Urban modeling is an important tool for efficient policy designing in a big city. Surabaya, a big city are now recognized as complex systems through which nonlinear and dynamic processes occur. The paper present a methodological framework for urban modeling from socio economic point of view, which suggested framework incorporates a set of fuzzy systems. In this case, the variable consist of manufacture, hospital, school and shopping centre. Combining with spatial analysis in GIS, the result is a dynamic model was shown to be capable of replicating the trends and characteristics of an urban environment, in this case the city of Surabaya

Adi, Firmansyah

Dadet, Pramadihano

Ira, Prasetyaningrum

Wahjoe, Tjatur Sesulihatien

English

EEPIS Repository

The 13th Industrial Electronics Seminar 2011 (IES 2011)  Electronic Engineering Polytechnic Institute of Surabaya (EEPIS), Indonesia, October 26, 2011   ISBN: 978-979-8689-14-7  450  Constructing Fuzzy for Socio Economic Urban Growth Dynamic  In Surabaya Based on GIS   Dadet Pramadihano, Wahjoe Tjatur Sesulihatien, Ira Prasetyaningrum, Adi Firmansyah Politeknik Elektronika Negeri Surabaya Institut Teknologi Sepuluh Nopember Kampus PENS-ITS Keputih Sukolilo Surabaya 60111 Telp (+62)31-5947280, 5946114, Fax. (+62)31-5946114   Abstract  Urban modeling is an important tool for efficient policy designing in a  big  city.  Surabaya,  a  big  city  are now recognized  as  complex  systems  through  which  non-linear and dynamic processes occur. The paper present a methodological  framework  for  urban  modeling  from socio  economic  point  of  view,  which  suggested framework incorporates a set of fuzzy systems. In this case,  the  variable  consist  of  manufacture,  hospital, school  and  shopping  centre.  Combining  with  spatial analysis  in  GIS,  the  result  is  a  dynamic  model  was shown  to  be  capable  of  replicating  the  trends  and characteristics of an urban environment, in this case the city of Surabaya.   Keyword : urban modeling, socio economic, fuzzy, GIS  1. Introduction Surabaya  is  the  capital  city  of  East Java.Geographically,  it  lies  between  the  eastern  and western  parts  of  Indonesia,  so  this  city  plays  an important  role  of  the  development  of  eastern  part  of Indonesia. Since the city of Surabaya has grown as an important  center  for  economic  development  both  at national and regional levels, the city itself is becoming much larger than before and now it can be classified as a metropolitan area because the number of the population already  exceeded  one  million  people.  In  2010 population in Surabaya reach over 2,7 million.      In parallel with this rapid growth in population, the urban expansion  of  the  city  has  been  similarly  dramatic, growing from a geographical extent of less than 1 km² in the 1920s to over 1,150 km² in 2004 [1]. Many factor affect developing urban countries [2] include land cover factor  (as  water,  town/village,  green,  agricultural,  and urban  built-up  area),  physical  factors  (road  network, railway lines, city center/subcenter, bridges, rivers)and socio-economic  factors  (population  and  employment). Generally,  main  problem  in  managing  and  planning urban  development  is  determining  the  urban  model. Every city has different characteristic , it is make a sense that the method to build a model is different[3][4]. The paper emphasize on socio economic factor., There are several ways to build urban model. The paper focus to construct  urban  model  by  combine  fuzzy  and  spatial analysis  of  socio  economic  factor  The  result  is visualized in Geographic Information System (GIS).  2. Method Incorporate First, it is important to choose the proper factor among socio economic factor. For this purpose, 764 data are gathered from 2005 to 2009. Table 1 shows some of socio  economic  data.  Because  some  economic  and social factor are intangible, it should be represented by something real. For example, education is represented by  number  of  school,  hospital  for  vital  community service, shopping centre represent entertain, etc .  Table 1. Socio Economic data Surabaya on 2009 Kecamatan Population per km Manufact school hospital Shop Cent Surabaya Pusat           Tegalsari  26655.94  12  32  2  5 Genteng  14498.71  6  24  2  2 Bubutan  30442.67  19  15  2  3 Simokerto  35728.67  26  15  2  3 Surabaya Utara           Pabean Cantikan 20251.88  26  8  1  2 Semampir  23503.39  18  33  3  0 Krembangan  26759.83  31  21  2  1 Kenjeran  16918.26  59  16  2  0 Bulak  5460.746  20  9  1  0 Computer Science and Engineering, Information Systems Technologies and Applications 451  Because there are many data, it should be decided which is significant in socio urban developing. Statistically, all data  is  tested  in  SPSS  software  to  find  rating  of significances.Figure 1 illustrate an example the result of SPSS test. Figure 1 illustrate an example the result of SPSS test.   