 the same datasets as BIBREF7
EXTRACTIVE
same datasets as BIBREF7
EXTRACTIVE
There is a wide variety of structured data that can be used to describe locations. In this work, we have restricted ourselves to the same datasets as BIBREF7 . These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg. temperature, avg. precipitation, avg. solar radiation, avg. wind speed, and avg. water vapor pressure). In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids). Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type.
Predicting the distribution of 100 species across Europe, using the European network of nature protected sites Natura 2000 dataset as ground truth. For each of these species, a binary classification problem is considered. The set of locations INLINEFORM0 is defined as the 26,425 distinct sites occurring in the dataset. There is a wide variety of structured data that can be used to describe locations. In this work, we have restricted ourselves to the same datasets as BIBREF7 . These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg. temperature, avg. precipitation, avg. solar radiation, avg. wind speed, and avg. water vapor pressure). In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids). Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type. Predicting people's subjective opinions of landscape beauty in Britain, using the crowdsourced dataset from the ScenicOrNot website as ground truth. The set INLINEFORM0 is chosen as the set of locations of 191 605 rated locations from the ScenicOrNot dataset for which at least one georeferenced Flickr photo exists within a 1 km radius.
1810.12091
Introduction </s> Users Ġof Ġphoto - sharing Ġwebsites Ġsuch Ġas ĠFlickr Ġoften Ġprovide Ġshort Ġtextual Ġdescriptions Ġin Ġthe Ġform Ġof Ġtags Ġto Ġhelp Ġothers Ġfind Ġthe Ġimages . ĠWith Ġthe Ġavailability Ġof ĠGPS Ġsystems Ġin Ġcurrent Ġelectronic Ġdevices Ġsuch Ġas Ġsmartphones , Ġlatitude Ġand Ġlong itude Ġcoordinates Ġare Ġnowadays Ġcommonly Ġmade Ġavailable Ġas Ġwell . ĠThe Ġtags Ġassociated Ġwith Ġsuch Ġge ore f eren ced Ġphotos Ġoften Ġdescribe Ġthe Ġlocation Ġwhere Ġthese Ġphotos Ġwere Ġtaken , Ġand ĠFlickr Ġcan Ġthus Ġbe Ġregarded Ġas Ġa Ġsource Ġof Ġenvironmental Ġinformation . ĠThe Ġuse Ġof ĠFlickr Ġfor Ġmodelling Ġurban Ġenvironments Ġhas Ġalready Ġreceived Ġconsiderable Ġattention . ĠFor Ġinstance , Ġvarious Ġapproaches Ġhave Ġbeen Ġproposed Ġfor Ġmodelling Ġurban Ġregions ĠB IB REF 0 Ġ, Ġand Ġfor Ġidentifying Ġpoints - of - interest ĠB IB REF 1 Ġand Ġitiner aries ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ. ĠHowever , Ġthe Ġusefulness Ġof ĠFlickr Ġfor Ġcharacter izing Ġthe Ġnatural Ġenvironment , Ġwhich Ġis Ġthe Ġfocus Ġof Ġthis Ġpaper , Ġis Ġless Ġwell - under stood . </s> Many Ġrecent Ġstudies Ġhave Ġhighlighted Ġthat ĠFlickr Ġtags Ġcapture Ġvaluable Ġecological Ġinformation , Ġwhich Ġcan Ġbe Ġused Ġas Ġa Ġcomplementary Ġsource Ġto Ġmore Ġtraditional Ġsources . ĠTo Ġdate , Ġhowever , Ġec ologists Ġhave Ġmostly Ġused Ġsocial Ġmedia Ġto Ġconduct Ġmanual Ġevaluations Ġof Ġimage Ġcontent Ġwith Ġlittle Ġautomated Ġexploitation Ġof Ġthe Ġassociated Ġtags ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠOne Ġrecent Ġexception Ġis ĠB IB REF 7 Ġ, Ġwhere Ġbag - of - words Ġrepresentations Ġderived Ġfrom ĠFlickr Ġtags Ġwere Ġfound Ġto Ġgive Ġpromising Ġresult Ġfor Ġpredicting Ġa Ġrange Ġof Ġdifferent Ġen viron ement al Ġphenomena . </s> Our Ġmain Ġhypothesis Ġin Ġthis Ġpaper Ġis Ġthat Ġby Ġusing Ġvector Ġspace Ġembed d ings Ġinstead Ġof Ġbag - of - words Ġrepresentations , Ġthe Ġecological Ġinformation Ġwhich Ġis Ġimplicitly Ġcaptured Ġby ĠFlickr Ġtags Ġcan Ġbe Ġutilized Ġin Ġa Ġmore Ġeffective Ġway . ĠVector Ġspace Ġembed d ings Ġare Ġrepresentations Ġin Ġwhich Ġthe Ġobjects Ġfrom Ġa Ġgiven Ġdomain Ġare Ġencoded Ġusing Ġrelatively Ġlow - dimensional Ġvectors . ĠThey Ġhave Ġproven Ġuseful Ġin Ġnatural Ġlanguage Ġprocessing , Ġespecially Ġfor Ġencoding Ġword Ġmeaning ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, Ġand Ġin Ġmachine Ġlearning Ġmore Ġgenerally . ĠIn Ġthis Ġpaper , Ġwe Ġare Ġinterested Ġin Ġthe Ġuse Ġof Ġsuch Ġrepresentations Ġfor Ġmodelling Ġgeographic Ġlocations . ĠOur Ġmain Ġmotivation Ġfor Ġusing Ġvector Ġspace Ġembed d ings Ġis Ġthat Ġthey Ġallow Ġus Ġto Ġintegrate Ġthe Ġtextual Ġinformation Ġwe Ġget Ġfrom ĠFlickr Ġwith Ġavailable Ġstructured Ġinformation Ġin Ġa Ġvery Ġnatural Ġway . ĠTo Ġthis Ġend , Ġwe Ġrely Ġon Ġan Ġadaptation Ġof Ġthe ĠGl o Ve Ġword Ġembed ding Ġmodel ĠB IB REF 9 Ġ, Ġbut Ġrather Ġthan Ġlearning Ġword Ġvectors , Ġwe Ġlearn Ġvectors Ġrepresenting Ġlocations . ĠSimilar Ġto Ġhow Ġthe Ġrepresentation Ġof Ġa Ġword Ġin ĠGl o Ve Ġis Ġdetermined Ġby Ġthe Ġcontext Ġwords Ġsurrounding Ġit , Ġthe Ġrepresentation Ġof Ġa Ġlocation Ġin Ġour Ġmodel Ġis Ġdetermined Ġby Ġthe Ġtags Ġof Ġthe Ġphotos Ġthat Ġhave Ġbeen Ġtaken Ġnear Ġthat Ġlocation . ĠTo Ġincorporate Ġnumerical Ġfeatures Ġfrom Ġstructured Ġenvironmental Ġdatasets Ġ( e . g . Ġaverage Ġtemperature ), Ġwe Ġassociate Ġwith Ġeach Ġsuch Ġfeature Ġa Ġlinear Ġmapping Ġthat Ġcan Ġbe Ġused Ġto Ġpredict Ġthat Ġfeature Ġfrom Ġa Ġgiven Ġlocation Ġvector . ĠThis Ġis Ġinspired Ġby Ġthe Ġfact Ġthat Ġsalient Ġproperties Ġof Ġa Ġgiven Ġdomain Ġcan Ġoften Ġbe Ġmod elled Ġas Ġdirections Ġin Ġvector Ġspace Ġembed d ings ĠB IB REF 10 Ġ, ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ. ĠFinally , Ġevidence Ġfrom Ġcateg orical Ġdatasets Ġ( e . g . Ġland Ġcover Ġtypes ) Ġis Ġtaken Ġinto Ġaccount Ġby Ġrequiring Ġthat Ġlocations Ġbelonging Ġto Ġthe Ġsame Ġcategory Ġare Ġrepresented Ġusing Ġsimilar Ġvectors , Ġsimilar Ġto Ġhow Ġsemantic Ġtypes Ġare Ġsometimes Ġmod elled Ġin Ġthe Ġcontext Ġof Ġknowledge Ġgraph Ġembed ding ĠB IB REF 13 Ġ. </s> While Ġour Ġpoint - of - dep art ure Ġis Ġa Ġstandard Ġword Ġembed ding Ġmodel , Ġwe Ġfound Ġthat Ġthe Ġoff - the - she lf ĠGl o Ve Ġmodel Ġperformed Ġsurprisingly Ġpoorly , Ġmeaning Ġthat Ġa Ġnumber Ġof Ġmodifications Ġare Ġneeded Ġto Ġachieve Ġgood Ġresults . ĠOur Ġmain Ġfindings Ġare Ġas Ġfollows . ĠFirst , Ġgiven Ġthat Ġthe Ġnumber Ġof Ġtags Ġassociated Ġwith Ġa Ġgiven Ġlocation Ġcan Ġbe Ġquite Ġsmall , Ġit Ġis Ġimportant Ġto Ġapply Ġsome Ġkind Ġof Ġspatial Ġsmoot hing , Ġi . e . Ġthe Ġimportance Ġof Ġa Ġgiven Ġtag Ġfor Ġa Ġgiven Ġlocation Ġshould Ġnot Ġonly Ġdepend Ġon Ġthe Ġoccurrences Ġof Ġthe Ġtag Ġat Ġthat Ġlocation , Ġbut Ġalso Ġon Ġits Ġoccurrences Ġat Ġnearby Ġlocations . ĠTo Ġthis Ġend , Ġwe Ġuse Ġa Ġformulation Ġwhich Ġis Ġbased Ġon Ġspat ially Ġsmoot hed Ġversion Ġof Ġpoint wise Ġmutual Ġinformation . ĠSecond , Ġgiven Ġthe Ġwide Ġdiversity Ġin Ġthe Ġkind Ġof Ġinformation Ġthat Ġis Ġcovered Ġby ĠFlickr Ġtags , Ġwe Ġfind Ġthat Ġterm Ġselection Ġis Ġin Ġsome Ġcases Ġcritical Ġto Ġobtain Ġvector Ġspaces Ġthat Ġcapture Ġthe Ġrelevant Ġaspects Ġof Ġgeographic Ġlocations . ĠFor Ġinstance , Ġmany Ġtags Ġon ĠFlickr Ġrefer Ġto Ġphotography Ġrelated Ġterms , Ġwhich Ġwe Ġwould Ġnormally Ġnot Ġwant Ġto Ġaffect Ġthe Ġvector Ġrepresentation Ġof Ġa Ġgiven Ġlocation . ĠFinally , Ġeven Ġwith Ġthese Ġmodifications , Ġvector Ġspace Ġembed d ings Ġlearned Ġfrom ĠFlickr Ġtags Ġalone Ġare Ġsometimes Ġoutper formed Ġby Ġbag - of - words Ġrepresentations . ĠHowever , Ġour Ġvector Ġspace Ġembed d ings Ġlead Ġto Ġsubstantially Ġbetter Ġpredictions Ġin Ġcases Ġwhere Ġstructured Ġ( scientific ) Ġinformation Ġis Ġalso Ġtaken Ġinto Ġaccount . ĠIn Ġthis Ġsense , Ġthe Ġmain Ġvalue Ġof Ġusing Ġvector Ġspace Ġembed d ings Ġin Ġthis Ġcontext Ġis Ġnot Ġso Ġmuch Ġabout Ġabstract ing Ġaway Ġfrom Ġspecific Ġtag Ġus ages , Ġbut Ġrather Ġabout Ġthe Ġfact Ġthat Ġsuch Ġrepresentations Ġallow Ġus Ġto Ġintegrate Ġnumerical Ġand Ġcateg orical Ġfeatures Ġin Ġa Ġmuch Ġmore Ġnatural Ġway Ġthan Ġis Ġpossible Ġwith Ġbag - of - words Ġrepresentations . </s> The Ġremainder Ġof Ġthis Ġpaper Ġis Ġorganized Ġas Ġfollows . ĠIn Ġthe Ġnext Ġsection , Ġwe Ġprovide Ġa Ġdiscussion Ġof Ġexisting Ġwork . ĠSection ĠSEC REF 3 Ġthen Ġpresents Ġour Ġmodel Ġfor Ġembed ding Ġgeographic Ġlocations Ġfrom ĠFlickr Ġtags Ġand Ġstructured Ġdata . ĠNext , Ġin ĠSection ĠSEC REF 4 Ġwe Ġprovide Ġa Ġdetailed Ġdiscussion Ġabout Ġthe Ġexperimental Ġresults . ĠFinally , ĠSection ĠSEC REF 5 Ġsummarizes Ġour Ġconclusions . </s> Vector Ġspace Ġembed d ings </s> The Ġuse Ġof Ġlow - dimensional Ġvector Ġspace Ġembed d ings Ġfor Ġrepresenting Ġobjects Ġhas Ġalready Ġproven Ġeffective Ġin Ġa Ġlarge Ġnumber Ġof Ġapplications , Ġincluding Ġnatural Ġlanguage Ġprocessing Ġ( N LP ), Ġimage Ġprocessing , Ġand Ġpattern Ġrecognition . ĠIn Ġthe Ġcontext Ġof ĠN LP , Ġthe Ġmost Ġprominent Ġexample Ġis Ġthat Ġof Ġword Ġembed d ings , Ġwhich Ġrepresent Ġword Ġmeaning Ġusing Ġvectors Ġof Ġtypically Ġaround Ġ300 Ġdimensions . ĠA Ġlarge Ġnumber Ġof Ġdifferent Ġmethods Ġfor Ġlearning Ġsuch Ġword Ġembed d ings Ġhave Ġalready Ġbeen Ġproposed , Ġincluding ĠSkip - gram Ġand Ġthe ĠContinuous ĠBag - of - Words Ġ( CB OW ) Ġmodel ĠB IB REF 8 Ġ, ĠGl o Ve ĠB IB REF 9 Ġ, Ġand Ġfast Text ĠB IB REF 14 Ġ. ĠThey Ġhave Ġbeen Ġapplied Ġeffectively Ġin Ġmany Ġdownstream ĠN LP Ġtasks Ġsuch Ġas Ġsentiment Ġanalysis ĠB IB REF 15 Ġ, Ġpart Ġof Ġspeech Ġtagging ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, Ġand Ġtext Ġclassification ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ. ĠThe Ġmodel Ġwe Ġconsider Ġin Ġthis Ġpaper Ġbuilds Ġon ĠGl o Ve , Ġwhich Ġwas Ġdesigned Ġto Ġcapture Ġlinear Ġregular ities Ġof Ġword - word Ġco - occ urrence . ĠIn ĠGl o Ve , Ġthere Ġare Ġtwo Ġword Ġvectors ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġfor Ġeach Ġword Ġin Ġthe Ġvocabulary , Ġwhich Ġare Ġlearned Ġby Ġminimizing Ġthe Ġfollowing Ġobjective : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġnumber Ġof Ġtimes Ġthat Ġword ĠIN LINE FORM 1 Ġappears Ġin Ġthe Ġcontext Ġof Ġword ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġis Ġthe Ġvocabulary Ġsize , ĠIN LINE FORM 4 Ġis Ġthe Ġtarget Ġword Ġbias , ĠIN LINE FORM 5 Ġis Ġthe Ġcontext Ġword Ġbias . ĠThe Ġweight ing Ġfunction ĠIN LINE FORM 6 Ġis Ġused Ġto Ġlimit Ġthe Ġimpact Ġof Ġrare Ġterms . ĠIt Ġis Ġdefined Ġas Ġ1 Ġif ĠIN LINE FORM 7 Ġand Ġas ĠIN LINE FORM 8 Ġotherwise , Ġwhere ĠIN LINE FORM 9 Ġis Ġusually Ġfixed Ġto Ġ100 Ġand ĠIN LINE FORM 10 Ġto Ġ0 . 75 . ĠInt uitive ly , Ġthe Ġtarget Ġword Ġvectors ĠIN LINE FORM 11 Ġcorrespond Ġto Ġthe Ġactual Ġword Ġrepresentations Ġwhich Ġwe Ġwould Ġlike Ġto Ġfind , Ġwhile Ġthe Ġcontext Ġword Ġvectors ĠIN LINE FORM 12 Ġmodel Ġhow Ġoccurrences Ġof ĠIN LINE FORM 13 Ġin Ġthe Ġcontext Ġof Ġa Ġgiven Ġword ĠIN LINE FORM 14 Ġaffect Ġthe Ġrepresentation Ġof Ġthis Ġlatter Ġword . ĠIn Ġthis Ġpaper Ġwe Ġwill Ġuse Ġa Ġsimilar Ġmodel , Ġwhich Ġwill Ġhowever Ġbe Ġaimed Ġat Ġlearning Ġlocation Ġvectors Ġinstead Ġof Ġthe Ġtarget Ġword Ġvectors . </s> Beyond Ġword Ġembed d ings , Ġvarious Ġmethods Ġhave Ġbeen Ġproposed Ġfor Ġlearning Ġvector Ġspace Ġrepresentations Ġfrom Ġstructured Ġdata Ġsuch Ġas Ġknowledge Ġgraphs ĠB IB REF 20 Ġ, ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ, Ġsocial Ġnetworks ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġand Ġtax onom ies ĠB IB REF 25 Ġ, ĠB IB REF 26 Ġ. ĠThe Ġidea Ġof Ġcombining Ġa Ġword Ġembed ding Ġmodel Ġwith Ġstructured Ġinformation Ġhas Ġalso Ġbeen Ġexplored Ġby Ġseveral Ġauthors , Ġfor Ġexample Ġto Ġimprove Ġthe Ġword Ġembed d ings Ġbased Ġon Ġinformation Ġcoming Ġfrom Ġknowledge Ġgraphs ĠB IB REF 27 Ġ, ĠB IB REF 28 Ġ. ĠAlong Ġsimilar Ġlines , Ġvarious Ġlex icons Ġhave Ġbeen Ġused Ġto Ġobtain Ġword Ġembed d ings Ġthat Ġare Ġbetter Ġsuited Ġat Ġmodelling Ġsentiment ĠB IB REF 15 Ġand Ġan ton y my ĠB IB REF 29 Ġ, Ġamong Ġothers . ĠThe Ġmethod Ġproposed Ġby ĠB IB REF 30 Ġimposes Ġthe Ġcondition Ġthat Ġwords Ġthat Ġbelong Ġto Ġthe Ġsame Ġsemantic Ġcategory Ġare Ġcloser Ġtogether Ġthan Ġwords Ġfrom Ġdifferent Ġcategories , Ġwhich Ġis Ġsomewhat Ġsimilar Ġin Ġspirit Ġto Ġhow Ġwe Ġwill Ġmodel Ġcateg orical Ġdatasets Ġin Ġour Ġmodel . </s> Emb edd ings Ġfor Ġgeographic Ġinformation </s> The Ġproblem Ġof Ġrepresenting Ġgeographic Ġlocations Ġusing Ġembed d ings Ġhas Ġalso Ġattracted Ġsome Ġattention . ĠAn Ġearly Ġexample Ġis ĠB IB REF 31 Ġ, Ġwhich Ġused Ġprincipal Ġcomponent Ġanalysis Ġand Ġstacked Ġaut oen cod ers Ġto Ġlearn Ġlow - dimensional Ġvector Ġrepresentations Ġof Ġcity Ġneighbourhoods Ġbased Ġon Ġcensus Ġdata . ĠThey Ġuse Ġthese Ġrepresentations Ġto Ġpredict Ġattributes Ġsuch Ġas Ġcrime , Ġwhich Ġis Ġnot Ġincluded Ġin Ġthe Ġgiven Ġcensus Ġdata , Ġand Ġfind Ġthat Ġin Ġmost Ġof Ġthe Ġconsidered Ġevaluation Ġtasks , Ġthe Ġlow - dimensional Ġvector Ġrepresentations Ġlead Ġto Ġmore Ġfaithful Ġpredictions Ġthan Ġthe Ġoriginal Ġhigh - dimensional Ġcensus Ġdata . </s> Some Ġexisting Ġworks Ġcombine Ġword Ġembed ding Ġmodels Ġwith Ġgeographic Ġcoordinates . ĠFor Ġexample , Ġin ĠB IB REF 32 Ġan Ġapproach Ġis Ġproposed Ġto Ġlearn Ġword Ġembed d ings Ġbased Ġon Ġthe Ġassumption Ġthat Ġwords Ġwhich Ġtend Ġto Ġbe Ġused Ġin Ġthe Ġsame Ġgeographic Ġlocations Ġare Ġlikely Ġto Ġbe Ġsimilar . ĠNote Ġthat Ġtheir Ġaim Ġis Ġdual Ġto Ġour Ġaim Ġin Ġthis Ġpaper : Ġwhile Ġthey Ġuse Ġgeographic Ġlocation Ġto Ġlearn Ġword Ġvectors , Ġwe Ġuse Ġtextual Ġdescriptions Ġto Ġlearn Ġvectors Ġrepresenting Ġgeographic Ġlocations . </s> Several