ProjectTitle Functiondensification LiubovKniazeva ChairofArchitecturalInformatics TechnicalUniversityofMunich &timevisualisation
2 Functiondensification ChairofArchitecturalInformatics Prof.Dr.-Ing.FrankPetzold PerformanceBasedDesign GerhardSchubert,İlaydaMemiş LiubovKniazeva 03762097 &timevisualisation
3 4 6 9 13 26 28 30 35 36 Introduction Data Typesanalysis Visualisation(main) Visualisation(extra) Visualisation(overview) Improvements Outlook Contact TableofContents
•Asmallexperimentalareaisvisualisedindifferentrepresentations, whichisthemainpartoftheprojectandconsistsofthemainblock, extrablockandoverviewofalltypes.
•Finally,possibleimprovementsandapplicationsfortheprojectare described.
•Asaresultacomparisonoftheareaefficiencybeforeandafter improvementshasbeenmade.
•Thentheanalysisofdifferentfunctiontypesduringthedayaremade.
•Firstthedatausedforanalysisandvisualisationsaredescribed.
Theprojectconsistsofthefollowingparts:
•Basedonanalysedinformation,improvementoftheexistingsituation issuggested.Inparticularnewfunctionsareaddedtotheexisting ones,consideringthetimeaspect.
themainaimofthisprojectwasto exploredifferentvisualisationformsthatcanbeusedtorepresenttime.Each optionemphasisesdifferentaspectsofthedata,thereforeitcanbeusedin differentsituations.
Inordertogetmaximumeffectivenessofspaceweshouldconsiderthe4th dimensionoftheworld-time.Thatiswhy
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Themainimpulsebehindtheprojectisthegrowingpopulationofourplanet and the limited space of our cities. All the cities continue to grow without using thewholepotentialofthespacealreadyprovidedwithinoccupiedterritory.
Introduction
5 Figure1. Representationofthe4-dimensionalworld
6 Data Forthisprojectthepopulartimedatafrom GoogleMaps wasused.Thisdata showshowbusyeachlocationtypicallyisduringdifferenttimesoftheday. Populartimesarebasedonaveragepopularityoverthelastfewmonths. Popularity for any given hour is shown relative to the typical peak popularity for the location for the week. For example, in Figure 2, 1 PM–2 PM on Wednesday isoneofthemorepopulartimesoftheweekforthisplace. Inordertogetthisdatathelibrary „populartimes“ onGitHubwasused. Givingtwoboundpointsforthesearchingarea(lowerleftandupperright)in formoflatitudeandlongitudecoordinateswecangetalistofallthePOIsin thisareawiththefollowinginformation: •coordinates(lat/log,e.g.[48.132986,11.566126] ) •functiontypes(list,e.g.[restaurant,food,point_of_interest] •populartimes(list[24x7]withthevaluesforeveryhourforeveryday inaweek) Asaresulttwodatasetsfordifferentareaswerecreated(Figures3,4)for differentpurposes: the„big scale“dataset foranalysing popularityof different functiontypesduringthedayandthe week.Andthe„smallscale“datasetas anexperimentalareaforvisualisations.
7 Figure2. PopulatimedataintheGoogleMaps
8 Figure3. Areafora„bigscale“dataset Thisdatasetcontainsinformationofalltheplaces with populartimesdatainthisarea. Itisusedtoanalysedifferent functiontypesduring theday. Figure4. Areafora„smallscale“dataset Thisdatasetcontainsinformationofalltheplaces regardlessofpopulartimesdataavailabilityinthis area. Itisusedtocreatevisualisationsonthemap.
9 Typesanalysis Thefirststageofanalysiswasaimedtofindaveragepopularityforevery functiontypeinordertounderstandatwhattimethesefunctionsaremostly demanded. FirstrepresentationsofitarepresentedinFigures5,6.InFigure5wecan see7columnswithaveragepopularityvaluesforeachdayoftheweekfrom MondaytoSundayseparately.InFigure6thereare3columns:weekdays averagevalues(fromMonday toFriday),forSaturday andSundayseparately. Itwasimportanttodividethesethreecategories,becausetherearesome functiontypesthatareabsolutelyclosedonSaturdayorSunday.Thatis whywecanseeahugedifferenceinthevaluesonweekdays,Saturdayand Sunday. Inordertogiveabetterandmorecompactrepresentationoftheanalysed datascatterplotwithdifferentsizesofpointsdependingonthepupalartime valueswascreated(Figure7).X-axisrepresentsdifferentfunctiontypes, Y-axisshowsdayhours(from0to23).Tomakeitmorereadableandeasy tounderstand,differentcoloursfordaytimes(night,morning,afternoon, evening)wereassigned(Figure8).Anothercolourschemerepresentseach hourindifferentcolours(Figure9). Alternative representation of the same data is violinplot (Figure 10), that shows datachangingsmoothlier.
