spatial patterns and revitalization mechanisms of industrial heritage at the theoretical level, and offer an operational technical pathway and policy reference for regional-scale industrial heritage conservation and regeneration at the practical level. The research framework outlines the main steps of the study (see Figure 1) to help readers understand the research logic and methodology.

Figure 1

Figure 1. Research framework.

2 Materials and methods

2.1 Data sources

Industrial heritage data were obtained from the National Industrial Heritage List released by China’s Ministry of Industry and Information Technology and from provincial industrial heritage lists issued by Heilongjiang, Jilin, Liaoning, and Inner Mongolia.

By 31 October 2025, the National Industrial Heritage List comprised seven batches (266 sites in total: 13 in 2017; 42 in 2018; 49 in 2019; 62 in 2020; 31 in 2021; 37 in 2023; and 32 in 2024). From this list, we identified 30 national-level sites located in Northeast China (Liaoning, Jilin, Heilongjiang, and eastern Inner Mongolia).

To expand the regional sample, we incorporated provincial-level lists from Liaoning (2022: 15 sites; 2023: 5; 2024: 5), Heilongjiang (2023: 28), and Jilin (2024: 10). Although the Inner Mongolia Autonomous Region has issued a provincial industrial heritage list, no heritage sites within the eastern part of the region fall into the study area. Duplicate records across national and provincial lists were identified through name–location matching and removed.

Finally, after consolidating, calibrating, and de-duplicating national and provincial lists, a total of 92 industrial heritage sites in Northeast China were obtained for spatial analysis (see Figure 2). To ensure temporal consistency, coordinates, attributes, and revitalization status were compiled and validated as of October 2025.

Figure 2

Figure 2. Distribution map of industrial heritage sites in northeast China.

Base maps were obtained from the Standard Map Service of China’s Ministry of Natural Resources. DEM data were obtained from the National Basic Geographic Information System. Population and GRP data were collected from the China City Statistical Yearbook and official provincial/municipal statistical yearbooks and bulletins.

2.2 Data collection

Industrial heritage data. Information for each site was compiled from officially disclosed names and addresses. Geographical coordinates (POI) were retrieved using the Baidu Maps API coordinate selector and converted to WGS84 coordinates via QGIS. All data were standardized in the WGS84 coordinate system and transformed into metric projections for spatial analysis.

Revitalization status (i.e., whether and how a site has been reused) was identified using government releases, cultural and tourism documents, credible media reports, and field investigations.

Auxiliary datasets included administrative boundaries and city-level indicators (permanent population and prefecture-level GRP) for spatial analysis and explanatory assessment.

2.3 Classification of revitalization patterns

To capture the utilization and transformation logic of industrial heritage in Northeast China, this study classifies each site into one of five revitalization patterns: cultural exhibition, commercial development, landscape-park reuse, static conservation, and in-production activation (Cho and Shin, 2014; Mo et al., 2022).

This classification was operationalized using four criteria (Table 1): primary functional use, economic attributes, cultural attributes, and spatial morphology (reuse form). Primary functional use identifies the dominant current activity and service orientation; economic attributes distinguish public-oriented utilization from profit-driven redevelopment or ongoing enterprise operation; cultural attributes assess whether industrial memory and educational interpretation are explicitly presented; and spatial morphology describes the prevailing physical form of reuse.

Table 1

Table 1. Typology and criteria of industrial heritage revitalization patterns.

Based on the above criteria, the study first conducted an initial coding using a dominant-function rule. Classification prioritized stable, long-term functional orientation and the principal operational mode. Evidence was triangulated across official plans and cultural tourism documents, institutional disclosures, media reports, and field investigation records. To improve reliability and reduce subjectivity, the preliminary results were reviewed by two or three experts in industrial heritage research or conservation planning. Because site uses may change with policy shifts and investment cycles, all classifications were standardized to the status as of November 2025 to ensure temporal consistency.

2.4 Urban typology

To examine the distribution of industrial heritage across urban types, this study classifies cities in Northeast China into three categories: core cities, resource-based cities, and nodal cities, based on national policies and regional development plans (Table 2).

Table 2

Table 2. City type classification.

2.5 Data preprocessing

All spatial coordinates were checked for accuracy and uniformly converted to the WGS84 geographic coordinate system. Site attributes were standardized in an Excel table and imported into ArcGIS 10.8 for spatial analysis.

2.6 Spatial distribution analysis

To examine the spatial distribution characteristics of industrial heritage, kernel density estimation (KDE), average nearest neighbor (ANN), and standard deviational ellipse (SDE) were applied (Wang et al., 2021; Korpilo et al., 2021).

Kernel density estimation (KDE) was used to estimate the continuous density surface of heritage sites across the study area (Dun et al., 2024; Zhao et al., 2024). The general form is given by Formula 1:

$f\left(x\right)=\frac{1}{nh}{\displaystyle \sum _{i=1}^n}k\left(\frac{x-x_i}{h}\right)(1)$

In the formula: $k\left(x\right)$: represents the kernel function; $h$: is the search radius (bandwidth) used to measure the influence range of each heritage site on its surrounding sites.; $x-x_i$: denotes the distance between a given industrial heritage site and another nearby site.