Ġmethods Ġalso Ġuse Ġword Ġembed ding Ġmodels Ġto Ġlearn Ġrepresentations Ġof ĠPoints - of - Interest Ġ( PO Is ) Ġthat Ġcan Ġbe Ġused Ġfor Ġpredicting Ġuser Ġvisits ĠB IB REF 33 Ġ, ĠB IB REF 34 Ġ, ĠB IB REF 35 Ġ. ĠThese Ġworks Ġuse Ġthe Ġmachinery Ġof Ġexisting Ġword Ġembed ding Ġmodels Ġto Ġlearn ĠPO I Ġrepresentations , Ġintuitive ly Ġby Ġletting Ġsequences Ġof ĠPO I Ġvisits Ġby Ġa Ġuser Ġplay Ġthe Ġrole Ġof Ġsequences Ġof Ġwords Ġin Ġa Ġsentence . ĠIn Ġother Ġwords , Ġdespite Ġthe Ġuse Ġof Ġword Ġembed ding Ġmodels , Ġmany Ġof Ġthese Ġapproaches Ġdo Ġnot Ġactually Ġconsider Ġany Ġtextual Ġinformation . ĠFor Ġexample , Ġin ĠB IB REF 34 Ġthe ĠSkip - gram Ġmodel Ġis Ġutilized Ġto Ġcreate Ġa Ġglobal Ġpattern Ġof Ġusers ' ĠPO Is . ĠEach Ġlocation Ġwas Ġtreated Ġas Ġa Ġword Ġand Ġthe Ġother Ġlocations Ġvisited Ġbefore Ġor Ġafter Ġwere Ġtreated Ġas Ġcontext Ġwords . ĠThey Ġthen Ġuse Ġa Ġpair - wise Ġranking Ġloss ĠB IB REF 36 Ġwhich Ġtakes Ġinto Ġaccount Ġthe Ġuser 's Ġlocation Ġvisit Ġfrequency Ġto Ġpersonal ize Ġthe Ġlocation Ġrecommendations . ĠThe Ġmethods Ġof ĠB IB REF 34 Ġwere Ġextended Ġin ĠB IB REF 35 Ġto Ġuse Ġa Ġtemporal Ġembed ding Ġand Ġto Ġtake Ġmore Ġaccount Ġof Ġgeographic Ġcontext , Ġin Ġparticular Ġthe Ġdistances Ġbetween Ġpreferred Ġand Ġnon - pre ferred Ġneighboring ĠPO Is , Ġto Ġcreate Ġa ĠâĢ ľ ge ographically Ġhierarchical Ġpair wise Ġpreference Ġranking Ġmodel âĢ Ŀ . ĠSimilarly , Ġin ĠB IB REF 37 Ġthe ĠCB OW Ġmodel Ġwas Ġtrained Ġwith ĠPO I Ġdata . ĠThey Ġordered ĠPO Is Ġspat ially Ġwithin Ġthe Ġtraffic - based Ġzones Ġof Ġurban Ġareas . ĠThe Ġordering Ġwas Ġused Ġto Ġgenerate Ġcharacteristic Ġvectors Ġof ĠPO I Ġtypes . ĠZone Ġvectors Ġrepresented Ġby Ġaveraging Ġthe Ġvectors Ġof Ġthe ĠPO Is Ġcontained Ġin Ġthem , Ġwere Ġthen Ġused Ġas Ġfeatures Ġto Ġpredict Ġland Ġuse Ġtypes . ĠIn Ġthe ĠCross Map Ġmethod ĠB IB REF 38 Ġthey Ġlearned Ġembed d ings Ġfor Ġspat io - tem poral Ġhots pots Ġobtained Ġfrom Ġsocial Ġmedia Ġdata Ġof Ġlocations , Ġtimes Ġand Ġtext . ĠIn Ġone Ġform Ġof Ġembed ding , Ġintended Ġto Ġenable Ġreconstruction Ġof Ġrecords , Ġneighbourhood Ġrelations Ġin Ġspace Ġand Ġtime Ġwere Ġencoded Ġby Ġaveraging Ġhots pots Ġin Ġa Ġtarget Ġlocation 's Ġspatial Ġand Ġtemporal Ġneighborhoods . ĠThey Ġalso Ġproposed Ġa Ġgraph - based Ġembed ding Ġmethod Ġwith Ġnodes Ġof Ġlocation , Ġtime Ġand Ġtext . ĠThe Ġconc aten ation Ġof Ġthe Ġlocation , Ġtime Ġand Ġtext Ġvectors Ġwere Ġthen Ġused Ġas Ġfeatures Ġto Ġpredict Ġpeoples ' Ġactivities Ġin Ġurban Ġenvironments . ĠFinally , Ġin ĠB IB REF 39 Ġ, Ġa Ġmethod Ġis Ġproposed Ġthat Ġuses Ġthe ĠSkip - gram Ġmodel Ġto Ġrepresent ĠPO I Ġtypes , Ġbased Ġon Ġthe Ġintuition Ġthat Ġthe Ġvector Ġrepresenting Ġa Ġgiven ĠPO I Ġtype Ġshould Ġbe Ġpredictive Ġof Ġthe ĠPO I Ġtypes Ġthat Ġfound Ġnear Ġplaces Ġof Ġthat Ġtype . </s> Our Ġwork Ġis Ġdifferent Ġfrom Ġthese Ġstudies , Ġas Ġour Ġfocus Ġis Ġon Ġrepresenting Ġlocations Ġbased Ġon Ġa Ġgiven Ġtext Ġdescription Ġof Ġthat Ġlocation Ġ( in Ġthe Ġform Ġof ĠFlickr Ġtags ), Ġalong Ġwith Ġnumerical Ġand Ġcateg orical Ġfeatures Ġfrom Ġscientific Ġdatasets . </s> Analy zing ĠFlickr Ġtags </s> Many Ġstudies Ġhave Ġfocused Ġon Ġanalyzing ĠFlickr Ġtags Ġto Ġextract Ġuseful Ġinformation Ġin Ġdomains Ġsuch Ġas Ġlingu istics ĠB IB REF 40 Ġ, Ġgeography ĠB IB REF 0 Ġ, ĠB IB REF 41 Ġ, Ġand Ġecology ĠB IB REF 42 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 43 Ġ. ĠMost Ġclosely Ġrelated Ġto Ġour Ġwork , ĠB IB REF 7 Ġfound Ġthat Ġthe Ġtags Ġof Ġge ore f eren ced ĠFlickr Ġphotos Ġcan Ġeffectively Ġsupplement Ġtraditional Ġscientific Ġenvironmental Ġdata Ġin Ġtasks Ġsuch Ġas Ġpredicting Ġclimate Ġfeatures , Ġland Ġcover , Ġspecies Ġoccurrence , Ġand Ġhuman Ġassessments Ġof Ġscenic ness . ĠTo Ġencode Ġlocations , Ġthey Ġsimply Ġcombine Ġa Ġbag - of - words Ġrepresentation Ġof Ġgeographically Ġnearby Ġtags Ġwith Ġa Ġfeature Ġvector Ġthat Ġenc odes Ġassociated Ġstructured Ġscientific Ġdata . ĠThey Ġfound Ġthat Ġthe Ġpredictive Ġvalue Ġof ĠFlickr Ġtags Ġis Ġroughly Ġon Ġa Ġpar Ġwith Ġthat Ġof Ġthe Ġscientific Ġdatasets , Ġand Ġthat Ġcombining Ġboth Ġtypes Ġof Ġinformation Ġleads Ġto Ġsignificantly Ġbetter Ġresults Ġthan Ġusing Ġeither Ġof Ġthem Ġalone . ĠAs Ġwe Ġshow Ġin Ġthis Ġpaper , Ġhowever , Ġtheir Ġstraightforward Ġway Ġof Ġcombining Ġboth Ġinformation Ġsources , Ġby Ġconc aten ating Ġthe Ġtwo Ġtypes Ġof Ġfeature Ġvectors , Ġis Ġfar Ġfrom Ġoptimal . </s> Despite Ġthe Ġproven Ġimportance Ġof ĠFlickr Ġtags , Ġthe Ġproblem Ġof Ġembed ding ĠFlickr Ġtags Ġhas Ġso Ġfar Ġreceived Ġvery Ġlimited Ġattention . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , ĠB IB REF 44 Ġis Ġthe Ġonly Ġwork Ġthat Ġgenerated Ġembed d ings Ġfor ĠFlickr Ġtags . ĠHowever , Ġtheir Ġfocus Ġwas Ġon Ġlearning Ġembed d ings Ġthat Ġcapture Ġword Ġmeaning Ġ( being Ġevaluated Ġon Ġword Ġsimilarity Ġtasks ), Ġwhereas Ġwe Ġuse Ġsuch Ġembed d ings Ġas Ġpart Ġof Ġour Ġmethod Ġfor Ġrepresenting Ġlocations . </s> Model ĠDescription </s> In Ġthis Ġsection , Ġwe Ġintroduce Ġour Ġembed ding Ġmodel , Ġwhich Ġcombines ĠFlickr Ġtags Ġand Ġstructured Ġscientific Ġinformation Ġto Ġrepresent Ġa Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġ. ĠThe Ġproposed Ġmodel Ġhas Ġthe Ġfollowing Ġform : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġparameters Ġto Ġcontrol Ġthe Ġimportance Ġof Ġeach Ġcomponent Ġin Ġthe Ġmodel . ĠComponent ĠIN LINE FORM 2 Ġwill Ġbe Ġused Ġto Ġconst rain Ġthe Ġrepresentation Ġof Ġthe Ġlocations Ġbased Ġon Ġtheir Ġtextual Ġdescription Ġ( i . e . ĠFlickr Ġtags ), ĠIN LINE FORM 3 Ġwill Ġbe Ġused Ġto Ġconst rain Ġthe Ġrepresentation Ġof Ġthe Ġlocations Ġbased Ġon Ġtheir Ġnumerical Ġfeatures , Ġand ĠIN LINE FORM 4 Ġwill Ġimpose Ġthe Ġconstraint Ġthat Ġlocations Ġbelonging Ġto Ġthe Ġsame Ġcategory Ġshould Ġbe Ġclose Ġtogether Ġin Ġthe Ġspace . ĠWe Ġwill Ġdiscuss Ġeach Ġof Ġthese Ġcomponents Ġin Ġmore Ġdetail Ġin Ġthe Ġfollowing Ġsections . </s> Tag ĠBased ĠLocation ĠEmb edd ing </s> Many Ġof Ġthe Ġtags Ġassociated Ġwith ĠFlickr Ġphotos Ġdescribe Ġcharacteristics Ġof Ġthe Ġplaces Ġwhere Ġthese Ġphotos Ġwere Ġtaken ĠB IB REF 45 Ġ, ĠB IB REF 46 Ġ, ĠB IB REF 47 Ġ. ĠFor Ġexample , Ġtags Ġmay Ġcorrespond Ġto Ġplace Ġnames Ġ( e . g . ĠBrussels , ĠEngland , ĠScandinav ia ), Ġlandmarks Ġ( e . g . ĠE iff el ĠTower , ĠEmpire ĠState ĠBuilding ) Ġor Ġland Ġcover Ġtypes Ġ( e . g . Ġmountain , Ġforest , Ġbeach ). ĠTo Ġallow Ġus Ġto Ġbuild Ġlocation Ġmodels Ġusing Ġsuch Ġtags , Ġwe Ġcollected Ġthe Ġtags Ġand Ġmeta - data Ġof Ġ70 Ġmillion ĠFlickr Ġphotos Ġwith Ġcoordinates Ġin ĠEurope Ġ( which Ġis Ġthe Ġregion Ġour Ġexperiments Ġwill Ġfocus Ġon ), Ġall Ġof Ġwhich Ġwere Ġuploaded Ġto ĠFlickr Ġbefore Ġthe Ġend Ġof ĠSeptember Ġ2015 . ĠIn Ġthis Ġsection Ġwe Ġfirst Ġexplain Ġhow Ġtags Ġcan Ġbe Ġweighted Ġto Ġobtain Ġbag - of - words Ġrepresentations Ġof Ġlocations Ġfrom ĠFlickr . ĠSub sequently Ġwe Ġdescribe Ġa Ġtag Ġselection Ġmethod , Ġwhich Ġwill Ġallow Ġus Ġto Ġspecialize Ġthe Ġembed ding Ġdepending Ġon Ġwhich Ġaspects Ġof Ġthe Ġconsidered Ġlocations Ġare Ġof Ġinterest , Ġafter Ġwhich Ġwe Ġdiscuss Ġthe Ġactual Ġembed ding Ġmodel . </s> Tag Ġweight ing . ĠLet ĠIN LINE FORM 0 Ġbe Ġa Ġset Ġof Ġgeographic Ġlocations , Ġeach Ġcharacterized Ġby Ġlatitude Ġand Ġlong itude Ġcoordinates . ĠTo Ġgenerate Ġa Ġbag - of - words Ġrepresentation Ġof Ġa Ġgiven Ġlocation , Ġwe Ġhave Ġto Ġweight Ġthe Ġrelevance Ġof Ġeach Ġtag Ġto Ġthat Ġlocation . ĠTo Ġthis Ġend , Ġwe Ġhave Ġfollowed Ġthe Ġweight ing Ġscheme Ġfrom ĠB IB REF 7 Ġ, Ġwhich Ġcombines Ġa ĠGa ussian Ġkernel Ġ( to Ġmodel Ġspatial Ġproximity ) Ġwith ĠPositive ĠPoint wise ĠMutual ĠInformation Ġ( P PM I ) ĠB IB REF 48 Ġ, ĠB IB REF 49 Ġ. </s> Let Ġus Ġwrite ĠIN LINE FORM 0 Ġfor Ġthe Ġset Ġof Ġusers Ġwho Ġhave Ġassigned Ġtag ĠIN LINE FORM 1 Ġto Ġa Ġphoto Ġwith Ġcoordinates Ġnear ĠIN LINE FORM 2 Ġ. ĠTo Ġassess Ġhow Ġrelevant ĠIN LINE FORM 3 Ġis Ġto Ġthe Ġlocation ĠIN LINE FORM 4 Ġ, Ġthe Ġnumber Ġof Ġtimes ĠIN LINE FORM 5 Ġoccurs Ġin Ġphotos Ġnear ĠIN LINE FORM 6 Ġis Ġclearly Ġan Ġimportant Ġcriterion . ĠHowever , Ġrather Ġthan Ġsimply Ġcounting Ġthe Ġnumber Ġof Ġoccurrences Ġwithin Ġsome Ġfixed Ġradius , Ġwe Ġuse Ġa ĠGa ussian Ġkernel Ġto Ġweight Ġthe Ġtag Ġoccurrences Ġaccording Ġto Ġtheir Ġdistance Ġfrom Ġthat Ġlocation : ĠIN LINE FORM 7 </s> where Ġthe Ġthreshold ĠIN LINE FORM 0 Ġis Ġassumed Ġto Ġbe Ġfixed , ĠIN LINE FORM 1 Ġis Ġthe Ġlocation Ġof Ġa ĠFlickr Ġphoto , ĠIN LINE FORM 2 Ġis Ġthe ĠHa vers ine Ġdistance , Ġand Ġwe Ġwill Ġassume Ġthat Ġthe Ġbandwidth Ġparameter ĠIN LINE FORM 3 Ġis Ġset Ġto ĠIN LINE FORM 4 Ġ. ĠA Ġtag Ġoccurrence Ġis Ġcounted Ġonly Ġonce Ġfor Ġall Ġphotos Ġby Ġthe Ġsame Ġuser Ġat Ġthe Ġsame Ġlocation , Ġwhich Ġis Ġimportant Ġto Ġreduce Ġthe Ġimpact Ġof Ġbulk Ġuploading . ĠThe Ġvalue ĠIN LINE FORM 5 Ġreflects Ġhow Ġfrequent Ġtag ĠIN LINE FORM 6 Ġis Ġnear Ġlocation ĠIN LINE FORM 7 Ġ, Ġbut Ġit Ġdoes Ġnot Ġyet Ġtake Ġinto Ġaccount Ġthe Ġtotal Ġnumber Ġof Ġtag Ġoccurrences Ġnear ĠIN LINE FORM 8 Ġ, Ġnor Ġhow Ġpopular Ġthe Ġtag ĠIN LINE FORM 9 Ġis Ġoverall . ĠTo Ġmeasure Ġhow Ġstrongly Ġtag ĠIN LINE FORM 10 Ġis Ġassociated Ġwith Ġlocation ĠIN LINE FORM 11 Ġ, Ġwe Ġuse ĠP PM I , Ġwhich Ġis Ġa Ġcommonly Ġused Ġmeasure Ġof Ġassociation Ġin Ġnatural Ġlanguage Ġprocessing . ĠHowever , Ġrather Ġthan Ġestimating ĠP PM I Ġscores Ġfrom Ġterm Ġfrequencies , Ġwe Ġwill Ġuse Ġthe ĠIN LINE FORM 12 Ġvalues Ġinstead : ĠIN LINE FORM 13 </s> where : ĠIN LINE FORM 0 </s> with ĠIN LINE FORM 0 Ġthe Ġset Ġof Ġall Ġtags , Ġand ĠIN LINE FORM 1 Ġthe Ġset Ġof Ġlocations . </s> Tag Ġselection . ĠInspired Ġby ĠB IB REF 50 Ġ, Ġwe Ġuse Ġa Ġterm Ġselection Ġmethod Ġin Ġorder Ġto Ġfocus Ġon Ġthe Ġtags Ġthat Ġare Ġmost Ġimportant Ġfor Ġthe Ġtasks Ġthat Ġwe Ġwant Ġto Ġconsider Ġand Ġreduce Ġthe Ġimpact Ġof Ġtags Ġthat Ġmight Ġrelate Ġonly Ġto Ġa Ġgiven Ġindividual Ġor Ġa Ġgroup Ġof Ġusers . ĠIn Ġparticular , Ġwe Ġobtained Ġgood Ġresults Ġwith Ġa Ġmethod Ġbased Ġon ĠK ull back - Le ib ler Ġ( K L ) Ġdivergence , Ġwhich Ġis Ġbased Ġon ĠB IB REF 51 Ġ. ĠLet ĠIN LINE FORM 0 Ġbe Ġa Ġset Ġof Ġ( mut ually Ġexclusive ) Ġproperties Ġof Ġlocations Ġin Ġwhich Ġwe Ġare Ġinterested Ġ( e . g . Ġland Ġcover Ġcategories ). ĠFor Ġthe Ġease Ġof Ġpresentation , Ġwe Ġwill Ġidentify ĠIN LINE FORM 1 Ġwith Ġthe Ġset Ġof Ġlocations Ġthat Ġhave Ġthe Ġcorresponding Ġproperty . ĠThen , Ġwe Ġselect Ġtags Ġfrom ĠIN LINE FORM 2 Ġthat Ġmaximize Ġthe Ġfollowing Ġscore : ĠIN LINE FORM 3 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġprobability Ġthat Ġa Ġphoto Ġwith Ġtag ĠIN LINE FORM 1 Ġhas Ġa Ġlocation Ġnear ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġis Ġthe Ġprobability Ġthat Ġan Ġarbitrary Ġtag Ġoccurrence Ġis Ġassigned Ġto Ġa Ġphoto Ġnear Ġa Ġlocation Ġin ĠIN LINE FORM 4 Ġ. ĠSince ĠIN LINE FORM 5 Ġoften Ġhas Ġto Ġbe Ġestimated Ġfrom Ġa Ġsmall Ġnumber Ġof Ġtag Ġoccurrences , Ġit Ġis Ġestimated Ġusing ĠBay esian Ġsmoot hing : ĠIN LINE FORM 6 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġparameter Ġcontrolling Ġthe Ġamount Ġof Ġsmoot hing , Ġwhich Ġwill Ġbe Ġtuned Ġin Ġthe Ġexperiments . ĠOn Ġthe Ġother Ġhand , Ġfor ĠIN LINE FORM 1 Ġwe Ġcan Ġsimply Ġuse Ġa Ġmaximum Ġlikelihood Ġestimation : ĠIN LINE FORM 2 </s> Location Ġembed ding . ĠWe Ġnow Ġwant Ġto Ġfind Ġa Ġvector ĠIN LINE FORM 0 Ġfor Ġeach Ġlocation ĠIN LINE FORM 1 Ġsuch Ġthat Ġsimilar Ġlocations Ġare Ġrepresented Ġusing Ġsimilar Ġvectors . ĠTo Ġachieve Ġthis , Ġwe Ġuse Ġa Ġclose Ġvariant Ġof Ġthe ĠGl o Ve Ġmodel , Ġwhere Ġtag Ġoccurrences Ġare Ġtreated Ġas Ġcontext Ġwords Ġof Ġgeographic Ġlocations . ĠIn Ġparticular , Ġwith Ġeach Ġlocation ĠIN LINE FORM 2 Ġwe Ġassociate Ġa Ġvector ĠIN LINE FORM 3 Ġand Ġwith Ġeach Ġtag ĠIN LINE FORM 4 Ġwe Ġassociate Ġa Ġvector ĠIN LINE FORM 5 Ġand Ġa Ġbias Ġterm ĠIN LINE FORM 6 Ġ, Ġand Ġconsider Ġthe Ġfollowing Ġobjective Ġ( which Ġin Ġour Ġfull Ġmodel Ġ( ĠEQ REF 7 Ġ) Ġwill Ġbe Ġcombined Ġwith Ġcomponents Ġthat Ġare Ġderived Ġfrom Ġthe Ġstructured Ġinformation ): ĠIN LINE FORM 7 </s> Note Ġhow Ġtags Ġplay Ġthe Ġrole Ġof Ġthe Ġcontext Ġwords Ġin Ġthe ĠGl o Ve Ġmodel , Ġwhile Ġinstead Ġof Ġlearning Ġtarget Ġword Ġvectors Ġwe Ġnow Ġlearn Ġlocation Ġvectors . ĠIn Ġcontrast Ġto ĠGl o Ve , Ġour Ġobjective Ġdoes Ġnot Ġdirectly Ġrefer Ġto Ġco - occ urrence Ġstatistics , Ġbut Ġinstead Ġuses Ġthe ĠIN LINE FORM 0 Ġscores . ĠOne Ġimportant Ġconsequence Ġof Ġthis Ġis Ġthat Ġwe Ġcan Ġalso Ġconsider Ġpairs ĠIN LINE FORM 1 Ġfor Ġwhich ĠIN LINE FORM 2 Ġdoes Ġnot Ġoccur Ġin ĠIN LINE FORM 3 Ġat Ġall ; Ġsuch Ġpairs Ġare Ġusually Ġcalled Ġnegative Ġexamples . ĠWhile Ġthey Ġcannot Ġbe Ġused Ġin Ġthe Ġstandard ĠGl o Ve Ġmodel , Ġsome Ġauthors Ġhave Ġalready Ġreported Ġthat Ġintroducing Ġnegative Ġexamples Ġin Ġvariants Ġof ĠGl o Ve Ġcan Ġlead Ġto Ġan Ġimprovement ĠB IB REF 52 Ġ. ĠIn Ġpractice , Ġevaluating Ġthe Ġfull Ġobjective Ġabove Ġwould Ġnot Ġbe Ġcomput ationally Ġfeasible , Ġas Ġwe Ġmay Ġneed Ġto Ġconsider Ġmillions Ġof Ġlocations Ġand Ġmillions Ġof Ġtags . ĠTherefore , Ġrather Ġthan Ġconsidering Ġall Ġtags Ġin ĠIN LINE FORM 4 Ġfor Ġthe Ġinner Ġsumm ation , Ġwe Ġonly Ġconsider Ġthose Ġtags Ġthat Ġappear Ġat Ġleast Ġonce Ġnear Ġlocation ĠIN LINE FORM 5 Ġtogether Ġwith Ġa Ġsample Ġof Ġnegative Ġexamples . </s> Struct ured ĠEnvironmental