Modelling Urban Development with Geographical Information Systems

and Cellular Automata

Yon Liu

0.. CRC Press V Ta)'tor & Franch Group BoX.~ l0111Yl "''W 'f(o'k

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~.ctilfte inven~ed, ind udtng pholoOOpylng, microfllmlns . 31' 2008026910

Contents P reface... . .. .......... ...... . ...................... ... ... ........ ..... . ...... . ... . ... . . ... ...... x i -r he i\uthor ... ....... .......... ........ ....... ............. ............. ........... ..... ........................ ... xiii

C hapter 1 Introduction to Urban Den;lopment Modelling.. .. ............. . .... .. ... ~- 1

I. 1 Models a nd Modelling ................ ............. . ... .. .. .. ........ ... .. ..... ..... 2 1.1.1 TheNeediori:>!odcls..... . ...... ... . ...... ...... .... . .. ... 2 1.1.2 Cbaraccel'istic< of l-.!odels. ....... . .... ... .. . 3 1.1.3 Types of Models....... . ... ........ .. . . ..... .... .4 1.1 .4 Procedures of Model B\lild ing ........... .. .... ... ... . . ... ....... 6 I. 1.5 The Pitfalls . .. .... ......... ............. ....... ..... .. .... .... ...... .... .. ... 7

1.2 Thcoreticul Appro1ches of Urba n Developme nl Modelling ..... ........... ...... ....... .............. ... ....... ... ... 7 I. 2. 1 Urban Ecclogical Approuch ... .................. ...... ........... .. 9 1.2.2 Socia l Physical Approach . ... ... ....... ................. .......... . . IO 1.2.3 Neoclassic I Approach ........ ... ..... ..... ... ....... . . . ....... . .. 11 1.2.4 Behavioural Approocb ...... .... .. ... .. ................... . ....... .. . 13 1.2.5 Systems Approach ..... ....... ...... .. ........... ............. ... .... . 14

1.3 Conlempon\ry Practice~ of Urban Developmcnl Modell ing....... . ...... . . ...... .... .... . . .. . 16 1.3.1 Cities as Self~Organising Systems ...... .. . .... ............ .. . 16 1.3.2 r:uzzy Set and Fuzzy Logic .... .... ....... ...... ....... ............ l9 1.3.3 GIS and Urba n Mode ll ing ... .. ....... ...... . .. 19

1.4 Problems and Prospects ..... .. ...................... ....... ....................... 20 1.4. 1_ Theoreticcll Probleu1s ............. .. .. .. .................. .......... .. 20 1.4.2 Technical Problems .... ... . . ... ... . ... ....... .. ..... . .... .. .. . . . 22 1.4.3 FuturePr06pccls .. ..... ...... ......... ..... .................. .. ....... . . . 22

1.5 Concluala in Urban Modelling..... ...... ....... . . ..... . .. 30

2.2.1 A n Urban Cellular Automata .. ... ......... .. ..... ... .. ......... .. 30

2.2.2 Advan~ages of Cellular Automata for Urb.HI :Vlodclli ng ....... ......... ......... ................... .................... .. 33 2:2.2. L Sim plicity in .Model Consln

4.3.2 Defining n Fuzty 13oundury of Sydney's 6 .3 The I rn1>nct o f t\e~ghbourhood Scale on Urban A'""' ... ................ .......................................... 105 the Model's Result~ ............................................................... 146

4.3.3 Vrualising S)dney~ Utban Oe,elopmcnl 111 Space and Tune........... . ........................... .............. 107

6.3.1 Rc>uh from the 1\lodel under Different :->erghbourbood Scoles.... ...................................... 146

4.4 Conclusion.............................. ..... .. ....................... .. ... ... .. .... 110 6.3.2 Sunulauon Accuractc:s of the Model O'-'er Ttme ... .... 149 6.3.3 '1ciehbourhootl Scale and Model Calibration .......... IS I

Cha p ter 5 Modelling the Urban l.lcvclopmem of Sydney: ~fodcl Specificalioo, Caltbmtiun a1ld Implementation .................... Ill 5.1 Model Spcl Transuion Rules ........... ............................. 160 5.2 .1 Model CulohrationPrincop1cs ................................... 120 7.2 Applications of Fuzzy~~ ond Fuzzy Lo,ie ...................... 160 5.2.2 SinuJhtion Ac..:urocy A~i>a Cocfficrent Analysis ...................... 126 7.5 Reappliebility of the Model ....... .......................................... 162

5.3 Model Implementation in GIS.... . .............. - ........ .. ... 128 5.3.1 Cellular Autornoto ;\lodellrng and GIS ... _ ............... 128

References ........................................................................... !63

5.3.2 The M eG IS Approach .. .. .................. ....... ............... 129 I ndex ......... .... ................ ... .... ..... ..................... ....... ........................ ....................... 171 5.3.3 Graphic lJ~:;cr hHcrfacc Dc~'gn .......... ............ ...... ... 130 5 .3.4 Model CHhhrntion .............. ...................................... 131

5.4 Conclusoon ........ . . .. .............................. ......................... 132

Choptor (, Modellmg rhc Urban Dcvelpmcnt or Sydney: f{esuhs nnLI Di.:>cussion ... .... .. .... ..... .......... .................. ............. . ..... 133 6.1 A Summary ofRl' h''"IIY Constrnined Dc'elopment ......... 142 6.2.3 Tramportatoon -Supponed De,elopment .................. 142 6.2.4 Urhan Ph111111ng Polide~; and Schemes ............... ...... l-l4 6.2.5 Orher TrnsihOII Rules ............................ ................. 145

Preface Urban c of prime agrjcul wral land and the destruction of natural landscape and p ublic open space. This has

~Hracted a lot of attemion to 1 he study of u rb~Hl developmem undec the theme o f global environmental change. Various trban models have been b tih for this purpose. Amongst these. models based on the principles of cellular automata are developing most rapidly.