Average nearest neighbor (ANN) compares the observed mean nearest-neighbor distance with that expected under complete spatial randomness to infer clustering or dispersion (Zhu et al., 2022; Gao et al., 2023). The general form is given by Formula 2:

$R=\frac{r_i}{r_e}(2)$

In the formula: R represents the nearest neighbor index; $r_i$: is the mean observed nearest neighbor distance of the industrial heritage sites; and $r_e$: is the theoretical mean nearest neighbor distance.

Standard Deviation Ellipse (SDE): The one-standard-deviation SDE summarizes the directional trend and dispersion of point distributions and typically contains ∼68% of observations (Sun et al., 2025).

Spatial Autocorrelation: Spatial autocorrelation was measured using Moran’s I to assess the overall clustering or dispersion of industrial heritage distributions (Huo et al., 2012). The general form is given by Formula 3:

$I=\frac{n}{W}\times \frac{{\mathrm{\Sigma }}_{i=1}^n{\sum }_{i=1}^n{w}_{ij}\left(x_i-\overline{x}\right)\left(x_j-\overline{x}\right)}{{\mathrm{\Sigma }}_{i=1}^n{\left(x_i-\overline{x}\right)}^2}(3)$

where $n$ is the number of spatial units, $x_i$ and $x_j$ are the observed values of the variable in units $i$ and $j$, $\overline{x}$ is the mean value of the variable, $w_{ij}$ is the element of the spatial weight matrix representing the spatial relationship between units $i$ and $j$ (e.g., contiguity or distance-based weights), and $W={\mathrm{\Sigma }}_i{\mathrm{\Sigma }}_j{w}_{ij}$ is the sum of all spatial weights.

2.7 Spatial clustering analysis

To further examine clustering at multiple spatial scales, Ripley’s $K$ function was employed to analyze how clustering intensity changes with distance (He et al., 2020). The general form is given by Formula 4:

$K\left(d\right)=\frac{A}{n^2}{\displaystyle \sum _{i=1}^n}{\displaystyle \sum _{j\ne 1}^n}I\left(d_{ij}\le d\right)(4)$

Where $K\left(d\right)$ is the estimated clustering intensity at distance d; $A$ is the study area, $n$ is the total number of heritage sites; $d_{ij}$ is the distance between point i and point j; $I\left(·\right)$ is an indicator function that equals 1 when $d_{ij}\le d$, and 0 otherwise.

Comparing observed $K\left(d\right)$ values with those expected under complete spatial randomness allows us to identify clustering or dispersion at different scales.

2.8 Spatial balance analysis method

To quantify how evenly industrial heritage is distributed among cities, the spatial Gini coefficient and a derived uniformity index were calculated (Hong et al., 2022).

The spatial Gini coefficient is given by Formula 5:

$G=\frac{{\mathrm{\Sigma }}_{i=1}^n{\mathrm{\Sigma }}_{j=1}^n\left|x_i-x_{\dot{J}}\right|}{2n\left(n-1\right)\mu }(5)$

$x_i$ and $x_{\dot{J}}$ represent the number of heritage sites in the $i$-th and $j$-th regions, respectively;

$n$ is the total number of cities; $\overline{x}$ denotes the average number of heritage sites across all cities; $\left|x_i-x_{\dot{J}}\right|$ represents the absolute difference in the number of heritage sites between any two cities.

The uniformity index is defined by Formula 6:

$C=1-G(6)$

$C$ represents the Uniformity Index, and $G$ denotes the Spatial Gini Coefficient.

When $C$ → 1, the heritage sites are evenly distributed; when $C$ → 0, the distribution is uneven, indicating significant concentration areas.

3 Analysis

3.1 Summary of industrial heritage types

Before analyzing revitalization patterns, we first examined the sectoral composition of industrial heritage in Northeast China to provide context for the region’s industrial base. Based on the GB/T 4754–2017 system and data from heritage lists and fieldwork, the 92 sites were classified into mining (B), manufacturing (C), energy production and supply (D), transportation (G), public facilities management (N), education (P), and culture (R) (see Figure 3).

Figure 3

Figure 3. Industrial heritage sectoral composition in northeast China.

The sectoral composition of industrial heritage in Northeast China is primarily dominated by mining and manufacturing. Mining accounts for about 24% of sites, including coal, metal, and oil–gas heritage, and represents the largest resource-based category. Manufacturing accounts for about 47%, covering steel, machinery, chemicals, food and liquor production, and tobacco, reflecting the region’s historical role as China’s core heavy industrial base. Energy production and supply accounts for ∼15%, mainly hydropower and thermal power facilities. Transportation-related heritage constitutes around 7%, including shipyards, docks, and railway repair facilities linked to the region’s historical port–rail system. Cultural, educational, and public-facility-related sites account for less than 3%, but hold substantial symbolic and historical value.