ĠData </s> There Ġis Ġa Ġwide Ġvariety Ġof Ġstructured Ġdata Ġthat Ġcan Ġbe Ġused Ġto Ġdescribe Ġlocations . ĠIn Ġthis Ġwork , Ġwe Ġhave Ġrestricted Ġourselves Ġto Ġthe Ġsame Ġdatasets Ġas ĠB IB REF 7 Ġ. ĠThese Ġinclude Ġnine Ġ( real - valued ) Ġnumerical Ġfeatures , Ġwhich Ġare Ġlatitude , Ġlong itude , Ġelevation , Ġpopulation , Ġand Ġfive Ġclimate Ġrelated Ġfeatures Ġ( av g . Ġtemperature , Ġavg . Ġprecipitation , Ġavg . Ġsolar Ġradiation , Ġavg . Ġwind Ġspeed , Ġand Ġavg . Ġwater Ġvapor Ġpressure ). ĠIn Ġaddition , Ġ180 Ġcateg orical Ġfeatures Ġwere Ġused , Ġwhich Ġare ĠCOR INE Ġland Ġcover Ġclasses Ġat Ġlevel Ġ1 Ġ( 5 Ġclasses ), Ġlevel Ġ2 Ġ( 15 Ġclasses ) Ġand Ġlevel Ġ3 Ġ( 44 Ġclasses ) Ġand Ġ116 Ġsoil Ġtypes Ġ( So il Gr ids ). ĠNote Ġthat Ġeach Ġlocation Ġshould Ġbelong Ġto Ġexactly Ġ4 Ġcategories : Ġone ĠCOR INE Ġclass Ġat Ġeach Ġof Ġthe Ġthree Ġlevels Ġand Ġa Ġsoil Ġtype . </s> N umer ical Ġfeatures . ĠN umer ical Ġfeatures Ġcan Ġbe Ġtreated Ġsimilarly Ġto Ġthe Ġtag Ġoccurrences , Ġi . e . Ġwe Ġwill Ġassume Ġthat Ġthe Ġvalue Ġof Ġa Ġgiven Ġnumerical Ġfeature Ġcan Ġbe Ġpredicted Ġfrom Ġthe Ġlocation Ġvectors Ġusing Ġa Ġlinear Ġmapping . ĠIn Ġparticular , Ġfor Ġeach Ġnumerical Ġfeature ĠIN LINE FORM 0 Ġwe Ġconsider Ġa Ġvector ĠIN LINE FORM 1 Ġand Ġa Ġbias Ġterm ĠIN LINE FORM 2 Ġ, Ġand Ġthe Ġfollowing Ġobjective : ĠIN LINE FORM 3 </s> where Ġwe Ġwrite ĠIN LINE FORM 0 Ġfor Ġset Ġof Ġall Ġnumerical Ġfeatures Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġvalue Ġof Ġfeature ĠIN LINE FORM 2 Ġfor Ġlocation ĠIN LINE FORM 3 Ġ, Ġafter Ġz - score Ġnormal ization . </s> C ateg orical Ġfeatures . ĠTo Ġtake Ġinto Ġaccount Ġthe Ġcateg orical Ġfeatures , Ġwe Ġimpose Ġthe Ġconstraint Ġthat Ġlocations Ġbelonging Ġto Ġthe Ġsame Ġcategory Ġshould Ġbe Ġclose Ġtogether Ġin Ġthe Ġspace . ĠTo Ġformal ize Ġthis , Ġwe Ġrepresent Ġeach Ġcategory Ġtype ĠIN LINE FORM 0 Ġas Ġa Ġvector ĠIN LINE FORM 1 Ġ, Ġand Ġconsider Ġthe Ġfollowing Ġobjective : ĠIN LINE FORM 2 </s> E val uation ĠT asks </s> We Ġwill Ġuse Ġthe Ġmethod Ġfrom ĠB IB REF 7 Ġas Ġour Ġmain Ġbaseline . ĠThis Ġwill Ġallow Ġus Ġto Ġdirectly Ġevaluate Ġthe Ġeffectiveness Ġof Ġembed d ings Ġfor Ġthe Ġconsidered Ġproblem , Ġsince Ġwe Ġhave Ġused Ġthe Ġsame Ġstructured Ġdatasets Ġand Ġsame Ġtag Ġweight ing Ġscheme . ĠFor Ġthis Ġreason , Ġwe Ġwill Ġalso Ġfollow Ġtheir Ġevaluation Ġmethodology . ĠIn Ġparticular , Ġwe Ġwill Ġconsider Ġthree Ġevaluation Ġtasks : </s> P redict ing Ġthe Ġdistribution Ġof Ġ100 Ġspecies Ġacross ĠEurope , Ġusing Ġthe ĠEuropean Ġnetwork Ġof Ġnature Ġprotected Ġsites ĠN atur a Ġ2000 Ġdataset Ġas Ġground Ġtruth . ĠFor Ġeach Ġof Ġthese Ġspecies , Ġa Ġbinary Ġclassification Ġproblem Ġis Ġconsidered . ĠThe Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġis Ġdefined Ġas Ġthe Ġ26 , 425 Ġdistinct Ġsites Ġoccurring Ġin Ġthe Ġdataset . </s> P redict ing Ġsoil Ġtype , Ġagain Ġeach Ġtime Ġtreating Ġthe Ġtask Ġas Ġa Ġbinary Ġclassification Ġproblem , Ġusing Ġthe Ġsame Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġas Ġin Ġthe Ġspecies Ġdistribution Ġexperiments . ĠFor Ġthese Ġexperiments , Ġnone Ġof Ġthe Ġsoil Ġtype Ġfeatures Ġare Ġused Ġfor Ġgenerating Ġthe Ġembed d ings . </s> P redict ing ĠCOR INE Ġland Ġcover Ġclasses Ġat Ġlevels Ġ1 , Ġ2 Ġand Ġlevel Ġ3 , Ġeach Ġtime Ġtreating Ġthe Ġtask Ġas Ġa Ġbinary Ġclassification Ġproblem , Ġusing Ġthe Ġsame Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġas Ġin Ġthe Ġspecies Ġdistribution Ġexperiments . ĠFor Ġthese Ġexperiments , Ġnone Ġof Ġthe ĠCOR INE Ġfeatures Ġare Ġused Ġfor Ġgenerating Ġthe Ġembed d ings . </s> In Ġaddition , Ġwe Ġwill Ġalso Ġconsider Ġthe Ġfollowing Ġregression Ġtasks : </s> P redict ing Ġ5 Ġclimate Ġrelated Ġfeatures : Ġthe Ġaverage Ġprecipitation , Ġtemperature , Ġsolar Ġradiation , Ġwater Ġvapor Ġpressure , Ġand Ġwind Ġspeed . ĠWe Ġagain Ġuse Ġthe Ġsame Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġas Ġfor Ġspecies Ġdistribution Ġin Ġthis Ġexperiment . ĠNone Ġof Ġthe Ġclimate Ġfeatures Ġis Ġused Ġfor Ġconstructing Ġthe Ġembed d ings Ġfor Ġthis Ġexperiment . </s> P redict ing Ġpeople 's Ġsubjective Ġopinions Ġof Ġlandscape Ġbeauty Ġin ĠBritain , Ġusing Ġthe Ġcrowds ourced Ġdataset Ġfrom Ġthe ĠSc enic Or Not Ġwebsite Ġas Ġground Ġtruth . ĠThe Ġset ĠIN LINE FORM 0 Ġis Ġchosen Ġas Ġthe Ġset Ġof Ġlocations Ġof Ġ191 Ġ6 05 Ġrated Ġlocations Ġfrom Ġthe ĠSc enic Or Not Ġdataset Ġfor Ġwhich Ġat Ġleast Ġone Ġge ore f eren ced ĠFlickr Ġphoto Ġexists Ġwithin Ġa Ġ1 Ġkm Ġradius . </s> Exper imental ĠSetup </s> In Ġall Ġexperiments , Ġwe Ġuse ĠSupport ĠVector ĠMachines Ġ( S V Ms ) Ġfor Ġclassification Ġproblems Ġand ĠSupport ĠVector ĠReg ression Ġ( S VR ) Ġfor Ġregression Ġproblems Ġto Ġmake Ġpredictions Ġfrom Ġour Ġrepresentations Ġof Ġgeographic Ġlocations . ĠIn Ġboth Ġcases , Ġwe Ġused Ġthe ĠS VM ĠIN LINE FORM 0 Ġimplementation ĠB IB REF 53 Ġ. ĠFor Ġeach Ġexperiment , Ġthe Ġset Ġof Ġlocations ĠIN LINE FORM 1 Ġwas Ġsplit Ġinto Ġtwo - thirds Ġfor Ġtraining , Ġone - six th Ġfor Ġtesting , Ġand Ġone - six th Ġfor Ġtuning Ġthe Ġparameters . ĠAll Ġembed ding Ġmodels Ġare Ġlearned Ġwith ĠAd ag rad Ġusing Ġ30 Ġiterations . ĠThe Ġnumber Ġof Ġdimensions Ġis Ġchosen Ġfor Ġeach Ġexperiment Ġfrom ĠIN LINE FORM 2 Ġbased Ġon Ġthe Ġtuning Ġdata . ĠFor Ġthe Ġparameters Ġof Ġour Ġmodel Ġin ĠEqu ation ĠEQ REF 7 Ġ, Ġwe Ġconsidered Ġvalues Ġof ĠIN LINE FORM 3 Ġfrom Ġ{ 0 . 1 , Ġ0 . 01 , Ġ0 . 001 , Ġ0 . 0001 } Ġand Ġvalues Ġof ĠIN LINE FORM 4 Ġfrom Ġ{ 1 , Ġ10 , Ġ100 , Ġ1000 , Ġ10 Ġ000 , Ġ100 Ġ000 }. ĠTo Ġcompute ĠKL Ġdivergence , Ġwe Ġneed Ġto Ġdetermine Ġa Ġset Ġof Ġclasses ĠIN LINE FORM 5 Ġfor Ġeach Ġexperiment . ĠFor Ġclassification Ġproblems , Ġwe Ġcan Ġsimply Ġconsider Ġthe Ġgiven Ġcategories , Ġbut Ġfor Ġthe Ġregression Ġproblems Ġwe Ġneed Ġto Ġdefine Ġsuch Ġclasses Ġby Ġdisc ret izing Ġthe Ġnumerical Ġvalues . ĠFor Ġthe Ġscenic ness Ġexperiments , Ġwe Ġconsidered Ġscores Ġ3 Ġand Ġ7 Ġas Ġcut - off Ġpoints , Ġleading Ġto Ġthree Ġclasses Ġ( i . e . Ġless Ġthan Ġ3 , Ġbetween Ġ3 Ġand Ġ7 , Ġand Ġabove Ġ7 ). ĠSimilarly , Ġfor Ġeach Ġclimate Ġrelated Ġfeatures , Ġwe Ġconsider Ġtwo Ġcut - off Ġvalues Ġfor Ġdisc ret ization : Ġ5 Ġand Ġ15 Ġfor Ġaverage Ġtemperature , Ġ50 Ġand Ġ100 Ġfor Ġaverage Ġprecipitation , Ġ10 Ġ000 Ġand Ġ17 Ġ000 Ġfor Ġaverage Ġsolar Ġradiation , Ġ0 . 7 Ġand Ġ1 Ġfor Ġaverage Ġwater Ġvapor Ġpressure , Ġand Ġ3 Ġand Ġ5 Ġfor Ġwind Ġspeed . ĠThe Ġsmoot hing Ġparameter ĠIN LINE FORM 6 Ġwas Ġselected Ġamong ĠIN LINE FORM 7 Ġbased Ġon Ġthe Ġtuning Ġdata . ĠIn Ġall Ġexperiments Ġwhere Ġterm Ġselection Ġis Ġused , Ġwe Ġselect Ġthe Ġtop Ġ100 Ġ000 Ġtags . ĠWe Ġfixed Ġthe Ġradius ĠIN LINE FORM 8 Ġat Ġ1 km Ġwhen Ġcounting Ġthe Ġnumber Ġof Ġtag Ġoccurrences . ĠFinally , Ġwe Ġset Ġthe Ġnumber Ġof Ġnegative Ġexamples Ġas Ġ10 Ġtimes Ġthe Ġnumber Ġof Ġpositive Ġexamples Ġfor Ġeach Ġlocation , Ġbut Ġwith Ġa Ġcap Ġat Ġ1000 Ġnegative Ġexamples Ġin Ġeach Ġregion Ġfor Ġcomputational Ġreasons . ĠWe Ġtune Ġall Ġparameters Ġwith Ġrespect Ġto Ġthe ĠF 1 Ġscore Ġfor Ġthe Ġclassification Ġtasks , Ġand ĠSpear man ĠIN LINE FORM 9 Ġfor Ġthe Ġregression Ġtasks . </s> Vari ants Ġand ĠBas eline ĠMethods </s> We Ġwill Ġrefer Ġto Ġour Ġmodel Ġas ĠEG EL Ġ( Emb edd ing ĠGE ographic ĠLocations ), Ġand Ġwill Ġconsider Ġthe Ġfollowing Ġvariants . ĠEG EL - Tags Ġonly Ġuses Ġthe Ġinformation Ġfrom Ġthe ĠFlickr Ġtags Ġ( i . e . Ġcomponent ĠIN LINE FORM 0 Ġ), Ġwithout Ġusing Ġany Ġnegative Ġexamples Ġand Ġwithout Ġfeature Ġselection . ĠEG EL - Tags + NS Ġis Ġsimilar Ġto ĠEG EL - Tags Ġbut Ġwith Ġthe Ġaddition Ġof Ġnegative Ġexamples . ĠEG EL - K L ( Tags + NS ) Ġadditionally Ġconsiders Ġterm Ġselection . ĠEG EL - All Ġis Ġour Ġfull Ġmethod , Ġi . e . Ġit Ġadditionally Ġuses Ġthe Ġstructured Ġinformation . ĠWe Ġalso Ġconsider Ġthe Ġfollowing Ġbas elines . ĠB OW - Tags Ġrepresents Ġlocations Ġusing Ġa Ġbag - of - words Ġrepresentation , Ġusing Ġthe Ġsame Ġtag Ġweight ing Ġas Ġthe Ġembed ding Ġmodel . ĠB OW - K L ( Tags ) Ġuses Ġthe Ġsame Ġrepresentation Ġbut Ġafter Ġterm Ġselection , Ġusing Ġthe Ġsame ĠKL - based Ġmethod Ġas Ġthe Ġembed ding Ġmodel . ĠB OW - All Ġcombines Ġthe Ġbag - of - words Ġrepresentation Ġwith Ġthe Ġstructured Ġinformation , Ġencoded Ġas Ġproposed Ġin ĠB IB REF 7 Ġ. ĠGl o Ve Ġuses Ġthe Ġobjective Ġfrom Ġthe Ġoriginal ĠGl o Ve Ġmodel Ġfor Ġlearning Ġlocation Ġvectors , Ġi . e . Ġthis Ġvariant Ġdiffers Ġfrom ĠEG EL - Tags Ġin Ġthat Ġinstead Ġof ĠIN LINE FORM 1 Ġwe Ġuse Ġthe Ġnumber Ġof Ġco - occ ur rences Ġof Ġtag ĠIN LINE FORM 2 Ġnear Ġlocation ĠIN LINE FORM 3 Ġ, Ġmeasured Ġas ĠIN LINE FORM 4 Ġ. </s> Results Ġand ĠDiscussion </s> We Ġpresent Ġour Ġresults Ġfor Ġthe Ġbinary Ġclassification Ġtasks Ġin ĠTables ĠT AB REF 23 ĠâĢĵ ĠT AB REF 24 Ġin Ġterms Ġof Ġaverage Ġprecision , Ġaverage Ġrecall Ġand Ġmacro Ġaverage ĠF 1 Ġscore . ĠThe Ġresults Ġof Ġthe Ġregression Ġtasks Ġare Ġreported Ġin ĠTables ĠT AB REF 25 Ġand ĠT AB REF 29 Ġin Ġterms Ġof Ġthe Ġmean Ġabsolute Ġerror Ġbetween Ġthe Ġpredicted Ġand Ġactual Ġscores , Ġas Ġwell Ġas Ġthe ĠSpear man ĠIN LINE FORM 0 Ġcorrelation Ġbetween Ġthe Ġrankings Ġinduced Ġby Ġboth Ġsets Ġof Ġscores . ĠIt Ġcan Ġbe Ġclearly Ġseen Ġfrom Ġthe Ġresults Ġthat Ġour Ġproposed Ġmethod Ġ( EG EL - All ) Ġcan Ġeffectively Ġintegrate ĠFlickr Ġtags Ġwith Ġthe Ġavailable Ġstructured Ġinformation . ĠIt Ġoutper forms Ġthe Ġbas elines Ġfor Ġall Ġthe Ġconsidered Ġtasks . ĠFurthermore , Ġnote Ġthat Ġthe ĠP PM I - based Ġweight ing Ġin ĠEG EL - Tags Ġconsistently Ġoutper forms ĠGl o Ve Ġand Ġthat Ġboth Ġthe Ġaddition Ġof Ġnegative Ġexamples Ġand Ġterm Ġselection Ġlead Ġto Ġfurther Ġimprovements . ĠThe Ġuse Ġof Ġterm Ġselection Ġleads Ġto Ġparticularly Ġsubstantial Ġimprovements Ġfor Ġthe Ġregression Ġproblems . </s> While Ġour Ġexperimental Ġresults Ġconfirm Ġthe Ġusefulness Ġof Ġembed d ings Ġfor Ġpredicting Ġenvironmental Ġfeatures , Ġthis Ġis Ġonly Ġconsistently Ġthe Ġcase Ġfor Ġthe Ġvariants Ġthat Ġuse Ġboth Ġthe Ġtags Ġand Ġthe Ġstructured Ġdatasets . ĠIn Ġparticular , Ġcomparing ĠB OW - Tags Ġwith ĠEG EL - Tags , Ġwe Ġsometimes Ġsee Ġthat Ġthe Ġformer Ġachieves Ġthe Ġbest Ġresults . ĠWhile Ġthis Ġmight Ġseem Ġsurprising , Ġit Ġis Ġin Ġaccordance Ġwith Ġthe Ġfindings Ġin ĠB IB REF 54 Ġ, ĠB IB REF 38 Ġ, Ġamong Ġothers , Ġwhere Ġit Ġwas Ġalso Ġfound Ġthat Ġbag - of - words Ġrepresentations Ġcan Ġsometimes Ġlead Ġto Ġsurprisingly Ġeffective Ġbas elines . ĠInterestingly , Ġwe Ġnote Ġthat Ġin Ġall Ġcases Ġwhere ĠEG EL - K L ( Tags + NS ) Ġperforms Ġworse Ġthan ĠB OW - Tags , Ġwe Ġalso Ġfind Ġthat ĠB OW - K L ( Tags ) Ġperforms Ġworse Ġthan ĠB OW - Tags . ĠThis Ġsuggests Ġthat Ġfor Ġthese Ġtasks Ġthere Ġis Ġa Ġvery Ġlarge Ġvariation Ġin Ġthe Ġkind Ġof Ġtags Ġthat Ġcan Ġinform Ġthe Ġprediction Ġmodel , Ġpossibly Ġincluding Ġe . g . Ġuser - specific Ġtags . ĠSome Ġof Ġthe Ġinformation Ġcaptured Ġby Ġsuch Ġhighly Ġspecific Ġbut Ġrare Ġtags Ġis Ġlikely Ġto Ġbe Ġlost Ġin Ġthe Ġembed ding . </s> To Ġfurther Ġanalyze Ġthe Ġdifference Ġin Ġperformance Ġbetween ĠBo W Ġrepresentations Ġand Ġembed d ings , ĠFigure ĠT AB REF 29 Ġcompares Ġthe Ġperformance Ġof Ġthe ĠGl o Ve Ġmodel Ġwith Ġthe Ġbag - of - words Ġmodel Ġfor Ġpredicting Ġplace Ġscenic ness , Ġas Ġa Ġfunction Ġof Ġthe Ġnumber Ġof Ġtag Ġoccurrences Ġat Ġthe Ġconsidered Ġlocations . ĠWhat Ġis Ġclearly Ġnoticeable Ġin ĠFigure ĠT AB REF 29 Ġis Ġthat ĠGl o Ve Ġperforms Ġbetter Ġthan Ġthe Ġbag - of - words Ġmodel Ġfor Ġlarge Ġcorpor a Ġand Ġworse Ġfor Ġsmaller Ġcorpor a . ĠThis Ġissue Ġhas Ġbeen Ġallev iated Ġin Ġour Ġembed ding Ġmethod Ġby Ġthe Ġaddition Ġof Ġnegative Ġexamples . </s> ĠCon clusions </s> In Ġthis Ġpaper , Ġwe Ġhave Ġproposed Ġa Ġmodel Ġto Ġlearn Ġgeographic Ġlocation Ġembed d ings Ġusing ĠFlickr Ġtags , Ġnumerical Ġenvironmental Ġfeatures , Ġand Ġcateg orical Ġinformation . ĠThe Ġexperimental Ġresults Ġshow Ġthat Ġour Ġmodel Ġcan Ġintegrate ĠFlickr Ġtags Ġwith Ġstructured Ġinformation Ġin Ġa Ġmore Ġeffective Ġway Ġthan Ġexisting Ġmethods , Ġleading Ġto Ġsubstantial Ġimprovements Ġover Ġbaseline Ġmethods Ġon Ġvarious Ġprediction Ġtasks Ġabout Ġthe Ġnatural Ġenvironment . </s> Acknowled gments </s> S hel an ĠJe aw ak Ġhas Ġbeen Ġsponsored Ġby ĠHC ED ĠIraq . ĠSteven ĠSch ock a ert Ġhas Ġbeen Ġsupported Ġby ĠE RC ĠStarting ĠGrant Ġ6 37 277 .
