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A Land Use Model for Supporting Redevelopment Decisions in the Osaka Metropolitan Area
APPLICABILITY OF CONTROL SCIENCE METHODOLOGY TO MANAGEMENT SYSTEMS
A LAND USE MODEL FOR SUPPORTING REDEVELOPMENT DECISIONS IN THE OSAKA METROPOLITAN AREA Y. Suzuki*, P. S. Pak*, K. Tsuji*, F. Yamada*, T. Murakami**, K. Hirose*** and Y. Noto*** • Department of Electn'cal Engineering, Osaka University, Suita, Osaka, Japan • ·Comprehensive Planning Bureau of Osaka Municipal Office, Osaka, Japan ••• Kansai Institute of Information Systems, Osaka, Japan Abstract. A land use model for Osaka Metropolitan Area is constructed based on the available mesh data. The model describes the mechanism of the transition of land use in a large city with land price as a characterizing index. Commercial, industrial and vacant areas are related to the numbers of people in manufacturing, tertiary industries, residence, etc., and these numbers are related to land price. Detailed equations derived through the cross-sectional regressign an~lysis, are presented. The model is constructed for each mesh of 500 x500 , and is able to draw the spatial pattern of land use in the future under various control policies. Keywords.
Land use; mesh data; location model.
I. INTRODUCTION The concentration of population and industries in large cities is a phenominon commonly observed in both industrialized and developing countries. In Japan, this concentration has been going on extensively during the era of high economic growth over the last two decades. One of the most important subjects that needs immediate attention is the problem of management and control of cities that have already become excessively large. The ultimate purpose of this management and control is to improve the level of social welfare for the people who live and work in large cities, providing social capital, supplying water and energy, maintaining the level of natural environment properly, etc. However, before we think about any concrete policies for achieving the above purpose, it is necessary to analyse the process of socioeconomic development of large cities, i.e., the process of population changes, industrial development and utilization of city space.
In this study, the problem of land use in large cities is considered directly and concretely, based on the knowledge of the land theory. As we mentioned above, the key which allows researches in this direction is the availability of data. Fortunately, we had an opportunity of making use of the considerably well-equipped mesh data for Osaka, the second largest city in Japan. There, the mixed use of land by small industries and residents has caused the degradation of the living environment, and a chronic decrease of population has been one of the concerned problems. In the following sections, we present a model which expresses the change of land use on mesh level and draw a future land use pattern in Osaka City based on this model. 11. AVAILABLE MESH DATA The municipal office of Osaka City started making mesh data in various terms several The size of each mesh is of yea~s ago. m 500 x 500. The data relevant to this paper are as follows:
The researches in this direction so far have been largely done by using approaches in economics where an econometrical model was constructed by uniting the whole city as one. However, this approach is, in a sense, single dimensional and lacks the view-point of land use in cities which is perhaps most important for deriving concrete policies for managing the city environment. There is also another approach which is represented by the land theory (Alonso, 1964). However, the difficulty in applying this approach was that the data relevant to land use was lacking, or even if the data were available, it took too much time and effort for processing the data.
(a) Population of each industry group. The Industrial Statistics in 1966 and 1969 have been made into mesh data. Population in wholesale and business (NWHC) Population in retail and service (NRET) - Population in manufacturing industries (NIND) NWHC includes the wholesale, banking and insurance, real estate, transportation and communication, utility and construction industries. (b) Night-time population(residents) (NPOP)
R()1
802
Y. Suzuki, e t a Z.
The mesh data based on the results of the National Censuses in 1965 and in 1970 are available. Using these data, NPOP in 1966 and in 1969 are estimated by simple linear interpolation, i.e., NPOP(1966)=NPOP(1965)+0.26NPOP(1965,1970), NPOP(1969)=NPOP(1965)+O.86NPOP(1965,1970), where 6NPOP(1965,1970)=NPOP(1970)-NPOP(1965). (c) Land use The mesh data based on an investigation in 1965 are available. We regard these data as the data for 1966. The following items are relevant: -
Residential areas (LRES) Industrial areas (LINO) Commercial areas (LCOM) Vacant lots (LOPN) Parks and green zone (LPRK) Rivers and water surface (LRIV)
3 . 1 Structure of the model The preparation of the mesh data has started recently and the data are available only at two different time points (1966 and 1969 as explained in the previous section) at this moment. Therefore, we first investigated, for each ward, the past trends of land use, population and populations in various industry groups by making use of the Statistics of Osaka City. With this general knowledge and by paying special attention to the changes of land use for each ward, we finally partitioned the city into 4 blocks and arrive at the model shown in Fig.3 . The variables used in the model are listed in Table 1 .
(d) Total floor area of residences (TFLS) The data for 1969 are available. (e) Number of passengers at railway stations (NPAS) The number of passengers at each station is available from the Statistics of Osaka City which is published annually. We have made these data into mesh data by locating each station on the map. (f) Land price
The land prices in 1966 and in 1969 at about 2,000 sample points are available. The land price of each mesh is determined by averaging the price over all sample points that fall into the same mesh. The land price is deflated using the consumer price index in 1970.
~":0~3 Central Business District (CBD)
Lee -gJ Periphery
c=J
Coa stal Industrial Zone
c=J Outskirts
Zone
Ill. A LAND USE MODEL BASED ON MESH DATA The fundamental idea behind the model structure, which will be decided in the following, is best expressed in Fig.l where the process of transition of land use is considered to be governed basically by land price. When the land price stays low, the vacant lots are converted to either industrial or residential areas. As the land price goes up high, these areas will be converted to commercial areas. In the following, the model will be described in some detail.
Uprise of land price (Advancement of urbanization) Fig. 1 Fundamental Transition of Land Use
Fig.2. Partition of Osaka City into Blocks The 4 blocks are shown in Fig.2, together with the wards of Osaka City. The central part forms the so-called Central Business District (CBD),where the management offices of various enterprises, the wholesale and retail stores and the service industries are highly concentrated. Factories are located along the shore of Osaka Bay and the Yodo River as well as in some parts of the city . The surroundings of the CBD are the areas where commerce and residences are dominant. The outskirts area still keeps some vacant lots, and these lots are being converted to either the residential or the manufacturing areas. The basic ideas for construct i ng the model were as follows: In the CBD, the chief factor is that concentration induces more concentration. There the opportunities of meeting people and obtaining informations useful for the people and/or enterprises are plentiful and this is very important. Thus, every industry makes every effort to locate its main office in the CBD, because in fact there are no other places which will bring similar benefits. The land price in the CBD is extremely high, but we imagine that the retail
803
Land Use Model for Supporting Redevelopment De c isions and service industries can afford this high expense. They may construct skyscrapers in order to utilize their land most efficiently. Thus, the land price of the CBD can be explained mainly by the degree of concentration of the retail and service industries. Also, the increase in land price is the chief cause of driving out the people or the industries that have traditionally been there for a long time. This is simply because, if possible, they will have a greater chance of getting more benefits by disposing of their lands.
