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Creation of future urban environmental scenariosusing a geographically explicit land-use model: a casestudy of TokyoYoshiki Yamagata a , Hajime Seya a & Kumiko Nakamichi a ba Center for Global Environmental Research , National Institute for Environmental Studies ,Onogawa 16-2, Tsukuba , 3058506 , Japanb Graduate School of Science and Engineering , Tokyo Institute of Technology , Tokyo ,JapanPublished online: 10 Jul 2013.

To cite this article: Yoshiki Yamagata , Hajime Seya & Kumiko Nakamichi (2013) Creation of future urban environmentalscenarios using a geographically explicit land-use model: a case study of Tokyo, Annals of GIS, 19:3, 153-168, DOI:10.1080/19475683.2013.806358

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Annals of GIS, 2013Vol. 19, No. 3, 153–168, http://dx.doi.org/10.1080/19475683.2013.806358

Creation of future urban environmental scenarios using a geographically explicit land-usemodel: a case study of Tokyo

Yoshiki Yamagataa*, Hajime Seyaa and Kumiko Nakamichia,b

aCenter for Global Environmental Research, National Institute for Environmental Studies, Onogawa 16-2, Tsukuba 3058506, Japan;bGraduate School of Science and Engineering, Tokyo Institute of Technology, Tokyo, Japan

(Received 2 April 2013; final version received 7 May 2013)

In the present study, a large-scale geographically explicit land-use model was developed for projecting the geographical dis-tribution of urban environmental variables, such as population density and fraction of urban and green vegetation land cover,for different urban forms. These variables form key inputs for regional climate models, yet they are sometimes addressed inan ad hoc manner. This study employs a land-use equilibrium model based on urban economic theory, which endogenouslyprojects the geographical distribution of households, residential floor space/rent and land area/rent. The model can deal withnot only urban growth but also urban shrinkage, which is becoming an important issue for developed countries, includingJapan, confronting population decrease. The model is calibrated for the Tokyo Metropolitan Area at the micro-district level.Using the model, this paper demonstrates an extreme urban compact city scenario for the year 2050, and it is compared tothe business as usual (BAU) scenario.

Keywords: land-use model; the Tokyo Metropolitan Area; compact urban form; scenario analysis

1. Introduction

The objective of the present study was to develop a large-scale geographically explicit land-use model for projectingthe geographical distribution of urban environmental vari-ables, such as population density and fraction of urbanand green vegetation land cover for different urban forms.These variables form key inputs for regional climate mod-els (Kusaka et al. 2012), yet they are sometimes addressedin an ad hoc manner.

Recently, in connection with the planning of low-carbon cities, many urban planners have become aware ofthe usefulness of the compact city concept (e.g. Newmanand Kenworthy 1999; Jenks, Burton, and Williams 1996;OECD 2012). Indeed, these studies have indicated thatcities with low-residential density rely disproportionatelyon automobile transportation, and therefore CO2 reduc-tion could be achieved by changing to a more compacturban form, which would lead to an increased use of pub-lic transportation and reduced car trip length.1 Now, thenext challenge is to test whether a compact city would alsoreduce CO2 emissions from the residential sector, studiesfor which are relatively limited (Ewing and Rong 2008;Nakamichi, Seya, and Yamagata 2013). CO2 emissions areusually estimated by multiplying the unit emission inten-sity by the number of households or residential floor areas

*Corresponding author. Email: yamagata@nies.go.jp

(spaces), and therefore a toolbox which enables us to esti-mate these variables under several urban forms is useful.

The real estate system, in a geographically explicitmanner, is often modelled as a part of land-use models.To date, numerous methods for modelling land-use2 change(e.g. Irwin and Geoghegan 2001; Verburg et al. 2004;Matthews et al. 2007; Irwin 2010; Barasa et al. 2011),statistical/empirical analysis of land use (Qi et al. 2013;Wu et al. 2013) and creation of future land-use scenar-ios (e.g. Rounsevell et al. 2006; Reginster and Rounsevell2006; Yue, Fan, and Liu 2007; Sohl et al. 2012)3 havebeen proposed. Koomen et al. (2007) categorized land-usechange models into: (i) economic principles, (ii) spatialinteraction, (iii) cellular automata, (iv) statistical analysis,(v) optimisation, (vi) rule-based, (vii) multi-agent modelsand (viii) microsimulation, with overlap occurring betweenmodels. The commonly used Markov models, for example,can be categorized into statistical analysis. Each approachhas its own merits and demerits. Recent studies in cate-gory (iv) consider spatial autocorrelation among land-useclasses because neighbouring zones tend to be catego-rized into the same class (e.g. Overmars, de Koning, andVeldkamp 2003; Aguiar, Câmara, and Escada 2007; Ruiz,López, and Páez 2009; Brady and Irwin 2011; Chakirand Le Gallo 2012; Sidharthan and Bhat 2012). Anotherrecent tendency is the movement toward computationally

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demanding techniques ((vii) and (viii)). Representativeurban microsimulation models include UrbanSim (Waddell2002, 2011), ILUTE (Miller et al. 2004; Farooq andMiller 2012) and IRPUD (Wegener 1982, 2011). Agent-based land-use models are summarized in Robinson et al.(2007). The spatial resolutions of these models are typ-ically very fine, and may also consider very detailedattributes of agents (e.g. age, income, sex, etc. of eachhousehold), resulting in time consuming data gatheringand/or synthesizing processes (Patterson and Bierlaire2010; Kakaraparthi, Kockelman, and Asce 2011).

Our model relates to the approaches (i) and (iv).Specifically, this study employs a land-use equilibriummodel based on urban economic theory (e.g. Anas 1982,1984; Anas and Liu 2007; Ueda et al. 2013), which endoge-nously projects the geographical distribution of house-holds, residential floor space/rent and land area/rent. Themodel can naturally deal with not only urban growth butalso urban shrinkage, which is becoming an important issuefor developed countries confronting population decrease(Wegener 1982).

