A progressive development of a visual analysis interface of climate-related VGI

Abstract

This paper describes the progressive development of three approaches of successively increasing analytic functionality for visually exploring and analysing climate-related volunteered geographic information. The information is collected in the CitizenSensing project within which urban citizens voluntarily submit reports of site-specific extreme weather conditions, their impacts, and recommendations for best-practice adaptation measures. The work has pursued an iterative development process where the limitations of one approach have become the trigger for the subsequent ones. The proposed visual exploration approaches are: an initial map application providing a low-level data overview, a visual analysis prototype comprising three visual dashboards for more in-depth exploration, and a final custom-made visual analysis interface, the CitizenSensing Visual Analysis Interface (CS-VAI), which enables the flexible multifaceted exploration of the climate-related geographic information in focus. The approaches developed in this work are assessed with volunteered data collected in two of the CitizenSensing project’s campaigns held in the city of Norrköping, Sweden.

Identifying relevant volunteered geographic information about adverse weather events in Trondheim using the CitizenSensing participatory system

“…These shortcomings might have been partially avoided if the participants had followed instructions more closely and we had detected and followed up on these issues early in the campaigns to ensure the observations were registered according to a protocol. Our study confirms the findings by Navarra et al (2021) that the specification level of instructions given to participants, as well as participants’ preconceptions of the task, influences the content and quality of the collected VGI. For instance, participants might be influenced by their pre-understanding of the events of heavy rainfall and low temperature.…”

“…This resulted in the need for advanced data examination that would allow for in-depth analysis of the collected data, for instance, by means of GIS software. Although we used GIS in the post-collection data exploration, in which more than one key-variable was analysed to reveal spatiotemporal patterns, customised visual analytics tools would be favourable to allow effective multidimensional data exploration (Navarra et al, 2021).…”

“…Therefore, VGI implies several challenges and features specific shortcomings related to both data collection processes as well as collected data and, as a result, may be treated as of low relevance to decision making processes, in which reliable information is of essential importance. Examples of challenges include the need for user-friendly data collection tools (Fast and Rinner, 2018), difficulties in engaging volunteered contributors (Goodchild, 2007), heterogeneity in data coverage between different places (Haklay, 2010), positional inaccuracy (Jackson et al, 2013), and uneven data distribution over time (Navarra et al, 2021).…”