Urban de\clopmel' ship grade, fuzzy logic

an analytical tool to evaluate 1 ht: impacts of \AI IOUS factors physical. SOCi(')Cconomic. and inM.itutional-n urbandc\C:JormenL Throush lhc imp1eaoentation of "a1aous lr.ln

~10on rules, 1he model genera:cs difftrt:nl ~cenanos of urb3n development. 1l.erefore. the model is useful for urban plnnners to answ~r v,;hat ir' qucstjons.

There are scv...:n chapters in 1 h1S book. The first chapter provides a conlcxt of urbtm modtlli ng and a theorelical as well as practical re\'tew of modelling techniques in urban d eveloprntnl research. 'fhe second chBptcr introduces tbe c:elluiBr automata approHch. Rtscnrch on urban dc:~elopment b~ed on the cellular automata approoch tS surveyed :1ncl th~; rxoh!ems rui11>Cd by u~ing this appro,,ch are idcn1lfh:d. nased on n thorough undcr$tanding of urban modelling and lhc (tpplicaljQnl\ of the celln-l:lr au1omata in llus field. Chap1c:1 3 develops a fuzz.y con~lr:uned cellular automata model or urban dt\S the Sydney dnlnhase, I he ctllular automata model of urttnn developmcn11~ tesled and cahbrated in ChapterS. Through th1s resting artd cahbrauon. tbe model bused to undcrsrand S)'tlney's uJb:ln deve1opmcnl inn cciJul.:tr env1ronment, nnd Lo cvalu::ue the impact of vnriou.s fac-wrs on Sydney'~ urban dcvdop1uenL These f.oc tors include I he ph)'sicn l constraint, transportation net ~Aork. and urbA!l planning 111 1elation to ''dlious arca.s plnnned for uob:ln dc,-elopmcnt. By var)IOJ! the >ite of1h

I {

1 Introduction to Urban Development Modelling

Ul'l:>an deveJopmcot twd I he migrationofpopulation from rufall.o uban areas arcsig-nilicantglob3l phenomcna. Jncrcasingl)', rnoresmaH, isolaLed pOpt . .ll:pc~;Led 10 be urban dwellers in 2030 ( Uilited Nations 2006)

T he majority of urb

-2 Modelllrlg Urban DNclopment wllh GlS and Cellular Automata

Cities are characterised by an immense cornplc:1.1 .1 THE N 0 roR Momts 'I he idea of using models jn scientific research is by no mean~ new. Thts iden comes from the way people react wath the re."'l ~orld in '""'hich they Ji\e. Praclically, all systems In the re:~.l world arc excccdmgl). c:omplex.. ~rherc:fore. these systems arc

lnlr-oduclion to Urban De-\,Ciopment Modelling 3

consrantJy explored by the UbC of simplified paue~n~ of symbols, t'ules. and proces~ (Apostol 1961; Mcad0\\5 1957). With the use of models, the complc'>ystenu of real-ity can be simpl ified so t11aL lhey can be understood a nd rnonaged.

11e appl.icalion of models in sciemific re~rch 1S import.;~nl in many (t:o,pects. In on~ asrlcct, ahh

10 Modelling \.hbom Uevetopment '' ith GIS and Cellular Automata

. 1 . odioining areas . As the city grc\v and chungcd. some new d i'llllcts became muJup c 'J 11ractivc lhan others. Forcxamplet heavy mdustry was ~11 t he mass oflhc lnte.s' (Wi lson 1984: 205). T his is a mncro-s.:ale or agareaative approach, the succcs:, of which hu~ bt:1.m the ease or using u through aggrcgming the neoclasslcnl lllOdcls o f consumel's nncl p roducers (Robinson 1998).

Although the socinl physical apJWMCh wns applied w idely ;,. urbao planning models, I he lintitalions of 1his approach arc \e.ry clear. The fundamental limitalion s 1ha:1 u. Jails to make an. adcquat~~~~ntat~n _?!_~e behav~ural proce~s 1hat leGd< to 1nd1vtduals ~eltcung a panocu13r JOurney 10 wor~. ~lodels deClition nmong economic nctivjties and ~ocial grou1>::. in a n urban nrcu. According 10 the economic theory of equilibrium, the !lliOC:aLion of urb.ln land to various users in both quantita tive a nd Jocatio!lal a~peccs ;_, controlled by supply-and-demand relauonslups obeying the general rule of least cost'J:and ma~mum benclils. or the uuliry maxirnisa1ion rult in an equilibrium sys .. fCRl , Under severe limitina u~sumption~. a typit:al model of urban ccooo:nics shows urbnn SII'Ucture as lhe reflection of spanal p~1ucrns of tra1tspor1 costs and urb:Jn 1o ltd ~ re nt T he ussumption~ 1nighl be a ~oncentl'ic, homogeneous city ~l...s>_n~ _ ~mgl l.! J_c_ 1U~: th.c concemrau on of pnxhJctJon of a cumpositc consumption good; housii\g ...

d~mond relating on ly 10 plot size, Jocalion, and enernatitie.(; and the ignorance of public ector policies. fiamples of th

Modelling Urb.m De-velopmen t with CIS and Cdlular Automata

For a predictie model, pn:docuons nboutlbc real world can be made directly from the model. For a descriptive rnodcl, it can reveal much nbout the structur-e or the rtal-v.l mode It

lk:l\:fl(JH'/Q mndtti-

Normuhc modo lk ~tatemcm of 1~ d~oy

1bt real "'Odd phtnorr.enJ '"'akttact!d ~ s,ymbohc ir.)S;~ ue rdllr.ed u.ntctuNI:y iu a model~ thu 10 cte3le oew t.Wory

~kldels&:aJ v.arhcr.!y a rwt of rhc sy$-:em t~ir:g tr.(l(le:lfd ~r a subs~s1e.-n of t.'tc tcotllly

\1o:pcctb:l ., occ.ur undu ru~t>d conditiOn._

~todtl$ ~ltr.JIIIIJ ell the equ:thbriurn sm:nc~ feonura

\\trocls i'OOC"tntt'aa'IIJ (II\ rnxC$SQ and (mction.;s thtocgb ti::n::.