Overall, the sectoral composition indicates a heavy-industry and energy-oriented profile. Mining and heavy manufacturing together exceed 70%, aligning with the region’s strategic industrial positioning during the First Five-Year Plan and reflecting the combined influence of resource endowment and the planned-economy industrial layout (Zhang et al., 2023).

3.2 Revitalization patterns

At the regional level (see Figure 4a), 74 industrial heritage sites (approximately 80%) have undergone some form of revitalization, while 18 sites (around 20%) remain unrevitalized or are only under basic protection. This suggests a relatively high level of adaptive reuse, although approximately one-fifth of the sites still exhibit limited activation potential.

Figure 4

Figure 4. (a) Revitalized and non-revitalized industrial heritage sites. (b) Types of Revitalization of Industrial Heritage.

Regarding revitalization patterns (see Figure 4b), in-production activation is the most common (35%), reflecting continued industrial operation and the “living heritage” duality of production and cultural value. Cultural exhibition follows with 21 sites (23%), many of which have been converted into museums or interpretive venues that preserve industrial memory. Landscape–park reuse accounts for 14 sites (15%), integrating ecological restoration and public space functions. Static conservation includes 18 sites (19%), characterized by minimal intervention, while commercial development remains limited to 7 sites (8%).

Overall, industrial heritage in Northeast China exhibits a “public–production” dual structure in revitalization outcomes. On the one hand, the prominence of in-production reuse highlights the ongoing role of industrial sectors in resource-based cities. On the other hand, cultural exhibition and landscape–park reuse together account for nearly 40%, demonstrating strong emphasis on public cultural functions and ecological improvement. The relatively low proportion of commercial development reflects policy orientations prioritizing conservation and public interest (Ministry of Industry and Information Technology of the People’s Republic of China, 2023).

3.3 Spatial distribution characteristics

3.3.1 Kernel density analysis

To visualize clustering intensity and density patterns, we applied Kernel Density Estimation (KDE), which effectively captures the continuous spatial trend of point densities and is well-suited for examining large-scale regional differences and hotspots (see Figure 5).

Figure 5

Figure 5. Kernel density map of industrial heritage sites in northeast China.

Industrial heritage in Northeast China exhibits a “multi-core clustering with extensive low-density areas” pattern. High-density clusters are concentrated in a few key urban agglomerations, while large areas exhibit low or zero density, highlighting significant spatial inequality. We identified four major clusters:

3.3.1.1 Central–southern liaoning core cluster

Centered on Shenyang, with Anshan, Fushun, and Benxi forming a gradient of secondary centers, this is the largest and most intense hotspot in the region. KDE values here fall within the highest legend classes, demonstrating the decisive influence of the long-standing central–southern Liaoning industrial base on spatial heritage patterns.

3.3.1.2 Harbin–daqing corridor

A strip-shaped high-density area extends from Harbin to Daqing, reflecting a corridor of equipment manufacturing, petrochemicals, and resource-based industries. KDE values are in the medium–high range with strong spatial continuity, indicating the structural role of early industrial construction and oilfield development.

3.3.1.3 Changchun central cluster

A large-scale density patch has formed in and around Changchun. As one of China’s early industrial bases and the nation’s leading automobile manufacturing center, Changchun has accumulated a substantial and diverse array of industrial heritage sites. Together with adjacent cities, it constitutes a structurally coherent industrial heritage core area in central Northeast China.

3.3.1.4 Coastal cluster

Coastal areas exhibit higher kernel density values than surrounding inland regions. This spatial pattern reflects the significant shaping influence of modern port industries, shipbuilding, and contemporary manufacturing on the distribution of coastal industrial heritage sites.

Overall, the KDE pattern aligns closely with the historical trajectory of industrialization in Northeast China. The high-density clusters overlap with early PRC key industrial bases and resource-rich areas (e.g., Daqing oilfields and the Liaodong mining belt), illustrating the lasting spatial imprint of early industrial layouts on contemporary heritage distribution.

3.3.2 Standard deviational ellipse analysis

To further examine directionality, overall spread, and structural features, a standard deviational ellipse (SDE) was constructed based on heritage points (see Figure 6).

Figure 6

Figure 6. Standard deviational ellipse of industrial heritage sites in northeast China.

The mean center of heritage distribution is located at (124.28379°E, 43.286154°N), in the transitional area between Songyuan and Changchun. This location is not a density maximum, but a “balanced center” formed by the combined influence of multiple industrial hubs: the Harbin–Daqing belt pulls the center northwestward, while the central–southern Liaoning base pulls it southeastward.

The ellipse is rotated about 43.44°, trending northeast–southwest and substantially overlapping with the historical Chinese Eastern and South Manchurian railway axes. As the earliest modern transport backbone in the region, this railway corridor has profoundly shaped the urban system, industrial layout, and logistics network. The SDE direction is essentially a spatial projection of this historic influence: industrial heritage is distributed along the railway axis, reflecting long-term constraints imposed by transport infrastructure.

