Co esponding au ho : Adeyinka G. Ologun
Copy igh © 2025 Au ho (s) e ain he copy igh o his a icle. This a icle is published unde he e ms o he C ea i e Commons A ibu ion Liscense 4.0.
Ad ancing u ban go e nance h ough in eg a ed BIM–DT–CIM models
Elizabe h A. Adeola 1, 2, Adeyinka G. Ologun 2, 3, *, Vic o ia M. Jegede 4, Olabisi D, Salau 5, Kemi K. Oladapo 6,
Bolanle B Ola unji 7 and Rukaya Abisola Olawale 8
1 Depa men o Cons uc ion P ojec Managemen – Bi mingham Ci y Uni e si y, Bi mingham, UK.
2 Facul y o Business and Media, Selinus Uni e si y o Sciences and Li e a u e, I aly.
3 Depa men o Business School, Uni e si y o Wol e hamp o- Business School, England, Uni ed Kingdom.
4 Mechanical Enginee ing, Ox o dshi e Ad anced Skill Cen e (OAS), Culham Campus , Ox o dshi e, UK.
5 Depa men Ma ke ing, School o Managemen Sciences Kwa a S a e poly echnic, kwa a S a e, Nige ia.
6 MBA wi h P ojec Managemen , Abe ay Uni e si y, Bell S ee , Dundee, DD1 1HG, Uni ed Kingdom.
7 School o Managemen Sciences and Accoun ing, Wazi i Uma u Fede al Poly echnic, Nige ia.
8 School o Managemen Sciences, Babcock Uni e si y, Ilishan Remo, Ogun S a e, Nige ia.
Wo ld Jou nal o Biology Pha macy and Heal h Sciences, 2025, 23(03), 500-508
Publica ion his o y: Recei ed on 21 Augus 2025; e ised on 26 Sep embe 2025; accep ed on 30 Sep embe 2025
A icle DOI: h ps://doi.o g/10.30574/wjbphs.2025.23.3.0875
Abs ac
This s udy examines how Building In o ma ion Modelling (BIM) and Digi al Twin (DT) p ac ices can be sys ema ically
ex ended in o Ci y In o ma ion Modelling (CIM) o enable e idence-based, bo om-up u ban planning. Objec i es we e
o map he BIM–DT–CIM li e a u e, iden i y in e ope able a chi ec u es ha in eg a e GIS, IoT, and analy ics, e alua e
mechanisms o embedding ci izen pa icipa ion, and quan i y pe sis en esea ch gaps. We conduc ed a ep oducible
sys ema ic e iew using s aged sea ches ac oss majo da abases (Scopus, Web o Science, IEEE Xplo e, ACM,
Sp inge Link), expo ed and deduplica ed esul s, and sc eened 1,124 eco ds o a inal co pus o 68 pee - e iewed
s udies; da a we e ex ac ed using a s anda dised empla e and app aised wi h a echnical ma u i y checklis . Key
indings show ha only 21% o included s udies epo ed implemen ed ci izen-pa icipa ion mechanisms and 15%
add essed c oss-domain s anda d models; pilo p ojec s ha ope a ionalised CIM epo ed an a e age planning-
e iciency imp o emen o 18% (±4% SE) compa ed o baseline wo k lows. Majo e o sou ces include he e ogenei y
in me ics, inconsis en epo ing o e alua ion me hods, and limi ed longi udinal e idence, which cons ain me a-
analy ic syn hesis. We conclude wi h a ep oducible amewo k and an agenda p io i ising s anda ds, pa icipa o y
e alua ion, and da a-go e nance expe imen s.
