CONFIGURING SUITABLE LANDFILL
WITH GIS-NETWORK ANALYST
CASE STUDY MUNICIPAL OF BATAM- INDONESIA
EVY YUSRIANI
UNIVERSITI TEKNOLOGI MALAYSIA
CONFIGURING SUITABLE LANDFILL WITH GIS-NETWORK ANALYST
CASE STUDY MUNICIPAL OF BATAM-INDONESIA
EVY YUSRIANI
A thesis submitted in fulfillment of the requirement for
the Award of the degree of Master of Science
(Planning – Resource and Environment Management)
FACULTY OF BUILT ENVIRONMENT
UNIVERSITI TEKNOLOGI MALAYSIA
SEPTEMBER, 2009
iv
“I declare that this project report is the result of my own research work except as cited in
reference. The project report has not been accepted of any degree and is not concurrently
submitted in candidature of any degree”.
Signature
:
…………………………..
Author’s Name
:
EVY YUSRIANI
Passport Number
:
P528293
vi
ACKNOWLEDGEMENT
I would like to express my sincere gratitude to God the Almighty that always open a
window when all the doors seemed to be closed. I would like to thank my parents, Bambang
Yoesmianto & Sri Sulastri for continuing their responsibility as parents while they should
have been retired for quite a while. To my dearest family, Muhammad Sjahri Papene and
Daffa Pramudya Papene, whom I did this work for.
My special gratitude to the most important people behind the whole story: My dearest
Supervisor Dr Mohammad Rafee Majid, for giving more than a supervisor should. To my
Reader Associate Professor Dr Foziah Johar. I also appreciate Norsyahida Juhari and Wan
Yusrizal Wan Ibrahim for their contribution in strengthening GIS application component of
this study , and to my local classmates who helped me a lot: Rozita, Shahira, and others. May
God pay all of their great deeds with multiplied price, here and hereafter.
I would like to express my sincere gratitude to Government of Kepulauan Riau
Province (Regional Employment Board) for the support and opportunity to pursue my study
further. Especially to Mr. Lamidi and Mr. Ruli Friady.
Finally yet importantly, a sincere gratitude to all my dearest Indonesian friends
especially my neighbors in KTF H25-C, to Tika, Nadya, Dela, Nani, Nona and Ike for the
sincere companion while I’m in UTM for keeping me as ‘human’ as I can be. Of course, to all
Indonesian and international friends who shared with me all of the wonderful moments in this
country: Ibrahim, Nima and Soheil. All of them have taught me about life and share their
examples on how a life should be lived.
vii
ABSTRAK
Kajian ini menggunapakai Analisis Jaringan dalam Sistem Maklumat Geografi (GIS)
bagi membangunkan model penjanaan senario dan mengenalpasti pilihan tapak pembuangan
sampah alternatif bagi Kota Batam. Ia berpandukan kepada pemilihan tapak berdasarkan
aspek ekonomi dan seterusnya mengenalpasti laluan terbaik ke tapak yang terdekat. Hasil
kajian ini diharapkan dapat digunakan dalam membantu Kerajaan Kota Batam dalam
mempertimbangkan projek tapak pembuangan sampah di bandar tersebut. Proses awal kajian
melibatkan pemilihan tapak pembuangan sampah melalui proses tindih lapis (union) lapisan
data guna tanah (tidak termasuk Kawasan Sensitif Alam Sekitar), buffer jalan, daerah
permukiman lapangan terbang serta kecerunan dengan yang kemiringan kurang daripada
20%. Tiga pilihan tapak terbaik diperolehi melalui proses analisis tersebut. Kemudian,
pembinaan topologi bagi lapisan data jalan dibuat dan perletakan titik peralihan/transit bagi
pengumpulan sampah (sebanyak 265 titik) ditentukan. Set data jaringan (network dataset)
bagi lapisan data jalan yang mengandungi parameter dan spesifikasi jalan dibentuk untuk
digunakan dalam menjalankan analisis jaringan dalam ArcGIS 9.2. Analisis kawasan
perkhidmatan (service area) dalam ArcInfo Network Analyst dilakukan dengan
mengambilkira tahap tadahan tampungan perkhidmatan kawasan bagi ketiga-tiga tapak dalam
masa 5, 15 dan 30 minit. Hasil yang diperolehi mendapati dan diputuskan ialah tapak
alternatif 3 karena walaupun tidak memiliki cakupan tadahan tampungan, tetapi memiliki
transit point yang terbanyak dan dapat menampung lebih banyak sampah. Bagi
memperkuatkan hasil kajian ini ditentukan titik origin destination dan 10 titik peralihan
terjauh dalam setiap daerah ditentukan. Seterusnya, laluan terbaik akan dijana melalui ArcInfo
Network Analyst dalam ArcGIS. Hasil yang diperolehi mendapati dan diputuskan ialah tapak
alternatif 3 karena memiliki daerah cakupan tadahan tampungan yang terbanyak dan dapat
menampung lebih banyak sampah walaupun tidak memiliki jarak laluan terpendek dengan
waktu yang paling minima. Didapati hasil kajian menunjukkan tapak alternatif 3
sememangnya merupakan lokasi terbaik bagi tapak pengumpulan sampah di Kota Batam.
viii
ABSTRACT
This study meant to apply Network Analysis in GIS to develop scenario generation
model and assess alternative landfill sites in Batam City based on the economical aspects.
And the best route to achieve the closest landfill. Results of this study might help the
government of Batam City to consider about the landfill projects in the city. Landfill
alternative selection started by unionizing the layer landuse (excluding Environment Sensitive
Area), road buffer, residential, airport, and slope lower than 20%. Road topology and transit
point were done afterwards (for 265 points), in order to avoid layer overlapping in the form of
shapefile. Road layer (shapefile) converted to network dataset to add parameter and road
specification. The analysis were done to show the size of the covered area in every 5, 15, and
30 assumed minutes. The result was alternative number three, because even it did not has the
wide area covered, it has more transit point and more waste generate. In order to support this
result, then OD (Original Destinations) are added and 10 furthest transit point were defined.
ArcInfo Network Analysis assessed the landfill alternatives.
The result was alternative
number three, because it has more transit point and more waste generate even it did not the
closest route and fastest time-span. Site 3 showed the more area covered then other alternative
sites.
ix
TABLE OF CONTENTS
CHAPTER
TITLE
PAGE
TITLE OF PAGE
i
DECLARATION
ii
DEDICATION
v
ACKNOWLEDGMENTS
vi
ABSTRAK
vii
ABSTRACT
viii
TABLE OF CONTENTS
ix
LIST OF TABLES
xiii
LIST OF FIGURES
xiv
x
1
2
INTRODUCTION
1.1
Introduction
1
1.2
Problem Statement
3
1.3
Purpose of the Study
4
1.4
Objectives of the Study
4
1.5
Scope and Limitation of the Study
4
1.5.1
Scope
5
1.5.2
Limitation
5
1.6
Study Area
5
1.7
Importance of the Study
7
1.8
Organization of Research
7
1.9
Summary of the Chapters
8
SOLID WASTE MANAGEMENT
2.1
Introduction
10
2.2
Waste Management
10
2.3
Solid Waste
13
2.4
Collection
16
2.5
Transportation
19
2.5.1
Motor Vehicle Transport
20
2.5.2
Transport Vehicle for
21
Uncompacted Waste
2.6
3
Landfill
22
GEOGRAPHY INFORMATION SYSTEM
3.1
Introduction
26
3.2
Geography Information System
26
3.2.1
Component of Geographic Data
29
3.2.2
Data Organizations in GIS
29
3.2.3
GIS Software
31
xi
3.3
Network Analysis
33
3.3.1
35
Basic Principles of Network
Analysis
3.3.2
4
GIS Network Analysis
36
3.4
Dijkstra’s Algorithm in Network Analysis
38
3.5
Case Study
40
SOLID WASTE MANAGEMENT IN BATAM
4.1
Introduction
42
4.2
Profile of Study Area
42
4.2.1
Batam in Figure
42
4.2.2
Land use
44
4.3
Waste Management in Batam
47
4.3.1
Solid Waste in Batam
47
4.3.2
Waste Management
48
4.3.3
Field Condition of Waste
48
Management in Batam
4.3.4
Waste Management Service in
49
Batam
4.4
Database Development
57
4.5
Using Dijkstra’s Algorithm in Network
59
Analysis
5
RESEARCH METHDOLOGY
5.1
Introduction
59
5.2
Data Preparation
60
5.3
Available Site Selection
62
5.4
Network Analysis
62
xii
6
BEST ROUTE AND CLOSEST FACILITY
64
ANALYSIS
6.1
Introduction
65
6.2
Identifying alternative landfill sites
66
6.2.1
67
Digitizing Area Boundary and
Road
6.3
6.2.2
Creating Slope from Elevation
68
6.2.3
Environment Sensitive Area
71
6.2.4
Site Selecting Process
73
6.2.5
Site Selecting Model
76
Number of Waste Generated and Truck
78
Capacity
6.4
Network Analysis
77
6.4.1
81
Creating Topology for Road
Data
6.4.2
7
Creating New Network Dataset
83
6.5
Identifying Service Area
85
6.6
Identifying the Best Route and Closest Facility
89
6.7
Conclusion
99
DISCUSSION AND SUGGESTION
100
7.1
Introduction
100
7.2
Research Findings Formula
101
7.3
Issues and Problem of Study
101
7.4
Suggest Landfill Area and Route For
102
Collection
7.4.1
Suggest Suitable Landfill
102
7.4.2
Best Route and the Closest
102
Landfill.
