Work

Redefining retail catchment with mobile geolocation data: Insights from New Zealand

Year: 2024

Type: article

Abstract: This study pioneers a transformative approach to defining and measuring retail catchment areas, moving from traditional isochrone-based models to a behavioural, evidence-based framework that capitalises on mobile location data. Departing from conventional methods that rely on static geographic boundaries or potential travel times, we employ geofencing and geohash techniques to map the actual movements and behaviours of shoppers. This research offers an understanding of retail catchment areas by analysing an extensive dataset with over 117 million data points from approximately 1.6 million users in Auckland. Utilising the DBSCAN clustering algorithm and the concave hull method, we analyse and visualise the geographic extent of catchment areas based on the home-like locations of mall visitors. This refined approach enables us to deepen our comprehension of consumer travel patterns and shopping motivations, empowering retail managers to craft more targeted marketing and operational strategies. Our findings reveal marked deviations from traditionally assumed catchment boundaries, providing fresh insights into consumer behaviour and market dynamics. By redefining catchment areas to reflect actual consumer behaviour and spatial interactions, this research underscores the critical need for more data-driven approaches in the retail sector to adapt to evolving consumer preferences and behaviours.

Source: Journal of Retailing and Consumer Services

DOI: 10.1016/j.jretconser.2024.104089

Authors: Yihan Guan, Ka Shing Cheung, Chung Yim Yiu

Institution: University of Auckland

Language: English

Topic: Human Mobility and Location-Based Analysis

Subfield: Transportation

Field: Social Sciences

Domain: Social Sciences

SDG: Sustainable cities and communities

Open Access status: hybrid

Estimated APC paid: $3,800