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FMI Open Data Impact Survey 2019

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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.

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FMI Open Data Impact Survey 2019

  1. 1. The Impact Assessment of FMI OpenData Notes from survey employed by Spatineo 10.8.2020 Roope Tervo
  2. 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. 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. 4. How our services are used?
  5. 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. 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. 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. 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. 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. 10. 25% of respondents use FMI open data with other open data
  11. 11. Who use our services?
  12. 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. 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. 14. What data is used?
  15. 15. Users find observations the most important accoding the survey
  16. 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. 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. 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. 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. 20. Editoitu data halutuinta
  21. 21. Impacts
  22. 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. 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. 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. 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

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