From Leapfrog Expansion to Infill Renewal: Urban Spatial Evolution of Panjin 1990-2020 and Infrastructure Transformation of a Resource-Based City

From Leapfrog Expansion to Infill Renewal: Urban Spatial Evolution of Panjin from 1990 to 2020 and Infrastructure Transformation in Resource-Based Cities

Introduction Global urbanization continues to accelerate. Urban land accounts for less than 1% of the Earth's surface, yet it accommodates over 50% of the population and 70%–90% of economic activities. Since the reform and opening-up, China's urbanization rate has jumped from 17.9% in 1978 to 63.89% in 2020, with the built-up area expanding sixfold. Against this macro background, the spatial evolution of resource-based cities presents a unique and complex picture. Panjin City in Liaoning Province, as a typical resource-based city, has an urban expansion trajectory from 1990 to 2020 that not only reflects a city's development process but also reveals the deeper logic of urban spatial restructuring against the backdrop of resource depletion. Based on a recent study published in *Scientific Reports*, this article reinterprets the Panjin case from an international infrastructure analysis perspective, offering insights for global urban renewal and infrastructure investment.

Research Methods: Deep Integration of Remote Sensing and Urban Planning This study adopted a novel deep learning model, combined with a time-spectral-texture combinatorial optimization approach, to identify pixel-level land cover change trajectories of impervious surfaces. Piecewise linear regression models were used to determine the timing of urban expansion. This methodology not only achieved a trajectory classification accuracy of 93.10% and a macro F1 score of 92.44%, but also identified the timing of urban expansion with 84.24% accuracy. This high-precision spatiotemporal mapping technology provides urban planners with evidence-based decision-making tools. Globally, remote sensing and big data analytics are reshaping the underlying logic of infrastructure planning, and the Panjin case epitomizes this trend.

Core Findings: A Profound Shift in Expansion Speed and Patterns The research data show that Panjin's built-up area increased from 312.75 square kilometers in 1990 to 489.49 square kilometers in 2020, a growth rate of 56.51%, with an average expansion speed of 5.89 square kilometers per year. More importantly, spatial compactness showed a declining trend, and the expansion pattern underwent a significant transformation around 2016—from early leapfrog and edge expansion to infill expansion. This transformation is highly significant: leapfrog expansion often leads to disorderly urban sprawl and inefficient infrastructure allocation, while infill expansion implies that the city places greater emphasis on optimizing and renewing existing space. This aligns closely with the global trend of shifting from extensive growth to smart growth.## The Transformation Dilemma of Resource-Based Cities and International Comparisons The case of Panjin is not an isolated one. Reference studies have pointed out that coal mining areas, oil cities in Alberta, Canada, and the Ruhr region in Germany all experienced similar development trajectories. Resource-based cities often rely on a single resource industry during periods of economic prosperity, leading to disorderly urban land expansion. When resources gradually become depleted and urban development stagnates, existing built-up areas face problems such as aging infrastructure and low land use efficiency. The shift in Panjin's expansion model indicates that the city is seeking a transition from outward expansion to endogenous renewal, which has direct guiding implications for infrastructure investment directions: shifting from supporting infrastructure construction for new development zones to urban renewal, stock renovation, and efficiency improvement projects.

The New Logic of Infrastructure Investment: From Increment to Stock The traditional logic of infrastructure investment focused on serving urban expansion, including building new transportation networks, energy pipelines, and public facilities. However, Panjin's infill expansion model after 2016 signals that infrastructure demand is undergoing structural changes. Infill expansion implies redevelopment of existing urban areas, which requires infrastructure investment to focus on upgrading, smart operations, and functional optimization. For example, the renovation of aging pipeline networks, the management of traffic congestion, the improvement of energy efficiency, and the embedding of digital infrastructure may all become new investment priorities. For infrastructure investors, identifying changes in urban expansion models is an important prerequisite for seizing the right timing for capital allocation.

Lessons from Urbanization in the Global South Panjin's experience offers valuable references for countries in the Global South. Many cities in developing countries are in a phase of rapid expansion and face the risk of infrastructure fragmentation brought about by leapfrog expansion. Using remote sensing technology to monitor urban expansion can help policymakers anticipate spatial form changes in advance, thereby planning infrastructure investment more scientifically. China's practice demonstrates that shifting from leapfrog expansion to infill renewal is an inevitable path for cities toward sustainability. This process involves not only the restructuring of physical space but also the coordination of financing models, governance capacity, and long-term strategies.

Conclusion: Data-Driven Urban Governance and the Future of Infrastructure The analysis of Panjin's urban expansion from 1990 to 2020 provides a micro-level yet profound sample. It shows that urban spatial evolution is not disorderly or random, but is comprehensively influenced by resource endowments, economic cycles, and policy orientations. At the crossroads of resource-based city transformation, precise remote sensing monitoring and spatial analysis provide a reliable basis for infrastructure investment. For the global infrastructure industry, this means that decision-making no longer relies on empirical judgment but is based on empirical data. In the future, as more cities apply similar methods, infrastructure investment will become more precise and efficient, truly serving the long-term sustainable development of cities.