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Article

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

1
Department of Geoinformatics, Cartography and Remote Sensing, Faculty of Geography and Regional Studies, University of Warsaw, Krakowskie Przedmieście 30, 00-927 Warsaw, Poland
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Department of Grassland and Landscape Studies, Faculty of Agrobioengineering, University of Life Sciences, 15 Akademicka St., 20-950 Lublin, Poland
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Department of Geoinformatics and Applied Computer Science, Faculty of Geology, Geophysics and Environmental Protection, AGH University of Science and Technology, Mickiewicza Av. 30, 30-059 Cracow, Poland
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Department of Clinical and Environmental Allergology, Jagiellonian University Medical College, Botaniczna 3, 31-531 Kraków, Poland
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2021, 10(2), 46; https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020046
Received: 2 December 2020 / Revised: 5 January 2021 / Accepted: 20 January 2021 / Published: 22 January 2021
(This article belongs to the Special Issue Citizen Science and Geospatial Capacity Building)
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. View Full-Text
Keywords: crowdsourced data quality; GeoWeb; citizen science; outdoor air pollution; symptom mapping crowdsourced data quality; GeoWeb; citizen science; outdoor air pollution; symptom mapping
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MDPI and ACS Style

Samulowska, M.; Chmielewski, S.; Raczko, E.; Lupa, M.; Myszkowska, D.; Zagajewski, B. Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping. ISPRS Int. J. Geo-Inf. 2021, 10, 46. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020046

AMA Style

Samulowska M, Chmielewski S, Raczko E, Lupa M, Myszkowska D, Zagajewski B. Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping. ISPRS International Journal of Geo-Information. 2021; 10(2):46. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020046

Chicago/Turabian Style

Samulowska, Marta, Szymon Chmielewski, Edwin Raczko, Michał Lupa, Dorota Myszkowska, and Bogdan Zagajewski. 2021. "Crowdsourcing without Data Bias: Building a Quality Assurance System for Air Pollution Symptom Mapping" ISPRS International Journal of Geo-Information 10, no. 2: 46. https://0-doi-org.brum.beds.ac.uk/10.3390/ijgi10020046

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