Assuring quality and waiting time in real-time spatial crowdsourcing

Wu, Zhibin; Peng, Lijie; Xiang, Chuankai

doi:10.1016/j.dss.2022.113869

Cited by 11 publications

(1 citation statement)

References 46 publications

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“…Many studies have concentrated on several aspects of the dependability of spatial data obtained through crowdsourcing [49][50][51][52]. For instance, several studies have focused on aspects of urban planning and public engagement [53].…”

Section: Data Quality and Reliabilitymentioning

confidence: 99%

The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics

Alamri

2024

IJGI

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Crowdsourced spatial analytics is a rapidly developing field that involves collecting and analyzing geographical data, utilizing the collective power of human observation. This paper explores the field of spatial data analytics and crowdsourcing and how recently developed tools, cloud-based GIS, and artificial intelligence (AI) are being applied in this domain. This paper examines and discusses cutting-edge technologies and case studies in different fields of spatial data analytics and crowdsourcing used in a wide range of industries and government departments such as urban planning, health, transportation, and environmental sustainability. Furthermore, by understanding the concerns associated with data quality and data privacy, this paper explores the potential of crowdsourced data while also examining the related problems. This study analyzes the obstacles and challenges related to “geospatial crowdsourcing”, identifying significant limitations and predicting future trends intended to overcome the related challenges.

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Section: Data Quality and Reliabilitymentioning

confidence: 99%

The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics

Alamri

2024

IJGI

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Crowdsourcing solutions for innovation: An evolutionary examination of participant behavior strategy

Wang,

Li,

Zheng

et al. 2024

Manage Decis Econ

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Despite numerous enterprises embracing crowdsourcing to access several innovative solutions, the prevalence of information asymmetry among different participants has led to an increase in the submission of low‐quality solutions and payment disputes. To improve the efficiency of crowdsourcing solutions for innovation, this study aims to employ an evolutionary game model to capture the dynamic interaction and decision‐making process of the requesters, platforms, and solvers. Initially, we dissect the relevant factors influencing the behavioral decisions of participants to construct a tripartite evolutionary game model. Subsequently, we analyze five potential evolutionarily stable strategies and conditions. Ultimately, we simulate the dynamic evolution of participant decision‐making behavior and the sensitivity of related parameters. The simulation results depict that the initial selection probabilities of populations bear no correlation to the system stability, which only influences the time required to reach equilibrium. The participant's behaviors are affected by price, loss, penalty, compensation, cost, and reputation recognition. Reward and punishment mechanisms help effectively mitigate the emergence of free‐riding and collusion. These findings provide important implications for the sustainable development of crowdsourcing solutions for innovation.

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Exploring the inhibitors for competitive AI software development through cloud driven transformation

Hasteer,

Sindhwani,

Behl

et al. 2023

Ann Oper Res

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Assuring quality and waiting time in real-time spatial crowdsourcing

Cited by 11 publications

References 46 publications

The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics

The Geospatial Crowd: Emerging Trends and Challenges in Crowdsourced Spatial Analytics

Crowdsourcing solutions for innovation: An evolutionary examination of participant behavior strategy

Exploring the inhibitors for competitive AI software development through cloud driven transformation

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