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HOW TRANSPORTATION INFLUENCES THE INTERACTION RESIDENTIAL AND BUSSINESS ALLOCATION IN BANDUNG CITY INDONESIA by NAJID Research Student Post Graduate Civil Engineering Department ITB Gd.Labtek I Lantai 2 Jl.Ganesha 10 Bandung –40132 Indonesia Telp/Fax : 65 - 022 - 2502350 e-mail :
[email protected]
Hang Tuah SALIM Lecturer Post Graduate Civil Engineering Department ITB Gd.Labtek I Lantai 2 Jl.Ganesha 10 Bandung –40132 Indonesia Telp/Fax : 65 - 022 - 2502350 e-mail :
[email protected]
Ofyar Z.TAMIN Lecturer Post Graduate Civil Engineering Department ITB Gd.Labtek I Lantai 2 Jl.Ganesha 10 Bandung –40132 Indonesia Telp/Fax : 65 - 022 - 2502350 e-mail :
[email protected]
Ade SJAFRUDDIN Lecturer Post Graduate Civil Engineering Department ITB Gd.Labtek I Lantai 2 Jl.Ganesha 10 Bandung –40132 Indonesia Telp/Fax : 65 - 022 - 2502350 e-mail :
[email protected]
ABSTRACT : Inconsistency on land use planning commonly caused by land use changed that make urban sprawl at almost big cities in Indonesia especially in Java Island. Urban sprawl that happened make irregurality and in efficiency in urban trip. Those fenomenon give the indication how needs to understand the behaviour of demand in choosing the location of their residential and bussiness. In this paper, bussiness activities is limited as retail activities. The relationship between residential and bussinees (retail) allocation as Lowry model has to explore to explain how land changed has done. In this paper behaviour of residential location choice modelled by stated preference analysis. Scope of research are : • Identification factors that influence residential and retail location choice in Bandung City. • Building the model that can explain behaviour of residential and retail location choice and their interaction. • Measuring the sensitivity demand respond on changing the value of each variable in the model. We found some variable (attribute) for residential allocation choice such as accessibility to the work place, accessibility to the school, accessibility to the market (shopping centre), accessibility to the main road, accessibility to the hospital, flood condition, air condition and land price and some variable (attribute) for retail allocation choice such as accessibility to CBD, accessibility to residential, accessibility to bus station, retail position, flood condition and land price. Surveys have conducted at several residential location at the east and south area in Bandung city and at several shopping centre at the centre and south area in Bandung City. Keywords : land use, accessibility, location choice, land price, utility function.
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1.BACKGROUND Based on Lowry theory that residential allocation will attract retail (businees) allocation and vice versa, so those allocation are dynamic process until they fulfill the land capacity. Therefore sometimes happened the allocation is not appropriate to the land use planning that give the worst effect to the infrastructure especially the road infastructure. Parengkuan (1991) told that land supply problem being worst since land use changed cases, such as residential land use in city planning change to business land use in reality. Development by private sometimes are not appropriate with city master plan since land supply aren’t enough with the demand. One of the instrument that will be land use policy to control those all problem in land use is land and building tax (Pajak Bumi dan Bangunan or PBB). From the study before shows that building tax has more relationship with city lang development than land tax in Bandung City (Parengkuan,1991). Winarso (1995) told that land use change easy to change and then those changed would given legality in the next evaluation. This condition is not true and sometimes happened unsatisfied and conflict on the public domain. Those changed has big impact to the public cost especially if the changed to the more commercial land use like shopping centre, office, etc. In the 1980-1990 period Bandung city has population growth rate 1.86% per-year and total of 2.056.915 people in 1990 with population density 122.95 people per-hectare (source : Sensus 1990). Commuting rate from the sub urban to the Bandung City for working trip is dominate the trip in the sub urban its self. Base of this that can be predicted that population in Bandung City at day is 1.5 times than at night (Bappeda,1997). In 1990 land use in Bandung city dominated are houses (52.56%), park or rice field (41.53 %), industry (3,65 %), social facility (3.33 %) and trade economy (2.68 %). Base on activity sprawl that commercial and service activity has tendency to develop to the north direction such as Jl.Merdeka-Dago, Jl.Sukajadi, Jl.Setiabudi and to the south direction. Industry development concentrated in Ujungberung and Gedebage area. There is indication that sub urban, south and east area will be residential area and allocation of fungtional activity. Nevertheless almost activity still concentrated in old area of city especially in city centre area (Bappeda, 1998). The next development of the new occupation centre can be see by more development of big scale housing that built in 1980. These development influenced by housing development policy by banking credit. In the early 1990 centre of development more settle with big, medium and little scale of trade and service development at the Soekarno-Hatta road and development of graduate school. The other land use development base on domination activity, policy, strategy of land use by explaining of the city functional and some of constraint to control and optimizing land use and efficiency of the whole activity (Bappeda, 1998). 2.OBJECTIVES • Identification factors that influence residential and business (retail) location choice. Proceedings of the Eastern Asia Society for Transportation Studies, Vol.4, October, 2003
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• Building the model that give explaining of residential and business (retail) location choice behaviour. • Explaining the characteristic of residential and business (retail) location choice. • To understand the interaction between behaviour of residential choice and retail choice behaviour.
