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.