R = 0.806 4) Population of tertiary industries:
(5)
NTRD =: NWHL + NRET
(6)
5) Population of daytime working people: a) For the meshes in the CBD: NWOK =: NTRD
(7)
b) For the meshes in a non-CBD area: NWOK =: NTRD + NIND
(8)
6) Land price:
=
98.127 + 0.04115 NWHL <9.690> <15.437>
During the course of constructing the model, the land theory has been helpful, but many modifications have been necessary as is always the case when the problem of the real world is to be handled properly. Therefore, the analysis of the data and the identification of the underlying structure were performed alternately by utilizing all results as the feedback information for making refinements. Consequently, it should be emphasized that the structure shown in Fig.3 had neither been given beforehand nor had been simply assumed so.
7) Night-time population:
3.2 Identification of the model
8) Population of manufacturing industries:
3.2.1. Dynamics of population
1) Population of wholesale and business:
<17.660> R 6NPOP
=
=
1.1856 NWHC_ l <42.47 >
(1)
- 160.77 In PRLD_ + 0.34987 LOPN_ l l <9.564> <-7.833> R
NIND
=
<2.645>
+ 220.747 In PRLD_ <2.304>
(2)
l
R
(3)
b) For the meshes in a non-CBD area:
(4)
NWHL =: NWHC 3) Population of retail and service:
0.02863 NPOP_ + 0.24617 NWOK_ l l <20.625 > <3.117 >
---....
+ 0.006849 NPAS + 0.018956 NPAS <7.525>
l
= 0.937
(11)
NIND
=
240.08 + 0.83898 NIND_l <3.248> <25.443> ...............
+ 0.18706 LIND_ - 0.15860 LRES_ l l <1.515>
<-2.359>
R
=
0.928
(12)
NIND
=
------
0.89832 NIND_l + 0.19022 LIND_l <29.512> <3.284 >
=
(13)
0.955
=
~
0.88854 NIND_l + 0.26537 LIND_l <3.305> <42.260 >
+ 2081.1 ( l/PRLD_ )
NWHL =: NWHC + NIND
<6.997 >
(10)
b) For the meshes in the Periphery of the CBD:
NIND
a) For the meshes in the CBD:
=
0.668
d) For the meshes in the Outskirts Zone:
R 0.925 2) Population of wholesale and business in a broad sense:
NRET
=
-1213.6 + 1.02423 NIND_l <-2.140> <18.213>
R
-294.67 + 1.2465 NWHC_ l <-1.978> <46.356>
+ 84.382 In PRLD_
(9)
663.38 - 0.038857 NPOP_ l <5.547> <-6.305 >
R = 0.961 b) For the meshes in a non-CBD area: NWHC
0.810
c) For the meshes in the Coastal Industrial Zone:
a) For the meshes in the CBD:
=
+ 0.10535 NRET
a) For the meshes in the CBD:
Equations (1) through (14) below represent the results of cross-sectional regression analysis. It should be noted that the variables in these equations are defined for each mesh. The n~bers with < > represent the tvalues and R is the mUltiple correlation coefficient with the degree of freedom adjusted. The suffix -1 indicates the delay of one period (3 years), i.e., the values of 3 years before are used for the variables with -1. 6 indicates the variation in one period. The variable with the symbol/"-.. indicates that the value is evaluated by averaging over 8 meshes adjacent to the relevant mesh.
NWHC
PRLD
l
<1. 347 > R
=
0.961
(H)
It can be seen from Eq.(l) that in the CBD, the NWHC increases exponentially, and this represents the mechanism that the concentration induces more concentration in the CBD. On the other hand, for the meshes outside of the CBD, the NWHC tends to gather around the area where the urbanization has advanced, i.e., areas where the land price (PRLD) is high. The logarithm on the PRLD indicates that the incentive of the wholesale and business people to a mesh becomes saturated when its land price gets overly expensive.
804
Y. Suzuki, et al.
Equations (3) and (4) are the definitions of the NWHL for each district. The definitions were necessary because of the nature of the data we used. That is, the NIND implies the number of people who work in the manufacturing industries and includes office workers as well. Since there are no significant factories in the CBD, the NIND in the CBD is treated as the population of office workers . Equation (5) shows that the population of retail and service in a specific mesh can be explained by the number of people who live and work there (NPOP and NWOK) , and by the number of people who get there or pass through the mesh (we have expressed this by NPAS). N~S is also included in the explanatory factors because the size of each mesh is of sOom x sOOm and therefore the people would certainly walk over to the adjacent meshes from the railway stations in the relevant mesh. This impact and the fact that only 128 meshes out of 1016 have railway s~tions have resulted in the coefficient of NPAS being greater than that of NPAS in Eq.(s). The NTRD is defined by Eq.(6). Using this definition , the NWOK can be defined as in Eqs.(7) and (8). The PRLD is determined by the population of tertiary industries as shown in Eq.(9) where the impacts by the NWHL and the NRET are evaluated separately. Equation (10) indicates that the night-time population is basically declining and that the population decreases when the land price is high and increases when there are many vacant lots . This equation seems to reflect the actual s i tuation fairly well. The value of r.=0.668 seems to be too low, however; this is because the regression has been taken over the values with considerably small changes and this does not necessarily imply that the equation is unreliable. In fact, performing the regression over the NPOP itself has resulted in the following equation : NPOP = 663.40 + 0.96114 NPOP_ l <155.96> <5.547 > - 160.77 In PRLD + 0.34986 LOPN_ l <-7.833> <9.563> R = 0.990
(IS)
Since 6NPOP = NPOP - NPOP_ ' Eq.(lO) is l identical to Eq.(ls) within the roundoff error, and R=0.990 assures that Eq.(lO) i s also reliable. The equations with respect to the NIND differ considerably from block to block. In the CBD, the NIND represents mainly office workers rather than factory workers as mentioned before. Therefore, the pursuit for economical benefits is dominant and this structure is reflected in Eq.(ll). In the periphery of the CBD, the situation is different; here the industrial sites are being converted to residential sites. This is explained in Eq.(12) where the second term indicates that the NIND is decreasing . The third and the fourth terms show that the integration of the industrial
sites are in progress and that the existence of residence has the effect of decreasing the industrial sites, respectively. Equation (13) shows that the NIND in the Coastal Industrial Zone is tending to decrease although it may be increasing in the meshes where the surrounding meshes have many manufacturing industries. In the Outskirts Zone, the NIND increases in those meshes where the surrounding meshes have many manufacturing industries and where the land price is relatively low(Eq.(14». 3.2.2
Dynamics of land use
With the changes in population of each group, the type of land use changes. Unfortunately, the mesh data of the land use are available only for one year and therefore it is not possible to obtain a model which describes the changes of land use by cross-sectional regression analysis similar to the one in Section 3.2.1. Thus, we decided to first calculate the fundamental unit per person for each type of land use and then to obtain the changes of land use by multiplying the unit by the changes in population that can be estimated from the equations derived in the previous section. The method of calculating the fundamental unit may be different depending on the data to be used. It may be calculated from the statistical data other than the mesh data. However, we intend to make full use of the mesh data because it is our primary purpose to obtain the changes of land use for each mesh. Here, the following method is adopted to achieve this purpose . The variables and the parameters associated with the land use portion of the model are listed in Table 2. a) Commercial area It can be said that the commercial area is determined by the population of tertiary industries(NTRD). The relation between the commercial area and the NTRD may not be linear simply because the land space is inherently limited whereas the uprise of the NTRD could be absorbed by increasing the floor space. We have assumed that the relation can be expressed as follows: LCOM = Y (NTRD)Yl o or In LCOM = In Yo + Yl In NTRD o < Yl < 1.