The ellipse is elongated, with estimated major and minor semi-axes of approximately 622 km and 219 km, respectively, yielding a ratio of about 2.84:1. This indicates that dispersion along the major axis (northeast–southwest) is much greater than in the perpendicular direction, forming a pronounced linear spatial structure. Major cities such as Harbin, Changchun, Shenyang, and Dalian evolved into industrial hubs along this axis and later hosted key First Five-Year Plan projects, reinforcing a corridor-type clustering of industrial heritage.

In sum, the SDE analysis reveals a strongly directional, historically contingent, and path-dependent spatial structure. Its dominant axis coincides with the railway corridor, reflecting the joint effects of “railway–industry–city” dynamics in shaping industrialization and the long-term structural inertia of transport infrastructure (Li et al., 2023).

3.3.3 Global Moran’s I

Global Moran’s I was applied at the prefecture-level city scale to test spatial dependence in heritage counts (see Figure 7).

Figure 7

Figure 7. Global spatial autocorrelation of industrial heritage sites in northeast China.

The resulting z-score lies within −1.65 to +1.65, corresponding to the non-significant range for random distribution. This suggests that city-level heritage counts are not significantly different from spatial randomness at the regional scale. In other words, though KDE and SDE identify local hotspots and directional structures, these do not coalesce into a region-wide clustering pattern. Local hotspots remain relatively isolated, and there is no continuous cluster spanning multiple cities.

3.3.4 Average nearest neighbor

Global Moran’s I may obscure fine-scale clusters. To capture microscale clustering, the average nearest neighbor nearest neighbor (ANN) was used (see Figure 8).

Figure 8

Figure 8. Average nearest neighbor index of industrial heritage sites in northeast China.

The ANN index is 0.2597 (<1), with a z-score of −13.5095, indicating a highly significant local-scale clustering pattern in which nearest-neighbor distances are far smaller than expected under randomness. While Moran’s I points to an approximately random pattern at the macro-scale, the NNI reveals clear micro-scale aggregation, complementing the KDE and SDE findings that highlight tight clusters within key industrial belts.

3.4 Multi-scale clustering (Ripley’s K)

Single-scale measures are inadequate for capturing variations across spatial scales. To examine scale dependence in heritage site distribution, this study applies Ripley’s K function for multi-scale spatial point-pattern analysis, assessing clustering or dispersion at different distances (see Figure 9).

Figure 9

Figure 9. Spatial clustering analysis of industrial heritage sites in northeast China.

The observed K curve consistently exceeds the expected curve under random conditions across most distance ranges, surpassing the confidence envelope and indicating significant clustering at multiple scales. In the 0–260 km range, observed K values greatly exceed expected values, implying tight clustering within and around urban agglomerations.

As distance increases beyond about 260–400 km, the observed curve gradually approaches the expected curve and, in some intervals, approaches the confidence band, indicating weaker clustering and a tendency toward uniformity or mild dispersion at larger scales. This reflects the breaking down of inter-city cluster connectivity at macro-scales, due to vast distances, heterogeneous industrial bases, and the expansive geography of Northeast China.

Overall, Ripley’s K reveals a “small-scale clustering–medium-scale intensification–large-scale dispersion” pattern. This “sparse–dense–sparse” spatial structure captures the joint effects of historical industrial evolution and the spatial logics of city clusters, transport corridors, and industrial bases, together with the uneven distribution of resources and urban development.

3.5 Spatial balance analysis

The spatial Gini coefficient for heritage counts among cities is $G=0.594$, and the corresponding uniformity index is $C=0.406$, The high spatial Gini coefficient and low uniformity index together indicate a highly uneven distribution, with industrial heritage strongly concentrated in a small number of core and corridor cities, while most cities hold relatively few sites.

3.6 Coupling between spatial patterns and revitalization modes

Overlaying kernel density, the SDE axis, and revitalization types reveals a clear hierarchy of revitalization patterns across different urban areas (see Figure 10).

Figure 10

Figure 10. Spatial distribution characteristics and revitalization types of industrial heritage in northeast China.

In high-density urban clusters, such as the Shenyang–Anshan–Fushun–Benxi area, heritage sites are concentrated in the highest KDE classes. NNI and Ripley’s K indicate tight local clusters, with multiple revitalization types, including in-production, cultural exhibition, landscape-park, static conservation, and commercial development. In-production and cultural exhibition are most prevalent, with the remaining types forming secondary clusters. These city clusters thus host the most complete set of revitalization patterns.

In corridor areas, such as the Harbin–Daqing belt, heritage points are distributed along the mid-section of the SDE major axis. Ripley’s K remains above the expected values at intermediate distances, indicating a continuous, linear corridor-like clustering pattern. Revitalization is predominantly in-production, accompanied by scattered cultural exhibition and landscape–park sites, while static conservation and commercial development are rare. Compared with high-density urban clusters, the revitalization spectrum is more concentrated and involves fewer types, forming a corridor structure characterized by a single dominant revitalization pattern.

