Authors

Zhiyan Zhang, Sonali Mhatre

Research Question

Does remote work influence voting outcomes differently for Democrats and Republicans in the NY metropolitan area?

Background

Remote work during the COVID-19 pandemic widened existing political divides in the U.S. Professional, Democratic-leaning industries adopted remote flexibility, while labor-intensive, Republican-leaning sectors had fewer options. This imbalance deepened partisan segregation, reducing cross-party interactions and reshaping residential and voting patterns. In the New York metro area, remote work enabled Democratic-leaning professionals to relocate, influencing voter turnout and shifting political balances. Our study examines these trends to inform policymakers on how remote work may be shaping political geography.

Methodology

Machine learning, statistical analysis, and spatial tools are used to examine how remote work influences partisan patterns in the New York metro area. Classification models like logistic regression, random forest, gradient-boosted trees, and SVM predicted whether neighborhoods lean Democratic or Republican, based on remote work potential and demographics. Application of OLS regression to measure how remote work levels impact the share of Democratic voters in each area. To visualize patterns, scatter plots, heatmaps, clustering, and maps are used to show how remote work aligns with political geography. This combined approach provides clear insights into how remote work may be shaping the political landscape, offering valuable guidance for policymakers.

Deliverables

• Technical Report
• Visualization Dashboard

Datasets