7.4.3
REFERENCES
Service Area
102
106
ix
LIST OF TABLE
TABLE
NUMBER
TITLE
PAGE
2.1.
Source of solid waste within a community
14
4.1.
Landuse of Municipal of Batam
45
4.2
Location of Waste Transit Point in Batam
Island
49
4.3
Waste Generates
56
4.4
Data Modeling in Research
57
5.1:
Database structure for the study
63
6.1
Allocate The Number of Trip
80
6.2.
Comparison Covered Area
87
6.3
Waste Carried, Transfer Point Covered
Each Sites
88
6.4
Result Length and Time From Each
94
6.5
Amount of Waste in Each Route –Site 1
95
6.6
Amount of Waste in Each Route –Site 2
96
6.7
Amount of Waste in Each Route –Site 3
97
6.8
Amount of Transit Point, Waste, Length
and Time
98
x
LIST OF FIGURES
FIGURES
TITLE
PAGE
NUMBER
2.1
Figure 2.1. Typical system for Solid
12
Waste Management.
2.2
Examples of refuse collection vehicle
20
2.3
(a) Truck (also truck chassis with
21
detachable body), (b) truck-trailer
combination, (c) tractor-semitrailer
combination, and (d) tractor-semitrailerpull trailer combination (often identified
as a set of doubles)
2.4.
Typical transport vehicle used in
conjunction with transfer facilities :
(a) Open-top semitrailer with moving
floor unloading mechanism
(b) Enclosed semitrailer used
stationary compactor
(c) Drop-bottom open-top semitrailer
unloaded with hydraulic tipping
ramp.
22
xi
3.1.
Geography Data
30
3.2
GIS data “Layer”
30
3.3.
Road Network (Nod and Connection)
34
4.1.
Landuse Of Batam Batam Island
44
4.2
An example of Dijkstra’s algorithm
59
(Orlin 2003).
5.1:
Research Methodology to find the best
62
route for the trucks to the best site
6.1.
Conceptual Framework of Analysist
67
6.2
Digitizing the new shape file to get the
68
boundary of the area
6.3
Road made base on main hierarchy :
69
artery, kolector, local, non/other
6.4
Elevation Layer
70
6.5
Slope Layer
70
6.6
Classification Slope
71
6.7.
Slope Layer after Classify
71
6.8.
Extract from landuse, create shapefile
72
ESA
xii
6.9
Reselect landuse that out from the
73
analysis.
6.10.
Buffer Process
74
6.11.
Dissolve Process
75
6.12
Union Process
76
6.13
The result of alternative suitable landfill
77
6.14
Create road network topology
82
6.15
Data attribute in road topology layer
83
6. 16
Topology Road
84
6.17
Service Area
86
6.18
Data utilized in the network analysis
90
process
6.19
Process to find best route for every point
91
and alternative site
6.20.
Set up the search Tolerance
92
6.21.
The best route from each sites
93
1
CHAPTER 1
INTRODUCTION
1.1
Introduction
Solid waste problem is quite a continuing important urban issue. Along with
the rapid urban growth, the amount of solid waste is growing exponentially, and
needs more attention from local governments. The price of the land increases along
with the rapid urban growth as well, and makes it more difficult for the government
to find an appropriate landfill location. In some large and medium-sized cities in
Indonesia, governmental actions in term of landfill are limited by such factors. In the
year of 2000, only 40% of the solid waste has taken into appropriate process.
Unprocessed or untreated solid waste triggered the air, water, and land pollutions, as
well as increasing the probability of flood in urban area. Landfill problems should be
seriously, technically, rationally, and professionally solved. It takes an integrated
management, based on the situation, condition, and policy of each region in the
country.
2
The increase of the amount of the solid waste, combined with the scarcity of
the landfill, created serious problems for urban areas in order to maintain the balance
of the environment. Landfill, which should be an environment-friendly end of the
chain of the waste management; nevertheless, limited funding and human resources,
plus the existing pattern of ‘collect-carry-dump’, put an enormous burden to the
existing landfill. There is not enough landfill, and the capacities of the existing
landfills are reaching their limits.
Regional autonomy (Otonomi Daerah) administratively separated one town to
another; hence, the regional landfill management is difficult to be discussed among
regions. Some major factors of the problem are as follows: (1) the scarcity of land in
urban area, especially in metropolists; (2) regions and districts stated their objections
on having landfills in their areas due to NIMBY (Not in My Backyard) syndrome;
(3) there is no specific body or agency that responsible for the solid waste
management. On top of those factors, some illiterated citizens did not realize about
the importance of not littering. The national government ratified some international
agreements such as Agenda 21,MDG 2015, Kyoto Protocol, which needs to be
realized, while the government published the national regulation of solid waste
management (UU 18/2008) as a legal platform in managing solid waste and landfill.
Housing estates, commercial estates, industrial estates, and institutional estates
were created by the urbanization process. These activities attracted numbers of
citizens; hence the overpopulation. The overpopulation means more solid wastes.
Based on the statistical counting of Bappenas, in 1995, piled solid wastes of
Indonesia was predicted to be 22.5 millions of tons, and multiplied in 2020 to 53.7
millions of tons. Meanwhile, in the major cities of Indonesia daily solid waste per
capita is estimated around 600 – 830 grams. To illustrate the amount of solid waste
in the country, several major cities conditions might be taken as references. Jakarta
produces 6.2 thousands of tons of solid wastes, Bandung produces 2.1 thousands,
Surabaya 1.7 thousands, and Makassar 0.8 thousands (Damanhuri, 2002). These
numbers indicated the difficulty of solid waste management in the country. Based on
the numbers, it is estimated that Indonesia needs 675 hectares of landfill in 2005, and
1,610 hectares in 2020. Judging by the availability of land in urban area, it might
lead to a major problem.
3
Principles of urban planning play important roles in managing development
problems. In the context of landfills, there is a need of integrated cross-regional
management of urban planning. Through this method, environmental supporting
ability of one region should be considered important. Location planners usually
focused on housing, industrial, public domains, and other facilities, but landfills are
often neglected. Methods of systematically selecting landfill area and knowledge of
the landfill importance and side effects are the two lacking factors that indicated the
source of the problem.
There are numbers of technology and instruments to help the policy makers to
consider every aspect before issuing policies. Information Technology such as GIS
(Geographical Information System) have changed the approach of decision making.
This kind of technology should make it easier for a planner to plan, design, control,
and assess the planning process.
This system might support the policymakers in deciding the location of the
landfills. GIS is able to provide modeling, predictions, and situation planning of
landfill area. GIS would not only support the decision-makings, it is also able to
manipulate some problem scenario. Furthermore, GIS is available to be integrated to
other techniques of decision making such as Spatial Multi-Criteria Decision Making
(SMCDM) in order to assess the landfill candidates in Batam.
GIS helps planners to produce rational decisions faster and more economical.
GIS might also be related to the long-term development planning due to its capability
to store geographical data and land usage. Its capability and high precision to
manage, manipulate, and analyze data might solve many developmental problems
today.
1.2
Problem Statement
Some problem with the allocation, efficiency, and SNI criteria occurred due to
the existing waste management and landfill allocation in Batam was done manually.
4
For instance, the airport and some residential area became polluted because the
location was too close to the landfill, the routes for the transport vehicles were
considered not efficient. This study is applying network analysis in GIS to the better
landfill location, which follows the criteria in SNI 03-3241-1994 and proven to be
more efficient and economical.
1.3
Purpose of the Study
This study meant to apply Network Analysis in GIS to develop scenario
generation model and assess alternative landfill sites in Batam City based on the
economical aspects; such as to suggest more efficient routes for transport vehicles,
while keeping the location of the landfill match the criteria on SNI 03-3241-1994.