Land-use equilibrium models are typically constructedusing relatively large zones (e.g. municipality level), espe-cially when one’s study area is large. Our challenge in thisstudy was calibrating the model at the micro-district level(finely divided region based on the seven-digit postcode,called cho-cho-moku in Japanese) for the whole TokyoMetropolitan Area. By doing so, we can look at the impli-cations of the district-scale compact city policies such asthe relaxation of the regulation on floor area ratio aroundtrain stations. Using the model, this paper demonstrates asomewhat extreme urban compact city scenario for 2050,and compares it to the business as usual (BAU) scenario.

2. Summary of our model

2.1. Brief description of our model

The structure of our model is similar to the work by Ueda,Tsutsumi, and Nakamura (1995) and Tomita, Terashima,and Nakayama (2007), and is inspired by the Anas and

co-workers model (Anas 1982, 1984; Anas and Liu 2007).Ueda, Tsutsumi, and Nakamura’s (1995) model reflects theJapanese real estate market situation in which land andbuildings are traded separately. The structure of our modelis represented in Figure 1. In this model, we excludedfirm or business agents because it is difficult to modelthe choice behaviour of firm location with high accuracyat the micro-zone level. We also excluded transportationfrom the model because flow data for transportation origin-destination (‘Person-Trip Survey’ data) for Tokyo is onlyavailable at the more aggregated zone level, and therefore itis difficult to calibrate the model at the cho-cho-moku levelwithout downscaling the variables. The major assumptionsof our model are summarized as follows:

(1) There exists a spatial economy whose coverage isdivided into zones i (i = 1, . . . , I).

(2) The society is composed of three types of agents:households, developers and absentee landlords.The behaviour of each agent is formulated on thebasis of microeconomic principles, that is, util-ity maximization by the households and profitmaximization by the developers and the absenteelandlords.

(3) The households are divided into seven categoriesshown in Table 1.

(4) The total number of households (or population) inthe metropolitan area is given (closed city).

(5) The households choose their locations in accor-dance with indirect (maximized) utility and zone-specific attributes.

(6) There is one residential land market and residentialfloor (building) market in each zone. These marketsreach equilibrium simultaneously.

2.1.1. Household’s utility maximization behaviour

In typical land-use equilibrium model studies, a house-hold’s indirect utility function may be specified as

Indirect utility(Zonal attractiveness)

Choice of location

Floor spacedemand Floor space supply

Land market

Income

Floor rent

Household

Developer

Land supply

Landlord

Land demand

Floor market

Profit maximization

Profit maximization

Utility maximization

Land rent

Otherattributes

Figure 1. Structure of our model.

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Table 1. Household types.

Household type s (Equation (8))

[1] One-person (65 years of age or over) 1[2] One-person (under 65 years of age ) 1[3] Married couple only (either of them

65 years of age or over)2

[4] Married couple only (under 65 yearsof age)

2

[5] Married couple with child(ren) 3.14[6] Single parent with child(ren) 2.17[7] Other types 2.85

Vi = ln yi − αa ln ri, (1)

where ln(·) denotes the natural logarithm of the variable (·).V denotes the indirect (maximized) utility; y, income perperson in a household; r, floor rent per unit. Due to the lim-itations in data availability and for computational reasons,we made some practical assumptions. Firstly, we assumedthat yi is fixed, which leads to a partial equilibrium model,not a general equilibrium model. Second, yi is identical forzones i if its higher zone (i.e. municipality) j is the same.Hence in this paper, we focus on the changes in r. By apply-ing Roy’s identity to Equation (1), the individual demandfunction of each good was derived as:

ai = αayi

/ri, zi = αzyi, (2)

where αz + αa = 1,αz,αa > 0,

where a denotes the consumption level of the residen-tial floor space (area) per person in a household; z, thatof the composite good per person in a household (whoseprice is assumed to be one). The parameters can easilybe estimated by applying ordinarily least squares (OLS)to Equation (2). One problem of applying this simple log-linear utility function is that it cannot properly representa household’s real preference in our study area. In theTokyo metropolitan area, αa ln ri and population are posi-tively correlated because people tend to live in the centralarea where floor rent is much higher. This positive corre-lation makes it unfeasible for scenario analyses. Hence, itis important to control the ‘quality’ of the floor in orderto avoid such omitted variable biases. To the best of ourknowledge, none of the economic land-use model litera-ture provides a solution to this problem. In order to conducta quality control, Yamagata and Seya (2013) employed thepure-repacking approach suggested by Walsh (2007), but inthis study we adopted a much simpler approach – quality-adjusted price (see e.g. Mehta 2007). The indirect utility isnow specified as

Vi = ln yi − αa ln ri, (3)

where ri = ri/ψi is the quality-adjusted price (ψi is aquality-adjusting parameter). By applying Roy’s identity toEquation (3), individual (residential) floor space functionwas derived as:

ai = αayi

/ri, zi = αzyi, (4)

or equally ai = αayi

/ri,

where ai = ai × ψi denotes the quality-adjusted demand.We specify the quality-adjusting parameter ψi as

ψi ≡ exp(x′iβ + εi). (5)

We adopted this approach because it is natural to supposethat the rent (or individual demand) of floor area is dif-ferentiated by some local attributes p × 1 vector xi (withparameter vector β and i.i.d. error εi). Then we have

ln ai − ln yi + ln ri = lnαa + xi′β + εi. (6)

The parameters can easily be estimated by applying OLSto Equation (6). In the case of β = 0, (where 0 is a p × 1vector whose element is given by zero), the demand func-tion Equation (4) will be back to Equation (2). Hence, thespecification in Equation (4) is a natural generalization ofthe conventional log-linear demand function.