(16)
wnere
The result of the regression based on the above relation is given in Eq.(17), which indicates that the assumed relation explains the real situation fairly well, and the equation is used in our model for calculating LCOM from NTRD. In LCOM
-0.52519 + 0.78167 In NTRD <-3.415> <35.524> R = 0.822
(17)
Since Eq.(17) is obtained using only one year's data, one might argue with the validity of applying the results to future years. However, we view this as follows. Among the
805
Land Use Model for Supporting Redevelopment Decisions 1016 meshes are contained the extremely urbanized meshes .a nd the extremely unurbanized meshes; in fact, we may claim that there are meshes at every stage of urbanization and these stages are in effect represented by the NTRD. Therefore we believe that the equation may be applied in the future without introducing serious error.
b) Residential area The residential area(LRES) is related to the night-time population (NPOP) as follows: NPOP*ATFS (18) LRES RBUK The total floor space per person (ATFS) changes with the improvement of housing situation and with the growth of personal income.
Table 1
Meaning Population of wholesale and business
Symbol
..........
....
NWHC
"',
,,
...
\
lPerson
NRET
Population of retail and service
jPerson
NTRD
Population of tertiary jPerson industries
NWOK
Population of daytime working people
lPerson
NIND
Population of manufacturing industries
lPerson
NPOP
Night-time population lPerson
NPAS
Number of passengers at railway stations
lPerson
PRLD
Price of land
1'1,000
LRES
Residential area
a
LIND
Industrial area
a
\
I
I
, ...
_-------- ----------'
.....
Fig. 3(a) Flowchart of the Model
O
:Variables
~:Exogenous
L----J variables
O
:Parameters
(the CBD) ~ :Concurrent relation
LCOM
Commercial area
a
LOPN
Vacant lot
a
TFLS
Total floor space of residence
m2
ATFS
Total floor space of residence per person
m2 person
RBUK
Bulk ratio of
AILA
Area of industrial land per person
Table 2 Svmbol
...... ,
,,
~
\ \ \
,
Cl
R
I I I
I I I
,.,
/ ~
Ai I
Cl
Fig. 4(b)
Flowchart of the Model(for Non-CBD Area)
lPerson
Population of wholesale and business (in a broad sense)
\
I
Unit
NWHL \ I
Variables in the Model
i I
residenc~
a person
Variables and Parameters Relevant to Land Use Unit
MeaninlZ Total floor space per person averaged over Osaka City Growth ratio of
m2 person
~
Industrial area per person averaged over i-th block where i-I Periphery of CBD ia2 Coastal Ind. Zone i-3 Outskirts Zone Growth ratio of Ai I
a person
806
Y. Suzuki, e t al .
With the aid of the statistics on housing which is carried out every five years, we found that the total floor space per person of Osaka City (denote this by ~(N)l) can be related to the year of Showa N 2 quite accurately by the following equation: ~(N)
Zone. Now define i
aI(N)
a~(N), i=1,2,3 as
A~(N)
and assuming that this value is identical for every mesh in the same block, we obtain
= 0.36701 N <89.904 >
(27)
(19) R = 0.990 The linear relation expressed by Eq.(19) suggests that if we define the rate of increase by ~(N) ~(4l)
(26)
N
41
(20)
and assuming that this rate is identical for every mesh, we can estimate the ATFS at the year Showa N as follows: ATFS(N) = ATFS(44)*a (N) R N = 47,50, ..• ,65
(21)
The bulk ratio of the residence also changes; however, we assume that the changes are small enough and RBUK is equal to RBUK_ . Thus we l obtain NPOP*ATFS LRES (22) RBUK_ l c) Industrial area The fundamental unit for estimating the industrial area (AlLA) can be calculated for the year 1966. However, the AlLA varies with time depending on the degree of mechanization and rationalization. Here, this variation is estimated by making use of the data of the population of manufacturing industries and the industrial area for each ward that are available from the Statistics of Osaka City. That is, by summing up the relevant data for each ward over each block, the averaged industrial area per person can be calculated for each block (denote this by Ai)' Since these data are available annually, regression can be taken on these data and the results are shown in the following: Ai
37.157 - 0.10333 N, R = 0.988 (23) <73.02> <-8.949>
A2 I
-641.64 +194.91 In N, R=0.932 (24) <-3 . 923 > <4.547 >
A3 = -236.32 + 69.12 In N, R=0.874 (25) I <-2.933> <3.274> where N is the year expressed by Showa and where the superscripts 1,2 and 3 refer to the Periphery of the CBD, the Coastal Industrial Zone and the Outskirts Zone, respectively. Since the people in the manufacturing industries in the CBD are treated as office workers, we did not obtain any regression equations in the CBD. The above equations indicate that the unit Ai decreases in the Periphery of the CBD and increases in the Coastal Industrial Zone and in the Outskirts Note that ATFS is defined for each mesh whereas AR(N) is defined for the whole city of Osaka. 2 1966 corresponds to the year of Showa 41.
where AlLA(N) denotes the value of the AlLA at the year of Showa N. d) Vacant lots The net increase of the commercial, residential and industrial areas will result in the decrease of the vacant lots. In our model, the changes of the vacant lots are balanced by the changes of the other areas. The slight amount of the manufacturing areas in the CBD is included in this balancing calculations. Therefore, these areas will be converted mainly to the commercial areas in this model. The changes in land use types other than those mentioned so far (e.g., roads, parks and green zone) were not treated. IV. PREDICTION OF FUTURE LAND USE In this section, the model developed in Section 3 ig used to predict the future pattern of land use up to 1990. 4.1 Assumptions The regression equations described in Section 3.2.1 were obtained based on the data of 1966 and 1969. Therefore, the equations may not hold in the future exactly as they are now. However, since these equations are derived through the process of exploring possible economical and land theoretical causalities among the various factors, it can be assumed that the structure of these equations will be the same in the future although their coefficients may change. For example, the coefficient of Eq.(l) represents the growth ratio of the population of the wholesale and business industries and it varies with the market condition. Also, the coefficients of NWHC_ and PRLD in Eq.(2) l may change in the future. The same arguments may apply for other equations as well. These changes can be estimated only when the mesh data for many different years are given to us, and this is not possible at the moment because only the data for two different years are available. Thus,the predictions which will be described in this section presume that the equations in Section 3.2.1 will hold in the future. However, in order to avoid the errors that may be introduced by using these equations, we propose the constraint that for each block or for the whole city, the total value of predicted population in each group is equal to the prescribed value that will be given to the model exogenously. We refer to this value as the "control total" in the
Land Use Model for Supporting Redevelopment Decisions following description. In other words. model is used to allocate these control into each mesh. i.e •• a two-dimensional In this sense. assigning control totals responds to a policy.