ModeL$ a.re based on the notion or c'l:ac ~aion., ~b

6 Mode-lling Urb~.tn Development \Vith C IS and Cellular Avtou1.a.ta

E.tcpl for lhe fortgoong classolicataons. mathematical model' con also be classi fic:d according to tbcit objectwc~~ the techniques in use, or the lhcorie 01' hnxnheses underpinning them. A rc::"iew of con\cntional models of urb:ln development based 011 their Utlderlying theoretical nppt'Onchcs is presented in Scct10n 1.2.

1.1.4 PROCEDURtS O F M ODE! B!JII.DI'IG

Although ,odels vary significar)ll)' from one ty pe to another. lllc.y shure common procedu res in the process of buildin" them. Figure 1.2 shows the ... orioas stages of a m(ldelling process ~,., has been v.cll accepted by many model buoldos (Caldwell and Ran\ 1999).

According to the flo\\ chan. the hrst ~tagc of rnodcl cnnnrucuon 1s to be clear about the objche cityccntn: (Hensball t96n

8 ft..\odelling Urban Developmenl W1lh GIS and Ctllul.lr Automata

Many models o f urban development are rclot.:d to von fhilnc n's model. F'or instance, Weber 's (1909} ladusl,.lal Locat1'orr could be reg(lrMt wides~>read o(models in urban geography was durin& I he perind of thequrmtitatl\C rewJiurion in geography. \\-htch bc~o~n m the late t9.50s and continued

, till rhe late 1960s (Bany L981) Tt 1is devclopr'llent Cflltle almost e;viouS !>hift of hllere.sts in the late l970s .. 'from usmg mathcnatical model~ 10 9u:Jiitative a nalyse-; in urban l'esearch. This shift .was mainuincd ull the late J98ltx and open $}Stems ptovrded altcmaU\"C wttys to understand c;ities m cvofuuonary and complc't ~y.stcms (Allen 1997). 1lte dc,clopment of lite geogmplucal information system (GIS) and the inte-gration of n ( ii S with llrb3n modelling ha'e also rn.cil itated urban modell ing Vtilh rich clatn sources and new tcch ni'l"'es. T hese new developments h:wc pushed lhe eOOrts of u1 ban developmcru modcHing hHo a new era.

The reS-t of1his secuon reviews various urh:ln modelling approaches und practices 10 the httra1ure. Howe\er. IllS not tJ'e intcn1ioo of the author 10 eo,cr the vast \Ol ume ()[ v.ork. underlaken in urba.n modelling as ~uch an effonts be)ond the scope of this book. Instead, the thcorehca1 approaches underpanning lhcse modelling efforts arc s~.unmaric;ed and an outline of the key (hem~ o f urban modelling is produced. It should be noted lhal althoug h there a re obvious dif(ere nces bel ween the vadous a1,1,roi'lchc:.s d1scussed 11'1 the ll>l lowing text, lh

\

12 Modelling Urtun Development with GIS and Cellular Automata

res:dcotial land dcvc!OpOI

14 Modelli ng Urban Development with CIS and Cellular Automata

case che dl,trJbution of households 10 available )and. t\hhough group dec-isions v.-ere rcannJcd D) Jr..c.y dc:ci~ions in the cor:ceptual franlC\''Ork , lhc.y were assumed to be koO\\n (Chapon aDd Weiss 1962b

~t::_f'ame of us O\erernRJ:aasis on indvdu31 beh.l\iOur rntbcr than group beba,-iour, and other weaknesses suet: as the overly \lmrlistic vtew of the relationshtp bem"trnints (lJttssetl and Short J 989).

1.2.5 S>'SHMS A PPROACH

ll1c sysu.;nu~ rtpproa:;h was. firs.t used in urban mcxlelling m the 1960:,. 11 was b:lSM M.he nouons of the Ge.neral Syem< Theory. According to vo11 Rertalanffy (1968), e\er)th1ng t.xtst.s m a sor-1 of syu~m in which it becomes an cJc:mem. AJ~e~emcnt~ of the ~ys1cm are linled and Interrelated and are also linked to the s)'ilems ~ ronmcnt. For in.sumc:c. an urb3u system consi.s1c: of a set or clements or subs) stems, liuch as populauon, land, emplo}ment, scrvic~ and tronspon. to mention arc"' All c:lc:::mt ntc: "1th1n the systern are it.tcract1ng \\ ithcach Olher 1hrough \Ocial. eco:tem:c. and sp.1tialmechanisms while hey are ai(,O uuerncun.c with elements in 1he environ-tl'l4!nt. The s.igmficance of any one element dot~ not depcrh.t on Itself but oo jtS r~4

Lion~lltl) !; with oth~r;. It s the links betY.-ecn lhc different clenu~nlS of the system d1~1t -dclcrm inc its e~ohuiun and so permit the process of c hange ln the ~ystem. Thus, th.c focus of the :.ystcms approach is not on Any :angle clcmenl but the ccmuec11QilS a nd procesc thnt ltnk a ll the clemems (Ch isholm 1967). '!'h is '"llgests the npplicntion or sys tcus nnatysis 111 dealing with 1he syswm.

'The implementation of sys_tems analysis involves two kC)' steps: the first is. the de llui1i~)n of a JHtrltcular system a~ the objccl of study. hnd the ~econd the way of dcseri btnt the q rntl urc and behavi(lllr of the S)'~tc m. In rega.n.l to 1 he definition of systems. C ho rley and Kennedy (1971) identified four thlfetent typcs of systemhologJcal system, the cascading system. the PI'OCtss-~SI>Onse ~ystcm, and 1he con1rol ~y~tern (1-lgurc 1.3). A morphological sys1cm represents lhe stalic relation "\hips as links between elements. whereas linl.:$ 10 a c:a~adang system pass energy from one element 1.0 another. The proccss-rc~~ $iys1cm cornbmes the first two types or ~)'stems. but the focus of studie..~ on 1h1s S)Siem IS on tbe p1ocess rather than the fo rm. "uh the emphasis on causal relationpeni ngs that WIJ:>ugc on the system and do llOt themselves give 1 he syMcm i ts imrinsic growth and

>~ability c haracteris tics" (Forre~tcr 1969: 12). The feed back loops are the fundamen-la l b~1ld1ng blocks oflbc system. Thed)namic bcluwiour of1he system is ,gecerated w1thm feedback loops lhat are contei,cd in 1ermc: of le, els of stocks {level variables).