Introduction Users of photo-sharing websites such as Flickr often provide short textual descriptions in the form of tags to help others find the images. With the availability of GPS systems in current electronic devices such as smartphones, latitude and longitude coordinates are nowadays commonly made available as well. The tags associated with such georeferenced photos often describe the location where these photos were taken, and Flickr can thus be regarded as a source of environmental information. The use of Flickr for modelling urban environments has already received considerable attention. For instance, various approaches have been proposed for modelling urban regions BIBREF0 , and for identifying points-of-interest BIBREF1 and itineraries BIBREF2 , BIBREF3 . However, the usefulness of Flickr for characterizing the natural environment, which is the focus of this paper, is less well-understood. Many recent studies have highlighted that Flickr tags capture valuable ecological information, which can be used as a complementary source to more traditional sources. To date, however, ecologists have mostly used social media to conduct manual evaluations of image content with little automated exploitation of the associated tags BIBREF4 , BIBREF5 , BIBREF6 . One recent exception is BIBREF7 , where bag-of-words representations derived from Flickr tags were found to give promising result for predicting a range of different environemental phenomena. Our main hypothesis in this paper is that by using vector space embeddings instead of bag-of-words representations, the ecological information which is implicitly captured by Flickr tags can be utilized in a more effective way. Vector space embeddings are representations in which the objects from a given domain are encoded using relatively low-dimensional vectors. They have proven useful in natural language processing, especially for encoding word meaning BIBREF8 , BIBREF9 , and in machine learning more generally. In this paper, we are interested in the use of such representations for modelling geographic locations. Our main motivation for using vector space embeddings is that they allow us to integrate the textual information we get from Flickr with available structured information in a very natural way. To this end, we rely on an adaptation of the GloVe word embedding model BIBREF9 , but rather than learning word vectors, we learn vectors representing locations. Similar to how the representation of a word in GloVe is determined by the context words surrounding it, the representation of a location in our model is determined by the tags of the photos that have been taken near that location. To incorporate numerical features from structured environmental datasets (e.g. average temperature), we associate with each such feature a linear mapping that can be used to predict that feature from a given location vector. This is inspired by the fact that salient properties of a given domain can often be modelled as directions in vector space embeddings BIBREF10 , BIBREF11 , BIBREF12 . Finally, evidence from categorical datasets (e.g. land cover types) is taken into account by requiring that locations belonging to the same category are represented using similar vectors, similar to how semantic types are sometimes modelled in the context of knowledge graph embedding BIBREF13 . While our point-of-departure is a standard word embedding model, we found that the off-the-shelf GloVe model performed surprisingly poorly, meaning that a number of modifications are needed to achieve good results. Our main findings are as follows. First, given that the number of tags associated with a given location can be quite small, it is important to apply some kind of spatial smoothing, i.e. the importance of a given tag for a given location should not only depend on the occurrences of the tag at that location, but also on its occurrences at nearby locations. To this end, we use a formulation which is based on spatially smoothed version of pointwise mutual information. Second, given the wide diversity in the kind of information that is covered by Flickr tags, we find that term selection is in some cases critical to obtain vector spaces that capture the relevant aspects of geographic locations. For instance, many tags on Flickr refer to photography related terms, which we would normally not want to affect the vector representation of a given location. Finally, even with these modifications, vector space embeddings learned from Flickr tags alone are sometimes outperformed by bag-of-words representations. However, our vector space embeddings lead to substantially better predictions in cases where structured (scientific) information is also taken into account. In this sense, the main value of using vector space embeddings in this context is not so much about abstracting away from specific tag usages, but rather about the fact that such representations allow us to integrate numerical and categorical features in a much more natural way than is possible with bag-of-words representations. The remainder of this paper is organized as follows. In the next section, we provide a discussion of existing work. Section SECREF3 then presents our model for embedding geographic locations from Flickr tags and structured data. Next, in Section SECREF4 we provide a detailed discussion about the experimental results. Finally, Section SECREF5 summarizes our conclusions. Vector space embeddings The use of low-dimensional vector space embeddings for representing objects has already proven effective in a large number of applications, including natural language processing (NLP), image processing, and pattern recognition. In the context of NLP, the most prominent example is that of word embeddings, which represent word meaning using vectors of typically around 300 dimensions. A large number of different methods for learning such word embeddings have already been proposed, including Skip-gram and the Continuous Bag-of-Words (CBOW) model BIBREF8 , GloVe BIBREF9 , and fastText BIBREF14 . They have been applied effectively in many downstream NLP tasks such as sentiment analysis BIBREF15 , part of speech tagging BIBREF16 , BIBREF17 , and text classification BIBREF18 , BIBREF19 . The model we consider in this paper builds on GloVe, which was designed to capture linear regularities of word-word co-occurrence. In GloVe, there are two word vectors INLINEFORM0 and INLINEFORM1 for each word in the vocabulary, which are learned by minimizing the following objective: DISPLAYFORM0 where INLINEFORM0 is the number of times that word INLINEFORM1 appears in the context of word INLINEFORM2 , INLINEFORM3 is the vocabulary size, INLINEFORM4 is the target word bias, INLINEFORM5 is the context word bias. The weighting function INLINEFORM6 is used to limit the impact of rare terms. It is defined as 1 if INLINEFORM7 and as INLINEFORM8 otherwise, where INLINEFORM9 is usually fixed to 100 and INLINEFORM10 to 0.75. Intuitively, the target word vectors INLINEFORM11 correspond to the actual word representations which we would like to find, while the context word vectors INLINEFORM12 model how occurrences of INLINEFORM13 in the context of a given word INLINEFORM14 affect the representation of this latter word. In this paper we will use a similar model, which will however be aimed at learning location vectors instead of the target word vectors. Beyond word embeddings, various methods have been proposed for learning vector space representations from structured data such as knowledge graphs BIBREF20 , BIBREF21 , BIBREF22 , social networks BIBREF23 , BIBREF24 and taxonomies BIBREF25 , BIBREF26 . The idea of combining a word embedding model with structured information has also been explored by several authors, for example to improve the word embeddings based on information coming from knowledge graphs BIBREF27 , BIBREF28 . Along similar lines, various lexicons have been used to obtain word embeddings that are better suited at modelling sentiment BIBREF15 and antonymy BIBREF29 , among others. The method proposed by BIBREF30 imposes the condition that words that belong to the same semantic category are closer together than words from different categories, which is somewhat similar in spirit to how we will model categorical datasets in our model. Embeddings for geographic information The problem of representing geographic locations using embeddings has also attracted some attention. An early example is BIBREF31 , which used principal component analysis and stacked autoencoders to learn low-dimensional vector representations of city neighbourhoods based on census data. They use these representations to predict attributes such as crime, which is not included in the given census data, and find that in most of the considered evaluation tasks, the low-dimensional vector representations lead to more faithful predictions than the original high-dimensional census data. Some existing works combine word embedding models with geographic coordinates. For example, in BIBREF32 an approach is proposed to learn word embeddings based on the assumption that words which tend to be used in the same geographic locations are likely to be similar. Note that their aim is dual to our aim in this paper: while they use geographic location to learn word vectors, we use textual descriptions to learn vectors representing geographic locations. Several methods also use word embedding models to learn representations of Points-of-Interest (POIs) that can be used for predicting user visits BIBREF33 , BIBREF34 , BIBREF35 . These works use the machinery of existing word embedding models to learn POI representations, intuitively by letting sequences of POI visits by a user play the role of sequences of words in a sentence. In other words, despite the use of word embedding models, many of these approaches do not actually consider any textual information. For example, in BIBREF34 the Skip-gram model is utilized to create a global pattern of users' POIs. Each location was treated as a word and the other locations visited before or after were treated as context words. They then use a pair-wise ranking loss BIBREF36 which takes into account the user's location visit frequency to personalize the location recommendations. The methods of BIBREF34 were extended in BIBREF35 to use a temporal embedding and to take more account of geographic context, in particular the distances between preferred and non-preferred neighboring POIs, to create a “geographically hierarchical pairwise preference ranking model”. Similarly, in BIBREF37 the CBOW model was trained with POI data. They ordered POIs spatially within the traffic-based zones of urban areas. The ordering was used to generate characteristic vectors of POI types. Zone vectors represented by averaging the vectors of the POIs contained in them, were then used as features to predict land use types. In the CrossMap method BIBREF38 they learned embeddings for spatio-temporal hotspots obtained from social media data of locations, times and text. In one form of embedding, intended to enable reconstruction of records, neighbourhood relations in space and time were encoded by averaging hotspots in a target location's spatial and temporal neighborhoods. They also proposed a graph-based embedding method with nodes of location, time and text. The concatenation of the location, time and text vectors were then used as features to predict peoples' activities in urban environments. Finally, in BIBREF39 , a method is proposed that uses the Skip-gram model to represent POI types, based on the intuition that the vector representing a given POI type should be predictive of the POI types that found near places of that type. Our work is different from these studies, as our focus is on representing locations based on a given text description of that location (in the form of Flickr tags), along with numerical and categorical features from scientific datasets. Analyzing Flickr tags Many studies have focused on analyzing Flickr tags to extract useful information in domains such as linguistics BIBREF40 , geography BIBREF0 , BIBREF41 , and ecology BIBREF42 , BIBREF7 , BIBREF43 . Most closely related to our work, BIBREF7 found that the tags of georeferenced Flickr photos can effectively supplement traditional scientific environmental data in tasks such as predicting climate features, land cover, species occurrence, and human assessments of scenicness. To encode locations, they simply combine a bag-of-words representation of geographically nearby tags with a feature vector that encodes associated structured scientific data. They found that the predictive value of Flickr tags is roughly on a par with that of the scientific datasets, and that combining both types of information leads to significantly better results than using either of them alone. As we show in this paper, however, their straightforward way of combining both information sources, by concatenating the two types of feature vectors, is far from optimal. Despite the proven importance of Flickr tags, the problem of embedding Flickr tags has so far received very limited attention. To the best of our knowledge, BIBREF44 is the only work that generated embeddings for Flickr tags. However, their focus was on learning embeddings that capture word meaning (being evaluated on word similarity tasks), whereas we use such embeddings as part of our method for representing locations. Model Description In this section, we introduce our embedding model, which combines Flickr tags and structured scientific information to represent a set of locations INLINEFORM0 . The proposed model has the following form: DISPLAYFORM0 where INLINEFORM0 and INLINEFORM1 are parameters to control the importance of each component in the model. Component INLINEFORM2 will be used to constrain the representation of the locations based on their textual description (i.e. Flickr tags), INLINEFORM3 will be used to constrain the representation of the locations based on their numerical features, and INLINEFORM4 will impose the constraint that locations belonging to the same category should be close together in