We defined the [h] (h = 1, . . . , H), the householdagent type. Then, with indirect utility and some additionalzone-specific attributes f [h]

i (a vector of explanatory vari-ables including intercept, whose dimension varies with [h])given, the location choice behaviour of each household canbe formulated as an aggregate logit model as

P[h]i = exp(v[h]

i )∑i′

exp(v[h]

i′ ), (7)

where v[h]i = δ[h]Vi + f ′[h]

i χ [h],

where δ[h] is a scalar parameter and χ [h] is a parameter vec-tor. We considered the characteristics of each householdtype by these parameters. The zonal aggregate demand forfloor area in each zone is given by:

A[h]i = ai

∑h∈H

N [h]P[h]i s[h]

i , (8)

where A denotes the zonal aggregate demand for the floorarea; s denotes the number of persons for each house-hold type (Table 1); and N [h] denotes the total number ofhouseholds of each type in the whole region.

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2.1.2. Developers’ profit maximization behaviour

A developer is assumed to behave so as to maximize profitas

∏[DH]

i= max

A[DH]i ,L

[DH]i ,K

[DH]i

riA[DH]i − piL

[DH]i − mK[DH]

i ,

(9)

s.t. A[DH]i = ν · (L[DH]

i )μ1 · (K[DH]i )μ2 , (10)

where [DH] denotes the behaviour of the developeragent. Also,

∏[DH]i denotes a profit function of the devel-

oper; Ai[DH], floor space which the developer supplies;

Li[DH], land area supplied to the developer; pi, land rent per

unit; m, the price for construction material; K[DH]i , mate-

rial inputted for the production of floor service; μ1,μ2, ν,parameters (0 < μ1 + μ2 < 1). We assume that the capital(real estate material) can freely move inside or outside ofthe focused area with no trade and transport costs, result-ing in the price m being identical for every zone. Solvingthe profit maximization problem and re-parameterizationyields the following floor space supply function and landdemand function (Ueda, Tsutsumi, and Nakamura 1995):

A[DH]i = φ1,i · r

μ1+μ21−μ1−μ2i · p

− μ11−μ1−μ2

i , L[DH]i

= φ2,i · r1

1−μ1−μ2i · p

− 1−μ21−μ1−μ2

i .

(11)

The parameters φ1,i, φ2,i > 0 can be obtained by calibra-tion (back transformation to reproduce the observations).μ1,μ2 can be estimated by OLS based on the followingrelationship.

piL[DH]i = μ1riA

[DH]i , mK[DH]

i = μ2riA[DH]i . (12)

2.1.3. Absentee landlords’ profit maximizationbehaviour

An absentee landlord in each zone is assumed to behave soas to maximize profit, given by the following equation

∏[LH]

i= piL

[LH]i − C(L[LH]

i ), (13)

where [LH] denotes the behaviour of the absentee landlordagent. Also, [LH]

i denotes a profit function of the land-lord; L[LH], land supply; C(·) and a cost function, whichis the cost of maintaining the land. In this study, C(·) wasspecified as

C(L[LH]i ) = −σiL

AVi ln

(1 − L[LH]

i

LAVi

), (14)

where LAVi denotes the available area of the land and σi is

a parameter which is obtained by calibration to reproducethe observations in each zone. Subsequently, we have thefollowing land supply function.

L[LH]i =

(1 − σi

pi

)LAV

i . (15)

With this equation, we can simulate the impact of land-useregulation by varying the value of LAV

i , which representsthe maximum value of the land supply quantity.

2.1.4. Market equilibrium condition

The equilibrium state of an urban economy is defined bytwo conditions (Ueda et al. 2013). One is that no house-hold has any incentive to relocate or to change its location(location equilibrium), expressed as

∑i

N [h]i = N [h]. (16)

The other condition is demand–supply balancing, definedas

A[h]i = A[DH]

i , L[DH]i = L[LH]

i . (17)

3. Model construction

3.1. Study area

Figure 2 represents our study area (the Tokyo MetropolitanArea). The number of zones in the Tokyo MetropolitanArea is 22,603, and the average zonal area and its stan-dard deviation are 0.70 and 2.48 (km2), respectively. Notehere that the standard deviation is relatively high comparedto the average, owing to substantial regional differences inzonal area (min.: 0.0010; max.: 231.2). That is, the areais small in the middle of Tokyo but large in the subur-ban regions (Figure 2). The total population was about3.6 million in 2005.

3.2. Data

We gathered data for the year 2005. The socio-economicdata are summarized in Table 2. Some variables that couldnot be prepared were predicted. The zonal representativevalues for land rent and floor rent were predicted from thepoint referenced land rent and floor rent data, respectively(Table 2) using ordinary kriging (Cressie 1993). Also, landareas of higher zones were allocated to their micro districtsbased on the (standardized) product of population numberand area. The use of more sophisticated areal interpola-tion methods such as Young and Gotway (2007), Polasek(2010), or Lin, Cromley, and Zhang (2011) is to be includedin future research. The available area of the residential

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0 12.5 25 50km

N

S

W E

Figure 2. Study area (micro districts called cho-cho-moku).

Table 2. Socio-economic data.

Data Data source Year Agency

Number of population Population census 2005 MIC1

Number of employees Population census 2005 MICIncome Statistical survey of salary in the private sector 2007 National Tax AgencyLand rent Officially assessed land price 2005 MLIT2

Floor rent Provided by a private company 2005 At home corp.Land area Fixed property tax cadastre3 2005 MICFloor area Population census 2005 MICPrice for Construction material Statistics on building material and labour

demand/Building construction navigation2006/2008 MLIT/Construction

Research Institute.Material inputted for production

of floorStatistics on building construction 2005 MLIT

Notes: 1MIC: Ministry of International Affairs and Communications, Japan.2MLIT: Ministry of Land, Infrastructure, Transport and Tourism, Japan.3Land area for residential use and commercial use. Commercial use includes that for office, hotel, bank, (department) store, hospital and theatre.

land was prepared using the area zoned for residential usebased on city planning law (hereinafter residential zones)in Japan (Ministry of Land, Infrastructure and Transport2003). Here, an important factor that must be consideredis land-use mixing, that is, areas zoned for commercialuse (commercial zones), also contain residential buildings.Unfortunately, there is no easily accessible data about thenumber of residential buildings located in the commercialzones, but fortunately we were provided GIS data of eachindividual building in Yokohama city by the local govern-ment, and could, therefore, verify that approximately 7% ofresidential buildings are located in the commercial zones.Considering this percentage and the possibility of regionaldifferences in it, and the fact that there is still room for

zoning conversion from commercial to residential in futurescenarios, we included 20%4 of the areas of commercialzones for residential use.