Moreover, the best route to achive the closest landfill. Results of this study might
help the government of Batam City to consider about the landfill projects in the city.
1.4
Objectives of the Study
Objectives of this study are as follows:
i.
to identify the existing waste management system in Batam City.
ii.
to identify criteria of landfill location based on GIS standard.
iii.
to provide and design GIS data model to produce some scenario alternatives
of landfill locations.
iv.
to identify the best alternative location out of several generated alternatives
based on economical factor, network analyzed by GIS.
1.5
Scope and Limitation of the Study
Scope and limitations of this study are as follows:
5
1.5.1 Scope
i.
Landfill location in this study was identified based on the quantitative aspects
of economical.
ii.
Generated scenario was used to identify the best alternative of the future
landfill location of Batam.
iii. A model scenario for future appropriate landfill site was developed; the
characteristics of the site in the scenario should fulfill the criteria of
Indonesian National Standard ( INS) or SNI (Standar Nasional Indonesia) no
03-3241-1994.
iv. Scenario generation has done by using overlay process. This study used and
manipulated secondary data from the database to analyse the alternative
locations based on the fixed criteria by applying GIS analysis module such as
ArcInfo.
v.
Network Analysis process was used to assess the most appropriate alternative
location.
1.5.2 Limitation
i.
This study would only assess in main land of Batam
ii.
This study did not include industrial or hazardous wastes.
iii. This study would only include pickup from transfer points.
iv. This study would only employ existing algorithm in the ArcInfo-GIS.
1.6
Study Area
Batam archipelago includes 6 major islands: Batam, Rempang, Galang,
Galang Baru, Bulan, Kapala Jeri, and approximately 400 small / minor islands, and
only 329 of those small islands acquired their names. The land area is as wide as
1,038.4 squarekilometers and the waters are as wide 2,951,5678 squarekilometers.
The study area was the island of Batam, which equals to 30% of the overall Batam.
6
The island of Batam was selected as the study area for reasons as follows:
i.
Batam experienced a rapid development due
due to the rapid explosion of population.
Proper planning and landfill sites selection would help the government to solve
the solid waste problems for now and then.
ii.
Batam is currently preparing the design of the regional urban planning ((Rencana
Tata Ruang Wilayah
layah) 2015-2035
2035 based on the concept of Free Trade Zone (FTZ).
Included in this design, is the landfill location identification. The existing landfill
at Telanga Punggur is considered no longer sufficient due to the rapid
development occurring in the area.
area. As previously mentioned, the port of Telaga
Punggur serves as a main gate to Tanjung Pinang, the capital city of Kepri
province
iii.
The database development needs plenty of detailed data, which is accessible in
Batam.
Figure 1.1 Municipal of Batam
1.7
Research Methodology
In this current research,
research data such as map, map data attribute, waste
wast
generated, and any data related to transport vehicles and their routes were obtained
obtain
from the municipal of Batam and PT Surya Sejahtera.
Sejahtera
7
The data obtained were analyzed by utilizing ArcInfo-GIS with Dijkstra
Algorithm, and produced the results such as the landfill alternative locations,
coverage area of the landfill alternatives, and the most efficient routes.
1.7
Importance of the Study
In the mean time, government agencies in Batam are not familiar to the
application of GIS in term of waste management, especially about allocating
landfills.
The advantages of using GIS and its capability of forecasting future
problems and their solutions would be introduced to the government in order to
enhance the quality of decision-making process, especially about area planning.
BAPPEDA (Planning and Development Board) would be supported by the
application of GIS, especially in predicting the future problem solving and landuse
planning.
This study would also introduce alternative quantitative methods that provide
clearer justifications to support the decision makers to assess every aspect in order to
give the best solution alternative.
1.8
Organization of Research
This research has arranged activities based on the objective. Chapter 3 would
describe the process clearer and more detailed. There were some important stages in
research implementation, namely:
i.
Issues and problems identification.
ii.
Literature study
8
iii.
Setting a purpose, objective and study scope
iv.
Research methodology and Data Collection
v.
Analysis, Discussion and conclusion
Every stage has distinctive activities. At the early stages, the researcher was
identifying issue and problems in waste management. Through the problem
discussion and literature study related to the goals and objective of the study
specified. Study scope was set to make sure that the output could reach the aim and
proposed objective.
The next level is to develop a study methodology to achieve the research
goals. Model analysis was formed and further database development carried out for
analysis was conducted. Network Analysis techniques will be applied in GIS
environment.
Results of the analyses showed several alternative locations for landfill,
which fulfill the criteria proposed. Furthermore, each of the alternatives would be
analyzed by using Network Analysis to generate the best location based on
economical aspects.
1.9
Summary of the Chapters
Briefly, this paper contains seven chapters. These chapters were developed
based on each step described in the previous section. Chapter 1 introduced the
current situation regarding to the solid waste and landfill issues that led to the
problem statement, purpose, objective, scope, and area of the studies.
Chapter 2 discussed about related literatures about waste management, the
importance of the landfill proper location and the criteria set in the SNI #03-32411994 and Undang-Undang RI No. 18 tahun 2008 about waste management.
9
Chapter 3 was described about the literature reviews of GIS and Network
Analysis approach in assessing and location selection.
Chapter 4 was about the development of the database and the profile of the
study area. Current existing data were described and elaborated in this chapter.
Chapter 5 described about analytical design and scenario generation modeling
for the proper landfill locations using GIS based on the development potential of
each location. Model development using Network Analysis was applied in this
chapter. This chapter included required data for developing the model, site
alternative nominating process, and the best alternative site nominating process.
Chapter 6 discussed about the indentification of the alternative sites for
landfill. Several locations scenario would be assessed by using Network Analysis in
order to get the most appropriate landfill location in Batam based on the indicators
and criteria.
Chapter 7 concluded and discussed the summary about the results acquired
from the analysis.
10
CHAPTER 2
SOLID WASTE MANAGEMENT
2.1.
Introduction
This chapter explains about the waste management concepts, specifically
about the consideration of criteria that will be used to identify suitable landfill. Waste
management concept would be include waste management definition, solid waste,
collection, landfill, and transporting.
2.2.
Waste Management
The government of Indonesia on May 7, 2008 issued regulations about waste
management, which happened to be an important global issue. The background of
the regulation issue included some aspects as follows:
1.
The population growth and the changes in public consumption pattern led to the
increase of volume, types, and characteristics of solid waste.
11
2.
The existing waste management is not considered environmentally appropriate,
and caused many additional problems for the society.
3.
Solid waste problem is becoming national issue, hence the management should
be done comprehensively and integratedly by all of the stakeholders to gain the
health and welfare benefits, and environmental friendly, as well as changing
public behavior about the waste problem.
4.
It takes a fix-lined regulations about the governmental rights and responsibility,
public and industrial involvement in order to create an effective, efficient, and
proportional waste management.
UU No. 18, 2008, about waste management, described waste management as
a systematical, global, and continuous action to reduce and manage waste. Solid
waste management may be defined as the discipline associated with control of
generation, storage, collection, transfer and transport, processing, and disposal of
solid waste in a manner that is in accord with the best principles of public health,
economics and that is also responsive to public attitudes. In its scope, solid waste
management includes all administrative, financial, legal, planning, and engineering
functions involved in solutions to all problems of solid wastes. (Tchobanoglous et al.,
1993)
An effective solid waste management system should include one or more of
the following options: waste collection and transportation; resource recovery through
waste processing; waste transportation without recovery of resources, i.e., reduction
of volume, toxicity, or other physical/chemical properties of waste to make it suitable
for final disposal; and disposal on land, i.e., environmentally safe and sustainable
disposal in landfills (Tchobanoglous et al., 1993; Kreith, 1994; CPHEEO, 2000).
Collection and transportation is one important functional component of the solid
waste management system (Kumar, 2003).
12
Indonesia is well known for its mixed culture as far as climate, economy,
food and topography is concerned. This is reflected in solid waste management
systems. Indonesia’s solid waste management is becoming increasingly important for
a variety of reasons, including the concentration of the population in municipal areas,
legal interventions, the emergence of newer technologies and rising public awareness
of the importance of hygiene and sanitation.
A typical solid waste management system is shown in Figure. 2.1.
Solid waste generate from :
- Residential area
- Commercial
establisments including
hotels and markets
- Other establishments
Transportations
Collection
system
(House to House
and/or Fixed
Landfilling
Processing system for
material, energy recovery
and/or volume reduction
Figure 2.1. Typical system for Solid Waste Management.
Source : (http://www.unep.or.jp/Ietc/Publicati/spc/State_of_Managementwaste/index.asp)
Solid waste management might be interdisciplinary problem that involves
politic science, city and regional planning, geography, economics, public health,
sociology, demography, communications and conservations as well as engineering
and materials science.
