01. Intro: Research Questions - dataandcrowd/VGI GitHub Wiki
Title: Barriers to the Participation of Volunteered Geographic Information: A Systematic Review
Introduction
Volunteering geographic information (VGI) or geographic crowdsourcing has been beneficial to our society with regard to gathering adequate information where one cannot acquire from (Mobasheri et al., 2017; Nicolosi et al., 2020; Zhiyuan et al., 2017). In the era of Web2.0, the use of the internet has enabled users from downloading the data as a receiver-only mode to assert their own views of their surroundings and share opinions and data to help local decision-making regardless of where they live (Sui et al., 2013). A good example is the online crowds who voluntarily built a web map after the earthquake in Haiti (Zook et al., 2010). With the success of crowdsourcing for solving real-world problems and the attention on web volunteer mapping, the role of the crowd has become more important than ever.
Despite the increasing attention in VGI and open mapping, recent scholarship has pointed out that most contributions in VGI were made by a biased sample of crowds that were featured as the elite, western, English-speaking male (Gardner et al., 2020). In other words, those who are under-represented are unlikely to appear on the map. However, it is important to take into consideration the under-represented group because the diversity of crowd can deliver various information on the map e.g. elevators in shopping malls and breastfeeding rooms in city centres can help women with buggies to navigate their journeys. Additionally, the details of missing data that was not able to be filled with the current user group e.g. names of local areas that are different from the officially recognised names need to be mapped in parallel.
Although biased samples have been thoroughly discussed in previous papers, the context was either to assess the likelihood of an imbalance between groups quantitatively or qualitatively or have raised sampling errors that are statistically not accepted (Brown, 2017; Gardner et al., 2020; Zhang & Zhu, 2018). In addition, empirical studies focus on one topic in isolation thus does not make it suitable in all applications (Alvarez Leon & Quinn, 2019; Haworth et al., 2018; Meier et al., 2017). To our knowledge, none of the published reviews identified or analysed the available evidence from a strict systematic approach. To get more ideas of which under-represented group is highlighted, this review will not constrain the discipline nor type of platforms.
This paper conducts a systematic review to identify and analyse the main components of under-represented participants in various VGI projects and to seek diverse participation in future geospatial crowdsourcing.
Research Questions (SPIDER Framework)
Overall question: Does the contribution of under-represented participants in crowdsourced mapping more likely to create diverse outcomes?
| SPIDER | Description |
|---|---|
| Sample | Under-represented groups |
| Phenomenon of Interest | More contribution in crowdsourced mapping |
| Design | Survey, questionnaire, case study |
| Evaluation | Create diverse outcomes |
| Research Type | Mixed |