DS504/CS586: Big Data Analytics Application I Prof. Yanhua Li

Welcome to

Time:6:00pm–8:50pmRLoca2on:AK232

Fall2016

•  18 critiques & Next Thur we have the last critique. –  Already graded 8 of them. –  Plan to grade 1-2 more.

•  Grading –  Projects (40%)

•  Project 1 (10%) •  Project 2 (30%)

–  Finalreportsinthediscussionforum(by11:59pm12/13);–  Self-and-peerevalua2onformforproject2(by11:59PM12/13);

– WriPenwork(30%):•  Cri2ques+Projectreports(20%)•  Quiz(10%,with5%each)

– Oralwork(30%):•  Presenta2on

•  Final Project Presentation – 22 minutes each group (including Q&A) –  Schedule:

•  12/15Thu

5

Next Class: Summary and Discussion

v  Review of the semester v  Plus the last critique/review

Ar2ficialNeuralNetworks(ANN)

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Ar2ficialNeuralNetworks(ANN)

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Ar2ficialNeuralNetworks(ANN)

•  Modelisanassemblyofinter-connectednodesandweightedlinks

•  Outputnodesumsupeachofitsinputvalueaccordingtotheweightsofitslinks

•  Compareoutputnodeagainstsomethresholdt

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GeneralStructureofANN

Activationfunction

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Neuron iInput Output

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TrainingANNmeanslearningtheweightsoftheneurons

Real-worldproblemsarealwaysmessy

•  Mul2plemodels

•  Keyfeatures

•  DataSparsity

•  Whatdowedotosolveaclassifica2on/inference/predic2onproblem?–  DataCleaning

–  Featureselec2on

–  Inferencemodel

–  Evalua2on

•  Anexampleofhowtosolverealworldapplica2onproblem

U-Air:WhenUrbanAirQualityMeetsBigData

Authors:YuZheng,MicrosogResearchAsia

Background •  Airquality

–  NO2,SO2–  Aerosols:PM2.5,PM10

•  WhyitmaPers–  Healthcare–  Pollu2oncontrolanddispersal

•  Reality–  Buildingameasurementsta2on

isnoteasy–  Alimitednumberofsta2ons

(poorcoverage)

Beijingonlyhas22airqualitymonitorsta2onsinitsurbanareas(50kmx40km)

Air quality monitor station

2PM,June17,2013

Challenges•  Airqualityvariesbyloca2ons

non-linearly•  Affectedbymanyfactors

–  Weathers,traffic,landuse…–  Subtletomodelwithaclearformula

0 40 80 120 160 200 240 280 320 360 400 440 4800.00

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Por

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Deviation of PM2.5 between S12 and S13

>35%Prop

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A) Beijing (8/24/2012 - 3/8/2013)

WedonotreallyknowtheairqualityofalocaGonwithoutamonitoringstaGon!

Challenges •  Exis2ngmethodsdonotworkwell

–  Linearinterpola2on–  Classicaldispersionmodels

•  GaussianPlumemodelsandOpera2onalStreetCanyonmodels•  Manyparametersdifficulttoobtain:Vehicleemissionrates,street

geometry,theroughnesscoefficientoftheurbansurface…

–  Satelliteremotesensing•  Sufferfromclouds•  Doesnotreflectgroundairquality•  Varyinhumidity,temperature,loca2on,andseasons

–  Outsourcedcrowdsensingusingportabledevices•  Limitedtoafewgasses:CO2andCO•  Sensorsfordetec2ngaerosolarenotportable:PM10,PM2.5•  Alongperiodofsensingprocess,1-2hours

30,000+USD,10ug/m3 202×85×168（mm）

InferringReal-TimeandFine-GrainedairqualitythroughoutacityusingBigData

Meteorology Traffic POIs RoadnetworksHumanMobility

Historicalairqualitydata Real-2meairqualityreports

Applica2ons •  Loca2on-basedairqualityawareness

–  Fine-grainedpollu2onalert–  Rou2ngbasedonairquality

•  Deployingnewmonitoringsta2ons•  Asteptowardsiden2fyingtherootcauseofairpollu2on

S2

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S6S4

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B) Shanghai

Difficul2es •  1.Howtoiden2fyfeaturesfromeachkindofdatasource

•  2.Incorporatemul2pleheterogeneousdatasourcesintoalearningmodel–  Spa2ally-relateddata:POIs,roadnetworks–  Temporally-relateddata:traffic,meteorology,humanmobility

