The New Retail is an emerging e-commerce model in recent years. It claims innovation in creating competitive advantages through intensive Internet-based data to promote online-to-offline interactions. Whether this self-claimed innovation would cause a different rationale in the choice of store locations is a subject worthy of investigation. In this article, taking Beijing’s Starbucks and Luckin coffee stores as cases, we build a two-layer business location choice model to examine the problem. The model consists of a macroscale (city-wide) submodel and a microscale (street block) submodel. Results show that the macroscale store location choice logics for the two are consistent, with differences predicted by economic geographical laws, thus rejecting the hypothesis of the big-data-driven location choice advantage for the New Retail. Results from the microscale model, however, suggest that the difference between the two brands is better explained with cognition and behavioral theories, where the delivery agents, rather than the customers’ environmental cognition characteristics, shape the New Retail’s peculiar store location preference over less “fancy” urban locales. We argue this unique location preference is indeed something new in the New Retail. We conclude the article with discussions on the relevance of the cognitive and behavioral perspectives in the business location choice problem, and suggest incorporating the microscale layer that explicitly considers these aspects in addressing the problem in real-world settings.

Background: Classic locational principles predict a picture of urban intensity decaying from the city center to the periphery under ideal assumptions. However, various exogenous factors can influence the real-world urban intensity and often deviate from the theoretical pattern. The specific mechanisms are worthy of exploration and are of potential theoretical and practical significance. Methods: In this paper, we consider two city districts with typical urban locations, namely, Changping and Chaoyang in Beijing, and construct mechanistic models of the status quo urban intensity (floor area ratio, FAR) utilizing multisource spatiotemporal big data. We further compare these models with the “theoretically ideal” FAR patterns as would be predicted by applied locational theories. Results: We find that the status quo FAR distribution generally conforms to the theoretical predictions but still exhibits regional deviations that can be explained by historical inertia and influence from particular policies. Conclusion: We conclude this paper with discussions on the findings’ methodological and practical implications for urban planning institutions, especially in a transition economy context.

Context The value realization of the ecosystem services (ES) with directional ecosystem service flows (ESF) is sensitive to the way ES supply and demand are spatially coupled, of which the nature of the propagation dynamics of ESFs and the mechanisms of effective demand generation remain to be clarified.Objectives The paper focuses on a typical directionally flowed urban ES, the park-recreational service, and proposes a formal model for analyzing its ES value realization mechanism, which discerns the ES value contribution of distance-decaying ES supply from the that of the demand-side factors. A cellular big-data supported empirical study conducted in Ningbo, China is conducted as a case illustrating the application of the model.Methods Statistical physical analysis on people’s park visitation behavior is performed to uncover the distance decay patterns of recreational ESF propagation. Further, with the conceptualization of the ES-consuming propensity, which captures the demand-side factors influencing effective ES demand generation including population demographics, socio-economic status, and urban place semantics, we construct spatial regression models that explain these factors’ contributions in ES value realization.Results The statistical physical analysis reveals both universality and park type-specific idiosyncrasies in the ESF distance decay patterns, corroborating the findings in previous studies. The spatial regression models yield up to 80% explanatory power, and results show that the ES-consuming propensity accounts for one quarter of the variation of realized ES value, with endogenous factors dominating effective ES demand generation while exogenous factors less influential.