Temporal/commercial spacialities in Mexico City. A critical data approach

Keywords: big data, twitter data, everyday life, economic structure
Session Type: Virtual Paper Abstract
Day: Tuesday
Session Start / End Time: 3/1/2022 05:20 PM (Eastern Time (US & Canada)) - 3/1/2022 06:40 PM (Eastern Time (US & Canada))
Room: Virtual 9

Abstract

Urban space takes different forms throughout the day. Despite its relevance to urban spaces, everyday dimension has not been much considered. This can be explained by the difficulty to get real-time information about city. However, with the rise of big data and massive information based on internet and technologies, nowadays, everyday spatial and temporal dimension can be studied. In this sense, we have two main objectives in this paper. Firstly, we analyze the temporal dimension of commercial neighborhoods in central places in the Metropolitan Zone of Mexico City (MZMC). The second objective is to make a reflection of the role of big data, particularly Twitter data, on forming commercial urban spaces. To achieve those goals, we use spritzer stream of Twitter from MZMC since march 2017 to august 2021. Twitter data has been processed in temporal and text variables. On first objective we use a Latent Dirichlet Allocation (LDA) algorithm to cluster the latent topic on Twitter text. Based on this algorithm, we find out different commercial structures throughout the day. These findings support the idea of different temporal structures. In particular, this differentiation is guided by economic and societal practices on the city, e.g either knowledge intensive services on the CBD or gentrified practices. On the second target, we approach Twitter data from critical data studies perspective. This approach allows us to get some conclusions about the role of new strategies of commercial establishments based on digital platforms like Twitter.