the space. We will discuss each of these components in more detail in the following sections. Tag Based Location Embedding Many of the tags associated with Flickr photos describe characteristics of the places where these photos were taken BIBREF45 , BIBREF46 , BIBREF47 . For example, tags may correspond to place names (e.g. Brussels, England, Scandinavia), landmarks (e.g. Eiffel Tower, Empire State Building) or land cover types (e.g. mountain, forest, beach). To allow us to build location models using such tags, we collected the tags and meta-data of 70 million Flickr photos with coordinates in Europe (which is the region our experiments will focus on), all of which were uploaded to Flickr before the end of September 2015. In this section we first explain how tags can be weighted to obtain bag-of-words representations of locations from Flickr. Subsequently we describe a tag selection method, which will allow us to specialize the embedding depending on which aspects of the considered locations are of interest, after which we discuss the actual embedding model. Tag weighting. Let INLINEFORM0 be a set of geographic locations, each characterized by latitude and longitude coordinates. To generate a bag-of-words representation of a given location, we have to weight the relevance of each tag to that location. To this end, we have followed the weighting scheme from BIBREF7 , which combines a Gaussian kernel (to model spatial proximity) with Positive Pointwise Mutual Information (PPMI) BIBREF48 , BIBREF49 . Let us write INLINEFORM0 for the set of users who have assigned tag INLINEFORM1 to a photo with coordinates near INLINEFORM2 . To assess how relevant INLINEFORM3 is to the location INLINEFORM4 , the number of times INLINEFORM5 occurs in photos near INLINEFORM6 is clearly an important criterion. However, rather than simply counting the number of occurrences within some fixed radius, we use a Gaussian kernel to weight the tag occurrences according to their distance from that location: INLINEFORM7 where the threshold INLINEFORM0 is assumed to be fixed, INLINEFORM1 is the location of a Flickr photo, INLINEFORM2 is the Haversine distance, and we will assume that the bandwidth parameter INLINEFORM3 is set to INLINEFORM4 . A tag occurrence is counted only once for all photos by the same user at the same location, which is important to reduce the impact of bulk uploading. The value INLINEFORM5 reflects how frequent tag INLINEFORM6 is near location INLINEFORM7 , but it does not yet take into account the total number of tag occurrences near INLINEFORM8 , nor how popular the tag INLINEFORM9 is overall. To measure how strongly tag INLINEFORM10 is associated with location INLINEFORM11 , we use PPMI, which is a commonly used measure of association in natural language processing. However, rather than estimating PPMI scores from term frequencies, we will use the INLINEFORM12 values instead: INLINEFORM13 where: INLINEFORM0 with INLINEFORM0 the set of all tags, and INLINEFORM1 the set of locations. Tag selection. Inspired by BIBREF50 , we use a term selection method in order to focus on the tags that are most important for the tasks that we want to consider and reduce the impact of tags that might relate only to a given individual or a group of users. In particular, we obtained good results with a method based on Kullback-Leibler (KL) divergence, which is based on BIBREF51 . Let INLINEFORM0 be a set of (mutually exclusive) properties of locations in which we are interested (e.g. land cover categories). For the ease of presentation, we will identify INLINEFORM1 with the set of locations that have the corresponding property. Then, we select tags from INLINEFORM2 that maximize the following score: INLINEFORM3 where INLINEFORM0 is the probability that a photo with tag INLINEFORM1 has a location near INLINEFORM2 and INLINEFORM3 is the probability that an arbitrary tag occurrence is assigned to a photo near a location in INLINEFORM4 . Since INLINEFORM5 often has to be estimated from a small number of tag occurrences, it is estimated using Bayesian smoothing: INLINEFORM6 where INLINEFORM0 is a parameter controlling the amount of smoothing, which will be tuned in the experiments. On the other hand, for INLINEFORM1 we can simply use a maximum likelihood estimation: INLINEFORM2 Location embedding. We now want to find a vector INLINEFORM0 for each location INLINEFORM1 such that similar locations are represented using similar vectors. To achieve this, we use a close variant of the GloVe model, where tag occurrences are treated as context words of geographic locations. In particular, with each location INLINEFORM2 we associate a vector INLINEFORM3 and with each tag INLINEFORM4 we associate a vector INLINEFORM5 and a bias term INLINEFORM6 , and consider the following objective (which in our full model ( EQREF7 ) will be combined with components that are derived from the structured information): INLINEFORM7 Note how tags play the role of the context words in the GloVe model, while instead of learning target word vectors we now learn location vectors. In contrast to GloVe, our objective does not directly refer to co-occurrence statistics, but instead uses the INLINEFORM0 scores. One important consequence of this is that we can also consider pairs INLINEFORM1 for which INLINEFORM2 does not occur in INLINEFORM3 at all; such pairs are usually called negative examples. While they cannot be used in the standard GloVe model, some authors have already reported that introducing negative examples in variants of GloVe can lead to an improvement BIBREF52 . In practice, evaluating the full objective above would not be computationally feasible, as we may need to consider millions of locations and millions of tags. Therefore, rather than considering all tags in INLINEFORM4 for the inner summation, we only consider those tags that appear at least once near location INLINEFORM5 together with a sample of negative examples. Structured Environmental Data There is a wide variety of structured data that can be used to describe locations. In this work, we have restricted ourselves to the same datasets as BIBREF7 . These include nine (real-valued) numerical features, which are latitude, longitude, elevation, population, and five climate related features (avg. temperature, avg. precipitation, avg. solar radiation, avg. wind speed, and avg. water vapor pressure). In addition, 180 categorical features were used, which are CORINE land cover classes at level 1 (5 classes), level 2 (15 classes) and level 3 (44 classes) and 116 soil types (SoilGrids). Note that each location should belong to exactly 4 categories: one CORINE class at each of the three levels and a soil type. Numerical features. Numerical features can be treated similarly to the tag occurrences, i.e. we will assume that the value of a given numerical feature can be predicted from the location vectors using a linear mapping. In particular, for each numerical feature INLINEFORM0 we consider a vector INLINEFORM1 and a bias term INLINEFORM2 , and the following objective: INLINEFORM3 where we write INLINEFORM0 for set of all numerical features and INLINEFORM1 is the value of feature INLINEFORM2 for location INLINEFORM3 , after z-score normalization. Categorical features. To take into account the categorical features, we impose the constraint that locations belonging to the same category should be close together in the space. To formalize this, we represent each category type INLINEFORM0 as a vector INLINEFORM1 , and consider the following objective: INLINEFORM2 Evaluation Tasks We will use the method from BIBREF7 as our main baseline. This will allow us to directly evaluate the effectiveness of embeddings for the considered problem, since we have used the same structured datasets and same tag weighting scheme. For this reason, we will also follow their evaluation methodology. In particular, we will consider three evaluation tasks: Predicting the distribution of 100 species across Europe, using the European network of nature protected sites Natura 2000 dataset as ground truth. For each of these species, a binary classification problem is considered. The set of locations INLINEFORM0 is defined as the 26,425 distinct sites occurring in the dataset. Predicting soil type, again each time treating the task as a binary classification problem, using the same set of locations INLINEFORM0 as in the species distribution experiments. For these experiments, none of the soil type features are used for generating the embeddings. Predicting CORINE land cover classes at levels 1, 2 and level 3, each time treating the task as a binary classification problem, using the same set of locations INLINEFORM0 as in the species distribution experiments. For these experiments, none of the CORINE features are used for generating the embeddings. In addition, we will also consider the following regression tasks: Predicting 5 climate related features: the average precipitation, temperature, solar radiation, water vapor pressure, and wind speed. We again use the same set of locations INLINEFORM0 as for species distribution in this experiment. None of the climate features is used for constructing the embeddings for this experiment. Predicting people's subjective opinions of landscape beauty in Britain, using the crowdsourced dataset from the ScenicOrNot website as ground truth. The set INLINEFORM0 is chosen as the set of locations of 191 605 rated locations from the ScenicOrNot dataset for which at least one georeferenced Flickr photo exists within a 1 km radius. Experimental Setup In all experiments, we use Support Vector Machines (SVMs) for classification problems and Support Vector Regression (SVR) for regression problems to make predictions from our representations of geographic locations. In both cases, we used the SVM INLINEFORM0 implementation BIBREF53 . For each experiment, the set of locations INLINEFORM1 was split into two-thirds for training, one-sixth for testing, and one-sixth for tuning the parameters. All embedding models are learned with Adagrad using 30 iterations. The number of dimensions is chosen for each experiment from INLINEFORM2 based on the tuning data. For the parameters of our model in Equation EQREF7 , we considered values of INLINEFORM3 from {0.1, 0.01, 0.001, 0.0001} and values of INLINEFORM4 from {1, 10, 100, 1000, 10 000, 100 000}. To compute KL divergence, we need to determine a set of classes INLINEFORM5 for each experiment. For classification problems, we can simply consider the given categories, but for the regression problems we need to define such classes by discretizing the numerical values. For the scenicness experiments, we considered scores 3 and 7 as cut-off points, leading to three classes (i.e. less than 3, between 3 and 7, and above 7). Similarly, for each climate related features, we consider two cut-off values for discretization: 5 and 15 for average temperature, 50 and 100 for average precipitation, 10 000 and 17 000 for average solar radiation, 0.7 and 1 for average water vapor pressure, and 3 and 5 for wind speed. The smoothing parameter INLINEFORM6 was selected among INLINEFORM7 based on the tuning data. In all experiments where term selection is used, we select the top 100 000 tags. We fixed the radius INLINEFORM8 at 1km when counting the number of tag occurrences. Finally, we set the number of negative examples as 10 times the number of positive examples for each location, but with a cap at 1000 negative examples in each region for computational reasons. We tune all parameters with respect to the F1 score for the classification tasks, and Spearman INLINEFORM9 for the regression tasks. Variants and Baseline Methods We will refer to our model as EGEL (Embedding GEographic Locations), and will consider the following variants. EGEL-Tags only uses the information from the Flickr tags (i.e. component INLINEFORM0 ), without using any negative examples and without feature selection. EGEL-Tags+NS is similar to EGEL-Tags but with the addition of negative examples. EGEL-KL(Tags+NS) additionally considers term selection. EGEL-All is our full method, i.e. it additionally uses the structured information. We also consider the following baselines. BOW-Tags represents locations using a bag-of-words representation, using the same tag weighting as the embedding model. BOW-KL(Tags) uses the same representation but after term selection, using the same KL-based method as the embedding model. BOW-All combines the bag-of-words representation with the structured information, encoded as proposed in BIBREF7 . GloVe uses the objective from the original GloVe model for learning location vectors, i.e. this variant differs from EGEL-Tags in that instead of INLINEFORM1 we use the number of co-occurrences of tag INLINEFORM2 near location INLINEFORM3 , measured as INLINEFORM4 . Results and Discussion We present our results for the binary classification tasks in Tables TABREF23 – TABREF24 in terms of average precision, average recall and macro average F1 score. The results of the regression tasks are reported in Tables TABREF25 and TABREF29 in terms of the mean absolute error between the