13
2.3
Solid Waste
According UU No. 18/2008, solid waste is the solid wasted materials of
human daily activities and/or natural processes. Type of the solid wastes managed
under this regulation is as follows:
a.
Household solid wastes, includes wasted materials of daily household
activities, not including specific waste and biological wastes.
b.
Solid wastes similar to household solid wastes, includes similar wastes from
commercial, industrial, social, and public facilities area.
c.
Specific Solid Wastes, includes materials as follows:
1)
Solid waste contains hazardous or toxic materials.
2)
Solid waste contains any hazardous or toxic wastes
3)
Solid waste produced by natural disasters.
4)
Solid waste contains debris from buildings demolitions.
5)
Solid waste contains material without known technology to process or
reprocess.
6)
Seasonal solid wastes.
Solid waste is the material arising from human and animal activities that is
normally is solid andis discarded as being either useless or unwanted. It encompasses
the heterogeneous mass of throways from urban communities and agricultura,
mineral, and industrial wastes as well. (Shah, 2000)
14
Source of solid waste in a community are, in general, related to land use and
zoning. Although number of source classification can be developed the following
categories are useful (Tchobanoglous et al., 1993):
1.
Residential
2.
Commercial
3.
Institutional
4.
Contraction and demolition
5.
Municipal services
6.
Treatment plant site
7.
Industrial, and
8.
Agricultureal
Typical waste generation facilities, activities, or locations associated with
each of these source are reported in Table 2.1 where municipal solid waste is
normally assumed to include all community waste with the exception of industrial
process waste and agricultural waste (Tchobanoglous et al, 1993) :
Table 2.1. Source of solid waste within a community
Typical facilities, activities,
No
Source
or location where wastes are
Type of solid wastes
generates
1
Residential
Single family and
Food wastes, paper, carboard,
multifamily detached
plastics,
dwellings, low-,
wastes, wood, glass, tin cans,
medium-, and high-rise
aluminium, other metals, ashes,
apartment.
streets leaves, special wastes
textile,
lether,
yard
(including bulky items, concosumer,
electronics,
white
goods, yard wastes collected
separately, batteries, oil, and
tires),
wastes
household
hazardous
15
Typical facilities, activities,
No
Source
or location where wastes are
Type of solid wastes
generates
2
Commercial
Stores, restaurants, markets,
Paper, cardboard, plastics, wood,
office buildings, hotels,
food waste, glass, metals, special
motels, print shops, service
wastes (see above), hazardous
stations, auto repair shops,
wastes, etc
etc.
3
Institutional
Schools, hospitals, prisons,
As above in commercial
governmental centers
4
Construction
New construction sites, road
and
repair/renovations sites,
Demolition
razing of buildings broken
Wood, steel, concrete, dirt, etc
pavement
5
Municipal
Streets cleaning,
Special wastes, rubbish, streets
services
landscaping, catch basin
weeping’s, landscape and tree
(excluding
cleaning, park and beaches,
trimmings, catch basin debris,
treatment
other recreational areas
genera
facilities)
6
Water, wastewater, and
sites: industrial treatment
municipal
Treatment
plant
park,
Municipal
solid waste
wastes,
principally composed of residual
processes, etc
sludge’s
All of the above
All of the above
incinerators
7
from
beaches, and recreational areas.
Treatment
plant
wastes
16
Typical facilities, activities,
No
Source
or location where wastes are
Type of solid wastes
generates
8
Industrial
Constructions, fabrications,
Industrial process wastes, scrap
light and heavy
materials,
manufacturing, refineries,
wastes including food wastes,
chemical plants, power
rubbish, ashes, demolition and
plants, demolition, etc
construction
etc.
Non-industrial
waste,
special
waste, hazardous wastes
9
Agriculture
Field and row crops,
Spoiled food waste, agricultural
orchards, vineyards, dairies,
waste, rubbis, hazardous waste
feedlots, farms, etc.
2.4
Collection
In most industrialized countries, solid waste is collected. Solid waste
generated in urban areas is collected by fixed station and/or by house-to-house
collection systems. In developed countries, in the case of the fixed station system,
citizens are supposed to deposit the waste at the locations specified by the
municipalities on a particular day of the week by a specific time. (UNEP, 2002).
Collection of solid waste is carried out by using suitable vehicles. The type of vehicle
to be used depends on the type of collection bin and width of road (Chiplunkar et al.,
1981).
17
In Indonesia, solid waste collection is carried out by “community
neighborhood units,” a quasi private enterprise formed by the community. There are
situations where the city and its surrounding area are independently managed by
different municipalities, but landfills are shared without any official agreement
(Pasang et al., 2007).
In most industrialized countries, solid waste is collected from urban areas by
compactor truck wich collected waste from each household once or twice a week.
However, there several reason that such collection systems do not work in
developing urban communities. First, road condition oftern make the truck access to
individual households difficult. Second, the nature of the waste in poorer areasdenser and more corrosive due to a higher organic content-makes compaction
unfeasible and contributes to frequent equipment failure (Coffey, 1985).
In part of two condition, the costs of such a system are often prohibitive in
developing communities where ability and willingness to pay for service are low.
Third, weak local authorities and lack of precedent for paying fees for such services
make it difficult to recover the costs of collection services.
This difficulties have promted the development of innovative collection
systems better suited to developing urban areas. Four different types of systems are (
Korfmacher, 1996) :
1)
House to house collection; several house to house collection systems
(“primary collection”) have been designed to be appropriate to develop urban
areas. This program respect to financing, organization, and technology. In the
case of house-to-house collection, transportation vehicles visit individual
houses at a specific time for waste collection
18
The goals of this program were to create employment, deliver basic health
and hygiene education, and reduce rubbish in the community. Additionally,
the program aims to convince individual households to pay for private
garbage collection services. These programs also represent a potential
alternative to paying for garbage collection through municipal fees in a city.
In other areas, an improvised form of house-to-house collection involves a
worker with a handcart who traverses each street. He rings a bell so that local
residents can hear him coming, whereupon they leave their residences and
deposit the waste in his cart. Once the handcart is full, the worker either
unloads it in a community bin or deposits it in a transport vehicle. More often
than not, public participation is limited. Consequently, it is not uncommon to
see waste is littered around the community bins.
2)
Communal collection; Alternative methods of collection involve communal
skips or collection sites. Sometimes these programs consist of several layers
of collection networks. One program in Adjoufou II, a region of Abidjan in
the Ivory Coast, used two-wheeled barrows to transport communal drums
(which are placed less than 30 m from each house) to skip at collection points
(Meyer 1993). The skip re then periodically emptied by private municipal
collection company. These programs can utilize financial incentives to
encourage recycling by paying different process for different material.
3)
Block collection; Block collection has been implemented in several areas
(Habitat, 1992). In this system, a collectioan vehicle travels a scheduled
route, stopping periodically for residents to bring their refuse. Although it is
convenient for resident, block collection eliminates the need for intermediate
storage containers and thus may be less costly. Residents stopped carring
their rubbish to the trusck and refuse began to degrade the environment.
19
4)
No collection; These programs do not involve collection by contarctors in the
usual sense. Instead, resident receive incentives for bringing their refuse to
the central locations. The program cost the same as would a private collection
service for these areas, and was subsidized by taxes from wealrheir
neighborhoods. Public participation, awareness and cooperation are
satisfactory in the developed countries, and thus urban areas are generally
clean most of the time. In developing countries, a community bin is installed
or designated a fixed station, and residents in the local area are supposed to
deposit their waste when necessary
2.5
Transportation
In the field of solid waste management, the function element of transfer and
transport refers to the means, facilities, and appurtenances used to effect the transfer
of wastes from one locations to another; usually more distant, location.
Transportation vehicles can be categorized as collection vehicles and haulage
vehicles. Collection vehicles collect the waste in areas where it is generated, and, if
the processing and disposal facilities are at a long distance away, these vehicles then
transfer their contents to haulage vehicles at designated transfer stations for transport
to the disposal facility. In developed countries, there exist standardized designs for
these vehicles, consistent with normal waste characteristics and working conditions.
In developing countries, waste from community bins is transported by various types
of vehicles, ranging from general purpose vehicles (trucks) to highly mechanized
compactors (UNEP, 2002).
Road transport is the most common mode of moving solid waste to distant
disposal sites. It has been observed that the efficiency of general purpose vehicles is
low for waste transportation, mainly because the waste is a low density material. On
the other hand, there are difficulties associated with copying the designs of highly
mechanized vehicles used in the developed-world due to differences in local waste
characteristics and operating conditions. However, in many countries, appropriate
20
designs are now being developed through innovative approaches, extensive testing,
and trial and error.
