Student Hub Cafe
December 2025

Data Gathering

Open Street Map via Overpass API was used to gather the locations of cafes and academic facilities.
Swiss (GTFS) was used to pull the information of public transportation information.

Information was converted into 3 tables.

Data Synthesis

Data was sorted into a geographical map of BERN with a 500m X 500m grid.
QGIS was used to map the information.

Analysis

The evidence does not point to a single “best” location. Instead, it redefines the problem: cafés in Bern cluster around transport intensity, not educational density, creating a structural separation between where students study and where cafés operate. This opens a limited set of strategic location archetypes, each with distinct commercial implications, risks, and dependencies. What matters most is not footfall volume alone, but how predictable, controllable, and brand-compatible that footfall is under real operational constraints.

What the evidence means for our objectives

Primary objective (student-focused study and collaboration)
The spatial data indicates that the objective is not naturally served by the current café ecosystem. Campus-dense areas are systematically under-served by cafés, while café-dense areas are shaped by transient, multi-purpose traﬃc flows driven by transport hubs.

Implication: achieving a credible “student study and collaboration” positioning requires consciously choosing whether to follow or to counter prevailing commercial geography. This is a strategic decision less so a tactical one.

Secondary objectives (commercial viability, brand coherence, scalability):
Transport-centric locations align with proven café density but dilute student specificity. Campus-proximate locations align with the target user but sit outside demonstrated café clustering, increasing execution risk.

Constraints shaping all outcomes

Budget constraints
• Transport-dense areas imply higher rents but lower demand uncertainty
• Campus-adjacent areas imply lower rents but higher demand uncertainty

Staﬃng capacity
• Commuter peaks require flexible, surge-capable staﬃng
• Study-focused models favour flatter, longer dwell-time staﬃng profiles

Regulatory exposure
• Campus-proximate locations may face stricter zoning or operating-hour constraints
• Central hubs may impose noise, crowd-control, or heritage restrictions

Time-to-market
• Existing café corridors reduce setup friction
• Novel locations extend validation timelines

Brand positioning
• Transport hubs push the brand towards “convenience with seating”
• Campus adjacency allows “academic third place” positioning but demands consistency

What matters most (and what is secondary)

Critical
• Alignment between location logic and brand promise
• Predictability of repeat usage versus raw footfall
• Exposure to uncontrollable competition dynamics

Secondary
• Maximum transport accessibility once a minimum threshold is met
• Marginal diﬀerences between adjacent grid cells
• Perfect data completeness (the structural patterns are already clear)

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