predicted and actual scores, as well as the Spearman INLINEFORM0 correlation between the rankings induced by both sets of scores. It can be clearly seen from the results that our proposed method (EGEL-All) can effectively integrate Flickr tags with the available structured information. It outperforms the baselines for all the considered tasks. Furthermore, note that the PPMI-based weighting in EGEL-Tags consistently outperforms GloVe and that both the addition of negative examples and term selection lead to further improvements. The use of term selection leads to particularly substantial improvements for the regression problems. While our experimental results confirm the usefulness of embeddings for predicting environmental features, this is only consistently the case for the variants that use both the tags and the structured datasets. In particular, comparing BOW-Tags with EGEL-Tags, we sometimes see that the former achieves the best results. While this might seem surprising, it is in accordance with the findings in BIBREF54 , BIBREF38 , among others, where it was also found that bag-of-words representations can sometimes lead to surprisingly effective baselines. Interestingly, we note that in all cases where EGEL-KL(Tags+NS) performs worse than BOW-Tags, we also find that BOW-KL(Tags) performs worse than BOW-Tags. This suggests that for these tasks there is a very large variation in the kind of tags that can inform the prediction model, possibly including e.g. user-specific tags. Some of the information captured by such highly specific but rare tags is likely to be lost in the embedding. To further analyze the difference in performance between BoW representations and embeddings, Figure TABREF29 compares the performance of the GloVe model with the bag-of-words model for predicting place scenicness, as a function of the number of tag occurrences at the considered locations. What is clearly noticeable in Figure TABREF29 is that GloVe performs better than the bag-of-words model for large corpora and worse for smaller corpora. This issue has been alleviated in our embedding method by the addition of negative examples.  Conclusions In this paper, we have proposed a model to learn geographic location embeddings using Flickr tags, numerical environmental features, and categorical information. The experimental results show that our model can integrate Flickr tags with structured information in a more effective way than existing methods, leading to substantial improvements over baseline methods on various prediction tasks about the natural environment. Acknowledgments Shelan Jeawak has been sponsored by HCED Iraq. Steven Schockaert has been supported by ERC Starting Grant 637277.
what dataset is used in this paper?
a1dac888f63c9efaf159d9bdfde7c938636f07b1
what Ġdataset Ġis Ġused Ġin Ġthis Ġpaper ?
Ġthe Ġsame Ġdatasets Ġas ĠB IB REF 7
<s> what Ġdataset Ġis Ġused Ġin Ġthis Ġpaper ? </s> Introduction </s> Users Ġof Ġphoto - sharing Ġwebsites Ġsuch Ġas ĠFlickr Ġoften Ġprovide Ġshort Ġtextual Ġdescriptions Ġin Ġthe Ġform Ġof Ġtags Ġto Ġhelp Ġothers Ġfind Ġthe Ġimages . ĠWith Ġthe Ġavailability Ġof ĠGPS Ġsystems Ġin Ġcurrent Ġelectronic Ġdevices Ġsuch Ġas Ġsmartphones , Ġlatitude Ġand Ġlong itude Ġcoordinates Ġare Ġnowadays Ġcommonly Ġmade Ġavailable Ġas Ġwell . ĠThe Ġtags Ġassociated Ġwith Ġsuch Ġge ore f eren ced Ġphotos Ġoften Ġdescribe Ġthe Ġlocation Ġwhere Ġthese Ġphotos Ġwere Ġtaken , Ġand ĠFlickr Ġcan Ġthus Ġbe Ġregarded Ġas Ġa Ġsource Ġof Ġenvironmental Ġinformation . ĠThe Ġuse Ġof ĠFlickr Ġfor Ġmodelling Ġurban Ġenvironments Ġhas Ġalready Ġreceived Ġconsiderable Ġattention . ĠFor Ġinstance , Ġvarious Ġapproaches Ġhave Ġbeen Ġproposed Ġfor Ġmodelling Ġurban Ġregions ĠB IB REF 0 Ġ, Ġand Ġfor Ġidentifying Ġpoints - of - interest ĠB IB REF 1 Ġand Ġitiner aries ĠB IB REF 2 Ġ, ĠB IB REF 3 Ġ. ĠHowever , Ġthe Ġusefulness Ġof ĠFlickr Ġfor Ġcharacter izing Ġthe Ġnatural Ġenvironment , Ġwhich Ġis Ġthe Ġfocus Ġof Ġthis Ġpaper , Ġis Ġless Ġwell - under stood . </s> Many Ġrecent Ġstudies Ġhave Ġhighlighted Ġthat ĠFlickr Ġtags Ġcapture Ġvaluable Ġecological Ġinformation , Ġwhich Ġcan Ġbe Ġused Ġas Ġa Ġcomplementary Ġsource Ġto Ġmore Ġtraditional Ġsources . ĠTo Ġdate , Ġhowever , Ġec ologists Ġhave Ġmostly Ġused Ġsocial Ġmedia Ġto Ġconduct Ġmanual Ġevaluations Ġof Ġimage Ġcontent Ġwith Ġlittle Ġautomated Ġexploitation Ġof Ġthe Ġassociated Ġtags ĠB IB REF 4 Ġ, ĠB IB REF 5 Ġ, ĠB IB REF 6 Ġ. ĠOne Ġrecent Ġexception Ġis ĠB IB REF 7 Ġ, Ġwhere Ġbag - of - words Ġrepresentations Ġderived Ġfrom ĠFlickr Ġtags Ġwere Ġfound Ġto Ġgive Ġpromising Ġresult Ġfor Ġpredicting Ġa Ġrange Ġof Ġdifferent Ġen viron ement al Ġphenomena . </s> Our Ġmain Ġhypothesis Ġin Ġthis Ġpaper Ġis Ġthat Ġby Ġusing Ġvector Ġspace Ġembed d ings Ġinstead Ġof Ġbag - of - words Ġrepresentations , Ġthe Ġecological Ġinformation Ġwhich Ġis Ġimplicitly Ġcaptured Ġby ĠFlickr Ġtags Ġcan Ġbe Ġutilized Ġin Ġa Ġmore Ġeffective Ġway . ĠVector Ġspace Ġembed d ings Ġare Ġrepresentations Ġin Ġwhich Ġthe Ġobjects Ġfrom Ġa Ġgiven Ġdomain Ġare Ġencoded Ġusing Ġrelatively Ġlow - dimensional Ġvectors . ĠThey Ġhave Ġproven Ġuseful Ġin Ġnatural Ġlanguage Ġprocessing , Ġespecially Ġfor Ġencoding Ġword Ġmeaning ĠB IB REF 8 Ġ, ĠB IB REF 9 Ġ, Ġand Ġin Ġmachine Ġlearning Ġmore Ġgenerally . ĠIn Ġthis Ġpaper , Ġwe Ġare Ġinterested Ġin Ġthe Ġuse Ġof Ġsuch Ġrepresentations Ġfor Ġmodelling Ġgeographic Ġlocations . ĠOur Ġmain Ġmotivation Ġfor Ġusing Ġvector Ġspace Ġembed d ings Ġis Ġthat Ġthey Ġallow Ġus Ġto Ġintegrate Ġthe Ġtextual Ġinformation Ġwe Ġget Ġfrom ĠFlickr Ġwith Ġavailable Ġstructured Ġinformation Ġin Ġa Ġvery Ġnatural Ġway . ĠTo Ġthis Ġend , Ġwe Ġrely Ġon Ġan Ġadaptation Ġof Ġthe ĠGl o Ve Ġword Ġembed ding Ġmodel ĠB IB REF 9 Ġ, Ġbut Ġrather Ġthan Ġlearning Ġword Ġvectors , Ġwe Ġlearn Ġvectors Ġrepresenting Ġlocations . ĠSimilar Ġto Ġhow Ġthe Ġrepresentation Ġof Ġa Ġword Ġin ĠGl o Ve Ġis Ġdetermined Ġby Ġthe Ġcontext Ġwords Ġsurrounding Ġit , Ġthe Ġrepresentation Ġof Ġa Ġlocation Ġin Ġour Ġmodel Ġis Ġdetermined Ġby Ġthe Ġtags Ġof Ġthe Ġphotos Ġthat Ġhave Ġbeen Ġtaken Ġnear Ġthat Ġlocation . ĠTo Ġincorporate Ġnumerical Ġfeatures Ġfrom Ġstructured Ġenvironmental Ġdatasets Ġ( e . g . Ġaverage Ġtemperature ), Ġwe Ġassociate Ġwith Ġeach Ġsuch Ġfeature Ġa Ġlinear Ġmapping Ġthat Ġcan Ġbe Ġused Ġto Ġpredict Ġthat Ġfeature Ġfrom Ġa Ġgiven Ġlocation Ġvector . ĠThis Ġis Ġinspired Ġby Ġthe Ġfact Ġthat Ġsalient Ġproperties Ġof Ġa Ġgiven Ġdomain Ġcan Ġoften Ġbe Ġmod elled Ġas Ġdirections Ġin Ġvector Ġspace Ġembed d ings ĠB IB REF 10 Ġ, ĠB IB REF 11 Ġ, ĠB IB REF 12 Ġ. ĠFinally , Ġevidence Ġfrom Ġcateg orical Ġdatasets Ġ( e . g . Ġland Ġcover Ġtypes ) Ġis Ġtaken Ġinto Ġaccount Ġby Ġrequiring Ġthat Ġlocations Ġbelonging Ġto Ġthe Ġsame Ġcategory Ġare Ġrepresented Ġusing Ġsimilar Ġvectors , Ġsimilar Ġto Ġhow Ġsemantic Ġtypes Ġare Ġsometimes Ġmod elled Ġin Ġthe Ġcontext Ġof Ġknowledge Ġgraph Ġembed ding ĠB IB REF 13 Ġ. </s> While Ġour Ġpoint - of - dep art ure Ġis Ġa Ġstandard Ġword Ġembed ding Ġmodel , Ġwe Ġfound Ġthat Ġthe Ġoff - the - she lf ĠGl o Ve Ġmodel Ġperformed Ġsurprisingly Ġpoorly , Ġmeaning Ġthat Ġa Ġnumber Ġof Ġmodifications Ġare Ġneeded Ġto Ġachieve Ġgood Ġresults . ĠOur Ġmain Ġfindings Ġare Ġas Ġfollows . ĠFirst , Ġgiven Ġthat Ġthe Ġnumber Ġof Ġtags Ġassociated Ġwith Ġa Ġgiven Ġlocation Ġcan Ġbe Ġquite Ġsmall , Ġit Ġis Ġimportant Ġto Ġapply Ġsome Ġkind Ġof Ġspatial Ġsmoot hing , Ġi . e . Ġthe Ġimportance Ġof Ġa Ġgiven Ġtag Ġfor Ġa Ġgiven Ġlocation Ġshould Ġnot Ġonly Ġdepend Ġon Ġthe Ġoccurrences Ġof Ġthe Ġtag Ġat Ġthat Ġlocation , Ġbut Ġalso Ġon Ġits Ġoccurrences Ġat Ġnearby Ġlocations . ĠTo Ġthis Ġend , Ġwe Ġuse Ġa Ġformulation Ġwhich Ġis Ġbased Ġon Ġspat ially Ġsmoot hed Ġversion Ġof Ġpoint wise Ġmutual Ġinformation . ĠSecond , Ġgiven Ġthe Ġwide Ġdiversity Ġin Ġthe Ġkind Ġof Ġinformation Ġthat Ġis Ġcovered Ġby ĠFlickr Ġtags , Ġwe Ġfind Ġthat Ġterm Ġselection Ġis Ġin Ġsome Ġcases Ġcritical Ġto Ġobtain Ġvector Ġspaces Ġthat Ġcapture Ġthe Ġrelevant Ġaspects Ġof Ġgeographic Ġlocations . ĠFor Ġinstance , Ġmany Ġtags Ġon ĠFlickr Ġrefer Ġto Ġphotography Ġrelated Ġterms , Ġwhich Ġwe Ġwould Ġnormally Ġnot Ġwant Ġto Ġaffect Ġthe Ġvector Ġrepresentation Ġof Ġa Ġgiven Ġlocation . ĠFinally , Ġeven Ġwith Ġthese Ġmodifications , Ġvector Ġspace Ġembed d ings Ġlearned Ġfrom ĠFlickr Ġtags Ġalone Ġare Ġsometimes Ġoutper formed Ġby Ġbag - of - words Ġrepresentations . ĠHowever , Ġour Ġvector Ġspace Ġembed d ings Ġlead Ġto Ġsubstantially Ġbetter Ġpredictions Ġin Ġcases Ġwhere Ġstructured Ġ( scientific ) Ġinformation Ġis Ġalso Ġtaken Ġinto Ġaccount . ĠIn Ġthis Ġsense , Ġthe Ġmain Ġvalue Ġof Ġusing Ġvector Ġspace Ġembed d ings Ġin Ġthis Ġcontext Ġis Ġnot Ġso Ġmuch Ġabout Ġabstract ing Ġaway Ġfrom Ġspecific Ġtag Ġus ages , Ġbut Ġrather Ġabout Ġthe Ġfact Ġthat Ġsuch Ġrepresentations Ġallow Ġus Ġto Ġintegrate Ġnumerical Ġand Ġcateg orical Ġfeatures Ġin Ġa Ġmuch Ġmore Ġnatural Ġway Ġthan Ġis Ġpossible Ġwith Ġbag - of - words Ġrepresentations . </s> The Ġremainder Ġof Ġthis Ġpaper Ġis Ġorganized Ġas Ġfollows . ĠIn Ġthe Ġnext Ġsection , Ġwe Ġprovide Ġa Ġdiscussion Ġof Ġexisting Ġwork . ĠSection ĠSEC REF 3 Ġthen Ġpresents Ġour Ġmodel Ġfor Ġembed ding Ġgeographic Ġlocations Ġfrom ĠFlickr Ġtags Ġand Ġstructured Ġdata . ĠNext , Ġin ĠSection ĠSEC REF 4 Ġwe Ġprovide Ġa Ġdetailed Ġdiscussion Ġabout Ġthe Ġexperimental Ġresults . ĠFinally , ĠSection ĠSEC REF 5 Ġsummarizes Ġour Ġconclusions . </s> Vector Ġspace Ġembed d ings </s> The Ġuse Ġof Ġlow - dimensional Ġvector Ġspace Ġembed d ings Ġfor Ġrepresenting Ġobjects Ġhas Ġalready Ġproven Ġeffective Ġin Ġa Ġlarge Ġnumber Ġof Ġapplications , Ġincluding Ġnatural Ġlanguage Ġprocessing Ġ( N LP ), Ġimage Ġprocessing , Ġand Ġpattern Ġrecognition . ĠIn Ġthe Ġcontext Ġof ĠN LP , Ġthe Ġmost Ġprominent Ġexample Ġis Ġthat Ġof Ġword Ġembed d ings , Ġwhich Ġrepresent Ġword Ġmeaning Ġusing Ġvectors Ġof Ġtypically Ġaround Ġ300 Ġdimensions . ĠA Ġlarge Ġnumber Ġof Ġdifferent Ġmethods Ġfor Ġlearning Ġsuch Ġword Ġembed d ings Ġhave Ġalready Ġbeen Ġproposed , Ġincluding ĠSkip - gram Ġand Ġthe ĠContinuous ĠBag - of - Words Ġ( CB OW ) Ġmodel ĠB IB REF 8 Ġ, ĠGl o Ve ĠB IB REF 9 Ġ, Ġand Ġfast Text ĠB IB REF 14 Ġ. ĠThey Ġhave Ġbeen Ġapplied Ġeffectively Ġin Ġmany Ġdownstream ĠN LP Ġtasks Ġsuch Ġas Ġsentiment Ġanalysis ĠB IB REF 15 Ġ, Ġpart Ġof Ġspeech Ġtagging ĠB IB REF 16 Ġ, ĠB IB REF 17 Ġ, Ġand Ġtext Ġclassification ĠB IB REF 18 Ġ, ĠB IB REF 19 Ġ. ĠThe Ġmodel Ġwe Ġconsider Ġin Ġthis Ġpaper Ġbuilds Ġon ĠGl o Ve , Ġwhich Ġwas Ġdesigned Ġto Ġcapture Ġlinear Ġregular ities Ġof Ġword - word Ġco - occ urrence . ĠIn ĠGl o Ve , Ġthere Ġare Ġtwo Ġword Ġvectors ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġfor Ġeach Ġword Ġin Ġthe Ġvocabulary , Ġwhich Ġare Ġlearned Ġby Ġminimizing Ġthe Ġfollowing Ġobjective : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġnumber Ġof Ġtimes Ġthat Ġword ĠIN LINE FORM 1 Ġappears Ġin Ġthe Ġcontext Ġof Ġword ĠIN LINE FORM 2 Ġ, ĠIN LINE FORM 3 Ġis Ġthe Ġvocabulary Ġsize , ĠIN LINE FORM 4 Ġis Ġthe Ġtarget Ġword Ġbias , ĠIN LINE FORM 5 Ġis Ġthe Ġcontext Ġword Ġbias . ĠThe Ġweight ing Ġfunction ĠIN LINE FORM 6 Ġis Ġused Ġto Ġlimit Ġthe Ġimpact Ġof Ġrare Ġterms . ĠIt Ġis Ġdefined Ġas Ġ1 Ġif ĠIN LINE FORM 7 Ġand Ġas ĠIN LINE FORM 8 Ġotherwise , Ġwhere ĠIN LINE FORM 9 Ġis Ġusually Ġfixed Ġto Ġ100 Ġand ĠIN LINE FORM 10 Ġto Ġ0 . 