Figure 1. Result of SPSS Test  Figure 1 illustrate correlation between number of manufacture  as  representative  of  industry  and population.  Population  is  chosen  as  representative  of urban  development  in  big  city  [3][4][7]  After  test amount  data,  the  result  shows  there  are  4  significant variable  such  as  hospital,  manufacture,  school  and shopping centre. Then, all data are engagedstatistically to choose the significant variables. Second, determine suitable method. In the urban system, there are many complex combinations of factors and  subsystems.  So,  the  tools  such  as  mathematical theories  and  models  must  be  used  with  full consciousness  of  complexity,  Fuzzy  solving  methods were  proposed  because  it  has  flexibility  to  simplify complexity  in  urban  modeling.[  F.  Habib  and  A. Shokoohi]. In Fuzzy Logic, variables consist of partially overlapping qualitative fuzzy sets. Refer to [ 5] , Fuzzy Mamdani  is  choosen.  Membership  function  is  build from all variable mention before. Figure 2 shows one of input membership and output function.  (a)  (b) Figure 2. Membership Function  (a). Input (b). Output  Each  fuzzy  set  is  described  by  a  linguistic variable  familiar  to  its  quality  while  quantitative (numerical) information is appointed to the proper fuzzy sets  by  the  correspondent  membership  function.  The knowledge  base  is  represented  as  linguistic “IF…THEN”  rules,  connecting  hypotheses  to conclusions through a certainty factor. In this research, rule  is  performed  to  determine  development  in  term VERY  LOW,  LOW,  LOW  ENOUGH,  MEDIUM, HIGH ENOUGH, HIGH, VERY HIGH. Figure 3 shows part of rule base in this research   Figure 3. Rule base  Then,  the  knowledge  extraction module  for  the suitability systems incorporates the following approach. First  we  calculate  the  percentage  of  urban  cells  that belong to each fuzzy set of each input variable. Then we divide  the  value  returned  for  each  set  with  the percentage of all cells (develop or not) that belong to this  fuzzy  set.  Finally  we  normalize  these  values  for each  single  variable  separately.  Finally  accumulation corresponds  to  compromising  different  individual conclusions into a final result. Fuzzy logic combined to GIS  provides  a  proper  framework  for  expressing  and mapping the urban growth dynamics.    R1  :  IF  industri  =  tinggi  v  sekolah=sedikit  v  rumah sakit=sedikit v swalayan =sedikit THEN perkembangan = sangat lambat R2  :  IF  industri  =  sedang  v  sekolah=sedang  v  rumah sakit=sedikit v swalayan =sedikit THEN perkembangan = lambat R3  :  IF  industri  =  sedikit  v  sekolah=banyak  v  rumah sakit=sedikit v swalayan =sedikit THEN perkembangan = cukup lambat R4  IF  industri  =  sedikit  v  sekolah=sedang  v  rumah sakit=sedikit v swalayan =sedikit THEN perkembangan = sedang R5 : IF industri = sedikit v sekolah= sedang v rumah sakit= sedikit v swalayan = sedang THEN perkembangan = cukup cepat R6  :  IF  IF  industri  =  sedikit  v  sekolah=  sedang  v  rumah sakit=dikit v swalayan = sedang Computer Science and Engineering, Information Systems Technologies and Applications 452 3. Architecture of The Model in GIS The goal is to develop a model that simulates in a realistic way the urban growth evolution while it allows the communication of its knowledge. The overall system is described by flow diagram in figure 4, combine GIS and fuzzy. These are to:   reproduce efficiently the underlying spatio-temporal patterns of the urban growth dynamics,   express  in  comprehensible  linguistic  terms  the knowledge acquired and   sustain  a  generic  form  of  the  model  that  is disengaged from data limitations.  Figure 5. Flow diagram overall  In order to be useful and realistic, urban models depend on real-world data such existing urban land uses and growth patterns, existing road network, location of various facilities, availability of infrastructure facilities etc. that can be integrated and mapped in a modelling scenario.  Figure  5  shows  the  object  of  research, Surabaya, in map.   Figure 6. Political Map of Surabaya  This  picture  shows  political  geography  of Surabaya.  