Revealing spatiotemporal urban activity patterns: A machine learning study using Google Popular Times

Academic Edi o : Wol gang Kainz Recei ed: 20 Ma ch 2025 Re ised: 9 May 2025 Accep ed: 29 May 2025 Published: 3 June 2025 Ci a ion: Ba ena-He án, M.; Mod ego-Mon o e, I.; G ijalba, O. Re ealing Spa io empo al U ban Ac i i y Pa e ns: A Machine Lea ning S udy Using Google Popula Times. ISPRS In . J. Geo-In . 2025,14, 221. h ps://doi.o g/10.3390/ijgi14060221 Copy igh : © 2025 by he au ho s. Published by MDPI on behal o he In e na ional Socie y o Pho og amme y and Remo e Sensing. Licensee MDPI, Basel, Swi ze land. This a icle is an open access a icle dis ibu ed unde he e ms and condi ions o he C ea i e Commons A ibu ion (CC BY) license (h ps://c ea i ecommons.o g/ licenses/by/4.0/). A icle Re ealing Spa io empo al U ban Ac i i y Pa e ns: A Machine Lea ning S udy Using Google Popula Times Mikel Ba ena-He án * , I zia Mod ego-Mon o e and Ola z G ijalba CAVIAR (Quali y o Li e in A chi ec u e) Resea ch G oup, Depa men o A chi ec u e, Uni e si y o he Basque Coun y UPV/EHU, Plaza Oña i 2, 20018 Donos ia-San Sebas ián, Spain; i zia [email p o ec ed] (I.M.-M.); [email p o ec ed] (O.G.) *Co espondence: mikel.ba [email p o ec ed] Abs ac : Ex ensi e scien i ic e idence unde sco es he impo ance o iden i ying spa io em- po al pa e ns o in es iga ing u ban dynamics. The ecen p oli e a ion o loca ion-based social ne wo ks (LBSNs) acili a es he measu emen o u ban hy hms h ough geo empo- al in o ma ion, p o iding deepe insigh s in o he unde lying causes o u ban ib ancy. This s udy p esen s a me hodology o analyzing he spa io empo al use o ci ies and iden i ying occupancy pa e ns aking in o conside a ion u ban o m and unc ion. The analysis elies on da a ob ained om Google Popula Times (GPT), ans o ming he ela- i e occupancy o a la ge numbe o poin s o in e es (POI) classi ied in o i e ca ego ies, o es ima ing he numbe o people agg ega ed wi hin u ban nodes du ing a ypical day. As a esul , his esea ch assesses he u ili y o his da a sou ce o e alua ing he changing dynamics o a ci y ac oss bo h space and ime. The me hodology employs geog aphic in- o ma ion sys em (GIS) ools and a i icial in elligence echniques. The esul s demons a e ha by analyzing geo empo al da a, we can classi y u ban nodes acco ding o hei hou ly ac i i y pa e ns. These pa e ns, in u n, ela e o ci y o m and u ban ac i i ies, showing a ce ain spa ial concen a ion. This esea ch con ibu es o he g owing body o knowledge on machine lea ning (ML) me hods o spa io empo al modeling, laying he g oundwo k o u u e s udies ha can u he explo e he complexi y o u ban phenomena. Keywo ds: spa io- empo al analysis; ime se ies clus e ing; u ban dynamics; loca ion-based social ne wo k; geog aphic in o ma ion sys ems; ime geog aphy 1. In oduc ion 1.1. Gene al O e iew The ela ionship be ween he spa ial cha ac e is ics o a ci y and human beha io has been s udied o many decades. T adi ionally, he scien i ic app oach has been sec- o al: mo phological o u ban s udies ha e been conduc ed wi hin he mo e echnical o a chi ec u al ields, while analyses o human beha io and dynamics adi ionally o ig- ina ed om humanis ic pe spec i es such as sociology and psychology. Howe e , he con inuous ad ancemen o geog aphic in o ma ion sys em (GIS) ools, he p oli e a ion o geoloca ed da a, and eme ging a i icial in elligence (AI) echniques a e opening up a new ield o esea ch. In his con ex , loca ion-based social ne wo ks (LBSNs) p o ide in o ma ion abou human ac i i y wi hin geo- e e enced digi al en i onmen s. Speci ically, Google Popula Times (GPT), whose spa io empo al da a a e openly accessible in eal ime and a ailable globally, o e s ex ensi e geoloca ed da a om poin s o in e es (POI) ha cap u e he ISPRS In . J. Geo-In . 2025,14, 221 h ps://doi.o g/10.3390/ijgi14060221 ISPRS In . J. Geo-In . 2025,14, 221 2 o 23 occupancy le els o es ablishmen s and public spaces. Howe e , while he use o his sou ce in u ban s udies has seen conside able p og ess, signi ican me hodological ad ancemen s a e s ill equi ed o ensu e ha he esul s a e bo h ep esen a i e and alid o s a egic u ban design. In his ega d, machine lea ning (ML) eme ges as an indispensable ool. I de el- ops algo i hms and s a is ical models ha allow compu e sys ems o lea n om and p ocess la ge olumes o da a, enabling a mo e comp ehensi e unde s anding o human ac i i y pa e ns. The e o e, new echniques associa ed wi h he p oli e a ion o LBSNs, combined wi h he enhanced capabili ies o AI in big da a analysis, a e d i ing signi ican ad ancemen s in he me hodological de elopmen o spa io empo al analysis o u ban li e. These new me hods will os e