English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Socio-economic indicators provide context for assessing a country's overallcondition. These indicators contain information about education, gender,poverty, employment, and other factors. Therefore, reliable and accurateinformation is critical for social research and government policing. Most datasources available today, such as censuses, have sparse population coverage orare updated infrequently. Nonetheless, alternative data sources, such as calldata records (CDR) and mobile app usage, can serve as cost-effective andup-to-date sources for identifying socio-economic indicators.  This work investigates mobile app data to predict socio-economic features. Wepresent a large-scale study using data that captures the traffic of thousandsof mobile applications by approximately 30 million users distributed over550,000 km square and served by over 25,000 base stations. The dataset coversthe whole France territory and spans more than 2.5 months, starting from 16thMarch 2019 to 6th June 2019. Using the app usage patterns, our best model canestimate socio-economic indicators (attaining an R-squared score upto 0.66).Furthermore, using models' explainability, we discover that mobile app usagepatterns have the potential to reveal socio-economic disparities in IRIS.Insights of this study provide several avenues for future interventions,including user temporal network analysis to understand evolving networkpatterns and exploration of alternative data sources.

Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France