Modelling and simulating spatial distribution pattern of urban growth using integration of gis and cellular automata

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MODELLING AND SIMULATING

SPATIAL DISTRIBUTION PATTERN OF URBAN

GROWTH USING INTEGRATION OF GIS AND

CELLULAR AUTOMATA

(Case Study in Bandung Area

–

West Java Province)

ERWIN HERMAWAN

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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STATEMENT

I, Erwin Hermawan hereby states that this thesis entitled:

MODELLING AND SIMULATING

SPATIAL DISTRIBUTION PATTERN OF URBAN GROWTH USING INTEGRATION OF GIS AND CELLULAR AUTOMATA (CASE STUDY IN BANDUNG AREA – WEST JAVA PROVINCE)

is a result of my own work under the supervision of advisory board during the period of February 2009 until March 2011 and that it has not been published ever. The content of this thesis has been examined by the advisory board and external examiner.

Bogor, April 2011

Erwin Hermawan G051060011

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ABSTRACT

ERWIN HERMAWAN (2011). Modelling and Simulating Spatial Distribution Pattern of Urban Growth using Integration of GIS and Cellular Automata (Case study in Bandung Area – West Java Province). Under the supervision of HARTRISARI HARDJOMIDJOJO and IDUNG RISDIYANTO.

This research has a purpose to study and develop a model that can representing and simulating spatial distribution pattern of urban growth in Bandung Area. Integration of Geographic Information System (GIS) and Cellular Automata (CA) modelling was implemented to develop Urban Growth Spatial Distribution Model (UGSDM) in Bandung Area. GIS with spatial analysis used to prepare and process necessary data that are later incorporated into CA-based model.

Two scenarios have been implemented to calibrate and validate the result from simulation model. In the first scenario, driving forces and constrained factors (slope > 15%, protected forest, open green space and water body) as an input in the model simulation. Whereas, the second scenario is the simulation model will be run without slope > 15 % as a constraints in model input. Khat Coefficient for first and Second

scenario in 1991 – 2000 simulation model is for about 76,80 % and 82,66 % respectively, whereas in 2000 – 2007 is for about 76,26 % and 79,27 % respectively. Base on the result, second scenario is the best to be applying in model simulation and can be used to predicting urban growth simulation. This scenario will be reduces the impact of the mismatched cells in simulation model and making the simulation accuracies more realistic to reflect the actual condition in the real world.

The result from urban development simulation in 2015 and 2030, shows that urban development in Bandung area will continue in two ways: one is through the in-filling of areas within the currently existing urban in Cimahi and Bandung City, and another is through expanding toward the southeast of the Bandung area.

Keywords: Geographic Information Systems, Cellular Automata, Urban Growth Spatial Distribution Model and Bandung Area.

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ABSTRAK

ERWIN HERMAWAN (2011). Pemodelan dan Simulasi Pola Distribusi Spasial Pertumbuhan Perkotaan menggunakan integrasi SIG dan Cellular Automata [ Studi Kasus wilayah Bandung, Provinsi Jawa Barat] Di bawah bimbingan dari HARTRISARI HARDJOMIDJOJO dan IDUNG RISDIYANTO.

Penelitian ini bertujuan untuk mempelajari dan membangun sebuah model yang dapat merepresentasikan dan mensimulasikan pola distribusi spasial pertumbuhan perkotaan di wilayah Bandung. Integrasi dari Sistem Informasi Geografis (SIG) dan Pemodelan Cellular Automata di implementasikan untuk membangun Model Spasial Pertumbuhan Perkotaan di wilayah Bandung. Analisa SIG di gunakan untuk mempersiapkan dan memproses data yang di butuhkan untuk kemudian di integrasikan ke dalam sebuah model berbasiskan Cellular Automata.

Dua skenario telah di implementasikan untuk mengkalibrasi dan memvalidasi hasil dari model simulasi. Pada skenario pertama, faktor penggerak dan faktor pembatas (kemiringan lereng > 15%, hutan lindung, ruang terbuka hijau dan tubuh air) digunakan sebagai input dalam model simulasi. Sedangkan, skenario kedua adalah model simulasi yang di jalankan tanpa menggunakan kemiringan lereng sebagai input di dalam model. Koefisien Khat yang dihasilkan untuk skenario pertama

dan kedua secara berturut-turut adalah sebesar 76,26 % and 79,27 % . Berdasarkan hasil tersebut, skenario kedua memberikan hasil yang terbaik untuk di aplikasikan dalam model simulasi dan dapat digunakan dalam prediksi pertumbuhan perkotaan. Skenario ini mengurangi dampak ketidak-sesuaian cell pada model simulasi dan membuat akurasi model simulasi lebih realistik untuk menggambarkan keadaan sebenarnya di dunia nyata.

Hasil dari simulasi perkembangan perkotaan di tahun 2015 dan 2030 menunjukan bahwa perkembangan perkotaan di wilayah Bandung akan berlangsung dalam dua cara : pertama memenuhi dan berkembang sepanjang wilayah perkotaan yang telah telah terbangun di Cimahi dan kota Bandung dan perkembangan lainnya adalah berkembang ke arah wilayah Bandung bagian tenggara.

Kata Kunci: Sistem Informasi Geografis, Cellular Automata, Model Spasial Pertumbuhan Perkotaan dan Wilayah Bandung.

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SUMMARY

This research has a purpose to study and develop a model that can representing and simulating spatial distribution pattern of urban growth in Bandung Area. Integration of GIS and Cellular automata modelling was used to develop the urban growth simulation model. GIS with spatial analysis used to prepare and process necessary data that are later incorporated into CA-based model.

Analyze of markov chain change detection during 1991-2007 in Bandung area was done to analyze and detection trend probability conversion from Paddy field, Plantation, shrub and forest into built up-area. The result was shown that the largest increase of built-up area for each year development is the result from land conversion of paddy field into built-up area. Radial sprawl for built-up area development was impact to conversion from Paddy field to built-up area and make paddy field as the highest weighted for landcover priority conversion to built-up area.

Extraction driving forces and constraint information has been done using multi-criteria evaluation to derive weighted factors for urban development. Based on the result of weighted calculation, neighbourhood effect is the most important influence factors in built-up area development during 1991 – 2000 in study area. The weighted was contributing 48% for urban development. This condition was agreed with the theory, urban development usually takes place first in the adjacent area to the developed built-up area.

Furthermore, The analysis result from multicriteria evaluation of driving forces and determine of landuse conversion priority using markov change detection were used as an information and data input in system configuration and development of urban growth simulation model.

In this research, Integration of GIS and Cellular automata modelling was used to develop a simulation model that can represent spatial distribution pattern of urban growth in Bandung Area. CA can be considered as analytical engine of GIS, which plays in important role in modelling and simulation of spatial-temporal processes. Another approach to integrate CA and GIS in this research are to develop a standalone CA program that can use data from GIS. Data interchange and compatibility can be achieved through file conversion protocols.

All of the data are contained in a grid-like shape based vector polygon with the

spatial resolution of the cell in 100 x100 m. it’s means, each cell on the model

represent an area of 10.000 m2 or 1 Ha on the ground. Several reasons to select in this cell size to input into the model development. The first one is the spatial data were used in this research have different of spatial resolution and scale. The lowest spatial resolution of spatial data is SRTM data which have spatial resolution in 90 x 90 m. Based on standardization of spatial data, all of the spatial data should be converted into the lowest of spatial resolution, so in this study all of the spatial data have been

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converted and standardized into spatial resolution for each cell 100 x 100 m. The second one is limitation of the computer specification to run and process the spatial database in the model simulation. Bandung as a study area , around of area is for about 329.510 Ha. This cell size was chosen to simplify in computing of the spatial database in the model processing.

Each cell in the database has unique coordinate system which describe in Cartesian coordinate. The same column in the lattice has the same of y-coordinates, the other way the same row has the same of x-coordinate. The attributes cell contains information of driving forces (Slope, Neighbourhood, distance from road and Urban Hierarchy Index), landcover priority and cell state in year –t (Urban existing, Urban allocation and constraint area).

Hybrid approach was implemented to integrate between tabular and spatial data as a Spatial Database Management System (SDBMS) in development of Urban Growth Spatial Distribution Model (UGSDM) in Bandung Area. In this study, UGSDM was developed to modelling and simulating spatial distribution of urban growth in Bandung area using integration of GIS and cellular automata methods.

Model calibration is the process of modifying the input parameters in an attempt to match field conditions within some acceptable criteria until the output from the model matches an observed set of data. In this study, two scenarios were applied to calibrate the model. In the first scenario, driving forces and constrained factors (slope > 15 %, protected forest, open green space and water body) as an input parameter in the model simulation. Whereas, the second scenario is the simulation model will be run without slope as an input parameter.

