The retail industry plays a vital role in the socio-economic development of urban areas, closely reflecting the scale of urbanization and the consumption patterns of residents (Dunne et al., 2011). As urban populations grow and their consumption power increases, the spatial pattern and evolution of retail commercial outlets have become central topics in urban studies, with implications for economic planning, social dynamics, and policy development (Levy and Grewal, 2023; Fernie et al., 2015). Retail outlets, as core elements of urban commercial activities, not only serve as centers for consumption but also provide insight into the transformation of urban spatial structures and economic patterns (Guy, 2002). The retail industry, as a critical component of urban economies, has undergone significant spatial and functional transformations in recent decades (Getis and Getis, 1966).

Various factors have driven these changes, including rapid urbanization, evolving consumer preferences, and technological advancements (Hao et al., 2021). Retail outlets, once concentrated in city centers, are now expanding into suburban areas and beyond, spurred by strategic policies and the development of retail networks (Wang and Coe, 2021). As highlighted by researchers from Universiti Kebangsaan Malaysia (UKM), the evolution of retail distribution patterns reflects broader socio-economic trends and has implications for both urban planning and economic growth (Ahamad et al., 2022; Azmi et al., 2023; Shaharudin Bin et al., 2011; Shaharuddin Bin et al., 2010; Othman and Jaafar, 2016; Saleh et al., 2016). Moreover, e-commerce is what Rayport and Jaworski referred to in 2003 as trade in goods and services on the Internet. Its integration with physical retail spaces has become a key factor in reshaping retail strategies (Rayport and Jaworski, 2003; Nertinger et al., 2022; Goel, 2023; Kirby-Hawkins et al., 2019). This study aims to explore these trends through a systematic review of the literature, focusing on the factors driving the distribution and size evolution of commercial outlets.

The spatial organization of retail outlets, including their distribution, density, and organizational structure, is a key indicator of urban development (Baviera-Puig et al., 2016; Kickert et al., 2020). For instance, Figure 1 presents the kernel density distribution of retail outlets in the Fengtai District of Beijing, based on point of interest (POI) data collected from Gaode Map in 2023. The dataset includes all retail categories under the Retail Trade division as defined in the National Economic Industry Classification and Codes (GB/T 4754-2011). The data were cleaned, processed, and validated before being analyzed using ArcGIS 10.6. The kernel density estimation calculates the concentration of retail outlets per square kilometer (Benoit and Clarke, 1997; Roig-Tierno et al., 2003).

The base map was obtained from the Alibaba Cloud data visualization platform (central coordinate: 116.27915, 39.8534). The coordinate reference system used is WGS 1984, projected with the Web Mercator projection (EPSG: 3857).

The spatial distribution of retail outlets interacts closely with urban form, manifesting in retail clusters near transport hubs, commercial centers, and high-density residential areas. This reflects broader changes in the economic landscape, influenced by urban planning, shifting consumer preferences, and the adoption of digital retail technologies (Zhang, 2008). In developed regions such as the United States, Europe, and Japan, the evolution of retail spatial structures has closely paralleled urban expansion and socio-economic change (Levy and Grewal, 2023; Guy, 2002). Retail activity has progressively shifted from traditional central business districts (CBDs) to decentralized suburban shopping malls during the post-war period, and more recently, to compact, mixed-use developments and transit-oriented retail clusters (Zhang, 2008). Furthermore, the integration of e-commerce has introduced hybrid spatial forms, blurring the boundaries between physical and digital retail environments (Rayport and Jaworski, 2003; Zhang, 2008; Burger et al., 2014; Kirby-Hawkins et al., 2019). Early studies on urban commercial location models, such as those developed by the Chicago School, laid the foundation for understanding the hierarchical and distributional patterns of urban retail systems (Yang, 2000; Guy, 2002; Hughes and Jackson, 2015).

The spatial patterns of retail outlets are shaped not only by urban planning and land-use policies but also by market forces, technological innovation, and changing consumer demands. With the rise of e-commerce, the retail landscape has witnessed a convergence of online and offline commercial spaces, introducing new complexities in spatial organization—such as the emergence of hybrid retail formats, the proliferation of last-mile delivery hubs, and the strategic placement of click-and-collect locations within urban cores (Rayport and Jaworski, 2003; Levy and Grewal, 2023; Feichtinger and Gronalt, 2021; Kirby-Hawkins et al., 2019). Recent studies on retail outlets have concentrated on analyzing urban commercial centers, hierarchical structures, and agglomeration effects, which have been crucial in understanding the dynamics of retail spatial patterns (Guy, 2002). As urbanization accelerates globally, these patterns have become increasingly multifaceted and interdisciplinary, drawing on regional economics, urban planning, and consumer behavior studies.

