Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Yang, Pan; Ng, Tze Ling; Cai, Ximing

doi:10.1029/2018wr024447

Cited by 14 publications

(10 citation statements)

References 81 publications

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“…Problems caused by financial incentives: Lave (2012) discussed the ethics of utilizing citizen scientists as a source of unpaid labor and Cieslik et al (2019), regarding mobilization of people of low socioeconomic status, stated that financial compensation, or other non‐monetary incentives, are right and necessary. Yang, Ng, and Cai (2019) warned that neglecting participant heterogeneity when designing or implementing an incentive allocation program risks undercutting the ultimate outcome due to differences in attitudes, beliefs, and experiences that can cause people to respond quite differently to the same incentives. There can be adverse impacts of financial incentives on participant motivation (Frey, 1997) and data quality (Walker et al, 2016).…”

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confidence: 99%

The benefits and negative impacts of citizen science applications to water as experienced by participants and communities

2020

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Citizen science is proliferating in the water sciences with increasing public involvement in monitoring water resources, climate variables, water quality, and in mapping and modeling exercises. In addition to the well-reported scientific benefits of such projects, in particular solving data scarcity issues, it is common to extol the benefits for participants, for example, increased knowledge and empowerment. We reviewed 549 publications concerning citizen science applications in the water sciences to examine personal benefits and motivations, and wider community benefits. The potential benefits of involvement were often simply listed without explanation or investigation. Studies that investigated whether or not participants and communities actually benefitted from involvement, or experienced negative impacts, were uncommon, especially in the Global South. Assuming certain benefits will be experienced can be fallacious as in some cases the intended benefits were either not achieved or in fact had negative impacts. Identified benefits are described and we reveal that more consideration should be given to how these benefits interrelate and how they build community capitals to foster their realization in citizen science water projects. Additionally, we describe identified negative impacts showing they were seldom considered though they may not be uncommon and should be borne in mind when implementing citizen science. Given the time and effort commitment made by citizen scientists for the benefit of research, there is a need for further study of participants and communities involved in citizen science applications to water, particularly in low-income regions, to ensure both researchers and communities are benefitting.

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confidence: 99%

The benefits and negative impacts of citizen science applications to water as experienced by participants and communities

2020

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“…The second set of data is on individuals' daily activities (e.g., daily routines and contacting networks), the usage of social media for person-to-person communication (e.g., the frequency of using social media, the number of social friends and the influencing weights of their opinions), and citizens' attitude towards epidemic risk and their social and economic conditions that may affect the adoption of disease prevention behaviors. These data can be acquired by following the approaches in choice experiment to conduct questionnaires and surveys in the neighborhood community, combined with data retrieval (e.g., crowd-sourcing and web crawler) and advanced data mining techniques for geo-tagged social media data ( Determann et al, 2014 ; Injadat et al, 2016 ; Jain and Katkar, 2015 ; Michaud et al, 2013 ; Roby et al, 2018 ; Viboud and Vespignani, 2019 ; Yang et al, 2019 ). The third set of data is on the characteristics and clinical features of infectious diseases, such as the basic reproductive number, the duration of the latent period, recovery period, and the discharge and fatality rate.…”

Section: Discussion and Future Directionsmentioning

confidence: 99%

How do social media and individual behaviors affect epidemic transmission and control?

Chen

Liu

et al. 2021

Science of The Total Environment

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“…Following Yang et al. (2019), the moving distance is assumed to follow a uniform distribution:

L_{i, t} \sim U (0, L_{\max})

where L max is the maximum distance the sensor can move during a time step. Again, L max follows a uniform distribution U (1 km, 5 km), which corresponds to a range of moving speeds from 12 km/h to 60 km/h with a time step of 5 min.…”

Section: Methodsmentioning

confidence: 99%

Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach

Niu

Yang

Zheng

et al. 2021

Water Resources Research

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In recent years, crowdsourcing, an alternative data acquisition approach that involves the collection of data from individual citizens through the internet, social media, and smartphones, has been increasingly investigated, especially in the field of geophysics (Ebert et al., 2018;Wu & Wang, 2019). Compared to traditional geophysical data collection approaches (which largely rely on expensive professional instruments; de Vos, Droste, et al., 2019), the crowdsourced approach uses human judgments or the low-cost sensors of common citizens as its data source (Zhang et al., 2021). It thus offers a way of obtaining massive data cost-effectively. In some developing countries, crowdsourcing can even be a major source of geophysical data (Pingali, 2017).The crowdsourcing approach has been demonstrated to increase the spatial and temporal representativeness of geophysical observation networks and has been applied in a broad range of areas, for example, climate research (Meier et al., 2017), air quality (Schneider et al., 2017), ecology (Hunt et al., 2017, geography (Fan et al., 2016), and especially rainfall. Especially, in the past two decades, the number of personal weather stations (PWSs) in the US has grown exponentially from nearly 2,000 in 2001 to almost 100,000 in 2019 (Chen et al., 2019), significantly outnumbering the 9,300 professional rain gauges operated or managed by the National Oceanic and Atmospheric Administration (NOAA; Durre et al., 2013). In recent years, crowdsource-based rainfall monitoring has become even more attractive (Haklay, 2013) because of the continuous developments in information extraction from smartphones (Guo et al., 2019), low-cost sensors (e.g., surveillance cameras; Jiang et al., 2019), microwave links (Overeem et al., 2016), and moving cars (Rabiei et al., 2016). The utilization of crowdsourced precipitation data has provided an essential supplement to traditional measurements based on ground gauges and radars (

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Reward‐Based Participant Management for Crowdsourcing Rainfall Monitoring: An Agent‐Based Model Simulation

Cited by 14 publications

References 81 publications

The benefits and negative impacts of citizen science applications to water as experienced by participants and communities

The benefits and negative impacts of citizen science applications to water as experienced by participants and communities

How do social media and individual behaviors affect epidemic transmission and control?

Automatic Quality Control of Crowdsourced Rainfall Data With Multiple Noises: A Machine Learning Approach

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