3.THEORY BASED Development of expressway has changed city environment like the personal behaviour, social economic structure and influence regional planning and transportation facility (Transportation 20 : 305 – 323, 1993). When level of service of transportation facility higher then before since the project conducted , so the transportation cost (travel time) and land value will be change follow by market equilibrium. The whole changed take the result as the land use changed since utility or profit level from each social economic sector has changed (Transportation 20 : 267 – 283, 1993). Land use policy will increase land value with many variation (Ned Levine, Urban Studies, Vol. 36 No.12 2047-2068, 1999). As the case in Trinidad, very fast increasing land value since not only economic and population growth as economic theory but also as the result from institution constraint dan environment policy that hampered land supply (Ayse Pamuk & david E Dowall, Urban Studies, Vol 35, No.2, 285-299, 1998). In the Granada case land value follow by multicentric behaviour (Jorge Chica Olmo, Urban Studies, vol 32 No.8, 1331-1334, 1995). Economic Theory from city size tell that optimal city size can be realised if economic agglomeration like regional income balance to in-economic agglomeration like traffic congested and air pollution (Xiao Ping Zheng, Urban Studies, vol. 35 No.1 95-112, 1998). In the meantime relationship between regional income and housing needs is not elastic, but relationship between regional income and housing quality is adequate elastic. The relationship between housing price and housing needs is very elastic in Pakistan (Hafiz A.Pasha & Muhammad S Butt, Urban Studies, Vol.33 No.7. 1141 – 1154, 1996). Housing needs is influenced by the policy of land use such as policy to reduce development intensity of land have impact reduce housing own in California (Ned Levine, Urban Studies, Vol.36. No.12.2047-2068,1999). The relationship between housing needs and transportation development have many modelled and wellknown with name land use and transportation interaction model. Almost of those model made base on needs of application of the certain city with certain approach and certain modelling technique too. 4.PRINCIPAL OF STATED PREFERENCE DESIGN Almost stated preference technique use experimental design to built hypotetical alternative to represent to the respondent. Experimental design has mentioned to ensure that each attribute represent to the respondent is independent. Combination from each alternative is full factorial design. Total options on the stated preference approach aren’t too much since that’s can be so Proceedings of the Eastern Asia Society for Transportation Studies, Vol.4, October, 2003
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tired to the respondent. Kroes and Sheldon (1988, p14) suggest between 9 to 16 options acceptable depend on the survey location. 5.UTILITY FUNCTION Utility function is attractiveness measurement each hypotetical scenario that presenting to respondent. This function reflect impact of intention or perception respondent across the whole attribute in the stated preference questions. Generally utility function has linear form as the equation below : U1 = a0 + a1.X1 + a2.X2 +… + an.Xn +e ………………………..…………….(1) Where :
U1 a0 a1…an X1… Xn e
= = = = =
utility alternative 1 constant model coefficient model attribute value random error