3.3. Model calibration

3.3.1. Floor space and land area model

For the quality control variables for the floor space demandequation, we employed x1: average elevation; x2: aver-age slope; x3: liquefaction risk; x4: earthquake risk; x5:building-to-land ratio (%); x6: floor area ratio (%); x7: pro-portion of residential zone; x8: proportion of urbanizationcontrol zone; x9: proportion of commercial zone; x10: nat-ural logarithm of the zone area; x11: office density (number

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Table 3. Variable description.

Variable Unit Description Source

Ave. elevation m Average elevation National land numerical informationAve. slope angle Average slope National land numerical informationLiquefaction risk – Risk index of liquefaction (from 0: no risk to

3: high risk)Wakamatsu, Matsuoka, and Hasegawa

(2006)

Earthquake risk – Occurrence probability (0∼1) of earthquakeswith ground motions equal to or larger thanJMA12 seismic intensity 6−

Japan seismic hazard information station

Building-to-land ratio % Building-to-land ratio UDS Co., Ltd.Floor area ratio % Floor area ratio UDS Co., Ltd.Residential Ratio Proportion of residential zone UDS Co., Ltd.U_C Ratio Proportion of urbanization control zone UDS Co., Ltd.Commercial Ratio Proportion of commercial zone UDS Co., Ltd.ln(area) km2 Natural logarithm of the zone area Nippon Statistics Center Co., LtdOffice density – Number of offices in 2006/area Establishment and enterprise censusW.Office density – Spatial weighted average of Number of

offices in 2006/areaEstablishment and enterprise census

Office agglomeration – (Number of offices + Spatial weightedaverage of number of offices in 2006)/area

Establishment and enterprise census

Dist. sta. m Distance to the nearest stations National land numerical informationDist. Tokyo sta. m Distance to Tokyo Station National land numerical information

Number of commuters – Number of annual commuters of the nearesttrain station

National land numerical information

Notes: 1JMA: The Japan Meteorological Agency2The JMA seismic intensity scale is a seismic scale used in Japan to measure the intensity of earthquakes. The JMA scale runs from 0 to 7. We used theoccurrence probability (0∼1) of earthquake whose seismic intensity is 6−, which corresponds to the intensity that makes it is difficult to remain standing.The probability is available through ‘Seismic hazard maps’ (http://www.j-shis.bosai.go.jp/en/shm), which is a hazard map of long-term probabilisticevaluations of earthquake occurrence and strong motion evaluation.

Table 4. Parameter estimates for the floor space demand func-tion and the developer models.

Variable Coef. t

Households Ave. elevation −0.00007465 −2.20Ave. slope 0.6387 8.64Liquefaction risk −0.003783 −2.15Earthquake risk −0.02926 −3.09Building-to-land ratio −0.002402 −16.8Floor area ratio 0.0001824 5.66Residential 0.02469 4.98U_C 0.03294 6.49Commercial 0.1498 17.4+ Municipality

dummy variables− −

ln(α) −0.8583 −72.7Adjusted R2 0.504

Developer μ1 0.2664 220Adjusted R2 0.691

μ2 0.1699 544Adjusted R2 0.931

of offices/area) and x12: density of spatial weighted aver-age of the number of offices.5 The detailed descriptions ofthe variables are summarized in Table 3. Table 4 shows theparameter estimates for the floor space demand functionof the households and those for the floor space supply and

land demand function of the developers. The results indi-cate that all of the parameters are statistically significantat the 5% level at least, which suggests the importance ofquality adjustment.

3.3.2. Location choice model

For introducing the zone-specific attributes to the loca-tion choice model, we employed the following variables:f 0: intercept; f 1: average elevation; f 2: average slope; f 3:liquefaction risk; f 4: earthquake risk; f 5: proportion of res-idential zone; f 6: squared proportion of residential zone;f 7: proportion of urbanization zone; f 8: squared proportionof urbanization control zone; f 9: proportion of commercialzone; f 10: squared proportion of commercial zone; f 11: nat-ural logarithm of the zone area; f 12: distance to the nearesttrain station; f 13: squared distance to the nearest train sta-tion; f 14: office agglomeration (number of offices + spatialweighted average of number of offices/area) and f 15: num-ber of annual commuters of the nearest train station. Thedetailed descriptions of the variables are summarized inTable 3. As explanatory variables, we added two risk fac-tors – liquefaction and earthquake risks. Studies suggestthat earthquake risk is negatively correlated with hous-ing prices in Japan (Nakagawa, Saito, and Yamaga 2007;Naoi, Seko, and Sumita 2009), but it is not clarified in

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the existing literature whether or not it will significantlyaffect the probability of choice of location. Although otherrisk factors such as Tsunami, storm surge and flooding areimportant factors that should be considered, we excludedthem because quantifying and creating hazard maps forthese factors for whole Tokyo seemed to be a difficulttask.