Surabaya  is  divided  by  5  district  namely Surabaya  Tengah  (Central  of  Surabaya),  Surabaya Timur  (East  Surabaya),  Surabaya  Barat(  West Surabaya),  Surabaya  Utara(North  Surabaya)  and Surabaya Selatan(South Surabaya). Each district consist of  group  area  called  Kecamatan,  Every  district  have different number of kecamatan. Kecamatan itself cover a numbers of area called Kelurahan. Every kecamatan has different numbers of kelurahan. Figure 1 shows all kecamatan in Surabaya.  To meet GIS and fuzzy, one technique in GIS, grid system, is employed. All map is divided in square, in this case 2km by 2 km, and every grid has specific database include geographical data and data for fuzzy input. Figure 6 shows result of Surabaya after gridded   Figure 6. Political Map of Surabaya  Then, all data in grid is proceed in fuzzy system to  produce  result  in  term  of  Very  Low,  Low,  Low Enough,  Medium,  High  Enough,  High,  Very  High development.  Last,  this  result  is  analyzed  in  GIS software and visualized in different color.  4.  Result And Discussion Socio economic factor is chalenge because it is dynamic and complex. In a big city, where infrastructure is  establish,  this  factor  play  significant  role  in  urban development.  From  the  statistical  correlation  test,  4 independent variable is decided, that is hospital, school, manufacture,  and  shopping  centre  while  independent variable is population. The result is shown in figure 6.   Computer Science and Engineering, Information Systems Technologies and Applications 453  (a)  (b) Figure 7. Correlation factor (a) Hospital to population (b) School to population  Figure 6 explain negative and positive correlation factor. For school, correlation is negative. School in this case consist of elementary, junior high school and high school, privat and public school.. When it is checked in spatial data, school is spread in group randomly. Private school  in  Surabaya  is  growing  up,  compete  public school.Their market are middle to high class whose has personal  car.  Private  school  usually  located  in  real estate, in which land price is relatively expensive. This is  the  explanation  why  this  variable  decrease  urban development.  In contrast, hospital means public service. When hospital  is  build,  the  environment  change.  It  attract people  to  live  surround  for  economic  reason,  such  as trading, rent a room and so on. This is the reason why correlation is positive. Another result shown in table 2.  Table 2. Correlation between manufacture and population Kecamatan  Kepadatan 2009  Industri Tegalsari  26655.94  12 Genteng  14498.71  6 Bubutan  30442.67  19 Simokerto  35728.67  26 Pabean Cantikan  20251.88  26 Semampir  23503.39  18 Krembangan  26759.83  31 Kenjeran  16918.26  59 Bulak  5460.746  20 Tambak Sari  24924.62  78 Gubeng  20463.5  26 Rungkut  4649.382  26 Tenggilis Mejoyo  10133.94  45 Gunung Anyar  5169.643  24 Sukolilo  4456.722  42 Mulyorejo  6460.476  22 Sawahan  32210.39  37 Wonokromo  27116.87  22  Table  2  shows  the  correlation  between manufacture  and  population.  From  all  kecamatan, manufacture  shows  the  negative  correlation.  Different reason with school, it because manufacture need a big area.  It  means  only  a  few  people  live  in  there,  and causing  the  development  stagnant.  Period  2004-2009 was used in order to extract a knowledge  base  which was  applied  in  the  2010  to  2014.  The  fifth  period  is chosen  because  usually  policy  for  city  planning  is revised  in  4  year  Figure  6  shows  the  result  after  all process from preparation to data training occur.   Figure 8 fuzzy for every grid  In figure 6 all of grid contain value of fuzzy result that is translated  in  linguistic.  The  result  in  term  of  word  is shown in table 3  Table 3. Result for Surabaya Pusat Kecamatan  Result Surabaya Pusat    Tegalsari  Cepat Genteng  Sedang Bubutan  Cepat Simokerto  Cepat  Computer Science and Engineering, Information Systems Technologies and Applications 454 The  weighting  value  can  be  determined  by  the knowledgebase operation of fuzzy models. A decision-maker can analyze the suitability for each space and any fuzzy degree. The function of computing weighting in Mamdani fuzzy  models can be coded to be a part of system  model.  The  combination  of  Mamdani  fuzzy model and GIS would be effective for GIS database and enforce the ability of special decision-making. Then the result is visualized in GIS, as shown in figure 9   Figure 9 Fuzzy for every grid in one kecamatan  From figure 9, it is clear that there some grid has same value (see the color). That because the grid lied on the same Kecamatan. Actually, it is not so accurate . For example, one kecamatan consist of 20 grid, a hospital lied  on  grid  18  while  19  others  kecamatan  has  no hospital. Of course weighting in grid 18 different with others  grid  even  though  they  are  lied  on  the  same kecamatan.  