In secondary centers such as Changchun, KDE values are moderate and heritage counts are intermediate. These locations lie within the northern coverage of the SDE ellipse. Ripley’s K indicates moderate clustering at small to medium scales. Revitalization types include all five patterns, with in-production and cultural exhibition slightly dominant; overall, the structure is relatively balanced.

In low-density peripheral areas (northern and eastern Northeast China and parts of the interior), KDE indicates scattered heritage points located near or beyond the outer edges of the SDE ellipse. Ripley’s K converges toward the expected line at larger distances, and Moran’s I suggests no significant global clustering. Revitalization patterns here are limited: most cities have one or two types, mainly in-production or static conservation. Cultural exhibition and landscape–park reuse occur infrequently, and commercial development is rare.

In summary, these differences across high-density clusters, corridors, secondary centers, and low-density areas indicate a clear spatial hierarchy in revitalization patterns, which is consistent with the SDE axis and the localized clustering revealed by the ANN index.

3.7 Urban characteristics and revitalization patterns

Industrial heritage revitalization is shaped not only by historical transport corridors and industrial layouts, but also by city-level factors such as economic strength, population size, and functional roles. Different city types may therefore exhibit distinct revitalization choices. This section examines the relationships between population size, economic strength, city type, and revitalization patterns.

3.7.1 Population size and its association with revitalization patterns

Overlaying population size with revitalization types reveals clear differences across population levels. Large cities such as Shenyang, Dalian, Changchun, and Harbin display diverse revitalization patterns, incorporating multi-sector combinations such as in-production, cultural exhibition, landscape-park, static conservation, and commercial development (see Figure 11). Large cities such as Anshan, Fushun, and Benxi also exhibit multiple types, though slightly fewer, mainly in-production, cultural exhibition, landscape–park, and static conservation.

Figure 11

Figure 11. Population size and industrial heritage revitalization type.

Medium-sized cities such as Jilin, Qiqihar, and Yingkou typically display two to three revitalization types, most commonly “in-production + cultural exhibition,” with landscape–park reuse present in some cases, and commercial development being rare. Smaller cities such as Tieling, Liaoyang, Dandong, and Jiamusi have fewer heritage sites and simpler pie charts, primarily dominated by in-production and static conservation, with cultural exhibition and landscape–park reuse appearing less frequently.

In the smallest cities (e.g., Heihe, Hegang, Jixi, Shuangyashan, Qitaihe), heritage points are highly scattered, and the pie charts are mostly single-type (in-production or static conservation), with few cultural exhibitions, landscape–park, or commercial sites present. A clear gradient emerges: the larger the population, the more numerous and diverse the revitalization patterns; the smaller the population, the more single and conservative the structure, implying that population size not only affects the choice of revitalization types but also underpins the social and cultural foundations of sustainable reuse (Rabie, 2016).

3.7.2 Economic strength and differentiation in revitalization patterns

Overlaying GRP (Gross Regional Product) classifications with revitalization types reveals significant differences across economic levels (see Figure 12). Cities with high GRP (darkest colors) host more heritage sites and exhibit a broader range of revitalization patterns, encompassing all five types (in-production, cultural exhibition, landscape–park, static conservation, and commercial development). Cities in the second tier (GRP 150–300 billion yuan) also exhibit relatively diverse patterns but fewer combinations, primarily focusing on in-production, cultural exhibition, and static conservation, with landscape–park and commercial development being less frequent.

Figure 12

Figure 12. Economic structure and industrial heritage revitalization type.

Medium-sized cities (GRP 80–150 billion yuan), such as Jilin, Qiqihar, Yingkou, and Jinzhou, generally display two or three revitalization types, most commonly “in-production + cultural exhibition,” with occasional landscape–park reuse and limited commercial development. Cities with lower GRP (below 80 billion yuan) exhibit fewer heritage sites and simpler revitalization patterns, primarily focused on in-production and static conservation, with diminishing frequencies of cultural exhibition and landscape–park reuse, and virtually no commercial development. Again, an economic gradient emerges: the stronger the economic base, the more diverse the revitalization patterns; weaker economies correspond to more single and conservative patterns.

3.7.3 Urban typologies and the spatial differentiation of revitalization patterns

Overlaying city type (core, node, resource-based) and revitalization patterns (see Figure 13) reveals distinct differences in the number and composition of revitalization modes.

Figure 13

Figure 13. Urban typology and industrial heritage revitalization type.

Core cities host numerous heritage sites, with pie charts displaying multiple revitalization sectors, including in-production, cultural exhibition, landscape-park, static conservation, and commercial development. These cities exhibit the most diverse and spatially concentrated revitalization patterns.

Nodal cities, which have an intermediate number of revitalization types, typically display two to three sectors, with “in-production + cultural exhibition + static conservation” combinations being the most common. Landscape–park reuse appears in some nodal cities, while commercial development is relatively uncommon. Their heritage sites are often distributed in strips or clusters along transport and industrial corridors.

R Resource-based cities exhibit fewer revitalization types. Pie charts are mainly single- or dual-type structures, dominated by in-production and static conservation. Cultural exhibition and landscape–park reuse occur infrequently, and commercial development is rare. Heritage sites exhibit scattered distributions.

