Measuring the Wisdom of the Crowd: How Many is Enough?
Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle o...
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| Autor*in: |
Walter, Volker [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2022 |
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| Schlagwörter: |
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| Anmerkung: |
© The Author(s) 2022 |
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| Übergeordnetes Werk: |
Enthalten in: PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science - Springer International Publishing, 2017, 90(2022), 3 vom: 06. Apr., Seite 269-291 |
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| Übergeordnetes Werk: |
volume:90 ; year:2022 ; number:3 ; day:06 ; month:04 ; pages:269-291 |
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| DOI / URN: |
10.1007/s41064-022-00202-2 |
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SPR047557729 |
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| 520 | |a Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. | ||
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10.1007/s41064-022-00202-2 doi (DE-627)SPR047557729 (SPR)s41064-022-00202-2-e DE-627 ger DE-627 rakwb eng Walter, Volker verfasserin (orcid)0000-0002-2714-5061 aut Measuring the Wisdom of the Crowd: How Many is Enough? 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. Wisdom of the crowd (dpeaa)DE-He213 Paid crowdsourcing (dpeaa)DE-He213 Crowdworker (dpeaa)DE-He213 MicroWorkers (dpeaa)DE-He213 Spatial data collection (dpeaa)DE-He213 Quality evaluation (dpeaa)DE-He213 Kölle, Michael aut Collmar, David aut Enthalten in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Springer International Publishing, 2017 90(2022), 3 vom: 06. Apr., Seite 269-291 (DE-627)SPR038131196 2363-7145 nnns volume:90 year:2022 number:3 day:06 month:04 pages:269-291 https://dx.doi.org/10.1007/s41064-022-00202-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_70 GBV_ILN_72 AR 90 2022 3 06 04 269-291 |
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10.1007/s41064-022-00202-2 doi (DE-627)SPR047557729 (SPR)s41064-022-00202-2-e DE-627 ger DE-627 rakwb eng Walter, Volker verfasserin (orcid)0000-0002-2714-5061 aut Measuring the Wisdom of the Crowd: How Many is Enough? 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. Wisdom of the crowd (dpeaa)DE-He213 Paid crowdsourcing (dpeaa)DE-He213 Crowdworker (dpeaa)DE-He213 MicroWorkers (dpeaa)DE-He213 Spatial data collection (dpeaa)DE-He213 Quality evaluation (dpeaa)DE-He213 Kölle, Michael aut Collmar, David aut Enthalten in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Springer International Publishing, 2017 90(2022), 3 vom: 06. Apr., Seite 269-291 (DE-627)SPR038131196 2363-7145 nnns volume:90 year:2022 number:3 day:06 month:04 pages:269-291 https://dx.doi.org/10.1007/s41064-022-00202-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_70 GBV_ILN_72 AR 90 2022 3 06 04 269-291 |
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10.1007/s41064-022-00202-2 doi (DE-627)SPR047557729 (SPR)s41064-022-00202-2-e DE-627 ger DE-627 rakwb eng Walter, Volker verfasserin (orcid)0000-0002-2714-5061 aut Measuring the Wisdom of the Crowd: How Many is Enough? 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. Wisdom of the crowd (dpeaa)DE-He213 Paid crowdsourcing (dpeaa)DE-He213 Crowdworker (dpeaa)DE-He213 MicroWorkers (dpeaa)DE-He213 Spatial data collection (dpeaa)DE-He213 Quality evaluation (dpeaa)DE-He213 Kölle, Michael aut Collmar, David aut Enthalten in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Springer International Publishing, 2017 90(2022), 3 vom: 06. Apr., Seite 269-291 (DE-627)SPR038131196 2363-7145 nnns volume:90 year:2022 number:3 day:06 month:04 pages:269-291 https://dx.doi.org/10.1007/s41064-022-00202-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_70 GBV_ILN_72 AR 90 2022 3 06 04 269-291 |