Meanwhile, Validation is the evaluation of the predicted modelling results. Simulation accuracy assessments were applied in model validation process. It can be defined as the task of comparing two maps: one generated by the model (data to be assessed), and the other based on the ground truth (the reference data). The reference data were assumed accurate and forms the standard for comparison. The error matrix approach and kappa analysis are methods in accuracy assessment that applied in this study.

In this research, Model simulation was running from 1991 – 2000 and 2000 – 2007 to calibrate and validate the model result. Khat Coefficient for first and Second

The result from urban development simulation in 2015 and 2030, shows that urban development in Bandung Area will continue in two ways: one is through the in-filling of areas within the currently existing urban in Cimahi and Bandung City, and another is through expanding toward the southeast of the areas.

Keywords: Geographic Information Systems, Cellular Automata, Urban Growth Spatial Distribution Model and Bandung Area.

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Copyright @2011, Bogor Agricultural University Copyright are protected by law,

1. It is prohibited to cite all of part of this thesis without referring to and mentioning the source:

a. Citation only permitted for the sake of education, research, scientific writing, report writing, critical writing or reviewing scientific problem b. Citation does not inflict the name and honor of Bogor Agricultural

University

2. It is prohibited to republish and reproduce all part of this thesis without written permission from Bogor Agricultural University

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MODELLING AND SIMULATING

SPATIAL DISTRIBUTION PATTERN OF URBAN

GROWTH USING INTEGRATION OF GIS AND

CELLULAR AUTOMATA

(Case Study in Bandung Area

–

West Java Province)

ERWIN HERMAWAN

A Thesis submitted for the Degree of Master of Science in Information Technology for Natural Resources Management Program Study

GRADUATE SCHOOL

BOGOR AGRICULTURAL UNIVERSITY

BOGOR

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Research Title : Modelling and Simulating Spatial Distribution Pattern of Urban Growth using Integration of GIS and Cellular Automata (Case study in Bandung Area – West Java Province)

Name : Erwin Hermawan Student ID : G051060011

Study Program : Master of Science in Information Technology for Natural Resources Management

Approved by, Advisory Board

Dr. Ir. Hartrisari Hardjomidjojo, DEA Supervisor

Idung Risdiyanto, S.Si, M.Sc Co-Supervisor

Endorsed by,

Program Coordinator

Dr. Ir. Hartrisari Hardjomidjojo, DEA

Dean of Graduate School

Dr. Ir. Dahrul Syah, M.Agr.Sc

Date of Examination : January 25, 2011

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ACKNOWLEDGEMENTS

First of all, I would like to express my highest gratefulness to Allah SWT, for His blessing, His will, His power, His help and His guidance to me during my study.

I wish to show my gratitude to my supervisor Dr. Ir. Hartrisari Hardjomodjojo, DEA. and my co-supervisor Idung Risdiyanto, S.Si, M.Sc. for their patience, advices, guidance, valuable inputs, time, encouragement and constructive criticism during the supervision of my research. and also I would like to thank to Dr. Ibnu Sofyan, M.Eng., as the external examiner of this thesis for his positives ideas and inputs.

I would like to thank to my kindly classmates MIT-NRM IPB batch 2006 for their friendliness, togetherness and best years in study time, and also to all MIT students generally. I thanks also MIT secretariat and staff for their cooperation and friendliness during study. Moreover, to all MIT lectures, I really appreciate for their sharing of valuable knowledge and experiences. Highest thanks is also addressed to my friend, Andri Susanto, S.Si to discuss about GIS and Remote sensing actually in Spatial Database Programming.

My deep appreciation is extended to my family members ; my father H. Mumuh Efendi, my Mother, Hj. A. Kurniasih and my sister Riska Rostikawati, SE., for being a source of motivation, encouragement, love, cares, patience and above all prayed for me during finishing my study in IPB.

Last but not least, my lovely “An-Nisa Larasati Budhiyono, ST.” thanks for your love, cares and support at the end of my research study.

This thesis is dedicated to my family, Bogor Agricultural University and my country Indonesia.

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CURRICULUM VITAE

Erwin Hermawan was born in Sumedang, West Java, Indonesia, on January 26th 1982. He spent his childhood in Sumedang until study in senior High School at SMU 1 Cimalaka, Sumedang in 2000. He achieved his undergraduate degree in 2005 from Bogor Agricultural University, Faculty of Mathematics and Natural Science, majoring in Meteorology.

In 2006, Erwin Hermawan pursued his Master Degree at Master of Science in Information Technology for Natural Resource Management (MSc in IT for NRM) Master Degree program of Bogor Agricultural University. His final project for thesis is entitled “ Modelling And Simulating Spatial Distribution Pattern of Urban Growth using Integration of GIS And Cellular Automata (Case Study in Bandung Area – West Java Province)”.

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Page

CONTENTS………. i

LIST OF TABLES……… iv

LIST OF FIGURES……….. v

LIST OF APPENDICES……….……….. vii

I. Introduction………...……… 1

1.1. Background……….. 2

1.2. Research Objective………...………… 2

1.2.1. Main Objectives………... 2

1.2.2. Specific Objectives………...……… 2

1.3. Scope, Limitation and Assumption of Research………..…… 3

1.4. Time and Study Area of Research……….……….…. 3

1.5. General of Research Methodology………..………. 4

1.6. Thesis Output………...……… 7

1.7. Thesis Structure………..…….. 7

II. Analysis of Markov Chain Change Detection and Driving Forces of Urban Growth Development in Bandung Area during 1991 – 2007……. 9

2.1. Introduction……….. 9

2.1.1. Background……….…. 9

2.1.2. Objective………..… 9

2.2. Literature Review ………...…. 9

2.2.1. Urban Growth and Urban Sprawl……….…… 9

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2.2.3. Markov Change Detection……… 13

2.2.4. Multi-criteria Evaluation for Built-up Area Suitability…… 14

2.3. Methodology……….. 15

2.3.1. Preparation and Processing of Necessary Data……… 15

2.3.2. Priority of Landcover conversion using Markov Change Detection……….. 16

2.3.3. Calculation of Driving Forces……….. 19

2.3.4. Multi-criteria Evaluation of Driving forces……….. 22

2.4. Result and Discussion……….……….……… 25

2.4.1. Data Preparation and Processing of Driving Forces ……... 25

2.4.2. Weighting Factors calculation of Driving Forces……….. 28

2.4.3. Change detection in Bandung Area………. 29

2.4.4. Transitional Matrix and Weighted Factors of Landcover Priority ……….…… 33

2.5. Conclusion………..………… 38

III. Development of Urban Growth Simulation Model Using Integration of GIS and Cellular Automata ……….……. 39

3.1. Introduction……….. 39

3.1.1. Background……….…. 39

3.1.2. Objective………..… 39

3.2. Literature Review ………...…. 40

3.2.1. The Concept of Cellular Automata……….. 40

3.2.2. CA in Urban Modelling ……….……….. 40

3.2.3. Integrating GIS and CA for Urban Dynamics Modelling… 43

3.2.4. Model Calibration and Validation ………...…… 44

3.3. Methodology……….... 46

3.3.1. Database and Model Development ……….……... 46

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iii 3.3.3. Population Projection ……….………. 50 3.3.4. Relation between Amount of Population and Built-up

Area ………. 50 3.3.5. Configuration of Model, Calibration and Validation ….… 51 3.4. Result and Discussion……….……….……… 52 3.4.1. Configuration of Model Structure and Element………...… 52 3.4.2. Population Projection and Land Demand for Built-up Area 55 3.4.3. Logical Framework and Program Flow ………….………. 57 3.4.4. Calibration and Validation Model using Two Scenarios … 60 3.4.5. Projection of Urban Growth Spatial Distribution Model… 64 3.4.6. Urban Land Carrying Capacity in Bandung Area………… 66 3.5. Conclusion………..………… 67 IV. General Conclusions and Recommendations

4.1. Conclusions………... 69 4.2. Recommendation……….. 70

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LIST OF TABLES

Page

2.1 Raster Data……….. 15

2.2 Vector Data………. 15

2.3 Non-Spatial Data……….……… 16

2.4 List of Software………..………. 16

2.5 Driving forces and Constraints Regulation for Urban Development….. 19

2.6 UHI Processing………..………. 21

2.7 Weighted for Driving Forces using Ranking Methods………..………. 28

2.8 Landcover Classification Type in study area……….………. 29

2.9 Landcover Change in Study Area during 1991 – 2007……...………… 32

2.10 Percentage of Landcover Change in Study Area during 1991 – 2007… 32 2.11 Landcover Change Transitional Matrix, 1991– 2000 (in hectares)….... 34

