1. Combining Two Datasets into a Single Map Animation Salla Multimäki 1, Antti Mäkilä 2, Paula Ahonen-Rainio 1 1) Department of Real Estate, Planning and Geoinformatics 2) Department of Computer Science MMEA WP1 Result Seminar Vaisala 23.9.2015

2. 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?

3. 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

4. Dataset combination 1/2 http://ankka.github.io/p sychic- nemesis/examples/9a. html

5. 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 / m 3 ) 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

6. Dataset combination 2/2

7. 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?

8. 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

9. 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

10. 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.