Th~ level ~ariab!es are. prosressi,ety a~ered over llln c by the ra1e of chan~ (ra1e '-anables). fhe rare vanablcs are affectai by variou~ posalive and negative fetdback

~ops wnhm ~c system. Through 1he usc of a special computer language ca lled DYNAMO destgned by worker; at the Musachusett> lnMitutc of Technology 11\HT). S)f'lcm Dynamics has been used lo sirnulat~ chc: d)namic behaviour of urban sys-rcms und pcovide prediel.ioru on urban dtv.eloprnc nt under different condition). Thi s u:c:hnique was regarded as providmg n laborntOr)' for s trategic and tacttcat research (Forrester 1969). However,

l(o Mod~lling Urhan Development with GIS and Cellular Automata

"llle system~ approach presented rc~ers wnh a '-'"a} of co:.struuing models beyond 1he simple cause- effect or sttmulus:-response re:l:uiun,h1ps. 1bcrcfore. ~ m have led lO the undc::rstn.nding of u rban dcvc1opmt.:nt as ! tO i n~guln1 J)IOCCSS in I he n1anncr of biful'CIHiOn nnd chao~ (Allen 1997; Batty and Longley 1994; W alo n 198la,b). Prac liclly, the emergence of new di~11al

(

(

IU Modelli llS t;rbat\ D evelopment with C IS nl'td Cellular Automata

beh:t~~:iour th.'lt underlies many phcnOil\ena chardCitfi\Cd by dendritic g;rowrh. such as growth or rr0-'\1 on 8 windo,'Ypanc. lighting. and bparks. [t Yr3~ first introduc~ by Dally (1991) iniO urban gn>wlh r.todelliog. f-olio~> in& lhe rules proposed by W1nen and S..nder (1981). ll:uty de'-cloped a model of Ol.A 10 modnmlleJ 10 u bm'l rnodell ing without muc h inu:rnctions for over , ,...,.o decades (Sui 1998). II was nol unti l the late J980s thnt OIS I'Cseuchcrs tried to mregrare thejr techniques with urb3n modelli ng in the hope of improving the analytical capabilities

20 Modelling Urban Development with C IS and Other dassafications on the i ntegnaticn of GIS aod urb:tn modelling approaches alsocxost. For instance, Sai (1998) Identified four different approach chat have been w1dcly used by resea.-chct'$. Thc.:se n~ciOOc en\bedding GlSIIke funct ionalities imo urban modelhng packogel, M th ose illustrated by Rirkin al. (1996),1'utnam ( 1992),

22 Modelling Lrban Deve'opmcnt wilh GIS and Ccllul.at Automata

1 .4 .2 TtCH'IICAL PROOL[ MS Anotl\cr type nf problem will' u ban mode IIi 1'\g conl.:erns technical is~~ ~e~. One of these

relate~ to data cwallability and dma handling~ the othc.r relates to the wuy GIS can be applied 1n unplcmenting urban nlOdels and in m.uupulatingand \1couali.sing da1.a.

Con,cntionally. dala ar: collected by researchers undertaking (f)ecific resc..vch projecrs. \Vith the-applications of GIS and r~motc ~nsing technology. more and more dnto bccone available f oan both commercial data prlwidcr:,. government orgaojsations, nnc.J profes...;ionn l institutions. However, problems exlsL in lhe compl-rabilily and accuracy of datonnd the vtay m \Vh iciHhey are hand led . 'J'hcsc problems relate to the vaiat ion in the ~pntlal areal unit that is frequellt1y d e.sined for differe nt purpOses. 1hc )c'\el or degree of aggregation OJ ~3Ual data. and the way of sampling for spatial data acqujsiti()n. The IIC(.:Utacy or datn also de-pends on data pro\iders and the w.ay dma a re stored and rrc:;cnted. h IS frorn tlus perspectl"e that there exists a general Inc "' of good datn tbr s pecirtc research purposes. Thercrvrc, ~earching for and ach ieving such go()d daw i11 a ll 1nodeJ Jing effons bccou\es an in1p0rt ant task.

The rApid gro\\lh of GIS :1nd its integration w1th urban modelling has provided modellers whh n('w pla1forms for data managtmem and visuallu_tion (Nyer&-c:S t99S). HO"e\er, this integrauon u essc:n11ally technical in nature, and u has not touched upon rundament31 issue_., m either urban modcJiing or GIS (Sui 1998}. llus is du~ tO the difference. in the sp.atitd data rcprc'\entnuoo schc.:rnes irwoh'cd in urblln mode lling nnd GIS (Abel, Ki lby, and Davis 199,1). lJssenLially, I he devclopmcm of GlS is l:oa~ed UJ)On a Jimitcdmop metaphor {l:lurrough ood Frank 1995: Harris and Bauy 1993), wbert geographic~;~! features in sp1tte a re captured in n1Rp loyers ejther as point~. lines. and polygons. or as raster cells. and these fcaiUre an: temporally fi"ed (Raper and Li,ingstonJnlroduclion to Urban Oeve!opmem \\odc-lllng 2l

Tl1o establibhment of ccnai n. mod~ Is demands certain k inds ur i nformatjon (Echcu iquc 1975). 1lle~fore, rc:-~>enn::hcrs cnn locus on looking only for rhc information lhoy nccJ.

