
Ridhi Purohit developed robust data workflows and metrics for the UI-Research/mobility-from-poverty repository, focusing on city- and county-level analysis of voter turnout and economic connectedness. She engineered end-to-end pipelines in R and Python, integrating multi-year datasets, automating AWS S3 storage, and implementing data cleaning, validation, and imputation for missing subgroups. Her work included enhancing demographic accuracy, aligning metrics with external schemas, and modernizing UI styling with CSS and Bootstrap Icons. By standardizing data processing and documentation using Quarto and R Markdown, Ridhi delivered reproducible, governance-ready datasets and reports, improving data quality, usability, and scalability for research and decision-making.
March 2025 performance summary for UI-Research/mobility-from-poverty: Delivered end-to-end data integration for the 2025 metrics release, consolidating data from multiple years (2018, 2023), implementing missing-subgroup imputation, standardizing data loading/processing, and applying thorough county-level quality checks to produce a coherent final dataset. Implemented demographic accuracy improvements for race/ethnicity categorization, correcting mappings for 'Other Races and Ethnicities' and ensuring consistent labeling for Hispanic individuals to support reliable demographic analyses in reports. Enhanced reporting and documentation to boost usability and methodological transparency: clarified CI calculation methodology (noting the use of qnorm), updated publication dates/generator versions, adjusted county data points for accuracy, and improved HTML/Quarto presentation. Achieved production readiness with crosswalk fixes, data-gap remediation, and rendering of the final combined files. Technologies/skills demonstrated include data pipeline standardization, data imputation, crosswalk processing, comprehensive QA across counties, and improved documentation and frontend presentation.
March 2025 performance summary for UI-Research/mobility-from-poverty: Delivered end-to-end data integration for the 2025 metrics release, consolidating data from multiple years (2018, 2023), implementing missing-subgroup imputation, standardizing data loading/processing, and applying thorough county-level quality checks to produce a coherent final dataset. Implemented demographic accuracy improvements for race/ethnicity categorization, correcting mappings for 'Other Races and Ethnicities' and ensuring consistent labeling for Hispanic individuals to support reliable demographic analyses in reports. Enhanced reporting and documentation to boost usability and methodological transparency: clarified CI calculation methodology (noting the use of qnorm), updated publication dates/generator versions, adjusted county data points for accuracy, and improved HTML/Quarto presentation. Achieved production readiness with crosswalk fixes, data-gap remediation, and rendering of the final combined files. Technologies/skills demonstrated include data pipeline standardization, data imputation, crosswalk processing, comprehensive QA across counties, and improved documentation and frontend presentation.
Month: 2025-02 | Repository: UI-Research/mobility-from-poverty. This month focused on delivering robust data metrics and a polished UI, with enhancements to the Digital Access Metric and the Voter Turnout data workflow, along with a UI styling upgrade. Key outcomes include: (1) Digital Access Metric enhancements with final evaluation function, updated saving/validation per UMF guidelines, pipeline improvements, and subgroups (geographic, demographic, income) for quality and granularity; (2) Voter Turnout Data Processing (2016) workflow added with final evaluation, quality checks, and integration of sources, file paths, and HTML outputs; (3) UI styling modernization through Bootstrap Icons integration for visual consistency and improved UX.
Month: 2025-02 | Repository: UI-Research/mobility-from-poverty. This month focused on delivering robust data metrics and a polished UI, with enhancements to the Digital Access Metric and the Voter Turnout data workflow, along with a UI styling upgrade. Key outcomes include: (1) Digital Access Metric enhancements with final evaluation function, updated saving/validation per UMF guidelines, pipeline improvements, and subgroups (geographic, demographic, income) for quality and granularity; (2) Voter Turnout Data Processing (2016) workflow added with final evaluation, quality checks, and integration of sources, file paths, and HTML outputs; (3) UI styling modernization through Bootstrap Icons integration for visual consistency and improved UX.
January 2025 performance highlights: Delivered enhanced digital access and Economic Connectedness (EC) metrics, ensured data quality and reproducibility, and completed repository cleanups. Focus areas included metric enhancements, alignment with external schemas (Social Capital Atlas), and scalable data files for subgroups/geographies under UMF guidelines. Result: more accurate, governance-friendly metrics for UMF decision-making and reduced maintenance burden.
January 2025 performance highlights: Delivered enhanced digital access and Economic Connectedness (EC) metrics, ensured data quality and reproducibility, and completed repository cleanups. Focus areas included metric enhancements, alignment with external schemas (Social Capital Atlas), and scalable data files for subgroups/geographies under UMF guidelines. Result: more accurate, governance-friendly metrics for UMF decision-making and reduced maintenance burden.
December 2024: Delivered two end-to-end data workflows and AWS storage utilities for UI-Research/mobility-from-poverty, enabling robust city-level turnout analysis and scalable data storage. No major defects fixed this month; stability and reproducibility improvements were achieved through automated quality checks, robust data processing, and streamlined storage operations.
December 2024: Delivered two end-to-end data workflows and AWS storage utilities for UI-Research/mobility-from-poverty, enabling robust city-level turnout analysis and scalable data storage. No major defects fixed this month; stability and reproducibility improvements were achieved through automated quality checks, robust data processing, and streamlined storage operations.

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