Some of these variables, especially f 14 and f 15, canchange in the future. However, projecting these variablesrequires modelling the choice of location of businesses

and firms, which is difficult to predict with high accu-racy. Hence, we postpone it to future research, and inthis study, we regarded these variables as constant overtime.6

Table 5 represents the parameter estimates by the max-imum likelihood method for the location choice model.While the majority of the zone specific variables are statis-tically significant at the 5% level, their magnitudes differ.For instance, the type 1 households (One-person (65 yearsof age or over)) place high importance on low rent (high

Table 5. Parameter estimates for the location choice model.

(a)

Type 1 Type 2 Type 3

Variable Coef. z Coef. z Coef. z

Intercept −16.99 −35.9 −15.78 −33.2 −15.06 −34.7Indirect–utility 0.7200 15.3 0.6094 12.9 0.5217 12.1Ave. elevation −0.00003252 −0.179 0.001570 8.46 −0.00008810 −0.640Ave. slope −3.653 −8.46 −10.84 −21.4 −2.791 −8.33Liquefaction risk −0.04766 −4.95 −0.07677 −7.86 −0.05468 −6.78Earthquake risk 0.7080 15.2 0.7860 16.5 0.4916 12.6Residential 1.216 10.1 2.043 17.2 1.407 13.1Residential∧2 −0.2607 −2.17 −1.121 −9.60 −0.2332 −2.20U_C 0.1927 1.36 −0.4398 −3.06 1.069 9.18U_C∧2 −1.299 −8.73 −1.108 −7.09 −1.959 −16.1Commercial 4.164 31.3 3.471 27.0 2.667 22.2Commercial∧2 −3.773 −26.5 −3.100 −22.2 −2.453 −18.4log (area) 0.6135 72.5 0.6125 71.6 0.6321 91.8Dist. sta. −0.0005290 −36.2 −0.0006723 −45.7 −0.0003748 −32.2Dist. sta.∧2 0.00000003881 20.2 0.00000005841 36.0 0.00000002307 14.0Office W. Office

density0.00008460 10.2 0.00005362 6.14 0.00006340 7.13

Number of commuters 0.000001032 8.34 0.000001855 16.5 0.000001442 12.7

(b)

Type 4 Type 5 Type 6 Type 7

Coef. z Coef. z Coef. z Coef. z

−12.71 −28.3 −10.88 −24.0 −10.96 −23.0 −10.76 −20.00.2510 5.60 0.07165 1.58 0.07869 1.65 0.1025 1.910.0003170 2.12 0.0005534 4.14 0.0004757 3.19 −0.0005326 −3.58

−6.380 −16.6 −6.825 −19.1 −6.419 −16.3 −4.106 −11.3−0.03099 −3.76 −0.02887 −3.67 −0.01035 −1.21 −0.001805 −0.204

0.6903 17.5 0.5321 14.1 0.6748 16.5 0.3966 9.442.805 25.9 2.836 26.8 2.331 20.8 1.217 9.93

−1.499 −14.2 −1.461 −14.2 −1.108 −10.0 −0.3448 −2.820.8592 7.42 1.184 10.7 0.9860 8.18 0.7099 5.51

−2.118 −17.3 −2.243 −19.3 −2.096 −16.6 −1.015 −7.752.353 19.7 1.445 12.0 3.131 25.2 2.851 20.6

−1.430 −10.8 −0.7807 −5.72 −2.232 −16.2 −2.198 −14.60.6515 93.9 0.6514 99.2 0.6442 88.7 0.7071 94.4− − − − − − −0.0002132 −20.5− − − − − − 0.00000001698 14.3− − − − − − 0.00007134 7.16− − − − − − 0.0000007047 4.83

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0e+00 2e−04 4e−04 6e−04 8e−04 1e−03

0e+0

02e

−04

4e−0

46e

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8e−0

41e

−03

Figure 3. Observed probability of choice of location and its predictive value (y-axis: Predictive probability; x-axis: observed probability).

indirect-utility), but this is less important to type 5 house-holds (Married couple with child(ren)). Large-floor spaceper person is relatively important for the type 5 households.Unfortunately, as shown in Figure 3 (type 1 example), it isdifficult to predict the probability of choice of location at aspatially fine level with high accuracy. Hence, for the sce-nario analysis, we added a constant to the location choicemodel, which ensured a 100% fit to the observations (calledconstant adjustment, which is one of the typical practi-cal solutions for future projection models (Pfaff 1977;Clements 1995)).

4. Scenario creation

4.1. Scenario settings

In creating the 2050 scenario, we assumed that the numberof each household type would change to [1]: 2.07, [2]: 1.07,[3]: 1.39, [4]: 0.66, [5]: 0.69, [6]: 1.32, [7]: 0.85 (ratio tothe number in 2005), which was estimated by log-linearextrapolation of estimates for the year 2030 produced bythe National Institute of Population and Society Research,Japan.

In this study, we compared the dispersion city scenarioversus the compact city scenario. Needless to say, thereis no unique way to create future dispersion and compactscenarios.7 For the former, we adopted the business as usual(BAU) scenario. The population density in the dispersioncity scenario is shown in Figure 4. For the compact city

scenario, the proximity of workplace to home is importantfor reducing trip length. Hence, we first quantified thedegree of spatial agglomeration of office space using a spa-tial clustering technique. We employed the Moran scatterplot by Anselin (1995, 1996). The Moran scatter plot plotsthe standardized office density value against its standard-ized spatial lag. A spatial lag of an office density is thespatial weighted average of office density in its surrounding(contiguity) zones.