Fo.the llllegrauon or urban ~odtlhng Wilh GIS. Si (199R) nrgucd tbal lhe l)lilh -~e.n~ could not be solved 1f th1s nuegrahon cominueJ lObe lt'Cat('d as a technical I),)UI!. He sugg~d a new ~!'ne\\~rt for urbAn modelling based onthene"IY dcvcl opmg gcograptucollnforrnnhons.ctencc. Modeb under this rmme"'Or-l::should enable

rc.~ea~chers to desc_rlbt: lhc emerging urbnn form in more cou1prebenshe \\11:}'~. lQ c.:xp la1~' lhc und?rlytng processes co ul nbuung to the e-rnergem:c of new fouw;, filld lO prcsc:nbc cffecuve u(hnu r>oJicjes to rcdircc:1 the unde;;rlying process IO promote the most desirable u rbon fOrll\S (Sui 1998).

1.5 CONCLUSION

Th1 . .; chap1er pr~vidcs u~ ovcn iew of the conh.:xl on urb1n modelling and a theorelic.ul n.!t well ~1 .s pracucai_I"CVICw.ofmodell ing techniques in urbnn dcvctorwnenl rcsc:H'ch. Conttnuang from 1h1s ovcrv1ew, the fol lowing c lt.lplers f ocus more o n urban model I Jns

ha~ed on 1he cellular aulomata appro3ch. 111c structure of I he book is as follows Chapter 2 firs! imroduce.~ lhe cellular automata approach and us applicali~n 1n

urba11 modelling. ReStarch on urban d('\'elopment based on ahc cellular auromor.n I '\ surve}ed, and ~he problems raised hy u~ina lhis approach nrc idcttified. Ba~ed o n

~he u.n( lerstandmg of urban modelling ond the applicmjons of ch~; cellular autornatH Ill thiS .field, Ch~pter 3 i mroduces fuzzy set and ru z:ty logic app1oachcs in urhnn

r)l l'ld~ lhng_, an_d ll dcvcl~ps a ceJJular ttUIOilhlllt rnodeJ of llfhttn dc,cJopmenl u~ ing fuu) eun,trame~ tr:mSIUOn r~les. A_ complete proces:~ of developing a fuz~y l

2 /

Cellular Automata and Its App lication m Urban Modelling

Recent studies of nonlinear and opc:n s)stenH have led tu the understandjng or citjc;) ns evolutionary and complex S)"Stem~ (Aileo 1997). Ciue~ are looked at as self-organising S)!3olemi. which are remarkably suited 10 comr>uttuional s.~mub lion (Clarke and G ayda> 1998; Wolfram 1984). A cel lular nu1oma1on s churuc tcr isc;i.l by phase trans itions th at can gcncnue cornplex patterns rhrough s jmp!c transition ru les. As such , th is lcchn iq ue Sterns ideally suited 10 modelling the complexity of urban sys1cms (Cinrke and Goydos 1998; Bouy 1995). In this cl oUJI ter, the: prlnciples o f mata simulauon are discu~ed. and the appl ica tions of1h1s simulauon technique in modclhns urban de,elopment are re,iewed. 1,uough lhis rev1cw, the progress and limit3.lions of 1'le cc11ular automata ror urbou dcvelopmenL modelli ng are ident ified. leading lO the de velopment of a fotzy cons1rtdned cellular au!Omala model 0 1 urban dcvclopmcnl in the follow .. ing choptes.

2.1 CELLULAR AUTOMATA MODELLING

2.1.1 CELLULAR A utOMATA M oDELLING: A GAME A cellular autonuuon (CA) is a d:scrcLc dynnm ic syslern in which sp ace is divided into rcgulflr spa liul c ells, a nd lime p(ogu;.:.s~c::. in discrele step~. E~lch cell in the syt. 1em ha~ one of a finite number of SIRles The Stole of each cell i.s upda:c!d accordtng 10 local ruler., 1har s. the ~uue of a cell at a gl\en lime dcp..:nd> on 1ti0 own slate an.i lhc Slalcs of ns neighbOuK a the prev:oJ.< IInne Slep (Wolfram 1984~

1 l1e ri!Search on the design and ap1'11 ica1 ion of cellular aulom:ua da1es t-ack 10 the dawn of digital compuuuio n. Ahn Tuting, a n E nglish mathenuuici Alamos Na1ional I 3baratQry, '''"'working on lhc problem of self.replicatm &

~yMcm

Modclll ng Urban DevciQprn ent with CIS and Cellular Automata

in t\VOdimcn~ionol and thre

28 Modelling Urban Oevelptncnt with C l S and C~tlular Auto.,ata

2.1.2.1 Five Basic Elements of a Cellular Auton~alon Ac:cordins co che foregoing definnaon, a eists of fhe bl.Sic e.lcmc: nts:

o. Th~ ct/1, "Inch" the b3sicspaual un11 m a ccllulnr spact. Cells in a cellular autcmacon are ao-anged in a spatialcessellatton A twu..-ynchronoo;ly to all cells ithin the systetn. Ho'4c"cr, a numher of modifJC.ations 111 defining trJnSttion rules have been

~>~rved in the literature, which 'Ar:iJI be dtc;(u~sed tn the foll()"'ir.g sections. c. Tl1~ tlme, ''hk:h specifies 1he tempoml dimtn~inn i n which a cellular autom-

oton exists. A CCOI'dlllg to tbc ddini1 ion of cellular aul

30 .\1\odeiHng Lrb.m Development ''-ith CIS ~nd Cellular Aulomala

Ccllu lnr ~\lomata also pos~~s the chnrncteristk: of a n open system that is capa ble o f self orgunisa~ion. This is a process in \.Vhich the system ilcrcuses its iotenlal organis:u ion m compJexiy wJtliOtJt bei ng guided or managed by an outside source. Such a ~lf .. t\rganising sys.tem l)'pically displays cmcr-gem propcrtie~ 111 'vhich OO\'C) and cohc:R::nt sr.-ucrures and pauerns arise from turbulence and ch.&os (Go!dstein 1999j. For uostance, Wolfmm ( 1983) shows thnt wuh a "disordortd" onitial state that was randomly chosen, a s1mplc one-djmensional cellu lar automaton is capable or genera ting ~mne structu re in the lb rm of many tritulg ular ''clcul'ings: The spon-tance u thntsed land puccl~. Ustng the Moore Neighbourhood. the

r::tn~cton cf ahe suue ot ('Aolteds js go\-emed by the foUowing ru!e. Rule 1:

1 ....

