Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping

Crowdsourcing is one of the spatial data sources, but due to its unstructured form, quality noisy crowd judgments a challenge. In this study, we address problem detecting and removing crowdsourced bias as prerequisite for better-quality open-data output. This study aims find most robust assurance system (QAs). To achieve goal, design logic-based QAs variants test them on air crowdsourcing database. By extending paradigm urban pollution monitoring from particulate matter concentration levels air-quality-related health symptom load, also builds new perspective citizen science (CS) monitoring. The method includes geospatial web (GeoWeb) platform well based conditional statements. A four-month campaign resulted in 1823 outdoor reports, with rejection rate up 28%, depending applied. focus was not digital sensors’ validation eliminating logically inconsistent surveys technologically incorrect objects. As effectiveness may depend location society structure, that opens cross-border opportunities replication research other geographical conditions.