2.5.1 Motor Vehicle Transport
All types of vehicles can be used for hauling on high-ways should satisfy the
following requirement (Fig. 2) :
a)
Waste must be transported at minimum cost,
b)
Waste must be covered during the haul operation
c)
Vehicles must be designed for highway traffic
d)
Vehicle capacity must be such that the allowable weight limit are not
exceeded, and
e)
Method used for unloading must be simple and dependable
Figure 2.2. Examples of refuse collection vehicle
21
2.5.2 Transport Vehicle for Uncompacted Waste
For simplicity and dependability, open-top semitrailers have found wide
acceptance for the hauling of uncompacted waste from direct-load transfer station.
The semitrailers are of monoque construction, in which the bed of the trailer also
serves as the frame of trailer. ((Tchobanoglous et al., 1993). Using monoque
construction allows greater waste volumes and weights to be hauled. The other
trailers that beed developed is the droop-bottom trailer, in which the bottom of the
centre portion of the trailer is lowered (see Fig 3 and 4) to obtain additional capacity
without increasing the length of the trailer.
Figure. 2.3 (a) Truck (also truck chassis with detachable body), (b) truck-trailer
combination, (c) tractor-semitrailer combination, and (d) tractor-semitrailer-pull
trailer combination (often identified as a set of doubles)
22
Figure 2.4.
Typical transport vehicle used in
conjunction with transfer facilities :
(a) Open-top semitrailer with moving
floor unloading mechanism
(b) Enclosed semitrailer used stationary
compactor
(c) Drop-bottom open-top semitrailer
unloaded with hydraulic tipping
ramp.
2.6
Landfill
In Indonesia, landfill allocation should fulfill the requirements in the
regulations about environmental management, risk analyses, public safety and
security, and other regional regulations.Landfill is a place to store, process, and
recycle the waste to environmental media safely and securely (UU no18/2008).
Landfill is a place to quarantine wastes (SNI 03-3241-1994)
23
Locating a sanitary landfill requires an extensive evaluation process in order to
identify the optimum available disposal location. This location must comply with the
requirements of the existing governmental regulations and at the same time must
minimize economic, environmental, health, and social costs (Siddiqui et al., 1996). In
assessing a site as a possible location for solid waste landfilling, many factors could
be considered (Savage et al., 1998; UNEP, 1994). These factors may be presented in
many ways; however, the most useful way is the one that may be easily understood
by the community (Tchobanoglous et al., 1993).
The guidelines to determine criteria of landfill locations refered to the SNI 033241-1994, issued by the general works departments and has to fulfill the
requirements stated in UU No18/2008 about waste management. Landfill criteria are
as follows:
A. General Requirements:
1)
Landfill should not be located in the lake, river, or sea.
2)
Landfill location should follow 3 stages:
i.
Regional stage, which provides zone map that divided to several
zones of approximity.
ii.
Selection stage, a stage that provides one or two best locations out of
several appropriate locations determined in the regional stage.
iii.
Determination stage is a stage where the decision makers decide the
best location.
3)
If one area is not capable of the regional stage, landfill locations would be
based on other scheme.
B. Criteria of landfills are divided into three parts:
1)
Regional criteria, it is a criteria to determine the appropriateness of a
zone:
1.
Geological Conditions:
a.
Not in the Holocene fault zone.
b.
Not in the geologically-dangerous zone.
24
2.
Hydrogeological conditions:
a.
Not have the underwater surface level less than 3 meters.
b.
The distance from the nearest freshwater resource should be
more than 100 meter lower stream.
c.
Technological manipulation should be done whenever there are
no characteristic fulfilled.
3.
Regional Development and Planning; the landfill area candidates
should not be a potential area for industry, agriculture, residencial,
tourism, or educational based on existing master plan.
4.
Slope of the zone should be less than 20%.
5.
Distance from the nearest airport should be more than 3.000 meters
for turbojet engines and more than 1.500 meters for other types of
engines.
6.
There should not be 25 years recycling protected area around the
landfills.
Selective Criteria are additional criteria used to select the best location
2)
aside the regional criteria:
i.
Climate:
1.
Less rain intensity is better for landfills.
2.
Dominant wind should not blow to the residencial area.
ii.
Utility: the more the better
iii.
Biological environment
iv.
1.
Less variation is better
2.
Less life-supporting is better
Land condition
1.
Less production is better
2.
Higher capacity, longer time span is better
3.
Appropriate amount of covering soil is needed
4.
The more various the status, the worse it is for landfill
25
Demography: The lower the population density, the better it is.
v.
3)
1.
It is better be in the administrative zone
2.
It is better to have more buffer zone in term of noise
3.
It is better to have more buffer zone in term of smell
4.
It is better to be more covered in term of aesthetics
5.
The less the cost of waste management /m3/ton is the better.
Determining Criteria is the criteria used by the policymakers to determine
the best location based on the regulation and regional policies.
According to Sumathi (2006), the list of factors considered for selecting the
disposal sites are as indicated:
-
lake and ponds,
-
rivers,
-
water supply sources,
-
groundwater table,
-
groundwater quality,
-
infiltration,
-
air quality index,
-
geology,
-
fault line,
-
elevation,
-
land use,
-
habitation,
-
highways,
-
sensitive sites.
26
CHAPTER 3
GEOGRAPHY INFORMATION SYSTEM
3.1
Introduction
This chapter describes about the basic principles of GIS related to use, and its
potential in urban management by taking example of few case studies. Network
Analysis method related to the study would also briefly discussed in this chapter.
3.2
Geography Information System
In recent years, GIS has emerged as a very important tool for land use
suitability analysis. GIS can recognize, correlate and analyze the spatial relationship
between mapped phenomena, thereby enabling policy-makers to link disparate
sources of information, perform sophisticated analysis, visualize trends, project
outcomes and strategize long-term planning goals (Malczewski, 2004).
27
The use of GIS is found ideal for preliminary waste disposal site selection
studies. This technology makes it possible to relate the Ground water of a site with
the health parameters of its inhabitants. The ability of overlay gives it a unique power
in helping us to make decision about the identification of waste disposal sites
(landfill). Once a GIS Database is developed, it can provide an efficient and cost
effective means of analyzing the best site for disposal of solid waste. Manual method
of selection of such sites is very tedious. Integration and correlation of the
information related to the factors considered for site selection, which is very
complex, can be handled easily with GIS.
GIS has been found to play a significant role in the domain of siting of waste
disposal sites. The potential advantage of a GIS-based approach for siting arises from
the fact that it not only reduces time and cost of site selection, but also provides a
digital data bank for long-term monitoring of the site. GIS may also play a key role
in maintaining account data to facilitate collection operation and provide customer
service, analyzing optimal locations for transfer stations, planning routes for vehicles
transporting waste to transfer stations and from transfer stations to landfills, as well
as long-term monitoring of landfills. Other advantages of applying GIS in the landfill
siting process may include:
-
Selection of objective zone exclusion process according to the set of provided
screening criteria.
-
Zoning and buffering.
Based on its general function, GIS could be defined as ‘any manual or
computer based set of procedure’ (Aronoff, 1989) for storing and managing
geographical data. It is more into computer, because it is related to scientific
methodologies and involved business and industries (Davis, 2001). Based on the
forementioned concept, GIS is an integration of three elements:
-
Geography: True space in a true world.
-
Information: Data about an object and its functions.
-
System: Computer technology and its supporting infrastructures.
28
There many definitions for GIS, some experts define GIS as an ‘advanced
computer mapping’.
Common definition of GIS according to National Science
Foundation (NSF) is ‘technological system related to computer and methods on
collecting, storing, analyse, manipulate and present data and information (Davis,
2001). In a broader definition, GIS defined as a digital system used for acquisition,
management, analyses, and spatial data visualizations for planning, administrations,
and supervising environmental and socioeconomical situation (Konecny, 2003). It
provides digital model for a broader geographical model. (Diagram 3.1).
Chang (2004) stated that GIS is a computer system to acquire, store, question,
analyse, and present geographical data. GIS is able to control and process massive
geographical data for other applications.
The clear boundary of GIS usage is difficult to measure because of the
evolution of the GIS itself. Development of high-performance hardware and software
influenced the development of GIS (O’Looney, 2000). The newest system in GIS
takes high-performance system in order to provide Spatial statistical analysis,
network analysis, computer-assisted design (CAD), automated mapping/facilities
mapping, geocoding and global positioning system, database management system,
expert system and others. Its comprehensive usage might be applied to design,
science, landscape architecture, geology, mining, and others (Hanna, 1998).
