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.
The effects of malls in urban societies in the U.S. have been extensively studied in the literature of the social sciences. Among the most prominent works, L. Cohen studied their emergence in the context of suburbanization @cite_29 ; J. Goss depicted them as new civic spaces that manipulate behavior through the configuration of space, in order to provoke dispositions and facilitate consumption @cite_10 ; Wakefield and Baker have applied factor analysis to identify the determinants of customer excitement @cite_27 . O'Reilly measured the effects of mall size and distance on mall selection and proposed the Law of Retail Gravitation: he suggested that larger retail centers present a higher attraction to customers, who would be willing to travel longer distances to arrive to them @cite_35 ; Huff studied the concept of trading area and proposed a model for defining the catchment area of a retail center @cite_2 .
@cite_35 @cite_29 @cite_27 @cite_2 @cite_10
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Abstract Competition between malls and newer forms of shopping centers has led mall developers and management to consider alternative methods to build excitement with customers. In this study, we examine the relationship between three factors (tenant variety, mall environment and shopping involvement) on shoppers' excitement and desire to stay at a mall. Results show that the three factors have a differential influence on excitement and desire to stay, which in turn are found to influence repatronage intentions and outshopping. 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. In this paper we propose a novel approach to localization in very large indoor spaces (i.e., 200+ store shopping malls) that takes a single image and a floor plan of the environment as input. We formulate the localization problem as inference in a Markov random field, which jointly reasons about text detection (localizing shop's names in the image with precise bounding boxes), shop facade segmentation, as well as camera's rotation and translation within the entire shopping mall. The power of our approach is that it does not use any prior information about appearance and instead exploits text detections corresponding to the shop names. This makes our method applicable to a variety of domains and robust to store appearance variation across countries, seasons, and illumination conditions. We demonstrate the performance of our approach in a new dataset we collected of two very large shopping malls, and show the power of holistic reasoning. Abstract Shopping is the most important contemporary social activity, and, for the most part, takes place in the shopping center. Developers and designers of the retail built environment exploit the power of place and an intuitive understanding of the structuration of space to facilitate consumption and thus the realization of retail profits. They strive to present an alternative rationale for the shopping center's existence, manipulate shoppers' behavior through the configuration of space, and consciously design a symbolic landscape that provokes associative moods and dispositions in the shopper. These strategies are examined to obtain an understanding of how the retail built environment works, and how we might work against it.