Vernacular boundaries of historic neighborhoods in the city of Lisbon

i Ve nacula bounda ies o his o ic neighbo hoods in he ci y o Lisbon Mónica So ía Roncancio Blanco ii Ve nacula Bounda ies o His o ic Neighbo hoods in he Ci y o Lisbon Disse a ion supe ised by P o . D . Ma co Oc  io T indade Painho, PhD NOVA In o ma ion Managemen School Lisbon, Po ugal Co-supe ised by: Vicen e De Aze edo Tang NOVA In o ma ion Managemen School Lisbon, Po ugal P o . D . S en Cas eleyn, PhD Uni e si a Jaume I Cas ell, Spain Feb ua y, 2024 iii Decla a ion o O iginali y I decla e ha he wo k desc ibed in his documen is my own and no om someone else. All he assis ance I ha e ecei ed om o he people is duly acknowledged and all he sou ces (published o no published) a e e e enced. This wo k has no been p e iously e alua ed o submi ed o NOVA In o ma ion Managemen School o elsewhe e. Lisbon, Feb ua y 26, 2024 Mónica So ía Roncancio Blanco [ he signed o iginal has been a chi ed by he NOVA IMS se ices] i Acknowledgmen s Fi s , I ex end my deepes g a i ude o P o . D . Ma co Painho o his suppo and guidance h oughou his academic jou ney. He has been an inc edible p o esso and human being. I am also hank ul o D . S en Cas eleyn and Vicen e De Aze edo Tang o hei insigh s and sugges ions as my hesis supe iso s. Thei pa ience and guidance ha e been in aluable. Addi ionally, My belo ed iend Nyi Nyi Nyan Lin dese es special men ion o his in aluable suppo o e hese 6 mon hs, o e ing always accu a e ecommenda ions o imp o e my p ojec . This p og am has a o ded me he oppo uni y o ul ill my d eam o s udying ab oad, allowing me o be pa o h ee Eu opean Uni e si ies and o es ablish academic, p o essional, and pe sonal connec ions. Finally, my hea el hanks go o my amily and iends o hei daily suppo om a a . This new academic and pe sonal achie emen is undoub edly o hem. This hesis is one o he ou pu s o a p ojec unded by he Fundação pa a a Ciência e a Tecnologia (FCT), named "Ci yMe," wi h he P ojec Re e ence (EXPL/GESURB/1429/2021). Ve nacula bounda ies o his o ic neighbo hoods in he ci y o Lisbon Abs ac This esea ch in es iga es he delinea ion o e nacula bounda ies in Lisbon's his o ic neighbo hoods: Al ama, Mou a ia, and Bai o Al o, he s udy explo es he in eg a ion o pe cei ed bounda ies de i ed om esiden s' ske ches and geo- agged da a om online ac i i ies. U ilizing a wo-phase me hodology, he esea ch i s ex ac s pe cei ed bounda ies h ough web-based su eys ca ego ized by esiden s' leng h o s ay, dis inguishing be ween sho - e m (less han 10 yea s) and long- e m esiden s (mo e han 10 yea s). This app oach allows o an o e lay analysis, iden i ying Co e and Domain egions based on consensus h esholds. Secondly, he s udy e ie es geo- agged bounda ies using A- DBSCAN and alpha-shape algo i hms o analyze online ac i i y, o e ing a compa a i e analysis wi h he pe cei ed bounda ies. The indings e eal a nuanced unde s anding o how esiden s and online use s concep ualize neighbo hood spaces, highligh ing disc epancies and con e gences be ween pe cei ed and digi al mappings. By calcula ing he In e sec ion O e Union (IOU) and F-sco es, he esea ch quan i a i ely assesses he o e lap be ween di e en da a sou ces, iden i ying he mos accu a e delinea ions ha e lec he his o ic neighbo hoods' spa ial eali y. This s udy con ibu es o u ban planning and policymaking by p o iding insigh s in o esiden s' spa ial pe cep ions, emphasizing he impo ance o conside ing bo h li ed expe iences and digi al oo p in s in he mapping o u ban a eas. The esea ch unde sco es he po en ial o combining adi ional su ey me hods wi h inno a i e geo-spa ial echnologies o enhance he p ecision and ele ance o u ban geog aphic s udies. Sus ainable De elopmen Goals (SGD): i Keywo ds Ve nacula Bounda ies Pe cei ed Bounda ies Use Gene a ed Con en Clus e ing Analysis Geog aphic In o ma ion Science Spa ial analysis His o ic neighbo hoods ii Ac onyms A-DBSCAN – App oxima e Densi y-Based Spa ial Clus e ing o Applica ions wi h Noise GIS – Geog aphic In o ma ion Sys ems IOU – he In e sec ion O e Union NBHD –Neighbo hood UGC – Use Gene a ed Con en iii Index o he Tex Acknowledgmen s .................................................................................... i Abs ac ....................................................................................................... Keywo ds ................................................................................................... i Ac onyms .................................................................................................. ii Index o he Tex ................................................................................... iii Index o Tables ........................................................................................... x Index o Figu es ....................................................................................... xi 1. In oduc ion ...................................................................................... 1 1.1 Aim and Resea ch Ques ions ......................................................... 2 1.2 P ojec Objec i es ......................................................................... 2 1.3 App oach o Achie ing he P ojec Objec i es ......................... 2 1.4 Thesis O ganiza ion ........................................................................ 3 2.Li e a u e Re iew .................................................................................. 4 2.1 S udying Neighbo hoods, Ve nacula bounda ies, and Place. .................................................................................................................... 4 2.1.1 Neighbo hoods ............................................................................... 4 2.1.2 Ve nacula Bounda ies .................................................................. 5 2.1.3 Places .............................................................................................. 6 2.2 Da a Sou ces o Pe cei ed Bounda ies in Neighbo hood Mapping .................................................................................................... 7 2.2.1 Online Web-Based Su ey ............................................................. 7 2.2.2 Use -Gene a ed Con en (UGC) .................................................... 8 2. 3 Me hods o Bounda ies Ex ac ion ........................................... 8 2.3.1 Pe cep ion-based neighbo hood delinea ion ................................. 8 2.3.2 Bounda ies Based on Online Ac i i y ........................................... 9 2.4 The S udy Si e ................................................................................... 9 2.4.1 Lisboa ............................................................................................. 9 2.4.2 Al ama .......................................................................................... 10 2.4.3 Mou a ia ...................................................................................... 11 2.4.4 Bai o Al o .................................................................................... 12 3. Me hodology .................................................................................... 13 3.1 Online Web-Based Su ey ............................................................ 14 3.1. 1 Da a collec ion & P ocessing ...................................................... 14 3.1. 2 Pe cei ed Bounda ies Ex ac ion .......................................... 19 ix 3.2 Use Gene a ed Con en ............................................................... 22 3.2.1 Da a collec ion ............................................................................. 22 3.2.2 Geo ags selec ion & Ex ac ion. .................................................. 24 3.2.3 Spa ial Clus e ing ........................................................................ 27 3.3 Compa a i e Analysis o Pe cei ed and Geo- agged Bounda ies ............................................................................................. 29 4. Resul s & Discussion ..................................................................... 30 4.1 Pe cei ed Bounda ies Ag eemen Rep esen a ion ................ 