2.12 Landcover Change Transitional Matrix, 2000 – 2007 (in hectares)…... 35

2.13 Landcover Transitional Probabilities, 1991–2000………….…………. 36

2.14 Landcover Transitional Probabilities, 2000 – 2007……… 36

2.15 Probability Trend Conversion from each Landcover Type to Built-up Area during 1991 – 2007………. 37

2.16 Weighted for each Landcover type………...……….. 37

3.1 Definitions of Terms in an Error Matrix and Their Computations….… 45

3.2 List of Software………..………. 47

3.3 Data Input to Predict Land Demand for Settlement Area……..………. 55

3.4 Built-up Area,Population and Density in 1991, 1997, 2000 and 2007... 56

3.5 First Scenario Simulation Result………. 62

3.6 Second Scenario Simulation Result……….. 63

3.7 Urban Development base on suitability of Slope Range….……..…… 63

3.8 Khat Coefficient for Two Scenarios... 64

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LIST OF FIGURES

Page

1.1 Research of Study Area.……….……….……… 4

1.2 General Methodology of Research………..……… 6

2.1 Forms of Sprawl………..….. 11

2.2 Priority of Conversion using Markov Change Detection………...……. 18

2.3 Calculating of Road Network Effect……….….. 22

2.4 The Expected Relation between Slope and Urban Land………….…… 22

2.5 Slope Derivation from DEM………..……. 23

2.6 The Multi-criteria Evaluation Process……….……… 23

2.7 Neighbourhood Effect………...….. 25

2.8 Road Network effect………..……. 26

2.9 Slope Effect……….……… 26

2.10 Urban Hierarchy Index……….... 27

2.11 Constraints Area……….. 27

2.12 Landcover Type Classification in Bandung Area, 1991………. 30

2.13 Landcover Type Classification in Bandung Area, 1997………. 30

2.14 Landcover Type Classification in Bandung Area, 2000………. 31

2.15 Landcover Type Classification in Bandung Area, 2007………. 31

2.16 Landcover Type Areas in 1991, 1997, 2000 and 2007………... 33

3.1 Two dimensional Cellular Automata (Torrens,2000)………... 42

3.2 Database Schematic………. 46

3.3 Hybrid Approach to build GIS Application Software ………. 47

3.4 Schematic Diagram of the Proposed………... 49

3.5 Regression and Correlation Analysis to get Demand for Prediction Model…. 50

3.6 Model Calibration and Validation Process……….. 51

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vi 3.8 Lattice and Cells Attributes………...….. 54 3.9 Relation between The Increase of Population and Built-up Area……. 56 3.10 Logical Framework of Model Development between…………..……. 57 3.11 A flowchart Showing The Simulation of The Model……… 59 3.12 Urban Development in 1991 – 2000 using 1st Scenario….…………. 60 3.13 Urban Development in 2000 – 2007 using 1st Scenario……..……… 61 3.14 Urban Development in 1991 – 2000 using 2nd Scenario…….……… 61 3.15 Urban Development in 2000 – 2007 using 2nd Scenario…….……… 62 3.16 Urban Development Simulation in Bandung Area, 2015……… 65 3.17 Urban Development Simulation in Bandung Area, 2030……… 65 3.18 Urban Land Carrying Capacity in Bandung Area………...….. 67

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vii

LIST OF APPENDICES

Page

1 Performance of UGSDM Application.……….…………... 75 2 Source Code of UGSDM Application.……….…………... 78

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I. INTRODUCTION

1.1. Background

Urban development and the migration of the population from rural to urban areas are considered as significant global phenomena. This has attracted a lot of attention to the study of urban development under the theme of global environmental change (Liu, 2009). Through a better understanding of the mechanism of urban development process, the urban planner and manager could be reduce the risk of wrong or bad decisions and enhance the effectiveness of new plans. Various urban models have been built a model to get a better understanding of the urban expansion phenomena.

In order to be useful and realistic, urban models depend on real world data that can be integrated and mapped in a modelling scenario. Existing urban land uses and growth patterns, existing road network, location of various facilities, availability and infrastructure facilities are the example of the data can be used in urban modelling scenario. Geographic Information Systems (GIS) has emerged as a prime framework for the integration and management of a range of spatial real world data. However, to use GIS alone as modelling tool have been received with skepticism as it has limited modelling functionalities and has constraints in handling temporal data sets. Furthermore, modelling and simulations in the spatial domain extensively have done using the techniques of Cellular Automata (CA), although there are different approaches to model processes in space and time including the geostatistical approaches. Cellular Automata (CA) plays in important role in modelling and simulation of spatial-temporal processes.

Nevertheless, GIS and CA in combination can be used as a strong couple to model the urban growth to take advantages of both the techniques. In case of GIS, its spatial data analysis capacities may be insufficient to handle complex urban dynamics. The integration of the dynamic strength of CA with the effective spatial representation found in GIS thus may be beneficial to achieve realistic representation of a phenomenon such as urban growth (Wolff and Wu, 2004).

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2 In this research, a simulation model to study and prediction the spatial distribution pattern of urban growth in Bandung Area was developed by using integration of CA and GIS modelling. CA can be considered as analytical engine of GIS, which plays in important role in modelling and simulation of spatial-temporal processes. Another approach to integrate CA and GIS in this research are to develop a standalone CA program that can use data from GIS. Data interchange and compatibility can be achieved through file conversion protocols.

Bandung area including Bandung Municipality, Bandung Regency, Cimahi Municipality and West Bandung Regency were selected as study area in this research. Bandung is capital city of west java province and one of metropolitan city in Indonesia, which has a very density population in the city center. The new constructions mainly taken place in the fringe urban areas of Bandung area. In this research, urban growth driving forces as a factor influences in urban development was defined and translated into the model structure to regulate the Urban CA model. Some scenario defining CA rules was generated to calibrate and validate the simulation model. The best simulation scenario will be used and applied to simulating and predicting spatial distribution pattern of urban growth in Bandung area in the future years.

1.2. Research Objective

1.2.1.Main Objective

This research has a purpose to study and develop a model that can represent spatial distribution pattern of urban growth in Bandung Area. Integration of GIS and Cellular automata modelling was used to develop the urban growth simulation model.

1.2.2.Specific Objective

The specific objectives of this research are:

1) To define and analyze driving forces of urban growth and landcover priority conversion using markov chain change detection in Bandung Area during 1991 - 2007.

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3 2) To develop model capable of representing spatial distribution pattern of urban growth in Bandung area using integration of GIS and Cellular Automata Modelling.

3) To generate alternative scenario was implemented in urban CA simulation model.

1.3. Scope, Limitation and Assumption of Research

Scope, limitation and assumption of this research are:

1) Study area as a boundary of system in this research is Bandung area, including : Bandung Municipality, Bandung Regency, Cimahi Municipality and West Bandung Regency

2) Integration of GIS and Cellular Automata was applied to achieve the goal from this research.

3) In the realiality of urban development, natural hazard can be added as a constrain factor. In this research natural hazard didn’t included in modeling of urban growth. Slope effect can be assumed as a factor related to natural hazard assessment.

1.4. Time and Study Area of Research

This research was conducted from August 2009 until December 2010, including data preparation, data processing and make a final report in MIT Laboratory, Bogor -

West Java. Meanwhile, data collecting was taken in Bandung – West java.

The study area is Bandung Area which located in region of West Java Province.

Recently, Bandung Area can be divided into four regions, there are Bandung

Municipality, Bandung Regency, Cimahi Municipality and West Bandung Regency as shown in Figure 1.1. Geographically, it is located between 107022’– 1080,53’ east longitude and 60,41’– 70,19’ east longitude.

Topographically, The highland point area in the north is 1.050 m and the lowland in the south, which elevation 675 m above sea level. The land in South Bandung until the line of grade crossing. The land is relatively flat, meanwhile the north part is mountainous where one could see Bandung shape and gaze at the whole city panorama. Geological condition and soil in Bandung city area have alluvial soil layer from. Tangkuban Perahu

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4 mount explosion. The kind of materials in the north generally andosol in the south and east consist of greey alluvial with clay sediment while in central and west scattered andosol.

Figure 1.1 Research of Study Area.

The climate of Bandung mostly influenced by the mountains, so that the weather is humid and cool. Nowadays, a change to weather occurs. The temperature average extremely rose, which in October 2009 the temperature reach out 31, 6 0C. That phenomena mainly caused by air pollution from motor cycle and global heat. Even that so, the average of rainfall frequency is still high.

1.5. General of Research Methodology

General of research methodology for Modelling Spatial Distribution Pattern of Urban Growth using Cellular Automata in Bandung area, can be seen in Figure 1.2. Preparation of the data into model input consists of spatial and non-spatial

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5 data. Spatial data can be divided into raster and vector data. Topographical maps (Rupa Bumi Indonesia) which covering Bandung area was used as vector data, including layers of Administrative boundary, landuse and Road Network. Meanwhile, satellite imagery including SRTM-DEM, SPOT 5 and Landsat TM imagery were used as a raster data in this research. Statistical data, including time series of population and village potential statistics of Bandung area in 1991, 1997, 2000 and 2007 was used as non-spatial data to predict population projection.