Figure 5 illustrates the concept of the Moran scatterplot. With both axes standardized, the Moran scatter plotcan be divided into four quadrants. Quadrant I (top rightcorner) shows high office density zones that are also sur-rounded by high office density zones (high–high (HH)).Quadrant II (top left corner) shows low office densityzones with high office density neighbourhoods (low–high(LH)). Quadrant III (bottom left corner) shows low officedensity zones surrounded by low office density zones (low–low (LL)). Quadrant IV (bottom right corner) shows highoffice density zones with low office density neighbour-hoods (high–low (HL)). For simplicity, henceforth, we referto the quadrants as HH, LH, LL and HL. HH and LL indi-cate a positive spatial autocorrelation; same office densitylevel zones are spatially clustered, also called ‘hot spots’and ‘cool spots’ for HH and LL, respectively. LH andHL indicate a negative spatial autocorrelation; dissimilaroffice density level zones are clustered. Figure 6a showsthe result of the spatial clustering. We assume that the

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Legend

Population density(people per km2)

>70,000

<70,000

<33,000

<20,000

<16,000

<12000

<9000

<6000

<4000

<1500

0 12.5 25 50km

N

Figure 4. Population density in 2050 under the BAU scenario.

Mean

Not affordableneighborhoods

Affordable

Affordableneighborhoods

Not-affordable

Hot spot (High-High (HH))

High affordability zones that arealso surrounded by highaffordability zones.

Helper (High-Low (HL))

High affordability zones that aresurrounded by low affordabilityzones.

Helped (Low-High (LH))

Low affordability zones that aresurrounded by high affordability

zones.

Cool spot (Low-Low (LL))

Low affordability zones that arealso surrounded by low

affordability zones.

Figure 5. Explanation of the Moran scatter plot.

zones which are categorized into HH or HL are urbancentres, and the available residential land LAV

i is restrictedby 50% if the zonal centroid’s distance from the nearesturban centre is over 500 million. The result is shown inFigure 6b, which suggests a polycentric pattern along therail lines. Moreover, we assume that households get subsi-dies (or imposed fixed property tax) corresponding to theratio: {ln(office density+2)/mean (ln(office density+2)}.8

The household’s income is multiplied by this ratio, andtherefore the total amount of the income in the study areadoes not change. The amount of subsidy is just cancelledout by the imposed fixed property tax.

4.2. Results of scenario creation

Figure 7a, b and c represent the geographical distributionof floor space, land area and population under the com-pact city scenario as a ratio to the dispersion city scenario,that is, {compact/dispersion}, respectively. It is noted thatthese values increase in the zones around the ‘city centres’.Figure 8a and b show the ratio of floor space to land area,representing the ‘actual’ floor–land ratio (AFAR), undertwo urban form scenarios. This value is useful as a proxyto the number of stairs. In the compact city scenario, thevalue of AFAR increases around the city centres, reflectingthe increase in high-rise buildings in these zones.

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Legend

0 12.5 25 50kmW E

S

NHigh-High/High-Low

Not regulated

Legend

0 12.5 25 50kmW E

S

N

Not significantHigh-HighLow-LowLow-HighHigh-Low Regulated

(a) (b)

Figure 6. (a) Results of spatial clustering using the local Moran scatter plot. (b) The zones whose land is regulated vs not regulated.

Legend Legend

Floor spaceCompact / Dispersion

LandCompact / Dispersion

0

N

S

W E12.5 25 50

km0

N

S

W E12.5 25 50

km1.25 >>

<< 1.25

<< 1.00

<< 0.750

1.25 >>

<< 1.25

<< 1.00

<< 0.750

LegendPopulation

Compact / Dispersion

0

N

S

W E12.5 25 50

km1.05 >>

<< 1.05

<< 1.00

<< 0.950

(a) (b)

(c)

Figure 7. (a) Projected floor space distribution under the compact city scenario as a ratio to the dispersion city scenario. (b) Projectedland area distribution under the compact city scenario as a ratio to the dispersion city scenario. (c) Projected population distribution underthe compact city scenario as a ratio to the dispersion city scenario.

Table 6 represents the total amount of the floor spaceand land area in the study area. It is suggested that thecompact city may contribute to the reduction of total floorspace, resulting in a decrease in CO2 emissions from the

residential sector if we use floor space-based unit inten-sity. Such intensity usually differs between condominiumsand detached houses, but the housing type mix can be pre-dicted with reasonable explanatory power via the logistic

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Legend

DispersionFloor / Land

0

N

S

W E

12.5 25 50km

2.0 >><< 2.0<< 1.5<< 1.0<< 0.50 0

N

S

W E

12.5 25 50km

Legend

CompactFloor / Land

2.0 >><< 2.0<< 1.5<< 1.0<< 0.50

(a) (b)

Figure 8. (a) Projected value of floor space/land area distribution under the dispersion city scenario. (b) Projected value of floorspace/land area distribution under the compact city scenario.

Table 6. Total amount of floor space and land area (km2).

Dispersion Land 2003.1Floor 918.0

Compact Land 1357.3Floor 863.7

Table 7. Parameter estimates for the share of detached housesestimation model.

Coef. t

Intercept 8.336 83.0Dist. to the nearest sta. 0.0001495 19.6Share of type 1 −14.04 −42.2Share of type 2 −12.50 −112Share of type 3 2.644 7.98Share of type 4 −14.54 −53.7Share of type 5 −7.028 −50.7Share of type 6 −16.21 −42.5

Adjusted R2 0.598Sample size 18, 519

regression model by introducing the share of each house-hold type and distance to the train stations as explanatoryvariables. The results are shown in Table 7. Based on theestimation results, we calculated the average value of theratio of detached houses in the compact city to those ofthe dispersed city, which is 1.02, with a minimum value of0.525. Hence, the ratio of detached houses may increase onaverage in the compact city, but the ratio may decrease inparticular zones, i.e. around the city centres.

Figure 9a and b suggest that the ratio of type1 house-holds may increase around the city centres, while that ofthe type 5 households may decrease. These results are rea-sonable in our setting because floor rent in the suburban

area increases because of land regulation (restriction), andtype 1 households, that place high importance on low-floorrent, may avoid suburban areas and move to zones aroundthe city centres, but type 5 households, that place highimportance on spacious dwellings, may remain in the sub-urban areas. From the planning point of view, realizing acompact city without large increases in floor rent in thecity centre area is important because otherwise it may cre-ate difficulties for low-income households. The simulationresults of this study suggest that land restriction in sub-urban areas can be an effective measure for achieving acompact city. However, in implementing such restrictions,while considering the benefits and costs of a policy onemust carefully consider aspects such as quality of life anddisaster resilience.