a deepe unde s anding o u ban dynamics and acili a e mo e in o med decision making in u ban planning. This pape in oduces an inno a i e me hodology o he spa io empo al analysis o occupancy pa e ns, in eg a ing he unique u ban o m and he a ious ac i i ies ha un old wi hin ine-g ained u ban uni s o analysis. By employing his app oach, we can iden i y u ban hy hms h oughou ime pe iods o each u ban node, allowing us o e alua e he dynamics o a eas wi h a ying le els o u ban ib ancy and ul ima ely in o m s a egic u ban planning decisions. The me hodology is applied o he case s udy o Donos ia-San Sebas ián. 1.2. Li e a u e Re iew The s udy o human daily ac i i y ou ines h ough space and ime in u ban se ings began in he la e 1960s, leading o he de elopmen o a new ield o esea ch: ime geog a- phy. This made i possible o analyze human beha io al pa e ns in eg a ing bo h spa ial and empo al dimensions hough spa io empo al p isms [ 1 ]. O he classical app oaches in sociology and human geog aphy, such as ime-use su eys and spa io empo al dia ies, allowed u ban planne s in he 1970s o be e unde s and he unc ioning o ci ies and e eal socio-spa ial dynamics by in oducing he a iable o ime. As such, he u ban ab ic is unde s ood as a con ex o beha io [ 2 ] in luencing la ge-scale ime o ganiza ion and he hy hms o human ac i i y in e e yday li e [ 3 ]. In his con ex , he Theo y o Na u al Mo emen and Space Syn ax u he emphasizes how he con igu a ion o he u ban g id p i ileges ce ain spaces o h ough mo emen [ 4 ], he eby shaping hese socio-spa ial dy- namics and ein o cing he hy hms o daily ac i i y. Ci ies a e hus composed o mul iple ime ames and luid empo ali ies o e en s [5]. F om an ecological pe spec i e, cha ac e izing he daily lows o di e en communi ies wi hin a ci y’s spa ial s uc u e [ 6 , 7 ] led o he conclusion ha simila u ban con ex s o en sha e empo al egula i ies [ 8 ]. Fo ins ance, Goodchild’s spa io empo al analyses, combining social da a and ca og aphy, showed ha he p ima y o ganizing dimension o u ban space is he ela ionship be ween esidence and wo k [ 9 ], while socio-economic s a us is also a c i ical a iable, in luencing he ime spen on a ious ac i i ies [10]. Addi ionally, ac i i y censuses in la ge ci ies demons a e hy hms o social beha - io [ 11 ], akin o he “mechanical pe iods” o social li e, shaped no only by he u ban con ex bu also by sha ed wo k schedules, habi s, and adi ions ha c ea e a hy hm and sense o place. Rou ine human ac i i ies o en ake place in p oxima e, small spaces and a e linked o sho pe iods o ime [ 12 ]. Consequen ly, he ele ance o a place is ied o i s empo al hy hm, and ice e sa; ce ain hy hms end o spa ialize [13]. O e he las decade, a new pa adigm has eme ged in which u ban hy hm da a a e linked o loca ions a he han indi iduals [ 14 ], highligh ing a signi ican associa ion be ween buil en i onmen ac o s and u ban ib ancy [ 15 ]. This makes i possible o iden i y he mo emen , mee ing, and es ing pa e ns o people in ci ies, gi ing each ISPRS In . J. Geo-In . 2025,14, 221 3 o 23 place a dis inc i e, inhe en ly hy hmic cha ac e [ 16 ]. Fo ins ance, by combining Jane Jacobs’ pa ame e s o u ban i ali y [ 17 ] wi h obse a ions o pedes ian lows a u ban in e sec ions, poly hy hms—pa e ns c ea ed by ou ines, social in e ac ions, and anspo sys ems—can be de ec ed [ 18 ]. Thus, each ac i i y ch ono ype [ 19 ] es ablishes a empo al connec ion be ween spa ially sepa a ed places [20]. In his con ex , nume ous s udies ha e analyzed ci ies om his spa io empo al di- mension, linking he cha ac e is ics o he buil en i onmen o he dynamics gene a ed wi hin i . Fo ins ance, li es yle changes b ough abou by globaliza ion and inc eased ime use demands [ 21 ] mani es as ex eme u ban i ali y and socio-cul u al iden i y in 24/7 ci y cen e s. These en i onmen s ex end beyond adi ional wo king hou s, e ealing mul iple di isions in isi a ion in ensi y and ac i i ies [ 22 ], leading o bo h spa ial and empo al seg ega ion [ 23 ]. This lack o inclusi i y s ems om he limi ed a ie y o se ices ha mee he needs o di e en social g oups [ 24 ]. Addi ionally, he ise in he nigh ime economy ans o ms ci ies in o spaces o s anda dized consump ion, a ac ing homogeneous g oups o consume s, ou is s, and en ep eneu s [ 25 ], which esul s in challenges o esiden s as gen i ica ion and he exclusion o ce ain social g oups [ 26 ]. In esponse, u ban s a e- gies a e eme ging ha seek o in eg a e and manage he day ime, e ening, and nigh ime economies based on cus ome expe ience and pe cep ion in ci y cen e s [27]. Mo eo e , he exposu e o people o he e ogeneous social con ex s depends on hei indi idual cha ac e is ics and he ac i i y spaces