In Latin America, shopping malls seem to offer an open, safe and democratic version of the public space. However, it is often difficult to quantitatively measure whether they indeed foster, hinder, or are neutral with respect to social inclusion. In this work, we investigate if, and by how much, people from different social classes are attracted by the same malls. Using a dataset of mobile phone network records from 387,152 devices identified as customers of 16 malls in Santiago de Chile, we performed several analyses to study whether malls with higher social mixing attract more people. Our pipeline, which starts with the socio-economic characterization of mall visitors, includes the estimation of social mixing and diversity of malls, the application of the gravity model of mobility, and the definition of a co-visitation model. Results showed that people tend to choose a profile of malls more in line with their own socio-economic status and the distance from their home to the mall, and that higher mixing does positively contribute to the process of choosing a mall. We conclude that (a) there is social mixing in malls, and (b) that social mixing is a factor at the time of choosing which mall to go to. Thus, the potential for social mixing in malls could be capitalized by designing public policies regarding transportation and mobility to make some malls strong social inclusion hubs.
Retail location has also been studied in the context of urban mobility: @cite_4 have studied the impact of workplace proximity, housing, and retail accessibility on mobility, showing that job-housing proximity has a larger impact on total mobility than retail location. On the other side, Galati and Greenhalgh studied the inner mobility, contact duration and inter-contact time of mall customers @cite_9 . In the context of big-data marketing, Chen have used a large dataset for optimizing retail store location @cite_13 .
@cite_13 @cite_9 @cite_4
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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. Which land-use strategy yields greater reductions in vehicular travel: improving the proximity of jobs to housing or bringing retail and consumer services closer ro residential areas? We probe this question by examining the degree to which job accessibility is associated with reduced work travel and how closely retail and service accessibility is correlated with miles and hours logged getting to shopping destinations. Based on data from the San Francisco Bay Area, we find that jobs-housing balance reduces travel more, by a substantial margin. The article concludes by discussing policy measures that have been introduced in California to bring housing, workplaces, and retail centers closer together. Location has been considered as a determinant factor for retail location’s success since the 1970s. Various researches have inspected different aspects in order to understand the market and select the best site for each store, from classifying the location to generating hierarchical trading areas. Previous researches done by human geographers may provide great insights into one specified site. However, their traditional methods are likely to be too expensive and difficult to expand to the whole city and even the whole nation. This chapter explores methods based on a large amount of data, which are currently being used as commercial solutions. By inspecting POI data and other social media data within pre-built grids, different types of business districts could be discovered, and site characteristics could be summarized. Two famous fast-food retailers have been explored in this study. Results indicate that more than 70 of stores can be explained by business district classifications. Also, results indicate that similar POI aggregations can be analyzed, from which 83 and 90 of stores’ locations can be explained for two retailers separately. Besides, the distance relationship between POIs and stores can contribute to explain the location of retail stores. A measure of location relative to employment is often included in hedonic housing price models. This is most often distance to the center, based on the monocentric model, which does not consider the decentralization of employment in urban areas. This paper tests the perfor-mance of alternative measures of location, considering both distance and time to the center and to multiple employment centers and measures of accessibility to employment and change in accessibility. The measures using multiple employment centers and accessibility perform better than simple distance to the center, with the combination of accessibility to employment and change in accessibility doing best.