In summary, a clear hierarchical gradient emerges: core cities exhibit the most diverse revitalization structures, nodal cities show moderate diversity, and resource-based cities exhibit the most single-pattern structures. On the map, this manifests as a progressive reduction in type diversity from urban clusters toward peripheral areas.

4 Discussion

4.1 Multi-scale clustering and path-dependent spatial structure

First, the multi-scale clustering pattern revealed by KDE, ANN, and Ripley’s K confirms that the distribution of industrial heritage in Northeast China is highly structured, rather than random. The coexistence of “macro-level dispersion and micro-level clustering” reflects cumulative historical and institutional processes rather than contemporary planning alone.

At the historical level, the alignment between the SDE major axis and the Chinese Eastern and South Manchurian railways demonstrates that industrial heritage is deeply embedded in early twentieth-century transport-industrial corridors. These corridors connected resource areas, industrial bases, and ports, later becoming the backbone of the planned economy industrial layout. Even after several rounds of restructuring, this early framework has not been erased; rather, it has been reinforced by the concentration of First Five-Year Plan projects along the same axis.

At the institutional level, state-led industrialization since the 1950s concentrated steel, petrochemical, power, and machinery projects in a small number of strategic nodes such as Shenyang, Anshan, Benxi, Dalian, Changchun, and Harbin. This top-down investment strategy produced steep hierarchies of industrial agglomeration. Thus, the heritage clusters in these areas can be interpreted as spatial legacies of historically prioritized industrial development, while low-density peripheral areas generally correspond to places historically marginalized in the national industrialization process.

In essence, the spatial pattern of industrial heritage in Northeast China is anchored by long-term historical trajectories, institutional forces, and transport infrastructures. This path-dependent structure and the spatial inertia it creates provide the basic geographical framework within which contemporary revitalization strategies must operate.

4.2 Space–function coupling: spatial structure and revitalization diversity

Second, the results demonstrate that revitalization patterns are not simply local, project-level choices, but are strongly shaped by multi-scale clustering structures. A clear “space–function coupling” or co-shaping mechanism can be identified, in which spatial concentration produces spatial overlay effects and enables more complex combinations of uses.

In high-density urban clusters, the large number of sites and their spatial proximity generate both economies of scale and network externalities. Functionally related clusters make it possible to develop integrated tourism products, cultural routes, and joint branding, thereby reducing the marginal cost of adding new sites. Under such conditions, in-production activation can be combined with cultural exhibition, landscape–park reuse, and commercial development, creating complex hybrid spaces that simultaneously support production, culture, and leisure.

In medium-density corridors, industrial heritage is distributed in continuous but less dense strips along historic and contemporary transport routes. Here, the key asset is continuity rather than nodal density. Linear strategies—such as industrial heritage routes, thematic tourism corridors, and educational itineraries—are more feasible. These often revolve around one or two dominant functions (typically in-production activation and exhibition), which are extended along the corridor to form coherent narratives of industrial and transport history.

In low-density peripheral areas, by contrast, heritage sites are spatially isolated. Scale effects are weak, service sharing is limited, and it is difficult to organize visitor flows into coherent circuits. As a result, revitalization strategies must rely more heavily on site-specific conditions and tend to be conservative, emphasizing static conservation, basic display, or low-intensity ecological restoration. The prevalence of single- or dual-type revitalization structures in these areas is therefore a rational response to spatial fragmentation and limited demand rather than a mere sign of “lagging” development.

Taken together, these patterns suggest that spatial structure conditions the feasible range of revitalization choices. The more concentrated the spatial structure, the more diverse and intensive the revitalization patterns that can be sustained; the more fragmented the structure, the more likely that single-function, conservative trajectories will prevail.

4.3 Urban functional roles and revitalization

Third, differences in revitalization patterns across city types reveal the mediating role of urban hierarchy and governance capacity. Population size, GRP, and functional positioning do not only correlate with the number of heritage sites; they also shape the “depth” and “direction” of reuse.

Core cities occupy the top of the urban hierarchy and function as administrative, economic, and cultural centers simultaneously. They have stronger fiscal capacities, more diversified industrial structures, and larger markets for cultural consumption. Under these conditions, industrial heritage can be integrated into city-wide strategies—for example, as anchors for creative industry clusters, waterfront regeneration projects, or cultural districts—rather than treated as isolated monuments. The empirical finding that core cities host all five revitalization types and display the most complex combinations reflects this multi-scalar embedding of heritage into broader agendas of city-making.

Nodal cities, in contrast, perform intermediate roles along transport and industrial corridors. They benefit from high accessibility and can attract flows of goods, people, and information, but their economic bases and governance capacities are more limited. Consequently, they tend to adopt “medium-depth” revitalization strategies dominated by in-production activation, cultural exhibition, and occasionally landscape–park reuse. These strategies reinforce their roles as regional gateways and relay nodes without requiring the heavy, long-term investments associated with large-scale commercial development.