hey equen [ 28 ]. Obse ing hese spaces helps iden i y beha io al pa e ns, he physical cha ac e is ics o s ee s ha encou age s a- iona y and pe sis en ac i i ies [ 29 ], as neighbo hood cha ac e and dynamics a e shaped by u ban o m [ 30 ], and ac i i y dis ibu ions ha e lec socio-spa ial cha ac e is ics [ 31 ]. E en in he mos in ima e spaces, like s ee s and squa es, he e is a di e si y and luidi y in he encoun e s and mo emen s ha cons i u e he u ban ab ic [32]. U ban dynamics, as e idenced h ough le els o ac i i y, comme cial a ailabili y, and household con ibu ions o he economy, shape bo h public li e and he socio-spa ial o ga- niza ion o ci ies [ 33 ]. The scale and size also play a c ucial ole, as pa e ns o socializa ion di e by u ban densi y, wi h s onge connec ions in compac ci ies, due o physical and design ac o s like public spaces, building ypologies, and isual spaciousness [ 34 ]. The quali a i e expe ience o u ban en i onmen s can hus be desc ibed h ough empo al, spa ial, isual, and connec i i y me ics o u ban o m [35]. Wi h he ad ancemen o in o ma ion echnologies and he inc easing spa io empo al esolu ion o da a, in eg a ing space and ime in o GIS en i onmen s p esen s a challenge, especially when ying o isualize a ci y’s mul i- unc ionali y [ 36 ]. In e es ingly, hese ech- nologies ha e al e ed he iming and loca ion o ac i i ies [ 37 ], eshaping socio-economic pa e ns in u ban a eas. Compu a ional imp o emen s and GIS ad ancemen s [ 38 ] ha e played a c ucial ole in anspo planning [ 39 ] and ha e e i ed he s udy o socio-u ban dynamics [40]. Recen esea ch on u ban dynamics has inc easingly adop ed spa io empo al pe spec- i es and big da a sou ces, e lec ing a g owing in e es in unde s anding he complexi y o ci y li e h ough digi al aces [ 41 ], whe e ML enhances spa ial analysis by iden i ying complex pa e ns, in eg a ing di e se da a sou ces, and adap ing dynamically o u ban challenges. A wide a ie y o da a sou ces ha e been explo ed, including mobile phone eco ds [ 42 – 45 ], social media con en [ 46 – 49 ], ansi sma ca d da a [ 50 , 51 ], bike sha ing sys ems [ 52 – 54 ], axi GPS ajec o ies [ 55 – 57 ], and inancial ansac ions [ 58 ]. These s udies add ess di e se hema ic angles—such as u ban ib ancy [ 15 , 53 , 59 ], unc ional land use and POIs de ec ion [ 46 – 48 , 60 ], commu ing and mobili y pa e ns [ 50 , 51 , 61 ], conges ion anal- ISPRS In . J. Geo-In . 2025,14, 221 4 o 23 ysis [ 55 , 56 ], and esilience o dis up ions [ 62 ]—demons a ing he ichness and po en ial o spa io empo al u ban esea ch. In e ms o analy ical echniques, ecen con ibu ions showcase an e ol ing me hod- ological landscape. Clus e ing me hods emain cen al, wi h k-means equen ly used o unco e land use pa e ns o mobili y dynamics [ 48 , 51 , 53 , 57 ], while o he s udies adop mo e sophis ica ed app oaches such as Dynamic Time Wa ping (DTW) combined wi h k-medoids o delinea e unc ional zones based on building-le el social media ac i i y [ 46 ], o hie a chical DTW o iden i y cyclical beha io al pa e ns in bicycle usage [ 52 ]. Modi- ied DBSCAN and uzzy clus e ing algo i hms ha e been employed o de ec commu ing lows and conges ion dynamics wi h g ea e empo al nuance [ 50 , 54 , 55 ]. Neu al ne wo ks ha e also been in eg a ed o hou ly popula ion densi y es ima ion [ 48 ] and i ali y a ea classi ica ion [ 59 ], while g a i y-based models [ 42 ] and spa io empo al low clus e ing s a egies [61] ha e eme ged o cap u e in e ac ion in ensi ies and mobili y ends. Beyond echnique, he hema ic ocus on u ban hy hms is pa icula ly ele an . Mul iple s udies examine in a-u ban a ia ions in ac i i y in ensi y, empo ali y, and unc ion—highligh ing he co-dependence be ween land use, mobili y, and buil en i on- men s uc u es [ 15 , 59 , 60 , 63 ]. O he s explo e he esilience o u ban sys ems unde ex e nal shocks, such as ex eme wea he , by le e aging empo ally ich da ase s like GPT o de ec shi s in daily ou ines [62]. Despi e hese ad ances, impo an gaps emain. Fi s , ew s udies ely on publicly accessible and globally consis en da ase s, such as GPT, which o e scalable and eplicable insigh s in o human ac i i y while a oiding many p i acy issues inhe en o mobile o inancial da a. Second, while ad anced clus e ing and modeling echniques a e widesp ead, he e is a lack o me hodological s anda diza ion, which hinde s compa a i e analysis ac oss ci ies o egions. Thi d, ela i ely ew con ibu ions o e in eg a ed spa ial and empo al g anula i y, which is essen ial o unde s and he ine-scale hy hms o u ban li e, pa icula ly a he in a-neighbo hood le el. In his con ex , he p esen s udy con ibu es a eplicable and ligh weigh me hodol- ogy ha le e ages