Stratifying the Digital Divide: Analysis of Socio-Economic Influences on Internet Performance
Fuente:
arXiv
Enregistré dans:
| Auteurs principaux: | , , , , , |
|---|---|
| Format: | Preprint |
| Publié: |
2026
|
| Sujets: | |
| Accès en ligne: | |
| Tags: |
Ajouter un tag
Pas de tags, Soyez le premier à ajouter un tag!
|
| _version_ | 1866916064278872064 |
|---|---|
| author | Kalamadi, Shivani Bej, Aditya Singh, Sachin Kumar Srinivasavaradhan, Varshika Belding, Elizabeth Gamero-Garrido, Alexander |
| author_facet | Kalamadi, Shivani Bej, Aditya Singh, Sachin Kumar Srinivasavaradhan, Varshika Belding, Elizabeth Gamero-Garrido, Alexander |
| contents | Despite numerous technological advancements, the digital divide remains a pressing issue affecting millions worldwide. We present a framework for diagnosing internet inequality at the Census Block Group level by pairing approximately 170 million crowdsourced Ookla speed tests (2021--2025) with U.S. Census demographics across six metropolitan regions. After quantifying and correcting for sampling bias, we use Random Forest regression with permutation importance to identify the socio-economic drivers of download speed, upload speed, and latency. Population density dominates all three metrics at the regional level, but this dominance is an artifact of scale: once areas are stratified into density bins, its influence vanishes in medium- and higher-density neighborhoods, revealing that socio-economic conditions are the true differentiators of internet quality in most urban settings. After controlling for density, income and racial composition emerge as the primary drivers, income consistently dictating upload speed and racial composition proving to be a stronger predictor of download speed than either income or education. Our findings demonstrate that internet inequality is locally configured: no single national narrative explains it, and effective policy demands region-specific intervention. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_30809 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Stratifying the Digital Divide: Analysis of Socio-Economic Influences on Internet Performance Kalamadi, Shivani Bej, Aditya Singh, Sachin Kumar Srinivasavaradhan, Varshika Belding, Elizabeth Gamero-Garrido, Alexander Networking and Internet Architecture Despite numerous technological advancements, the digital divide remains a pressing issue affecting millions worldwide. We present a framework for diagnosing internet inequality at the Census Block Group level by pairing approximately 170 million crowdsourced Ookla speed tests (2021--2025) with U.S. Census demographics across six metropolitan regions. After quantifying and correcting for sampling bias, we use Random Forest regression with permutation importance to identify the socio-economic drivers of download speed, upload speed, and latency. Population density dominates all three metrics at the regional level, but this dominance is an artifact of scale: once areas are stratified into density bins, its influence vanishes in medium- and higher-density neighborhoods, revealing that socio-economic conditions are the true differentiators of internet quality in most urban settings. After controlling for density, income and racial composition emerge as the primary drivers, income consistently dictating upload speed and racial composition proving to be a stronger predictor of download speed than either income or education. Our findings demonstrate that internet inequality is locally configured: no single national narrative explains it, and effective policy demands region-specific intervention. |
| title | Stratifying the Digital Divide: Analysis of Socio-Economic Influences on Internet Performance |
| topic | Networking and Internet Architecture |
| url | https://arxiv.org/abs/2605.30809 |