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Enhancing Spatial Statistics with SSW on Web Platform

Learn how SSW in practice improves the national geoportal's statistics view with examples of analysis methods and Oskari Pilots. Discover the role of Scrum methodology, user stories, and testing procedures in developing this open-source web application.

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Enhancing Spatial Statistics with SSW on Web Platform

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  1. SSW - SpatialStatistics on Web Tuuli Pihlajamaa, Marja Tammilehto-LuodeSeptember 10th 2015 Nordic Forum for Geography and Statistics

  2. Contents • Introduction • Oskari platform • SSW in practise: • DevelopmentbyScrummethodology • Testingprocedure • User stories in theproject • Examples of theanalysismethods to bedeveloped • Oskari Pilot in Statistics Finland • Concludingremarks Etunimi Sukunimi

  3. Introduction • Objectives of theproject • To providespatialstatistics on theweb • To improvethenationalgeoportalfromthepoint of view of statistics • To gainexperienceabout an open sourcewebapplication • To promotecooperationbetweenthe NSI and theNMA • Focus on usability of grid-base data – use of Inspire data and concept • Eurostatgrant 2014-2015 • Inspiringcooperationwiththe National LandSurvey • SF projectleader, 4 SF projectmembers, 4 NLS projectmembers + scrum team • SupportedbybothDGs Etunimi Sukunimi

  4. Oskari platform Open source applications • Created and maintained by the National Land Survey • Built on standard Open Source components (OpenLayers, Geotools, Geoserver…) • Promotes extension of functionalities in a coordinated manner – integration of applications • Open Source - MIT/EUPL dual licensing • Guidelines, source code and all the content on the Developer Web Site and in GitHub • Oskari network (more than 30 user organisations), management group, integrator (e.g. responsible for maintaining the Developer Web Site) • The National Geoportal is an implementation of using the Oskari platform Etunimi Sukunimi

  5. SSW in practise

  6. DevelopmentbyScrummethodology • The Oskari software development in National LandSurveyof Finland (later NLS) is donebased on agile software developmentmethodologyScrum • In thisprojectonesprintlastedusuallytwoweeks • StatisticsFinland’s(later SF) part in theprojectwas to writeuserstoriesfor NLS and testthatthestorieswereimplementedright • NLS dividedtheuserstories into smallerstoriesto beused for development EtunimiSukunimi

  7. Testingprocedure • NLS provided a demo environment, that SF coulduse to testthetools and functionsthatweredeveloped, beforelaunchingthem in Paikkatietoikkuna • SF gavefeedback and reportedifthetoolsneededimprovements • Mostlytestingwasdonebythemembers in theproject team, butbesidesthatweorganizedalsotwoworkshops, wheretheapplicationwastestedbyusers outside theprojectgroup Etunimi Sukunimi

  8. User stories in theproject • 13 storieswere to befinished • Functionsincluded in thestories: • Key ratioscomputationbased on differentareaselections (freehand, areacode, sectors and buffers) • Handlingthepopulationgrid data in calculations, dealing data withprotected/no-data values • Differencecomputation, calculatingdifference in populationgrid data valuesbetweendifferentyears • Filtering databased on thevalues in thedata orbased on theresults of keyratioscomputation • Spatial join, joining data based on location • Heatmapanalysis EtunimiSukunimi

  9. Previousanalysismethods to bedeveloped • Buffer • creating buffers around features (buffers for multiple points, buffers for lines and polygons) for buffer analysis • Key ratios computation • calculating median was added • handling the grid data with no-data values/protected values • filtering data by using results from key ratios computation Etunimi Sukunimi

  10. Buffer- creating buffers around features (buffers for multiple points, buffers for lines and polygons) for buffer analysis Etunimi Sukunimi