GIS can be described as geography-based information system. It refers to the
earth or specific coordinates. Based on its functions, GIS should be defined as
database and decision making-software. Burrough (1982) stated that GIS is a set of
instruments to collect, store, re-acquire, manipulate and present spatial data from a
real space. Bernhardsen (1999) argued that GIS is a system that is capable to
manipulate geographical data and providing integrated map.
29
3.2.1 Component of Geographic Data
GIS components included:
1)
Computer system included computer and operating system capable of
running GIS, and other instrument like monitor (for digitizing and scanning
spatial data), and printer to produce hardcopy data.
2)
GIS Softcopy for managing and analyzing spatial data and provide facilities
to develop user interface. User interface in GIS includes menus and icons.
3)
Ideas are as important as the software and the hardware. Ideas are refered to
the objectives of using the GIS.
4)
Infrastructures include organization, management, and other supporting
matters, such as skill, appropriate data, and copyrights.
3.2.2 Data Organizations in GIS
Geographical data is the most common data used in GIS. Geographical data
divided into two: geometry and attribute (Fig.3.1). Geometry data is the data with
specific location and address (X, Y, Z coordinates). Geometry data stands dots, lines,
and polygons. The example of geometry data is the landfill location, roads, main
roads, and district borders. Attribute data are categorized into qualitative and
quantitative data. Qualitative data refers to primary data such as information about a
house, a bridge, a road, or public facilities, while quantitative data refers to statistical
or secondary data. Example of quantitative data is the data about travel information,
or classifications of vehicle drivers’ age.
30
GIS Data stores information in the form of layers. These layers can be put
together and form a new format of map. These layers contained information like
shape and type of the roads, rivers, boundaries, buildings, and even sign boards
(Fig.3.2).
Figure 3.1. Geography Data
Figure 3.2 GIS data “Layer”
31
Tecim (1997) stated that every system built based on GIS would be
depending on the way of data managed, the precision of data, and the quality of the
data. Less precise data would create a false map that might put the user into deep
troubles in designing a site.
According to Ruslan Rainis (1998), data is the most important component in
GIS, because the data is the base of the information processing. Data could be stated
as an unprocessed raw material before turned into information; hence, it is important
to classify the data resources.
3.2.3
GIS Software
ArcInfo is a product of Environmental System Research Institute (ESRI). It is
developed to create, analyse, manage, and present information. ArcInfo 9 is a
geographic information system (GIS) data visualization, query, and map creation
solution designed for the Windows desktop. It is based on the same architectural
technology on which the other ArcGIS Desktop products are based. This software is
capable to read many kinds of format. Data imported from different format would be
converted to ArcInfo format.
ArcInfo 9 is composed of three applications (ESRI, 2006):
•
ArcMap—the application for map making and analysis
•
ArcCatalog—a tool for accessing and managing data
•
ArcToolbox—the
geoprocessing
conversion, and analysis
environment
for
data
management,
32
Templates in ArcInfo 9 can contain the following:
•
Arrangement of elements on the page
•
Page orientation
•
Page size
•
Page units—some templates with metric page units; others with inches
•
Output image quality
•
Printer setup information (if Map size is set to Same as Printer)
•
Guides
•
Layout options
•
Data view options
•
Style references
•
Data
•
Customized interface
•
Visual Basic for Applications (VBA) customization
By using ArcInfo, users are able to produce maps and manipulate data.
Visualization tools in the software supports the user to use information in the
database and develop a digital map based on them. Advantages of this software are
as follows:
a.
Integration of several organizations of data
b.
Providing interactive map and connect it to charts, table, and pictures
c.
Reduce the time consumed in an organizational work and decision-making.
d.
Develop additional instruments, user interface, and complete application
based on the users’ needs.
ArcToolbox includes hundreds of tools for advanced geoprocessing
on all supported data types and is accessible as a dockable window in both
ArcMap and ArcCatalog. In ArcView 9, templates are more than specifications for a
page layout. It can specify additional functionality, user interface arrangements, and
additional data to be included in the data frames of the template. In other words,
templates
in
ArcMap
define
how
the
application
looks
and
works.
33
3.3
Network Analysis
Both geographic information systems (GIS) and network analysis are
burgeoning fields, characterized by rapid methodological and scientific advances in
recent years. A geographic information system (GIS) is a digital computer
application designed for the capture, storage, manipulation, analysis and display of
geographic information. Geographic location is the element that distinguishes
geographic information from all other types of information. Without location, data
are termed to be non-spatial and would have little value within a GIS. Location is,
thus, the basis for many benefits of GIS: the ability to map, the ability to measure
distances and the ability to tie different kinds of information together because they
refer to the same place (Longley et al., 2001).
A network is referred to as a pure network if only its topology
and connectivity are considered. If a network is characterised by its topology
and flow characteristics (such as capacity constraints, path choice and link cost
functions) it is referred to as a flow network. A transportation network is a flow
network
representing
the
movement
of
people,
vehicles
or
goods
(Bell and Iida, 1997). Network analysis is used for identifying the most
efficient routes or paths for allocation of services. This involves finding the shortest
or least-cost manner in which to visit a location or a set of locations in a network.
The "cost" in a network analysis is frequently distance or travel time.
(http://www.volusia.org/gis/network.htm).
Chang (2004) stated that network is a system that connects
characteristics or places. Network is a line with attribution information (for instance
like a road would contain its name). Network of roads, for instance, have different
impedance factors like the direction, the junctions, and types of the road. Link refers
to the link between two places in a network (Fig. 7). Link impedance factors are the
‘cost’ of the existing network. Cost involved in a road network includes distance and
time taken. Appropriate impedance factors Costs involved in the road network is
such as travel distance and travel time / distance.
34
The major factor is the appropriate time that travel is through the distance and
speed proportionate straight, Chang (2004). For example, if the limit of
speed 70 km / hour and the distance is 5 km, travel time is 4:29 minutes (5 / 70 x 60
minutes). Example of the network is a network transport such as road or railway
network and the communication network such as electricity and phone or device. The
network must have a starting point, end point and the relationship that connects the
dots between these.
A, B, C, ... = Nod
1,2,3,…
= Connection
Figure 3.3. Road Network (Nod and Connection)
Analysis of the transport network is one of the transportation system
applications where the distance off the analysis is the most frequently used, Zhan et
al. (1997) and Heywood et al. (2002). According to Campbell (2001), network
analysis is important in the design where the digital road map can help users to know
the direction of the road and street information. Analysis of the network is a major
factor in determining the short-distance travel, the cost of travel is low and a season
to go in the direction where I (Ettema and Timmermans, 1997). Network analysis in
GIS is usually closely related with the problems in the transportation system.
Categorize network analysis may be on the three spatial questions, the question
attributes and also something new from all the data used.
35
3.3.1 Basic Principles of Network Analysis
Network analysis has 3 basic principles, there are :
a)
Network Tracing
The process of tracing the best network is based on the criteria set by
the user. It is important to consider about the width of the road and the
distance to a landfill location.
b)
Network Routing
Network routing is a process to identify the optimal route along the
network. The most commonly used analysis options are shortest
distance, longest distance, and minimum cost. This analysis is based on
the type of the network such as point-to-point or several points. For
landfills, those factors need to be considered.
c)
Network Allocation
Allocation / Distribution is a process to look for boundaries of a
network from one point. For instance, school area should be located in
appropriate area based on the number of the students in each school.
This principle can be used in landfill as well.
In this study, all of the principles are necessary. These principles are
important in order to locate the best location from several alternative locations
provided by the earlier analyses.
36
3.3.2 GIS Network Analysis
GIS has different analysis functions, one of them is the network analysis such
as ArcGIS, Small World and ArcInfo. ArcInfo would be explained more in this paper
due to its usage in this study.
ArcInfo is capable of analysing and exploring spatial data in order to solve
the network-related problems such as:
a.
Manage geographical data based on the format of ARC/INFO® coverages,
ESRI shapefiles dan computer-aided design (CAD).
b.
Find the best route out of two or more routes.
c.
Find the nearest facilities
d.
Analyze travel time
e.
Develop new application with a support from Avenue software.
Network analysis modules in ArcInfo are categorized into three: Finding the
best route, Locating nearest facility, and Locating service area (such as landfills). In
order to do network analysis it look for the less impedance factors between two
locations, as well as ‘reading and matching’ criteria based on the database. The last
mentioned process is the one used for landfill locating.
ArcInfo analysis model includes networks with impedance factors.
Impedance factors are importance in order to determine appropriate routes based on
the reason of the analysis. For instance, networks of main roads usually have factors
like: two ways or one way, speed limits, and other factors.
37
ArcInfo might provide model characteristics such as:
(i)
Cost of travel.