30 4.1.1 Al ama .......................................................................................... 30 4.1.2 Mou a ia ...................................................................................... 31 4.1.3 Bai o Al o .................................................................................... 32 4.2 Geo- agged ac i i y de i ed Bounda ies. ................................. 34 4.2.1 Al ama .......................................................................................... 34 4.2.2 Mou a ia ...................................................................................... 35 4.2.3 Bai o Al o .................................................................................... 35 4.3 Compa a i e Analysis o pe cei ed and Geo- agged Bounda ies ............................................................................................. 37 4.3.1 Al ama .......................................................................................... 37 4.3.2 Mou a ia ...................................................................................... 41 4.3.3 Bai o Al o .................................................................................... 45 5. Final Discussion ............................................................................. 49 5.1 Answe ing Resea ch Ques ions ............................................... 49 5.2 Limi a ions ...................................................................................... 51 5.3 Fu u e Scope ................................................................................... 52 6. Conclusions ..................................................................................... 53 7. Bibliog aphic Re e ences ............................................................. 55 Appendix ................................................................................................... 58 4 Chap e 2 2.Li e a u e Re iew 2.1 S udying Neighbo hoods, Ve nacula bounda ies, and Place. 2.1.1 Neighbo hoods Neighbo hoods, wi hin a sociological con ex , a e acknowledged as esiden ial zones cha ac e ized by signi ican social and cul u al composi ions. They a e places whe e esiden s sha e simila cha ac e is ics, d i en by a collec i e s uggle o u ban space and esou ces. This concep ualiza ion o neighbo hoods ep esen s one o he ini ial a emp s o de ine hese a eas. I was p oposed by u ban sociologis s Pa k and Bu gess in 1925. Thei seminal wo k, “The Ci y,” laid he ounda ion o ea ly 20 h-cen u y u ban planning s udies, ma king a signi ican momen in his ield, (Pa k e al.,1925). In he ollowing yea s, con ibu ions like hose om u ban planne Ke in Lynch would highligh i e key elemen s de ining he ci y's image, d awn om esea ch on men al mapping and collec i e pe cep ions. Among hese elemen s, "dis ic s" and "edges" a e closely ied o he concep o neighbo hood s uc u e. Edges ep esen ac ual o pe cei ed bounda ies, delinea ed as linea ea u es dema ca ing he pe iphe ies o a eas known as dis ic s (Lynch, 1964). These dis ic s a y in size om medium o la ge and showcase a di e si y o cha ac e is ics, including ypes o buildings, esiden demog aphics, opog aphy, ac i i ies, his o ical signi icance, and le els o upkeep. Cu en ly, ele ance in he depic ion o hese esiden ial a eas lies in he ac ha neighbo hoods boos social li e, access o public se ices, and su eillance, connec ing and exchanging esou ces wi h o he neighbo hoods (Bae & Mon ello, 2018). On he o he hand, he neighbo hood con o ma ion is ied o he esiden s' in e ac ions, which a e essen ial o main aining hei ib a ion and di e si y h ough wo k, housing, and ec ea ional ac i i ies. Acco ding o Coul on s udy, neighbo hoods a e a collec i e and geog aphical cons uc ion, al hough hese dimensions a e oo ed in social and psychological condi ions, hey a e bound o geog aphic space and as a esul , ob aining meaning ul spa ial 5 ep esen a ions o neighbo hoods is necessa y o he enac men o local policies (Coul on e al., 2013). 2.1.2 Ve nacula Bounda ies Due o he exis ence o unclea bo de lines, e nacula egions a e dis inguished by s ong cul u al linkages ha p oduce spa ial expansions ha a e no sha p, such as ci y cen e o his o ic neighbo hoods. Some w i e s, such as W. Zelinsky (Zelinsky, 1980), in es iga ed how people pe cei e space and how place awa eness o egional consciousness c ea es an a achmen sense based on popula cul u e. One o his mos no able p ojec s was he iden i ica ion o cul u al egions in No h Ame ica by analyzing he equency o local place names and businesses. Schola s ha e ound p ac ical implica ions in he analysis o e nacula egions because o icial adminis a i e uni s o census blocks do no necessa ily ep esen neighbo hood o egion ex ension as spa ial and social en i ies (Hollens ein & Pu es, 2010). Consequen ly, adminis a i e a e ac s a e o e lain in a a ie y o ac o s associa ed o collec i e o indi idual people beha io s be ween social, economic, and en i onmen al ea u es such as housing sys ems, land use, and accessibili y and whose esiden ’s pe cep ion s a e emains in cons an change hus e nacula s udies going ocus on he ac o s' ex ension, con ibu ing o policymaking. Fo E ans (E ans & Wa e s, 2007), we li e daily in e nacula egions ha we accen ua e wi h geog aphical e ms ha a e no o mally ep esen ed. Fo example, "High c ime a eas". The e ms ac as a ool o unde s and he ne wo ks o sociolinguis ic communi ies wi h sha ed unde s andings; while o Coul on, he e nacula egions scale is ano he ea u e o he s udy whose dwelle s pe cep ion can be smalle o bigge acco ding o hei own expe iences (Coul on e al., 2013). Inco po a ing e nacula egions in o esea ch in oduces signi ican challenges due o he subjec i e na u e o da a de i ed om indi idual and communi y pe cep ions, leading o a ied de ini ions and in e p e a ions. These challenges include di icul ies in da a collec ion, whe e he di e si y in pe sonal desc ip ions o su oundings can esul in inconsis encies when mapped agains o icial geog aphic bounda ies, Addi ionally, he eliabili y o subjec i e pe cep ions aises ques ions, as pe sonal biases and expe iences may hea ily in luence he delinea ion o e nacula a eas, (Deng, 2016). To add ess hese issues, inno a i e me hodologies a e equi ed o ha monizing e nacula bounda ies wi h o icial geog aphic da a, necessi a ing he de elopmen o in o ma ion sys ems ha p io i ize lay use pe spec i es o e adi ional adminis a i e geog aphy. Such sys ems employ echniques like c owdsou ced mapping and pa icipa o y 6 GIS o in eg a e e nacula a eas in o s anda d da ase s, b idging he gap be ween subjec i e pe cep ions and objec i e da a. 2.1.3 Places The con ibu ion in he de ini ion o "Place" is wide due o ields such as geog aphy, psychology, and an h opology among o he s being in ol ed. The unique iden i y and signi icance seem o be ema kable ea u es o place de ini ion, acco ding o he geog aphe Edwa d Relph in his book "Place and Placelessness" he claims his esea ch me hod is "a phenomenology o place" ha elies on he in e p e a i e s udy o human expe ience. Fo him, a place is a geog aphical a ea wi h a dis inc iden i y and signi icance o people. Human expe iences, memo ies, and cul u al impo ance a e all p esen in places. They os e a eeling o iden i ica ion and a achmen o people and communi ies. Belonging a place shapes people's pe sonali ies c ea ing deep connec ions wi h he sense o sel , places in luence how communi ies o m, how people ela e o each o he , and how social no ms a e es ablished; on he o he hand, cul u al and his o ical meaning a e con en in hese places displaying s o ies, cul u al he i age, social memo ies and iding wi h en i onmen al ela ionships. As an example o he a o emen ioned. Two au ho s He nandez's esea ch on place iden i y and Anac a e al.' s udy on spa ial ep esen a ion h ough ou e ins uc ions con ibu e o ou unde s anding o how indi iduals connec wi h and de ine hei neighbo hoods. He nandez's wo k del es in o he emo ional bonds people o m wi h hei en i onmen , measu ing he in ensi y o place a achmen and place iden i y h ough ques ionnai es,(He nández e al., 2007). These ques ionnai es assess a ious en i onmen al scales and neighbo hood, ci y, and island o measu e he ' ype o bond' ha indi iduals ha e wi h each. This app oach unde sco es he signi icance o place in shaping pe sonal iden i y and social dynamics. On he o he hand, Anac a app oach he concep o neighbo hood om a spa ial pe spec i e, explo ing how people cogni i ely map a eas wi h indis inc bounda ies when gi ing ou e di ec ions, (Anac a e al., 2017). They ocus on 'Neighbo hood' as a ca ego y wi hin ske ch mapping, highligh ing he social use o sha ed acili ies and he homogenei y o esiden ial o s uc u al cha ac e is ics as de ining elemen s. By combining he indings om bo h s udies, we gain a comp ehensi e iew o how place iden i y and spa ial cogni ion in e play in neighbo hoods' concep ualiza ion and li ed expe ience, enhancing ou insigh s in o u ban social geog aphy and he psychology o space. 