Data processing using various statistics methods and spatial analysis technique in GIS have been applied to derive the data input into the development model. On the other hand, landcover type classification was get from references map and derive from satellite imagery in 1991, 1997, 2000 and 2007. Markov chain change detection was applied to analyze trend of change detection during the periode 1991 – 2007 in Bandung area. The result from this analysis is trend of landcover priority conversion into built-up area. Furthermore, land demand for up area was derived from relation between population projection and built-up area in the same years.

Driving forces and constraints, which are influence for urban development was derived based on regulation from study literature. Standardization value for each driving forces and constrain into 0 – 1, and then Multicriteria evaluation as a transition rule was applied to get weighted from each driving forces and there

contributes for urban development. Landcover priority that’s means priority for

each type of landcover conversion into built up area was combine with potential value which derive from driving forces and constraints.

Furthermore, land demand for built-up area was related into ranking of potential value. In this step, the model will be indicated that the number of the cells who have higher potential values will be translated into built-up area development base on amount of land demand requirement for built-up area in year - t+n. Model calibration and validation using some scenario was applied to calculate simulation accuracies of the urban growth spatial distribution model. The best scenario from the result will be applied in model simulation and can be used to predict spatial distribution of urban growth simulation in Bandung Area.

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6 Driving Factors

 Neighbourhood Effect  Road Network  Urban Hierarchy Index  Slope 0 – 15 %

Constraints Factors

 Slope > 15 %

 Forest Protected, Green Space Area, Water Body

Standardization Factors Landcover

Classification in 1991, 1997, 2000 and 2007 from Satellite Imagery

Markov Chain Change Detection Population

Data in 1991, 1997, 2000 and

2007

Built-up Area in 1991, 1997, 2001

and 2007

Projection Population in year t+n

Land Demand of Built-up Area in Year t+n Relation between

Population and Built-up Area

Weighted using Ranking Methods

Multicriteria Evaluation to get Potential Cell

Transition Potential Value

Ranking of Potential Value

Allocate Potential Value into Built-up Area in Year t+n

Conversion priorities of each landcover type into

Built-up area

Calibration & Validation Model with Actual Built Up Area in

year t+n

Future Spatial Distribution of Urban Growth Simulation in Bandung Area Yes

No Reference of

Landuse landcover map in 2000 and

2007

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7 1.6. Thesis Output

Several output of this research are:

1) A model simulation to support decision related to projection population and to know spatial distribution pattern of urban growth in Bandung area. 2) Alternative maps based on scenario created to predict spatial distribution of

built-up area.

1.7. Thesis Structure

The thesis was organized into three chapters. The first chapter describes introduction chapter includes: (1) Background, (2) Research objective (3) Scope . limitation and assumption of research (4) Time and study area of research, (5) General of Research Methodology, (6) Thesis Output and (7) Structure of Thesis document.

Generally, the result and analyze from this research activity are described in chapter 2 and chapter 3. In the second chapter, analysis of markov chain change detection and driving forces of urban growth in Bandung area was analyzed and discussed. Data processing using various statistics methods and spatial analysis technique in GIS have been applied to derive the data input into the development

model. Population projection and it’s relation with demand of built-up area, multicriteria evaluation of driving forces and determine of landuse conversion priority using markov change detection were applied in this data processing and was used as an information and data input in system configuration and development of urban growth simulation model.

The Third chapter describes development of urban growth simulation model, calibration and validation of the model. In this research, two scenarios were applied in urban growth cellular automata simulation model to calibrate the model. Validation using accuracy assessments with error matrix and kappa coefficient were applied to validate the best of scenario result. Finally, general conclusion and recommendation are drawn in fourth chapter as last chapter of this research.

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II. ANALYSIS OF MARKOV CHANGE DETECTION AND

DRIVING FORCES OF URBAN GROWTH

DEVELOPMENT IN BANDUNG AREA

DURING 1991 - 2007

2.1. Introduction

2.1.1.Background

Driving forces and constrains are the keys factors in the urban growth development context. Driving forces can be defined as causes or factors influence for urban growth modeling. In this research, slope, road network, Neighbourhood effect and Urban Hierarchy Index were selected as a driving forces input in modeling of urban growth development in Bandung Area. Whereas, protected forest, slope greater than 15% , open green space and water body were selected as a constraints factor in this study area.

Furthermore, trend of priority conversion between each landcover type into built-up area during 1991 – 2007 in Bandung area was analyzed using markov chain change detection technique. The analysis result from this processing were used as an information and data input in system configuration and development of urban growth simulation model.

2.1.2.Objective

The objectives of this study is to define and analyze driving forces of urban growth and landcover priority conversion using markov chain change detection in Bandung Area during 1991 - 2007.

2.2. Literature Review

2.2.1.Urban Growth and Urban Sprawl

Urban growth indicates a transformation of the vacant land or natural environment to the construction of urban fabrics including residential, industrial and infrastructure development, it mostly happened in the fringe urban areas. Urban expansions are driven by population growth, social economic development.

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10 (Shenghe and Sylvia, 2002). It has always been the focus of the social economic debates due to its impacts on social economic aspects as well as the impacts on the huge consumption of cultivated lands and damage to the environmental sustainability.

Urban sprawl, also known as suburban sprawl, is the spreading of a city and its suburbs over rural land at the fringe of an urban area. Sprawl generally infers to the increase in built-up and paved area with impacts such as loss of agricultural land, open space, and ecologically sensitive habitats. Also, sometimes sprawl is equated with growth of town or city (radial spread). In simpler words, as population increases in an area or a city, the boundary of the city expands to accommodate the growth; this expansion is considered as sprawl. Sprawl also takes place on the urban fringe or the peri-urban region, at the edge of an urban area or along the highways (Sudhira et al., 2004a).

Sprawl development consists of three basic spatial forms: low-density (radial) sprawl, ribbon and leapfrog development (Barnes et al., 2001). Radial sprawl is the consumptive use of land for urban purposes along the margins of existing areas. This type of sprawl was supported by piece meal extensions of basic urban infrastructures such as water, sewer, power, and roads. Ribbon sprawl is development that follows major transportation corridors outward from urban cores. In this case, lands adjacent to corridors are developed, but those without

direct access remain in rural uses/covers. Over time these nearby “raw” lands

maybe be converted to urban uses as land values increase and infrastructure is extended perpendicularly from the major roads and lines. The leapfrog development is a discontinuous pattern of urbanization, with patches of developed lands that are widely separated from each other and from the boundaries, albeit blurred in cases, of recognized urbanized areas (Barnes et al., 2001). This form of development is the most costly with respect to providing urban services such as water and sewerage. Figure 2.1 shows the different forms of sprawl.

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11 Radial Sprawl Ribbon Sprawl Leapfrog Sprawl

Figure 2.1 Form of Sprawl

In industrialized countries the future growth of urban populations will be comparatively modest since their population growth rates are low and over 80% of their population already live in urban areas. Conversely, developing countries are in the middle of the transition process, when growth rates are highest. The exceptional growth of many urban agglomerations in many developing countries is the result of a threefold structural change process: the transition away from agricultural employment, high overall population growth, and increasing urbanization rates (Grubler, 1994).

Mapping and quantification of urban sprawl provides a picture of location of sprawl, type and patterns of sprawl, which helps to identify the environmental and natural resources threatened by such sprawls. Analysing the sprawl over a period of time will help in understanding the nature and growth of this phenomenon and thereby visualizing the likely scenarios of future sprawl (Sudhira et al., 2004a). 2.2.2.Urban Growth Driving Forces and Constraints

Urban growth driving forces can be simply defined as causes or factors influence for urban growth modeling and in the opposite, whereas constraints are limitations imposed by nature or by human beings that do not permit certain action to be taken (Keeney, 1980). Definition for each driving forces and constraints for urban development in this study was explained below.

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12 1) Driving forces

a) Neighbourhood effect

Urban form itself has a very heavy influence on the urban development. At a small scale, a city tends to grow in the areas nearby the developed urban area. Neighborhood (the total number of developed cells), in another word, the transition from non-urban to urban was much affected by the adjacent area. If the vicinity of a cell is developed area, this cell has much higher potential to transit to urban form if it was non-urban before.

b) Road network

Road network have proved to be the key factor in urban evolution process. It heavily impacts the urban form and land price and even the location of city center and sub-center. While the relationship between road network and urban form is far from clear. Closer the distances from the city centre and the roads, the land has higher probability of becoming developed.

c) Urban Hierarchy Index

An urban hierarchy categorizes centers according to their importance in terms of the functions that they provide. Functional importance is reflected in the range and amount of services and facilities. There is usually (but not always) related to the size of the urban population. Scalogram or Gultman Scaling is one of methods to ranks cities and municipalities in a region by their functional complexity based on the number and types of facilities that are located within them (Rustandi, 2008). The uses of the scalogram are to:

 Categorize settlements into levels of functional complexity,  Determine the types and diversity of services and facilities,

 Indicate the sequence in which settlements tend to accumulate functions,

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13  Show the degree of access that people have to services and

facilities, and

 Assist in deciding appropriate investment for settlements on a hierarchical basis.

d) Slope effect

Ordinarily, built-up area will grow where there are flatten areas. The first assumption of this method is that some areas are not developed because they are too steep. The second assumption is that increasing slope implies a higher building cost. With this assumption follow, that there should be a lower percentage of cells within higher slope intervals, compared to lower slope intervals. In this case, slope will constrain the urban form mainly depends on the slope.