  11. Key ratioscomputation- handling the grid data with no-data values/protected values Etunimi Sukunimi

  12. Key ratioscomputation– filtering data byusingresultsfromkey ratios computation Etunimi Sukunimi

  13. New analysismethods to becreated • Buffers and sectors (Multiple Buffer) • creating buffers and sectors for analysis • Difference computation • calculatingdifference in populationgrid data valuesbetweendifferentyears • Spatial join • enriching data based on spatial location • using spatial join in key ratios computation Etunimi Sukunimi

  14. Buffers and sectors (Multiple Buffer) - creating buffers and sectors for analysis Creatingbuffers and sectors Etunimi Sukunimi

  15. Difference computation - calculatingdifference in populationgrid data valuesbetweendifferentyears Etunimi Sukunimi

  16. Spatial join – enriching data based on spatial location • Example: Givingpostalcode for educationalinstitutions Etunimi Sukunimi

  17. Spatial join - using spatial join in key ratios computation, case buffers and sectors Using populationgrid data for theaggregation Etunimi Sukunimi

  18. Spatial join - using spatial join in key ratios computation, case buffers and sectors Key ratios of populationbysectors Etunimi Sukunimi

  19. Heatmap(Kerneldensity) • ChooseWMS-layerfotheanalysis • Choosetheradius of kernels, pixels per celland weightproperty Etunimi Sukunimi

  20. Heatmap(Kerneldensity) • Results: Etunimi Sukunimi

  21. Oskari Pilot in Statistics Finland • Aim of thepilot is to haveexperience of thetechnicalimplementationof Oskari and it’susability in SF. ResultswillsupporttheGIS technologyreview. • To reachtheaim, wewill: • Buildinternalcatalogserviceof spatialstatistics data of StatisticsFinland • Documenttheprocess, howto build Oskari services, based on practicalexperience Etunimi Sukunimi

  22. Approach to the Oskari Pilot Firststage: Technical understanding of Oskari platform • Building a testenvironment • How to implementOskari functionsin thePilotservice • Questionsregardingupdateand theadministrationof the intranet service • Definingthedemands of openinga publicservice(maybe in thefuture) Second stage: Building a pilotservice • Definingtheservice (data and functions) • Implementing data and metadata in theservice • ImplementingOskari functionsto thePilotservice Etunimi Sukunimi

  23. Firststage: Technical understanding - Intranet application in function Etunimi Sukunimi

  24. Second stage: Pilotservice to bebuild • Viewingservicein intranet • Open data from SF interfaceservicesand other data • Readyservicewillsupportstatisticsproduction and increasetheunderstanding of the dataavailable in SF Etunimi Sukunimi

  25. Data in thepilotservice • INSPIRE-data (newest) • Municipality-basedstatisticalunits • Grid net for statistics 1 km x 1km • PopulationDistribution • Production- and Industrial Facilities • EducationalInstitutions • PAAVO – Open data bypostalcodearea • Municipalsub-areas • Prices of dwellings in housing companies – by postal code area Etunimi Sukunimi

  26. Pilotservice– draftfromthe data list and metadata from National Geoportal Etunimi Sukunimi

  27. Pilotservice– dataproducer’stools • Layeradministration • Layerrightsadministration • User administration Etunimi Sukunimi

  28. Currentsituation of Oskari Pilot and results • Technical understandingis stillevolving • Results of thepiloting - bytheend of theyear 2015 • SSW projectends at theend of theyear • Resultsaremeant to supportthe GIS technologyreview, thatwillbedonebytheend of theyear Etunimi Sukunimi

  29. Concludingremarks • Mutual interest – a concretecooperationproject • Learning bydoing – learningfromeachothers • Promotedfurthercooperation • Open source application on the web feasible - Further development is promising (graphs and tables) • Cooperation with other Oskari platform users • Final report due January 2016 (Interim report available) • http://www.paikkatietoikkuna.fi/web/en/map-window • http://www.oskari.org/ Etunimi Sukunimi

  30. Questions? tuuli.pihlajamaa@stat.fi marja.tammilehto-luode@stat.fi

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