7 2.2 Da a Sou ces o Pe cei ed Bounda ies in Neighbo hood Mapping In he ield o neighbo hood mapping, i is essen ial o add ess he concep o cogni i e egions in o mal a eas cha ac e ized by unclea bounda ies, membe ship s a us, ex ension, loca ion, and shape, which a e in luenced by he dis ibu ion o communi ies o he pe cep ions o esiden s. These egions' spa ial p ope ies a e also shaped by cul u al ac o s, adding o hei complexi y. A comp ehensi e unde s anding o socio-demog aphics and en i onmen al cha ac e is ics is i al o achie ing accu a e spa ial delinea ion. This p ocess in ol es p ecisely de ining he bounda ies and ea u es o hese a eas, conside ing hei in o mal na u e and he impac o cul u al and social elemen s. Cogni i e egions p esen signi ican challenges in geog aphic in o ma ion science, mainly due o hei agueness, he di icul y in ep esen ing hem accu a ely in GIS ools, hei a ying scales, and hei lack o homogenei y o consis ency. To cap u e he di e se pe cep ions and expe iences o esiden s, se e al da a sou ces a e used, including su eys and in e iews ha e i y he in e iewees' de ined cha ac e iza ion, pa icipa o y mapping exe cises, analysis o social media, and examina ion o his o ical eco ds. Addi ionally, p eexis ing adminis a i e (no ably census) da a and o he agency-de ined zones se e as p oxies o neighbo hood delinea ions. This p ojec employs wo o hese da a sou ces, which will be explained in de ail below. 2.2.1 Online Web-Based Su ey Su eys ha e been a adi ional me hod o comp ehending cogni i e bounda ies. They in ol e di ec ly que ying local inhabi an s abou hei pe cep ions o neighbo hood limi s. In ad ancing he mapping o di use a eas, su eys ha e been u ilized no only in delinea ing neighbo hood a eas bu also in add essing issues a ec ing neighbo hoods. Fo ins ance, he s udy by E ans & Wa e s employed a su ey o assess he high le els o c ime in LA ci y, iden i ying a eas wi h a high isk o c ime. This app oach allowed esea che s o ga he nuanced insigh s in o how esiden s men ally map hei su oundings, (E ans & Wa e s, 2007). The s udy by Wes e hol e al., used a web mapping-based su ey ha le esponden s map and a e a eas in he ci y ela ed o poin s o in e es ex ac ed om Google Places, showing how indi iduals' sense o place in luences hei pe cep ion o neighbo hood bounda ies, (Wes e hol e al., 2022). 8 2.2.2 Use -Gene a ed Con en (UGC) UGC is ano he ich da a sou ce o s udying human beha io , sen imen , and pe cep ion o he u ban en i onmen . Social media analysis p o ides a mode n, da a- ich sou ce o explo ing pe cei ed bounda ies. The s udy by Hollens ein & Pu es demons a es how use -gene a ed con en on pla o ms like Flick can o e insigh s in o how people pe cei e and in e ac wi h u ban spaces. This analysis allows o he ex ac ion o cogni i e bounda ies based on he agg ega ion o indi idual expe iences and exp essions cap u ed h ough social media, (Hollens ein & Pu es, 2010). Mo eo e , he wo k o Huang emphasizes he po en ial o combining "big da a," ep esen ed by geo ags and images om Ins ag am and Twi e , wi h "small da a," such as ques ionnai es and ske ch maps. Thei s udy unde lines he impo ance o analyzing social media con en o map he cogni i e landscapes o ci ies, e ealing how esiden s and isi o s pe cei e hem. Findings sugges ha social media analy ics can p o ide a eliable measu e o pe cei ed ci y images, wi h pla o ms like Ins ag am explaining public pe cep ion associa ed wi h ou is a ac ions and landma ks, while Twi e is linked o he place’s ele ance o e e yday li e enues o communi ies. These esul s ha e p ac ical implica ions, especially in u ban planning, o e ing aluable in o ma ion o policymake s and ci izens alike, (Huang e al., 2021). 2. 3 Me hods o Bounda ies Ex ac ion 2.3.1 Pe cep ion-based neighbo hood delinea ion I s me hod in ol es ga he ing unique insigh s h ough men al mapping, highligh ing he cogni i e mapping o neighbo hoods as desc ibed by (Dal on & Hu ell, 2023). These mappings a e de ined as g aphic and subjec i e ep esen a ions ha cap u e no jus he physical layou bu also he emo ional and cul u al associa ions indi iduals hold wi h hei communi ies. This app oach measu es cogni i e ep esen a ions by sampling esiden s, collec ing socio-demog aphic ea u es, and e ie ing men al ep esen a ions h ough su eys o in e iews. The collec ed da a hen acili a es spa ial analysis abou co e a eas. The le el o ag eemen among he men al maps se es as a measu e o membe ship, e ealing collec i e pe cep ions o neighbo hood bounda ies. 9 2.3.2 Bounda ies Based on Online Ac i i y (Deng, 2016) discussed he in e ac ions among esiden s and how hese social p ocesses ela e o geog aphic pa e ns and neighbo hood o ma ion, ocusing on physical ne wo ks. The me hod gains ele ance wi h he inco po a ion o Use -Gene a ed Con en (UGC), as i cha ac e izes a neighbo hood and e eals de ails o i s social composi ion and en i onmen , ans o ming geog aphical da a in o neighbo hood pa e ns. A case s udy in Spain u ilized da a om social media pla o ms like Google Places, Fou squa e, Twi e , and Ins ag am. P e ious esea ch has shown he complemen a i y o hese ou sou ces o in e ing aluable insigh s ega ding he spa io empo al use o ci y spaces and people’s p e e ences, aiming o le e age he a ailabili y o use -gene a ed da a sou ces (Be nabeu-Bau is a e al., 2023). Addi ionally, s udies by (Gao e al., 2017a) and (Tang e al., 2022) use use -gene a ed con en o de e mine he delimi a ions o cogni i e egions. They pe o m clus e analysis using algo i hms such as A-DBSCAN o iden i y poin clus e s based on he collec ed da a, aiming o ind co e a eas ha ep esen a consensus among a signi ican numbe o use s who ag ee ha hese a eas ep esen egional o neighbo hood ex ensions. 2.4 The S udy Si e 2.4.1 Lisboa Lisbon, he capi al o Po ugal, is a dynamic ci y wi h a ich his o y. I s a ed as a Phoenician ade pos , was aken o e by he Romans and hen he Muslims and became he depa u e po o he amous Po uguese explo e s. I 's one o he oldes ci ies in Eu ope. Nowadays, i 's a popula des ina ion o in e na ional s uden s, ou is s, and o eign esiden s (Tang e al., 2021). Each phase o Lisbon's de elopmen esul ed in a ious se lemen s, now ecognized as he his o ic neighbo hoods in he ci y cen e . Fo his s udy, h ee neighbo hoods, Al ama, Mou a ia, and Bai o Al o, ha e been selec ed. These a eas, signi ican o hei his o ic impo ance and he need o clea delinea ion, a e si ua ed wi hin di e se adminis a i e bounda ies Figu e 1. Some o he main cha ac e is ics o each his o ic neighbo hood a e depic ed in he nex sec ion. 10 Figu e 1. F eguesías Bounda ies and Pe cei ed His o ic Neighbou hoods loca ion map 2.4.2 Al ama Named a e he A abic al-hamma. The hill whe e Al ama is loca ed, along wi h he nea by cas le hill, we e among he i s se led a eas ha con ibu ed o Lisbon's o igins. The pa o Al ama s e ches along he hill be ween he Chu ch o São Miguel and San o Es ê ão, wi h Rua da Reguei a a i s co e. The neighbo hood ex ends up o whe e he Escolas Ge ais used o be, a esidence o uni e si y s uden s un il he 16 h cen u y. A he hill's lowe end lies he sho e o he Tejo Ri e , once a cen e o i e comme ce and a ishing neighbo hood du ing he 13 h and 14 h cen u ies. Al ama has always been a ou is a ac ion due o being he oldes pa o he ci y, Moo ish- oo ed a chi ec u e ha p ese es he ancien medie al A ab oad mall wi h alleyways, na ow s ee s and s ai s ha make i amous as well as i s p oximi y o he São Jo ge cas le and i s connec ion wi h Fado, he adi ional Po uguese olk music. This neighbo hood has an impo an social isibili y ha is widely ecognized in aspec s such as li e a u e, pain ing, and music in he ci y o Lisbon, becoming a his o ical he i age spo (Da Cos a, 2008). In pas cen u ies, Al ama was home o bo h a is oc a s and wo king-class indi iduals. The di e se backg ounds o i s esiden s os e ed a s ong sense o communi y, oo ed in long- e m enancy and he social connec ions 11 o med among neighbo s (Da Cos a, 2008). Howe e , ecen challenges such as o e - ou ism, he ise in lodging phenomena, and damage o p ope ies indica e ha Al ama is unde going signi ican ans o ma ions, g adually mo phing in o a p edominan ly ou is dis ic (Cocola-Gan & Gago, 2021). A p esen , Al ama is enowned as Lisbon's mos pic u esque neighbo hood, o en seen as a jou ney back in ime. I 's a popula spo -on Ins ag am, equen ly showcased by ou is s, ERASMUS s uden s, and empo a y esiden s, (Bap is a e al., 2018). 