the totals space. cor-
The constraint is implemented by the following modification coefficient: y~ =
CTNXYZ+ l / L NXYZ+ l
(28)
where NXYZ+ 1 denotes the predicted value of population In a particular group obtained by using the equations in Section 3.2.1 and CTNXYZ+ denotes the control total. Summal tion is taken over a specified block or over the whole city. We also assume that the equations with regard to land use will hold in the future. 4.2 Future values of the control total In our model. the base year is taken to be 1969. Therefore. it is necessary to give exogenously the values of the control total in 1972.1975 ••..• 1990. For the years 1972 and 1975 the actual values are available from the Statistics of Osaka City. For the years beyond 1978 we determined the values of the control total according to the future planning of Osaka City (we call this the Master Plan). In the Master Plan. the figures of Osaka City in 1990 are estimated under several different planning ideas. Here the following three different types are considered. Plans A and B assume moderate and strong actions for stimulating the development of the city. whereas Plan C does not assume any positive actions. The values of the contro~ totals in 1975 and 1990 are listed in Table 3. Linear interpolation is used to estimate these values in the other years. NPAS is another exogenous variable used in the model. The values for NPAS were also estimated by referring to the values shown in the Master Plan. 4.3 Results and discussions Under the assumptions in Section 4.1. the population in each group and the land use pattern in the future under three different plans were estimated. Figure 5 is the mesh map which shows the night-time population in 1990 under Plan A. As can be seen from Fig.5. the ranking of the meshes in the CBD in 1990 is much lower than that in 1966 (see Fig. 4). This indicates that the evacuation of residents will continue. The night-time population is als0 decreased in the Coastal Industrial Zone and this is caused by the increase of manufacturing industries in this area. High rankings of the night-time population is observed in the southern part of the city. Note also that its density increases significantly in this part (rank 9 implies 10.728 ~ 12.677 person in 1966. but 14.198 ~ 37.701 in 1990). In the eastern part of the city. the population is decreased because the land price in 1966 was already too high for living and this caused the residents to move out from this area.
807
Figure 7 illustrates the pattern of land use in 1990 which is estimated by the model and Fig. 6 the actual pattern in 1966. Each mesh is classified into one of the types defined in Table 4. The numbers in the table assume the priority in the process of classification; when a mesh satisfies two or more criteria simultaneously. it is classified into the type with the smallest number. The type "miscellaneous" is introduced because there are some meshes near the city border which do not belong to any of the above 10 types. In the mesh map of 1990.the type of parks and green zone does not appear because this type is not treated in this model. Table 5 shows the number of meshes which belong to each type. From this table we can make comparison among the land use patterns under Plans A.B and C. We can see that under every plan the commercial areas and the residential areas increases significantly while the industrial area remains nearly the same and the composite area decreases remarkably. The increases of the high density commercial area and the residential area are most significant under Plan B. reflecting that the population is set up high (3.5 million). The high density commercial area does not increase much under Plan C. indicating that the function of the city might be retarded if the people keep moving out of the city. V. CONCLUSION Based on the available mesh data a dynamic model which explains the changes of populations and land use for each mesh has been constructed. The key frame of the model is land price. which is taken into account as one of the model variables. The future land use patterns under several Master Plans have been drawn by using this model. The fundamental indices in the Master Plans have been used as the control totals for the model. These patterns have met the thoughts of the specialists in the municipal office reasonably well and it has been assured that the model can comply with their desire to have scientific supporting tools for making redevelopment decisions. Table 3 Control Totals and Its Future Values 1975 1990 Items of Master Plans Control Total A C B Wholesale and business 93.4 133 148 106 CBD 52.6 78 90 56 Non-CBD 40.8 55 58 50 73 Retail and service 67.8 91 98 Night-time population 272.0 300 350 250 Manufacturing indust. 59.3 67 71 60 10.3 18 16 CBD 19 Periphery of CBD 12.7 12 12 10 Coastal Indust. Zone 12.3 13 14 12 22 26 Outskirts Zone 24.0 24 (Unit: 10.000) REFERENCE
~
Alonso. W. (1964). Location and Land Use. Harvard Univ. Press . Cambridge. Mass.
Y. Suzuki, et al.
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Fig. 4 Night-time Population (1966)
..,
frk)4S
"040 IIIIO)S .. NO)O
00'10
NO'O 11\1015 N010 _065
,,'00 H09S .. 090 _015
_oeo
'IJ
l1li"'111'"'l1li111-. .............. I0Il.( [EE E EEE fE (( ((EE El
W.WWW"" • • W".",",."'.Iw((tEE't:((f((lEEE((
0000 UUJJ:Ja~J :JOOOOI}t);J OIl')O l)Q DOt) 0000001') 9 eal '.tt5S441 H1l1 'IU' 1Ul '4 45566 71a89 0505 OSiJSUSuSuSO"J" SJ SJ'iOSQ 50snsoso Sf")
os .lit" ( 1
'"
1 5
,-
016)
l llt.)(
1J'" )
114 )
h'1"'.. J It-
1 '1'i , 'u",.l '1i11
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"'6 11
1" ,, "<11-
'"0'
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7"07-
to}')
ic.H-
(
-c.
lE A 0 ( (
1 SS)
"
(
1}1) "9 ) 1 I
"
\) !)
""
U191S '1) Jl101 '1) ...... w...... " ......... [[Et ([ [[Et t E EE E E E OOUOO,,",OUJUUu 000 fJJ O J1lJUOOOOOOO on UODO J 9 ea7 7tto~)4' 1 } l l l 1 :J Ul 11151' 'SS 6671 158/1 o SOS O~ JSu')US\J') 1.1\11 S ') "J S JSO 'i0 SOSo so sosn
,."
