# A Bayesian Spatial Model to Correct Under-Reporting in Urban Crowdsourcing

arXiv: https://arxiv.org/abs/2312.11754 · HTML: https://mlsanity.com/paper/2312.11754/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 53 references that are in the map.
- First posted on arXiv 18 Dec 2023.

## Builds on (most cited first, 15 of 15)

- [Learning from positive and unlabeled data: a survey](https://mlsanity.com/paper/1811.04820/) (2018)
- [Characterizing Fairness Over the Set of Good Models Under Selective Labels](https://mlsanity.com/paper/2101.00352/) (2021)
- [Flood Prediction Using Machine Learning Models: Literature Review](https://mlsanity.com/paper/1908.02781/) (2019)
- [Fair Allocation through Selective Information Acquisition](https://mlsanity.com/paper/1911.02715/) (2019)
- [Coarse race data conceals disparities in clinical risk score performance](https://mlsanity.com/paper/2304.09270/) (2023)
- [Counterfactual Prediction Under Outcome Measurement Error](https://mlsanity.com/paper/2302.11121/) (2023)
- [Detecting disparities in police deployments using dashcam data](https://mlsanity.com/paper/2305.15210/) (2023)
- [Inferring Multidimensional Rates of Aging from Cross-Sectional Data](https://mlsanity.com/paper/1807.04709/) (2018)
- [Domain constraints improve risk prediction when outcome data is missing](https://mlsanity.com/paper/2312.03878/) (2023)
- [Equity in 311 Reporting: Understanding Socio-Spatial Differentials in the Propensity to Complain](https://mlsanity.com/paper/1710.02452/) (2017)
- [Risk Scores, Label Bias, and Everything but the Kitchen Sink](https://mlsanity.com/paper/2305.12638/) (2023)
- [Rapid Mixing Swendsen-Wang Sampler for Stochastic Partitioned Attractive Models](https://mlsanity.com/paper/1704.02232/) (2017)
- [Accounting for spatial varying sampling effort due to accessibility in Citizen Science data: A case study of moose in Norway](https://mlsanity.com/paper/1911.11467/) (2019)
- [Tracking disease outbreaks from sparse data with Bayesian inference](https://mlsanity.com/paper/2009.05863/) (2020)
- [Reflections from the Workshop on AI-Assisted Decision Making for Conservation](https://mlsanity.com/paper/2307.08774/) (2023)

## Built on by (most cited first, 6 of 6)

- [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)
- [Proxy-Guided Measurement Calibration](https://mlsanity.com/paper/2603.09288/) (2026)
- [Multi-Task Anti-Causal Learning for Reconstructing Urban Events from Residents' Reports](https://mlsanity.com/paper/2603.11546/) (2026)
- [Towards Inclusive Mobility Modeling: Characterizing and Evaluating Elderly Trajectory Patterns in Urban Systems](https://mlsanity.com/paper/2606.31207/) (2026)

## Abstract (arXiv)

Decision-makers often observe the occurrence of events through a reporting process. City governments, for example, rely on resident reports to find and then resolve urban infrastructural problems such as fallen street trees, flooded basements, or rat infestations. Without additional assumptions, there is no way to distinguish events that occur but are not reported from events that truly did not occur--a fundamental problem in settings with positive-unlabeled data. Because disparities in reporting rates correlate with resident demographics, addressing incidents only on the basis of reports leads to systematic neglect in neighborhoods that are less likely to report events. We show how to overcome this challenge by leveraging the fact that events are spatially correlated. Our framework uses a Bayesian spatial latent variable model to infer event occurrence probabilities and applies it to storm-induced flooding reports in New York City, further pooling results across multiple storms. We show that a model accounting for under-reporting and spatial correlation predicts future reports more accurately than other models, and further induces a more equitable set of inspections: its allocations better reflect the population and provide equitable service to non-white, less traditionally educated, and lower-income residents. This finding reflects heterogeneous reporting behavior learned by the model: reporting rates are higher in Census tracts with higher populations, proportions of white residents, and proportions of owner-occupied households. Our work lays the groundwork for more equitable proactive government services, even with disparate reporting behavior.