2.4.3 Mou a ia Mou a ia neighbo hood is loca ed be ween he alley o he Plaça Ma in Moniz and he hills o he cas le and G aça neighbo hood ha was he a ea designa ed o he de ea ed Moo s a e he occupa ion o he ci y by D. A onso Hen iques as pa o he Ch is ian conques o e he Muslims in 1147. Due o i s b oken opog aphy he neighbo hood de eloped a layou i egula , he ini ial occupa ion su ounded he cas le hill going down he slope o he wa e alley ha exis ed in he pas and is cu en ly Almi an e Reis A enue. Mou a ia is no able o i s medie al layou , a e lec ion o A ab a chi ec u al in luences, cha ac e ized by small s ee s, alleys, and na ow s ai cases. Addi ionally, he neighbo hood's his o ical seg ega ion, as i was no included wi hin he Fe nandina’s wall in he 14 h cen u y, mean ha i did no appeal o he weal hie classes o ha pe iod, (Da Cos a, 2008). Mou a ia's his o ical ac i i ies we e cen e ed a ound i s la ge Mosque, which was loca ed whe e he Chu ch o Nossa Senho a do Soco o is cu en ly loca ed. Addi ionally, in he a ea ha is now Ma im Moniz Squa e, he e was once a smalle mosque along wi h public ba hs. This a ea held signi ican comme cial impo ance, as Moo ish c a sman se ed a la ge Ch is ian cus ome . Howe e , in 1496, he expulsion o Jewish and Muslim mino i ies om Po ugal led o he ans o ma ion o Mosques and Islamic s uc u es in o chu ches and con en s. These eligious buildings we e p ima ily u ilized by he nobili y and cle gy, who played a pi o al ole in inco po a ing Mou a ia in o he b oade ci yscape, (Langens, 2022). Ea ly in he 19 h cen u y, he neighbo hood ell in o po e y and c ime, wi h ba s and p os i u ion abounding. Howe e , his agic pe iod was he pe ec se ing o he appea ance o he adi ional Po uguese musical gen e called Fado, wi h which he adis a Ma ía Se e a ga e i be e cul u al s a e o he “Mo o” neighbo hood. Un o una ely, in he 20 h cen u y and in he name o he mode niza ion o he a ea, he s uc u e o he neighbo hood was al e ed, collapsing se e al o he ep esen a i e monumen s o he 17 h cen u y, (F anco, 2016). E en ually, as pa o Po uguese decoloniza ion p ocesses and cu en immig a ion phenomena, 12 Mou a ia is conside ed he mul icul u al encla e o he ci y cen e , being a comme cial, gas onomic, and social cen e ha allows mul ie hnic ela ions, (Oli ei a, 2019). I is impo an o no e, in ecen yea s, he municipal policies ha e es ablished s a egies aimed a he egene a ion o Mou a ia. These s a egies in ol e ans o ming i s s igma ized image in o ha o a ehabili a ed space, bo h in e ms o in as uc u e and by p o iding access o cul u al ac i i ies. These ini ia i es a e designed o enhance he neighbo hood's iden i y, adi ion, and di e si y (Es e ens e al., 2019). 2.4.4 Bai o Al o The Bai o Al o h o e be ween he end o he 15 h and 16 h cen u ies as a esponse o he e oca ion o he manda e ha limi ed he ci y o de elop only wi hin he medie al Fe nandina wall, which sough o make he cen al a ea o he ci y and he po he social and comme cial axes. The modi ica ion allowed his neighbo hood o begin he p ocess o Lisbon as a mode n ci y whe e he popula classes would begin hei loca ion ex e nally, mos ly on he i e bank in ish ades, which changed o e ime on he no he nmos pa . Palaces, con en s, and chu ches led by me chan s and bou geois amilies o ha ime we e loca ed on his hill, (Da Cos a, 2008). Wi h i s e olu ion, Bai o Al o was cha ac e ized by being a neighbo hood wi h g ea e en ila ion han he ci y cen e , especially in imes o he plague, and due o i s opog aphic condi ions, he ainwa e lowed di ec ly in o he Tejo Ri e due o i s long and egula s ee s, which gua an eed be e heal h condi ions, howe e . wi h na ow s ee s ha a he ime educed he condi ions o cha ac e is ic mobili y ha e en oday pe sis s as a neighbo hood wi h a la ge comme cial concen a ion, highligh ing mee ing a eas and social ec ea ion o he ci y's esiden s, (No e, 2020). In con empo a y imes, Bai o Al o has ea ned a epu a ion as a bohemian encla e, a ibu ed o he many jou nalis s and li e a y au ho s who ha e esided he e. Howe e , s a ing in he 1990s, he neighbo hood began o de elop a ib an nigh li e, which has since become he ci y's p emie nigh li e spo . This e olu ion is la gely due o he low o s uden s d awn by a o dable en s and he con enience o local ameni ies (Bap is a e al., 2018). 13 Chap e 3 3. Me hodology The me hodology o his s udy cen e s on explo ing he spa ial de ini ions o h ee his o ic neighbo hoods in Lisbon – Al ama, Mou a ia, and Bai o Al o. This explo a ion is achie ed h ough he analysis o da a ob ained om cogni i e mapping, ex ac ed ia an online web-based su ey, and geo agged online ac i i y om use -gene a ed con en (UGC). Bo h da ase s a e in eg al componen s o he Ci yMe p ojec , an ini ia i e unded by Po ugal's Fundação pa a a Ciência e a Tecnologia (FCT). The Ci yMe p ojec is designed o deepen ou unde s anding o how ci izens men ally map he ci y, he eby enhancing spa ial analysis and communi y pa icipa ion in public policies and u ban planning. A key con ibu ion o he Ci yMe p ojec is he de elopmen o a ully unc ional public websi e (h ps://ci yme.no aims.unl.p /) ha acili a es pa icipan da a collec ion h ough a web-based map su ey and p o ides access o sou ces o geo agged con en om UGC. The wo k low o he s udy is di ided in o wo main lines. The i s phase ocuses on ex ac ing ep esen a i e bounda ies o he h ee his o ic neighbo hoods. This p ocess in ol es collec ing pa icipan ske ches h ough he web-based map su ey. This de elopmen al line de ails da a collec ion and p ocessing me hods, ex ac ion echniques o pe cei ed bounda ies, and culmina es in he ep esen a ion o he ag eemen on pe cei ed bounda ies o Al ama, Mou a ia, and Bai o Al o. The second phase o he wo k low add esses he e ie al o ep esen a i e bounda ies om a ious sou ces o geo- agged online ac i i y. This pa o he s udy ou lines he speci ics o da a collec ion, con en selec ion c i e ia, coding p ocesses, clus e ing ex ac ion, and ul ima ely, he gene a ion o bounding shapes o he his o ic neighbo hoods. The inal phase o he s udy is whe e hese wo phases con e ge, enabling a quan i a i e compa ison o he spa ial de ini ions de i ed om bo h cogni i e mapping and geo agged online ac i i y. This concluding phase in ol es calcula ing me ics o unde s and he ela ionship be ween he neighbo hood ex ensions gene a ed by he wo me hodologies. The igu e 2 illus a es he s uc u e o he s udy me hodology. 20 Figu e 9. O e lap ag eemen maps by polygon coun o sho - and long- e m esiden sin Al ama, Mou a ia, and Bai o Al o his o ic neighbo hoods! 