Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities.
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 indic...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0246785 |
| _version_ | 1819012592572563456 |
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| author | Lorenzo Donadio Rossano Schifanella Claudia R Binder Emanuele Massaro |
| author_facet | Lorenzo Donadio Rossano Schifanella Claudia R Binder Emanuele Massaro |
| author_sort | Lorenzo Donadio |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-21T01:46:30Z |
| format | Article |
| id | doaj.art-6c2333b214ee4c348aadee6e9b7ab085 |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-21T01:46:30Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-6c2333b214ee4c348aadee6e9b7ab0852022-12-21T19:20:00ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01163e024678510.1371/journal.pone.0246785Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities.Lorenzo DonadioRossano SchifanellaClaudia R BinderEmanuele MassaroThe 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.https://doi.org/10.1371/journal.pone.0246785 |
| spellingShingle | Lorenzo Donadio Rossano Schifanella Claudia R Binder Emanuele Massaro Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities. PLoS ONE |
| title | Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities. |
| title_full | Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities. |
| title_fullStr | Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities. |
| title_full_unstemmed | Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities. |
| title_short | Leveraging insurance customer data to characterize socioeconomic indicators of Swiss municipalities. |
| title_sort | leveraging insurance customer data to characterize socioeconomic indicators of swiss municipalities |
| url | https://doi.org/10.1371/journal.pone.0246785 |
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