~e-
,,"'t
nl)
lJ
on
61 )
." ••• 17)
••
Fig.6 Land Use in 1966
Table 4
"tOao
.. 01S 11010 flOOS 11060 'WOS'i NIlSU IIIU4 S 1104 0 III OB "01 :) "Ul S IIIU1 0 "'0' S 11010 "UU\
suus Sal U G
501 ') SOlJ 5Ul S
SOJ O
SOB 50'0 504 \ SOSO 50S \ 5UttO
500\
SUI;,) 507 \
so at,) 5Ud \
5090 ~09 S 5100 510\ 5110
Fig. 5 Night-time Population (1990)
Estimated Land Use in 1990 under Plan A
Definition of the Types of Land Use
Index Symbol 1 Blank 2 P 3
B
4 5
C I
6
A
"'OO 111095 "U90 "0& 'i
Fig.7
7
H
8
R
9
L
10 11
U M
Table 5
Type of Land Use Criterion Rivers and water surfaCE Over 50% Parks and green zone Over 10% High density l.ommercial area commercial area is over 30% Over 15% Commercial area Industrial area Over 40% Both the res iComposite area of dential and the residence and ndustrial area factories are over 15% High density Residential residential area area over 60% Residential area Over 30% Low density Residential residential area area over 5% Vacant lots Over 50% Miscellaneous Areas other than above
Number of Meshes in Each Type of Land Use
~
Land use type
1990 (estimated) 1966 Master Plan actual B A C
Hi~h
density commercial area Commercial area
53 70
57 72
37 65
35 63
Industrial area Composite area of residence and factories High density residential area Residential area Low density residential area
53
52
57
54
69
68
67
114
124 66 79
163 58 151
82 62 117
4 131 65
A LAND USE MODEL FOR SUPPORTING REDEVELOPMENT DECISIONS IN THE OSAKA METROPOLITAN AREA Y. Suzuki*, P. S. Pak*, K. Tsuji*, F. Yamada*, T. Murakami**, K. Hirose*** and Y. Noto*** • Department of Electn'cal Engineering, Osaka University, Suita, Osaka, Japan • ·Comprehensive Planning Bureau of Osaka Municipal Office, Osaka, Japan ••• Kansai Institute of Information Systems, Osaka, Japan Abstract. A land use model for Osaka Metropolitan Area is constructed based on the available mesh data. The model describes the mechanism of the transition of land use in a large city with land price as a characterizing index. Commercial, industrial and vacant areas are related to the numbers of people in manufacturing, tertiary industries, residence, etc., and these numbers are related to land price. Detailed equations derived through the cross-sectional regressign an~lysis, are presented. The model is constructed for each mesh of 500 x500 , and is able to draw the spatial pattern of land use in the future under various control policies. Keywords.
Land use; mesh data; location model.
I. INTRODUCTION The concentration of population and industries in large cities is a phenominon commonly observed in both industrialized and developing countries. In Japan, this concentration has been going on extensively during the era of high economic growth over the last two decades. One of the most important subjects that needs immediate attention is the problem of management and control of cities that have already become excessively large. The ultimate purpose of this management and control is to improve the level of social welfare for the people who live and work in large cities, providing social capital, supplying water and energy, maintaining the level of natural environment properly, etc. However, before we think about any concrete policies for achieving the above purpose, it is necessary to analyse the process of socioeconomic development of large cities, i.e., the process of population changes, industrial development and utilization of city space.
In this study, the problem of land use in large cities is considered directly and concretely, based on the knowledge of the land theory. As we mentioned above, the key which allows researches in this direction is the availability of data. Fortunately, we had an opportunity of making use of the considerably well-equipped mesh data for Osaka, the second largest city in Japan. There, the mixed use of land by small industries and residents has caused the degradation of the living environment, and a chronic decrease of population has been one of the concerned problems. In the following sections, we present a model which expresses the change of land use on mesh level and draw a future land use pattern in Osaka City based on this model. 11. AVAILABLE MESH DATA The municipal office of Osaka City started making mesh data in various terms several The size of each mesh is of yea~s ago. m 500 x 500. The data relevant to this paper are as follows:
The researches in this direction so far have been largely done by using approaches in economics where an econometrical model was constructed by uniting the whole city as one. However, this approach is, in a sense, single dimensional and lacks the view-point of land use in cities which is perhaps most important for deriving concrete policies for managing the city environment. There is also another approach which is represented by the land theory (Alonso, 1964). However, the difficulty in applying this approach was that the data relevant to land use was lacking, or even if the data were available, it took too much time and effort for processing the data.
(a) Population of each industry group. The Industrial Statistics in 1966 and 1969 have been made into mesh data. Population in wholesale and business (NWHC) Population in retail and service (NRET) - Population in manufacturing industries (NIND) NWHC includes the wholesale, banking and insurance, real estate, transportation and communication, utility and construction industries. (b) Night-time population(residents) (NPOP)
R()1
802
Y. Suzuki, e t a Z.
The mesh data based on the results of the National Censuses in 1965 and in 1970 are available. Using these data, NPOP in 1966 and in 1969 are estimated by simple linear interpolation, i.e., NPOP(1966)=NPOP(1965)+0.26NPOP(1965,1970), NPOP(1969)=NPOP(1965)+O.86NPOP(1965,1970), where 6NPOP(1965,1970)=NPOP(1970)-NPOP(1965). (c) Land use The mesh data based on an investigation in 1965 are available. We regard these data as the data for 1966. The following items are relevant: -
Residential areas (LRES) Industrial areas (LINO) Commercial areas (LCOM) Vacant lots (LOPN) Parks and green zone (LPRK) Rivers and water surface (LRIV)
3 . 1 Structure of the model The preparation of the mesh data has started recently and the data are available only at two different time points (1966 and 1969 as explained in the previous section) at this moment. Therefore, we first investigated, for each ward, the past trends of land use, population and populations in various industry groups by making use of the Statistics of Osaka City. With this general knowledge and by paying special attention to the changes of land use for each ward, we finally partitioned the city into 4 blocks and arrive at the model shown in Fig.3 . The variables used in the model are listed in Table 1 .
(d) Total floor area of residences (TFLS) The data for 1969 are available. (e) Number of passengers at railway stations (NPAS) The number of passengers at each station is available from the Statistics of Osaka City which is published annually. We have made these data into mesh data by locating each station on the map. (f) Land price
The land prices in 1966 and in 1969 at about 2,000 sample points are available. The land price of each mesh is determined by averaging the price over all sample points that fall into the same mesh. The land price is deflated using the consumer price index in 1970.
~":0~3 Central Business District (CBD)
Lee -gJ Periphery
c=J
Coa stal Industrial Zone
c=J Outskirts
Zone
Ill. A LAND USE MODEL BASED ON MESH DATA The fundamental idea behind the model structure, which will be decided in the following, is best expressed in Fig.l where the process of transition of land use is considered to be governed basically by land price. When the land price stays low, the vacant lots are converted to either industrial or residential areas. As the land price goes up high, these areas will be converted to commercial areas. In the following, the model will be described in some detail.