21 This s udy employed he Minimum-Maximum s anda diza ion me hod o make uni o m he coun ield o o e lapping en i ies, es ablishing alues anging om 0 o 100 (Pe cen age). Consensus h esholds we e chosen, suppo ed by he li e a u e, which shows pe cen ages ha embody he esiden 's delinea ion bounda ies and hei iden i y e ec s using ske ch maps (Bae & Mon ello, 2018).Con o ming o he s udy, he a eas o g ea es ag eemen we e >75 pe cen which we will call he Co e egion, and a eas indica ing a leas 50 pe cen bu no mo e han 75 pe cen ag eemen , i will call Domain egion. The ca ego iza ion in o "Co e" and "Domain" egions was chosen based on Meinig's con ibu ion, who he ex en o he egion occupied by Mo mon cul u e o e ime in concen ic ci cles, ocusing on he s a e o U ah and sou heas e n Idaho in he Uni ed S a es o Ame ica. His model, as p esen ed in 1965, is adop ed and adap ed o he neighbo hood le el in his s udy, (Meinig,1965). The in eg a ion o hese ca ego ies wi h consensus h esholds is depic ed in Figu e 10. Figu e 10. Neighbo hood Regions Scheme A his s age, he ou come yields he s udy's g ound u h, which becomes an in aluable p ima y sou ce encapsula ing uni ied bounda ies and esiden s' de ailed pe cep ions o neighbo hoods. This g ound u h se es as a benchma k o e alua ing he accu acy o maps p oduced om use - gene a ed con en , and i plays a i al ole in disce ning he di e ences and simila i ies be ween local pe cep ions and digi al depic ions de eloped in he nex s age o he s udy. 22 3.2 Use Gene a ed Con en 3.2.1 Da a collec ion The s udy inco po a es a second da ase de i ed om use -gene a ed con en . This da ase is composed by geo agged pos s, pages, ad e isemen s, poin s o in e es (POIs), and hei espec i e a ibu es, including ex ual da a, all o which we e ga he ed om a a ie y o social media pla o ms, i ’s impo an o no e ha he da a ex ac ed om hese sou ces came om public p o iles o use s. The ex ac ion o his da a was made possible using bo h o icial and uno icial APIs, and i was unde aken as an in eg al pa o he Ci yMe p ojec . The Ci yMe p ojec o e s a unique and comp ehensi e pe spec i e on he spa ial dynamics o Lisbon by combining su eyed da a (Using in he i s s age o his esea ch) wi h use -gene a ed con en om social media pla o ms. This app oach p o ides a mo e holis ic unde s anding o he a ea; in consequence, A key elemen o he Ci yMe p ojec UGC sec ion is he Ci y Me-UGC online eposi o y, accessible a h ps://gi hub.com/Ci yMe-p ojec /Ci yMe-UGC, enabling o pe o m spa ial analyses ha compa e and cha ac e ize egions based on bo h su eyed da a and he use -gene a ed con en . In his segmen o he s udy, he ocus is on he me hodologies used o ex ac ing ep esen a i e bounda ies om use -gene a ed con en . Da a was ga he ed om h ee social ne wo ks Flick , Twi e , and Ins ag am speci ically a ge ing men ions o he names o his o ic neighbo hoods ele an o his s udy wi hin he ex ual con en . The ex ac ion p ocess was ca ied ou h ough Py hon coding. This app oach allowed o a comp ehensi e analysis o use in e ac ions and e e ences o hese his o ic neighbo hoods. The ollowing con en p o ides a b ie o e iew o he s uc u e o he h ee sou ces and desc ibes he unique cha ac e is ics and con ibu ions o each pla o m o he esea ch. Flick Flick ope a es as a digi al pla o m o pho o managemen , enowned o i s ex ensi e collec ion o geo agged images, as no ed by (Gao e al., 2017a). On Flick , use s ha e he capabili y o upload and dissemina e hei pho og aphs, ei he wi hin speci ic Flick g oups o o a b oade audience. These images a e o en accompanied by use -gene a ed ags, p o iding desc ip i e con ex , and a e geo agged wi h p ecise coo dina es. This 23 ea u e is pa icula ly use ul o esea ch, as Flick is p edominan ly u ilized by ou is s, which aligns wi h Lisbon's s a us as a sough -a e a el des ina ion. The geo e e encing o pho og aphs on Flick can be accomplished ei he au oma ically o manually, le e aging he loca ion- based echnologies embedded in sma phones and came as. Twi e Twi e is ecognized as a p ominen social ne wo k pla o m, p ima ily u ilized o sha ing opinions and news epo ing. Acco ding o (Li e al., 2013), use -gene a ed da a om Twi e o e s aluable insigh s in o he cha ac e is ics o a ious loca ions and he demog aphics o people who equen hese places, whe he hey a e esiden s, wo ke s, o ou is s. This aspec o Twi e makes i a signi ican sou ce o unde s anding social dynamics and public pe cep ion. Fu he mo e, as poin ed ou by (Sal a o e e al., 2021), Twi e da a can se e as a supplemen a y sou ce o in o ma ion, complemen ing s a is ical da a ob ained om o icial sou ces. The b ie , ye exp essi e messages on Twi e o en e lec people's ins an eac ions and pe cep ions abou a ious aspec s o hei en i onmen , such as neighbo hood sa e y and c ime le els. These wee s no only p o ide indi idual pe spec i es bu can also igge g oup eac ions, o e ing a eal- ime glimpse in o public sen imen . Ins ag am The sha ed ex s on Ins ag am o e se e al bene i s, as ou lined by (Be nabeu-Bau is a e al., 2023). These include insigh s in o he empo al and spa ial ends o people a speci ic loca ions, he spon aneous eac ions and pe cep ions indi iduals ha e o hei su oundings now o pos ing, and he iden i ica ion o pa e ns ela ed o ac i i ies in u ban spaces. Such pa e ns e eal he di e si y o p e e ences, mobili y, and iden i y o ci ies a a ious poin s o in e es . Ini ially, he selec ion o geo e e enced con en on Ins ag am was done manually. Howe e , his p ocess la e e ol ed o u ilize in eg a ed posi ioning echnology, enhancing he accu acy and e iciency o geo e e encing. This echnological shi has allowed o a mo e p ecise and comp ehensi e unde s anding o how people in e ac wi h and pe cei e hei u ban en i onmen s. 24 3.2.2 Geo ags selec ion & Ex ac ion. In his esea ch, we ocused on s anda dizing he da a a ibu es ga he ed om in e ac ions on Flick , Twi e , and Ins ag am. The key a iables s anda dized we e he men ion o neighbo hood names wi hin he ex ual con en and hei geog aphical loca ions. This da a was compiled o e a decade, om 2012 o 2022, a pe iod du ing which con en ela ed o he ci y o Lisbon was a ailable ac oss all hese pla o ms. The un il e ed da ase s comp ised: 5,669,531 eco ds o Twi e , 50,946 o Flick , and 35,657 o Ins ag am. The s anda diza ion p ocess was essen ial o ensu ing consis ency and accu acy in he analysis o he da a collec ed om hese di e se social media sou ces. To ob ain he ele an neighbo hood desc ip ion o geo agged da a om social pla o ms, names o neighbo hoods as keywo ds we e ex ac ed om he ex ual a ibu es o he geo agged da a such as he "s a us", "desc ip ion" and " ags" by il e ing ou exac men ions o he keywo ds and associa ed ex ac ed neighbo hood keywo ds (Al ama, Mou a ia and Bai o Al o) wi h he poin coo dina es. In case o mul iple neighbo hood names we e men ioned in he same da a en y, Euclidean dis ances be ween he da a poin and cen oids o men ioned neighbo hoods (Using g ound u h al eady gene a ed as e e ence) we e calcula ed and he nea es neighbo hood o he poin was assigned as he neighbo hood a ibu e o he poin . Coo dina es o poin s om ce ain pla o ms such as Ins ag am and Twi e a e no o ganic, whe e geo agged coo dina es o he da a a e no e ie ed om he use 's ac ual posi ioning sys em bu a he assigned wi h a p e-popula ed coo dina e associa ed wi h he name o he place he use is checking in. This na u e assigns edundan and o e lapping coo dina e poin s o he geo agged da a ega dless o he use 's ac ual loca ion. This becomes p oblema ic when calcula ing he (minimum) dis ance band be ween neighbo ing poin s in iden i ying and c ea ing clus e s since a signi ican amoun o poin s ha e iden ical coo dina es which a ec s he s a is ical calcula ions esul ing nea sub-me e dis ances o he dis ance band. These iden ical/ edundan /o e lapping poin s we e emo ed ill only one unique poin pe coo dina e is le . To add ess he imbalance in geo agged da a om sou ces like Twi e , which signi ican ly exceeds ha om o he sou ces, ou s udy implemen s a me hod o calib a e he da a olume. Following he app oach by (Gao e al., 2017a), which iden i ied a simila disc epancy, we i s es ablish he (a e age/maximum) con ibu ion le el o no mal o o dina y use s. This le el is de e mined by calcula ing he da a o pos s p oduced by he op 10% o ac i e use s, ep esen ing he 90 h pe cen ile o he use base. A h eshold is hen se a his le el, and any use con ibu ions exceeding i 25 a e scaled down acco dingly. U ilizing his adjus ed h eshold ensu es ha each use 's da a con ibu ion is p opo iona e and consis en wi hin he da ase , which is hen used o u he analysis. As a esul , we ob ained he il e ed da ase s wi h he h ee names o his o ical neighbo hoods om e e y sou ce. Impo an o men ion ha he Flick egis e s calling o Mou a ia did no yield esul s. Looking o an explana ion, i was ound ha Mou a ia was men ioned, bu he loca ion o he pos was caugh by Al ama cen oid in luence a ea. Figu e 11 illus a es he quan i ies de i ed a e pe o ming he da a mining p ocess. Figu e 12 displays he ex ac ed da a and a ep esen a ion o he pos ings used as he ex ac ion sou ce. Figu e 11.Numbe o pos s ex ac ed based on he name o each neighbou hood om e e y UGC sou ce. 26 Figu e 12.Dis ibu ion and depic ion o selec ed pos s om h ee di e en UGC sou ces ac oss he his o ic neighbo hoods o Lisbon. 