4.3. Projecting urban environmental variables underthe compact and dispersion scenarios

In this subsection, we project the geographical distributionof the urban environmental variables under the compactand dispersion scenarios in the year 2050 based on the pre-ceding results. We project the variables which are usefulfor regional climate models (Kusaka et al. 2012). Becauseregional climate models are usually mesh based, we pre-pared the variables at the resolution of the Japanese tertiarymesh (approximately 1km2). The number of mesh blocks is14,156. Our data set includes:

(1) population density(2) floor space (residential and commercial)(3) land area (residential and commercial)(4) ratio of detached houses (residential)(5) ratio of condominiums (residential)(6) ratio of actual floor area (floor space/land area)(7) ratio of each land cover in each zone

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Legend

Type1Compact / Dispersion

0N

S

W E 12.5 25 50 km

1.10 >><< 1.10<< 1.00<< 0.900

Legend

Type5Compact / Dispersion

0N

S

W E 12.5 25 50 km

1.05 >><< 1.05<< 1.00<< 0.950

(b)(a)

Figure 9. (a) Ratio of the distribution of type 1 households (One-person (65 years of age or over)) under the compact city scenario as aratio to the dispersion city scenario. (b) Ratio of the distribution of type 5 households (Married couple with child(ren)) under the compactcity scenario as a ratio to the dispersion city scenario.

These data sets were prepared in ArcGIS Shapefile format,and are available from the first author on request. Althoughanthropogenic heat is also one of the key inputs9 for theregional climate model, we prepared floor space, ratio ofdetached houses and ratio of actual floor area instead,which can be used as a surrogate for anthropogenic heat.

The variables from A) to F) were predicted by interpo-lating the zonal values in the mesh, weighted simply by area(areal weighting interpolation). The variable G) was calcu-lated as follows. First, land-cover data for 2006 were takenfrom the National Land Numerical Information data set(http://nlftp.mlit.go.jp/ksj-e/gml/gml_datalist.html),10 andfrom the data, the area of ‘land for building’ (hereinafterurban class) in each mesh block k (k = 1, . . . , K), wasprepared as zurban

k . Second, the actual residential and com-mercial land uses for the zone were interpolated for eachmesh block, by areal weighting, prepared as Lk . Then, therelationship between zurban

k and Lk was determined by thefollowing simple regression model:

zurbank = ψ + Lkς + εk , (18)

where ψ is the intercept, ς is the regression coefficient andεk is the standard error term. Once the parameters were sta-tistically estimated, future urban class areas in each meshblock could be predicted based on:

�zurbank = ψ + (Lk +�Lk)ς + ek − zurban

k , (19)

where �Lk is the change in the residential land area, and�zurban

k is the corresponding changes in urban class. ek

denotes the residual from the regression model, which wasadded for constant adjustment. The parameter estimates forthis regression model are shown in Table 8. Both, the inter-cept and the coefficient estimate for Lk , are positive and

Table 8. Parameter estimates for the land-use/land-cover con-version model for building land.

Coef. t

Intercept 30, 920.06 13.0Lh 1.328 138Adjusted R2 0.574

statistically significant at the 1% significance level. Theadjusted R2 is 0.574.

Once�zurbank was calculated, −�zurban

k was allocated tothe other land-cover classes, and future land covers wereprojected. We adopted two strategies for this allocation.The first strategy was to simply allocate −�zurban

k to theforest class, which we call the ‘re-vegetation scenario’. Thesecond strategy, which we term the ‘logit scenario’, allo-cated −�zurban

k based on the occupancy probability in eachland-cover class, which was calculated based on a multino-mial logit model. The dependent variable was the highestcoverage class in each mesh block, and the explanatoryvariables adopted were g0: intercept; g1: average eleva-tion; g2: average slope; g3: distance to the nearest trainstation/1000 (km) and g4: distance to Tokyo Station/1000(km). The parameter estimates for the multinomial logitmodel are shown in Table 9 (the ‘golf course’ class waseliminated from the model because there was no meshblock in which the highest occupancy class was golf course;the parameters of the cropland class were standardized tozero). The McFadden pseudo R2 value (likelihood ratioindex) is 0.358, which suggests that the fit to the observa-tions is rather good. Some interpretations of the parameterestimates are as follows: with respect to the estimate forvariable g1, the t-value of the paddy field class is thelargest and that of the urban class is second largest, whichis intuitively acceptable. With respect to the estimates forvariables g2 and g4, those of the forest class are positive

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Table 9. Parameter estimates for the land-cover class occupancy estimation model.

Variable Intercept Ave. elevation Ave. slope Dist. sta./1000 Dist. Tokyo sta./1000

Land-cover class Coef. Z Coef. z Coef. z Coef. z Coef. z

Other agriculturalland

1.049 4.67 0.002064 2.18 −0.1448 −10.9 0.01886 0.709 0.02594 5.92

Other land 1.474 5.62 0.002914 1.73 −0.1906 −8.95 −0.2120 −5.23 −0.0001351 −0.0254Rivers and lakes 1.333 5.18 0.006025 5.10 −0.2273 −11.3 −0.1567 −4.26 0.003299 0.637Trunk

transportationland

0.5713 0.484 0.009973 1.24 −0.01137 −0.0486 −0.3077 −0.334 −0.1823 −2.23

Land forbuilding(Urban)

5.739 27.0 −0.001122 −1.05 −0.1199 −9.07 −0.7600 −22.5 −0.03125 −7.25

Wasteland −2.068 −3.17 0.003148 2.45 0.02052 0.738 −0.04436 −0.575 −0.01135 −0.846Forest 0.3771 1.68 0.001923 2.39 0.08168 7.04 0.02881 1.11 0.01307 3.00Paddy field 2.924 13.4 −0.05476 −24.7 −0.05736 −4.04 −0.1166 −4.37 0.02649 6.16

and statistically significant (1% level), whereas those ofthe urban class are negative but also statistically significant(1% level). These results are also intuitively acceptable.With respect to the estimate for variable g4, the absolutevalue of the t-value of the urban class is largest.