10 Figure5 Figure6 Figure7. Typeanalysis(onecolour) Representationstagesforfunctiontypesanalysis
11 Figure8. Typeanalysiswithdaytimecolours (night,morning,afternoon,evening) Figure9. Typeanalysiswithhourscolourgradient
12 Figure10. Violinplot
13 Visualisation Afterreceivingthegeneralfunctiontypesanalysisthenextstepwasto representdataonthemap. Intheprojectdifferentvisualisationtypesweretriedout.Eachofthem hasadvantagesanddisadvantages.Moreoverthedifferentaccentofdata representationisemphasised. Allthevisualisationweremadewithdifferenttools: Rhinoceros+Grasshopper,Pythonorwebsite ‘Kepler.gl’ Followingtypesaredescribedfurther: (1)TransparentClouds (2)KDEplot (3)Hexbinplot (4)3DScatterplot (5)3DColumns (6)CloudClusters
14 1 TransparentClouds Advantages: •differenceduringtheday Disadvantages: •video •overlapping •difficultto read Focus: differenceduringtheday Tool: Rhinoceros+Grasshopper Figure11. ImagefromvideoforThursdayat12:00
15 FirsttriestovisualisedataonthemapwasmadeinRhinocerosusing Grasshopper. Usingdifferenttransparencydependingonthevaluesandsettingsuch parametersasdayoftheweekandhourastablepicturewascreated.By changingthedayofweekandthehouritispossibletocreateavideofrom thestable picturesfor eachhour. Unfortunately,this videoshould bemade by hand,aswellasattachingabackgroundmap.Thisrequiresextratimefrom theuser. Anotherprobleminthisvisualisationtypewascoordinateconversionfrom degreestometres.Aftersomeresearchthispartwasimplementedinpython codeusingHaversineformula. Inthisvisualisationtypewecannotseeclearlymultiplicatedvaluesinthe areaswiththehighPOIsdensity.Inordertoimproveitsomeplaceswitha highPOIsdensitywereunited inaclusterandrepresented asablockwithan averagevalueofallincludedinblockplaces.Howeveritstilldidn‘tprovide enoughqualityandclarity. Thesubsequent visualisationwas thereforedecided tobe implementedusing Pythonand therelevant librariesto getmore opportunitiesand freedomin the visualisation process. In this way almost all the work was automated including thebasemapattachmentandvideocreation.
16 2 KDEplot Advantages: •easytoread Disadvantages: •video •normalisedforeachhour Focus: highestdensityareas Tool: Python+Matplotlib,Seaborn Figure12. ImagefromvideoforThursdayat12:00 Thursday12:00
Moreover the process of video creating was also automated: pictures for each hourarecreated,unitedinagifandthendeleted.
KDEplot visualisationshows thevalues relativelyto eachhour. Inthat waywe cansee themost popularplace ata specifichour, howeverwe cannotsee the differencefor aspecific placeduring theday. Duringthe courseof theproject, nosolutionwasfoundforthepresentationofthedifferencesduringtheday. However,thismaybestillpossiblewiththeuseofothertoolssuchasQGIS oranotherPythonlibrary.
Inordertosolvetheproblemwithmultiplicatedvaluesinhighdenseareas, kerneldensityestimation(KDE)wasselected.Thisvisualisationiscreated usingpythonMatplotlibandSeabornlibraries.Coordinateconversation isimplementedusingpyprojlibrary,backgroundmapisattachedusing Contextilylibrary.
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18 3 Hexbinplot Advantages: •differenceduringtheday Disadvantages: •video •toodetailed(difficultto read) •smallbins Focus: differenceduringtheday Tool: Python+Matplotlib Thursday12:00 Figure13. ImagefromvideoforThursdayat12:00
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Anothervisualisationtypewasimplementedusinghexbinplot(Matplotlib library).Comparedtothe firsttype(TransparentClouds) theaveragevaluefor poiswithin eachbin isfound. Inthis waywe canavoid possibleoverlapping in highdenseareasandcreateamorepleasantoverviewofdata.
Stillthistypeisrepresentedinvideoformat.Comparedtothesecond visualisationtype(KDEplot)wecanseedifferencesinpopularityduringthe dayandinformationisrepresentedinasmaller,moredetailedscale.Wecan clearly see the most popular places, but the focal areas with high POIs density arenotsoclearlyvisible.
20 3Dscatterplot Advantages: •interactive •3-dimensional(showstimeandcoordinates) •differenceduringtheday Disadvantages: •unreadable •impossibletoaddmapasabackground Focus: differenceduringtheday Tool: Python+Matplotlib,Pyplot 4 Figure14. 3Dscatterplot
3D scatter plot was the first implementation of this idea. This type is interactive (youcanselectanypointandseevaluesinit),butnosolutionforattaching thebackgroundmapwasfound.Moreoveritisprettydifficulttoscalethe areaandadaptanappearance.Thatiswhyforthenextvisualisationitwas decidedtoreturnbacktoRhinocerostohavemorefreedominworkwith geometrymodelling.