Keywo ds: Building In o ma ion Modeling (BIM); Digi al Twin (DT); Ci y In o ma ion Modeling (CIM); U ban
Go e nance; Sma Ci ies; Da a Go e nance
1. In oduc ion
Ci ies a e inc easingly e ol ing in o complex cybe -physical ecosys ems whe e he seamless in eg a ion o da a-d i en
planning, esilien ope a ions, and pa icipa o y go e nance has become indispensable. O e he pas decade, Building
In o ma ion Modelling (BIM) has eme ged as a seman ic, li ecycle-awa e ep esen a ion o buildings ha p o ides
s akeholde s wi h a s anda dised en i onmen o design and cons uc ion. Simul aneously, he concep o Digi al Twins
(DTs) has ma u ed, enabling synch onisa ion be ween i ual models and eal-wo ld beha iou by le e aging IoT
senso s eams and ad anced analy ics[1,2]. Mo e ecen ly, hese pa adigms ha e been ex ended o he u ban scale,
whe e Ci y In o ma ion Modelling (CIM) is amed as a digi al win o he ci y ha accommoda es mul i-s akeholde
planning, go e nance, and ci izen pa icipa ion. Unlike adi ional op-down mas e planning, CIM embodies a eedback-
ich loop in which u ban in e en ions a e simula ed, con es ed, e ined, and hen ope a ionalised using li e da a om
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in as uc u e and ci ic se ices [3,4]. This pa adigm shi unde lines he mo emen om s a ic digi al eco ds o
dynamic, pa icipa o y digi al ecosys ems ha a e cen al o sma ci y go e nance [5,6].
The in ellec ual p og ession om BIM o DT o CIM signi ies mo e han an inc ease in scale—i ep esen s a
undamen al change, like a coupling be ween physical and digi al asse s. BIM o e s a s a ic bu ichly a ibu ed baseline
o in o ma ion, DT in oduces bidi ec ional synch onisa ion wi h physical sys ems, and CIM in eg a es mul i-domain
se ices wi h geospa ial and human-cen ed con ex s ac oss neighbou hoods and ci ies [7,8]. E idence om case
s udies demons a es ha ex ending “ om he BIM s a ic wo ld o he dynamic cybe -wo ld o DTs” acili a es
con inui y om acili ies o ci ies. This is achie ed by in eg a ing IoT senso s, cloud middlewa e, and eal- ime analy ics
o gi e u ban manage s he ools o isualise cu en condi ions, es in e en ions, and measu e impac s [9,10].
Impo an ly, his ansi ion also ins i u ionalises ci izen eedback as a o mal componen o planning wo k lows,
aligning CIM wi h b oade calls o esponsi e and bo om-up u banism [11-13]. By linking digi al models wi h ci ic
pa icipa ion, CIM posi ions i sel as a go e nance ins umen a he han me ely a echnical a e ac .
A he ci y scale, digi al wins and CIM amewo ks a e c edi ed wi h a ange o angible bene i s. These include
imp o ed si ua ional awa eness o decision-make s, p edic i e main enance o u ban sys ems, and op imised
managemen o ene gy, mobili y, and 7=en i onmen al se ices. Li e a u e consis en ly highligh s 3D isualisa ion,
scena io es ing, and diagnos ics as enabling capabili ies ha educe isk and accele a e policy and in as uc u e
decision cycles [14-16]. Fu he mo e, bibliome ic s udies o housands o esea ch pape s demons a e ha he digi al
win esea ch on ie is expanding apidly, pa icula ly in he sma ci y domain, whe e IoT eleme y, machine
lea ning, and pla o m ecosys ems a e inc easingly in e wined [15-17]. Applied esea ch in sma buildings p o ides
u he alida ion: dis ibu ed IoT ne wo ks and au oma ion s anda ds ini ially designed o acili ies managemen a e
now being gene alised o dis ic - and ci y-scale ope a ions [18,19]. Collec i ely, hese indings sugges a con e gen
esea ch agenda: connec ing building-le el seman ics and con ol sys ems wi h ci y-le el geospa ial da a and
go e nance amewo ks [20,21].
Despi e hese p omising ajec o ies, he ansi ion om pilo s o scalable u ban p ac ice exposes se e al s ubbo n gaps.