Nowadays,  monitoring  of  urban development is done for every kecamatan, even though it is not accurate. If the stakeholder want to get more accurate data, it is suggested to observe by segmentation like a grid. Figure 10 illustrate an example for showing the urban development of three kecamatan intersect   (a)  (b)  Figure 10. Thematic Map (a) Political Map (b) Socio economic Map  Figure  9  explain  how  to  change  thematic  map from  political to  socio  economic  map.  This  picture  is capture of three  kecamatan,namely Tegalsari, Bubutan, Genteng, all are located in Surabaya Pusat. The darkest color is Kecamatan Tegalsari, Medium color is Bubutan and The  light  color  is  Genteng.  From  figure  10,  it  is clear  that  urban  development  across  the  kecamatan border, so it is running to closest neighborhoods which has  similar  or  less  similarity  in  variable.  Political geography  thematic  map  should  is  not  suitable  to applied  when  socio  economic  variable  is  used  for. Unfortunately, this result can not express how fast the development  is.  To  know,  we  need  another  adding method  that  suitable  for  handling  flow  of  changes  in urban  development  caused  by  time  (year)  and  spatial data. The  method  is  using  cellular  automata.  And figure 11 is the implementation about cellular automata.    Figure 11. Prediction with Cellular Automata    From the visualization of results of the first test that leads to the prediction of the development of the District of Central Surabaya in the past, it seems that an adequate infrastructure in the Centre of Surabaya, such as hospitals, schools, and much less self-serving, due to the city centre. Then, the prediction of the direction of the development of the city to go north, South, and part to  the  East  in  North  Surabaya,  South  of  Surabaya, Surabaya,  Surabaya  and  East  near  the  central  which Computer Science and Engineering, Information Systems Technologies and Applications 455 have the infrastructure that menmadai but has a small land  area,  so  many  residents  who  moved  to  North Surabaya, Surabaya, East and South and some began to develop  in  some  areas  because  the  area  of  West Surabaya  District  Sukomanunggal  there  are  many supermarkets  such  as  malls  and  hence  estimate  the direction of the legendary moves in there. For most of the sub-region of West Surabaya do not experience the predicted  development,  because  in  areas  where infrastructure is still not too many that are available. In the  following  years  the  direction  perkermbangan moving toward the East Surabaya and West Surabaya in Surabaya  to  Central,  North  and  South  not  likely  be occupied as a place of settlement, which is full. In the final stage there is no more empty land because all the land in the city of Surabaya is already in use.   5. Conclusion This  paper  has  combine  a  fuzzy  and  GIS automata  urban  growth  modelfor  simulating  the complex  dynamics  of  urban  growth  in  urabaya, Indonesia. The use of these methods provided a more efficient and accurate means of calibrating the model, The inclusion of fuzzy logic allowed for the formulation of  more  realistic  transition  rules.  The  model  was rigorously  validated  through  the  application  of quantitative  and  qualitative  methods.  These  methods were selected based on their capability to assess changes in spatial structure over time. The model was tested by simulating  the  spatial  patterns  of  urban  growth  over several different development stages using the calibrated values.  empirical  spatial  patterns  of  Surabaya’s  urban growth  which  had  occurred  in  reality.  And  the conclusion  is  prediction  of  human  settlements development  by  using  cellular  automata  method  can help  for  analysis  and  decision-making  on macroeconomic  development  Surabaya  city.    Also Development  with  a  focus  on  Surabaya  city  centre where the movement of population flows from the city centre to the suburbs.    References [1].  Sugiarto,  Boni.  2005,  Model  Perkembangan Spasial  Surabaya  Metropolitan  Area  (Studi Kasus : Kabupaten Sidoajo Dan Kota Surabaya), Surabaya : FTSP-ITS. [2].  Hariyanto,  Teguh.  2005,  Pengembangan  Sistem Informasi Geografis Untuk Prediksi Penggunaan Dan  Perubahan  Lahan  Menggunakan  Citra Ikonos Multispektral. Surabaya : FTSP-ITS. [3].  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Sigrun Kabisch, Annegret Haas, Dagmar Haase, Beyond  Growth-  Urban  Development  In Shrinking Cities as a challenge for Modelling   

