Finnish Meteorological Institute conducted the impact assesment of its open data. The survey was employed by Spatineo. FMI open data portal gets over 10 data requests each second and the open data have remarkable affect on Finnish society.

1. The Impact
Assessment of FMI
OpenData
Notes from survey employed by
Spatineo
10.8.2020 Roope Tervo

2. A survey had two main goals
Assess the impact of FMI open
data for FMI’s customers
Find out what not-open data
customers would need in the
future
Spatineo
Impact
Digital Survey
Digital Survey

3. Some basic information
Survey done by Spatineo
during 2019
2018 data from FMI
OpenData portal (WMS +
WFS) and AWS
Observation download UI
not included
Fingerprint:
ip-address + operating
system + user-agent +
referrer
3

4. How our services are
used?

5. Most users use
interpolated timeseries
Most users use
interpolated timeseries
(WFS) while large datasets
are disseminated via AWS
Requests
Data
Amount
Fingerprints
Source Amount %
Source Amount [TB] %
Source Amount %
2018 data

6. Most users use
interpolated timeseries
Timeseries API is crucial
Requests
Data
Amount
Fingerprints
Source Amount %
Source Amount [TB] %
Source Amount %
AWS is important for
heavy users

7. Summer is the busiest time of the year
Requests to all interfaces by date
Problems with Oracle 3 x load compared to
beginning of the year

8. Removing API-key did have an effect
Company requests to all interfaces
Possibility to request
without API-key
Observations
Weather forecsast
Company fingerprints to all interfaces

9. Manual and catalogue needs enhancement
0 % 10 % 20 % 30 % 40 % 50 % 60 % 70 % 80 % 90 % 100 %
AWS instructions
Manual
WFS quick tutorial
WMS quick tutorial
Catalog
Material quality accoding to the survey
Confusing Quite confusing OK Good Not used
N=951
Needs focus

10. 25% of respondents use
FMI open data with
other open data

11. Who use our services?

12. Companies are the most active user sector
55 %30 %
9 %
All Requests
Anonymous Companies
Public sector Other
FMI
81 %
6 %
11 %
Identified Requests
Companies Public sector
Other FMI

13. Company sector usage by requests
Weather forecast
Observations
Single radar dbz
Thunder strikes
Radar metadata
Solar observations
Radar composites
Harmonie NWP
Single radar VRAD
Road observations
IT Weather Energy Consumer services e-commerce traffic

14. What data is used?

15. Users find observations the most important
accoding the survey

16. Three top datasets cover majority of requests
34 %
26 %
15 %
5 %
8 %
Requests by dataset
Station specific weather observations (111 M requests)
Weather forecast model HIRLAM (86 M requests)
Lightning strikes (48 M requests)
Radar precipitation amounts (rr) (15 M requests)
Single radar radar reflectivity (dbz) (9 M requests)
Radar composite precipitation amount (rr) (8 M requests)
Sun radiation observations (8 M requests)
Radar composite reflectivity (dbz) (7 M requests)
Road weather observations (7 M requests)
Other (27 M requests)

17. All datasets had requested at least few times
69
142
291
291
2669
2824
4074
4228
48
84
148
123
84
266
236
1202
51
0 500 1000 1500 2000 2500 3000 3500 4000 4500
Climate scenarious
Climate normals (yearly)
Air quality forecast Enfuser
Ice model HELMI
Radioactivity
Air quality forecast SILAM
Soundings
Hydrocrafy and current model HBM
Climate normals (monthly)
Requests and fingerprints to least popular datasets
Fingerprints Requests

18. Companies use mostly weather forecast
6
2,6
6
24,8
37,9
0
2,6
0
1,5
1,5
0
0,3
0
7,8
3,6
0 5 10 15 20 25 30 35 40
Single radar reflectivity
Road weather observations
Thunder strikes
Weather observations
Weather forecast HIRLAM
Dataset requests by data (million requests)
Other Public sector Companies

19. Public sector use a lot road weather
observations
10 %
36 %
10 %
21 %
20 %
3 %
Public sector usage by requests
Sea level forecast
Road weather observations
Sea level observations
Weather observations
Weather forecast HIRLAM
Other

20. Editoitu data halutuinta

21. Impacts

22. 22
”Tuotamme merisään tilannekuvaa reaaliajassa
sovelluksen karttapohjalle. Käytämme myös historiatietoa
vastavasti. Tällä hetkellä siis vain rikastamme
karttakäyttöliittymää ajankohtaisella tai historiallisella
vallinneella säätilalla, loppukäyttäjä voi käyttää tietoa
arvioidessaan varsinaista dataamme, eli alusten
liiketietoja ja polttoaineenkulutuksia.”
”Fysiikan opetuksessa peruskoulussa ja lukiossa.”
”Asiakastyytyväisyyden mittaustulosten lisätietona.”
” Vesilaitosten jätevesivirtaamat ja vuotovedet”
”Helsingin kaupunkipyöräjärjestelmän aktiivisuuden
"sääjoustojen" hahmotteluun. Tekeillä. Ks.
https://twitter.com/tellinkibotti?lang=fi ;
https://bit.ly/tellinkiappi”

23. 23
”Taloyhtiöt liukastumistapaukset, johon tarvittu ko.
hetkenä vallinnutta säätilaa ja siihen liittyviä yksittäisiä
tietoja. Vakuuutusyhtiöt tarvitsevat näitä tietoja.”
”Vesilaitosten jätevesiverkon käyttöytymisen
ennustamiseen. Sadevesistä ~40% valuu
jätevesiverkkoon, joten se vaikuttaa virtauksiin
voimakkaasti.”
”Olen katsellut säätutkakuvista, että voinko ajaa
avoautolla töihin, ja toisaalta joko minulla on kiire kotiin.”
”Rakensin eteiseemme sääpaneelin, joka näyttää
havainnon ja ennusteen alueellemme. Käytämme tätä
lasten päiväkotivaatteiden valitsemiseen aamuisin.
https://kiedontaa.blogspot.com/2015/10/weather-
display-update.html”
“Weather and sea observations, used to estimate solar
power production”

24. FMI open data have
significant impact on
business
14% of the private company
respondents said that FMI
open data have generated
new business during last 3
years
N = 389; 14 % of 389 = 54 companies

25. ”
Our services will
create added value
for our customers,
boosting their
competitiveness and
promoting new
business and exports.
”
Companies are the heavy users
à Support for business
Different user groups use
different datasets
Summer times three times as
busy as winter time
à high availability requirement