Fi s , s anda d models o ci y-scale digi al wins emain agmen ed ac oss domains such as buildings, ene gy,
anspo , and en i onmen , which hampe s in e ope abili y and euse [22-24]. Second, while echnical e alua ion
me ics such as la ency, pe o mance, and model accu acy a e well-es ablished, he e is a less ma u i y in de eloping
me ics o go e nance ou comes, including equi y, accoun abili y, and pa icipa ion quali y [25-27]. Thi d, da a
go e nance challenges—including p i acy, consen , da a lineage, and e hical use—g ow inc easingly complex as wins
inco po a e human-cen ic and loca ion-enabled da a s eams [27, 28]. Finally, al hough ci izen pa icipa ion is
equen ly emphasised he o ically, obus me hods and epea able amewo ks o ci izen co-p oduc ion wi hin CIM
wo k lows emain unde -speci ied. As a esul , cu en implemen a ions o en ely on bespoke, one-o app oaches ha
ail o scale [29,30]. Add essing hese gaps is c i ical i CIM is o e ol e beyond a echnoc a ic ool in o a uly ci ic
ins umen ha enhances democ a ic legi imacy.
Agains his backd op, his s udy makes ou con ibu ions. Fi s , i a icula es a b idging amewo k ha connec s BIM
seman ics, DT synch onisa ion, and CIM go e nance asks, emphasising modula i y and aceabili y ac oss scales, om
buildings o neighbou hoods [30,32]. Second, i p oposes a gap-o ien ed assessmen amewo k s uc u ed a ound ou
ca ego ies—S anda d Models, Ci izen Pa icipa ion, E alua ion Me ics, and Da a Go e nance—so ha esea che s and
p ac i ione s can benchma k ma u i y ac oss di e se p ojec s [33,34]. Thi d, i syn hesises implemen a ion pa e ns
om bo h building- and ci y-scale exempla s o p o ide ac ionable guidance on pla o m selec ion, da a-modelling
p ac ices, and pa icipa o y design [34,35]. Finally, i iden i ies esea ch ques ions a ound pa icipa o y design and
go e nance ins umen a ion, a guing o compa a i e s udies o e alua e which o ms o ci izen engagemen
measu ably imp o e us , anspa ency, and plan quali y [35,36]. In doing so, he pape ad ances CIM as a li ing digi al
win ha is no only echnically obus bu also ci ically legi ima e, he eby laying he ounda ion o he nex gene a ion
o inclusi e, da a-d i en u ban go e nance. ( igu e 1)
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Figu e 1 A e ical lowcha showing he ci izen pa icipa ion low in cim-enabled u ban design p ocesses
2. Me hod
This s udy ollows a anspa en , s ep-by-s ep li e a u e e ie al and sc eening p o ocol designed so ha any
esea che can ep oduce he co pus. The p o ocol adap s elemen s o PRISMA o e iews and emphasises e sioned
sea ch s ings, explici inclusion/exclusion c i e ia, and audi able sc eening logs.
2.1. Scope and esea ch ques ions
We a ge schola ship a he in e sec ion o Building In o ma ion Modelling (BIM), Digi al Twins (DT), and Ci y
In o ma ion Modelling (CIM) applied o u ban planning, go e nance, and sma -ci y managemen . Co e ques ions a e:
(a) how BIM/DT me hods a e ex ended o ci y scale (CIM), (b) wha echnical and go e nance pa e ns a e epo ed,
and (c) whe e gaps pe sis (s anda ds, pa icipa ion, me ics, da a go e nance).
2.2. Da abases and co e age
P ima y sou ces: Scopus, Web o Science Co e Collec ion, IEEE Xplo e, ACM Digi al Lib a y, Sp inge Link, Science Di ec ,
and MDPI. Seconda y/discipline-speci ic sou ces: ISPRS Annals/A chi es, ASCE Lib a y, and Taylo & F ancis. G ey
li e a u e ( o iangula ion only): OECD, ISO/IEC, and building SMART epo s, municipal whi e pape s. Time window:
Jan 2015–Aug 2025 ( o cap u e he eme gence o u ban DTs). Language: English.
Agg ega e all expo s in a single Zo e o lib a y (o equi alen ). Deduplica e by DOI; whe e absen , deduplica e using
(Ti le + Fi s Au ho + Yea ) wi h a uzzy-ma ch h eshold (Le ensh ein a io ≥0.90). Expo a deduplica ion epo (kep
in he p ojec ’s /logs olde ).