Uprise of land price (Advancement of urbanization) Fig. 1 Fundamental Transition of Land Use
Fig.2. Partition of Osaka City into Blocks The 4 blocks are shown in Fig.2, together with the wards of Osaka City. The central part forms the so-called Central Business District (CBD),where the management offices of various enterprises, the wholesale and retail stores and the service industries are highly concentrated. Factories are located along the shore of Osaka Bay and the Yodo River as well as in some parts of the city . The surroundings of the CBD are the areas where commerce and residences are dominant. The outskirts area still keeps some vacant lots, and these lots are being converted to either the residential or the manufacturing areas. The basic ideas for construct i ng the model were as follows: In the CBD, the chief factor is that concentration induces more concentration. There the opportunities of meeting people and obtaining informations useful for the people and/or enterprises are plentiful and this is very important. Thus, every industry makes every effort to locate its main office in the CBD, because in fact there are no other places which will bring similar benefits. The land price in the CBD is extremely high, but we imagine that the retail
803
Land Use Model for Supporting Redevelopment De c isions and service industries can afford this high expense. They may construct skyscrapers in order to utilize their land most efficiently. Thus, the land price of the CBD can be explained mainly by the degree of concentration of the retail and service industries. Also, the increase in land price is the chief cause of driving out the people or the industries that have traditionally been there for a long time. This is simply because, if possible, they will have a greater chance of getting more benefits by disposing of their lands.
R = 0.806 4) Population of tertiary industries:
(5)
NTRD =: NWHL + NRET
(6)
5) Population of daytime working people: a) For the meshes in the CBD: NWOK =: NTRD
(7)
b) For the meshes in a non-CBD area: NWOK =: NTRD + NIND
(8)
6) Land price:
=
98.127 + 0.04115 NWHL <9.690> <15.437>
During the course of constructing the model, the land theory has been helpful, but many modifications have been necessary as is always the case when the problem of the real world is to be handled properly. Therefore, the analysis of the data and the identification of the underlying structure were performed alternately by utilizing all results as the feedback information for making refinements. Consequently, it should be emphasized that the structure shown in Fig.3 had neither been given beforehand nor had been simply assumed so.
7) Night-time population:
3.2 Identification of the model
8) Population of manufacturing industries:
3.2.1. Dynamics of population
1) Population of wholesale and business:
<17.660> R 6NPOP
=
=
1.1856 NWHC_ l <42.47 >
(1)
- 160.77 In PRLD_ + 0.34987 LOPN_ l l <9.564> <-7.833> R
NIND
=
<2.645>
+ 220.747 In PRLD_ <2.304>
(2)
l
R
(3)
b) For the meshes in a non-CBD area:
(4)
NWHL =: NWHC 3) Population of retail and service:
0.02863 NPOP_ + 0.24617 NWOK_ l l <20.625 > <3.117 >
---....
+ 0.006849 NPAS + 0.018956 NPAS <7.525>
l
= 0.937
(11)
NIND
=
240.08 + 0.83898 NIND_l <3.248> <25.443> ...............
+ 0.18706 LIND_ - 0.15860 LRES_ l l <1.515>
<-2.359>
R
=
0.928
(12)
NIND
=
------
0.89832 NIND_l + 0.19022 LIND_l <29.512> <3.284 >
=
(13)
0.955
=
~
0.88854 NIND_l + 0.26537 LIND_l <3.305> <42.260 >
+ 2081.1 ( l/PRLD_ )
NWHL =: NWHC + NIND
<6.997 >
(10)
b) For the meshes in the Periphery of the CBD:
NIND
a) For the meshes in the CBD:
=
0.668
d) For the meshes in the Outskirts Zone:
R 0.925 2) Population of wholesale and business in a broad sense:
NRET
=
-1213.6 + 1.02423 NIND_l <-2.140> <18.213>
R
-294.67 + 1.2465 NWHC_ l <-1.978> <46.356>
+ 84.382 In PRLD_
(9)
663.38 - 0.038857 NPOP_ l <5.547> <-6.305 >
R = 0.961 b) For the meshes in a non-CBD area: NWHC
0.810
c) For the meshes in the Coastal Industrial Zone:
a) For the meshes in the CBD:
=
+ 0.10535 NRET
a) For the meshes in the CBD:
Equations (1) through (14) below represent the results of cross-sectional regression analysis. It should be noted that the variables in these equations are defined for each mesh. The n~bers with < > represent the tvalues and R is the mUltiple correlation coefficient with the degree of freedom adjusted. The suffix -1 indicates the delay of one period (3 years), i.e., the values of 3 years before are used for the variables with -1. 6 indicates the variation in one period. The variable with the symbol/"-.. indicates that the value is evaluated by averaging over 8 meshes adjacent to the relevant mesh.
NWHC
PRLD
l
<1. 347 > R
=
0.961
(H)
It can be seen from Eq.(l) that in the CBD, the NWHC increases exponentially, and this represents the mechanism that the concentration induces more concentration in the CBD. On the other hand, for the meshes outside of the CBD, the NWHC tends to gather around the area where the urbanization has advanced, i.e., areas where the land price (PRLD) is high. The logarithm on the PRLD indicates that the incentive of the wholesale and business people to a mesh becomes saturated when its land price gets overly expensive.