27 3.2.3 Spa ial Clus e ing By ex ac ing a da ase o geo- e e enced social media pos ings om h ee di e en sou ces, each ca ego ized by neighbo hood, he esea ch concen a ed on employing a coding p ocess o iden i ying clus e s o poin s. This goal was accomplished using he App oxima e DBSCAN (A-DBSCAN) clus e ing algo i hm, de eloped by (A ibas-Bel e al., 2021) .The s udy aimed o gain insigh s in o u ban a eas by analyzing he densi y o poin s, pa icula ly hose associa ed wi h buildings wi hin hese a eas. Fo he spa ial clus e ing in his esea ch, p e-ex ac ed geo ags we e u ilized, acili a ing he iden i ica ion o high-densi y clus e s om each sou ce, and de ining hei bounda ies. A-DBSCAN algo i hm is use ul o he calcula ion and gene a ion o shapes ha ep esen he spa ial ex en o neighbo hoods, illus a ing he clus e ing pa e ns o social media geo ags, as highligh ed by (Tang e al., 2022). The applica ion o his algo i hm equi es wo inpu pa ame e s. The i s one, known as he eps pa ame e , ep esen s he maximum dis ance be ween wo poin s necessa y o hem o be conside ed pa o he same neighbo hood o clus e . Fi s ly, a nea es neighbo analysis was pe o med on he da ase . This analysis in ol es calcula ing he dis ance om each poin in he da ase o i s closes neighbo . The pu pose o his s ep is o unde s and he ypical dis ances be ween poin s in he da ase , which is c ucial o de e mining he app op ia e scale o clus e ing. A e comple ing he nea es neighbo analysis, he nex s ep was o calcula e a speci ic pe cen ile o hese dis ances as in (Tang & Painho, 2023, Tang e al., 2022) s udies. The calcula ed pe cen ile alue is hen used as he ‘eps’ pa ame e in he clus e ing algo i hm. The choice o which pe cen ile o use can depend on he da ase and he desi ed sensi i i y o he clus e ing algo i hm o ou lie s o noise. Tha was he case o he cu en s udy we e he pe cen ile alues oscilla e be ween he 90 h o he 99 h pe cen ile pe social pla o ms. The second pa ame e is called ’Minimum Poin s’ and e e s o he minimum numbe o neighbo ing poin s equi ed o a poin o be conside ed pa o a clus e . Gi en ha each da a sou ce exhibi s a ia ions in he numbe o en ies, we op ed o use pe cen ages o choose Minimum Poin s as in (Gao e al., 2017) esea ch. In his s udy, he pe cen ages chosen we e 3% and 5% o he o al numbe o pos ings pe da a sou ce, o model he ague na u e o cogni i e egions. 28 Finally, he A-DBSCAN algo i hm was un i e a i ely. Table 2 shows he combina ion o pa ame e s by sou ce and neighbo hood o gene a e a s able and ep esen a i e delinea ion based on he dis ibu ion o geo ags. The cons uc ion o ha polygons was hen unde aken o app oxima e he bounding shape o he his o ic neighbo hoods, using he α-shape algo i hm due o i s abili y o p o ide a smoo hed delinea ion,(Edelsb unne e al., 1983). I is impo an o no e ha in addi ion o calcula ing polygons o each pla o m i was c ea ed a shape de i ed om all sou ces o each o he his o ic neighbo hoods unde s udy. The p ojec aims o e eal pa e ns o consis ency and a iance in he iden i ica ion o neighbo hood bounda ies by compa ing his p ima y da a wi h he insigh s de i ed om use -gene a ed con en analysis, as discussed in his phase. These indings will be u he explo ed in he subsequen phase. Table 2. A-DBSCAN pa ame e s alues by sou ce and neighbo hood. NBHD/Pa ame e s eps (%) mins(%) eps (%) mins(%) eps (%) mins(%) eps (%) mins(%) Al ama 0.99 0.03 0.99 0.03 0.99 0.03 0.99 0.05 Mou a ia 0.95 0.03 0.90 0.05 0.95 0.05 Bai o_Al o 0.99 0.03 0.90 0.03 0.90 0.03 0.90 0.05 Flick Twi e Ins ag am Combined 29 3.3 Compa a i e Analysis o Pe cei ed and Geo- agged Bounda ies The inal s age o he s udy in eg a es he ini ial segmen s, unde aking a quan i a i e compa ison o spa ial de ini ions de i ed om cogni i e mapping and geo agged online ac i i ies wi hin he his o ic neighbo hoods o Al ama, Mou a ia, and Bai o Al o. This compa ison aims o measu e he ag eemen be ween neighbo hood ex ensions as iden i ied by each me hod. Cen al o his analysis a e wo me ics: he In e sec ion O e Union (IOU) and he F- sco es. The IOU me ic quan i ies he o e lap be ween wo shapes, wi h alues anging om 0 o 1 whe e 0 ep esen s no o e lap and 1 indica es pe ec o e lap. Simila ly, F-sco es assess he accu acy and ele ance o his o e lap by calcula ing p ecision and ecall, p o iding a balanced measu e o a me hodology’s e ec i eness in accu a ely cap u ing and comp ehensi ely co e ing neighbo hood bounda ies as was calcula ed in (Tang e al., 2022). F- sco es also ange om 0 o 1, whe e 0 signi ies he wo s p ecision and ecall, and 1 he bes . Calcula ions o bo h IOU and F-sco es a e g ounded in he “g ound u h” es ablished by pe cei ed bounda ies ob ained in he i s s age o he s udy and he polygons gene a ed om use ac i i ies. Py hon was u ilized o hese compu a ions, acili a ing he coding and analysis o he esul s. This app oach p o ides a quan i a i e basis o discussing he spa ial de ini ion o his o ic neighbo hoods, allowing o a compa ison o he insigh s gained om bo h cogni i e mapping and geo- agged da a. 36 neighbo hood. Bo h Twi e and Ins ag am da a sugges ha use ac i i y in Bai o Al o is cen e ed a ound pa icula ho spo s o enues. This is suppo ed by he ac ha popula nigh li e a eas, such as Rua da Rosa, Rua da A alaia, Rua São Ped o de Alcan a a, and adi ional es au an s such as Tasca do Chico, a e loca ed wi hin hese ou lines, as con i med by (No e, 2020) .(The comple e loca ion o Bai o Al o's poin s o in e es can be ound in he Appendix). Figu e 18. Geo- agged ac i i y de i ed bounda ies o Bai o Al o. 37 4.3 Compa a i e Analysis o pe cei ed and Geo- agged Bounda ies The hi d phase encompasses a compa a i e analysis. This sec ion ou lines he compu a ion o me ics, including he In e sec ion O e Union (IOU), which measu es he ex en o o e lap be ween he iden i ied shapes, as well as he e alua ion o o e lapping a eas and F-sco es o each da ase . U ilizing he pe cei ed bounda ies de ined as he benchma k in he s udy's ini ial phase, hese a e compa ed agains he use -gene a ed shapes o de e mine he IOU and F-sco es. The maps and me ics a e accompanied by obse a ions, highligh ing he mos signi ican indings by his o ic neighbo hood acco ding o esiden s’ g oups and egions o consensus. 