THE~

l ltCICOreth recormoredcvelnped parcels (i c., urban pa-cds)in the Moo1-c Neighbourhood of n uonurban Jand parcel in qucst.ion, the non-urban land J}.(II'Cd will be developed into an urban s1a1e.

Wnh thi~_ransitio_n rule, lhe m~l gcncrn1es a ~ries of sccnutos or urban develop-ment ac dtfkrcnt umc frames. whtch are d1splaycd in Figure 2.2.

However. m a real s itu:uion, the-geogmphicnl condilioos v .. ithi n an area can ne\cr be unironR For ins111nce, t~igu ittciml difference may exist in 1hc terrain of the land ~ca~- In Ot'der to reduce I he co~ls in 1hc constwcljon and opcro1ion of mun icipal Llct lttlcs such as scwa.gc and water SUJ)J)I)', Ul'ban deveJopmeru m::.y be rei:[rict ed 10 a~as WJth a re_Jief of lc(S lhan 300m. lltcrefore. no d evtlopnlent wtlltake place 10 cells Wtl_h art-he( or more than 300m. l_n th~.Stuarion. a new rule needstobe 1m pie men ted '" the: model to reflect the terra1n rt~triction. This new rule can be presented liS anolher IF-THEN s1mcrncnt.

l ~ule 2: IF THcr-o

the relief of the landscape i~ more than 300 rn, the land pa1ctt will remain unde\eJOped (i e .. u stays as a non-urban pan:cl).

32

,_ ~

M odelling Urba1'1 Development wtth G IS d l"'d yslcm ir. a ceJ. lular !\pace.

2.2.2 A DVANTAGES OF ULLUlAR A UTOMATA FOR URBAN M OOlUING Wnh lhc full de\eJopn1en1 Of computer &rAphiCS, geographical information S)'S ltml\, rrnctals, and chaos and complex system" theoric.:s since the late 1980s, the nppliconions of cellular automara to urban modelling are rapidly gajning favour amons urban researchers. Tbis is because c ellular automata are intrinsically c::pa tml, which is inberenlly attracti\'c for their opplieation to geographical prt~blems (\Vh itt nnd Engelen l9 93). CeJtular a'Jtomntn nrc 11Cspcc ially appcopriatc in urban modelli ng where th e process of urban spread is ent irely local in nature and the aggregnte effects. s uch as growrh booms. arc fmttrgellf'' (Clarke and Gaydos 1998: 70 0). thal is, the ir behavlout is gencrt~ tcd 11 by I'Cpetit i\e appUcation o f lhe rules beyond the inioial condioion" (Clarke and Onydos 1998: 700). Wio h ohcsc

34 Modelling U1bau Development , .... th GIS 3nd Cellular Automata

ur.dcn tandings. cellular outomata have been widely used 1n geograpbacal modeJ-hng. especiolly m studies o( urban development (e g, Clarke and Goydos 1998; On 1984).

Apart from the intrinsic nature of celluhw aulo ma ta tOWllrd urbnn modc.lling, 1his applonch also posses~cs a IH.II Ii bt: l' of oth er adva nta geous rctH Urt!. that a re altraclive to u rb::en nodellen. which nrc outlined below.

2.2.2.1 Simplicity in Model Construct ion Compared to all other ulbJn stmulau~o models diseu~"d in Chapter I, !he de-elop-mem of wban models b3~d nn cellular automa1a are 'cry suaixhlfotward. which c.m be=. constructed ba.~ed on uuuithc understanding or the syslcm bt;i.og modelled

(Bc~tcnon and Torrens 200'1). As illustrated in Sec lion '2.2. l, an m ban cellular outonatu model c an be con-~tructed as a s imple. two-clirnenMona1 a Hay of cells with o ue of L\VO possible states: Ul'b~rn or ural. The lrfllll)ition of ccJls from one State (rurnl) to a nothc;r (uban) is based on a number o f simple rules. which c an be imple me nted ;nto the model as a set o f s imple "JF-THEN'' StlUements. Howc\'er, based on [he sclf:.oganisation a1\d .ll>rlf-n:production nature'i of the cellular autom ata, such B /j.tmplc design of the cellu-lar autotnala can generate very compi.CJC spatia) panems when the system J,rogresses 0\11!1 ume.

The simplid1y and ntUihVC. noture of the cellular automata nc.n only umplifies the pcUC:(!)S of model cons1rucuon but it also makes i1 easlcr ror modcllen to understand the de"clopmem of the system tmd intcrptet thc model's re~ult~. This is because the model m imics rhe wa)' in wh ch "we sludy, underswnd and describe the system and phcnorne na in the re:ll world'' (Bcneoson a nd Torrens 2004: I I).

2.2. 2.2 Modelling S1>atial Oyna mics Lo Support "Wha t If" E

36 Model ling Urban Development with CIS and Cellular Automata

Torsten Hage~r3nd's onnovallndffuson models were of this trend (Hagerstrand 1952). rrom rhcsc. ''iamodcls of nugration and of local sell lemont netwOfks ~lortill 1965). the spa ttal growth of urban a reas. and the: changes tn urban structure \\"ere treated as diffc:rem ly()es of d1ffus1on proc.esses. In the construction o( the diffusion 11 10dels, a notion of the neighbo n rhoocl eJle"t was included ( I mgorstrand 1967. 1965); lh is wos very close in principle to the cellular a utomata technology.