GPT and unsupe ised ML echniques o classi y occupancy pa e ns ac oss u ban space and ime. By ocusing on unc ional u ban hy hms and hei spa ial mani es a ion, i add esses bo h he me hodological agmen a ion and da a accessibili y limi a ions iden i ied in p io wo k. 2. Ma e ials and Me hods 2.1. S udy A ea In pu sui o applying he me hodology o a local con ex , he selec ed case s udy o alida ing he me hod is he municipali y o Donos ia-San Sebas ián (Figu e 1), a coas al ci y in no he n Spain. The ci y o igina ed as a walled medie al se lemen , and i s g ow h o e ime ex ended ac oss he we lands o he U umea Ri e and along he coas al edge. Today, i p esen s a clea mo phological s a i ica ion: he his o ical medie al co e, he 19 h-cen u y and pos mode n u ban expansions ha cha ac e ize he lowland dis ic s, and mo e ecen pe iphe al neighbo hoods and subu bs loca ed on su ounding hillsides and sloped e ain. Despi e he ci y’s mode a e size—174,529 inhabi an s wi hin he u ban a ea [ 64 ]—i s high popula ion densi y o 13,073 inhabi an s/km 2 exhibi s a “Medi e anean” li es yle. This, along wi h i s unc ional cha ac e is ics, e lec s sociospa ial dynamics whe e balanc- ing wo k and li e can be challenging [65]. ISPRS In . J. Geo-In . 2025,14, 221 5 o 23 Figu e 1. The adminis a i e di ision o Donos ia-San Sebas ián. Sou ce: GeoEuskadi. Own elabo a ion. As o 2022, in he Basque Coun y, 99% o indi iduals aged 16 o 74 egula ly use sma phones [ 66 ]. Addi ionally, 56.5% o in e ne use s engage wi h social ne wo ks, and 55.2% o companies use social ne wo ks o business pu poses [ 67 ]. This widesp ead adop ion suppo s he po en ial o LBSNs o u ban s udies in his con ex . S ee -le el u ban uses in Donos ia-San Sebas ián consis o a a ie y o ac i i ies wi h di e ing ope a ing hou s h oughou he day [ 68 ]. The majo i y o hese businesses include e ail, hospi ali y, and auxilia y p o essional se ices, ollowed by public se ices. This comme cial di e si y is highly concen a ed in he dense neighbo hoods loca ed in he la e a eas o he ci y, making i a p ime a ea o s udying u ban dynamics. 2.2. Me hodology To add ess his s udy’s objec i e o iden i ying and classi ying unc ional u ban hy hms, a combina ion o empo al, spa ial, and mo phological dimensions was equi ed. The i s s ep in ol ed agg ega ing POIs wi hin a mo phological g id ha cap u es he physical s uc u e o he ci y. Unlike con en ional adminis a i e bounda ies, he use o mo phologically homogeneous uni s allows o a mo e consis en compa ison o ac i i y ac oss u ban space, aligning wi h p e ious esea ch ha emphasizes he impo ance o he buil en i onmen in shaping beha io . Mo eo e , weigh ing each uni by he legal capaci y o i s POIs enables he iden i ica ion o a eas no simply by he coun o es ablishmen s, bu by hei po en ial in ensi y o use. Fo empo al clus e ing, we selec ed he k-shape algo i hm, which is speci ically designed o no malized ime se ies. Unlike classical clus e ing echniques such as k- means, k-shape accoun s o bo h he shape and alignmen o empo al pa e ns, making i pa icula ly sui able o cap u ing cha ac e is ic ac i i y p o iles while being obus o di e ences in ampli ude. This was c ucial o dis inguish usage pa e ns ha ollow simila hy hms e en i hei absolu e magni udes di e . This ype o unsupe ised ML is pa icula ly use ul in e ealing la en spa io empo al pa e ns ha a e no easily cap u ed ISPRS In . J. Geo-In . 2025,14, 221 6 o 23 by con en ional GIS o s a is ical echniques, allowing o a mo e nuanced and da a-d i en classi ica ion o u ban ac i i y p o iles. Finally, hea maps we e used o explo e he spa ial dis ibu ion o he esul ing clus e s. This spa ial smoo hing echnique highligh s concen a ions and dispe sions o ac i i y ypes ac oss he ci y, o e ing an in ui i e isualiza ion o unc ional zones and hei mo phological con ex . This in eg a i e app oach suppo s bo h analy ical igo and spa ial in e p e abili y, laying he g oundwo k o linking usage pa e ns o planning conside a ions. Figu e 2illus a es he i e s ages in which he me hodology is s uc u ed: (a) collec ion o aw da a and p ocessing, (b) de ini ion o he uni o analysis, (c) agg ega ion o capaci y- weigh ed POIs occupancy a es, (d) applica ion o ime se ies clus e ing echniques, and (e) spa ial analysis o he occupancy ends. Figu e 2. Me hod low diag am. This app oach in eg a ed wo ypes o spa ial da a: (1) an LBSN, (2) he adminis a i e egis y o u ban eal es a e. Rega ding LBSNs, Google Places is cu en ly one o he mos comp ehensi e global da abases o u ban s udies, o e ing aluable insigh s in o land use a ibu es and u ban dynamics. Howe e , Google Places lacks empo al da a, as POIs a e s a ic in na u e. The in oduc ion