4.3.1 Al ama The compa a i e analysis o social media da a in ela ion o he pe cei ed bounda ies o Al ama as de ined by sho - e m and long- e m esiden s e eals dis inc pa e ns in bo h he domain and co e egions. The esul s a e display in he igu es 19 and 20 and ables 6 and 7. Fo sho - e m esiden s In he domain egion , Flick s ands ou wi h he highes In e sec ion o e Union (IOU) sco e o 0.538 among all sou ces, indica ing a mode a e o e lap wi h esiden s' domain bounda ies, see Table 5. Meanwhile, Twi e shows he highes ecall alue o 0.755, cap u ing a signi ican po ion o he domain egion bu also including a eas beyond i . When conside ing he combined da a sou ces, he p ecision sco e peaks a 0.773, sugges ing ha he agg ega e o all da a mos accu a ely aligns wi h he domain a eas ecognized by esiden s. Mo ing o he co e egion, while all sou ces cap u e aspec s o he co e ha esiden s ag ee wi h a pe ec ecall , hey also encompass many a eas esiden s do no ecognize as pa o i . P ecision alues a e low ac oss all pla o ms, wi h he combined da a achie ing he highes p ecision a a ound 40%, indica ing ha nea ly hal o he a eas iden i ied by all sou ces all wi hin he s ic e consensus a ea. The F-sco e o he combined da a in he co e egion eaches 0.549, which is he highes ye e lec s he challenges in pinpoin accu acy o an a ea pe cei ed na owly. Fo long- e m esiden s Flick again depic s he highes IOU sco e o 0.580 in he domain egion, showing a s ong co ela ion wi h he a eas ha long- e m esiden s iden i y wi h Al ama, see Table 6. The combined da a has an IOU sco e o 0.394, sligh ly su passing Ins ag am, and a p ecision o 0.766, he highes among 38 all pla o ms, indica ing a mo e accu a e depic ion o consensus a eas when all da a sou ces a e conside ed. Flick also exhibi s a high ecall o 0.912, cap u ing mos o he domain egion as pe cei ed by esiden s. Fu he mo e, Flick ’s p ecision F-sco e is signi ican ly high a 0.734, cap u ing and comp ehensi ely co e ing neighbo hood bounda ies o long- e m esiden s. In he co e egion, IOU sco es d op ma kedly ac oss all pla o ms. Despi e including all a eas ha long- e m esiden s ag ee a e pa o Al ama, all pla o ms also cap u e a eas ou side o he consensus. The combined da a’s F-sco e is he mos no able a 0.612, showing ha he agg ega ion o all pla o ms, despi e indi idual lowe p ecision, o e s a mo e balanced measu e o accu acy be ween p ecision and ecall o he co e egion. Based on he me ic esul s, Flick 's delinea ion o Al ama eme ges as he mos ep esen a i e o he neighbo hood, pa icula ly when compa ed o he spa ial ag eemen o long- e m esiden s in he domain egion. The da a sugges s ha Flick use s, who a e likely o be pho og aphy en husias s, concen a e hei pos ings on e y speci ic si es wi hin Al ama. The beha io o use s on pho og aphy- ocused pla o ms like Flick ypically g a i a es owa ds he mos isually appealing loca ions wi hin a neighbo hood his o ical si es, iewpoin s, and places o signi ican in e es . This pa e n aligns wi h he obse a ions made by Hollens ein and Pu es (2010), who also iden i ied a simila end in use in e ac ions in hei s udy. In hei esea ch, hey a emp ed o use Flick 's place names and desc ip ions o delinea e e nacula bounda ies, as he me hods employed in ou s udy. Fu he mo e, Al ama's epu a ion as Lisbon's mos pic u esque neighbo hood, as desc ibed by (Bap is a e al., 2018), ein o ces he idea ha isually d i en pla o ms like Flick would na u ally align closely wi h he a eas ha esiden s and isi o s ind mos appealing. The inal delinea ion map o he his o ic Al ama neighbo hood, based on Flick geo ags and he pe cei ed bounda ies by long- e m esiden s, is de ailed in he Appendix. As a suppo in he esul s desc ip ions his map showcases enowned poin s o in e es , majo oad a e ies, and images ha cap u e he pic u esque and his o ic cha ac e de ining Al ama. 39 Figu e 19. Compa a i e map o pe cei ed and Geo- agged bounda ies o sho - e m esiden s in Al ama. Table 6. Quan i a i e esul s be ween use gene a ed con en da ase s bounda ies and Al ama Sho - e m esiden s g ound u h. Residen s Sho e m Domain Flick Twi e IG Combined IOU 0,538 0,523 0,385 0,310 O e lap Km² 0.276 0.281 0.274 0.127 Recall 0,741 0,755 0,736 0,341 P ecision 0,662 0,630 0,447 0,773 F-sco e 0,699 0,687 0,556 0,473 Co e Flick Twi e IG Combined IOU 0,191 0,179 0,130 0,378 O e lap Km² 79708,581 79708,581 79708,581 66878,3655 Recall 1,000 1,000 1,000 0,839 P ecision 0,191 0,179 0,130 0,408 F-sco e 0,321 0,303 0,230 0,549 40 Figu e 20. Compa a i e map o pe cei ed and Geo- agged bounda ies o Long- e m esiden s in Al Residen s Long e m Domain Flick Twi e IG Combined IOU 0,580 0,534 0,384 0,394 O e lap Km² 0.256 0.253 0.248 0.126 Recall 0,912 0,901 0,882 0,448 P ecision 0,614 0,567 0,404 0,766 F-sco e 0,734 0,696 0,555 0,565 Co e Flick Twi e IG Combined IOU 0,199 0,186 0,136 0,441 O e lap Km² 0.0829 0.0829 0.0829 0.0756 Recall 1,000 1,000 1,000 0,912 P ecision 0,199 0,186 0,136 0,461 F-sco e 0,332 0,314 0,239 0,612 Table 7. Quan i a i e esul s be ween use gene a ed con en da ase s Bounda ies and Al ama Sho - e m esiden s g ound u h. 41 4.3.2 Mou a ia The compa a i e analysis o social media pla o m da a in delinea ing he pe cei ed bounda ies o he Mou a ia neighbo hood p o ides insigh ul me ics o bo h sho - e m and long- e m esiden s. The esul s a e display in he igu es 21 and 22 and ables 8 and 9 . Fo Sho -Te m Residen s In he domain egion, Twi e demons a es a signi ican alignmen wi h he pe cep ions o sho - e m esiden s, boas ing an In e sec ion o e Union (IOU) sco e o 0.551 and a high ecall o 0.760.Twi e ’s da a cap u es a b oad a ea ha esiden s associa e wi h he Mou a ia domain, al hough i may ex end beyond he consensus bounda ies. Ins ag am, wi h an excep ionally high ecall o 0.940, iden i ies a la ge po ion o he domain. Howe e , i s p ecision sco e o 0.447 indica es i encompasses a eas ha all ou side he esiden -de ined domain o a g ea e ex en han Twi e . The combined sou ces shape, despi e a low IOU alue o 0.241, achie es he highes p ecision a 0.740, poin ing o a mo e accu a e ep esen a ion wi hin he consensus a ea. Rega ding he co e egion, Twi e 's p ecision is no ably low a 0.216, ye i nea ly encapsula es he en i e co e a ea wi h a ecall o 0.922. This implies ha while Twi e 's da a ex ensi ely co e s he co e egion acknowledged by esiden s, i is no as p ecise. Ins ag am's co e age is comple e conce ning he co e as ecognized by esiden s bu also includes conside able a eas beyond i . Fo Long-Te m Residen s The domain egion o long- e m esiden s again sees Twi e wi h a subs an ial IOU o 0.512 and a ecall o 0.716, cap u ing a signi ican pa o wha long- e m esiden s ega d as Mou a ia’s domain bu also eaching ou in o a eas ou side o he ag eed domain. Ins ag am p esen s an ex ensi e co e age wi h a ecall o 0.978, bu i s p ecision is only 0.475, meaning i includes mo e a eas ou side o he esiden consensus compa ed o Twi e . The ou pu om all da a sou ces, while p esen ing a smalle o e lap, aligns mos accu a ely wi h he a eas ag eed by long- e m esiden s. In he co e egion, he me ics a e gene ally lowe . Twi e 's IOU is 0.288, coupled wi h a ecall o 0.851, showing i encompasses mos o he co e as iden i ied by long- e m esiden s. Howe e , he p ecision o 0.303 sugges s some imp ecision in delinea ing speci ic bounda ies. The combined sou ces ou pu ea u es an IOU o 0.359 and he bes F-sco e o 0.528, indica ing a 42 be e equilib ium in cap u ing he co e's ex en while main aining bounda y accu acy. The delinea ion o Mou a ia by Twi e is shown o be he mos ep esen a i e o he neighbo hood, especially when compa ed wi h he spa ial ag eemen o long- e m esiden s in he domain egion. The selec ion o his delinea ion was based no solely on me ic esul s, which we e simila ac oss all aspec s calcula ed o bo h esiden g oups, bu also on he his o ical ex ension o he neighbo hood as desc ibed by his o ians. Acco ding o hem, Mou a ia is loca ed be ween he alley o Plaça Ma in Moniz and he hills o he cas le and G aça neighbo hood. This a ea was designa ed o he de ea ed Moo s a e he ci y's conques by D. A onso Hen iques in 1147, as pa o he Ch is ian conques o e he Muslims. The e o e, he decision was made o in eg a e he pe cei ed bounda ies o long- e m esiden s, which include he a ea be ween Cos a de Cas elo and Calçada de San o And é s ee s, as i be e cap u es hose desc ibed de ails and has mode a e alues in he me ics. Ano he peculia i y o he esul s is ela ed o he ac ha neighbo hood poin s o in e es , such as he Fado Vadio G a i i, known as a ibu e o he Fado music gen e and i s bi hplace in Mou a ia, he São C is ó ão chu ch, as ancien as i is amous, which acco ding o he Wo ld Monumen s Wa ch websi e is pa o Mou a ia,(Chu ch o São C is ó ão, 2016) a e no included wi hin he pe cei ed limi s. The exclusion o key cul u al and his o ical landma ks om Mou a ia's pe cei ed bounda ies p omp s a deepe e lec ion on he neighbo hood's e nacula ambigui y and he challenges in accu a ely delinea ing i s eal ex en . This cha ac e is ic o Mou a ia sugges s a nuanced complexi y in unde s anding i s spa ial iden i y. I unde sco es he necessi y o conside ing he s udy a ea's unique cha ac e is ics e en when applying Ex ac ion me hods. The inal delinea ion map o he his o ic Mou a ia