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Daily Urban Systems (DUSs) are not only an attractive concept for planning locations for jobs, housing, schools and retail, but also for managing services such as public transportation and health care. If we can match geographically demand and supply of goods and services, higher levels of spatial efficiency can be reached. Since 50 years most of the research delineating DUSs uses thresholds of commuting levels, thus identifying labor markets polarized towards central cities. Few research grasps the more recent complex interactions within metropolitan areas due to growth and decentralization of activities. In this paper, we use techniques of complex network theory, namely community detection, on nearly 4,500,000 Belgian commuting links to define DUSs. Secondly, we explore differences for DUSs by gender and by income group. The results confirm the usefulness of community detection techniques for delineating Daily Urban Systems. Commuting patterns of females and low and very low income commuters are geographically more restricted than those of male and high and very high income commuters.

The recent proliferation of big data sources has given rise to a data deluge. Network theory has become the standard methodology to frame, develop and analyze such massive datasets. In line with the critique of Schwanen ( 2016), we argue in this paper that initiatives confronting networkbased insights with (qualitative) location-and domain-specific insights are necessary in understanding, discussing and advancing the role network analysis can play in geography. By iterating a community detection algorithm to achieve different levels of communities and quantifying the borders between them through damping values (as proposed in Grauwin et al., 2017), we show how to derive the hierarchical structure within the logistics buyer-supplier network in Belgium. This allows for a richer geography, which has been missing in current big data studies.

The use of location models in retail businesses is well‐established, particularly in the grocery sector. Many alternative methods are in use today but the spatial interaction model (SIM) has a proven record of success. To date, that success relates purely to face‐to‐face activities, modeling and predicting visits by consumers to retail outlets. However, grocery retailers are cutting back on store investments and concentrating on investment in the convenience market and e‐commerce: the latter has now reached a 7.2% share of the U.K. grocery market, with continued growth forecast. Although spatial models are used extensively for helping to locate new convenience stores, so far e‐commerce has not been built into existing retail location models. Yet e‐commerce seems to be a spatial activity. Extensive evidence demonstrates the geography of demand and supply are as important in groceries e‐commerce as they are in face‐to‐face grocery retailing. We therefore take up the challenge of incorporating e‐commerce into classic location models. Methodologically, we find the standard distance deterrent term in the production‐constrained SIM unsuitable for modeling e‐commerce flows: we explore inverting this term and find extensive gains in prediction accuracy, an interesting finding that contributes to the ongoing applied SIM literature.

Shared (electric) mobility is still facing challenges in terms of reaching its potential as a sustainable mobility solution. Low physical and digital integration with public transport, a lack of charging infrastructure, the regulatory barriers, and the public nuisance are hindering the uptake and organization of shared mobility services. This study examines the case of the shared mobility hub, a location where shared mobility is concentrated, as a solution to overcome these challenges. To find ideas informing how a network of shared mobility hubs can contribute to sustainable urban mobility and to overcome the aforementioned challenges, a business model innovation approach was adopted. Focus groups, consisting of public and private stakeholders, collaboratively designed five business model (BM) blueprints, reaching a consensus about the value creation, delivery, and capture mechanisms of the network. The blueprints, defined as first-/last-mile, clustered, point-of-interest (POI), hybrid, and closed mobility hub networks, provide alternative solutions to integrate sustainable transportation modes into a coherent network, enabling multi- and intermodal travel behaviour, and supporting interoperability, sustainable land use, and ensured access to shared (electric) travel modes. However, which kind of network the local key stakeholders need to commit to depends on the local policy goals and regulatory context.