Technological advancements, particularly in Geographic Information System (GIS) and big data analytics, have significantly enhanced the study of spatial distributions. GIS tools have allowed for more precise and granular analysis of retail locations and their evolution over time (Benoit and Clarke, 1997; Roig-Tierno et al., 2003). Studies using point of interest (POI) data, such as those by Miao et al. (2021) in Beijing and Roig-Tierno et al. (2003) in Murcia, Spain, have provided valuable insights into urban spatial structures, focusing on the distribution and clustering of retail outlets. These methods have become indispensable in analyzing retail patterns, particularly in large cities, and have contributed to the development of location-based decision models (Hao et al., 2021). By leveraging POI data, these studies have furthered our understanding of the intricate dynamics of retail site selection, offering a nuanced view of the spatial behavior of consumers and retailers alike.

Despite the wealth of research on the spatial distribution of retail outlets in major urban centers, there remains a notable gap in the literature concerning small- and medium-sized cities. In the context of China’s urban hierarchy, these typically refer to cities with a resident population of less than 1 million (small) or between 1 and 5 million (medium) (Wei, 2015; Chen, 1991). Most existing studies, such as those by Miao et al. (2021), have concentrated on first-tier cities—a term commonly used in China to denote the most economically developed and globally connected metropolitan areas, such as Beijing, Shanghai, Guangzhou, and Shenzhen (Li et al., 2022; Wei, 2015). These cities often dominate academic research due to their size, data availability, and policy prominence, while the spatial dynamics of retail development in lower-tier or less densely populated urban areas remain underexplored. These cities present unique challenges and opportunities for retail location analysis as they may exhibit distinct patterns in terms of consumer behavior, market demand, and retail clustering (Hao et al., 2021). As a result, understanding the spatial distribution of retail outlets in small- and medium-sized cities could offer valuable insights into broader urban retail trends and help refine location-based decision-making models.

Clarke (2000) emphasized the need to understand the relationship between small- and medium-sized retailers and different levels of urban development. In Clarke’s study, small- and medium-sized retailers are defined in line with Organization for Economic Co-operation and Development (OECD) standards, where small enterprises typically employ 10–49 people and medium-sized enterprises 50–249 people, based on workforce size (Organisation for Economic Co-operation and Development (OECD), 2005). Recent studies by Zhang et al. (2021) have started to bridge this gap by analyzing retail evolution in southern and northern China, respectively. These studies highlight the varying dynamics of retail distribution and consumer behavior across regions with different levels of urbanization. For example, Zhang et al. (2021) explored how retail outlets in southern China have evolved in response to rapid economic growth and urban expansion, while Liu and Ma (2020) examined retail patterns in northern China. These studies contribute to a more nuanced understanding of retail evolution and demonstrate the importance of considering regional factors in retail location models. This systematic literature review aims to address significant gaps in the existing body of research regarding the spatial distribution and evolution of retail outlets in China (Yang et al., 2024; Yannan et al., 2020), with particular emphasis on the disparities between large and small cities. Two key objectives guide this review: (1) to analyze the characteristics of the spatial structure of urban retail outlets and (2) to explore the evolutionary process of retail development, focusing on the economic, social, and technological drivers that shape retail location dynamics.

This study provides a comprehensive analysis of the evolving spatial patterns of retail outlets, highlighting the dynamic and multifaceted factors that influence retail distribution across global, national, and urban scales. As retail outlets transform from simple, localized businesses into complex, integrated commercial hubs, they are profoundly shaped by technological advancements, government policies, transportation infrastructure, and changing consumer behaviors. The findings underscore that retail evolution is not a linear process but a complex interplay of economic, social, and technological forces that differ across regions and time periods (Levy and Grewal, 2023; Bonfrer et al., 2022; Zhang et al., 2023).

Furthermore, to better understand the complexity and hierarchical nature of retail spatial distribution, future research could adopt fractal dimension analysis, particularly the two-scale fractal dimension (He and Liu, 2023). This mathematical approach, widely used in urban geography and complex systems research, quantifies the self-similarity and spatial intricacy of distribution networks. Applying this method to retail clusters could reveal how spatial complexity evolves with urban expansion, decentralization trends, or policy interventions. Such an approach offers a powerful lens to analyze non-linear retail development patterns and complements the qualitative findings synthesized in this review.