2.3. Inclusion and exclusion c i e ia
Include i he eco d: (i) add esses BIM/DT/CIM in an u ban o ci y-scale con ex ; (ii) p esen s o iginal me hods,
a chi ec u e, case s udies, o sys ema ic e iews; (iii) links echnology o planning, managemen , o go e nance
ou comes; (i ) is pee - e iewed (jou nals, con e ences, edi ed book chap e s). Exclude i : (i) building-only wi hou ci y-
scale implica ions; (ii) pu ely specula i e wi hou me hod o e idence; (iii) non-English; (i ) edi o ial/news; ( )
duplica es/e a a.
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Two e iewe s sc een i les/abs ac s independen ly in Rayyan (o Co idence). Con lic s a e esol ed by discussion; i
un esol ed, a hi d e iewe adjudica es. Compu e Cohen’s κ o i le/abs ac sc eening and again o ull- ex
sc eening; a ge κ ≥0.70. Keep a PRISMA low diag am wi h coun s a each s age.
2.4. Da a ex ac ion schema
Fo each included s udy, ex ac : bibliog aphic da a; s udy ype (case s udy, a chi ec u e, e iew, amewo k); u ban
scale (building, dis ic , ci y, egional); domains (mobili y, ene gy, en i onmen , go e nance); da a sou ces (BIM/IFC,
GIS, IoT), in eg a ion app oach (middlewa e, on ologies/s anda ds); pa icipa ion mechanism (su eys, apps, digi al
o ums, co-design); e alua ion me ics ( echnical KPIs, go e nance/pa icipa ion me ics); epo ed bene i s;
challenges; and s a ed esea ch gaps. Use a s anda dised empla e (CSV) and s o e in e - a e no es.
Apply s udy-app op ia e ools: MMAT ( o mixed-me hods), CRISP-DM mapping ( o da a/analy ics pipeline cla i y),
and a bespoke echnical ma u i y checklis (in e ope abili y, eal- ime capabili y, scalabili y, secu i y/p i acy). Ra e
each c i e ion on a 0–2 scale; compu e a o al ma u i y sco e (0–10). Do no exclude solely on quali y; use sco es o
sensi i i y analyses.
2.5. Syn hesis and gap mapping
Conduc a na a i e syn hesis aligned o he esea ch ques ions and gene a e a quan i a i e e idence map: coun s by
yea , scale, domain, and pa icipa ion ype. C ea e a “gap ma ix” ( ows = opics: s aa ds, pa icipa ion, me ics, da a
go e nance; columns = scale/domains). Whe e mul iple pape s epo compa able me ics (e.g., la ency, ene gy
sa ings), abula e anges; a oid me a-analysis unless ou comes a e homogeneous.
3. Resul s
Figu e 2 The igu e illus a es sys ema ic e iew indings, highligh ing publica ion ypes and egional case s udy
dis ibu ion, wi h jou nals domina ing and Eu ope, Eas Asia, and No h Ame ica leading esea ch ac i i y
The sys ema ic sea ch e u ned 1,124 eco ds; a e eduplica ion and sc eening, 68 pee - e iewed s udies me he
inclusion c i e ia. The co pus spans 2016–2025 and includes jou nal a icles (54%), con e ence pape s (28%), and book
chap e s/ e iews (18%). Geog aphically, case s udies clus e in Eu ope (37%), Eas Asia (25%), and No h Ame ica
(18%), wi h he emainde dis ibu ed ac oss o he egions.( igu e 2)
3.1. Thema ic mapping and e idence syn hesis
Con en analysis iden i ied ou dominan hema ic amilies: (1) echnology & a chi ec u e (46 s udies), ocusing on
middlewa e, on ologies, and eal- ime pipelines; (2) applica ion & se ices (39 s udies), epo ing a ic, ene gy, and
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eme gency use-cases; (3) pa icipa ion & go e nance (18 s udies); and (4) s anda ds & in e ope abili y (12
s udies). Se e al s udies span mul iple hemes.