Al-Ahmadia*, Alison Heppenstallb, Linda Seeb and Jim Hoggb, Modeling Urban Growth Dynamics using Cellular Automata and GIS

Alison Heppenstallb, Linda Seeb and Jim Hoggb, Modeling Urban Growth Dynamics using Cellular Automata and GIS. Saudi Arabia :

Analisis Penginderaan Jauh Dan Sistem Informasi Geografis (Sig) Untuk Memantau Perkembangan Kawasan Permukiman Baru Di Kota Sidoarjo. Surabaya :

Annegret Haas, Dagmar Haase, Beyond Growth- Urban Development In Shrinking Cities as a challenge for Modelling

Konsep Dasar Sistem Informasi Geografis&quot;, http://arashirin.wordpress.com/2007/12/31/konse p-dasar-sistem-informasi-geografis (diakses tanggal 20

Manajemen Bencana Lumpur Sidoarjo Menggunakan Gis Berbasis Web. Surabaya :

Model Perkembangan Spasial Surabaya Metropolitan Area (Studi Kasus : Kabupaten Sidoajo Dan Kota Surabaya),

Modelling the influence of land use and land cover change on landscape functions,

Pengembangan Sistem Informasi Geografis Untuk Prediksi Penggunaan Dan Perubahan Lahan Menggunakan Citra Ikonos Multispektral. Surabaya :

Constructing Fuzzy for Socio Economic Urban Growth Dynamic In Surabaya Based on GIS

Constructing Fuzzy for Socio Economic Urban Growth Dynamic In Surabaya Based on GIS

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