Jn the early t960s, w ith 1he nssis tance of comput1' technology, nod els of urban g 1ow th wen.:. de\'eloped under the beluwiouraJ appruflch 'vilh more emphasis on indiVidu als' behaviour and their decJsion-mak i ng proccsl)c:S. One of these models 1>oesentod by Lathrop and Hamburx (1965) was developed in a cell-based frame-.,,-ork. to sjiJlullte the development of an urban area in western New York State. This model ''as rele ... -anl '" spuuco lhe prmciples of change m a cellular ~pace. An01her model. de,eloped by Chnp:n and"" collO~e a ccll .. splcc model si01ula1iog urban growth in the Detroit region (Tobler 1970). Using his fi r::a rule o f geography that "everyt hi ng is rela.Led to everything e lse, hut near t hi ngs are more related than distant things" (Tobler 1970: 236), thls model nllcmptcd to relate 1he PQI)Uiation growth of a cell (ra:pra:,..nting an area of one.hicol ~r~odcl ( M odel V ) shows lhnl l he land usc at Joc.at ion r.j is dt:pcndenl o n the land u~ of t he model itS(}Ir and al l tbe land uses i n the neighboul'hood of the location i,j.

(2.3)

''heres; is the land-use ea~egory (urban, ruraL ... ) atloc:a~ton i.j arumet;g~x is 1he land-use category at the same locaton at some o1hcr umc; g:_ .. 11 . 4~ represems aH llle land-use categories m the nc:il!hbourh

38 Modelling Uroan oc .. clopment w ith C IS 0fld Cellular Au tomata

:1re3 (deMh); and three dense ne~ghbounng Oae.s v.ould pruvide for a zone to reach the neCssnry populauon base ar.d thus ocquirc a>an growth (Couelelis 1997: 165). 11 was not until the 1990> that Coooclehs identified how cel-luln outomrun-bascd urban and region::~) mo rtels could b~: moved from tbc realm or in.suucthe metaphors to llt--:11 of potcnliet.Jly usefu\ qunntitattve forecasting tools through gcncralisiog the idea of space withm tt ccllu)ur flu tommon w proximal spac~. and the operations or a cel lular automaton 10 a tnorc scno1fl l geo-algcbra (Couclelts 1997; Takeyaona nnd Couclcl is l997).

2.3 CONTEMPO RARY CELLULAR AUTOMATA-I!ASW UROAN MODE LLING PRACTICES

Cellular autOill!la models luwe demonstrated thcar abtl1ty in a;cncroung different spa 1131 paucms I>Med nn locally d:fined uansouoo rules, \\hoeh ha"e a11noc:ed a lot of interes11n urban research. Ho\\-evcr, cQmpa~d to the standnrd cellular autOOtata model thal ~nctly defines us fh--ebasic elements. conttmporor)' celluiKr auwmatabased mod-el long pracuces h"e broadly interpreted the definong char.>eteristics of tloe sundard cellular "utomata, or relaxed some of its chtt11)C'tenstics to a

40 Modelling t.;rban Dlvcloprf'lcr)t w ith GIS .;~nd Cellu lar Automata

Another cellular u~otum::un 1nodcl developed by Cla ke and collc~gt.:es used a basic grid of 300m area. I fowever, wh le applying the model 10 the \Vashington!Batumore region, cal i brc\tjorn: Y.t:rc:. \ 1 ndertaken at d 1 rrcrcnt cell :;cole:,, including 2:10 m, 420 m, 840 m. and 1680 m (Clarke and OuydU$ 1998). Their results h'" thar, although not all n1les or factors arc sensuhe to the dwngc. or the cell scale, the scale of cells does ha\c an impact on the ret.ulrs of 1hc smulntion, e~ptc:"!ally an relation to cc::rrajn factors. such as road and >lope. Tiley susg.,sted lucran:hical approach in calibrating the model by .. first using coarse dnta to mvc~usate the sc:aling nature of each parameter in a d1fferem Cll) \elllng, then scalma up once rhe he~t data ranges are found- (Clarke and Gaydos 1998 710). n111 tindang "as supported by Samat (2006) wbo idcnaified that the cellular au1omara model produces reahslic urban fonn only up 10 a certain range of spaualresolutaon. mdtc:~.ting 1he s:odels of urban growrh configure the saar~ of cells using land-use or land cover lype. For in;~ancc, Wh ile and Engelen (2000. 1997. 1993) defined 0 hi~:rnrchy Of five )ttndUSC: States, ranging ffOfl1 \CIC~nt (the Jowes1 s1a1e), housing. andumy. 10 commerce (1he highe

42 Modelling Urban Oevelopuwnt wflh GIS and Cel lular Automata

r '

4-

::;: 'u :;. 1-j. l I

,r: . 1-+ ~~ H 1':'~ ,_I"" 1.. ~

-. I (a)

f i C U RE 2.7 L:ugc nei.ghb()'.uhood sizes used by White. und l~n~dert ( 1993) 3L\d Wu (L996}. ((n) A cu-culnr ncighbou,hocd of 113 cell. propO>cd by While and Engc'cn ( 1993): (b) a rect-angular neighbourhoonle nt in asmaJl a rea base; o thers s uch n~ 11 rbn n p lanning and the uans portntion net works are global controls ovel' lhe whole nrcn. Jo..forcovcr, developme nts