Inordertoavoidusingvideotorepresentdatathethirddimensionwas used.Twodimensionswerealreadyfixedforplacecoordinates:latitude andlongitude.Thustheideawastocreateacolumnconsistingof24voxels foreachplaceonthemap.Eachvoxeldisplayspopularityvalueswithits brightness.
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22 5 3Dcolumns Advantages: •inonepicture(novideo) •differenceduringtheday Disadvantages: •difficulttogetfastoverview •needtimetounderstand Focus: differenceduringtheday Tool: Rhinoceros+Grasshopper Figure15. 3DcolumsrepresentationforThursday
23 Thecoordinateconversationwasalreadyimplementedinpythonandthe datasetwithonlynecessaryandpost-processedinformationwascreated there. BackgroundmapwasgeneratedusingtheKDEplotfunctiontoshowthe densityofPOIs.Usingsuchaheatmapasabackgrounditiseasiertosee themainPOIsconcentration.Eachvoxelinthe24-voxelscolumnsdisplays popularityvaluesforeachhour. Howeverduetothesameheightofallcolumnsitisdifficulttogetafast overviewofthedata.Thusthisrepresentationissuitableformoredetailed observations.
24 6 CloudClusters Advantages: •inonepicture •interactive •density&timeeasytoread Disadvantages: •onlythemost popularhouris shown Focus: themostpopularhourofplaces Tool: Python+Keplerwebsite Interactive Map Figure16. ImageofinteractivemapwithCloudClusters
Forthisvisualisationonly themostpopularhour oftheplacewas used.Postprocessedandcleaneddatasetwascreatedinpythonandthenexportedfor visualisationinthe‘Kepler.gl’website.Themainadvantageofwhichishis interactiveness(usingQRcodeorlinkthisinteractivemapcanbeopened andmore thoroughlyexplored). Itis notonly possibleto getinformation eachpoint,butalsotozoom-inandzoom-outtheareatogetanoverviewon adifferentscale. Thesize ofthe circlesrepresents theamount ofpoints inthis area.In thisway itispossibletoavoidunwanteddataoverlapping.Thecolourofthecircle showstheaveragevalueforthemostpopularhourofallPOIsconsideredin thecircle. Howeveritwasn‘tpossibletofixspecificcoloursforeachvalue,sointhe caseofzoom-inorzoom-outtheminimumandmaximumvaluesvaryand colormapisadaptedaccordingly.Thatiswhyinthiscaseitisimportantto lookcarefullyatthelegend.
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Inthenextvisualisationitwasdecidedtousecolourstodisplaytime.The suitablegradientofcoloursfordayhourswasselected.
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IntheCloudClustervisualisationwecanclearlyseethemainconcentration pointandthemostpopularhoursforeachplace.
26 Extra
Usingthe‘Kepler’websiteitispossibletocreateaheatmap(Figure17). Unfortunatelyitisnotveryaccurate,thatiswhyitwasn‘tsuitableforfurther work. Anotheroptionwastorepresentinformationusing3Dhexbin(Figure18).In thiscasewecanrepresentdataforexampleforonespecifichour.However thecolourandtheheightofcolumnsrepresentthesamevalue,thatiswhyit isnot reallyreasonable touse thisoption, becauseit makesit moredifficult to perceivetheinformationdisplayedintheimage. Itwasalsopossibletocombinethesetwooptionsintoone(Figure19).
Alltheserepresentationsweren‘t developedwellenoughto findspecificuses forthem.Howeversomeofthemcouldhavepotentialtobeconsideredand adaptedinadifferentwayortodisplayothertypesofinformation.
Duringtheprojectdifferentvisualisationtypesanddifferenttoolswereused, some of them weren‘t so important as others. In this part some of these ‘extra’ types,thatweren‘tpresentedinthemainblock,arebrieflydescribed.
27 Figure17. Heatmaprepresentation usingKeplerwebsite Figure18. 3DHexbinrepresentation usingKeplerwebsite Figure19. CombinationofHeatmapand 3Dhexbinrepresentations usingKeplerwebsite
28 Overview Duringthefinalpresentationoftheprojectotherstudentsofthecourse wereaskedtoestimatesuggestedvisualisationtypes.Wecanseethatthe CloudClusteris consideredasthe easiesttoread. However,itis importantto mentionthateachtypeofvisualisationconveysdifferentaspectsofthedata andcanbeusedfordifferentaims. Figure20. Votingresults
(1)Transparentclouds (2)KDEplot (3)Hexbinplot (5)3Dcolumns (4)3Dscatterplot(wasn‘tpresentedinvoting) (6)Cloudclusters Figure21. Overviewofvisualisationoptions
Torestrictfunctionchoicethelistofcomplementaryfunctionswascreated. SincealmosteveryPOIcontainsmorethanonefunctiontype,thefirstidea forthelistcreatingwastofindintersectedtypesofdifferentpoisandcreate groupssuchas[cafe,food,restaurant]forexample.Howeverthislistwas stillnotsatisfactoryenough.Thereforethesegroupswerepost-processed by hand. Foreachexistingfunctionwerecalculatedweightedparametersof complementaryfunctionsfromthecorrespondinglistandonenewfunction withthebiggestvaluewasselected.