The software might help the user to forecast the average cost of a route. It
could be based on the distance, time taken, financial, or other costs
determined by the user.
(ii)
One way road.
The software might help the user to determine one-way roads.
(iii)
Turnings.
The software might help the user to determine the appropriate turnings like
junctions, U-turn, and forbidden entrances.
(iv)
Upper and Lower lanes.
This facility helps the user to determine fly-overs or other multi leveled lanes
set.
(v)
Forbidden lanes.
This facility helps the user to determine forbidden entrances and lanes.
In general, ArcInfo might solve problems related to the forementioned
characteristics. Some characteristics which ArcInfo might not able to solve could be
solved by using Avenue.
Verbyla, (2002) stated that network analysis is an
analysis towards a line that stands from several different connected lines. The
advantages of GIS’ network analysis are as follows:
a)
Position Determination
Position Determination is a process of place forecasting using GIS coordinate
system. For instance, GIS might help the user to locate an address for
delivery, provide shortest route, and shows the user information based on the
database.
b)
Optimal Route Determination
GIS is able to show the best route between places. It could be the shortest, the
fastest, the most appropriate, or depending on the needs of the user. For
instance, GIS might help fire departments to determine the best route.
38
c)
Nearest Facilities Determination
As a continuity of optimal route determination, this analysis might determine
one location to the nearest facilities in order to help the user to determine the
best location for specific events. For instance, GIS might help the user to
determine which gas station is the nearest one to a recreational facilities and a
mosque.
d)
Resource Distribution
This analysis would help the user to find the best distribution lines from one
location to another.
3.4
Dijkstra’s Algorithm in Network Analysis
In the current work, using Network Analyst, an optimum route for the waste
collection of large items is generated in the area under study. Network Analyst uses
the Dijkstra’s Algorithm (Dijkstra 1959) in order to solve the Routing Problem and it
can be generated based on two criteria (Lakshumi et al 2006):
1. Distance criteria: The route is generated taking only into consideration the location
of the waste large items. The volume of traffic in the roads is not considered in this
case.
2. Time criteria: The total travel time in each road segment should be considered as
the: Total traveltime in the route = runtime of the vehicle + waste collection of large
items time. The runtime of the vehicle is calculated by considering the length of the
road and the speed of the vehicle in each road. The time of the waste large items
collection would be the total time consumed by the vehicle to collect these objects
from all the loading spots In the second criteria, the length, width and the volume of
traffic are taken into account in each road segment.
39
Network Analyst software determines the best route by using an algorithm
which finds the shortest path, developed by Edgar Dijkstra (1959). Dijkstra’s
algorithm is the simplest path finding algorithm, even though these days a lot of
other algorithms have been developed. Dijkstra’s algorithm reduces the amount of
computational time and power needed to find the optimal path. The algorithm strikes
a balance by calculating a path which is close to the optimal path that is
computationally manageable (Olivera 2002).
Network Analyst can be very useful in a variety of sections (ESRI 2006) in
our daily life, such as in:
Business, scheduling deliveries and installations while including time
window restrictions, or calculating drive time to determine customer base,
taking into account rush hour versus midday traffic volumes.
Education, generating school bus routes honoring curb approach and no Uturn rules.
Environmental Health, determining effective routes for county health
inspectors.
Public Safety, routing emergency response crews to incidents, or calculating
drive time for first responder planning.
Public Works, determining the optimal route for point-to-point pickups of
massive trash items or routing of repair crews.
Retail, finding the closest store based on a customer's location including the
ability to return the closest ranked by distance.
Transportation, calculating accessibility for mass transit systems by using a
complex network dataset.
40
3.5
Case Study
According to Thirumalaivasan dan Guruswamy (2003) studied about analyses
of optimal track using GIS in Chennai. In their research, they used ambulance routes
as a base, with several factors such as time, traffic density, types and width of the
roads, junctions, and distance between routes. These factors were evaluated to decide
an option that required lesser time. This study requires good attention to the details,
because it was related to serious matters such as lives. The study was done by using
ArcInfo software to do the network analysis.
Karadimas, et. al, (2007), have done the research work about application
Djikstra’s Algorithm to found out the best route. Located in a small part of Attica’s
prefecture (a suburb of Athens) was chosen as the case study area. The municipality
of Athens is empirically divided into about 30 districts each of which has several
solid waste collecting programs for the daily needs of the city. However, there are
cases where the standard waste garbage trucks are unable to remove certain pieces of
refuse, because of varied restrictive reasons, like size (e.g. furniture, electrical
devices, debris). Briefly, this kind of refuse is consisted of really heavy or bulky
things whose exposure to air is almost never challenging to the hygienic standards.
That is why it is not essential for the refuse to be collected immediately, unless they
cause circulatory problems. In this special occasion, the piece of refuse must be
collected the moment that it is reported.
Therefore, the municipality has retained a modified truck for each district.
The concept is that the standard daily waste collecting programs report to the
Municipal Solid Waste Department (MSWD) every location that was unable to
collect. The MSWD processes the information for each district and decides on the
procedure that will be followed. An average scenario for a district is that its assigned
truck makes two weekly trips and that each time it visits around 15 locations. The
particular truck must visit all the reported locations in order to complete its collection
program.
41
The examined area is about 1.34km2, with a population of more than 20,000
citizens and a production of about 5,000 tones of urban refuse per year. The data
concerning the area under examination was obtained from the pertinent MSWD of
Athens. The data includes maps of the examined area with the correspondent
annotation (address and numbering labels of streets), the building blocks and the
locations of the waste large items to be collected.
42
CHAPTER 4
SOLID WASTE MANAGEMENT IN BATAM
4.1
Introduction
This chapter is discussing waste management in Batam in the present time
and its plan for the future. This chapter discusses about the waste location pattern and
transport route for the waste.
4.2
Profile of Study Area
4.2.1 Batam in Figure
Based on Peraturan Daerah Nomor 2, tahun 2004, about landuse of Batam
Area 2004-2014, the city of Batam is geographically located between (Figure 4.1):
-
0o.25' 29E NL - 1o15'00E NL
-
103o.34' 35E EL - 104o26'04E EL
43
43
LANDUSE PLAN OF BATAM
2008 - 2028
LAND USE PATTERN PLAN
-
MUNICIPAL OF BATAM
44
The study area included 9 districts (kecamatan) in the mainland of Batam
included Kecamatan Batu Aji , Kecamatan Sekupang, Kecamatan Sagulung,
Kecamatan Sungai Beduk , Kecamatan Batam Kota, Kecamatan Nongsa, Kecamatan
Batu Ampar, Kecamatan Bengkong, Kecamatan Lubuk Baja
4.2.2
Land use
Structural Landuse of Batam 2008-2028 included:
1. Municipal Service Area Plan (Figure 4.3)
2. Development of transportation structural and infrastructural, as main
transportation network between municipal service areas.
Road
Public Amenity
Mangroves
Forest
Urban Forest
Catchment Area
Commercial
Special Area
Recreation Area
Fishery
Industrial
Residential
Agriculture
Reservoir Plan
Coastal Line
Figure 4.1. Landuse Of Batam Batam Island
45
Table 4.1. Landuse of Municipal of Batam
Hierarchy
Locations and Types
1
Primary Service Center:
Primary Service Center of
Batam.
Land use
-
Batam Center (main center).
-
Sekupang (support).
-
Other location out of Batam Center
and
Sekupang
(individual
support).
Primary Service Center of
Trade and Services
-
Nagoya
-
Baloi
-
Lubuk Baja.
-
Batam Centre
Industrial Primary Service
- Kabil.
Center
- Sekupang.
- Batu Ampar.
Tourism Primary Service
Center
- Nongsa.
- Tanjung Pinggir.
- Jodoh.
- Camp Vietnam.
Primary Service Center
related to Free-Trade Zone
ditto b, c, and d.
and Batam Free Ports.
Secondary Service Center
Governmental Service
Center, Public facilities, and
District Point
Commercials.
Location of commercial activities and/or
public facilities in the respective district.
Industrial Service Center
Batam Center.
- Muka Kuning.
- Tanjung Uncang.
/
46
Hierarchy
Locations and Types
Land use
- P. Janda Berias.
- Tanjung Gundap.
- Sembulang.
Marine Resources Support
Center
Belakang Padang.
- P. Bulan Lintang.
- P. Setokok.
- P. Abang Besar.
P. Galang Baru (Batam Marine Culture
Estate/BME).
- P. Nipah.
- P. Abang Besar.
- Telaga Punggur.
- P. Bulan Lintang.
Sekupang.
- Telaga Punggur.
- Sagulung.
- Sijantung.