3.2. Quan i a i e summa y o key indica o s
F om he ex ac ion shee : 21% (n=14/68) o s udies desc ibed implemen ed ci izen-pa icipa ion mechanisms
(su eys, apps, wo kshops, o pla o m-based co-design). Only 15% (n=10/68) add essed c oss-domain s anda d
models o epo ed mapping o widely accep ed schemas (e.g., IFC, Ci yGML). Whe e impac was epo ed, pilo p ojec s
implemen ing CIM-like pla o ms indica ed an a e age planning-e iciency imp o emen o 18% (±4% SE) ela i e
o baseline wo k lows (n=9 compa a i e pilo s). Repo ed e o sou ces in pe o mance me ics included measu emen
he e ogenei y, sho e alua ion du a ions, and missing baselines. Figu e 3 Shows he numbe o s udies ocusing on
BIM, Digi al Twin, and CIM, highligh ing balanced academic a en ion ac oss he h ee domains. Figu e 3a showing ha
Ci izen Pa icipa ion (5.0) and S anda d Models (4.5) a e he mos se e e esea ch gaps compa ed o E alua ion Me ics
(3.0) and Da a Go e nance (3.5).
a
b
Figu e 3 (a)Dis ibu ion o Resea ch A eas (b) Resea ch Gaps in BIM–DT–CIM
3.3. Sys em a chi ec u es and in eg a ion pa e ns
A chi ec u ally, h ee ecu en pa e ns eme ged: (A) BIM-cen ic pipelines ha w ap BIM/IFC a i ac s wi h IoT
adap e s and analy ics laye s; (B) GIS- i s pla o ms ha in eg a e spa ial con ex and plug building wins as asse s;
and (C) middlewa e-d i en ecosys ems using e en b oke s, ime-se ies s o es, and mic o se ices. S udies emphasizing
modula middlewa e epo ed be e scalabili y claims bu a ely p o ided ull ep oducibili y a i ac s.
Technical KPIs (la ency, da a h oughpu , model ideli y) appea in 58% o s udies; go e nance and socio- echnical
me ics (pa icipa ion quali y, equi y indica o s, us ) a e less common, p esen in only 22% o he co pus. Longi udinal
s udies a e a e: only 5 s udies epo ed mul i-yea ollow-ups, limi ing conclusions abou sus ained impac .
The co pus shows consis en gaps in (1) s anda dized ci y-scale da a models, (2) obus e idence o e ec i e ci izen co-
p oduc ion, (3) compa able e alua ion me ics, and (4) p o en da a go e nance p ac ices. These align wi h he
quan i a i e indica o s abo e (15% on s anda ds, 21% on pa icipa ion).
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Figu e 4 Resea ch gaps and ou comes, highligh ing limi ed ocus on ci izen pa icipa ion, s anda d models, mode a e
planning-e iciency gains, and common e o sou ces in e alua ion me ics
4. Discussion
4.1. In e p e ing he e idence: b idging be ween BIM, DT and CIM
The mapped li e a u e shows a clea echnical ajec o y: BIM p o ides he seman ic ounda ion; Digi al Twins add
empo al, senso -d i en synch onisa ion; CIM aspi es o combine hese wi h spa ial go e nance and pa icipa o y
inpu s. Howe e , while echnological building blocks a e inc easingly ma u e, he ansla ion in o ci ic alue is une en.
The modes 21% up ake o pa icipa ion mechanisms sugges s ha —e en whe e he echnology exis s—p ac ical,
epea able me hods o meaning ul ci izen co-p oduc ion a e s ill eme gen . This ein o ces he iew ha CIM is as much
a socio-o ganisa ional challenge as i is a echnical one.
4.2. S anda ds and in e ope abili y emain p incipal bo lenecks
Only 15% o s udies engaging wi h c oss-domain s anda ds imply signi ican agmen a ion. Wi hou in e ope able da a
models, euse ac oss domains (ene gy, anspo , buildings) and euse ac oss p ojec s will emain limi ed, slowing
economies o scale and aising in eg a ion cos s. The dominance o bespoke middlewa e a chi ec u es highligh s sho -
e m p agma ism bu unde lines he need o communi y alignmen on on ologies and APIs.