Cel lular Au to ftlata and Its AppiH::iltion i n Urban ,\1odclllng 43

m information technology and telecC'Innnuu

44 Modell ing Urban Development with C IS ,lfld Cel lular Automata

~nu~ distorhon is espedatly si_gnjficaot ~hen a l3rge neighbourhood size applies. \\lti bei ng changed :3. Changes o f neighbourhood size when Sp!llia l SCflle nnd 1he neiglbourhood

type nrc kcpl constant 4. Chnnges of neighbourhood t)'JlC when spaun l scnlc is kcpl constant bm the

neighbourhood size is beiugchangepatio I scales of cells and neishboorhood. Their rc.>carch demonsrnred Lh!tr, all hough the sinwlation results of a geographkal cellular auromata model an sc:nsi-11\0I\)'berspeeti\'(:), rc)ultjng Jn d i ffcrent types of urban cellular nutomtun modtls. These approaches ran~c from the very sirnpJc to the very com-plex ones. For instance. the diffusion-li m~ed ngar~gnli()n model developed by BaHy and colleagues for modell ing the dynnm ic urbttn growth is a celhllar automaton in notuJC, nnd the rule applied in this model is s imple: u vocan1 cell wouJd convert to an OCC\IJlk:d one if it is within t.he neighbourhood (lr a n ('CCUJlicd c ell (Bauy. Long ley. nnd Fonhcrlngham L989). Howeve1', {)I her url>nn tnodcls based on cellu lar automata

u~e COII'Ibuled ru les to simulate Lhc complex bchuviot.u' of the system. This sectjon reviews urbtm cellula r 3\.ltomata models in t he llterorure with d istinct features in dcfinmg the transition rules.

2.3.4.1 Cons trained Cellular Automata

n,e cootemrw>rary cellular automal.a model of urban gro"'rh .started with research I by While and Engelen, who lirs1 developed and a1>plied a conmained c~llu~r

auroma1a model 10 simulare land-uS

I '

'

46 Modell Ins Urban Ocvelopment with CIS ;md Cellu l o;~.r AutomntC~

.. tbi~ model genewtes cont;traints \'prO.lCh ba~ed on ccllu lur automata theory. The constrained cellu lar auLomata n"lodCI h.ts evolved nnd been applied to simulate land use change m a nu111ber of citie~ su1cc i1s flrf'i l

48 Mor,l{'lli ng Utb:'ll'l Uevcl(lpmc l"'t with GIS and Cellular Autonala

tran~it.ion rules ar~ defined to mtegrnte with 3 decision-making process. that is, they are defined throuKh v.1guc. and suh;c

so ,\..1odelling Urb~n De-velopment with G IS and CelhJiar Automata

l.nput La)Tn HW.dt.n Layus

FIC URE 2.9 An del: v.illii h3ppens inside the black boJt as unknown lO the mOdellcrs and the u~rs. Ther;foic~

1bc._lnodel does not pro-..,de explicit knowledge: in understanding the proc"'tss of land. 0 ~"->'!moge Q,i and Yeh 2002}

2.3.4.6 S tochastic Cell ula r Automa ta Mode l RC$earch literature al~o ~hO\\~ the use o f a stocha, uc np]>roach in contigu,jng 1he parameter values o f a cellular nuii) IOOta model. One such ~cudy '"as underta ken by Wu (2002) '"''ho c.te~i\ed the 1niua! probability of sJmul~u ion from obwved sequcn~ llal land-we data. The inrtinl probability was updated dynamicdly through local rules based On the Slrcngth Of ncrgltbourhood dC\'elopment. fly applymg the model tosimulttc tbe proces.; of rural to urban land usecom'Crsion 1n Ch1nas Guangzbou tity, the model gcoera1ed renhsucstmulation results.

S imilarly, Alme ida ct ol. (2003) designe d a framework usmg probabilistic 111e thods thut ~lre based o n Bayc8' theory a nd the related '\.,.'tlj!hts of evid ence" upvroach for simulnli ng u rbon c hange (Al meida eL nl. 2003: lSI). The model was appl ied 10 a medium .. s:ilcd town in Brazil to con1bine vnrious .socio-econo.-nic a nd infrastructuml factors tu predict the probability of chnnge~ betwe en land-use 1)1pes. The research sh ows that the "weights of evtdencc" approach prO\' Ide s a pa r t.iC\Ji a rly simple and useful way of J llustratin~ how te-;s con"enuonal map d ata 1n raster ar.d in bmary form ca1t be used in a multhariaue frame"ork. thus linking. cellular automat3 and rasu:r GIS to more convention:. I and \\C:d o~ drivtrs of the growth process (Almeida et nl 2003: 507).

/ 2.3.5 MOOfLLI'IC Tl.\lf

Urbnn develoruJ,Ont is n pi'OCt.:.SS that occurs in space ~uld over ti me. In order Lo unders tand the dynamic process of such developrnenL, both lhe spatial and tcm poral di mensions o f this process need to be taken iruo accOJ.ml . .1\iany effons have been m ade at Lhe conceptual level 10 improve the cemporal dynamic repre~nration in a G IS (Ciaramunl and Theriault 1995; Pcuquct and Duan 1995; Peuquet 1994: Langrnn 1993; Hazcllon, Leahy. and Williamson 1992). llO\\

52 Modelling Urban Oevelopme:nt with GIS and Cellular Auto~ta

for practical lasb, rc~ulting i1l significanll)(ogrc!.S iuthe field ofsimula1in.g the sp.tt tia l arld lemporal dynomic5. of urban devclopmenl. However, there are Stll several UmHIS\Ve(ed queM iOnS ren1aining in lhis neld.

).'(ost of the cellular automata aJOdels or urb:ln growth configure the cells as blnary stute.s of either urban or nonur'ban. or wath spt:Ctfic hlnduse types; there 1s 3 sharp boundary between the non -urban and urban s_uues. or bel"'een the different types of land use. Therefore. those n>Odels >IOiulrue ool~ the con\'crsion of :1 nonul'ban to :arl urban proce"'(\ (Li and Yeh 2000~ Oarke and Gaydos 1998; Wu 1998a,b,c. 1996; Clarke, Hoppen, and Gnydos 1997). . AJLhough some re4\earch ha~ been undertaken 10 apply fu11.y set lhcory ~~~ dcHning the trunsition rules of a ccllulnr nutomata mode l ('Wu1998b, 1996. Dragion:a11y. 1 h~se

m~e1s urc implemented by scttin& up a starling ~ate nnd lhen lc~~n~ the mudcl run for a number of ltcratlons tantil the sunulatcd results ~t wilh U\e dam SC( for calibration. Con