2) Constrained factors:

Constraints are limitations imposed by nature or by human beings that do not permit certain action to be taken (Keeney, 1980). The specification of constraint is typically based on available resources and regulations and involves value or professional judgment.

The constrained factors indicate those limitations posed on the urban devel-opment such as water body is not allowed to develop into urban use, high slope terrain is not suitable for urban use, reserved area and public green area is not allowed to transfer into urban, etc.

2.2.3.Markov Changes Detection

Landuse and landcover changes are the result of the complex interaction between human and biophysical driving forces that act over wide range of temporal and spatial scale. Change detection is a task to compare two sets of imagery to identify changes of Landuse and landcover. The use of satellite data enables change detection to be done over broad spatial scales. Markov chains have been used to model changes in land use and land cover at a variety of spatial scales. This type of change detection technique is one application of change detection that can be used to predict future changes based on the rates of past change (Wijanarto, 2007).

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14 Land use studies using Markov chain models tend to focus on a much larger spatial scale, and involve both urban and non-urban covers (Drewett, 1969; Bourne, 1971; Bell, 1974; Bell and Hinojosa, 1977; Robinson, 1978; Jahan, 1986; Muller and Middleton, 1994). The method is based on probability that a given piece of land will change from one mutually exclusive state to another (Aavikson 1995). These probabilities are generated from past changes and then applied to predict future change. Brown et al. (2000) recently presented an approach to estimating transition probabilities between two binary images in a study of land use and land cover relationship in the Upper Midwest, USA. This approach, according to Huber (2001), needs to be improved and generalized in order to estimate properly Markov transitions from a pair of images.

The use of satellite imagery would create an opportunity for improved analysis. Moreover, the Markov models have been mostly employed for studies around a city or a slightly larger area, with a regional concentration in North America. The application of stochastic models to simulate dynamic systems such as land use and land cover changes in a developing nation is rare. Clearly, much work needs to be done in order to develop an operational procedure that integrates the techniques of satellite remote sensing, GIS, and Markov modelling for monitoring and modelling land use and land cover changes.

2.2.4.Multi-criteria Evaluation for Built-up Area Suitability.

White et al., (1997) suggest the utility of assigning suitability of any cell for being allocated into built-up in the CA transitional rules. This concept has also been subsequently applied in various other CA models (Singh, 2003; Sun, 2003). Multi-criteria evaluation (MCE) is a method for combining many layers or Multi-criteria. A criterion can be a factor or a constraint (Eastman, 1999). On the similar lines, the multi-criteria evaluation was carried out for evaluating suitability of land cover to be allocated into built-up. The availability of suitable land is another prime factor for urban sprawl. The chief land use / land cover classification carried out for the satellite imagery includes, already developed (built-up) area, water bodies (sea, rivers, streams, tanks, lakes, etc), area under vegetation (agriculture, plantation, paddy, etc) and area under open land (barren, uncultivated, unused open land). The themes of

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15 already developed areas and water bodies act as constraints for any further development. However, the probability of open land, barren land and uncultivated land for future built-up is very high. Similarly, the possibility of area under vegetation to be converted into built-up as a result of sprawl cannot be ruled out.

2.3. Methodology

2.3.1.Preparation and Processing of Necessary Data

Several spatial and non-spatial data were prepared and used to defined and analyze driving forces of urban growth and landcover priority conversion using markov chain change detection in Bandung Area during 1991 – 2007. Spatial data consist of raster and vector data can be listed in Table 2.1 and 2.2. Meanwhile, non -spatial data requirement can be seen in Table 2.3.

Table 2.1 Raster Data

No Data Coverage Aquitition Date Spatial Resolution

Source of The Data 1. Landsat 5

Path/Row : 121/065 & 122/065

 July, 5th and 28th 1991  Oct,15th and

22th 1997

30 m LAPAN 1)

2. SPOT 5 KKK/JJJ : 285/364 & 287/364

July, 17th 2007 20 m LAPAN 1)

3. DEM SRTM_f03_ s008e112

2000 90 m SRTM 2)

1) National Institute of Aeronautics and Space (Lembaga Penerbangan dan Antariksa Nasional) 2) Shuttle Radar Topographic Mission (SRTM). Can be free downloaded from the internet at

ftp://ftp.glcf.umiacs.umd.edu/glcf/landsat/WRS2/.

Table 2.2 Vector Data

No Data Type Date

Production Scale Source of The Data 1. Topographical

Map (Rupa Bumi Indonesia)

2000 1 : 25.0000

BAKOSURTANAL1)

2. Landuse Map 2000 & 2007 1 : 50.0000

BPN Bandung2)

1) National Coordinator for Survey and Mapping Agency ( Badan Koordinasi Survei dan Pemetaan Nasional)

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16 Table 2.3 Non-Spatial Data

Data Type Date Production Source of Data Population Data 1991 - 2008 BPS1) Village potential statistics (Potensi

Desa) 1991 and 2000 BPS

1) 1) Central Statistics Agency (Badan Pusat Statistik)

Several software were used to process the necessary data consist of Microsoft Office, GIS and Remote Sensing application software. The following application software can be seen in Table 2.4.

Table 2.4 List of Software Application

No Software Type Function

1. Arc GIS desktop 10 GIS Application Processing, Analyzing, Viewing and Manipulating Spatial Data.

2. Er-Mapper 7.1 Remote Sensing Application

Processing, Extracting & Analyzing Satellite Imagery Data.

3. Microsoft Excel 2007

Spreadsheet Application

Statistic & Tabular data analyzing.

2.3.2.Priority of Landcover Conversion using Markov Change Detection.

Markov Chain change detection is one application of change detection that can be used to predict future changes based on the rates of past change, based on probability that a given piece of land will change from one mutually exclusive state to another.

Markov chain models have several assumptions (Parzen, 1962; Haan, 1977; Wang, 1986; Stewart, 1994). One basic assumption is to regard land use and land cover change as a stochastic process, and different categories are the states of a

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17 of the process at time t, Xt, depends only on its value at time t-1, Xt-1,and not on

the sequence of values Xt-2, Xt-3,...,X0 that the process passed through in arriving at

Xt-1.It can be expressed as:









0 0 1 1 1

| , ,..., |

t j t i

P X a X a X a X a P X a X a

 

   

  

(1.1) Moreover, it is convenient to regard the change process as one which is discrete in time (t = 0,1,2,...). The P X



_t a_j|X_t_₁ a_i



, known as the one-step transitional probability, gives the probability that the process makes the transition from state ai to state aj in one time period. When steps are needed to implement this transition, the P X



_t a_j |X_t_₁a_i



is then called the step transition probability, Pij( ). If the

( )

P is independent of times and dependent only upon states ai, aj and , then the Markov chain is said to be homogeneous. The

treatment of Markov chains in this study will be limited to ﬁrst order

homogeneous Markov chains. In this event:

P X



t aj|Xt1ai



Pij

(1.2) Where Pij can be estimated from observed data by tabulating the number of

times the observed data went from state i to j, nij, and by summing the number of

times that state ai occurred, ni, then

_ij ij i n P n  (1.3)

As the Markov chain advances in time, the probability of being in state j

after a sufﬁciently large number of steps becomes independent of the initial state of the chain. When this situation occurs, the chain is said to have reached a steady state. Then the limit probability, Pj, is used to determine the value of Pij( ):

( )

lim _ijn _j

n P P _(1.4)

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( )

1, 2,..., ( ) 1 0

j i ij

i j

P PP j m state P P

 

 

In this research, markov chain change detection used to get landcover type priorities conversion to urban build up area based on matrix probabilities. The calculation process can be explained in Figure 2.2.

Markov Chain change detection in year t and t+1

Transition Area & Transitional Probability Matrix

Trend Prediction

Conversion Priority of Landcover type into Built up Area Image Classification

Result from Satellite Imagery in1991, 1997,

2000 and 2007

Figure 2.2 Priority of Conversion using Markov Change Detection

In the first step image classification processed using visual interpretation methods was done. The result from this processed can be divided into seven class type of landcover, there are ; built-up area, forest, paddy field, plantation, agricultural, shrub and water body.

Second step is to create a transition matrix of pixels in each class for two times. This is the same as the cross tabulation matrix, which can be used for accuracy assessment. From this step, trend prediction will be calculated using matrix probabilities. Landcover priorities scenarios can be derived from transitional probabilities of landcover type in year t that changed into built-up area in year t+1. Afterwards, the value of probabilities has been ranked from higher to lower probabilities. This step was done to get decision of landcover type priorities.