804
Y. Suzuki, et al.
Equations (3) and (4) are the definitions of the NWHL for each district. The definitions were necessary because of the nature of the data we used. That is, the NIND implies the number of people who work in the manufacturing industries and includes office workers as well. Since there are no significant factories in the CBD, the NIND in the CBD is treated as the population of office workers . Equation (5) shows that the population of retail and service in a specific mesh can be explained by the number of people who live and work there (NPOP and NWOK) , and by the number of people who get there or pass through the mesh (we have expressed this by NPAS). N~S is also included in the explanatory factors because the size of each mesh is of sOom x sOOm and therefore the people would certainly walk over to the adjacent meshes from the railway stations in the relevant mesh. This impact and the fact that only 128 meshes out of 1016 have railway s~tions have resulted in the coefficient of NPAS being greater than that of NPAS in Eq.(s). The NTRD is defined by Eq.(6). Using this definition , the NWOK can be defined as in Eqs.(7) and (8). The PRLD is determined by the population of tertiary industries as shown in Eq.(9) where the impacts by the NWHL and the NRET are evaluated separately. Equation (10) indicates that the night-time population is basically declining and that the population decreases when the land price is high and increases when there are many vacant lots . This equation seems to reflect the actual s i tuation fairly well. The value of r.=0.668 seems to be too low, however; this is because the regression has been taken over the values with considerably small changes and this does not necessarily imply that the equation is unreliable. In fact, performing the regression over the NPOP itself has resulted in the following equation : NPOP = 663.40 + 0.96114 NPOP_ l <155.96> <5.547 > - 160.77 In PRLD + 0.34986 LOPN_ l <-7.833> <9.563> R = 0.990
(IS)
Since 6NPOP = NPOP - NPOP_ ' Eq.(lO) is l identical to Eq.(ls) within the roundoff error, and R=0.990 assures that Eq.(lO) i s also reliable. The equations with respect to the NIND differ considerably from block to block. In the CBD, the NIND represents mainly office workers rather than factory workers as mentioned before. Therefore, the pursuit for economical benefits is dominant and this structure is reflected in Eq.(ll). In the periphery of the CBD, the situation is different; here the industrial sites are being converted to residential sites. This is explained in Eq.(12) where the second term indicates that the NIND is decreasing . The third and the fourth terms show that the integration of the industrial
sites are in progress and that the existence of residence has the effect of decreasing the industrial sites, respectively. Equation (13) shows that the NIND in the Coastal Industrial Zone is tending to decrease although it may be increasing in the meshes where the surrounding meshes have many manufacturing industries. In the Outskirts Zone, the NIND increases in those meshes where the surrounding meshes have many manufacturing industries and where the land price is relatively low(Eq.(14». 3.2.2
Dynamics of land use
With the changes in population of each group, the type of land use changes. Unfortunately, the mesh data of the land use are available only for one year and therefore it is not possible to obtain a model which describes the changes of land use by cross-sectional regression analysis similar to the one in Section 3.2.1. Thus, we decided to first calculate the fundamental unit per person for each type of land use and then to obtain the changes of land use by multiplying the unit by the changes in population that can be estimated from the equations derived in the previous section. The method of calculating the fundamental unit may be different depending on the data to be used. It may be calculated from the statistical data other than the mesh data. However, we intend to make full use of the mesh data because it is our primary purpose to obtain the changes of land use for each mesh. Here, the following method is adopted to achieve this purpose . The variables and the parameters associated with the land use portion of the model are listed in Table 2. a) Commercial area It can be said that the commercial area is determined by the population of tertiary industries(NTRD). The relation between the commercial area and the NTRD may not be linear simply because the land space is inherently limited whereas the uprise of the NTRD could be absorbed by increasing the floor space. We have assumed that the relation can be expressed as follows: LCOM = Y (NTRD)Yl o or In LCOM = In Yo + Yl In NTRD o < Yl < 1.
(16)
wnere
The result of the regression based on the above relation is given in Eq.(17), which indicates that the assumed relation explains the real situation fairly well, and the equation is used in our model for calculating LCOM from NTRD. In LCOM
-0.52519 + 0.78167 In NTRD <-3.415> <35.524> R = 0.822
(17)
Since Eq.(17) is obtained using only one year's data, one might argue with the validity of applying the results to future years. However, we view this as follows. Among the
805
Land Use Model for Supporting Redevelopment Decisions 1016 meshes are contained the extremely urbanized meshes .a nd the extremely unurbanized meshes; in fact, we may claim that there are meshes at every stage of urbanization and these stages are in effect represented by the NTRD. Therefore we believe that the equation may be applied in the future without introducing serious error.
b) Residential area The residential area(LRES) is related to the night-time population (NPOP) as follows: NPOP*ATFS (18) LRES RBUK The total floor space per person (ATFS) changes with the improvement of housing situation and with the growth of personal income.
Table 1
Meaning Population of wholesale and business
Symbol
..........
....
NWHC
"',
,,
...
\
lPerson
NRET
Population of retail and service
jPerson
NTRD
Population of tertiary jPerson industries
NWOK
Population of daytime working people
lPerson
NIND
Population of manufacturing industries
lPerson
NPOP
Night-time population lPerson
NPAS
Number of passengers at railway stations
lPerson
PRLD
Price of land
1'1,000
LRES
Residential area
a
LIND
Industrial area
a
\
I
I
, ...
_-------- ----------'
.....
Fig. 3(a) Flowchart of the Model
O
:Variables
~:Exogenous
L----J variables
O
:Parameters
(the CBD) ~ :Concurrent relation
LCOM
Commercial area
a
LOPN
Vacant lot
a
TFLS
Total floor space of residence
m2
ATFS
Total floor space of residence per person
m2 person
RBUK
Bulk ratio of
AILA
Area of industrial land per person
Table 2 Svmbol
...... ,
,,
~
\ \ \
,
Cl
R
I I I
I I I
,.,
/ ~
Ai I
Cl
Fig. 4(b)
Flowchart of the Model(for Non-CBD Area)
lPerson
Population of wholesale and business (in a broad sense)
\
I
Unit
NWHL \ I
Variables in the Model
i I
residenc~
a person
Variables and Parameters Relevant to Land Use Unit
MeaninlZ Total floor space per person averaged over Osaka City Growth ratio of
m2 person
~
Industrial area per person averaged over i-th block where i-I Periphery of CBD ia2 Coastal Ind. Zone i-3 Outskirts Zone Growth ratio of Ai I
a person
806
Y. Suzuki, e t al .
With the aid of the statistics on housing which is carried out every five years, we found that the total floor space per person of Osaka City (denote this by ~(N)l) can be related to the year of Showa N 2 quite accurately by the following equation: ~(N)
Zone. Now define i
aI(N)
a~(N), i=1,2,3 as
A~(N)
and assuming that this value is identical for every mesh in the same block, we obtain
= 0.36701 N <89.904 >
(27)
(19) R = 0.990 The linear relation expressed by Eq.(19) suggests that if we define the rate of increase by ~(N) ~(4l)
(26)
N
41
(20)
and assuming that this rate is identical for every mesh, we can estimate the ATFS at the year Showa N as follows: ATFS(N) = ATFS(44)*a (N) R N = 47,50, ..• ,65
(21)
The bulk ratio of the residence also changes; however, we assume that the changes are small enough and RBUK is equal to RBUK_ . Thus we l obtain NPOP*ATFS LRES (22) RBUK_ l c) Industrial area The fundamental unit for estimating the industrial area (AlLA) can be calculated for the year 1966. However, the AlLA varies with time depending on the degree of mechanization and rationalization. Here, this variation is estimated by making use of the data of the population of manufacturing industries and the industrial area for each ward that are available from the Statistics of Osaka City. That is, by summing up the relevant data for each ward over each block, the averaged industrial area per person can be calculated for each block (denote this by Ai)' Since these data are available annually, regression can be taken on these data and the results are shown in the following: Ai
37.157 - 0.10333 N, R = 0.988 (23) <73.02> <-8.949>
A2 I
-641.64 +194.91 In N, R=0.932 (24) <-3 . 923 > <4.547 >
A3 = -236.32 + 69.12 In N, R=0.874 (25) I <-2.933> <3.274> where N is the year expressed by Showa and where the superscripts 1,2 and 3 refer to the Periphery of the CBD, the Coastal Industrial Zone and the Outskirts Zone, respectively. Since the people in the manufacturing industries in the CBD are treated as office workers, we did not obtain any regression equations in the CBD. The above equations indicate that the unit Ai decreases in the Periphery of the CBD and increases in the Coastal Industrial Zone and in the Outskirts Note that ATFS is defined for each mesh whereas AR(N) is defined for the whole city of Osaka. 2 1966 corresponds to the year of Showa 41.