75 . ĠInt uitive ly , Ġthe Ġtarget Ġword Ġvectors ĠIN LINE FORM 11 Ġcorrespond Ġto Ġthe Ġactual Ġword Ġrepresentations Ġwhich Ġwe Ġwould Ġlike Ġto Ġfind , Ġwhile Ġthe Ġcontext Ġword Ġvectors ĠIN LINE FORM 12 Ġmodel Ġhow Ġoccurrences Ġof ĠIN LINE FORM 13 Ġin Ġthe Ġcontext Ġof Ġa Ġgiven Ġword ĠIN LINE FORM 14 Ġaffect Ġthe Ġrepresentation Ġof Ġthis Ġlatter Ġword . ĠIn Ġthis Ġpaper Ġwe Ġwill Ġuse Ġa Ġsimilar Ġmodel , Ġwhich Ġwill Ġhowever Ġbe Ġaimed Ġat Ġlearning Ġlocation Ġvectors Ġinstead Ġof Ġthe Ġtarget Ġword Ġvectors . </s> Beyond Ġword Ġembed d ings , Ġvarious Ġmethods Ġhave Ġbeen Ġproposed Ġfor Ġlearning Ġvector Ġspace Ġrepresentations Ġfrom Ġstructured Ġdata Ġsuch Ġas Ġknowledge Ġgraphs ĠB IB REF 20 Ġ, ĠB IB REF 21 Ġ, ĠB IB REF 22 Ġ, Ġsocial Ġnetworks ĠB IB REF 23 Ġ, ĠB IB REF 24 Ġand Ġtax onom ies ĠB IB REF 25 Ġ, ĠB IB REF 26 Ġ. ĠThe Ġidea Ġof Ġcombining Ġa Ġword Ġembed ding Ġmodel Ġwith Ġstructured Ġinformation Ġhas Ġalso Ġbeen Ġexplored Ġby Ġseveral Ġauthors , Ġfor Ġexample Ġto Ġimprove Ġthe Ġword Ġembed d ings Ġbased Ġon Ġinformation Ġcoming Ġfrom Ġknowledge Ġgraphs ĠB IB REF 27 Ġ, ĠB IB REF 28 Ġ. ĠAlong Ġsimilar Ġlines , Ġvarious Ġlex icons Ġhave Ġbeen Ġused Ġto Ġobtain Ġword Ġembed d ings Ġthat Ġare Ġbetter Ġsuited Ġat Ġmodelling Ġsentiment ĠB IB REF 15 Ġand Ġan ton y my ĠB IB REF 29 Ġ, Ġamong Ġothers . ĠThe Ġmethod Ġproposed Ġby ĠB IB REF 30 Ġimposes Ġthe Ġcondition Ġthat Ġwords Ġthat Ġbelong Ġto Ġthe Ġsame Ġsemantic Ġcategory Ġare Ġcloser Ġtogether Ġthan Ġwords Ġfrom Ġdifferent Ġcategories , Ġwhich Ġis Ġsomewhat Ġsimilar Ġin Ġspirit Ġto Ġhow Ġwe Ġwill Ġmodel Ġcateg orical Ġdatasets Ġin Ġour Ġmodel . </s> Emb edd ings Ġfor Ġgeographic Ġinformation </s> The Ġproblem Ġof Ġrepresenting Ġgeographic Ġlocations Ġusing Ġembed d ings Ġhas Ġalso Ġattracted Ġsome Ġattention . ĠAn Ġearly Ġexample Ġis ĠB IB REF 31 Ġ, Ġwhich Ġused Ġprincipal Ġcomponent Ġanalysis Ġand Ġstacked Ġaut oen cod ers Ġto Ġlearn Ġlow - dimensional Ġvector Ġrepresentations Ġof Ġcity Ġneighbourhoods Ġbased Ġon Ġcensus Ġdata . ĠThey Ġuse Ġthese Ġrepresentations Ġto Ġpredict Ġattributes Ġsuch Ġas Ġcrime , Ġwhich Ġis Ġnot Ġincluded Ġin Ġthe Ġgiven Ġcensus Ġdata , Ġand Ġfind Ġthat Ġin Ġmost Ġof Ġthe Ġconsidered Ġevaluation Ġtasks , Ġthe Ġlow - dimensional Ġvector Ġrepresentations Ġlead Ġto Ġmore Ġfaithful Ġpredictions Ġthan Ġthe Ġoriginal Ġhigh - dimensional Ġcensus Ġdata . </s> Some Ġexisting Ġworks Ġcombine Ġword Ġembed ding Ġmodels Ġwith Ġgeographic Ġcoordinates . ĠFor Ġexample , Ġin ĠB IB REF 32 Ġan Ġapproach Ġis Ġproposed Ġto Ġlearn Ġword Ġembed d ings Ġbased Ġon Ġthe Ġassumption Ġthat Ġwords Ġwhich Ġtend Ġto Ġbe Ġused Ġin Ġthe Ġsame Ġgeographic Ġlocations Ġare Ġlikely Ġto Ġbe Ġsimilar . ĠNote Ġthat Ġtheir Ġaim Ġis Ġdual Ġto Ġour Ġaim Ġin Ġthis Ġpaper : Ġwhile Ġthey Ġuse Ġgeographic Ġlocation Ġto Ġlearn Ġword Ġvectors , Ġwe Ġuse Ġtextual Ġdescriptions Ġto Ġlearn Ġvectors Ġrepresenting Ġgeographic Ġlocations . </s> Several Ġmethods Ġalso Ġuse Ġword Ġembed ding Ġmodels Ġto Ġlearn Ġrepresentations Ġof ĠPoints - of - Interest Ġ( PO Is ) Ġthat Ġcan Ġbe Ġused Ġfor Ġpredicting Ġuser Ġvisits ĠB IB REF 33 Ġ, ĠB IB REF 34 Ġ, ĠB IB REF 35 Ġ. ĠThese Ġworks Ġuse Ġthe Ġmachinery Ġof Ġexisting Ġword Ġembed ding Ġmodels Ġto Ġlearn ĠPO I Ġrepresentations , Ġintuitive ly Ġby Ġletting Ġsequences Ġof ĠPO I Ġvisits Ġby Ġa Ġuser Ġplay Ġthe Ġrole Ġof Ġsequences Ġof Ġwords Ġin Ġa Ġsentence . ĠIn Ġother Ġwords , Ġdespite Ġthe Ġuse Ġof Ġword Ġembed ding Ġmodels , Ġmany Ġof Ġthese Ġapproaches Ġdo Ġnot Ġactually Ġconsider Ġany Ġtextual Ġinformation . ĠFor Ġexample , Ġin ĠB IB REF 34 Ġthe ĠSkip - gram Ġmodel Ġis Ġutilized Ġto Ġcreate Ġa Ġglobal Ġpattern Ġof Ġusers ' ĠPO Is . ĠEach Ġlocation Ġwas Ġtreated Ġas Ġa Ġword Ġand Ġthe Ġother Ġlocations Ġvisited Ġbefore Ġor Ġafter Ġwere Ġtreated Ġas Ġcontext Ġwords . ĠThey Ġthen Ġuse Ġa Ġpair - wise Ġranking Ġloss ĠB IB REF 36 Ġwhich Ġtakes Ġinto Ġaccount Ġthe Ġuser 's Ġlocation Ġvisit Ġfrequency Ġto Ġpersonal ize Ġthe Ġlocation Ġrecommendations . ĠThe Ġmethods Ġof ĠB IB REF 34 Ġwere Ġextended Ġin ĠB IB REF 35 Ġto Ġuse Ġa Ġtemporal Ġembed ding Ġand Ġto Ġtake Ġmore Ġaccount Ġof Ġgeographic Ġcontext , Ġin Ġparticular Ġthe Ġdistances Ġbetween Ġpreferred Ġand Ġnon - pre ferred Ġneighboring ĠPO Is , Ġto Ġcreate Ġa ĠâĢ ľ ge ographically Ġhierarchical Ġpair wise Ġpreference Ġranking Ġmodel âĢ Ŀ . ĠSimilarly , Ġin ĠB IB REF 37 Ġthe ĠCB OW Ġmodel Ġwas Ġtrained Ġwith ĠPO I Ġdata . ĠThey Ġordered ĠPO Is Ġspat ially Ġwithin Ġthe Ġtraffic - based Ġzones Ġof Ġurban Ġareas . ĠThe Ġordering Ġwas Ġused Ġto Ġgenerate Ġcharacteristic Ġvectors Ġof ĠPO I Ġtypes . ĠZone Ġvectors Ġrepresented Ġby Ġaveraging Ġthe Ġvectors Ġof Ġthe ĠPO Is Ġcontained Ġin Ġthem , Ġwere Ġthen Ġused Ġas Ġfeatures Ġto Ġpredict Ġland Ġuse Ġtypes . ĠIn Ġthe ĠCross Map Ġmethod ĠB IB REF 38 Ġthey Ġlearned Ġembed d ings Ġfor Ġspat io - tem poral Ġhots pots Ġobtained Ġfrom Ġsocial Ġmedia Ġdata Ġof Ġlocations , Ġtimes Ġand Ġtext . ĠIn Ġone Ġform Ġof Ġembed ding , Ġintended Ġto Ġenable Ġreconstruction Ġof Ġrecords , Ġneighbourhood Ġrelations Ġin Ġspace Ġand Ġtime Ġwere Ġencoded Ġby Ġaveraging Ġhots pots Ġin Ġa Ġtarget Ġlocation 's Ġspatial Ġand Ġtemporal Ġneighborhoods . ĠThey Ġalso Ġproposed Ġa Ġgraph - based Ġembed ding Ġmethod Ġwith Ġnodes Ġof Ġlocation , Ġtime Ġand Ġtext . ĠThe Ġconc aten ation Ġof Ġthe Ġlocation , Ġtime Ġand Ġtext Ġvectors Ġwere Ġthen Ġused Ġas Ġfeatures Ġto Ġpredict Ġpeoples ' Ġactivities Ġin Ġurban Ġenvironments . ĠFinally , Ġin ĠB IB REF 39 Ġ, Ġa Ġmethod Ġis Ġproposed Ġthat Ġuses Ġthe ĠSkip - gram Ġmodel Ġto Ġrepresent ĠPO I Ġtypes , Ġbased Ġon Ġthe Ġintuition Ġthat Ġthe Ġvector Ġrepresenting Ġa Ġgiven ĠPO I Ġtype Ġshould Ġbe Ġpredictive Ġof Ġthe ĠPO I Ġtypes Ġthat Ġfound Ġnear Ġplaces Ġof Ġthat Ġtype . </s> Our Ġwork Ġis Ġdifferent Ġfrom Ġthese Ġstudies , Ġas Ġour Ġfocus Ġis Ġon Ġrepresenting Ġlocations Ġbased Ġon Ġa Ġgiven Ġtext Ġdescription Ġof Ġthat Ġlocation Ġ( in Ġthe Ġform Ġof ĠFlickr Ġtags ), Ġalong Ġwith Ġnumerical Ġand Ġcateg orical Ġfeatures Ġfrom Ġscientific Ġdatasets . </s> Analy zing ĠFlickr Ġtags </s> Many Ġstudies Ġhave Ġfocused Ġon Ġanalyzing ĠFlickr Ġtags Ġto Ġextract Ġuseful Ġinformation Ġin Ġdomains Ġsuch Ġas Ġlingu istics ĠB IB REF 40 Ġ, Ġgeography ĠB IB REF 0 Ġ, ĠB IB REF 41 Ġ, Ġand Ġecology ĠB IB REF 42 Ġ, ĠB IB REF 7 Ġ, ĠB IB REF 43 Ġ. ĠMost Ġclosely Ġrelated Ġto Ġour Ġwork , ĠB IB REF 7 Ġfound Ġthat Ġthe Ġtags Ġof Ġge ore f eren ced ĠFlickr Ġphotos Ġcan Ġeffectively Ġsupplement Ġtraditional Ġscientific Ġenvironmental Ġdata Ġin Ġtasks Ġsuch Ġas Ġpredicting Ġclimate Ġfeatures , Ġland Ġcover , Ġspecies Ġoccurrence , Ġand Ġhuman Ġassessments Ġof Ġscenic ness . ĠTo Ġencode Ġlocations , Ġthey Ġsimply Ġcombine Ġa Ġbag - of - words Ġrepresentation Ġof Ġgeographically Ġnearby Ġtags Ġwith Ġa Ġfeature Ġvector Ġthat Ġenc odes Ġassociated Ġstructured Ġscientific Ġdata . ĠThey Ġfound Ġthat Ġthe Ġpredictive Ġvalue Ġof ĠFlickr Ġtags Ġis Ġroughly Ġon Ġa Ġpar Ġwith Ġthat Ġof Ġthe Ġscientific Ġdatasets , Ġand Ġthat Ġcombining Ġboth Ġtypes Ġof Ġinformation Ġleads Ġto Ġsignificantly Ġbetter Ġresults Ġthan Ġusing Ġeither Ġof Ġthem Ġalone . ĠAs Ġwe Ġshow Ġin Ġthis Ġpaper , Ġhowever , Ġtheir Ġstraightforward Ġway Ġof Ġcombining Ġboth Ġinformation Ġsources , Ġby Ġconc aten ating Ġthe Ġtwo Ġtypes Ġof Ġfeature Ġvectors , Ġis Ġfar Ġfrom Ġoptimal . </s> Despite Ġthe Ġproven Ġimportance Ġof ĠFlickr Ġtags , Ġthe Ġproblem Ġof Ġembed ding ĠFlickr Ġtags Ġhas Ġso Ġfar Ġreceived Ġvery Ġlimited Ġattention . ĠTo Ġthe Ġbest Ġof Ġour Ġknowledge , ĠB IB REF 44 Ġis Ġthe Ġonly Ġwork Ġthat Ġgenerated Ġembed d ings Ġfor ĠFlickr Ġtags . ĠHowever , Ġtheir Ġfocus Ġwas Ġon Ġlearning Ġembed d ings Ġthat Ġcapture Ġword Ġmeaning Ġ( being Ġevaluated Ġon Ġword Ġsimilarity Ġtasks ), Ġwhereas Ġwe Ġuse Ġsuch Ġembed d ings Ġas Ġpart Ġof Ġour Ġmethod Ġfor Ġrepresenting Ġlocations . </s> Model ĠDescription </s> In Ġthis Ġsection , Ġwe Ġintroduce Ġour Ġembed ding Ġmodel , Ġwhich Ġcombines ĠFlickr Ġtags Ġand Ġstructured Ġscientific Ġinformation Ġto Ġrepresent Ġa Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġ. ĠThe Ġproposed Ġmodel Ġhas Ġthe Ġfollowing Ġform : ĠDIS PLAY FORM 0 </s> where ĠIN LINE FORM 0 Ġand ĠIN LINE FORM 1 Ġare Ġparameters Ġto Ġcontrol Ġthe Ġimportance Ġof Ġeach Ġcomponent Ġin Ġthe Ġmodel . ĠComponent ĠIN LINE FORM 2 Ġwill Ġbe Ġused Ġto Ġconst rain Ġthe Ġrepresentation Ġof Ġthe Ġlocations Ġbased Ġon Ġtheir Ġtextual Ġdescription Ġ( i . e . ĠFlickr Ġtags ), ĠIN LINE FORM 3 Ġwill Ġbe Ġused Ġto Ġconst rain Ġthe Ġrepresentation Ġof Ġthe Ġlocations Ġbased Ġon Ġtheir Ġnumerical Ġfeatures , Ġand ĠIN LINE FORM 4 Ġwill Ġimpose Ġthe Ġconstraint Ġthat Ġlocations Ġbelonging Ġto Ġthe Ġsame Ġcategory Ġshould Ġbe Ġclose Ġtogether Ġin Ġthe Ġspace . ĠWe Ġwill Ġdiscuss Ġeach Ġof Ġthese Ġcomponents Ġin Ġmore Ġdetail Ġin Ġthe Ġfollowing Ġsections . </s> Tag ĠBased ĠLocation ĠEmb edd ing </s> Many Ġof Ġthe Ġtags Ġassociated Ġwith ĠFlickr Ġphotos Ġdescribe Ġcharacteristics Ġof Ġthe Ġplaces Ġwhere Ġthese Ġphotos Ġwere Ġtaken ĠB IB REF 45 Ġ, ĠB IB REF 46 Ġ, ĠB IB REF 47 Ġ. ĠFor Ġexample , Ġtags Ġmay Ġcorrespond Ġto Ġplace Ġnames Ġ( e . g . ĠBrussels , ĠEngland , ĠScandinav ia ), Ġlandmarks Ġ( e . g . ĠE iff el ĠTower , ĠEmpire ĠState ĠBuilding ) Ġor Ġland Ġcover Ġtypes Ġ( e . g . Ġmountain , Ġforest , Ġbeach ). ĠTo Ġallow Ġus Ġto Ġbuild Ġlocation Ġmodels Ġusing Ġsuch Ġtags , Ġwe Ġcollected Ġthe Ġtags Ġand Ġmeta - data Ġof Ġ70 Ġmillion ĠFlickr Ġphotos Ġwith Ġcoordinates Ġin ĠEurope Ġ( which Ġis Ġthe Ġregion Ġour Ġexperiments Ġwill Ġfocus Ġon ), Ġall Ġof Ġwhich Ġwere Ġuploaded Ġto ĠFlickr Ġbefore Ġthe Ġend Ġof ĠSeptember Ġ2015 . ĠIn Ġthis Ġsection Ġwe Ġfirst Ġexplain Ġhow Ġtags Ġcan Ġbe Ġweighted Ġto Ġobtain Ġbag - of - words Ġrepresentations Ġof Ġlocations Ġfrom ĠFlickr . ĠSub sequently Ġwe Ġdescribe Ġa Ġtag Ġselection Ġmethod , Ġwhich Ġwill Ġallow Ġus Ġto Ġspecialize Ġthe Ġembed ding Ġdepending Ġon Ġwhich Ġaspects Ġof Ġthe Ġconsidered Ġlocations Ġare Ġof Ġinterest , Ġafter Ġwhich Ġwe Ġdiscuss Ġthe Ġactual Ġembed ding Ġmodel . </s> Tag Ġweight ing . ĠLet ĠIN LINE FORM 0 Ġbe Ġa Ġset Ġof Ġgeographic Ġlocations , Ġeach Ġcharacterized Ġby Ġlatitude Ġand Ġlong itude Ġcoordinates . ĠTo Ġgenerate Ġa Ġbag - of - words Ġrepresentation Ġof Ġa Ġgiven Ġlocation , Ġwe Ġhave Ġto Ġweight Ġthe Ġrelevance Ġof Ġeach Ġtag Ġto Ġthat Ġlocation . ĠTo Ġthis Ġend , Ġwe Ġhave Ġfollowed Ġthe Ġweight ing Ġscheme Ġfrom ĠB IB REF 7 Ġ, Ġwhich Ġcombines Ġa ĠGa ussian Ġkernel Ġ( to Ġmodel Ġspatial Ġproximity ) Ġwith ĠPositive ĠPoint wise ĠMutual ĠInformation Ġ( P PM I ) ĠB IB REF 48 Ġ, ĠB IB REF 49 Ġ. </s> Let Ġus Ġwrite ĠIN LINE FORM 0 Ġfor Ġthe Ġset Ġof Ġusers Ġwho Ġhave Ġassigned Ġtag ĠIN LINE FORM 1 Ġto Ġa Ġphoto Ġwith Ġcoordinates Ġnear ĠIN LINE FORM 2 Ġ. ĠTo Ġassess Ġhow Ġrelevant ĠIN LINE FORM 3 Ġis Ġto Ġthe Ġlocation ĠIN LINE FORM 4 Ġ, Ġthe Ġnumber Ġof Ġtimes ĠIN LINE FORM 5 Ġoccurs Ġin Ġphotos Ġnear ĠIN LINE FORM 6 Ġis Ġclearly Ġan Ġimportant Ġcriterion . ĠHowever , Ġrather Ġthan Ġsimply Ġcounting Ġthe Ġnumber Ġof Ġoccurrences Ġwithin Ġsome Ġfixed Ġradius , Ġwe Ġuse Ġa ĠGa ussian Ġkernel Ġto Ġweight Ġthe Ġtag Ġoccurrences Ġaccording Ġto Ġtheir Ġdistance Ġfrom Ġthat Ġlocation : ĠIN LINE FORM 7 </s> where Ġthe Ġthreshold ĠIN LINE FORM 0 Ġis Ġassumed Ġto Ġbe Ġfixed , ĠIN LINE FORM 1 Ġis Ġthe Ġlocation Ġof Ġa ĠFlickr Ġphoto , ĠIN LINE FORM 2 Ġis Ġthe ĠHa vers ine Ġdistance , Ġand Ġwe Ġwill Ġassume Ġthat Ġthe Ġbandwidth Ġparameter ĠIN LINE FORM 3 Ġis Ġset Ġto ĠIN LINE FORM 4 Ġ. ĠA Ġtag Ġoccurrence Ġis Ġcounted Ġonly Ġonce Ġfor Ġall Ġphotos Ġby Ġthe Ġsame Ġuser Ġat Ġthe Ġsame Ġlocation , Ġwhich Ġis Ġimportant Ġto Ġreduce Ġthe Ġimpact Ġof Ġbulk Ġuploading . ĠThe Ġvalue ĠIN LINE FORM 5 Ġreflects Ġhow Ġfrequent Ġtag ĠIN LINE FORM 6 Ġis Ġnear Ġlocation ĠIN LINE FORM 7 Ġ, Ġbut Ġit Ġdoes Ġnot Ġyet Ġtake Ġinto Ġaccount Ġthe Ġtotal Ġnumber Ġof Ġtag Ġoccurrences Ġnear ĠIN LINE FORM 8 Ġ, Ġnor Ġhow Ġpopular Ġthe Ġtag ĠIN LINE FORM 9 Ġis Ġoverall . ĠTo Ġmeasure Ġhow Ġstrongly Ġtag ĠIN LINE FORM 10 Ġis Ġassociated Ġwith Ġlocation ĠIN LINE FORM 11 Ġ, Ġwe Ġuse ĠP PM I , Ġwhich Ġis Ġa Ġcommonly Ġused Ġmeasure Ġof Ġassociation Ġin Ġnatural Ġlanguage Ġprocessing . ĠHowever , Ġrather Ġthan Ġestimating ĠP PM I Ġscores Ġfrom Ġterm Ġfrequencies , Ġwe Ġwill Ġuse Ġthe ĠIN LINE FORM 12 Ġvalues Ġinstead : ĠIN LINE FORM 13 </s> where : ĠIN LINE FORM 0 </s> with ĠIN LINE FORM 0 Ġthe Ġset Ġof Ġall Ġtags , Ġand ĠIN LINE FORM 1 Ġthe Ġset Ġof Ġlocations . </s> Tag Ġselection . ĠInspired Ġby ĠB IB REF 50 Ġ, Ġwe Ġuse Ġa Ġterm Ġselection Ġmethod Ġin Ġorder Ġto Ġfocus Ġon Ġthe Ġtags Ġthat Ġare Ġmost Ġimportant Ġfor Ġthe Ġtasks Ġthat Ġwe Ġwant Ġto Ġconsider Ġand Ġreduce Ġthe Ġimpact Ġof Ġtags Ġthat Ġmight Ġrelate Ġonly Ġto Ġa Ġgiven Ġindividual Ġor Ġa Ġgroup Ġof Ġusers . ĠIn Ġparticular , Ġwe Ġobtained Ġgood Ġresults Ġwith Ġa Ġmethod Ġbased Ġon ĠK ull back - Le ib ler Ġ( K L ) Ġdivergence , Ġwhich Ġis Ġbased Ġon ĠB IB REF 51 Ġ. ĠLet ĠIN LINE FORM 0 Ġbe Ġa Ġset Ġof Ġ( mut ually Ġexclusive ) Ġproperties Ġof Ġlocations Ġin Ġwhich Ġwe Ġare Ġinterested Ġ( e . g . Ġland Ġcover Ġcategories ). ĠFor Ġthe Ġease Ġof Ġpresentation , Ġwe Ġwill Ġidentify ĠIN LINE FORM 1 Ġwith Ġthe Ġset Ġof Ġlocations Ġthat Ġhave Ġthe Ġcorresponding Ġproperty . ĠThen , Ġwe Ġselect Ġtags Ġfrom ĠIN LINE FORM 2 Ġthat Ġmaximize Ġthe Ġfollowing Ġscore : ĠIN LINE FORM 3 </s> where ĠIN LINE FORM 0 Ġis Ġthe Ġprobability Ġthat Ġa Ġphoto Ġwith Ġtag ĠIN LINE FORM 1 Ġhas Ġa Ġlocation Ġnear ĠIN LINE FORM 2 Ġand ĠIN LINE FORM 3 Ġis Ġthe Ġprobability Ġthat Ġan Ġarbitrary Ġtag Ġoccurrence Ġis Ġassigned Ġto Ġa Ġphoto Ġnear Ġa Ġlocation Ġin ĠIN LINE FORM 4 Ġ. ĠSince ĠIN LINE FORM 5 Ġoften Ġhas Ġto Ġbe Ġestimated Ġfrom Ġa Ġsmall Ġnumber Ġof Ġtag Ġoccurrences , Ġit Ġis Ġestimated Ġusing ĠBay esian Ġsmoot hing : ĠIN LINE FORM 6 </s> where ĠIN LINE FORM 0 Ġis Ġa Ġparameter Ġcontrolling Ġthe Ġamount Ġof Ġsmoot hing , Ġwhich Ġwill Ġbe Ġtuned Ġin Ġthe Ġexperiments . ĠOn Ġthe Ġother Ġhand , Ġfor ĠIN LINE FORM 1 Ġwe Ġcan Ġsimply Ġuse Ġa Ġmaximum Ġlikelihood Ġestimation : ĠIN LINE FORM 2 </s> Location Ġembed ding . ĠWe Ġnow Ġwant Ġto Ġfind Ġa Ġvector ĠIN LINE FORM 0 Ġfor Ġeach Ġlocation ĠIN LINE FORM 1 Ġsuch Ġthat Ġsimilar Ġlocations Ġare Ġrepresented Ġusing Ġsimilar Ġvectors . ĠTo Ġachieve Ġthis , Ġwe Ġuse Ġa Ġclose Ġvariant Ġof Ġthe ĠGl o Ve Ġmodel , Ġwhere Ġtag Ġoccurrences Ġare Ġtreated Ġas Ġcontext Ġwords Ġof Ġgeographic Ġlocations . ĠIn Ġparticular , Ġwith Ġeach Ġlocation ĠIN LINE FORM 2 Ġwe Ġassociate Ġa Ġvector ĠIN LINE FORM 3 Ġand Ġwith Ġeach Ġtag ĠIN LINE FORM 4 Ġwe Ġassociate Ġa Ġvector ĠIN LINE FORM 5 Ġand Ġa Ġbias Ġterm ĠIN LINE FORM 6 Ġ, Ġand Ġconsider Ġthe Ġfollowing Ġobjective Ġ( which Ġin Ġour Ġfull Ġmodel Ġ( ĠEQ REF 7 Ġ) Ġwill Ġbe Ġcombined Ġwith Ġcomponents Ġthat Ġare Ġderived Ġfrom Ġthe Ġstructured Ġinformation ): ĠIN LINE FORM 7 </s> Note Ġhow Ġtags Ġplay Ġthe Ġrole Ġof Ġthe Ġcontext Ġwords Ġin Ġthe ĠGl o Ve Ġmodel , Ġwhile Ġinstead Ġof Ġlearning Ġtarget Ġword Ġvectors Ġwe Ġnow Ġlearn Ġlocation Ġvectors . ĠIn Ġcontrast Ġto ĠGl o Ve , Ġour Ġobjective Ġdoes Ġnot