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19 2.3.3.Calculation of Driving Forces

Data collection and regulation of driving forces and constraints for urban development was get from official agency. The regulation and source of the data was given in Table 2.5.

Table 2.5 Driving forces and constraints regulation for urban development Criteria Data type Regulation Source of the data

Driving Forces

Road Network1)

Closer the distances

from the roads Bakosurtanal, 2000 Urban

Hierarchy Index 1)

Based on the number and types of facilities that are located within the region

Village potential statistics & Statistical year book for Bandung area in 1991 and 200.

Slope 1)

Slope should be a lower than 15 %

DEM from SRTM Imagery with spatial resolution 90 m.

Neighbour hood1)

Adjacent area to the actual developed built-up area

Derived from Actual built-up area in 1991 and 2000

Constraints

Protected Forest2)

Not located in Protected forest and Water Body area

City's Development Planning Board (Bappeda, 2000)

Water body, Open Green space 1)

Not located in Protected forest and Water Body area

Classified from Landsat TM 1991 and 1997, and references map from Bakosurtanal, 2000 and BPN,2007.

Slope 1) Slope > 15 %

DEM from SRTM Imagery with spatial resolution 90 m.

1) According to ministry of public works SNI 03-1733-2004 about codes of housing design in urban areas.

2) According to KEPPRES No.32 year’s 1991 about protected area.

1) Neighbourhood Effect

Urban development usually takes place first in the adjacent area to the developed urban area. That is where CA model can make sense. Considering only the neighbourhood function, the potential value can be defined as:

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20 N fx y_, ( ,q Num) (1.5) Where f (q,Num) is a function of the neighbourhood for cell in location (x,y), q

is the developed cells in the neighbourhood and N is the number of the neighbours. The simplest way to define this is to use the q as the amount of developed cells in the neighbourhood and use Num as the total number of its adjacent cell in its neighbourhood. In this sense, f is define as

, /

x y

f q Num (1.6) Where q is the number of developed cells in the neighbourhood in this case existing urban cell and Num is the total cell number of the neighbourhood. In this research, 3 x 3 moore neighbourhood was applied, so equation 2.6 can be expressed as :









3 3 ,

3 3 1 ij x y

con S urban f     



(1.7)

Simple IF…THEN rules are used to drive the simulation  IF the tested cell is urban, THEN keep it urban

 IF the tested cell is non-urban THEN equation 2.7 will be applied  IF the tested cell is constraint, THEN keep it no change.

2) Urban Hierarchy Index (UHI)

Scalogram method was used to calculate UHI. The equation used is :

n k k k k k n HI F a    _  _  



(1.8)

Where : k

HI : Hierarchy index in sub district - k

F : Number of Availabele types in district - k

a : Number of Availabele types in district - k

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21 Village potential statistics data in 1991 and 2000 was used to calculate the UHI, the process was shown in Table 2.6.

Table 2.6 UHI Processing

No District Sub

district

Popula tion

Facilities Total

Facilities _Hierarchy

Indeks

F1 F2 F3 ...Fk...

n k k



1 A A1 ... .... .... .... ...

2 A A2 ... .... .... .... ...

3 B B1 ... .... .... .... ...

- ... ... ... .... . .... . .... . ...

i i i ... .... .... .... ...

Number of types a1 a2 a3 ...ak...

Number of facilities n1 n2 n3 ...nk...

Weighted 1

1 n a 2 2 n a 3 3 n a k k n a

3) Road network effect

The road network effect is usually calculated in terms of accessibility. In the case of the exponential decay function, the equation used is:

1 ij n d i j j

T

W e

 





_(1.9)

Where Ti is the accessibility of cell i, W is the weight for road class j, dtjis the distance from cell i to the nearest road class j, β is the exponent. The common concept on accessibility is that the effect of road, which influences the location choosing process for residential area, is a function of power exponential decay. Weighted calculation in equation 2.9 was calculated by overlaying between urban area and the result from reclassification process at a specific distance from road network. In this case, buffering proses was assumed at 3 km from road network. Figure 2.3 show this process.

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22 Road

Network Buffering 3 Km Intervals

Landcover Type

Built-up Area

Relation between Distance of Road Network Buffering into Built-up area

Weighted Factors Eksponensial Decay Function

Effect of RoadNetwork Distance into Built-up area Figure 2.3 Calculating of Road Network Effect

4) Slope Effect

In this study, regulation are being used for slope criteria base on ministry of public works SNI 03-1733-2004 about codec of housing design in urban areas, should be less than 15 % for urban developmnet.

Figure 2.4 The expected relation between slope and urban land

Slope derivation from DEM and how to get constraint area from slope processing, explained in a diagram in Figure 2.5.

0 _{Slope (%)} 15

elo

tt _{Maximum Slope}

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DEM From SRTM Imagery

Fill DEM

Derive Slope

Slope 0 – 15 %

Input as Driving Factors in MCE

Constraint Area

Yes

Figure 2.5 Slope derivation from DEM

2.3.4.Multi-criteria Evaluation of Driving Forces.

Multi-criteria evaluation (MCE) was used to evaluate the driving forces by combining or overlaying each criterion. In this research, slope, road network effect, hierarchy index and neighbourhood effect as criteria of driving forces. The first step is standardized each criterion score into range 0 – 1. Figure 2.6 show this processes.

Driving Factors - Neighbourhood Effect - Slope Effect

- Distance From Road Network - Urban Hierarchy Index

Liner Trasformation Scale into 0 -1

Standarized Value for each Driving Factors Development of

Built-up Area 1991 - 200

Mean Value for each driving factors related to Built-up area development 1991 - 2000

Weighted calculation using Ranking Methods

Weighted Factors For each driving Factors

Allocation of Potensial Cell Suitability for Built-up Area Area using MCE

Overlay

Built-up Area 1991

Built-up Area 2000

Erase

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24 The linier scale transformation methods was used to get standardized score from each criteria. The most often used procedure in this methods here are maximum and minimum score and the score range procedure (Voogd 1983; Massam 1988). Equation 2.10, was applied to get standardized criterion score.

max max min j ij ij j j

x







(1.10)

Where :

x : Standardized score for theobject-i andattribute - j

x : Raw score

max

x : Maximum score for the jth attribute min

x : Minimum score for the jth attribute

The seconds step is overlaying or combining each criterion, after the linier scale transformation was applied. The last process in this step is weighted calculation. Ranking methods will be used to create the importance weighted from each criterion. In the ranking methods, rank sum is one of popular approach (Stillwell et al, 1981). This method will be used to calculate the weighting for each criterion and can be formulated as:

_

_

1

j i k

n r

w

n r

 



 



(1.11)

Where w_i is the normalized weight for the jth criterion, n is the number of criteria under consideration (k = 1,2,………,n), and r_jis the rank position of the criterion. Each criterion is weighted (n r _k 1) and then normalized by the sum of all weights, that is





n r _k 1



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25 2.4. Result and Discussion

2.4.1.Data Preparation and Processing of Driving Forces

Neighbourhood effect, distance to road, UHI and slope with the value less than 15% were used as a driving forces for urban growth development. Neighbourhood effect was simulated from actual urban in 1991 to get neighbourhood value in year 2000. Village potential statistics data in 1991 and 2000 was calculated using scalogram methods to get value of UHI. Distance from road was calculated using exponential decay function, whereas slope less than 15% was derived from DEM SRTM with spatial resolution 90 M. Constraint area in the study area including: protected forest, slope greater than 15% , open green space and water body. All of the data has been converted into raster data with spatial resolution 100 x 100 m, and then converted again to point vector data. The linier scale transformation method was applied to get standardized score from each criteria. The result from this calculation are all of the data value will be range from 0 – 1 which 1 is maximum value. Figure 2.7 until 2.10 was shown spatial distribution of driving forces. Whereas, Figure 2.11 was shown constraints area in study area.

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26 Figure 2.8 Road Network Effect

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27 Figure 2.10 UHI Effect

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28 2.4.2.Weighting Factors Calculation of Driving Forces

Map overlay technique using intersection between developments of built-up area in 1991- 2000 and driving forces (neighbourhood effect, UHI, Slope and Distance from Road) were used to get average value for each driving forces. Ranking method was applied to calculate of weighted for each driving forces. Table 2.7 show the result and calculation process to get weighted for each driving forces.

Based on the result of weighted calculation in Table 2.7, neighbourhood effect is the most important influence factors in built-up area development during 1991 – 2000 in study area. The weighted was contributing 48% for urban development. This condition was agreed with the theory, urban development usually takes place first in the adjacent area to the developed built-up area. The others important influences factors in built-up area development process are distance from road, UHI and slope. Closer the distances from the roads having an effect to increase new development of up area. This condition was contributing about 24% weighted influence in built-up area development during 1991 - 2000.

