This paper addresses the challenge of optimizing large-scale retail expansion in competitive urban environments through a data-driven and automated approach to the Competitive Facility Location (CFL) problem. Traditional CFL methods often face limitations in handling large-scale scenarios, relying on manual pre-selection of candidate sites and imposing restrictions on the number of new locations. Our approach uses Adaptive Large Neighborhood Search (ALNS) enhanced with data enrichment techniques, such as community detection on road networks and population weighting based on mobility data. We developed 2 ALNS variants: Community Geometric Centroid (CGC-ALNS) and Population Weighted Centroid (PWC-ALNS). These methods automate the site selection process, eliminating the need for manual pre-selection and enabling evaluation of a large number of store locations. We benchmarked our approaches against ArcGIS, a widely used commercial software for CFL problems. The results demonstrate notable improvements in performance: CGC-ALNS consistently outperforms ArcGIS with up to a 2% increase in consumer count captured, while PWCALNS achieves even greater gains, with an average increase of 4.6% to 13.1% across various store distribution scenarios. Our key contributions include an automated, data-driven site selection process with no restrictions on the number of new sites, and significant performance improvements over existing commercial solutions.

In the national battle against COVID-19, harnessing population-level big data is imperative, enabling authorities to devise effective care policies, allocate healthcare resources efficiently, and enact targeted interventions. Singapore adopted the Home Recovery Programme (HRP) in September 2021, diverting low-risk COVID-19 patients to home care to ease hospital burdens amid high vaccination rates and mild symptoms. While a patient’s suitability for HRP could be assessed using broad-based criteria, integrating machine learning (ML) model becomes invaluable for identifying high-risk patients prone to severe illness, facilitating early medical assessment. Most prior studies have traditionally depended on clinical and laboratory data, necessitating initial clinic or hospital evaluations. None of these studies incorporated vaccination status, a crucial variable in a well-vaccinated population. This paper proposes a machine learning approach to nationwide risk stratification, offering intervention recommendations by harnessing nationwide datasets. Our best-performing ML model, XGBoost achieves an AUROC of 0.930 utilizing data from multiple data sources including patients’ demographic information, vaccination status and medical history. For broader applicability, we also propose a parsimonious XGBoost model with an AUROC of 0.885 with a selection of five commonly collected variables, namely age, number of vaccine doses taken and number of days since the first, second and booster doses. Importantly, both of our proposed models achieve robust predictive performance without requiring the collection of clinical or laboratory data from patients. We believe that the parsimonious model, leveraging easily attainable data, has the potential for broader adoption across diverse nations, ultimately delivering paramount value to their populations.

Conventional methodologies for new retail store catchment area and footfall estimation rely on ground surveys which are costly and time-consuming. This study augments existing research in footfall estimation through the innovative integration of mobility data and road network to create population-weighted centroids and delineate residential neighbourhoods via a community detection algorithm. Our findings are then used to enhance Huff Model which is commonly used in site selection and footfall estimation. Our approach demonstrated the vast potential residing within big data where we harness the power of mobility data and road network information, offering a cost-effective and scalable alternative. It obviates the reliance on often outdated census data and government urban planning records, positioning itself as a formidable driver of informed retail strategy. In doing so, our approach is poised to deliver substantial value to the retail industry.