Document Type
Dissertation
Rights
This item is available under a Creative Commons License for non-commercial use only
Disciplines
Computer Sciences, Information Science
Abstract
Advances in data analytics for big data have affected many different domains and are providing new insights from these data for their respective communities. One such community is conservation science. A part of section of this community, ornithologists and bird conservationists has at its disposal millions of volunteers willing to contribute to a new ‘big’ data set. This can be achieved by means of new technology and the use of citizen science. This is already underway in North America through the excellent work of the Cornell Laboratory of Ornithology (CLO). This institute has harnessed the technology and the people and science is benefiting from the results. We would like to understand if a gap exists between the approach of the CLO and comparable groups in Ireland and the United Kingdom and if so, what issues need to be addressed to enable adoption of the new methodologies outside North America. This paper researches the effect of data visualisation techniques such as geo representation and tabular displays on motivating a community of interest to achieve a critical mass for successful delivery of a desirable new ‘big data’ set. Our work includes a review of data visualisation as part of data analytics, a review of how data visualisation has been used by conservation science in the past, and what the state of the art looks like today. We try to find key features in the techniques which are valuable for motivating the stakeholders. We also try to find the impact of the new insights provided from large spatio-temporal data sets on possible new beneficiaries
Recommended Citation
Kendlin, F.: Applying data visualisations to open new perspectives in birdwatching. Masters Dissertation. Technological University Dublin, 2013.
Publication Details
A dissertation submitted in partial fulfilment of the requirements of Technological University Dublin for the degree of MSc in Computing (Data Analytics)
January 2013