Table 2.7 Weighted for Driving Forces using Ranking methods

Driving forces Average

Rank Sum Straight

Rank

Reciprocal Weight

(1/rj)

Normalized Weight Neighbourhood Effect 0.786 1 1.000 0.48

Urban Hierarchy Index 0.591 4 0.250 0.12 Slope 0.638 ₃ _0.333 _0.16 Road Network Effect 0.705 2 0.500 0.24

Total 2.083 1

Ordinarily, built-up area will grow where there are flatten areas. In this study, slope effect was contributing 16 % weighted influence in built-up area development. Amount of services facilities and number of public facilities in a region is once factor that affected in build-up area development. Scalogram method was applied to derive urban hierarchy index based on amount of services and number of public facilities in a region. The result from urban hierarchy index analysis contributes for about 12 % in urban development during 1991 – 2000.

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29 2.4.3.Change Detection in Bandung Area

Landuse/cover information is fundamental for understanding the spatial and temporal dynamics of urban development, which is the basis for urban development planning and sustainable land management. The landuse/cover cover maps for 1991 and 1997 derived from Landsat TM images whereas Landuse/cover in 2000 and 2007 used as reference map, respectively get from Indonesia Topographical Map (Bakosurtanal) and Landuse Map – (Badan Pertanahan Nasional). Spot 5 Image in 2007 were used also to derive landuse/cover classification and comparing the result with Landuse Map from BPN in 2007.

The selection of a landuse/cover classification scheme depends on various factors such as the collected data, the local planning system and the purpose of the application. In this study, seven kinds of landuse type are formulated derived from available data, there are : Built-up area, forest, Paddy Field, Plantation, Agricultural, Shrub and water Body. Table 2.8 was shown landcover classification type in study area and association with type of landuse.

Table 2.8 Landcover Classification type in study area

No Type of Landcover Description

1. Built-up area Residential, Commercial and Services, Industries 2. Agriculture Land Cropland, un-irrigated agriculture field

3. Paddy Field Paddy field with irrigation and non-irrigation 4. Plantation Perennial, annual and seasonal Plant

5. Forest Protection forest, Production Forest 6. Water Body Stream, Reservoir

7. Shrub Shrub,groves,barren land

Visual interpretation method was used in this study to derive landcover classification type. This method was applied cause provides a comparable interpretation of sequential images when consistent interpretation criteria. Image understanding and pattern recognition from satellite imagery can be solved and more clearly when image interpretation processing was done to derive landcover type classification. The result of landcover classification in Bandung Area in 1991, 1997, 2000 and 2007 displayed in Figures 2.12 – 2.15 respectively.

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30 Figure 2.12 Landcover Classification in Bandung Area, 1991

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31 Figure 2.14 Landcover Classification in Bandung Area, 2000

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32 Generally, based on Figure 2.12 – 2.15, urban built-up area has been concentrated in Bandung city and spreading over the time to the urban fringe of the city. Sprawl development type of built-up area in this study area is radial sprawl. In this case, the consumptive use of land for built-up area purposes along the margins of Bandung city as a capital city of Bandung area. This type of sprawl also supported by topographic structure as a flat area and in social economy, functional complexity based on the number and types of services and public facilities are located in this area.

Table 2.9 Landcover Change in Study Area during 1991 - 2007 No Landcover Area (Ha)

1991 1997 2000 2007 1 Agricultural 75.487 76.697 78.577 79.211 2 Build Up Area 33.382 35.243 36.861 38.437 3 Forest 53.067 50.305 48.270 47.418 4 Paddy Field 85.230 83.557 82.354 79.955 5 Plantation 42.976 43.748 43.704 44.279 6 Shrub 32.511 33.092 32.857 33.300 7 Water Body 6.848 6.859 6.878 6.901 Total Area 329.501 329.501 329.501 329.501

Table 2.10 Percentage of Landcover Change in Study Area during 1991 - 2007 No Landcover Percentage

1991 1997 2000 2007 1 Agricultural 22,91 23,28 23,85 24,04 2 Build Up Area 10,13 10,70 11,19 11,67 3 Forest 16,11 15,27 14,65 14,39 4 Paddy Field 25,87 25,36 24,99 24,27 5 Plantation 13,04 13,28 13,26 13,44 6 Shrub 9,87 10,04 9,97 10,11 7 Water Body 2,08 2,08 2,09 2,09 Total Area 100 100 100

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33 Figure 2.16 Landcover Type Area in 1991, 1997, 2000 and 2007

Based on Table 2.9 and 2.10, and also Figure 2.16, Landcover has changed significantly over the period from 1991 to 2007 in Bandung area. From 1991 to 2000, although paddy field and forest decreased by only 9.04 % and 3.37 %, the decrease quantity amounts to 2.762 ha and 1.673 ha, respectively. In contrast, built-up area was increased by 5.57 %. These trends continued in the period from 2000 to 2007. The paddy field decreased by 2.91 %, the speed is a little higher than that of last period. The trend of forested change is reversed, increasing by 1.77 %, from 2000 to 2007. The change trend of built-up area is relatively slow, although an increase trend is kept.

2.4.4.Transitional Matrix and Weighted Factor of Landcover Priority

Transitional matrix was used to identifying trend changes between each landcover for two time periods. This is basically the same as the cross tabulation matrix, which can be used for accuracy assessment.

The result from Cross tabulation matrix analysis describe in Table 2.7 and 2.8 , shows the land use and land cover change matrix from 1991 to 2000. From the table, it is clear that there has been a considerable change during the 9-year period. Built up area have increased in area (by 4.28 %) and in the other hand, Forest and Paddy Field has decreased in area (by 1.79 % and 2.89 % respectively).

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swap4 = IDXY(index)

nneighbourhood(index) = nneighbourhood(index + gap) idx(index) = idx(index + gap)

idy(index) = idy(index + gap) IDXY(index) = IDXY(index + gap) nneighbourhood(index + gap) = swap1 idx(index + gap) = swap2

idy(index + gap) = swap3 IDXY(index + gap) = swap4 doneflag = 0

End If Next

Loop Until doneflag = 1 gap = Int(gap / 2) Loop

End Sub

'Sorting Data Base on Field ID Public Sub ShellSort2()

Dim gap As Long, doneflag As Integer

Dim index As Long, swap1 As Long, swap2 As Long, swap3 As Long, swap4 As Single, swap5 As Long

FrmProcess.Caption = "Sorting Data Base on Potensial Value in Year's-" & namafile12 index = 0

gap = Int(UBound(IDXY) / 2) Do While gap >= 1

doneflag = 1

For index = 1 To UBound(IDXY) - gap If IDXY(index) > IDXY(index + gap) Then swap1 = IDXY(index)

swap2 = idx(index) swap3 = idy(index)

swap4 = nneighbourhood(index) IDXY(index) = IDXY(index + gap) idx(index) = idx(index + gap) idy(index) = idy(index + gap)

nneighbourhood(index) = nneighbourhood(index + gap) IDXY(index + gap) = swap1

idx(index + gap) = swap2 idy(index + gap) = swap3

nneighbourhood(index + gap) = swap4 doneflag = 0

End If Next

Loop Until doneflag = 1 gap = Int(gap / 2) Loop

End Sub

'Simulation Result For Year's t+1 Public Sub Simulation_Result() Settingpeta

FrmProcess.Caption = "Shapefile Data Processing For Year's-" & namafile12 & "Base On Year's " & B_year & " Simulation"

Set peta = LokasiPeta.FindGeoDataset(namafile) Set peta1 = New MapLayer

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Dim att_NB As mapobjects2.Recordset

Dim flidxy As mapobjects2.Field Dim flidx As mapobjects2.Field Dim flidy As mapobjects2.Field Dim fNbase As mapobjects2.Field Dim fNbase3 As mapobjects2.Field Dim strfld As New mapobjects2.Strings Set att_NB = peta1.Records

k = 0

FrmProcess.ProgressBar1.Value = 0 FrmProcess.ProgressBar1.Max = 329501 Close #4

att_NB.MoveFirst

Set flidxy = att_NB.Fields("IDXY") Set flidx = att_NB.Fields("IDX") Set flidy = att_NB.Fields("IDY") 'Set fNbase = att_NB.Fields("Ncell") Set fNbase = att_NB.Fields("Class") Set fNbase3 = att_NB.Fields("Class_2")

Open App.Path & "\data\INPUT_UPDATE.txt" For Input As #2

Open App.Path & "\data\" & "INPUT_PROSES" & namafile12 & ".txt" For Output As #3 Open App.Path & "\data\" & "INPUT_PROSES" & namafile12 & "_2.txt" For Output As #4 For k = 1 To 329501

