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Combining Two Datasets into a Single Map Animation

CLEEN_Ltd
CLEEN_Ltd

The amount of environmental data is increasing, and the data would be valuable to the society if they are delivered to the right processes at the right time. In the seminar, we show examples of available data, how they are produced and processed, and how the data can be used in new innovative applications. This presentation is part of the Environmental Data for Applications Seminar held on the 23rd of September 2015. The seminar was organised by the MMEA (Measurement, Measuring and Environmental Assessment) research programme under the Cleen Ltd (SHOK). The presentations are based on the research results related to environmental data interoperability. The participants included key players and partners in the field of environmental monitoring in Finland. More info at www.mmea.fi

Combining Two Datasets into a Single Map Animation

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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 Seminar
Vaisala 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/p
sychic-
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
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Combining Two Datasets into a Single Map Animation

  • 1. 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 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
  • 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 / 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
  • 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.

Editor's Notes

  1. Antin dippatyö MMEA confluencessa
  2. Outi Mäyrä gave and example of assumed correlation between rain, temperature and plant disease