where AlLA(N) denotes the value of the AlLA at the year of Showa N. d) Vacant lots The net increase of the commercial, residential and industrial areas will result in the decrease of the vacant lots. In our model, the changes of the vacant lots are balanced by the changes of the other areas. The slight amount of the manufacturing areas in the CBD is included in this balancing calculations. Therefore, these areas will be converted mainly to the commercial areas in this model. The changes in land use types other than those mentioned so far (e.g., roads, parks and green zone) were not treated. IV. PREDICTION OF FUTURE LAND USE In this section, the model developed in Section 3 ig used to predict the future pattern of land use up to 1990. 4.1 Assumptions The regression equations described in Section 3.2.1 were obtained based on the data of 1966 and 1969. Therefore, the equations may not hold in the future exactly as they are now. However, since these equations are derived through the process of exploring possible economical and land theoretical causalities among the various factors, it can be assumed that the structure of these equations will be the same in the future although their coefficients may change. For example, the coefficient of Eq.(l) represents the growth ratio of the population of the wholesale and business industries and it varies with the market condition. Also, the coefficients of NWHC_ and PRLD in Eq.(2) l may change in the future. The same arguments may apply for other equations as well. These changes can be estimated only when the mesh data for many different years are given to us, and this is not possible at the moment because only the data for two different years are available. Thus,the predictions which will be described in this section presume that the equations in Section 3.2.1 will hold in the future. However, in order to avoid the errors that may be introduced by using these equations, we propose the constraint that for each block or for the whole city, the total value of predicted population in each group is equal to the prescribed value that will be given to the model exogenously. We refer to this value as the "control total" in the
Land Use Model for Supporting Redevelopment Decisions following description. In other words. model is used to allocate these control into each mesh. i.e •• a two-dimensional In this sense. assigning control totals responds to a policy.
the totals space. cor-
The constraint is implemented by the following modification coefficient: y~ =
CTNXYZ+ l / L NXYZ+ l
(28)
where NXYZ+ 1 denotes the predicted value of population In a particular group obtained by using the equations in Section 3.2.1 and CTNXYZ+ denotes the control total. Summal tion is taken over a specified block or over the whole city. We also assume that the equations with regard to land use will hold in the future. 4.2 Future values of the control total In our model. the base year is taken to be 1969. Therefore. it is necessary to give exogenously the values of the control total in 1972.1975 ••..• 1990. For the years 1972 and 1975 the actual values are available from the Statistics of Osaka City. For the years beyond 1978 we determined the values of the control total according to the future planning of Osaka City (we call this the Master Plan). In the Master Plan. the figures of Osaka City in 1990 are estimated under several different planning ideas. Here the following three different types are considered. Plans A and B assume moderate and strong actions for stimulating the development of the city. whereas Plan C does not assume any positive actions. The values of the contro~ totals in 1975 and 1990 are listed in Table 3. Linear interpolation is used to estimate these values in the other years. NPAS is another exogenous variable used in the model. The values for NPAS were also estimated by referring to the values shown in the Master Plan. 4.3 Results and discussions Under the assumptions in Section 4.1. the population in each group and the land use pattern in the future under three different plans were estimated. Figure 5 is the mesh map which shows the night-time population in 1990 under Plan A. As can be seen from Fig.5. the ranking of the meshes in the CBD in 1990 is much lower than that in 1966 (see Fig. 4). This indicates that the evacuation of residents will continue. The night-time population is als0 decreased in the Coastal Industrial Zone and this is caused by the increase of manufacturing industries in this area. High rankings of the night-time population is observed in the southern part of the city. Note also that its density increases significantly in this part (rank 9 implies 10.728 ~ 12.677 person in 1966. but 14.198 ~ 37.701 in 1990). In the eastern part of the city. the population is decreased because the land price in 1966 was already too high for living and this caused the residents to move out from this area.
807
Figure 7 illustrates the pattern of land use in 1990 which is estimated by the model and Fig. 6 the actual pattern in 1966. Each mesh is classified into one of the types defined in Table 4. The numbers in the table assume the priority in the process of classification; when a mesh satisfies two or more criteria simultaneously. it is classified into the type with the smallest number. The type "miscellaneous" is introduced because there are some meshes near the city border which do not belong to any of the above 10 types. In the mesh map of 1990.the type of parks and green zone does not appear because this type is not treated in this model. Table 5 shows the number of meshes which belong to each type. From this table we can make comparison among the land use patterns under Plans A.B and C. We can see that under every plan the commercial areas and the residential areas increases significantly while the industrial area remains nearly the same and the composite area decreases remarkably. The increases of the high density commercial area and the residential area are most significant under Plan B. reflecting that the population is set up high (3.5 million). The high density commercial area does not increase much under Plan C. indicating that the function of the city might be retarded if the people keep moving out of the city. V. CONCLUSION Based on the available mesh data a dynamic model which explains the changes of populations and land use for each mesh has been constructed. The key frame of the model is land price. which is taken into account as one of the model variables. The future land use patterns under several Master Plans have been drawn by using this model. The fundamental indices in the Master Plans have been used as the control totals for the model. These patterns have met the thoughts of the specialists in the municipal office reasonably well and it has been assured that the model can comply with their desire to have scientific supporting tools for making redevelopment decisions. Table 3 Control Totals and Its Future Values 1975 1990 Items of Master Plans Control Total A C B Wholesale and business 93.4 133 148 106 CBD 52.6 78 90 56 Non-CBD 40.8 55 58 50 73 Retail and service 67.8 91 98 Night-time population 272.0 300 350 250 Manufacturing indust. 59.3 67 71 60 10.3 18 16 CBD 19 Periphery of CBD 12.7 12 12 10 Coastal Indust. Zone 12.3 13 14 12 22 26 Outskirts Zone 24.0 24 (Unit: 10.000) REFERENCE
~
Alonso. W. (1964). Location and Land Use. Harvard Univ. Press . Cambridge. Mass.
Y. Suzuki, et al.
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Table 5
Type of Land Use Criterion Rivers and water surfaCE Over 50% Parks and green zone Over 10% High density l.ommercial area commercial area is over 30% Over 15% Commercial area Industrial area Over 40% Both the res iComposite area of dential and the residence and ndustrial area factories are over 15% High density Residential residential area area over 60% Residential area Over 30% Low density Residential residential area area over 5% Vacant lots Over 50% Miscellaneous Areas other than above
Number of Meshes in Each Type of Land Use
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Land use type
1990 (estimated) 1966 Master Plan actual B A C
Hi~h
density commercial area Commercial area
53 70
57 72
37 65
35 63
Industrial area Composite area of residence and factories High density residential area Residential area Low density residential area
53
52
57
54
69
68
67
114
124 66 79
163 58 151
82 62 117
4 131 65