The analysis intention is to determine coefficient value model that known as weight perception or part of utility that reflect relatif effect each attribute to the whole utility. Random error factor reflect factors that loss of observation on the survey. Model is probabilistic model since base on the assumption that random error is include to the model. 6.MODEL APPROACH This research has intention to know which is attribute (variable) and how much the attribute determine residential and business location choice. For simpification purpose in analysis that model form is binary logit model. Probability that individu choose one location than the other one base on differentiate utility between those two location, so logit equation form expressed as below : P1 = eu1/( eu1 + eu1)
…………………………………………..…………….(2)
Base on linear model assumption, so differentiate utility can be expressed to difference in amount of n relevant attribute between two location and can be expressed below : U1 – U2 = a0 + a1.(X11 – X12 )+ a2.(X21 – X22 )+ a3.(X31 – X32 )+… + an(Xn 1 -Xn2 ) ………(3) Where :
U1 U2 a0 an Xn1 Xn2
= = = = = =
utility location 1 utility location 2 constant model coefficient model attribute n to location 1 attribute n to location 2
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Utility as individual respond can be expressed in the probabilistic form to choose particular location, as below : Ln[P1/(1-P1)] = a0 + a1(X11 - X12) + a2(X21 – X22) + a3(X31 – X32) + …..+ an(Xn1 – Xn2) ………...(4)
From the equation 3 and 4 can be formulate transformation equation that can be expressed below : U1 – U2 = ln (P1 / 1 – P1 ) ………………………………………………….….(5)
This transformation known with Berkson Theil transformation. 7.SURVEY DESIGN In this survey alternative choice given to respondent base on two location. Location 1 has attribute value like the existing condition and location 2 has attribute value better or worst than location one as imagination value. The existing condition as the survey conducted at the Vijaya Kusuma residential area (east Bandung) and Gading Junti residential area (south Bandung). Options question in the questionaire use rating technic with 5 point semantic scale : 1) Choose A ; 2) Probably choose A ; 3) Fairly ; 4) Probably choose B ; 5) Choose B. Atribut level is difference between attribute value B and attribute value A. Design full factorial has 28 = 256 alternative (options) since there are 8 attribute each 2 level. For retail allocation choice, there is 26 = 64 alternatif (options). Those options too much represent to the respondent and to reduce those options we take counfounding technique as Cochran and Cox,1957. We get only 16 options that will be present to the respondent (Cochran and Cox, 1957, PLAN 6 A.14 page 285 alternative blok 2) for residential allocation choice and (Cochran and Cox, 1957, Plan 6A.5 page 278) for retail allocation choice. We difference between land attribute (such as flood condition, air condition and land price) and travel attribute (such as accessibility to the work place, accessibility to the school, accessibility to the market place, accessibility to the main road and accessibility to the hospital) for residential allocation choice, and between land attribute (such as retail position, flood condition and land price) and travel attribute (such as accessibility to CBD, accessibility to residential and accessibility to bus station) for retail allocation choice.
