Mining Customer Journeys to Uncover Empirical Retail Agglomerations
Bibliographic record
Abstract
Shopping centers are a cornerstone of the retail system, with their success hinging on offering tenant mixes and layouts that stimulate cross-shopping. Despite the rise of e-commerce, physical retail remains vital as consumers increasingly seek blended digital–in-store experiences. Yet, traditional approaches to analyzing shopper behavior often rely on surveys or simple frequency counts, which fail to capture the complexity of customer journeys. This study addresses this gap by applying spatial big data and unsupervised machine learning to investigate empirical retail agglomerations. The research explores how structured, non-random co-visitation patterns within a shopping center can be systematically identified and leveraged within a tenant-mix strategy. Drawing on 24 million anonymized visits to a major Canadian shopping center, the study employs GeoAI and association rule mining, specifically the Apriori algorithm, to uncover high-frequency and high-lift co-visitation rules. Results reveal structured journeys that highlight strong co-visitation between anchors and specialty tenants, confirming that shopping center behavior is far from random. These patterns suggest optimal adjacencies and provide a data-driven framework for leasing and tenant layout. The study contributes theoretically by extending retail agglomeration research using unsupervised methods to examine behavioral clustering on a large-scale dataset empirically. For practitioners, the research approach offers actionable insights for leasing and tenant-mix optimization.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".