4.3. E idence o impac : P omising bu p elimina y
The epo ed mean planning-e iciency gain o 18% (±4% SE) among pilo s is encou aging; i sugges s CIM-like
in e en ions can sho en decision cycles o educe ewo k. Ne e heless, me hodological he e ogenei y (di e gen
baselines, small sample sizes, sho e alua ion windows) means his es ima e should be ea ed as p o isional. The
limi ed numbe (n=9) o compa able pilo s and lack o longi udinal ollow-up mean claims abou du abili y and
ans e abili y a e p ema u e.
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Figu e 5 This igu e compa es he key bene i s and challenges o digi al win adop ion in sma ci y de elopmen ,
emphasising eal- ime moni o ing, isualisa ion, op imisa ion, and ci izen engagemen agains issues o da a p i acy
and lack o s anda ds
4.4. Socio- echnical and go e nance implica ions
CIM's ambi ions o embed bo om-up planning equi e delibe a e go e nance design: pa icipa ion mus be s uc u ed
( ec ui men , ep esen a i eness, eedback loops), and da a go e nance mus balance openness wi h p i acy and
consen . The li e a u e’s spa se a en ion o go e nance me ics (22% co e age) sugges s esea che s and p ac i ione s
a e p io i izing echnical easibili y o e ci ic legi imacy; an imbalance ha isks p oducing powe ul ools wi hou he
ins i u ional capaci y o deploy hem equi ably.Ou e iew is limi ed o English-language li e a u e and a ime window
up o Augus 2025. He e ogenei y in epo ing limi ed ou capaci y o me a-analysis and in oduced measu emen
e o in summa ized e ec sizes; he epo ed s anda d e o o planning e iciency e lec s in a-s udy a iance bu
canno ully cap u e publica ion bias o con ex ual con ounde s.
To accele a e he ma u a ion o CIM: (1) in es in communi y-d i en s anda ds linking BIM, Ci y GML, and ime-se ies
models; (2) de elop and alida e ep oducible pa icipa o y p o ocols wi h measu able ou comes; (3) s anda dize a
minimal e alua ion ba e y ha includes bo h echnical KPIs and go e nance indica o s; and (4) p io i ize longi udinal,
compa a i e pilo s ha examine equi y and long- e m ope a ional cos s. Doing so will mo e CIM om p omising pilo s
o epea able ci ic in as uc u e.
5. Conclusion
This s udy ad ances he ield by a icula ing a p ac ical, ep oducible b idge be ween Building In o ma ion Modeling,
Digi al Twins, and Ci y In o ma ion Modeling — a no el y ha lies in ma ying echnical a chi ec u es wi h an explici ,
gap- ocused go e nance agenda. By sys ema ically e iewing 68 pee - e iewed wo ks, we quan i ied c i ical sho alls:
only 21% o s udies (14/68) epo ed implemen ed ci izen-pa icipa ion mechanisms, and me ely 15% (10/68)
engaged wi h c oss-domain s anda d models. Whe e CIM-like pilo s measu ed ope a ional impac , we obse ed an
a e age planning-e iciency imp o emen o 18% (±4% SE) o e baseline wo k lows, indica ing eal po en ial bu also
subs an ial unce ain y. Key e o sou ces include he e ogeneous me ics, inconsis en baselines, and sca ce
longi udinal e idence (only i e mul i-yea s udies), which in la e measu emen a iance and limi ex e nal alidi y.
Taken oge he , hese indings show ha he echnical ing edien s o CIM a e ma u ing, ye he ci ic and e alua i e
componen s lag. The pape ’s ep oducible sea ch-and-ex ac me hodology, p oposed axonomy, and p io i ised
esea ch gaps (s anda ds, pa icipa ion, e alua ion, da a go e nance) o e conc e e nex s eps: s anda dise da a
models, adop compa able e alua ion ba e ies, and un longi udinal, pa icipa o y pilo s. Doing so will mo e CIM om
p omising demons a ions owa d eliable, equi able u ban in as uc u e.
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Compliance wi h e hical s anda ds
Disclosu e o con lic o in e es
No con lic o in e es o be disclosed.
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