Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement

Abstract: The problem of identifying the optimal location for a new retail store has been the focus of past research, especially in the field of land economy, due to its importance in the success of a business. Traditional approaches to the problem have factored in demographics, revenue and aggregated human flow statistics from nearby or remote areas. However, the acquisition of relevant data is usually expensive. With the growth of location-based social networks, fine grained data describing user mobility and popularity of places has recently become attainable. In this paper we study the predictive power of various machine learning features on the popularity of retail stores in the city through the use of a dataset collected from Foursquare in New York. The features we mine are based on two general signals: geographic, where features are formulated according to the types and density of nearby places, and user mobility, which includes transitions between venues or the incoming flow of mobile users from distant areas. Our evaluation suggests that the best performing features are common across the three different commercial chains considered in the analysis, although variations may exist too, as explained by heterogeneities in the way retail facilities attract users. We also show that performance improves significantly when combining multiple features in supervised learning algorithms, suggesting that the retail success of a business may depend on multiple factors.

• The paper introduces Geo-Spotting, mining LBS data and using machine learning with geographic and mobility features to predict optimal retail placement.
• Combining geographic and user mobility features significantly improves the accuracy of optimal location predictions, often ranking the best spot within the top 5% of predictions.
• This approach provides a more precise, data-driven assessment of location potential for businesses, opening new avenues for urban analytics beyond retail.

Optimal Retail Store Placement Using Location-Based Services

The paper "Geo-Spotting: Mining Online Location-based Services for Optimal Retail Store Placement" explores a novel approach to determining optimal retail store locations by leveraging data from location-based services (LBS) such as Foursquare. Traditionally, selecting a store location relied heavily on analysis of demographics, revenue projections, and human flow statistics, which were often costly to obtain and analyze. The advent of location-based social networks provides an unprecedented opportunity to access granular data about user mobility patterns and venue popularity, offering a potentially more accurate and cost-effective method for location analysis.

Methodology and Data

The authors utilize a dataset from Foursquare collected in New York City, focusing on check-in data as a proxy for retail store popularity. This data allows for a nuanced examination of both geographic and mobility-related features. Geographic features pertain to the types and densities of venues in a given area, whereas mobility features capture user movement patterns, including transitions between venues. The research emphasizes two main types of signals: spatial (geographic) and user mobility.

The paper introduces various machine learning features crafted to predict store popularity. For geographic features, these include density, entropy of neighboring venue types, and competitiveness indicating the proximity of similar businesses. For mobility features, the authors quantify attributes such as area popularity, transition density, and quality based on user movement trends. These features are then evaluated individually and in combination using supervised learning algorithms like Support Vector Regression and M5 decision trees.

Key Findings

The empirical analysis reveals intriguing insights into the factors contributing to retail location success. Key findings include:

• Spatial Trends: There is a non-random spatial distribution favoring locations near transportation hubs or touristic attractions, which enhance retail store popularity.
• Mobility Influence: A significant portion of user movements (approximately 90%) occurs within 1 km, underscoring the importance of understanding local movement patterns.
• Supervised Learning Performance: Combining geographic and mobility features using supervised learning models consistently improves prediction accuracy, with the optimal retail location frequently ranked in the top 5% of predictions.
• Chain-Specific Insights: Different retail chains exhibit unique attraction patterns, necessitating tailored feature analysis per chain to accurately predict optimal locations.

Implications and Future Directions

The implications of this research are substantial for urban analytics and LBS-driven business strategies. By integrating fine-grained user mobility data, businesses can achieve more precise and dynamic assessments of location potential, reshaping how commercial strategies are devised in urban settings. Furthermore, the paper suggests that similar methodologies could be applied in broader urban analytics contexts, such as real estate value prediction or understanding urban growth patterns.

As LBS platforms continue to evolve and more detailed datasets become available, the framework proposed in this paper could be refined and applied to other metropolitan areas and retail sectors. Future research might explore the integration of additional data sources or the use of advanced machine learning techniques to further enhance predictive accuracy.

In conclusion, this work exemplifies the transformative potential of LBS data in optimizing retail store placement, bridging the gap between traditional land economy practices and modern data-driven insights, and it paves the way for future explorations into location intelligence.