Government policies emerge as a critical determinant in shaping the geographic spread of retail outlets, often serving as a guiding framework for urban and economic development (Zhang and Wei, 2015; Hao et al., 2021). The growing influence of consumer preferences—toward convenience, quality, and the overall shopping experience—has led to the rise of specialized retail formats and a shift toward larger, more integrated commercial spaces (Türk et al., 2014; Mihaela, 2015). In tandem with this, advancements in transportation and the rise of e-commerce have significantly altered the accessibility and sustainability of physical retail spaces (Hagberg and Fuentes, 2018; Bonfrer et al., 2022).

The continued decentralization of retail from city centers to suburban areas reflects broader societal trends of urbanization and counter-urbanization (Champion, 2001; Kickert et al., 2020). As cities evolve and consumer demands shift, retailers must adapt to new spatial dynamics, leveraging both physical and digital platforms to meet the diverse needs of their target markets.

To further illustrate the intra-urban disparity in retail development, Figure 5 presents a comparison between Dadongmen Street and Dahongmen Street in Fengtai District, Beijing. Data sources: Retail POI from Amap API; administrative boundaries from Beijing Municipal Open Data; base layers from Esri and OpenStreetMap. While Dadongmen exhibits a high-density clustering of retail outlets concentrated around key commercial corridors, Dahongmen shows a scattered and low-density pattern, lacking defined retail cores. This spatial contrast reinforces our broader findings regarding the uneven distribution of retail infrastructure within urban territories, driven by factors such as accessibility, zoning, and local demand profiles.

Furthermore, socio-cultural factors, particularly in culturally and religiously diverse regions, play a crucial role in shaping the design and location of retail outlets, ensuring they cater to the distinct values and preferences of local communities (Kulke and Suwala, 2016; Erkip and Ozuduru, 2015). Moreover, integrating two-scale economic modeling frameworks—such as a revised Evans model—offers an advanced approach to simulate how retail networks respond to macroeconomic fluctuations and policy interventions (Evans, 1985). These models can formalize the relationships between spatial heterogeneity (e.g., clustering intensity and entropy) and economic outcomes (e.g., consumption patterns and GDP growth). As emphasized by Batty (2007), the integration of spatial metrics into dynamic economic modeling can serve as a powerful tool to evaluate systemic resilience and complexity. Future empirical research could apply such models to multi-scalar datasets to bridge spatial theory and urban economic forecasting.

In addition, the application of two-scale population-economic dynamic models presents a promising avenue for forecasting retail demand (Anas et al., 1998). These models integrate micro-scale demographic changes—such as population flows, income mobility, and land-use transitions—with macroeconomic shifts including urban expansion strategies and national development policies. This multi-scalar framework captures the feedback loop between local retail transformations and structural economic evolution. Incorporating such models could enhance spatial planning accuracy and support adaptive retail infrastructure strategies under demographic and policy uncertainty.

This study emphasizes that the global retail landscape is at a critical juncture, driven by continuous changes in technological, social, and economic contexts. Future research should delve deeper into how these various factors interact and evolve, offering further insights into the future trajectory of retail distribution and the implications for both businesses and urban development. Understanding these forces will allow policymakers and retailers to better anticipate shifts in consumer behavior and adapt strategically to the changing retail environment (Zhang et al., 2023; Levy and Grewal, 2023).

To further explore the implications of spatial complexity in retail systems, future research could adopt quantitative modeling approaches that more explicitly formalize the link between spatial patterns and economic performance. A promising direction involves adapting simplified dynamic economic models—such as a modified Evans model (Evans, 1985)—to examine how variables such as fractal dimension or network entropy correlate with urban economic indicators, including GDP growth or retail productivity. This modeling framework could help quantify the hypothesis that greater spatial diversity and interconnectivity within retail networks foster enhanced economic resilience. Such an approach is consistent with recent work in urban complexity, which emphasizes the integration of spatial structure into economic modeling to better capture systemic urban behavior (Batty, 2005). By incorporating spatial metrics into economic analysis, researchers and policymakers would gain deeper insights into how retail spatial strategies contribute to balanced and sustainable urban development.

In light of the systematic review approach adopted in this study, it is important to reflect on the methodological scope and global relevance of the findings. While the review synthesized a relatively small number of studies, the insights derived reveal consistent patterns in the spatial evolution of retail systems across diverse contexts. This aligns with other systematic reviews in urban and retail research, which also emphasize the critical role of spatial configuration, governance regimes, and socio-economic transitions in shaping distributional outcomes (Németh et al., 2021; Zakharovskyi and Németh, 2021).

