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Archivio digitale delle tesi discusse presso l’Università di Pisa

Tesi etd-04132021-120259


Tipo di tesi
Tesi di laurea magistrale
Autore
GAYDAMAKA, ANNA
URN
etd-04132021-120259
Titolo
Poverty Research Based on Small Area Model-Based Estimators and Big Data Sources
Dipartimento
INFORMATICA
Corso di studi
DATA SCIENCE AND BUSINESS INFORMATICS
Relatori
relatore Prof.ssa Giusti, Caterina
relatore Prof. Rinzivillo, Salvatore
Parole chiave
  • auxiliary information
  • Big Data
  • poverty measures
  • Small Area Estimation
Data inizio appello
07/05/2021
Consultabilità
Non consultabile
Data di rilascio
07/05/2091
Riassunto
The presented thesis is an interdisciplinary study in the field of official statistics and big data. The main purpose of the thesis is to investigate the problem of poverty in small areas by analyzing population data obtained from official surveys and big data sources. To achieve this goal, the following tasks were completed. Analysis of methods of poverty estimation indicators in small areas has been conducted. The big data analysis methods for extracting poverty-related metrics from small area data was studied. The software tools for analyzing big data, small territories and calculating poverty metrics were developed. A case study on small area territories in Chile was designed and conducted.
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