Nowadays, the status quo of color steel building complexity has attracted the attention of many scholars, and its spatial logic analysis, spatiotemporal characteristics, spatiotemporal evolution, coating aging, and fire risk analysis are the focus of attention. However, the existing studies mainly focus on urban land use, urban expansion, industrial economy, environmental pollution, urban-rural differences, and other issues [
16,
17]. Research on the spatiotemporal evolution and distribution patterns of color steel plate buildings is still relatively scarce in terms of theoretical and methodological frameworks. This study combines GIS spatial analysis technology and spatial theory methods to study the spatial coupling relationship of color steel plate buildings and also analyzes the spatiotemporal evolution of color steel plate buildings in urban areas of Lanzhou City.
2.1. Study on the Spatiotemporal Evolution Rules and Characteristics of Typical Geographic Entities
Geographic entities are independent natural or artificial features in the real world with spatial location and common attributes [
18,
19], which may be objective houses and vegetation or political districts and contour lines abstracted by human beings. Geographic entities can act as a natural bridge between geographic information and various thematic information, enabling the integration and sharing of information. The data model based on geographic entities has many advantages. First, geographic entities as cognitive units are more in line with the cognitive habits of the general public and are easy to understand and use by non-specialists; second, geographic entities can provide a basis for linking geographic information and other thematic data; and third, they provide a reliable way to realize entity-level data updates [
20]. Geographic entity data adopts the entity-oriented modeling method, with geometric elements as the basic unit of spatial data expression and classification hierarchical organization [
21]. The conceptual model of geographic entity data is shown in
Figure 1, which consists of an entity layer and an element layer. The element layer is the constituent unit of geographic entities, expressed as points, lines, surfaces, and bodies, which represent single, connected, and homogeneous geometric objects in the space, and these points, lines, surfaces, and bodies are uniquely identified by the element identification code. The entity layer consists of one or more graphic elements, usually expressed by geometric complexes but can also be expressed by geometric primitives identified by entity codes [
22,
23].
Geographic entity data is the foundation and bridge of the geographic information platform. The production of map data is built based on basic geographic information data, firstly forming geographic entity data; then adding social and economic information based on geographic entity data to form governmental electronic map data; adding comprehensive social information based on governmental electronic map data and forming public electronic map data after encrypted processing [
8]. With the in-depth development of smart city construction based on geographic entity location information integration of natural resources, transportation, environmental protection, emergency disaster mitigation, population, economic and credit information, and other multi-sectoral data to build urban management, big data has become an inevitable trend in the development of the current smart city [
24]. The extension of information from the natural attributes of houses to their social attributes realizes the specialized needs of different departments while ensuring that the basic results meet relevant technical standards of the surveying and mapping industry. This can provide refined and accurate geographic information and location services for smart city construction, with extensive applications in areas such as comprehensive renovation of residential communities, finding illegal houses, and de-scaling urban development [
25].
2.2. Spatial and Temporal Evolution Law and Characteristics of Color Steel Plate Building Group
Rapid and disorderly urban growth has triggered urban expansion and irreversible land cover changes. Medium-sized cities, in particular, have become increasingly important in achieving sustainable urban development and have become the focus of research and policymaking. However, there is a clear gap in assessing urban sprawl in these cities [
26]. To address this challenge, the United Nations established 17 Sustainable Development Goals (SDGs) in 2015, with a particular emphasis on the goal of achieving sustainable cities and communities. The achievement of this goal requires researchers to be able to visualize and analyze the status and trends of SDG indicators [
27]. In this regard, Wang et al. developed a geospatial big data analysis engine based on SuperMap iObject for Java and Apache Spark [
28]. And based on this, they proposed an innovative solution: a spatiotemporal big data visualization framework that integrates open-source map libraries, visualization libraries, and modern web development techniques and utilizes Spark Streaming for real-time data processing. Meanwhile, they mapped the results in real-time to DataFlowLayer, which supports high-performance geospatial big data analytics by using GIScript (
https://github.com/skyswind/GIScript?tab=readme-ov-file, accessed on 25 May 2024) and iDesktop Cross (
https://supermap-idesktop.github.io/SuperMap-iDesktop-Cross/, accessed on 25 May 2024) to support high-performance spatial and temporal big data spatial analysis for more accurate assessment and planning of urban development [
29].
Among the many areas of sustainable urban development, the study of spatial differences in housing conditions is of particular importance. Housing is not only a fundamental issue in determining the sustainable development of cities and regions, but it is also crucial for understanding how housing conditions affect the economic and social dimensions [
30]. Peter et al. assessed the spatial constraints associated with inner-city residential construction by creating an Index of Residential Development (IoRD), which can help policymakers identify spatial development targets for further planning [
31]. Meanwhile, a study by Osman et al. revealed how the urban characteristics of unplanned settlements can affect sustainable development, especially in regions such as Egypt [
30]. In addition, Bai et al. used a multi-level fuzzy integrated evaluation method to construct community sustainable development evaluation indicators from a micro perspective, which provided a quantitative tool for community development [
32]. And de Siqueira et al. provided another perspective by evaluating the relationship between urban development actions and sustainability based on the LEED-ND indicator system [
33].
