A taxonomy of quality assessment methods for volunteered and crowdsourced geographic information

Degrossi, Lívia Castro; Albuquerque, João Porto de; Rocha, Roberto dos Santos; Zipf, Alexander

doi:10.1111/tgis.12329

Cited by 51 publications

(29 citation statements)

References 47 publications

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“…Social media data are uploaded by volunteers based on their experiences and opinions, producing “ambient geographic information" (Degrossi, Porto de Albuquerque, dos Santos Rocha, & Zipf, ). As user‐generated photos are used in this study, the uncertainty in and quality of the data should be tested (Goodchild & Li, ).…”

Section: Discussionmentioning

confidence: 99%

Extracting human emotions at different places based on facial expressions and spatial clustering analysis

Kang

Jia

Gao

et al. 2019

Transactions in GIS

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The emergence of big data enables us to evaluate the various human emotions at places from a statistical perspective by applying affective computing. In this study a novel framework for extracting human emotions from large‐scale georeferenced photos at different places is proposed. After the construction of places based on spatial clustering of user‐generated footprints collected from social media websites, online cognitive services are utilized to extract human emotions from facial expressions using state‐of‐the‐art computer vision techniques. Two happiness metrics are defined for measuring the human emotions at different places. To validate the feasibility of the framework, we take 80 tourist attractions around the world as an example and a happiness ranking list of places is generated based on human emotions calculated over 2 million faces detected from greater than 6 million photos. Different kinds of geographical contexts are taken into consideration to find out the relationship between human emotions and environmental factors. Results show that much of the emotional variation at different places can be explained by a few factors such as openness. The research offers insights into integrating human emotions to enrich the understanding of sense of place in geography and in place‐based GIS.

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Section: Discussionmentioning

confidence: 99%

Extracting human emotions at different places based on facial expressions and spatial clustering analysis

Kang

Jia

Gao

et al. 2019

Transactions in GIS

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“…The quality of VGI has been a hot topic from the beginning of this trend, and there are a lot of papers dealing with this topic. The need for specific data quality elements, metrics and methods is clearly pointed out in Gusminia et al (2017), Degrossi et al (2018), Senaratne (2017). • Statistical data are those produced by statistical agencies.…”

Section: New Types Of Data: the Challengesmentioning

confidence: 99%

Geospatial data quality (ISO 19157-1): evolve or perish

López¹,

González²,

Pau³

et al. 2020

rcarto

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Given the circumstance that the process for the revision of the international standard ISO 19157 is currently open, this article presents a critical reflection on its content, application and some challenges posed by the new types of data (e.g. big data, BIM data, etc.), that also have a geospatial component and to which, therefore, this international standard can be applied as well. Proposals are put forward going along three lines of improvement, on the one hand the consideration of new data quality elements and on the other, the reinforcement of the interoperability of this international standard with other standards related to data quality, and finally various improvements (e.g. standardization of evaluation methods, clearly introducing the life cycle, improvement of the definition of metaquality, etc.) of the standard, which come from experience.

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“…In this context, when pedagogical issues are raised, they tend to center on critiques of expectations put on citizens to receive "training" so that they can act as competent "smart citizens" (Gabrys 2016(Gabrys , 2010. This is connected to the frequent concerns with the quality of data resulting from citizen sensing (Degrossi et al 2018), in response to which some initiatives include the training of citizens in the ability to carry out high-quality data collection (Bordogna et al 2014). However, the task of decision making about which data to collect and defining the criteria for assessing the quality of the data are often assumed to be the sole remit of researchers, as pointed out by Haklay (2013a).…”

Section: Toward a Pedagogy Of Questionsmentioning

confidence: 99%