o GPT has signi ican ly changed his scena io. GPT calcula es occupancy pa e ns by analyzing isi da a, agg ega ed and anonymized, collec ed o e he p e ious ou o six weeks. The peak hou is used as he e e ence, wi h o he es ima es displayed in ela ion o his peak [ 69 ]. GPT has hus become an inc easingly popula ool in esea ch ela ed o u ban dynamics. (a) Collec ion and P ocessing o Ca og aphic and GPT Da a To collec and p ocess he necessa y POIs a ibu es, we employed he c awle -google- places ool a ailable on Gi Hub, using commi 12c124 [ 70 ]. Da a ex ac ion was conduc ed ia an uno icial API, which au oma ed he e ie al o POIs a ibu es by en e ing links ob- ained h ough a combina ion o selec ed Google Places ca ego ies and desi ed geog aphic loca ions. The da ase includes a a ie y o ields ela ed o each POI, whose key a ibu es o in e es —ob ained a e a se ies o p ep ocessing s eps o o ganize he da a in o he co esponding ields—a e: ISPRS In . J. Geo-In . 2025,14, 221 7 o 23 - Popula imes: Hou ly a e age occupancy as a pe cen age ela i e o he peak occu- pancy. - Ca ego y: The main ca ego y decla ed by he owne on Google My Business. - Geoloca ion: Geog aphical coo dina es. - Add ess: S ee name and doo numbe . Fo his s udy, da a we e collec ed om 1378 POIs loca ed in Donos ia-San Sebas ián on a F iday du ing Ap il and May 2022, allowing us o cap u e bo h ou ine and leisu e-d i en u ban dynamics wi hin a single day Figu e 3shows a b anched diag am ha ca ego izes he sea ches ca ied ou using Google Places, g ouped in o i e main classes. This classi ica ion—ba s and es au an s, shops, wellbeing, p o essional se ices, and ou doo s—was de ined o syn hesize he mul i ude o highly speci ic ca ego ies p o ided by Google Places in o b oade unc ional g oups, allowing o a mo e cohe en and in e p e able analysis o u ban ac i i y. The diag am also displays he numbe o POIs wi hin each class and hei pe cen age ela i e o he o al. Figu e 3. Classi ica ion o POIs in he municipali y o Donos ia-San Sebas ián. The size o he symbols is p opo ional o he numbe o POIs. Sou ce: Google Places. Own elabo a ion. Addi ionally, we collec ed he cadas al da abase o u ban p ope ies in he municipal- i y [ 71 ], which uniquely iden i ies each p ope y and p o ides de ailed u ban cha ac e is ics, including add ess, buil -up a ea, and he in ended use o each p ope y. A e bo h da ase s we e p epa ed, hey we e linked h ough hei s anda dized add esses o associa e he cadas al loo a ea wi h he POIs da a. In cases whe e no di ec ma ch exis ed be ween he wo da ase s, he POIs we e assigned he a e age loo a ea o ISPRS In . J. Geo-In . 2025,14, 221 8 o 23 i s espec i e sub-ca ego y o ensu e consis ency in he analysis and a oid missing alues ha could impac he in e p e a ion o spa ial dis ibu ion pa e ns. To es ima e he numbe o people occupying each POI, we e e enced he Spanish Technical Building Code (CTE), pa icula ly he Basic Documen on Fi e Sa e y (CTE DB SI). This egula ion de ines he maximum occupancy densi y o di e en p ope y ypes, measu ed in squa e me e s pe pe son. Using his s anda d, each POI is assigned a maximum occupancy alue (capaci y), which se es as a weigh ing ac o . This allows us o ans o m he ela i e GPT occupancy alues in o an absolu e numbe o es ima ed people pe hou . (b) Uni o Analysis: he Mo phological G id The uni o analysis o his esea ch, he elabo a ion s eps o which a e shown in Figu e 4, is based on a g id sys em adap ed o he ci y’s u ban mo phology. This i egula g id was gene a ed using Vo onoi polygons o igina ing om s ee in e sec ions, which a e he s a egic poin s o connec ion and decision o people in mo ion [ 72 ]. Thus, such clea isual join s p o ide a ine-g ained delimi a ion o unc ional nodes, main aining ela i e spa ial homogenei y a he han elying on a delimi a ion based on u ban blocks, which would conside only a single acade o each s ee hey encompass. Figu e 4. Design p ocess o he mo phological g id om he s ee ne wo k. (1) The o iginal s ee ne wo k includes all line ea u es mapped o each s ee (e.g., bike lanes, a ic di ec ions); (2) he simpli ied s ee ne wo k educes each s ee o a single cen al axis; (3) in e sec ions a e gene a ed a he c ossing poin s o hese axes, shown as ‘+’ symbols; (4) Vo onoi polygons a e c ea ed om he in e sec ions o de ine he mo phological g id, aligning wi h he u ban s uc u e. Colo s a e andomly assigned o aid in e p e a ion. Sou ce: GeoEuskadi. Own elabo a ion. (c) Weigh ed Occupancy Calcula ion The POIs a e spa ially linked o he co esponding cells wi hin he mo phological g id. As mul iple POIs may coexis wi hin a single cell, hei weigh ed con ibu ion o he hou ly occupancy calcula ion is de e mined by hei ela i e capaci y in p opo ion o he o al capaci y o all POIs wi hin ha cell. To es ima e he capaci y o each POI, a ca ego y-dependen occupancy densi y a io (Table 1) was applied o i s cadas al loo a ea. Pj= n ∑ i=1Ai δc,i(1) P= capaci y (maximum numbe o people); A= loo a ea (m2); δ= densi y (m2/pe son); ISPRS In . J. Geo-In . 