Numerical presentation from the attribute level at the location survey, expressed at the table 1 and table 2 below :
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Table 1 : Numerical presentation at location survey for residential location choice Attribute Gading Junti Vijaya Kusuma Permata Kopo Working place (minute) 30 – 60 45 – 60 45 – 60 Motorcycle 45 – 60 60 – 90 60 – 90 Transit School (minute) *) 30 10 20 Market place (minute) 30 30 10 Main road (minute) *) 20 20 10 Hospital (minute) 60 30 30 Flood condition Not any flood Not any flood Not any flood Air condition Middle Good Middle Land Price /m2 (rupiahs) 200.000 200.000 400.000 *) by foot ; others by transit (angkot) Table 2 : Numerical presentation at location survey for retail allocation choice Attribute ITC Kebon Kelapa Accessibility to CBD (minute) 10 Accessibility to Residential (minute) 15 Accessibility to Bus Station (minute) 1 Retail Position At Plaza Flood condition Not any flood Land Price /m2 (rupiahs) 5.000.000 Based on table 1 and tabel 2 above, We can Classify high and low attribute level can be expressed at table 3 and table 4 below : Table No 1 2 3 4 5 6 7 8
3 : High and low attribute level Attribute Working place (minute) School (minute) Market place (minute) Main road (minute) Hospital (minute) Flood condition Air condition Land Price /m2 (rupiahs)
Table No 1 2 3 4 5 6
4 : High and low attribute level Attribute Accessibility to CBD (minute) Accessibility to residential (minute) Accessibility to bus station (minute) Retail Position Flood Condition Land Price /m2 (rupiahs)
Low Level 60 30 30 20 60 little flood rather hot -200.000
High Level 30 10 10 10 30 Not any flood Cool -400.000
Low Level 45 30 21 individual store Little flood -2.500.000
High Level 10 15 1 in Plaza Not any flood -5.000.000
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Difference attribute level from two attribute level above that is high (+) and low (-), can be seen at the table 5 and table 6 below : Table 5 : Difference attribute level Atribut Selisih Level Rendah (-) Working place (minute) 0 School (minute) 0 Market place (minute) 0 Main road (minute) 0 Hospital (minute) 0 Flood condition 0 Air condition 0 2 Land Price /m (rupiahs) 0 Table 6 : Difference attribute level Atribut Accessibility to CBD (minute) Accessibility to residential (minute) Accessibility to bus station (minute) Retail Position Flood Condition Land Price /m2 (rupiahs)
Selisih Level Tinggi (+) 30 20 20 10 30 1 1 200.000
Selisih Level Rendah (-) 0 0 0 0 0 0
Selisih Level Tinggi (+) 35 15 20 1 1 2.500.000
8.DATA COLLECTION Survey was conducted at Vijaya Kusuma , Gading Junti and Permata Kopo residential area for segmen demand at dwellings type 21 with the questionaire tools. Land variable and their options can be seen at table 7 and table 8 below. Table 7 : Variable and the option choice No Variable 1 Working place (minute) 2 School (minute) 3 Market place (minute) 4 Main road (minute) 5 Hospital (minute) 6 Flood condition 7 Air condition 8 Land Price /m2 (rupiahs)
Options 30 and 60 10 and 30 10 and 30 10 and 20 10 and 30 1 (little flood) and 0 (not any flood) 1 (rather hot) and 0 (cool) 200.000 and 400.000
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Table 8 : Variable and the option choice No Variable 1 Accessibility to CBD (minute) 2 Accessibility to residential (minute) 3 Accessibility to bus station (minute) 4 Retail Position 5 Flood Condition 6 Land Price /m2 (rupiahs)
Options 10 and 45 15 and 30 1 and 21 1 (individual) and 0 (in Plaza) 1 (little flood) and 0 (not any flood) 5.000.000 and 2.500.000
9.DATA ANALYSIS Average social economic characteristic and average travel characteristic for working and shopping trip purpose can be seen below : - Average income/household - Family size - Average vehicle ownership - House Price in 1994 (at the recent) - House Ownership type - Average travel time (cost) to the work place - Average travel time (cost) to the shopping centre
