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

• URL: http://arxiv.org/abs/2301.09986v2
• Date: Sat, 4 Feb 2023 11:23:05 GMT
• Title: Predicting Socio-Economic Well-being Using Mobile Apps Data: A Case Study of France
• Authors: Rahul Goel, Angelo Furno, Rajesh Sharma
• Abstract summary: This work investigates mobile app data to predict socio-economic features. We present a large-scale study using data that captures traffic of thousands of mobile applications by approximately 30 million users. Using the app usage patterns, our best model can estimate socio-economic indicators.
• Score: 5.254432021398321
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Socio-economic indicators provide context for assessing a country's overall condition. These indicators contain information about education, gender, poverty, employment, and other factors. Therefore, reliable and accurate information is critical for social research and government policing. Most data sources available today, such as censuses, have sparse population coverage or are updated infrequently. Nonetheless, alternative data sources, such as call data records (CDR) and mobile app usage, can serve as cost-effective and up-to-date sources for identifying socio-economic indicators. This work investigates mobile app data to predict socio-economic features. We present a large-scale study using data that captures the traffic of thousands of mobile applications by approximately 30 million users distributed over 550,000 km square and served by over 25,000 base stations. The dataset covers the whole France territory and spans more than 2.5 months, starting from 16th March 2019 to 6th June 2019. Using the app usage patterns, our best model can estimate socio-economic indicators (attaining an R-squared score upto 0.66). Furthermore, using models' explainability, we discover that mobile app usage patterns 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 network patterns and exploration of alternative data sources.

Related papers

• Job-SDF: A Multi-Granularity Dataset for Job Skill Demand Forecasting and Benchmarking [59.87055275344965]
  Job-SDF is a dataset designed to train and benchmark job-skill demand forecasting models. Based on 10.35 million public job advertisements collected from major online recruitment platforms in China between 2021 and 2023. Our dataset uniquely enables evaluating skill demand forecasting models at various granularities, including occupation, company, and regional levels.
  arXiv  Detail & Related papers  (2024-06-17T07:22:51Z)
• Design and analysis of tweet-based election models for the 2021 Mexican legislative election [55.41644538483948]
  We use a dataset of 15 million election-related tweets in the six months preceding election day. We find that models using data with geographical attributes determine the results of the election with better precision and accuracy than conventional polling methods.
  arXiv  Detail & Related papers  (2023-01-02T12:40:05Z)
• On Inferring User Socioeconomic Status with Mobility Records [61.0966646857356]
  We propose a socioeconomic-aware deep model called DeepSEI. The DeepSEI model incorporates two networks called deep network and recurrent network. We conduct extensive experiments on real mobility records data, POI data and house prices data.
  arXiv  Detail & Related papers  (2022-11-15T15:07:45Z)
• A deep learning framework to generate realistic population and mobility data [5.180648702293017]
  Census and Household Travel Survey datasets are regularly collected from households and individuals. These datasets often represent a limited sample of the population due to privacy concerns or are given aggregated. We propose a framework to generate a synthetic population that includes both socioeconomic features (e.g., age, sex, industry) and trip chains (i.e., activity locations)
  arXiv  Detail & Related papers  (2022-11-14T14:05:09Z)
• ReGNL: Rapid Prediction of GDP during Disruptive Events using Nightlights [0.0]
  Regional GDP NightLight (ReGNL) is a neural network based model which is trained on a custom dataset of historical nightlights and GDP data. We find that ReGNL is disruption-agnostic and is able to predict the GDP for both normal years and for years with a disruptive event.
  arXiv  Detail & Related papers  (2022-01-19T14:10:37Z)
• Social media data reveals signal for public consumer perceptions [6.212955085775758]
  One of the most widely cited economic indicator is consumer confidence index (CCI) Numerous studies in the past have focused on using social media, especially Twitter data, to predict CCI. However, the strong correlations disappeared when those models were tested with newer data according to a recent comprehensive survey.
  arXiv  Detail & Related papers  (2020-12-26T03:58:20Z)
• Urban Sensing based on Mobile Phone Data: Approaches, Applications and Challenges [67.71975391801257]
  Much concern in mobile data analysis is related to human beings and their behaviours. This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
  arXiv  Detail & Related papers  (2020-08-29T15:14:03Z)
• Magnify Your Population: Statistical Downscaling to Augment the Spatial Resolution of Socioeconomic Census Data [48.7576911714538]
  We present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes. For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions. As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of 300 spatial resolution.
  arXiv  Detail & Related papers  (2020-06-23T16:52:18Z)
• Predicting Livelihood Indicators from Community-Generated Street-Level Imagery [70.5081240396352]
  We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery. By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
  arXiv  Detail & Related papers  (2020-06-15T18:12:12Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.