2025,14, 221 9 o 23 j= cell; i= POIs; c= ca ego y. Table 1. Ra io o he numbe o occupan s o he loo a ea o a habi able uni . Sou ce: Código Técnico de la Edi icación. Documen o Básico Segu idad en caso de Incendio (CTE DB SI). Own elabo a ion. In ended Use Acco ding o CTE Occupa ion (m2/Pe son) POI Ca ego y Adminis a i e 10 Lawye Ad e ising agency A chi ec Bank Managemen Company o ices Comme cial 2 Bu che ’s shop Beau y salon Clo hes shop G oce y Bake y Hai d esse Fishmonge s Pha macy G eeng oce ’s 3 Copy shop Cou ie se ice Compu e shop 5 Supe ma ke Public 1 Ba s 0.5 Pub Dance club Disco club 1.5 Res au an 5 Gym 10 Squa e Tou is a ac ion 25 1Pa k Hospi al 10 Den is Nu i ionis 15 Gynaecologis Physio he apis 1Acco ding o u ban s anda ds o open spaces (pa ks) in a medium ab ic a neighbo hood-ci y scale. Subsequen ly, he hou ly calcula ion o weigh ed occupancy was de e mined by combining he ela i e weigh o each POI wi hin he cell and i s co esponding occupancy alue om GPT. Fo each cell and hou ly in e al, i was calcula ed as ollows: O ,j= n ∑ i=1 Pi Pj ∗GPT ,i!(2) O= weigh ed occupancy a e (%); ISPRS In . J. Geo-In . 2025,14, 221 16 o 23 e al a eas, he dis ibu ion di e ges, o ming smalle , mo e isola ed concen a ions wi h lowe in ensi y. Finally, C5 is s ongly localized a ound emblema ic squa es wi hin he ci y cen e , pa icula ly he Ca hed al Squa e, as well as key plazas in he Pa e Vieja and An iguo. This clus e is i ually absen in pe iphe al neighbo hoods, ein o cing i s associa ion wi h cen ali y and symbolic u ban spaces. ISPRS In . J. Geo-In . 2025,14, 221 17 o 23 Figu e 9. The 5 clus e s in Donos ia-San Sebas ián and hei espec i e (a) ke nel densi y maps highligh ing he mos ep esen a i e a eas, (b) ada cha s o mixed-use composi ion by POIs ca ego ies, and (c) s acked a ea g aphs o empo al occupancy by POIs ca ego ies. Each clus e is ep esen ed by a speci ic colo . The i e ca ego ies a e colo -coded and iden i ied in he legend below. 4. Discussion The ML-based me hodology applied in his s udy enabled he classi ica ion o u ban space in o i e dis inc empo al clus e s, each e lec ing cha ac e is ic occupancy pa e ns de i ed om GPT da a. These clus e s e eal bo h daily hy hms o ac i i y and hei ISPRS In . J. Geo-In . 2025,14, 221 18 o 23 spa ial dis ibu ion, highligh ing he di e en ia ed oles ha neighbo hoods play wi hin he unc ional s uc u e o he ci y. Al hough all clus e ypes a e ep esen ed ac oss he ci y’s neighbo hoods—consis en wi h he mul i unc ional cha ac e o a compac , mixed-use, and complex ci y such as Donos ia-San Sebas ián— he KDE e eals dis inc spa ial dis ibu ions o each unc ional clus e . This allows o he associa ion o occupancy dynamics wi h he ci y’s mo pho- logical and s uc u al cha ac e is ics. As shown in he esul s, his case s udy enables he iden i ica ion o key axes o ho spo s o each occupancy pa e n. O e all, he cen al pa o he ci y and he adjacen neighbo hoods—mainly loca ed in la a eas wi h mo e homogeneous u ban s uc u es—hos highe le els o ac i i y ac oss all clus e ypes. In con as , mo e pe iphe al and opog aphically ele a ed a eas show signi ican ly lowe ac i i y le els, wi h ce ain esiden ial neighbo hoods—de eloped du ing he 1970s and 1980s o in mo e ecen yea s—showing an almos comple e absence o concen a ed u ban unc ions. This con i ms a clea cen e –pe iphe y g adien in he ci y’s po en ial o a ac people. A a ine scale, deepe analysis wi hin indi idual neighbo hoods e eals ha he mos in ense loca ions o each clus e end o align wi h pedes ian co ido s, public squa es, o main anspo a ion ou es. As demons a ed in Sec ion 3.2.2, he p oposed me hod enables he iden i ica ion o dis inc spa ial pa e ns based on bo h he ype o clus e and he exis ing u ban ab ic. These indings could o m he basis o u u e, mo e de ailed s udies on he ela ionship be ween occupancy pa e ns and u ban mo phology and unc ions a he neighbo hood scale. The empo al luc ua ions o each clus e also di e ma kedly, o e ing insigh in o he unc ional d i e s o human agglome a ion. Fo example, ba -o ien ed clus e s (C2 and C5) ha e he highes con as o agglome a ion a ios and show a high spa ial concen a ion. On he one hand, C5 has he absolu e highes e ening peak bu was p eceded by eally low mo ning ac i i y. On he o he hand, C2 shows a deep alley in he a e noon. The mo e e enly dis ibu ed mixed-use