Weightoftheseparameters(K1andK2)canbedefinedbytheuser.
Todefinethetime intervalfora newfunction,the limitpopularityvalueshould beset.Initiallythevalue„0“isconsideredasalimit,becauseitmeansthat theplaceisclosedatthattime.Howeverinordertoimproveefficiencyof thearea,hourswithasmallpopularityvaluecanbealsoconsidered.Inthe prototypealimitwasassignedvalue„50“.
Themainideainthebeginning wastofind„gaps“intime,when someplaces arenotused,andtofillitwithacomplementaryfunctionthatisdemandedat thattime. Theselectingalgorithm(Figure22)considerstwoparameters:maximum effectivenessofthenewfunction(summaryofthepopulartimedataat selectedtime)andquantityofthespecificfunctiontypeintheexperimental area(neitherspecificlocationnordistancebetweenplacesareconsidered).
30 Improvements
Afterdataanalysisandvisualisation,suggestionsweremadetoimprovethe efficiencyoftheinvestigatedarea.
Thisselecting processwasn‘t themain focusof theproject andwas usedjust asanexampletoseepossibleinfluenceonthevisualisation.
Therefore this process can be improved by adding other selecting parameters, suchaslocationofthefunctionsorfrequencyofuse.Besides,alistof complementaryfunctionscanbemademorethoroughly. Selectingprocessalgorythm
Figure22.
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Oncetheadditional featureshavebeen selected,wecan comparehowmuch ofthearea‘sefficiencyhasbeenimproved.
Firstofall,wecancomparebyhowmuchthenumberoffunctionplacesof eachtypehasincreasedintheselectedarea(Figure23).Alsocompared how manyhoursperweekeachplaceisnotusedatall,usedalittleorusedalot (Diagramsinfigures24,25,26).
Inthefirstdiagram(Figure24)wecanseeanalysisoftheinitialdata,where onlysomeofthePOIshavepopulartimedata.Intheseconddiagram(Figure 25)averagevaluesofthetypewereaddedtotheexistingfunctionswithout populartime data. The last diagram (Figure 26) shows the situation after adding newfunctionstotheexistingones(Thereforewehavethesamequantityof POIs,butanincreasedquantityoffunctions).
Figure23. Histogrammwitholdandnewfunctionquantitydependingonthefunctiontype
33 Figure24. Step1:Initialdata Figure25. Step2:Averagepopulartimedata Figure26. Step3:Newfunctionadded
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Figure27. 3D-Columnvisualisationwithold andnewfunctions
Toshowchangesafteraddingnewfunctionsthe3D-Columnsvisualisation wasused(Figure27).Inordertodistinguishexistingandnewfunctions, differentcoloursforthevoxelswereassigned.Thebrightness/darknessof eachvoxelshowsthepopularityvalue. Wecanclearlysee,whereexactlynewfunctionswereadded(bluetones), whichplacesandatwhattimearestillnotused(whitecolour)andhowmany newfunctionswiththebigpopularityvalueswereadded(allthedarkblue voxels).
35 Outlook Inthepreviouschapterssomeofthepossiblefutureimprovementswere alreadymentioned.Theseincludeimprovingtheselectionprocessfornew features,forexampleconsideringthelocationinrelationtoexistingfacilities, andthe frequencyof useby thepopulation (e.g.supermarkets areused every dayandcinemaseveryweek/month).Localimprovementstoeachofthe presentedvisualisationtypesarealsorequired. Thenextstepinthefurtherdevelopmentoftheprojectistocreateheatmaps foreachtypeoffunction.Inthisway,itwillbepossibletoevaluatewhether thesefunctionsareevenlydistributedovertheareaandwhereexactlyitis worthaddingnewones.Sincethefunctionsmaybedifferentatthesame locationduringtheday,theseheatmapswillchangeduringtheday. Apossibleapplicationofthisprojectcouldalsobetheidentificationof‚dead zones‘,suchasbusinessdistrictsthatcompletelydieoutintheevening.By addingnewfunctions,it willbepossibleto createmorelivelyand saferzones withmixeduse.
36 Contact LiubovKniazeva 03762097 1.SemesterMasterArchitecture
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