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.
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. The need for a network when there is no infrastructure is no longer limited to military and emergency applications; ad hoc networks can support private and public applications as well. Ad hoc networking has been a dynamically growing research area in recent years. In order to conduct informed and realistic design of forwarding policies and algorithms for mobile ad-hoc delay tolerant networks, it is important to gather appropriate real human mobility data. In this paper we study human mobility in a shopping mall environment. In such an environment, people using network devices such as mobile phones, PDA, etc. could be willing to communicate in a variety of ways, without the mediation of routing across the global Internet. The ultimate goal is to enable a multitude of users at any place in the shopping mall to access receive appropriate local information at any time. We discuss the implications of our results and make recommendations for the design of opportunistic forwarding algorithms for shopping mall environments. 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.