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

arXiv: https://arxiv.org/abs/1306.1704 · HTML: https://mlsanity.com/paper/1306.1704/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Jun 2013.

## Built on by (most cited first, 9 of 9)

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- [Risk Prediction on Traffic Accidents using a Compact Neural Model for Multimodal Information Fusion over Urban Big Data](https://mlsanity.com/paper/2103.05107/) (2021)
- [OpenSiteRec: An Open Dataset for Site Recommendation](https://mlsanity.com/paper/2307.00856/) (2023)
- [Modelling Cooperation and Competition in Urban Retail Ecosystems with Complex Network Metrics](https://mlsanity.com/paper/2104.13981/) (2021)
- [Modelling Urban Dynamics with Multi-Modal Graph Convolutional Networks](https://mlsanity.com/paper/2104.14633/) (2021)
- [Weak Signals in the Mobility Landscape: Car Sharing in Ten European Cities](https://mlsanity.com/paper/2109.09832/) (2021)
- [Clairvoyance: Intelligent Route Planning for Electric Buses Based on Urban Big Data](https://mlsanity.com/paper/2112.04682/) (2021)
- [Web Mining to Inform Locations of Charging Stations for Electric Vehicles](https://mlsanity.com/paper/2203.07081/) (2022)

## Abstract (arXiv)

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.