: 750.000 rupiahs. : 3-4 person. : 1 Motorcycle : Rp. 14 Million (Rp.30 Million) : Credit (15 years) : 54 minute (Rp.3.500) : 15 minute (Rp. 1600)
Distribution of respondent that has main reason to choose the residential location can be seen at table 9 and table 10 below : Table 9 : Distribution of respondent versus main reason to choose residential location Distribution of respondent Reason Total % Working place 7 11,67 School 9 15,00 Market place 6 10,00 Main road 4 6,67 Hospital 10 16,67 Flood condition 15 25,00 Air condition 4 6.67 2 Land Price /m 5 8,33 Total 60 100 Table 10 : Distribution of respondent versus main reason to choose retail location Distribution of respondent Reason Total % Accessibility to CBD 1 5.00 Accessibility to residential 4 20.00 Accessibility to bus station 10 50.00 Retail Position 2 10.00 Flood Condition 1 5.00 2 Land Price /m 2 10.00 Total 20 100 Proceedings of the Eastern Asia Society for Transportation Studies, Vol.4, October, 2003
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We can see from table above that accessibility especially accessibility to the work place, accessibility to the shopping centre (market place) and accessibility to main road are the strong reason to choose residential location beside the land price. By Regression Analysis (program SPSS) we get the utility function : U1 – U2 = 0,961 + 0,0054.Wt – 0,0079.Sc + 0,0124.Sh + 0,0072.Tr + 0,0045.Hp + 0,055.Bj + 0,579.Ud + 0,0084.Hg ………………….. ……………………………( 6 ) R 2 = 0,472 By Maximum Likelihood analysis (program Alogit) we get utility function : U1 – U2 = 9,216 – 0,2433.Wt + 0,3303.Sc + 0,0509.Sh + 0,0325.Tr + 0,0163.Hp + 1,019.Bj + 5,914.Ud + 0,00858.Hg ……………………. ………………………….( 7 ) 2 ρ = 0,4912 Where : Wt = difference accessibility to the work place (minute) Sc = difference accessibility to the school (minute) Sh = difference accessibility to the shopping place (minute) Tr = difference accessibility to the main road (minute) Hp = difference accessibility to the hospital (minute) Bj = difference flood condition / dummy variable Ud = difference air condition / dummy variable Hg = difference land price (rupiah) By Maximum Likelihood analysis (program Alogit) we get utility function : U1 – U2 = 7.507 – 0.1852.Wt + 0.2431.Pop + 0,0347.Tr + 0,6931.Pz + 4.169.Bj + 0,00523.Hg …….…………………. ………………………………( 8 ) 2 ρ = 0,4911 Where : Wt = difference accessibility to CBD (minute) Pop = difference accessibility to Residential (minute) Tr = difference accessibility to bus station (minute) Pz = difference retail position / dummy variable Bj = difference flood condition / dummy variable Hg = difference land price (rupiah) We can analysis with Maximum likelihood more satisfied than linear regression and utility Vijaya Kusuma residential area better than Permata Kopo residential area with differentiate 3.63 and Vijaya Kusuma has probability 97 % choosen for the segment demand dwelling with type 21. Based on equation 8, probability chosen ITC Kebon Kelapa relatif to it’s comparison is 27 % than it’s comparison location is 73%.
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The interaction between residential allocation and retail allocation by accessibility to the shopping (market) place for residential allocation choice and accessibilility to residential (population) for retail allocation choice. The sensitivity analysis for scenario change 25%, 50% and 75% of each atribute value to see the effect of differentiate utility. The sensitivity analysis for each attribut can be seen below at table 11 and table 12 below : Table 11 : Percentage change of differentiate utility for residential allocation choice Scenario Wt Sc Sh Tr Hp Hg - 25 % 30.0 -8.2 -3.8 -1.6 -1.2 -0.4 - 50 % 60.1 -16.3 -7.5 -3.2 -2.4 -0.8 - 75 % 90.1 -24.5 -11.3 -4.8 -3.6 -1.3 + 75 % + 50 % + 25 %
-90.1 -60.1 -30.0
24.5 16.3 8.2
11.3 7.5 3.8
4.8 3.2 1.6
3.6 2.4 1.2
1.3 0.8 0.4
