Digital Humanities and Big Microdata: New Approaches for Demographic Research

Abstract

During the last decades, Demography saw a huge amount of microdata, from an uncountable number of sources, being released in on-line worldwide datasets. These data, increasing in quantity on a daily basis, are commonly known as “big microdata” and demographers can use them in order to inquire new themes or reassess old questions in demographic research. Nowadays, the choice of which methodologies to use is one of the main issues. A considerable number of patterns and relations may hide themselves behind this impressive amount of available data. Our goal is to show how researchers can exploit the potentiality of big microdata with quantitative approaches—one from machine learning (cluster analysis) and another related to geoprocessing (network analysis)—in order to revisit old and new themes in two fields of demographic research (population and environment and internal migration) using a well-known source of data: the demographic census. In the first example, we overlap a cluster analysis (made with EM-algorithm) of demographic data to an environmental analysis of flooding and landslide for the metropolitan region of São Paulo (MRSP), with the aim of relating vulnerability to environmental risks. In the second one, we use network analysis in order to describe the intrametropolitan migration dynamic of the MRSP, as well as to discuss about some horizons of research combining vulnerability, environmental risks, and migrations. At the end of the chapter, we evaluate the potentiality of combining digital humanities and big microdata, in order to rediscover Demography.