•  3.Datasparseness(liPletrainingdata)–  Limitednumberofsta2ons–  Manyplacestoinfer

MethodologyOverview •  Par22onacityintodisjointgrids•  Extractfeaturesforeachgridfromitsaffec2ngregion

–  Meteorologicalfeatures –  Trafficfeatures–  Humanmobilityfeatures–  POIfeatures–  Roadnetworkfeatures

•  Co-training-basedsemi-supervisedlearningmodelforeachpollutant

–  PredicttheAQIlabels–  Datasparsity–  Twoclassifiers

MeteorologicalFeatures:Fm

•  Rainy,Sunny,Cloudy,Foggy•  Windspeed•  Temperature•  Humidity•  Barometerpressure

GoodModerate

UnhealthyUnhealthy-S

Very Unhealthy

AQIofPM10AugusttoDec.2012inBeijing

TrafficFeatures:Ft

•  Distribu2onofspeedby2me:F(v)•  Expecta2onofspeed:E(V)•  Standarddevia2onofSpeed:D

Good Moderate

km

Unhealthy-S Very UnhealthyUnhealthy

0≤v<20

20≤v<40

v≥ 40

E(v)

D(v)

GPStrajectoriesgeneratedbyover30,000taxisFromAugusttoDec.2012inBeijing

HumanMobilityFeatures:Fh •  Humanmobilityimplies

–  Trafficflow–  Landuseofaloca2on–  Func2onofaregion(likeresiden2alorbusinessareas)

•  Features:–  Numberofarrivals 𝑓↓𝑎 andleavings𝑓↓𝑙 

A) AQI of PM10 B) AQI of NO2

fl fl

fa faGoodModerate

UnhealthyUnhealthy-S

Very Unhealthy

GoodModerate

UnhealthyUnhealthy-S

Very Unhealthy

Numberofarrivalsfaandleavings(departures)fl

Parksvsfactories

Extrac2ngTraffic/HumanMobilityFeatures

•  Offlinespa2o-temporalindexing•  ta:arrival2me•  Traj:trajID•  Ii:theindexofthefirstGPSpoint(inthetrajectory)enteringagrid•  Io:theindexofthelastGPSpoint(inthetrajectory)enteringthegrid

POIFeatures:Fp•  WhyPOI

–  Indicatethelanduseandthefunc2onoftheregion–  thetrafficpaPernsintheregion

•  Features–  NumbersofPOIsovercategories–  Por2onofvacantplaces–  ThechangesinthenumberofPOIs

•  Factories,shoppingmalls,•  hotelandrealestates•  Parks,decora2onandfurnituremarkets

RoadNetworkFeatures:Fr •  Whyroadnetworks

–  Haveastrongcorrela2onwithtrafficflows–  Agoodcomplementaryoftrafficmodeling

•  Features:

–  Totallengthofhighways𝑓↓ℎ –  Totallengthofother(low-level)roadsegments𝑓↓𝑟 –  Thenumberofintersec2ons𝑓↓𝑠 inthegrid’saffec2ngregion

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Semi-SupervisedLearningModel

•  Philosophyofthemodel–  Statesofairquality

•  Temporaldependencyinaloca2on•  Geo-correla2onbetweenloca2ons

–  Genera2onofairpollutants•  Emissionfromaloca2on•  Propaga2onamongloca2ons

–  Twosetsoffeatures•  Spa2ally-related•  Temporally-related

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Geospace

A location with AQI labels A location to be inferred Temporal dependencySpatial correlation

POIs: Spatial

Fh Temporal

Road Networks: Fr

Ft FmMeteorologic:Traffic:Human mobility:

FpSpa2alClassifier

TemporalClassifierCo-Training

Co-Training-BasedLearningModel

•  Spa2alclassifier–  Modelthespa2alcorrela2onbetweenAQIofdifferentloca2ons–  Usingspa2ally-relatedfeatures–  BasedonaBPneuralnetwork

•  Inputgenera2on–  Selectnsta2onstopairwith–  Performmrounds

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ANNInput generation

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Co-Training-BasedLearningModel

•  Temporalclassifier–  Modelthetemporaldependencyoftheairqualityinaloca2on–  Usingtemporallyrelatedfeatures–  BasedonaLinear-ChainCondi2onalRandomField(CRF)