Table 12 : Percentage change of differentiate utility for retail allocation choice Scenario Wt Sc Sh Tr Hp Hg - 25 % 0.92 -1.21 -0.17 -3.44 -20.70 -0.03 - 50 % 1.84 -2.41 -0.34 -6.88 -41.40 -0.05 - 75 % 2.76 -3.62 -0.52 -10.32 -62.10 -0.08 + 75 % + 50 % + 25 %
-2.76 -1.84 -0.92
3.62 2.41 1.21
0.52 0.34 0.17
10.32 6.88 3.44
62.10 41.40 20.70
0.08 0.05 0.03
10.CONCLUSION • Utility Function with Maximum Likelihod analysis is better than Linear Regression analysis for this case. • Air condition has the highest sensitivity in the model, that contradictive with the early data survey (table 9) since probably respondent cannot compare all attribute together at the same situation. • Probability to choose ITC Kebon Kelapa is 27% and it’s comparisson is 73%, that shown the comparisson location has atribute of land value is too cheap than ITC Kebon Kelapa land value. • By using those model of utility function , the city government can control the residential and retail growth. 11.RECOMENDATION • Need to considering the whole attribute again and less than 8 attribute probably has better result. • The next study must considering the aother segment demand such as dwelling with type 36, 45 and so on or more expensive location. Proceedings of the Eastern Asia Society for Transportation Studies, Vol.4, October, 2003
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• The further study must give more obvious perspective between interaction residential and retail allocation. REFERENCES a) Books and Books Chapter Brotchie JF, et.al.(1980) Technique for Optimal Placement of Activities in Zones (TOPAZ), Berlin Heidelberg New York. Hadi,G.K (1995) Dampak Perubahan Guna Lahan Terhadap Kinerja Jaringan Jalan, Lalu Lintas dan Biaya Perjalanan, Tesis, ITB. Cochran, W.G. and Cox, G.M. (1968) Experimental Designs, John Wiley & Sons,Inc., New York. Kombaitan,B.(1999) Perubahan Struktur Ruang Perkotaan dan Perkembangan Pola Ruang Pergerakan Bekerja, Disertasi, ITB. Musa,I.(2000) Peranan Faktor Lokasi dalam Pemilihan Lokasi Industri Para pemanfaat Kawasan Industri di Indonesia, Disertasi, ITB. Rejeki,T.R.(2000) Pedoman Penentuan Indeks Perubahan Pemanfaatan Lahan Sebagai Penerapan Permendagri No.4 Thun 1996, Tesis, ITB. Santoso,I.(1986) The Developmentof Microcomputer version Of Leeds Integrated Land Use – Transport (LILT) Model, Thesis, University of London. Tamin,O.Z.(1997) Perencanaan & Pemodelan Transportasi, Penerbit ITB. Webster,F.V, et.al.(1990) Urban Land Use and Transportation Interaction, Gower Publishing Company. b) Journal papers Lubis,H.A.S. & Karsaman,R.H.(1997) Krisis Perencanaan Transportasi Kota, Perencanaan dan Manajemen Transportasi, Jurnal PWK.Vol. 8 no.3. Kombaitan,B.(1995) Perijinan Pembangunan Kawasan dalam Penataan Ruang, Aspek Hukum dalam Penataan Ruang, Jurnal PWK no. 17. Parengkuan,E.P.(1991) Studi Permasalahan Pajak Lahan Kota dalam Kaitannya dengan Penggunaan Lahan dan aspek Pengendalian Guna Lahan di Kotamadya Bandung, Jurnal Perencanaan Wilayah dan Kota, no.2 Triwulan 1. Sujarto,D. (1992) Wawasan Tata Ruang, Wawasan mengenai Tata Ruang dan Pembangunan, Jurnal PWK Juli, Edisi Khusus. Tamin,O.Z, Russ,B.F.(1997) Penerapan Konsep Interaksi Tata Guna Lahan-Sistem Transportasi dalam Perencanaan Sistem Jaringan Transportasi, Perencanaan dan Manajemen Transportasi, Jurnal PWK.Vol. 8 no.3.
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Winarso,H.(1995) Tarif Ijin Perubahan Guna Lahan Perkotaan Sebagai Bentuk Kontrol Pelaksanaan Penataan Ruang Kota, Aspek Hukum dalam Penataan Ruang, Jurnal PWK no.17. c) Papers presented to conferences Najid et.al.(2002) Pengaruh Transportasi pada Pemilihan Lokasi Tempat Tinggal di Kota Bandung, Proceeding Of FSTPT_V, University of Indonesia, November 2002. d) Others documents Bappeda (1998) Studi Sistem Transportasi Terpadu di Kotamadya DT II Bandung. Bureau of Transport Economics (1998) Urban Transport Models, Department Of Transport and Regional Services. Hague Consulting Group (1992) Alogit Users’ Guide Version 3.2.
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