No abstract

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

The United Nations (UN) sustainable development goals (SDGs), a strategy to guide the world’s social and economic transformation, highlight the issue of urban air pollution in SDG 11. Open data, as an output of citizen science (CS), are needed to supply and improve the SDG indicator system. Therefore, we propose a CS framework to extend the paradigm of urban air pollution monitoring from particulate matter concentration levels to air quality-related health symptom load, and foster the development of a tier-3 SDG indicator (which we call indicator 11.6.3). Building this new perspective for CS contributions to the achievement of SDGs, we address the problem of crowdsourced data bias as a prerequisite for better quality open data output. The aim of this study is to propose an air pollution symptom mapping framework for citizen-driven research and to find the most robust data quality assurance system (QAs) in this field. The method includes a GeoWeb application as well as data quality assurance mechanisms based on conditional statements, in order to reduce crowdsourced data bias. A four-month crowdsourcing campaign, released in Lubelskie voivodship (Poland), resulted in 1823 outdoor reports with a rejection rate of up to 28%, depending on the applied QA system (QAs). Testing the QAs variants, we find the most robust data bias solving method in survey-based symptom mapping. The framework output is shared via GeoWeb dashboards, including the 11.6.3 indicator evaluation. By familiarizing the public with citizen science, a city can track the progress of its SDG achievements and increase the transparency of the process through the use of GeoWeb.