Unknown

Dataset Information

0

Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities.


ABSTRACT: The availability of reliable socioeconomic data is critical for the design of urban policies and the implementation of location-based services; however, often, their temporal and geographical coverage remain scarce. We explore the potential for insurance customers data to predict socioeconomic indicators of Swiss municipalities. First, we define a features space by aggregating at city-level individual customer data along several behavioral and user profile dimensions. Second, we collect official statistics shared by the Swiss authorities on a wide spectrum of categories: Population, Transportation, Work, Space and Territory, Housing, and Economy. Third, we adopt two spatial regression models exploring both global and local geographical dependencies to investigate their predictability. Results show consistently a correlation between insurance customer characteristics and official socioeconomic indexes. Performance fluctuates depending on the category, with values of R2 > 0.6 for several target variables using a 5-fold cross validation. As a case study, we focus on predicting the percentage of the population using public transportation and we discuss the implications on a regional scope. We believe that this methodology can support official statistical offices and it could open up new opportunities for the characterization of socioeconomic traits at highly-granular spatial and temporal scales.

SUBMITTER: Donadio L 

PROVIDER: S-EPMC7928527 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

altmetric image

Publications

Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities.

Donadio Lorenzo L   Schifanella Rossano R   Binder Claudia R CR   Massaro Emanuele E  

PloS one 20210303 3


The availability of reliable socioeconomic data is critical for the design of urban policies and the implementation of location-based services; however, often, their temporal and geographical coverage remain scarce. We explore the potential for insurance customers data to predict socioeconomic indicators of Swiss municipalities. First, we define a features space by aggregating at city-level individual customer data along several behavioral and user profile dimensions. Second, we collect official  ...[more]

Similar Datasets

| S-EPMC6077280 | biostudies-literature
| S-EPMC9632483 | biostudies-literature
| S-EPMC6063431 | biostudies-literature
| S-EPMC8595966 | biostudies-literature
2024-04-23 | GSE264680 | GEO
| S-EPMC4244251 | biostudies-literature
| S-EPMC4046095 | biostudies-literature
| S-EPMC7452671 | biostudies-literature
| S-EPMC9189776 | biostudies-literature
| S-EPMC7274094 | biostudies-literature