Figures 10 and 11 indicate the geographical distribu-tion of the ratios of urban class and forest class underthe dispersion and compact city scenarios. It is noted thatthe urban class ratio in each mesh block decreases in thecompact city scenario relative to that of the dispersioncity scenario except around city centres. The differencebetween Figure 11a and b is slight because there is lit-tle allocation of −�zurban

k to the forest class. However, ifwe look at Figure 11c, forests increase especially aroundthe middle of Tokyo. Kusaka et al. (2012) pointed out thatsuch a ‘cool compact city’ may mitigate the urban heatisland phenomenon.11 Spatially explicit land-use scenarioswere only recently introduced as a part of the IPCC global

climate modelling. However, such scenarios would be moreimportant for urban and regional planners who need tolook at specific local adaptation measures against climatechange.

5. Concluding remarks

In the present study, a large-scale geographically explicitland-use model was developed for projecting the geograph-ical distribution of urban environmental variables, whichwere listed in the previous section, for future compactand dispersion urban forms. This study employed a land-use equilibrium model based on urban economic theoryfor scenario creation, so that we could deal with casesof not only urban growth, but also urban shrinkage. Ourchallenge in this study was calibrating the model for thewhole of Tokyo Metropolitan Area, at the micro-districtlevel. By doing so, we can look at the implications of

Legend

Ratio of land for buildingdispersion

0

N

S

W E12.5 25 50

km

0.8 >><< 0.8<< 0.6<< 0.4<< 0.2

Legend

Ratio of land for buildingcompact

0

N

S

W E12.5 25 50

km

0.8 >><< 0.8<< 0.6<< 0.4<< 0.2

(a) (b)

Figure 10. (a) Ratio of land for building (urban class) under the dispersion city scenario. (b) Ratio of land for building (urban class)under the compact city scenario.

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Legend

Ratio of land for buildingdispersion (logit)

0

N

S

W E12.5 25 50

km

0.8 >>

<< 0.8

<< 0.6

<< 0.4

<< 0.2

Legend

Ratio of land for buildingcompact (logit)

0

N

S

W E12.5 25 50

km

0.8 >>

<< 0.8

<< 0.6

<< 0.4

<< 0.2

Legend

Ratio of land for forestcompact (re-vegetation)

0

N

S

W E12.5 25 50

km

0.8 >>

<< 0.8

<< 0.6

<< 0.4

<< 0.2

(a) (b)

(c)

Figure 11. (a) Ratio of land for forest (forest class) under the dispersion city logit scenario. (b) Ratio of land for forest (forest class)under the compact city logit scenario. (c) Ratio of land for forest under the compact city re-vegetation scenario.

district-scale compact city policies such as the relaxationof the regulation for floor area ratio around train stations.Using the model, this paper demonstrated a rather extremeurban compact city scenario in 2050, and it was comparedto the dispersion city scenario (BAU).

The simulation results of this study suggested that landrestriction in the suburban area could be an effective mea-sure to realize a compact city without raising the floor rentin the urban centres, which may prevent low-income house-holds from living in such areas. In the implementation ofsuch restrictions, while considering the benefits and costsof a policy, one must also carefully consider aspects suchas quality of life and disaster resilience. Also, as discussedin Nakamichi, Seya, and Yamagata (2013), it is importantto consider the (i) co-benefits and trades off between sce-narios, (ii) climate change impacts including flooding riskfor attaining climate compatible urban development and(iii) taking into account the new technologies such as elec-tric vehicles (EVs) or photovoltaics (PVs). An important

aspect of future work will be to calibrate the model usingpast land-cover data (e.g. Ibeas et al. 2013).

AcknowledgementsThis study was funded by the ‘Research Program on ClimateChange Adaptation’ of the Ministry of Education, Culture,Sports, Science and Technology, Japan. We deeply appreciateSimon Benger of Flinders University and the reviewers for theirvarious useful comments and suggestions provided with regard tothis paper.

Notes1. Regarding the other merits and demerits of the compact city,

see OECD (2012). Antipova and Wang (2010) suggestedthat land use plays an important role in a traveller’s decisionon trip chaining.

2. Koomen et al. (2007) made a distinction between ‘landcover’ that can be observed and land use, the actual useto which the land is put. For more details regarding the

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relationship between land use and land cover, see Verburg(2006).

3. Rounsevell et al. (2006) and Reginster and Rounsevell(2006) for Europe, Yue, Fan, and Liu (2007) for China andSohl et al. (2012) for the United States.

4. We imposed the restriction that the commercial land areain each zone is always less than the available area of thecommercial use, and therefore 20% is a maximum value.

5. For the spatial weight, we used inversed distance. Theweight for the zone itself is set to zero (see Anselin 1988).

6. Ibeas et al. (2013) attempted to model the spatial interac-tions between workplace and residential location while theirmodel is based on more aggregated zones.

7. Xie and Ye (2007), Torrens (2008) and Pereira et al. (2013)attempted to quantify the characteristics of urban pattern.

8. Here, we added ‘2’ to deal with zones whose number ofoffices are zero.

9. Yue et al. (2012) suggested that anthropogenic heat releaseis one of the most important components leading to thevariances of Shanghai’s urban heat island

10. Paddy field; other agricultural land; forest; wasteland; landfor building; trunk transportation land; other land; rivers andlakes; beach; body of seawater and golf course.

11. This was based on a previous version of our scenario, so theresult may change with our new scenarios.

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