cells ep esen ed in C1 ha e a cha ac e is ic mo ning peak. Meanwhile, he shopping-o ien ed cells o C4 a e dispe sed h oughou he ci y, al hough highe in ensi ies clea ly mani es in cen al a eas. I s mul i unc ional cha ac e allows o a con inuous ac i i y pa e n, simila o C3, which ins ead shows he highes spa ial concen a ion and a di e en o e ing o ac i i ies ha end owa d wellbeing and ee spaces. These pa e ns di e in iming, in ensi y, and unc ional composi ion. Clus e s wi h p onounced e ening peaks d i en by ba s and es au an s (e.g., C5) con as wi h hose ha show symme ical mo ning and a e noon ac i i y linked o e ail (e.g., C4). Spa ially, hese pa e ns co espond o es ablished u ban hie a chies, echoing Ch is alle ’s cen al place heo y by iden i ying key cen e s in he his o ic co e and s uc u ed expansion a eas. Howe e , unlike s a ic spa ial models, ou da a-d i en app oach cap u es eme gen unc ional hie a chies based on eal- ime usage, aligning wi h ecen heo e ical upda es ha inco po a e empo al specializa ion in o u ban hie a chy models [20]. Ou esul s a e in line wi h ecen s udies highligh ing empo al specializa ion in u ban en i onmen s. Fo ins ance, he di e gence be ween midday and nigh ime check in beha io s ac oss neighbo hoods [ 64 ], sugges ing ime-speci ic unc ionali y independen o ca ego ical di e si y. In ou indings, he a eas wi h s ong e ening ac i i y (e.g., C5) a e no necessa ily he mos unc ionally di e se bu a e he mos in ense in one ca ego y (ba s and es au an s), ea i ming ha spa ial specializa ion and empo al hy hm do no always align wi h land use a ie y. F om a me hodological pe spec i e, ou app oach builds on he ad ances in u ban pa e n mining h ough ime se ies clus e ing. While p io wo k has applied his o ISPRS In . J. Geo-In . 2025,14, 221 19 o 23 single domains—such as bike sha ing sys ems [ 74 ] o nigh li e eco e y [ 75 ]—ou mul i- ca ego ical pe spec i e cap u es he coexis ence and laye ing o u ban unc ions, a de ining ai o compac ci ies like Donos ia-San Sebas ián. Ou decision o agg ega e he POIs wi hin mo phologically homogeneous u ban uni s also imp o es spa ial g anula i y. In con as o s udies ha ely on adminis a i e bounda ies o uni o m g ids [ 15 ], ou i egula mo phological g id aligns mo e closely wi h he s ee ne wo k and buil o m. This enhances spa ial ele ance and in e p e abili y. Mo eo e , by weigh ing he occupancy based on POIs ca ego y and cadas al su ace, we con e GPT’s ela i e me ics in o es ima ed coun s o people, add essing a majo sho coming o s udies using pu ely ela i e indica o s [62]. The p oposed amewo k was de eloped using open-sou ce ools, including Py hon and QGIS o spa ial p ocessing, ensu ing anspa ency and ep oducibili y. Gi en he ligh weigh na u e o he algo i hms and he localized scope o he analysis, he compu- a ional cos and en i onmen al impac emain minimal compa ed wi h mo e in ensi e AI amewo ks. Tha said, he me hodology has limi a ions. The mul isou ce s a egy equi es ha - monizing da a om di e en p o ide s, pa icula ly ma ching Google Places POIs wi h cadas al p emises. This p ocess in ol es add ess s anda diza ion, and is subjec o geolo- ca ion e o s, misma ches, o absen eco ds. In such cases, we assigned a e age ca ego y su ace alues, which in oduces some unce ain y in o he spa ial analysis. Es ima ing maximum occupancy also assumes ha he highes GPT- eco ded alue e lec s he legal capaci y o each enue, which may no align wi h ac ual peak ac i i y o compliance [ 76 ]. Fu he mo e, GPT’s olling a e age is sensi i e o excep ional e en s (e.g., holidays, closu es), po en ially skewing he baseline o no mal beha io . We also ecognize he dependency on a p op ie a y pla o m. Al hough GPT p o ides ine-g ained, publicly accessible da a, changes in i s access policies o da a a chi ec u e could a ec u u e esea ch con inui y. Mo e undamen ally, Google Places and GPT may o e ep esen comme cial ac i i y while unde ep esen ing sec o s like educa ion, heal hca e, o in o mal uses. Despi e he widesp ead use o sma phones and LBSNs, digi al beha io is s ill shaped by sociodemog aphic biases, meaning some popula ion g oups a e unde ep esen ed [77]. Finally, while his s udy segmen s u ban space based on mo phological s uc u e, i does no di ec ly quan i y u ban o m h ough a iables like block size, densi y, o connec i i y. Fu u e wo k should build on his amewo k by in eg a ing spa ial indica o s in o co ela ion models [ 59 , 63 ], o mo e clea ly a icula e how u ban o m in luences usage dynamics. 