How can spatiotemporal visualizations of VGI improve disaster risk management?
Nov 19th, 2014 by j0p
By Katja Bode and Felicia Linke
Introduction
VGI is increasingly used to support disaster risk management. This data can help to “update the situational awareness and to improve the operational response” (Terpstra et al. 2012).
However, it is often difficult to access the vast amount of data in an appropriate way. Visualization plays a key role to make this data easily understandable (Terpstra et al. 2012).
This blog entry gives an example of an approach to gathering useful information from tweets and concludes with the challenges of spatiotemporal visualization.
Approach
In the case of a disaster taking place it is very important to filter the relevant information shared on social media (like Twitter), which can actually contribute to the improvement of DRM strategies and its effectiveness. Crisis includes a large number of events, both personal and shared, many of which are not relevant in the field of DRM and are therefore not considered in the filtering process (such as a personal crisis or an economic crisis). In this discussion on helpfulness of data, it is necessary to restrict oneself to events that have constrained temporal and geographical extent (even if they are lengthy or broad) that affect large numbers of people and bring everyday life to a standstill.
The common steps which are necessary for making use of information shared shortly before, during and after a disaster on Twitter will be presented on the basis of the Application System Twitcident. Twitcident is a recently developed framework and Web-based system that automatically filters, searches and analyses tweets regarding incidents. The system was used during a storm in Belgium that hit the festival of Pukkelpop 2011 (which will be used as a case example).
The system contains three components.
FIRST STEP:
Data Collection à Search Harvest and Filter: The „raw data“ (i.e. tweets about a certain disaster) need to be sorted by their spatiotemporal occurrence in order to evaluate their relevance (further visualisation possible i.e. display data in a map)
Example from the Pukkelpop Festival: Gather all the Tweets, which are tweeted in the area of the festival side and in Belgium and the adjacent countries.
Fig. 1: Geo-annotated tweets plotted on Google maps (TERPSTRA et al. 2012, p.3).
SECOND STEP:
Data content assessment and validation/ interpretative analyses à All the data gathered in the first step need to be filtered again by specific words which are mainly used by Twitter-users during disasters.
Example from the Pukkelpop Festival: First filter for tweets including “Pukkelpop” or “PP11”.Then identify damage-related tweets by filtering tweets on keywords; i.e. ‘damage’, ‘devastate’, ‘collapse’, ‘destroy’, ‘ravage’/ Identify casualty-related tweets by filtering tweets on keywords: ‘casualty’, ‘injury’, ‘dead’, ‘died’, ‘death’, ‘kill’, ‘decease’ and conjugations of these terms.
THIRD STEP:
Analyse results for improving or establishing early-warning systems and coordinating first-aid-groups during the disaster.
Challenges in visualizing spatiotemporal data
There are many different types of spatial and spatiotemporal data which are increasingly made public and easily accessible. However, there are some difficulties before this data can be used in a certain way i.e. to be analysed for use in DRM.
Firstly there is a lot of data. Often this is too much to be analysed without filtering it before. Many times real time analysis are made and therefore it is necessary to handle the new data in a way that it fits in with previous data. Also, there might occur problems when different types or different qualities of data that may come from different sources are combined.
Another challenge is to support analysis at multiple scales and to find the right scale(s) for the data being used.
Users of social media often have different backgrounds so it is important to explore ways to understand and adequately support diverse users i.e. specialists and non-specialist.
It is necessary to present data in a way that users are able to explore it more easily. Instead of current complex GIS programs there should be used tools which allow different spatiotemporal visualization, support for analysis, and collaboration among others.
All these challenges need to be faced in order to develop an appropriate way to deal with spatiotemporal data. Key objectives should be to include characteristics of time and space, be visual, scalable, collaborative, lightweight and to deal with different and large data sets (Andrienko et al. 2010).
Literature:
Andrienko, G., Andrienko, N., Demsar, U., Dransch, D., Dykes, J., Fabrikant, S. I., … Tominski, C. (2010). Space, time and visual analytics. International Journal of Geographical Information Science, 24(10), 1577–1600.
Terpstra, T., & Vries, A. de. (2012). Towards a realtime Twitter analysis during crises for operational crisis management. In Proceedings of ISCRAM 2012 (pp. 1–9).