Ġdirectly Ġrefer Ġto Ġco - occ urrence Ġstatistics , Ġbut Ġinstead Ġuses Ġthe ĠIN LINE FORM 0 Ġscores . ĠOne Ġimportant Ġconsequence Ġof Ġthis Ġis Ġthat Ġwe Ġcan Ġalso Ġconsider Ġpairs ĠIN LINE FORM 1 Ġfor Ġwhich ĠIN LINE FORM 2 Ġdoes Ġnot Ġoccur Ġin ĠIN LINE FORM 3 Ġat Ġall ; Ġsuch Ġpairs Ġare Ġusually Ġcalled Ġnegative Ġexamples . ĠWhile Ġthey Ġcannot Ġbe Ġused Ġin Ġthe Ġstandard ĠGl o Ve Ġmodel , Ġsome Ġauthors Ġhave Ġalready Ġreported Ġthat Ġintroducing Ġnegative Ġexamples Ġin Ġvariants Ġof ĠGl o Ve Ġcan Ġlead Ġto Ġan Ġimprovement ĠB IB REF 52 Ġ. ĠIn Ġpractice , Ġevaluating Ġthe Ġfull Ġobjective Ġabove Ġwould Ġnot Ġbe Ġcomput ationally Ġfeasible , Ġas Ġwe Ġmay Ġneed Ġto Ġconsider Ġmillions Ġof Ġlocations Ġand Ġmillions Ġof Ġtags . ĠTherefore , Ġrather Ġthan Ġconsidering Ġall Ġtags Ġin ĠIN LINE FORM 4 Ġfor Ġthe Ġinner Ġsumm ation , Ġwe Ġonly Ġconsider Ġthose Ġtags Ġthat Ġappear Ġat Ġleast Ġonce Ġnear Ġlocation ĠIN LINE FORM 5 Ġtogether Ġwith Ġa Ġsample Ġof Ġnegative Ġexamples . </s> Struct ured ĠEnvironmental ĠData </s> There Ġis Ġa Ġwide Ġvariety Ġof Ġstructured Ġdata Ġthat Ġcan Ġbe Ġused Ġto Ġdescribe Ġlocations . ĠIn Ġthis Ġwork , Ġwe Ġhave Ġrestricted Ġourselves Ġto Ġthe Ġsame Ġdatasets Ġas ĠB IB REF 7 Ġ. ĠThese Ġinclude Ġnine Ġ( real - valued ) Ġnumerical Ġfeatures , Ġwhich Ġare Ġlatitude , Ġlong itude , Ġelevation , Ġpopulation , Ġand Ġfive Ġclimate Ġrelated Ġfeatures Ġ( av g . Ġtemperature , Ġavg . Ġprecipitation , Ġavg . Ġsolar Ġradiation , Ġavg . Ġwind Ġspeed , Ġand Ġavg . Ġwater Ġvapor Ġpressure ). ĠIn Ġaddition , Ġ180 Ġcateg orical Ġfeatures Ġwere Ġused , Ġwhich Ġare ĠCOR INE Ġland Ġcover Ġclasses Ġat Ġlevel Ġ1 Ġ( 5 Ġclasses ), Ġlevel Ġ2 Ġ( 15 Ġclasses ) Ġand Ġlevel Ġ3 Ġ( 44 Ġclasses ) Ġand Ġ116 Ġsoil Ġtypes Ġ( So il Gr ids ). ĠNote Ġthat Ġeach Ġlocation Ġshould Ġbelong Ġto Ġexactly Ġ4 Ġcategories : Ġone ĠCOR INE Ġclass Ġat Ġeach Ġof Ġthe Ġthree Ġlevels Ġand Ġa Ġsoil Ġtype . </s> N umer ical Ġfeatures . ĠN umer ical Ġfeatures Ġcan Ġbe Ġtreated Ġsimilarly Ġto Ġthe Ġtag Ġoccurrences , Ġi . e . Ġwe Ġwill Ġassume Ġthat Ġthe Ġvalue Ġof Ġa Ġgiven Ġnumerical Ġfeature Ġcan Ġbe Ġpredicted Ġfrom Ġthe Ġlocation Ġvectors Ġusing Ġa Ġlinear Ġmapping . ĠIn Ġparticular , Ġfor Ġeach Ġnumerical Ġfeature ĠIN LINE FORM 0 Ġwe Ġconsider Ġa Ġvector ĠIN LINE FORM 1 Ġand Ġa Ġbias Ġterm ĠIN LINE FORM 2 Ġ, Ġand Ġthe Ġfollowing Ġobjective : ĠIN LINE FORM 3 </s> where Ġwe Ġwrite ĠIN LINE FORM 0 Ġfor Ġset Ġof Ġall Ġnumerical Ġfeatures Ġand ĠIN LINE FORM 1 Ġis Ġthe Ġvalue Ġof Ġfeature ĠIN LINE FORM 2 Ġfor Ġlocation ĠIN LINE FORM 3 Ġ, Ġafter Ġz - score Ġnormal ization . </s> C ateg orical Ġfeatures . ĠTo Ġtake Ġinto Ġaccount Ġthe Ġcateg orical Ġfeatures , Ġwe Ġimpose Ġthe Ġconstraint Ġthat Ġlocations Ġbelonging Ġto Ġthe Ġsame Ġcategory Ġshould Ġbe Ġclose Ġtogether Ġin Ġthe Ġspace . ĠTo Ġformal ize Ġthis , Ġwe Ġrepresent Ġeach Ġcategory Ġtype ĠIN LINE FORM 0 Ġas Ġa Ġvector ĠIN LINE FORM 1 Ġ, Ġand Ġconsider Ġthe Ġfollowing Ġobjective : ĠIN LINE FORM 2 </s> E val uation ĠT asks </s> We Ġwill Ġuse Ġthe Ġmethod Ġfrom ĠB IB REF 7 Ġas Ġour Ġmain Ġbaseline . ĠThis Ġwill Ġallow Ġus Ġto Ġdirectly Ġevaluate Ġthe Ġeffectiveness Ġof Ġembed d ings Ġfor Ġthe Ġconsidered Ġproblem , Ġsince Ġwe Ġhave Ġused Ġthe Ġsame Ġstructured Ġdatasets Ġand Ġsame Ġtag Ġweight ing Ġscheme . ĠFor Ġthis Ġreason , Ġwe Ġwill Ġalso Ġfollow Ġtheir Ġevaluation Ġmethodology . ĠIn Ġparticular , Ġwe Ġwill Ġconsider Ġthree Ġevaluation Ġtasks : </s> P redict ing Ġthe Ġdistribution Ġof Ġ100 Ġspecies Ġacross ĠEurope , Ġusing Ġthe ĠEuropean Ġnetwork Ġof Ġnature Ġprotected Ġsites ĠN atur a Ġ2000 Ġdataset Ġas Ġground Ġtruth . ĠFor Ġeach Ġof Ġthese Ġspecies , Ġa Ġbinary Ġclassification Ġproblem Ġis Ġconsidered . ĠThe Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġis Ġdefined Ġas Ġthe Ġ26 , 425 Ġdistinct Ġsites Ġoccurring Ġin Ġthe Ġdataset . </s> P redict ing Ġsoil Ġtype , Ġagain Ġeach Ġtime Ġtreating Ġthe Ġtask Ġas Ġa Ġbinary Ġclassification Ġproblem , Ġusing Ġthe Ġsame Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġas Ġin Ġthe Ġspecies Ġdistribution Ġexperiments . ĠFor Ġthese Ġexperiments , Ġnone Ġof Ġthe Ġsoil Ġtype Ġfeatures Ġare Ġused Ġfor Ġgenerating Ġthe Ġembed d ings . </s> P redict ing ĠCOR INE Ġland Ġcover Ġclasses Ġat Ġlevels Ġ1 , Ġ2 Ġand Ġlevel Ġ3 , Ġeach Ġtime Ġtreating Ġthe Ġtask Ġas Ġa Ġbinary Ġclassification Ġproblem , Ġusing Ġthe Ġsame Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġas Ġin Ġthe Ġspecies Ġdistribution Ġexperiments . ĠFor Ġthese Ġexperiments , Ġnone Ġof Ġthe ĠCOR INE Ġfeatures Ġare Ġused Ġfor Ġgenerating Ġthe Ġembed d ings . </s> In Ġaddition , Ġwe Ġwill Ġalso Ġconsider Ġthe Ġfollowing Ġregression Ġtasks : </s> P redict ing Ġ5 Ġclimate Ġrelated Ġfeatures : Ġthe Ġaverage Ġprecipitation , Ġtemperature , Ġsolar Ġradiation , Ġwater Ġvapor Ġpressure , Ġand Ġwind Ġspeed . ĠWe Ġagain Ġuse Ġthe Ġsame Ġset Ġof Ġlocations ĠIN LINE FORM 0 Ġas Ġfor Ġspecies Ġdistribution Ġin Ġthis Ġexperiment . ĠNone Ġof Ġthe Ġclimate Ġfeatures Ġis Ġused Ġfor Ġconstructing Ġthe Ġembed d ings Ġfor Ġthis Ġexperiment . </s> P redict ing Ġpeople 's Ġsubjective Ġopinions Ġof Ġlandscape Ġbeauty Ġin ĠBritain , Ġusing Ġthe Ġcrowds ourced Ġdataset Ġfrom Ġthe ĠSc enic Or Not Ġwebsite Ġas Ġground Ġtruth . ĠThe Ġset ĠIN LINE FORM 0 Ġis Ġchosen Ġas Ġthe Ġset Ġof Ġlocations Ġof Ġ191 Ġ6 05 Ġrated Ġlocations Ġfrom Ġthe ĠSc enic Or Not Ġdataset Ġfor Ġwhich Ġat Ġleast Ġone Ġge ore f eren ced ĠFlickr Ġphoto Ġexists Ġwithin Ġa Ġ1 Ġkm Ġradius . </s> Exper imental ĠSetup </s> In Ġall Ġexperiments , Ġwe Ġuse ĠSupport ĠVector ĠMachines Ġ( S V Ms ) Ġfor Ġclassification Ġproblems Ġand ĠSupport ĠVector ĠReg ression Ġ( S VR ) Ġfor Ġregression Ġproblems Ġto Ġmake Ġpredictions Ġfrom Ġour Ġrepresentations Ġof Ġgeographic Ġlocations . ĠIn Ġboth Ġcases , Ġwe Ġused Ġthe ĠS VM ĠIN LINE FORM 0 Ġimplementation ĠB IB REF 53 Ġ. ĠFor Ġeach Ġexperiment , Ġthe Ġset Ġof Ġlocations ĠIN LINE FORM 1 Ġwas Ġsplit Ġinto Ġtwo - thirds Ġfor Ġtraining , Ġone - six th Ġfor Ġtesting , Ġand Ġone - six th Ġfor Ġtuning Ġthe Ġparameters . ĠAll Ġembed ding Ġmodels Ġare Ġlearned Ġwith ĠAd ag rad Ġusing Ġ30 Ġiterations . ĠThe Ġnumber Ġof Ġdimensions Ġis Ġchosen Ġfor Ġeach Ġexperiment Ġfrom ĠIN LINE FORM 2 Ġbased Ġon Ġthe Ġtuning Ġdata . ĠFor Ġthe Ġparameters Ġof Ġour Ġmodel Ġin ĠEqu ation ĠEQ REF 7 Ġ, Ġwe Ġconsidered Ġvalues Ġof ĠIN LINE FORM 3 Ġfrom Ġ{ 0 . 1 , Ġ0 . 01 , Ġ0 . 001 , Ġ0 . 0001 } Ġand Ġvalues Ġof ĠIN LINE FORM 4 Ġfrom Ġ{ 1 , Ġ10 , Ġ100 , Ġ1000 , Ġ10 Ġ000 , Ġ100 Ġ000 }. ĠTo Ġcompute ĠKL Ġdivergence , Ġwe Ġneed Ġto Ġdetermine Ġa Ġset Ġof Ġclasses ĠIN LINE FORM 5 Ġfor Ġeach Ġexperiment . ĠFor Ġclassification Ġproblems , Ġwe Ġcan Ġsimply Ġconsider Ġthe Ġgiven Ġcategories , Ġbut Ġfor Ġthe Ġregression Ġproblems Ġwe Ġneed Ġto Ġdefine Ġsuch Ġclasses Ġby Ġdisc ret izing Ġthe Ġnumerical Ġvalues . ĠFor Ġthe Ġscenic ness Ġexperiments , Ġwe Ġconsidered Ġscores Ġ3 Ġand Ġ7 Ġas Ġcut - off Ġpoints , Ġleading Ġto Ġthree Ġclasses Ġ( i . e . Ġless Ġthan Ġ3 , Ġbetween Ġ3 Ġand Ġ7 , Ġand Ġabove Ġ7 ). ĠSimilarly , Ġfor Ġeach Ġclimate Ġrelated Ġfeatures , Ġwe Ġconsider Ġtwo Ġcut - off Ġvalues Ġfor Ġdisc ret ization : Ġ5 Ġand Ġ15 Ġfor Ġaverage Ġtemperature , Ġ50 Ġand Ġ100 Ġfor Ġaverage Ġprecipitation , Ġ10 Ġ000 Ġand Ġ17 Ġ000 Ġfor Ġaverage Ġsolar Ġradiation , Ġ0 . 7 Ġand Ġ1 Ġfor Ġaverage Ġwater Ġvapor Ġpressure , Ġand Ġ3 Ġand Ġ5 Ġfor Ġwind Ġspeed . ĠThe Ġsmoot hing Ġparameter ĠIN LINE FORM 6 Ġwas Ġselected Ġamong ĠIN LINE FORM 7 Ġbased Ġon Ġthe Ġtuning Ġdata . ĠIn Ġall Ġexperiments Ġwhere Ġterm Ġselection Ġis Ġused , Ġwe Ġselect Ġthe Ġtop Ġ100 Ġ000 Ġtags . ĠWe Ġfixed Ġthe Ġradius ĠIN LINE FORM 8 Ġat Ġ1 km Ġwhen Ġcounting Ġthe Ġnumber Ġof Ġtag Ġoccurrences . ĠFinally , Ġwe Ġset Ġthe Ġnumber Ġof Ġnegative Ġexamples Ġas Ġ10 Ġtimes Ġthe Ġnumber Ġof Ġpositive Ġexamples Ġfor Ġeach Ġlocation , Ġbut Ġwith Ġa Ġcap Ġat Ġ1000 Ġnegative Ġexamples Ġin Ġeach Ġregion Ġfor Ġcomputational Ġreasons . ĠWe Ġtune Ġall Ġparameters Ġwith Ġrespect Ġto Ġthe ĠF 1 Ġscore Ġfor Ġthe Ġclassification Ġtasks , Ġand ĠSpear man ĠIN LINE FORM 9 Ġfor Ġthe Ġregression Ġtasks . </s> Vari ants Ġand ĠBas eline ĠMethods </s> We Ġwill Ġrefer Ġto Ġour Ġmodel Ġas ĠEG EL Ġ( Emb edd ing ĠGE ographic ĠLocations ), Ġand Ġwill Ġconsider Ġthe Ġfollowing Ġvariants . ĠEG EL - Tags Ġonly Ġuses Ġthe Ġinformation Ġfrom Ġthe ĠFlickr Ġtags Ġ( i . e . Ġcomponent ĠIN LINE FORM 0 Ġ), Ġwithout Ġusing Ġany Ġnegative Ġexamples Ġand Ġwithout Ġfeature Ġselection . ĠEG EL - Tags + NS Ġis Ġsimilar Ġto ĠEG EL - Tags Ġbut Ġwith Ġthe Ġaddition Ġof Ġnegative Ġexamples . ĠEG EL - K L ( Tags + NS ) Ġadditionally Ġconsiders Ġterm Ġselection . ĠEG EL - All Ġis Ġour Ġfull Ġmethod , Ġi . e . Ġit Ġadditionally Ġuses Ġthe Ġstructured Ġinformation . ĠWe Ġalso Ġconsider Ġthe Ġfollowing Ġbas elines . ĠB OW - Tags Ġrepresents Ġlocations Ġusing Ġa Ġbag - of - words Ġrepresentation , Ġusing Ġthe Ġsame Ġtag Ġweight ing Ġas Ġthe Ġembed ding Ġmodel . ĠB OW - K L ( Tags ) Ġuses Ġthe Ġsame Ġrepresentation Ġbut Ġafter Ġterm Ġselection , Ġusing Ġthe Ġsame ĠKL - based Ġmethod Ġas Ġthe Ġembed ding Ġmodel . ĠB OW - All Ġcombines Ġthe Ġbag - of - words Ġrepresentation Ġwith Ġthe Ġstructured Ġinformation , Ġencoded Ġas Ġproposed Ġin ĠB IB REF 7 Ġ. ĠGl o Ve Ġuses Ġthe Ġobjective Ġfrom Ġthe Ġoriginal ĠGl o Ve Ġmodel Ġfor Ġlearning Ġlocation Ġvectors , Ġi . e . Ġthis Ġvariant Ġdiffers Ġfrom ĠEG EL - Tags Ġin Ġthat Ġinstead Ġof ĠIN LINE FORM 1 Ġwe Ġuse Ġthe Ġnumber Ġof Ġco - occ ur rences Ġof Ġtag ĠIN LINE FORM 2 Ġnear Ġlocation ĠIN LINE FORM 3 Ġ, Ġmeasured Ġas ĠIN LINE FORM 4 Ġ. </s> Results Ġand ĠDiscussion </s> We Ġpresent Ġour Ġresults Ġfor Ġthe Ġbinary Ġclassification Ġtasks Ġin ĠTables ĠT AB REF 23 ĠâĢĵ ĠT AB REF 24 Ġin Ġterms Ġof Ġaverage Ġprecision , Ġaverage Ġrecall Ġand Ġmacro Ġaverage ĠF 1 Ġscore . ĠThe Ġresults Ġof Ġthe Ġregression Ġtasks Ġare Ġreported Ġin ĠTables ĠT AB REF 25 Ġand ĠT AB REF 29 Ġin Ġterms Ġof Ġthe Ġmean Ġabsolute Ġerror Ġbetween Ġthe Ġpredicted Ġand Ġactual Ġscores , Ġas Ġwell Ġas Ġthe ĠSpear man ĠIN LINE FORM 0 Ġcorrelation Ġbetween Ġthe Ġrankings Ġinduced Ġby Ġboth Ġsets Ġof Ġscores . ĠIt Ġcan Ġbe Ġclearly Ġseen Ġfrom Ġthe Ġresults Ġthat Ġour Ġproposed Ġmethod Ġ( EG EL - All ) Ġcan Ġeffectively Ġintegrate ĠFlickr Ġtags Ġwith Ġthe Ġavailable Ġstructured Ġinformation . ĠIt Ġoutper forms Ġthe Ġbas elines Ġfor Ġall Ġthe Ġconsidered Ġtasks . ĠFurthermore , Ġnote Ġthat Ġthe ĠP PM I - based Ġweight ing Ġin ĠEG EL - Tags Ġconsistently Ġoutper forms ĠGl o Ve Ġand Ġthat Ġboth Ġthe Ġaddition Ġof Ġnegative Ġexamples Ġand Ġterm Ġselection Ġlead Ġto Ġfurther Ġimprovements . ĠThe Ġuse Ġof Ġterm Ġselection Ġleads Ġto Ġparticularly Ġsubstantial Ġimprovements Ġfor Ġthe Ġregression Ġproblems . </s> While Ġour Ġexperimental Ġresults Ġconfirm Ġthe Ġusefulness Ġof Ġembed d ings Ġfor Ġpredicting Ġenvironmental Ġfeatures , Ġthis Ġis Ġonly Ġconsistently Ġthe Ġcase Ġfor Ġthe Ġvariants Ġthat Ġuse Ġboth Ġthe Ġtags Ġand Ġthe Ġstructured Ġdatasets . ĠIn Ġparticular , Ġcomparing ĠB OW - Tags Ġwith ĠEG EL - Tags , Ġwe Ġsometimes Ġsee Ġthat Ġthe Ġformer Ġachieves Ġthe Ġbest Ġresults . ĠWhile Ġthis Ġmight Ġseem Ġsurprising , Ġit Ġis Ġin Ġaccordance Ġwith Ġthe Ġfindings Ġin ĠB IB REF 54 Ġ, ĠB IB REF 38 Ġ, Ġamong Ġothers , Ġwhere Ġit Ġwas Ġalso Ġfound Ġthat Ġbag - of - words Ġrepresentations Ġcan Ġsometimes Ġlead Ġto Ġsurprisingly Ġeffective Ġbas elines . ĠInterestingly , Ġwe Ġnote Ġthat Ġin Ġall Ġcases Ġwhere ĠEG EL - K L ( Tags + NS ) Ġperforms Ġworse Ġthan ĠB OW - Tags , Ġwe Ġalso Ġfind Ġthat ĠB OW - K L ( Tags ) Ġperforms Ġworse Ġthan ĠB OW - Tags . ĠThis Ġsuggests Ġthat Ġfor Ġthese Ġtasks Ġthere Ġis Ġa Ġvery Ġlarge Ġvariation Ġin Ġthe Ġkind Ġof Ġtags Ġthat Ġcan Ġinform Ġthe Ġprediction Ġmodel , Ġpossibly Ġincluding Ġe . g . Ġuser - specific Ġtags . ĠSome Ġof Ġthe Ġinformation Ġcaptured Ġby Ġsuch Ġhighly Ġspecific Ġbut Ġrare Ġtags Ġis Ġlikely Ġto Ġbe Ġlost Ġin Ġthe Ġembed ding . </s> To Ġfurther Ġanalyze Ġthe Ġdifference Ġin Ġperformance Ġbetween ĠBo W Ġrepresentations Ġand Ġembed d ings , ĠFigure ĠT AB REF 29 Ġcompares Ġthe Ġperformance Ġof Ġthe ĠGl o Ve Ġmodel Ġwith Ġthe Ġbag - of - words Ġmodel Ġfor Ġpredicting Ġplace Ġscenic ness , Ġas Ġa Ġfunction Ġof Ġthe Ġnumber Ġof Ġtag Ġoccurrences Ġat Ġthe Ġconsidered Ġlocations . ĠWhat Ġis Ġclearly Ġnoticeable Ġin ĠFigure ĠT AB REF 29 Ġis Ġthat ĠGl o Ve Ġperforms Ġbetter Ġthan Ġthe Ġbag - of - words Ġmodel Ġfor Ġlarge Ġcorpor a Ġand Ġworse Ġfor Ġsmaller Ġcorpor a . ĠThis Ġissue Ġhas Ġbeen Ġallev iated Ġin Ġour Ġembed ding Ġmethod Ġby Ġthe Ġaddition Ġof Ġnegative Ġexamples . </s> ĠCon clusions </s> In Ġthis Ġpaper , Ġwe Ġhave Ġproposed Ġa Ġmodel Ġto Ġlearn Ġgeographic Ġlocation Ġembed d ings Ġusing ĠFlickr Ġtags , Ġnumerical Ġenvironmental Ġfeatures , Ġand Ġcateg orical Ġinformation . ĠThe Ġexperimental Ġresults Ġshow Ġthat Ġour Ġmodel Ġcan Ġintegrate ĠFlickr Ġtags Ġwith Ġstructured Ġinformation Ġin Ġa Ġmore Ġeffective Ġway Ġthan Ġexisting Ġmethods , Ġleading Ġto Ġsubstantial Ġimprovements Ġover Ġbaseline Ġmethods Ġon Ġvarious Ġprediction Ġtasks Ġabout Ġthe Ġnatural Ġenvironment . </s> Acknowled gments </s> S hel an ĠJe aw ak Ġhas Ġbeen Ġsponsored Ġby ĠHC ED ĠIraq . ĠSteven ĠSch ock a ert Ġhas Ġbeen Ġsupported Ġby ĠE RC ĠStarting ĠGrant Ġ6 37 277 .