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Training

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Labeleddata Unlabeleddata

Inference

InferenceProcess

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Temporally-relatedfeatures

Spa2ally-relatedfeatures

<𝑝↓𝑐1 , 𝑝↓𝑐2 ,…,𝑝↓𝑐𝑛 >

<𝑝′↓𝑐1 , 𝑝′↓𝑐2 ,…,𝑝′↓𝑐𝑛 >

× 𝑐= arg↓𝑐↓𝑖 ∈𝒞 𝑀𝑎𝑥( 𝑝↓𝑐𝑖 × 𝑝′↓𝑐𝑖 )

< pc1 , · · · , pcn >

< p0c1 , · · · , p0cn >

c = argci2CMax(P ciSC ⇥ P

ciTC)

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Evalua2on

Datasources Beijing Shanghai Shenzhen Wuhan POI 2012Q1 271,634 321,529 107,061 102,467

2012Q3 272,109 317,829 107,171 104,634

Road #.Segments 162,246 171,191 45,231 38,477 Highways 1,497km 1,963km 256km 1,193km Roads 18,525km 25,530kmKM 6,100km 9,691km #.Intersec. 49,981 70,293 32,112 25,359

AQI #.Sta2on 22 10 9 10 Hours 23,300 8,588 6,489 6,741 Timespans 8/24/2012-3/8/201

3 1/19/2013-3/8/20

13 2/4/2013-3/8/201

3 2/4/2013-3/8/2013

UrbanSize(grids) 5050km(2500) 5050km(2500) 5745km(2565) 4525km(1165)

•  Datasets

S1

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S8

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S21S3

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S9

S8

S1

S2

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S3S10

S5

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S6 S7

S8

A) Beijing B) Shanghai C) Shenzhen D) Wuhan

Evalua2on

•  GroundTruth–  Removeasta2on–  Crossci2es

•  Baselines–  LinearandGaussianInterpola2ons–  ClassicalDispersionModel–  DecisionTree(DT):–  CRF-ALL–  ANN-ALL

Evalua2on

PM10 NO2

Features Precision Recall Precision Recall

Fm 0.572 0.514 0.477 0.454 Ft 0.341 0.36 0.371 0.35 Fh 0.327 0.364 0.411 0.483 Fp+Fr 0.441 0.443 0.307 0.354

Fm+Ft 0.664 0.675 0.634 0.635 Fm+Ft+Fp+Fr 0.731 0.734 0.701 0.691

Fm+Ft+Fp+Fr+Fh 0.773 0.754 0.723 0.704

•  Doeseverykindoffeaturecount?

Evalua2on •  Overallperformanceoftheco-training

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0.65

0.70

0.75

0.80

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SC TC Co-Training

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NO2PM10

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U-Air Linear Guassian Classical DT CRF-ALL ANN-ALL

Accuracy

Evalua2on

GroundTruth

PredicGons

G M S U

G 3789 402 102 0 0.883

Recall

M 602 3614 204 0 0.818 S 41 200 532 50 0.646 U 0 22 70 219 0.704 0.855 0.853 0.586 0.814 0.828

Precision

•  ConfusionmatrixofCo-TrainingonPM10

Evalua2on

CiGes PM2.5 PM10 NO2

Prec. Rec. Prec. Rec. Prec. Rec.

Beijing 0.764 0.763 0.762 0.745 0.730 0.749

Shanghai 0.705 0.725 0.702 0.718 0.715 0.706

Shenzhen 0.740 0.737 0.710 0.742 0.732 0.722

Wuhan 0.727 0.723 0.731 0.739 0.744 0.719

•  PerformanceofSpa2alclassifier

Evalua2on

Procedures Time(ms) Procedures Time(ms) Feature

extracGon (pergrid)

Ft&Fh 53.2 Inference (pergrid)

SC 21.5 Fp 28.8 TC 13.1 Fr 14.4 Total 131

•  Efficiencystudy•  Singlegrid131s•  InferringtheAQIsforen2reBeijingin5minutes

Conclusion•  Inferfine-grainedairqualitywith

–  Real-2meandhistoricalairqualityreadingsfromexis2ngsta2ons–  Otherdatasources:meteorology,POIs,roadnetwork,humanmobility,and

trafficcondi2on

•  Co-Training-basedsemi-supervisedlearningapproach–  Dealwithdatasparsitybylearningfromunlabeleddata–  Modelthespa2alcorrela2onamongtheairqualityofdifferentloca2ons–  Modelthetemporaldependencyoftheairqualityinaloca2on

•  Results–  0.82withtrafficdata(co-training)–  0.76ifonlyusingspa2alclassifier

Ques2ons?