FrmProcess.ProgressBar1.Value = FrmProcess.ProgressBar1.Value + 1 Input #2, IDXY1, idx1, idy1, nneighbourhood1

flidxy2 = flidxy.Value flidx2 = flidx.Value flidy2 = flidy.Value fNbase2 = fNbase.Value 'fNbase4 = fNbase3.Value If nneighbourhood1 = 10000 Then

'Update Cell Year's t+1 Base On Year's t Simulation att_NB.Edit

If gclss = 1 Then

att_NB.Fields("Class_2").Value = "Urban Simulation" Else

att_NB.Fields("Class").Value = "Urban Simulation" End If

nneighbourhood1 = -2 att_NB.Update Else

End If

If gclss = 1 Then

strfld.Add att_NB.Fields("Class_2").Value Else

strfld.Add att_NB.Fields("Class").Value End If

Write #3, IDXY1, idx1, idy1, Round(nneighbourhood1, 6) Write #4, IDXY1, idx1, idy1, Round(nneighbourhood1, 6) att_NB.MoveNext

Next k

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Dim vmr As New mapobjects2.ValueMapRenderer

vmr.SymbolType = peta1.symbol.SymbolType If gclss = 1 Then

vmr.Field = "Class_2" Else

vmr.Field = "Class" End If

vmr.ValueCount = strfld.Count For kkk = 0 To strfld.Count - 1 vmr.Value(kkk) = strfld(kkk)

If vmr.Value(kkk) = "Constraint" Then vmr.symbol(kkk).Color = &HC0FFC0 If vmr.Value(kkk) = "Urban Existing" Then vmr.symbol(kkk).Color = &HFF& If vmr.Value(kkk) = "Non Urban" Then vmr.symbol(kkk).Color = &HFF8080 If vmr.Value(kkk) = "Urban Simulation" Then vmr.symbol(kkk).Color = &H80FFFF vmr.symbol(kkk).Outline = False

Next kkk

Set peta1.Renderer = vmr

frmmapwndow.Map1.Layers.Add peta1 peta_adm

ekspotimage frmmapwndow.Hide

frmmapwndow.Map1.Refresh frmmapwndow.Hide

frmmapwndow.legend1.LoadLegend frmmapwndow.Hide

frmmapwndow.legend1.setMapSource frmmapwndow.Map1 frmmapwndow.Hide

frmmapwndow.legend1.LoadLegend True frmmapwndow.Hide

frmmapwndow.legend1.ShowAllLegend frmmapwndow.Hide

frmmapwndow.legend1.Active(0) = True frmmapwndow.Hide

End Sub

'Inisialisation For Simulating t+1 Public Sub inisialisasi_data2() k = 0

Close #1

FrmProcess.Caption = "Inisilisation Data Year-" & B_year FrmProcess.ProgressBar1.Value = 0

FrmProcess.ProgressBar1.Max = 329501

Open App.Path & "\data\" & "INPUT_PROSES" & B_year & ".txt" For Input As #1 For k = 1 To 329501

FrmProcess.ProgressBar1.Value = FrmProcess.ProgressBar1.Value + 1 Input #1, IDXY2, idx2, idy2, nneighbourhood2

IDXY(k) = IDXY2 idx(k) = idx2 idy(k) = idy2

PBase(k) = nneighbourhood2

Fneighbourhood(idx(k), idy(k)) = PBase(k) Next k

End Sub

'Public Sub update_Data()

' FrmProcess.ProgressBar1.Value = 0 ' FrmProcess.ProgressBar1.Max = 331937 ' Set rsurban = New ADODB.Recordset ' strsql = "SELECT * FROM TBLINPUT"

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' rsurban.Open strsql, db1

' j = 0

' With rsurban '

' For j = 1 To 331937

' FrmProcess.ProgressBar1.Value = FrmProcess.ProgressBar1.Value + 1 '' strsql = "SELECT * FROM TBLINPUT"

'' strsql = strsql + " WHERE IDXY=" & j ' 'rsurban.Open strsql, db1

' '.MoveFirst

' strsql = "UPDATE TBLINPUT" & _

' " Set P2003 = '" & rsurban("PProcess") & "' " & _ ' " Where IDXY = '" & j & "'"

' db1.Execute strsql, , adCmdText '' Set rsurban = New ADODB.Recordset

'' rsurban.Open strsql, db1, adOpenDynamic, adLockOptimistic ' '.MoveNext

' Next j ' End With '

'MsgBox "selesai" 'End Sub

'Public Sub SQL_SELECTINSERT(PIDXY As Long, PUPDATE As Single) '' On Error GoTo lanjut

' 'Set rsurban = New ADODB.Recordset '' strsql = "SELECT P2004 FROM TBLINPUT" '' strsql = strsql + " WHERE IDXY=" & PIDXY ' 'rsurban.Open strsql, db1

' Set rs1 = New ADODB.Recordset ' rs1.Open strsql, db1

' With rs1 ' .MoveFirst

' db1.Execute "INSERT INTO TEST" & _ ' "VALUES('" & PUPDATE & "')" ' End With

' 'db1.Execute strsql, , adCmdText ''lanjut:

'' Select Case Err.Number '' Case 3021:

'' End Select

'' strsql = "UPDATE TBLINPUT" & _ '' " Set P2003 = '" & PUPDATE & "' " & _ '' " Where IDXY = '" & CStr(PIDXY) & "'" '' db1.Execute strsql, , adCmdText

''Open App.Path & "\data\update.txt" For Input As #1 ''While Not EOF(1)

'' ProgressBar1.Value = ProgressBar1.Value + 1 '' Input #1, IDXY, nupdate

'' db1.Execute "INSERT INTO TEST " & _

'' "VALUES('" & rs1("ID") & "','" & rs1("data") & "','" & rs1("Z1") & "','" & rs1("Z2") & "','" & rs1("Z3") & "','" & rs1("Z4") & "','" & rs1("Z5") & "','" & rs1("Z6") & "','" & rs1("Z7") & "','" & rs1("Z8") & "','" & rs1("Z9") & "','" & rs1("Z10") & "','" & rs1("Z11") & "','" & rs1("Z12") & "','" & rs1("Z13") & "','" & rs1("Z14") & "')"

''Wend 'End Sub

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' ProgressBar1.Value = 0

' ProgressBar1.Max = Land_Demand

' Open App.Path & "\data\update.txt" For Input As #1 ' While Not EOF(1)

' ProgressBar1.Value = ProgressBar1.Value + 1 ' Input #1, PIDXY, nupdate

' strsql = "UPDATE TBLINPUT" & _

' " Set PProcess = '" & CStr(nupdate) & "' " & _ ' " Where IDXY = '" & PIDXY & "'"

' db1.Execute strsql, , adCmdText '' Set rsurban = New ADODB.Recordset

'' rsurban.Open strsql, db1, adOpenDynamic, adLockOptimistic ' Wend

'End Sub

Public Sub ekspotimage() Dim strImagePath As String

strImagePath = App.Path & "\Data\Image result\" & "SC" & ckkid & "\" & namafile12 & ".JPG" frmmapwndow.Map1.ExportMapToJpeg strImagePath, 100, 0, 1, moNoSymbologyScaled If index = 1 Then

Call ShellExecute(frmmapwndow.hWnd, "Open", strImagePath, _ vbNullString, App.Path, 1)

End If

Unload frmmapwndow End Sub

Private Sub overlay_matrix() ' k = 0

' j = 0

' For k = 10 To 0 Step -2 ' For j = 10 To 0 Step -2

' overlay(k, j) = ((k + j) / 2) / 10 ' Next j

' Next k

overlay(100, 100) = 1 overlay(100, 75) = 0.88 overlay(100, 50) = 0.75 overlay(100, 25) = 0.63 overlay(100, 0) = 0.5 overlay(75, 100) = 0.88 overlay(75, 75) = 0.75 overlay(75, 50) = 0.63 overlay(75, 25) = 0.5 overlay(75, 0) = 0.38 overlay(50, 100) = 0.75 overlay(50, 75) = 0.63 overlay(50, 50) = 0.5 overlay(50, 25) = 0.38 overlay(50, 0) = 0.25 overlay(25, 100) = 0.63 overlay(25, 75) = 0.5 overlay(25, 50) = 0.38 overlay(25, 25) = 0.25 overlay(25, 0) = 0.13 overlay(0, 100) = 0.5 overlay(0, 75) = 0.38 overlay(0, 50) = 0.25 overlay(0, 25) = 0.13 overlay(0, 0) = 0

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End Sub

Private Sub peta_adm() Settingpeta

Set admn = LokasiPeta.FindGeoDataset("district.shp") Set admn1 = New MapLayer

Set admn1.GeoDataset = admn

admn1.symbol.Color = moBlack admn1.symbol.Size = 2

frmmapwndow.Map1.Layers.Add admn1 frmmapwndow.Map1.Refresh

'frmmapwndow.legend1.LoadLegend

'frmmapwndow.legend1.setMapSource frmmapwndow2.mapdisp2 'frmmapwndow.legend1.LoadLegend True