# Quantifying Spatial Under-reporting Disparities in Resident Crowdsourcing

arXiv: https://arxiv.org/abs/2204.08620 · HTML: https://mlsanity.com/paper/2204.08620/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 50 references that are in the map.
- First posted on arXiv 19 Apr 2022.

## Builds on (most cited first, 4 of 4)

- [The effect of differential victim crime reporting on predictive policing systems](https://mlsanity.com/paper/2102.00128/) (2021)
- [Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain](https://mlsanity.com/paper/1710.02452/) (2017)
- [Efficient Profile Maximum Likelihood for Universal Symmetric Property Estimation](https://mlsanity.com/paper/1905.08448/) (2019)
- [FixMyStreet Brussels: Socio-Demographic Inequality in Crowdsourced Civic Participation](https://mlsanity.com/paper/1708.02274/) (2017)

## Built on by (most cited first, 7 of 7)

- [From Individual Experience to Collective Evidence: A Reporting-Based Framework for Identifying Systemic Harms](https://mlsanity.com/paper/2502.08166/) (2025)
- [Fingerprinting New York City's Scaffolding Problem with Longitudinal Dashcam Data](https://mlsanity.com/paper/2402.06801/) (2024)
- [Bayesian Modeling of Zero-Shot Classifications for Urban Flood Detection](https://mlsanity.com/paper/2503.14754/) (2025)
- [Learning Disease Progression Models That Capture Health Disparities](https://mlsanity.com/paper/2412.16406/) (2024)
- [Urban Incident Prediction with Graph Neural Networks: Integrating Government Ratings and Crowdsourced Reports](https://mlsanity.com/paper/2506.08740/) (2025)
- [Predictive Hotspot Mapping for Data-driven Crime Prediction](https://mlsanity.com/paper/2602.23750/) (2026)
- [Proxy-Guided Measurement Calibration](https://mlsanity.com/paper/2603.09288/) (2026)

## Abstract (arXiv)

Modern city governance relies heavily on crowdsourcing to identify problems such as downed trees and power lines. A major concern is that residents do not report problems at the same rates, with heterogeneous reporting delays directly translating to downstream disparities in how quickly incidents can be addressed. Here we develop a method to identify reporting delays without using external ground-truth data. Our insight is that the rates at which duplicate reports are made about the same incident can be leveraged to disambiguate whether an incident has occurred by investigating its reporting rate once it has occurred. We apply our method to over 100,000 resident reports made in New York City and to over 900,000 reports made in Chicago, finding that there are substantial spatial and socioeconomic disparities in how quickly incidents are reported. We further validate our methods using external data and demonstrate how estimating reporting delays leads to practical insights and interventions for a more equitable, efficient government service.