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

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.

A Bayesian Approach to Estimate the Spatial Distribution of Crowdsourced Radiation Measurements around Fukushima

“…Some aspects of novel data sources can also be evaluated from a qualitative perspective, such as the origins of a crisis project, motivations, types of quality, resources, and if the training or expertise of the volunteers could influence the quality of collection [25]. Quality assurance protocols can be developed to handle crowdsourced [26] and enable contributors to flag potential issues in their own inputs helps to provide context even when using sensor devices. Even prior to crisis, establishing connections between citizen projects and organizations willing to perform evaluation could encourage the assessments of how projects can have their data used.…”

“…For some tasks, nonexperts have been found to perform just as well as experts and also collecting responses on levels of confidence for observations can produce more robust data [25]. VGI environmental monitoring work on air quality demonstrates a method for quality assurance (QA) protocols to handle the unstructured nature of crowdsourced data [26]. Another study recommends an approach of cross-comparison with a comparable data source [27].…”

Editorial on the Citizen Science and Geospatial Capacity Building

“…Samulowska et al [5] addressed the data bias in VGI and developed a geospatial web platform with a robust quality assurance (QA) approach. The article includes an extensive literature review on the CitSci contribution for air pollution mapping.…”

QUAD: A Quality Aware Multi-Unit Double Auction Framework for IoT-Based Mobile Crowdsensing in Strategic Setting

“…As the mobile users (or crowd workers) are equipped with sensing devices, so the researchers thought of utilizing these mobile users for sensing and collecting data for several real world applications and then distributing it to the community or organization. For example, measuring the air pollution level across the cities Pan et al (2017); Samulowska et al (2021); Li et al (2019), giving information about the road traffic Staniek (2021); Aubry et al (2014), noise pollution assessment Pődör and Szabó (2021); Rana et al (2010), information about the potholes Enigo et al (2016); Wu et al (2020), and many more Nagatani et al (2013); Poblet et al (2014). The process of completion of task(s) by the crowd workers or group of common people equipped with sensing devices in the form of an open call give rise to a new paragmatic field of study termed as mobile crowdsensing (a.k.a.…”