We acknowledge that variations in data availability, national research focus, and the exclusion of non-English sources may limit the representativeness of the evidence base. Nevertheless, the synthesis offers a valuable global overview of how retail landscapes are being transformed by urbanization and technological innovation. Future research could build on this work by incorporating multilingual corpora, region-specific frameworks, or automated mapping tools to enhance the breadth and granularity of analysis.

Despite the valuable insights generated through this review, several limitations merit discussion. First, the geographic concentration of studies in North America, Europe, and East Asia highlights a notable imbalance in the global literature on retail spatial patterns. Regions such as Africa, Latin America, and parts of Southeast Asia are conspicuously absent from the reviewed studies. This gap may stem from language-based publication bias, underrepresentation in mainstream academic databases such as Scopus and WoS, or limited research capacity and data availability in these regions. As a result, the findings of this review—though comprehensive within their context—may not fully reflect the diversity of retail spatial evolution worldwide.

Second, the methodological framework adopted—while grounded in a rigorous systematic review—relies on a relatively small and thematically clustered sample of 15 high-quality studies. This constrained sample size, due to strict inclusion criteria, affects the generalizability of the findings. While the identified patterns are robust within urbanized, high-income contexts, their applicability to less formalized or rapidly urbanizing regions remains uncertain.

Furthermore, scale remains a critical issue in retail spatial analysis. Most studies reviewed here focus either on national or urban contexts, with little engagement with intermediate scales (e.g., regional corridors and cross-border retail zones) or multi-scalar interactions (e.g., how global trends cascade into local retail morphologies). Future research should explicitly address these scale dynamics by employing multi-level modeling, comparative regional studies, or cross-scalar GIS analyses to deepen our understanding of retail system evolution in a globalized world.

To advance this field, future systematic reviews should incorporate multilingual databases, include gray literature, and actively seek contributions from underrepresented regions. Doing so would not only improve the global representativeness of research but also uncover alternative retail models and innovations emerging from the Global South.

Ultimately, enhancing methodological pluralism, data inclusivity, and regional equity in retail geography research will not only strengthen empirical generalizability but also support evidence-based policy formulation for inclusive and sustainable urban commercial planning worldwide.

This study reviewed 15 articles that explored trends in the distribution of retail business outlets and the factors influencing these trends, systematically analyzing the evolution of retail patterns at international, national, and urban levels over the past decade. The geographic diversity of the studies, covering regions in Europe, the Americas, Australia, and Asia, strengthens the credibility and generalizability of the findings, offering a comprehensive perspective on global retail distribution.

The key issues addressed in this study revolve around the primary trends and influential factors shaping retail distribution patterns. The research categorized retail distribution trends into three primary dimensions: (1) international trends, (2) inter-city variations within countries, and (3) intra-city dynamics. The influencing factors were further grouped into five major categories: (a) government policies, (b) consumer characteristics, (c) transportation infrastructure, (d) retail formats, and (e) socio-cultural influences. These factors interact in complex ways, contributing to the multifaceted nature of retail distribution across various scales.

The study’s findings underscore the significant role played by government policies, consumer demand, transportation infrastructure, retail formats, and socio-cultural factors in shaping retail distribution. These influences vary by region and scale, resulting in diverse retail patterns globally. For instance, while some regions experience retail decentralization and suburbanization, others may face urban consolidation, highlighting the impact of local contexts on retail evolution. This diversity is crucial for understanding the complexities of retail patterns and their alignment with broader societal and economic trends.

The conclusions drawn from this study offer valuable insights for both retailers and policymakers. For governments, the ability to analyze and understand retail industry developments provides a theoretical foundation for informed urban and commercial planning. Policymakers can use these insights to optimize spatial planning, design policies that foster retail growth, and ensure alignment with retail trends. For retail executives, understanding the evolution of retail layouts and the factors influencing their development can inform strategic decisions, helping to choose optimal locations that align with urban development plans and contribute to long-term business success.

Future research could build on this study by expanding the geographical scope and incorporating diverse cultural, economic, and environmental factors across different regions. This would provide a deeper understanding of how various socio-economic dynamics interact to shape retail distribution patterns. A more extensive exploration of these dimensions across various cultural and economic contexts will enhance our global understanding of the retail landscape and further refine strategies for adapting to the ever-evolving retail environment.