combining two datasets into a single map animation
TRANSCRIPT
Combining Two Datasets into a Single Map Animation
Salla Multimäki1, Antti Mäkilä2, Paula Ahonen-Rainio1
1) Department of Real Estate, Planning and Geoinformatics 2) Department of Computer Science
MMEA WP1 Result SeminarVaisala 23.9.2015
Motivation: Why to combine two different datasets into the same visualization?
Visual analysis• Is there spatial correlation between two phenomena?
• Instant• Lagged
• Finding anomalies• Are there areas where two phenomena do not match as
expected?Model evaluation
• How good is the correlation between model of the phenomenon and actual obseravtions?
Dataset combination 1/2
Rain radar (FMI) and rain model (SILAM)• Hypothesis: modelled and observed rain should meet• Visualization considerations:
• Viewer’s interest is in those areas where observations and model do not meet– Complementary colours– Combination forms neutral grey– Transparency levels
• No ”good/bad” or ”real/fake” associations• Classification simple enough
– Classification must be the same with both datasets
Dataset combination 1/2
http://ankka.github.io/psychic-nemesis/examples/9a.html
Dataset combination 2/2
Birch pollen concentration (SILAM) and relative air humidity (SILAM)
• Hypothesis: high air humidity ( > 70%) should remove high pollen concentrations ( > 50 grains / m3) Bartková-Ščevková, J. "The influence of temperature, relative humidity and rainfall on the occurrence of pollen allergens (Betula, Poaceae, Ambrosia artemisiifolia) in the atmosphere of Bratislava (Slovakia)." International Journal of Biometeorology 48.1 (2003): 1-5.
• Visualization considerations:• Two colours which together forms third,
easily separable colour• Natural associations: yellow pollen, blue
water• No classification, only binary values
because of the hypothesis
Dataset combination 2/2
Validation of the results
• Coming up in September: focus group interviews– three separate groups of 4-6 participants: students, GIS
professionals and meteorology professionals
• The focus groups are evaluating:– Used colours and their suitability for the task– Classification– Background map
• Analysis of e.g. following things:– Colour combinations– Effect of geometrical complexity of the datasets– Other suggestions?
Some preliminary results from the first focus group interview (GIS professionals)
Rain model and observations
• Was easier to interpret because of the logical movement of the areas
• Neutral grey is easily missed or mixed with other light values (or sea)
• Green areas of the model were experienced too dominant because of their geometry– Suggestion: show dappled radar images on the top of the more
solid model, no transparency– Suggestion: show only outlines of model areas
Some preliminary results from the first focus group interview (GIS professionals)
Pollen and air humidity
• The geometry and behaviour of the datasets have a great effect
• Blue was seen as a sea area• Green was seen as a third, separate phenomenon
– Suggestion: show only the areas where the datasets overlap (because that is what should not happen according to the previous research!)
• The task of the user was not as clear as with the other example
Publication of the results
• EuroCarto 2015: 1st ICA European Symposium on Cartography 10.-12.11.2015 Vienna, Austria
• Selected papers are intended to be published in the International Journal of Cartography and in a book of the series Lecture Notes on Geoinformation and Cartography by Springer.