5. Conclusions This pape p esen s a me hod o he spa io empo al analysis o u ban dynamics, ocusing on he concen a ion o people wi hin a ci y. I e alua es he e ec i eness o using Google Maps and land use da a, combined wi h ML echniques o measu e ine-g ained u ban occupancy pa e ns. The a ailabili y o geosocial da a h ough LBSNs o e s new oppo uni ies o de ailed s udies o he spa io empo al concen a ion o people wi hin ci ies. As he usage o hese pla o ms g ows ac oss a ious popula ion g oups, he po en ial and ep esen a i eness o hese da a a e expec ed o inc ease, p o ided ha public access o such da a emains a ail- able. Howe e , p e ious s udies ha e no conside ed ei he he capaci y o he in e ac ion be ween POIs. So, he in eg a ion o s a ic da a and publicly accessible geo empo al da a wi h ML ep esen s a signi ican ad ancemen in he s udy o u ban dynamics. This me hodology ISPRS In . J. Geo-In . 2025,14, 221 20 o 23 no only s anda dizes da a analysis o people agglome a ion bu also enables scalabili y o o he u ban con ex s. The indings demons a e ha by analyzing geoloca ed, ime-s amped big da a, i is possible o model he ac i i y pa e ns o speci ic u ban nodes. This me hodology enables de ailed spa io empo al analysis, o e ing aluable insigh s in o he hou ly beha io o people in a ci y in ela ion o u ban o m and i s unc ions ep esen ed by spa ially ela ed POIs. The es ima ion o he olume o people, using ca ego y-dependen densi y a ios and he loo a ea o POIs, weigh ed by GPT da a allows o an hou ly capaci y calcula ion o each loca ion. This app oach in ol es a e e se-enginee ing p ocess using pe cen age alues p o ided by Google om agg ega ed and anonymized da a, all o which is publicly accessible. Fu he mo e, his s udy con i ms ha he ela ionship be ween land use and ac i i y pa e ns emains a c i ical ac o in u ban li e. The case s udy also highligh s he impo ance o unde s anding he spa ial in e ac ion o di e en POIs ca ego ies in shaping u ban dynamics h oughou he day. The insigh s gained om clus e ing spa io empo al ac i i y pa e ns p o ide a amewo k o a ge ed u ban planning in e en ions, as he obse ed empo al hy hms in a eas wi h mixed land use ein o ce he ole o u ban o m and unc ion in shaping ac i i y pa e ns. I also allows o he iden i ica ion o cha ac e is ic u ban pa e ns o hy hms by in- co po a ing ML echniques in o he analysis o u ban big da a. Fu he mo e, he clus e ing o ime se ies da a o e s a clea isual ep esen a ion o u ban a eas ha sha e simila unc ionali y and ac i i y pa e ns, e en when hose a eas a e geog aphically discon inuous. By in eg a ing empo al and spa ial da a on he p esence o people in a ci y, a new dimension is in oduced o he complex analysis o u ban dynamics. In conjunc ion wi h he s udy o u ban s uc u e and o m, his app oach enables he iden i ica ion o imbalances occu ing wi hin a ci y, as well as he cha ac e iza ion o la ge dep essed o low- i ali y a eas. Addi ionally, i allows o he de ec ion o zones wi h excessi e occupancy, which may be conside ed sa u a ed. The e o e, he p oposed me hodology in oduces a no el app oach ha suppo s da a-d i en u ban planning decisions, which could be applied in he e alua ion and design o municipal policies in a ious a eas such as mobili y, he de elopmen o local economies, o he loca ion o public acili ies, o example. On he o he hand, as he me hodology is s anda dized, i is ans e able and applicable o o he ci ies. Mo eo e , his s udy lays he g oundwo k o mo e ad anced me hods ha can u he explo e he complexi ies o u ban phenomena. Fu u e esea ch could aim o c ea e mo e ep esen a i e samples o he popula ion, educing biases in social ne wo k da a ela ed o age, gende , o o igin. Ex ending he s udy pe iod o include seasonal a ia ions o mo e days o he week would also allow o compa a i e s udies o occupancy pa e ns, p o iding a mo e accu a e ep esen a ion o eali y and se ing as a aluable ool o ci y design and managemen . Au ho Con ibu ions: Concep ualiza ion: Mikel Ba ena-He án, Ola z G ijalba and I zia Mod ego- Mon o e; Da a cu a ion: Mikel Ba ena-He án; Fo mal analysis: Mikel Ba ena-He án; Funding acquisi ion: Ola z G ijalba; In es iga ion: Mikel Ba ena-He án, Ola z G ijalba and I zia Mod ego- Mon o e; Me hodology: Mikel Ba ena-He án; P ojec adminis a ion: Ola z G ijalba; So wa e: Mikel Ba ena-He án; Resou ces: Mikel Ba ena-He án and I zia Mod ego-Mon o e; Supe ision: Ola z G ijalba; Valida ion: Mikel Ba ena-He án, Ola z G ijalba and I zia Mod ego-Mon o e; Visualiza ion: Mikel Ba ena-He án and I zia Mod ego-Mon o e; W i ing—o iginal d a : Mikel Ba ena-He án, Ola z G ijalba and I zia Mod ego-Mon o e; W i ing— e iew and edi ing: Mikel Ba ena-He án, Ola z G ijalba and I zia Mod ego-Mon o e. All au ho s ha e ead and ag eed o he published e sion o he manusc ip . ISPRS In . J. 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