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10.1007/s41064-022-00202-2 doi (DE-627)SPR047557729 (SPR)s41064-022-00202-2-e DE-627 ger DE-627 rakwb eng Walter, Volker verfasserin (orcid)0000-0002-2714-5061 aut Measuring the Wisdom of the Crowd: How Many is Enough? 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. Wisdom of the crowd (dpeaa)DE-He213 Paid crowdsourcing (dpeaa)DE-He213 Crowdworker (dpeaa)DE-He213 MicroWorkers (dpeaa)DE-He213 Spatial data collection (dpeaa)DE-He213 Quality evaluation (dpeaa)DE-He213 Kölle, Michael aut Collmar, David aut Enthalten in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Springer International Publishing, 2017 90(2022), 3 vom: 06. Apr., Seite 269-291 (DE-627)SPR038131196 2363-7145 nnns volume:90 year:2022 number:3 day:06 month:04 pages:269-291 https://dx.doi.org/10.1007/s41064-022-00202-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_70 GBV_ILN_72 AR 90 2022 3 06 04 269-291 |
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10.1007/s41064-022-00202-2 doi (DE-627)SPR047557729 (SPR)s41064-022-00202-2-e DE-627 ger DE-627 rakwb eng Walter, Volker verfasserin (orcid)0000-0002-2714-5061 aut Measuring the Wisdom of the Crowd: How Many is Enough? 2022 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier © The Author(s) 2022 Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. Wisdom of the crowd (dpeaa)DE-He213 Paid crowdsourcing (dpeaa)DE-He213 Crowdworker (dpeaa)DE-He213 MicroWorkers (dpeaa)DE-He213 Spatial data collection (dpeaa)DE-He213 Quality evaluation (dpeaa)DE-He213 Kölle, Michael aut Collmar, David aut Enthalten in PFG – Journal of Photogrammetry, Remote Sensing and Geoinformation Science Springer International Publishing, 2017 90(2022), 3 vom: 06. Apr., Seite 269-291 (DE-627)SPR038131196 2363-7145 nnns volume:90 year:2022 number:3 day:06 month:04 pages:269-291 https://dx.doi.org/10.1007/s41064-022-00202-2 kostenfrei Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER GBV_ILN_70 GBV_ILN_72 AR 90 2022 3 06 04 269-291 |
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Measuring the Wisdom of the Crowd: How Many is Enough? |
| abstract |
Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. © The Author(s) 2022 |
| abstractGer |
Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. © The Author(s) 2022 |
| abstract_unstemmed |
Abstract The idea of the wisdom of the crowd is that integrating multiple estimates of a group of individuals provides an outcome that is often better than most of the underlying estimates or even better than the best individual estimate. In this paper, we examine the wisdom of the crowd principle on the example of spatial data collection by paid crowdworkers. We developed a web-based user interface for the collection of vehicles from rasterized shadings derived from 3D point clouds and executed different data collection campaigns on the crowdsourcing marketplace microWorkers. Our main question is: how large must be the crowd in order that the quality of the outcome fulfils the quality requirements of a specific application? To answer this question, we computed precision, recall, F1 score, and geometric quality measures for different crowd sizes. We found that increasing the crowd size improves the quality of the outcome. This improvement is quite large at the beginning and gradually decreases with larger crowd sizes. These findings confirm the wisdom of the crowd principle and help to find an optimum number of the crowd size that is in the end a compromise between data quality, and cost and time required to perform the data collection. © The Author(s) 2022 |
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| title_short |
Measuring the Wisdom of the Crowd: How Many is Enough? |
| url |
https://dx.doi.org/10.1007/s41064-022-00202-2 |
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true |
| author2 |
Kölle, Michael Collmar, David |
| author2Str |
Kölle, Michael Collmar, David |
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SPR038131196 |
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| doi_str |
10.1007/s41064-022-00202-2 |
| up_date |
2024-07-03T13:31:38.528Z |
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7.399168 |