neighbo hood, based on Twi e geo ags and he pe cei ed bounda ies by long- e m esiden s, is p o ided in de ail in he Appendix. As a suppo in he esul s desc ip ions his map highligh s enowned poin s o in e es , majo oad a e ies, and ea u es images ha cap u e he di e se and adi ional na u e de ining Mou a ia. 43 Figu e 21.Compa a i e map o pe cei ed and Geo- agged bounda ies o Sho - e m esiden s in Mou a ia. Table 8. Quan i a i e esul s be ween use gene a ed con en da ase s bounda ies and Mou a ia Sho - e m esiden s g ound u h. Residen s Sho e m Domain Twi e IG Combined IOU 0,551 0,434 0,241 O e lap Km² 0.0950 0.1176 0.0330 Recall 0,760 0,940 0,264 P ecision 0,668 0,447 0,740 F-sco e 0,711 0,605 0,389 Co e Twi e IG Combined IOU 0,212 0,127 0,285 O e lap Km² 0.0308 0.0334 0.0173 Recall 0,922 1,000 0,519 P ecision 0,216 0,127 0,388 F-sco e 0,350 0,225 0,444 44 Residen s Long e m Domain Twi e IG Combined IOU 0,512 0,470 0,259 O e lap Km² 0.0915 0.1250 0.0354 Recall 0,716 0,978 0,277 P ecision 0,643 0,475 0,795 F-sco e 0,677 0,639 0,411 Co e Twi e IG Combined IOU 0,288 0,193 0,359 O e lap Km² 0.0432 0.0507 0.0252 Recall 0,851 1,000 0,496 P ecision 0,303 0,193 0,565 F-sco e 0,447 0,323 0,528 Table 9. Quan i a i e esul s be ween use gene a ed con en da ase s bounda ies and Mou a ia Long- e m esiden s g ound u h. Figu e 22.Compa a i e map o pe cei ed and Geo- agged bounda ies o Long- e m esiden s in Mou a ia. 45 4.3.3 Bai o Al o In his compa a i e analysis, we del e in o he spa ial pe cep ions o Bai o Al o's Domain and Co e egions, as dis inguished by sho - e m and long- e m esiden s, using da a om Flick , Twi e , Ins ag am, and a combined app oach. The esul s a e display in he igu es 23 and 24 and ables 10 and 11. Fo Sho -Te m Residen s In he Domain egion, Flick 's encompassing cap u e o he a ea aligns wi h sho - e m esiden s' ecogni ion, e en hough wi h a p ecision o only 0.327, indica ing ex aneous a ea inclusion. Twi e , wi h a p ecision o 0.929 and ecall o 0.487, o e s a selec i e ye highly accu a e po ayal, showing cap u es less bu wi h g ea e ideli y. Ins ag am s ikes a balance wi h ecall and p ecision a es o 0.564 and 0.568, espec i ely, indica ing mode a e co e age and a e age p ecision. The combined da a sou ces achie e a p ecision o 0.879, closely mi o ing he a eas sho - e m esiden s ag ee, despi e no cap u ing he en i e y o he Domain. Wi hin he Co e egion, Flick 's da a, while all-encompassing wi h a ecall o 1.000, su e s in p ecision a 0.174, sugges ing i spans many a eas ou side he consensus. Twi e showcases a high IOU o 0.646, wi h ecall and p ecision a es o 0.778 and 0.792, espec i ely, indica ing a selec i e ye accu a e cap u e o he Co e. Ins ag am o e s a b oad, albei less p ecise, iden i ica ion, whe eas he agg ega e da a p o ides a balanced ep esen a ion wi h an IOU o 0.615, and ecall and p ecision a es o 0.753 and 0.770, espec i ely. Fo Long-Te m Residen s Flick again cap u es he en i e Domain ecognized by long- e m esiden s bu wi h low p ecision (0.348), indica ing he inclusion o ex ensi e non- consensual a eas. Twi e 's app oach, wi h a p ecision o 0.910, e lec s high accu acy, e en co e ing less o he Domain a ea. In he Co e egion, Flick 's co e age ex ends o e he en i e Co e and beyond, simila o i s app oach in he Domain. Twi e , wi h i s mode a e ecall and high p ecision, accu a ely cap u es he Co e. The combined da a sou ces show he highes p ecision o long- e m esiden s in he Co e, sugges ing ha an in eg a ed app oach o di e en da a sou ces yields he mos p ecise delinea ion. Acco ding o (F anco, 2016), he cons uc ion o he San Roque Chu ch in he 16 h cen u y. ma ked a ele an momen in he u ban his o y o Bai o Al o, ans o ming i in o a ocal poin o Lisbon's nobili y. This shi led o he cons uc ion o palaces on he hill's wes e n side, d i en by a desi e o be 52 5.3 Fu u e Scope Fu u e esea ch could bene i om expanding he ex ac ion o ex ual a ibu es ela ed o he speci ic economic, en i onmen al, and social cha ac e is ics o each neighbo hood. E o s could include explo ing di e se me hods o uni ying pe cei ed bounda ies, such as bounda y agg ega ion o adial a e aging due o, as men ioned in he me hod compa ison s udy by (Dal on & Hu ell, 2023). Addi ionally, expe imen ing wi h a ious clus e ing algo i hms like spec al clus e ing o Ke nel Densi y Es ima ion could p o e aluable. Inco po a ing insigh s om e hnog aphic s udies, like hose conduc ed by (Bap is a e al, 2018), which e eal he less posi i e social eali ies wi hin he h ee neighbo hoods, would be pa icula ly insigh ul. These eali ies, including issues such as ma ginaliza ion and social exclusion, a e e lec ed in he discou se on social media and could add dep h o his s udy's indings. I his app oach p o es success ul, i could be applied o o he his o ic neighbo hoods in he ci y, each dis inguished by i s unique cha ac e is ics, he eby enhancing ou unde s anding o u ban social dynamics. Chap e 6 6. Conclusions This s udy emba ked on he in ica e challenge o mapping ou he e nacula bounda ies o Lisbon's his o ic neighbo hoods: Al ama, Mou a ia, and Bai o Al o. By blending esiden s' insigh s wi h geo- agged da a, alongside me hods and ools o ex ac ing pe cep ions, we' e un eiled he ue po en ial o me ging cogni i e mapping wi h use - gene a ed con en . This me hod has p o en e ec i e in delinea ing he spa ial con ines o hese s o ied a eas, illumina ing he ich apes y o his o y and communi y li e ha 's in e wo en wi h he u ban ab ic o Lisbon. The indings shed ligh on he nuanced ways digi al pla o ms and social in e ac ions con ibu e o ou g asp o u ban bounda ies. Al ama's ep esen a ion h ough Flick 's lens eme ged as no ably p ecise, unde sco ing he impac o isual pla o ms in cap u ing he essence o locales, hus emphasizing he signi ican ole o isual pe cep ions in bounda y delinea ion. In Mou a ia, Twi e 's alignmen wi h long- e m esiden s' unde s anding highligh ed he pla o m's capabili y o cap u e he neighbo hood's essence ia social media dynamics. Meanwhile, Bai o Al o's de ailed mapping ia Twi e e ealed he in luence o his o ical and social unde pinnings in shaping u ban bo de s. Add essing he esea ch ques ions, i 's insigh ul o no e how esiden s spa ially de ine di e en his o ic neighbo hoods h ough a ious lenses, be i he leng h o esidence, he o ien a ion o s ee ne wo ks, o he spa ial ex en o a eas. A composi e app oach, which amalgama es mul iple da a sou ces, a o ds a iche , mo e accu a e ep esen a ion o he co e egions o e nacula bounda ies. Addi ionally, he indings ad oca e ha social media analy ics can se e as a eliable index o pe cei ed ci y images, wi h pla o ms like Ins ag am elucida ing public pe cep ions ied o ou is a ac ions and landma ks. The implica ions o his esea ch e e be a e h ough he ealms o u ban planning and policymaking, championing an app oach ha mo e closely esona es wi h he li ed expe iences and pe cep ions o ci y inhabi an s. A deepe unde s anding o subjec i e neighbo hood delinea ions can signi ican ly e ine s a egies in ou ism managemen and esou ce alloca ion, especially wi hin highly ma ginalized a eas. This hesis con ibu es o he en ichmen o e nacula geog aphy by o e ing new 54 insigh s in o he mode n u ban ab ic, shaped by a combina ion o social in e ac ions and digi al oo p in s. The success ul in eg a ion o spa ial and social dimensions showcased in his s udy unde sco es he e icacy o employing di e se me hodologies o da a ex ac ion, analysis, clus e ing, and delinea ion. 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No h Ame ica’s Ve nacula Regions. Annals o he Associa ion o Ame ican Geog aphe s, 70(1), 1–16. Appendix Figu e 25. Map o he His o ic Al ama Neighbo hood Delinea ion Based on Flick Geo ags and Pe cei ed Bounda ies by Long-Te m Residen s 59 Figu e 26.Map o he His o ic Mou a ia Neighbo hood Delinea ion Based on Twi e Geo ags and Pe cei ed Bounda ies by Long-Te m Residen s 60 Figu e 27.Map o he His o ic Bai o Al o Neighbo hood Delinea ion Based on Twi e Geo ags and Pe cei ed Bounda ies by Long-Te m.Residen s 61 Ve nacula Bounda ies o His o ic Neighbo hoods in he Ci y o Lisbon 2024 Mónica So ía Roncancio Blanco