Resource-based cities face the most severe constraints. Long-term dependence on coal, oil, or other extractive industries has left them with environmental degradation, economic restructuring pressures, and population loss. For these cities, industrial heritage is often closely associated with unresolved environmental liabilities and contested memories of decline. Under such conditions, prioritizing static conservation, ecological restoration, and low-intensity public space reuse can be seen as a form of risk management: it stabilizes physical structures, mitigates environmental and safety risks, preserves basic industrial memory, and maintains flexibility for future, more intensive interventions when economic and institutional conditions improve.

In this sense, the gradient from diversified patterns in core cities to single or dual patterns in resource-based cities captures not only spatial differences, but also deeper inequalities in governance capacity and development opportunities within the regional urban system. More broadly, this gradient reflects a multi-layered mechanism in which historical industrial layouts, contemporary spatial structures, urban functional roles, and socio-economic conditions interact to shape differentiated revitalization trajectories.

4.4 Comparative perspective: industrial heritage revitalization in northeast China within the East Asian context

From a broader East Asian perspective, the revitalization of industrial heritage in Northeast China shares significant commonalities with, but also diverges notably from, practices in South Korea and Japan. Unlike the Western context of long-term de-industrialization, East Asian countries generally experienced rapid, state-led modernization. Yet, depending on their urban functional roles and governance configurations, they have developed differentiated revitalization trajectories.

First, the experience of core cities in Northeast China (such as Shenyang and Changchun) shows clear parallels with the revitalization patterns observed in Incheon, South Korea. As Cho and Shin (2014) argue, industrial heritage conservation in Incheon is characterized by a persistent tension between “conservation” and “economization”, with many former industrial sites being converted into art platforms or commercial districts to drive waterfront and inner-city regeneration. In a similar way, our results indicate that core cities in Northeast China host the most diverse portfolios of revitalization types and tend to achieve functional transformation through the intensive integration of “Cultural Exhibition” and “Commercial Development”. However, there is an important difference in the underlying drivers: while Incheon’s model increasingly reflects community participation and market-driven aestheticization under a growth-oriented urban competitiveness framework, core cities in Northeast China remain strongly shaped by top-down, government-led planning. In this context, industrial heritage is mobilized primarily as a strategic resource for city branding, regional positioning, and the construction of national narratives.

Second, the revitalization challenges faced by resource-based cities in Northeast China (such as Fuxin and Daqing) exhibit structural similarities to those in industrial cities like Kitakyushu in Japan. Both regions confront the urgent task of shifting from heavy pollution and resource depletion toward ecological sustainability. Kitakyushu’s Eco-Town strategy famously converted industrial brownfields into environmental hubs, eco-industrial parks, and educational facilities, advancing a powerful narrative of “from pollution to ecology” (Yin, 2014). In a comparable vein, our data show that resource-based cities in Northeast China are dominated by “Static Conservation” and “Landscape–Park Reuse”, reflecting a shared “safety-first” logic in declining industrial regions, where ecological restoration and low-intensity use are prioritized over large-scale commercial redevelopment. Nevertheless, in contrast to Kitakyushu—which has been widely recognized as a model for circular-economy and green-growth development—many resource-based cities in Northeast China remain in relatively early stages of “In-production Activation” or basic conservation; (OECD, 2013; Zhao and Liu, 2021). This gap underscores the need to more effectively integrate ecological remediation with industrial restructuring in the next stage of regional transformation. Taken together, the revitalization of industrial heritage in Northeast China is driven by a multi-layered mechanism that links “historical path–spatial pattern–urban function–socio-economic conditions.”

4.5 A “core–corridor–resource” framework and the “core–radiation–infill” path

Based on the above mechanisms, revitalization strategies can be framed in three levels, tightly aligned with spatial structure: core cities act as multi-functional hubs; corridor regions organize and connect; resource-based cities focus on ecological restoration and protective updating.

In core cities such as Shenyang, Dalian, Harbin, and Changchun, dense heritage clusters and high-level urban functions support complex, multi-functional revitalization. Industrial heritage can be embedded into urban renewal and industrial upgrading strategies, hosting overlapping functions of cultural exhibition, creative industries, tourism, and commerce. These cities become regional heritage hubs and demonstration zones.

Corridor regions—such as Harbin–Daqing and Shenyang–Anshan–Benxi—feature continuous but less dense heritage distributions and strong historic coherence along transport routes. They are particularly suited to cross-city industrial heritage routes, thematic tourism corridors, and industrial landscape belts, organizing heritage resources into linear cultural networks.

Resource-based cities such as Daqing, Fuxin, Fushun, Hegang, Jixi, and Shuangyashan face significant environmental and economic constraints. Their heritage revitalization is best advanced through gradual ecological restoration, heritage park creation, and small-scale exhibition, improving environmental quality and preserving industrial memory while maintaining flexibility for future reuse.

Summarizing these strategies, this study proposes a “core–radiation–infill” path:

Core (C) – high-level, multi-functional activation centers in core cities; Radiation (R) – linear cultural and industrial corridors that radiate from core cities and link nodal cities; Infill (I) – bottom-up, low-intensity but widely distributed micro-revitalizations in resource-based and peripheral cities that “fill in” gaps in the regional network.

