Demography developed as an inherently spatial science until roughly the mid–twentieth century. Then, interest on micro–level processes focusing on individual as the agent of demographic action has become prominent in population studies, relegating spatial thinking to a disciplinary minority. Nonetheless, in recent years, the availability of cutting–edge tools and methodologies and the enhanced computer capabilities in collecting and managing big amounts of spatial data have fostered a re–emerging interest towards the spatial dimension embedding social phenomena. In the wake of this renaissance of matters of space and place, the present work intends to recapture the spatial dimension in the analysis of population dynamics to convey realistic and plausible meanings of the matters under study. Spatial thinking, spatial data preparation and methodology have been exploited to address three key demographic fields: morbidity and mortality, residential segregation, and migration. The first manuscript focusses on the possible influence of an environmental risk factor, volcanic activity, on the onset of thyroid cancer (TC). The present work illustrates an exploratory study of the spatial distribution of TC in Eastern Sicily, where Mount Etna, an active volcano, is located. Georeferencing the data provided by the Cancer Registry of Eastern Sicily, the phenomenon is visualised through geographical maps, highlighting the areas where TC incidence is significantly higher than expected. The results seem to confirm the consolidated opinion that the presence of a volcano can play a role in the spatial variation in TC incidence. Residential segregation is investigated in the two following chapters. The first one focusses on the residential choices of Sri Lankans in the main Italian cities. The uniqueness of the analysis relies on the fact that no other existing studies comparatively examine the spatial segregation of Sri Lankan communities in different southern European municipalities. Moreover, implementing a single geographic reference grid allowed the homogenisation of different areal unit arrangements and the comparison between urban contexts. Original results have emerged from the empirical analysis, detecting peculiar and similar residential behaviour in Sri Lankans’ settlement patterns across the cities considered. The following and fourth chapter comparatively analyses the allocation models of different foreign groups in the city of Messina (Italy). The case–control approach implemented allowed to disentangle the voluntary from the apparent source of segregation and hence, to assess similarities and differences across the various immigrant communities. The fifth and last manuscript proposes to introduce diffusionism, a theoretical framework typical of market research studies, in traditional migration studies to better clarify the dynamics underlying population movements. Classical migration studies distinguish chain migration, where social relationships and social networks channel between different places, from the impersonally organised migration. Whereas the former is mediated by interpersonal information exchange or imitative behaviours, the latter is mainly guided by economic motives or political instabilities. Despite the theoretical distinction between the two types of migration mechanisms, at the current stage of literature development, no practically viable methodology has been proposed to analyse population flows while disentangling the social from the impersonal drivers of migration. The aim of this work is to fill this gap by introducing an “all–inclusive” methodology which can be operationalised in practical applications. Here an extended version of the traditional Bass diffusion model is proposed. The Bass model is widely applied in market research to analyse the diffusion of new products, ideas, and behaviours. The version introduced in the present work aims to analyse population movements while distinguishing between sociological and structural motives to migrate.
Putting space in its place(s): spatial analysis of demographic processes
BITONTI, Francesca
2023-02-28
Abstract
Demography developed as an inherently spatial science until roughly the mid–twentieth century. Then, interest on micro–level processes focusing on individual as the agent of demographic action has become prominent in population studies, relegating spatial thinking to a disciplinary minority. Nonetheless, in recent years, the availability of cutting–edge tools and methodologies and the enhanced computer capabilities in collecting and managing big amounts of spatial data have fostered a re–emerging interest towards the spatial dimension embedding social phenomena. In the wake of this renaissance of matters of space and place, the present work intends to recapture the spatial dimension in the analysis of population dynamics to convey realistic and plausible meanings of the matters under study. Spatial thinking, spatial data preparation and methodology have been exploited to address three key demographic fields: morbidity and mortality, residential segregation, and migration. The first manuscript focusses on the possible influence of an environmental risk factor, volcanic activity, on the onset of thyroid cancer (TC). The present work illustrates an exploratory study of the spatial distribution of TC in Eastern Sicily, where Mount Etna, an active volcano, is located. Georeferencing the data provided by the Cancer Registry of Eastern Sicily, the phenomenon is visualised through geographical maps, highlighting the areas where TC incidence is significantly higher than expected. The results seem to confirm the consolidated opinion that the presence of a volcano can play a role in the spatial variation in TC incidence. Residential segregation is investigated in the two following chapters. The first one focusses on the residential choices of Sri Lankans in the main Italian cities. The uniqueness of the analysis relies on the fact that no other existing studies comparatively examine the spatial segregation of Sri Lankan communities in different southern European municipalities. Moreover, implementing a single geographic reference grid allowed the homogenisation of different areal unit arrangements and the comparison between urban contexts. Original results have emerged from the empirical analysis, detecting peculiar and similar residential behaviour in Sri Lankans’ settlement patterns across the cities considered. The following and fourth chapter comparatively analyses the allocation models of different foreign groups in the city of Messina (Italy). The case–control approach implemented allowed to disentangle the voluntary from the apparent source of segregation and hence, to assess similarities and differences across the various immigrant communities. The fifth and last manuscript proposes to introduce diffusionism, a theoretical framework typical of market research studies, in traditional migration studies to better clarify the dynamics underlying population movements. Classical migration studies distinguish chain migration, where social relationships and social networks channel between different places, from the impersonally organised migration. Whereas the former is mediated by interpersonal information exchange or imitative behaviours, the latter is mainly guided by economic motives or political instabilities. Despite the theoretical distinction between the two types of migration mechanisms, at the current stage of literature development, no practically viable methodology has been proposed to analyse population flows while disentangling the social from the impersonal drivers of migration. The aim of this work is to fill this gap by introducing an “all–inclusive” methodology which can be operationalised in practical applications. Here an extended version of the traditional Bass diffusion model is proposed. The Bass model is widely applied in market research to analyse the diffusion of new products, ideas, and behaviours. The version introduced in the present work aims to analyse population movements while distinguishing between sociological and structural motives to migrate.File | Dimensione | Formato | |
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