Detection of temporary color steel buildings and their spatial and temporal patterns and characterization are important for many developing cities, and areas with dense distributions of color steel buildings are usually problematic with high population densities and high levels of sustainability and risk [
34]. Therefore, in the context of exploring sustainable urban development, color steel buildings also serve as an important measure of sustainable urban development, which is distributed in large quantities in large and medium-sized cities and are widely used in buildings such as factories, warehouses, workshops, and private houses due to its advantages of low price and convenient installation [
35], etc. Therefore, the study of the spatial and temporal distribution pattern of the urban color steel building complex is important for understanding the city’s operating conditions, solving urban problems, and promoting sustainable urban development. Hou et al. proposed a new benchmark dataset for retrieving color steel sheds from Google Earth images with a total number of 2407 remote sensing images [
36]. Samat et al. used Sentinel-2A/B MSIL2A imagery to map blue and red color steel buildings across China [
37]. Hong et al. utilized remote sensing imagery and instrumental experiments to analyze the influencing factors of the reflectance of color steel plates and to investigate their spectral characteristics, providing technical support for information extraction [
38]. Sun et al. used CNN-based blue steel roof information extraction and Gaofen-2 imagery to analyze the distribution of colored steel buildings [
39]. Dong et al., considering the safety hazards posed by lightweight and heavy floating objects in railway operation environments, innovatively combined a large model to propose a dual-branch semantic segmentation network for extracting color steel building structures [
40]. This aims to mitigate safety incidents caused by heavy floating objects resulting from color steel building structures. Most of the existing research’s attention to the color steel plate building clusters in the city focuses on fire spacing [
41,
42], fire characteristics [
43], fire rescue countermeasures, and the illegal remediation of color steel plate buildings in the urban area, etc. And there are fewer studies on the extraction of its spatial elements, spatial distribution characteristics, etc., especially in urban industrial parks, where the large-volume color steel plate buildings are clustered and are paid less attention to. Industrial parks appeared at the end of the 19th century to carry industries and promote industrial agglomeration, and in the long course of development, the function of industrial parks has gradually changed from single to diversified. Due to the slow development of industry in Northwest China, the construction of industrial parks is also relatively simple. Color steel plate construction is low-cost and can realize large-span architecture, so it exists in a large number of industrial parks in the city. Based on Google Earth image data, Gao et al. analyzed the coupling relationship between industrial parks and color steel plate building clusters in Yinchuan City and found that the core density area of color steel plate building clusters overlapped with national and provincial industrial parks [
44]. Li et al. extracted data on color steel building clusters from Google Earth imagery and combined various spatiotemporal analysis methods to investigate the spatiotemporal distribution patterns and evolutionary trends of industrial parks in Xining City [
45]. Industrial parks, transportation networks, and land use are the main factors affecting the spatial distribution of color steel plate building clusters. In the key cities of Northwest China, there is a certain overlap between the color plate building clusters and industrial parks, and the study of the spatial and temporal distribution characteristics of the color plate building clusters is of great significance to the study of industrial parks in Northwest China and the analysis of the urbanization process [
46].
Li et al. investigated the spatial distribution characteristics of small color steel buildings and large color steel buildings by kernel density analysis, average nearest neighbor, and standard deviation ellipse correlation methods for Anning District, Lanzhou City, respectively [
47]. Ma and others selected color steel buildings in the Anning District of Lanzhou City as the research object and studied color steel buildings in terms of temporal and spatial changes, stability, degree of fragmentation, and aggregation characteristics, concluding that the distribution characteristics of color steel buildings in the region are significant [
48]. One of the main directions of current research is to deconstruct the geographical distribution of color steel buildings using remote sensing imagery data. Another important direction of research involves developing new methods and integrating various data sources to characterize aspects such as urban spatial morphology and development level related to color steel buildings. Zhang et al. used satellite imagery to dynamically monitor color steel buildings, revealing the spatial morphology and development level of the Munyaka Region in Kenya [
49]. Wang et al. through the analysis of the spatial layout and agglomeration characteristics of color steel plate houses in urban areas, drew the connection between large and small color steel plate houses and the spatial structure of other cities and pointed out that large and small color steel plate houses are mostly located in industrial districts and that small color steel plate houses are mostly found in urban villages and urban-rural junctions [
50]. Gao et al. selected the concentrated areas of color steel buildings in urban villages and industrial parks, carried out the determination of area and spacing, and registered the corresponding use [
51]. The extensive spatial analysis research on color steel buildings provides scientific support for sustainable urban development.
As shown in
Figure 2, we have outlined the current state of research on color steel plates. This research is driven by the trend of urbanization and the context of the Sustainable Development Goals (SDGs). The types of research on sustainable cities are diverse, encompassing urban infrastructure, urban buildings, urban greening, and more. Color steel buildings fall under the category of urban buildings in sustainable cities. Research on color steel buildings is based on foundational studies of base materials and chemical coatings, such as coatings research, and extends to geographic applications like remote sensing extraction and spatiotemporal analysis. However, these related studies mainly rely on government work reports, statistical bulletins, statistical yearbooks, and other statistical data for analysis, with large human influence factors and periodicity and timeliness limitations, making it impossible to conduct dynamic and continuous monitoring. There is a lack of intuitive and dynamic data that can effectively map the temporal and spatial changes of the color steel plate building complex, and the study of the spatial and temporal evolution law is insufficient.