This framework helps coordinate the tension between highly concentrated resources and weak activation capacity in peripheral areas, and supports a shift from point-based interventions to region-wide, networked governance of industrial heritage.

4.6 Limitations and future research

Despite the systematic analysis of spatial patterns and revitalization modes, the study has several limitations. First, it uses cross-sectional data as of 2025 and does not capture the dynamic evolution from abandonment to reuse, limiting the evaluation of long-term outcomes and temporal lags. Second, revitalization patterns are classified primarily based on literature, official lists, media reports, and fieldwork. Although expert validation was conducted, deeper institutional factors such as local governance capacity, community participation, and capital investment mechanisms are not fully captured. Third, the study area—Northeast China—has specific characteristics of resource-based industrial structures and planned-economy legacies. While the proposed “space–function coupling mechanism” and “core–radiation–infill” path likely have broader relevance, they require further testing and refinement through empirical research in other old industrial regions (e.g., the middle Yangtze River or North China energy cities).

In terms of future research, three directions are particularly promising. First, time-series analyses could be introduced to trace the transition of industrial sites from production to abandonment and reuse. Second, more attention could be given to institutional drivers, including local governance, community engagement, and financing arrangements, to reveal the social dynamics of revitalization. Third, cross-regional comparative studies could apply and test the framework proposed here, thereby refining the model and broadening its applicability for sustainable industrial heritage reuse.

5 Conclusion

This study systematically examines the spatial patterns, clustering structures, and revitalization modes of industrial heritage in Northeast China, analyzing how population size, economic strength, and urban typology shape these patterns.

First, industrial heritage in Northeast China follows a “local clustering and overall dispersion” pattern. KDE and SDE identify four major concentrations: the central-southern Liaoning urban agglomeration, the Harbin-Daqing corridor, the Changchun central cluster, and the Dalian coastal area, forming a recognizable multi-core structure. While global Moran’s I suggests no strong global autocorrelation, ANN and Ripley’s K reveal significant clustering at small-to-medium scales, confirming a multi-scaled pattern in which regional dispersion coexists with localized agglomeration.

Second, revitalization outcomes are dominated by in-production activation, cultural exhibition, and landscape–park reuse, indicating a dual functional profile of productive continuity and public-oriented reuse. Importantly, revitalization portfolios display a clear hierarchy across the regional urban system: core cities concentrate both site numbers and functional diversity, whereas intermediate corridor and nodal cities tend to adopt more stable portfolios centered on production and exhibition. Resource-based and peripheral cities show narrower and more conservative structures, dominated more often by static conservation and landscape–park reuse, with limited intensive redevelopment. Together, these findings indicate that spatial pattern and revitalization structure are closely coupled, resulting in a clear core-radiation-infill differentiation across the regional urban system.

Secondly, the results support a clear space-function coupling logic: spatial distribution conditions the feasible range of revitalization choices, and urban capacity shapes the achievable depth of reuse. In general, higher-density distributions benefit more from overlay effects and can sustain more diversified and intensive portfolios, while fragmented distributions face weaker integration conditions and therefore more frequently adopt conservative pathways. Moreover, population size, GRP, and city type systematically relate to revitalization depth: stronger cities are more likely to support multi-functional and intensive reuse, while resource-constrained cities tend to follow cautious and incremental trajectories.

Thirdly, building on the framework in Section 4.5, this study summarizes a differentiated “core–radiation–infill” pathway for old industrial bases. At the core level (C), core cities should embed industrial heritage into broader urban renewal and industrial upgrading agendas and develop multi-functional hubs where conditions allow. At the radiation level (R), corridor regions should organize cross-city linear cultural networks (e.g., routes and corridor narratives) to enhance connectivity and diffusion. At the infill level (I), resource-based and peripheral cities should prioritize flexible, low-intensity interventions—such as ecological restoration, heritage parks, small-scale display, and micro-renewal—to improve environmental quality and accessibility while maintaining flexibility for future upgrading. Overall, this pathway provides a practical framework for shifting from isolated site-based projects to regionally coordinated, networked governance.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material, further inquiries can be directed to the corresponding authors.

Author contributions

YF: Data curation, Project administration, Writing – review and editing, Writing – original draft, Formal Analysis, Methodology, Investigation, Conceptualization, Resources, Software, Funding acquisition. S-JL: Writing – original draft, Supervision, Writing – review and editing. M-SK: Writing – original draft, Project administration, Writing – review and editing. XW: Writing – review and editing, Writing – original draft, Data curation, Conceptualization, Formal analysis. WD: Writing – review and editing, Writing – original draft, Visualization.

Funding

The author(s) declared that financial support was received for this work and/or its publication. Development of Smart Tourism Products Based on MR Technology for the Red Culture of the Northeast Anti-Japanese United Army (Project No. 20240304012SF) by Xinying Wang.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Generative AI statement

The author(s) declared that generative AI was not used in the creation of this manuscript.

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