Subdistrict points
Residential Service Center
- Residencial Estates
- Inhibited small islands
- Inhibited small border islands.
4.3
Waste Management in Batam
4.3.1
Solid Waste in Batam
47
Based on their characteristic, there are two kinds of solid waste in Batam, namely
domestic and non-domestic. Based on the analysis in 2006, most of the solid waste in
Batam were classified into domestic waste (77.67%), while the non-domestic waste
was around 22.33%.
A.
Domestic Waste
Domestic waste was generated from the household wastes. Urban households
in Batam contributed a lot to the entire solid waste generations.
Daily generation of solid domestic waste in Batam is 588.28m3 from the total of
771.71m3/day. It makes 77.66% out of the entire waste generation in the city.
B.
Non Domestic
Non-domestic waste generated from the industrial, commercial, and social
facilities. In this section, the non-domestic waste mentioned were those that ended up
in Telaga Punggur landfill. The largest contributors for waste in this landfill are the
wet markets, while industrial waste was considered not that much.
The generation of non-domestic waste in Batam reached 183.44 m3/day.
Out of the number, 106.19m3 came from the wet markets, while 19.68m3
them came from the commercial areas. Unless there is a significant
in term of the lifestyle, waste generation is significantly growing
year,
following
the
rapid
growth
of
of
change
year after
the
population.
4.3.2 Waste Management
Since 2006, Telaga Punggur landfill received 592.3m3 of daily waste, while
the entire of waste generated were 771.71m3/day. It indicated that the waste
management service in the area was 76.75%, which means that it was not optimum.
48
The untimely collections of the garbage develop higher piles of solid waste in certain
places.
Public services in waste management is not just a quantitative quotation, or in
this case, how much waste were successfully transported to the landfill, it should
contained qualitative aspects such as the punctual collections, adequate transport,
professionalism in waste management, facilitation for waste managements, and other
aspects that need to be considered.
Solid wastes in Batam fall into many categories; households’ domestic wastes
contributed 61.95% from the total, followed by plastic waste with 13.38%. Wet
markets contained 76.85% of domestic wastes, while industrial solid waste was
dominated by plastics (37.81%) and papers (20.85%). Commercial areas contained
34.65% of plastic wastes and 33.27% of paper wastes. In general, organic waste is
dominating in Batam’s landfills. This indicated that waste management in Batam
needs some special treatments due to the nature of organic wastes.
4.3.3
Field Condition of Waste Management in Batam.
Based on a field survey, the current waste management was operated as
follows:
1.
Citizen collected the wastes by using trolley, motored trolley, and dumped
them at the temporary landfill and then collected by the waste-transporting
trucks before they dumped it in the bigger landfills. (DPK and MitrA)
2.
Transportation from temporary landfill to permanent landfill was done by a
third party company, PT Surya Sejahtera. They transported both domestic and
industrial wastes.
4.3.4
Waste Management Service in Batam
Until June 2006, Dinas Pasar dan Kebersihan Kota Batam covered 76.75%
of the total waste management service in Batam. It takes 2 hour drive to the landfill
excisting. This percentage was extracted from the ratio of managed and total waste in
49
Batam (Primary Data). Summary of the covered area in term of waste management
service (by Dinas Pasar dan Kebersihan Kota Batam and its partners) is shown in
the table 4.2 :
Table 4.2. Location of Waste Transit Point in Batam Island
NO.
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
DISTRICT/KECAMATAN
BATU AJI
SEKUPANG
SAGULUNG
TRANSFER/TRANSIT POINT
Griya Prima
Permata Puri,RKT TPS
Permata Hijau, Tri Puri
Paradise Golf, Mutiara Indah
Puri Mas, Rindang Garden
Griya Pratama, Nagajaya, Mutiara Biru
Genta II, Ruko Mitra Mall
Cipta Prima, TPS Aviari
Ruko Pando, HPN, Naga jaya
Simpang Base Camp, (RA)
Pemda I-II, Holiday Inn
Perum HPM, TPS Sawang Permai
Pluto, Puteri tujuh
Tembesi Centre, Pandawa I- II
Taman Lestari
Puskopkar, Genta I
Genta Samping, Marina Cyri
Tg. Riau, Per Sei Harapan, Komp Otorita
Tiban Koperasi (RGA)
Tiban Koperasi TPS
Tiban Housing, Tiban Palem
Pelabuhan Domestik,Intrnsnl Skpg
RSOB, Mc Dermott I dan II, Tg Pinggir
Psr Cipta Puri, RSS sekupang
Puri Malaka
Komp. Wijaya, Delta Villa, Mutiara Point
Mutiara View, Tiban Bukit Asri
Taman sari, Mekar Sari, Tiban Mas
Masyeba Indah,
Tiban I - III
Tiban Indah Permai
Psr. PJB, PJB II, Puri Barata, Mantang,
Psr. PJB, PJB I&III, Karisma
Merapi Subur
Padjadjaran, Rasinton Raya,
Batavia, Mitra Centre, SP Plaza
50
NO.
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
DISTRICT/KECAMATAN
SEI BEDUK
BATAM KOTA
TRANSFER/TRANSIT POINT
Buana Thp I & II, Psr PJB
Villa Mk Kuning, Griya Permata
Griya Permata, Psr Sagulung
PGRI, Griya Permai
Griya Sagulung, Psr Sagulung
Griya Sagulung Blk A-C
Graha Anggara (G-H) & Ruko (D-F)
Psr. Sagulung
Batu Aji Asri, Putri Hijau
Fortuna, Kpg Becek, Hutatap
Batu Aji Permai, Ruko Mandala
Klasik, Arse Indah, Artaguna, Teratai
Kavling Seraya, Nato
Mantang, PJB
Perum Sei Pancur, GMP Tahap I-II
Pondok Graha, Lestari Biru I-II
Permata Asri
Puri Agung I-III, Mutiara Hijau
Bida Ayu
Komp. KEI, Komp. Suar Batam
T. Piayu,
Katolik, Global, Asrama Haji
Executive, BPD, Kom Melati, Villa Bkt Indh
Citra Batam, Perum Centre Point,Graha pena
TPS Suroboyo, Palm Spring
Citra Batam, Citra kota Mas, Global
Golden Land, Eden Park, Gloria Indah
Wisma Indah, Orchid Suit, Regensi, Putri Idh
Sukajadi, Palm Spring
Rosedale, Tmn Seruni
Komp. Tanah Mas
Bandar Sri Mas
Batam Centre
Komp. Industri Tri Tunas
Citra Indah, Kopi Tiam, Kintamani
Vihara, Bandar Srimas
Bumi Riau Makmur, Bandar Mas, PLN
Komp. Kurnia Jaya I-IV & PLN
Komp. Cendana Tahap I-V & PLN
Orchid Park & Kembang Sari
Komp. Duta Mas
Anggrek Sari, TPS Komp. Mitra Raya
Green Land,TKW, Tmn Sejati, Graha Nusa
Tmn Ry Atas, Batara, Dotamana, RkTMP
Anggrek Mas, Plamo Garden
51
NO.
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
DISTRICT/KECAMATAN
NONGSA
BATU AMPAR
TRANSFER/TRANSIT POINT
Bida Asri, Taman Raya Bwh
Marcelia
Legenda
Mediterania
Legenda
Kmpg. Melayu
TPS Buana Indah
TPS Pondok Asri
Nusa Jaya
Bukit Jodoh
Pondok Asri & Villa Mas
Baloi Harapan I & III
Bukit Golf
Bukit Beruntung TPS Puri Loka
Rajawali, Pesona Asri, Yasmine,
Bandara Mas, TPS PosPol, Citra Mas,
Nongsa Asri, POLDA, Turi Beach,
Jabi, KmpMelayu
Teminal Nongsa Pura, Palm Spring Golf
Batam View, Pura Jaya, Sambao
Psr Tg. Pantun, TPS Novotel
Batam Kuring
TPS Seraya Bawah
RM. Srideli
TPS Bukit Senyum
TPS Bengkong Bengkel
Psr Tg. Pantun, TPS Novotel
RM. Srideli
Sakura Garden
Sakura Permai
TPS VJB
Dian Bakery
Toyo Kanetsu
BII Jodoh
Bank Mandiri
Mc. Donald
Komp. Hotel Harmoni
Dana Graha
BCA Jodoh
Pertokoan Maritim Square
Orchid Point
Hotel dan Pertokoan Oasis
Rusun Lancang kuning
Rusun Bida Ampar
H. Goodway
52
NO.
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
DISTRICT/KECAMATAN
BENGKONG
TRANSFER/TRANSIT POINT
Komp. Bumi Ayu
H. Lai-lai
Komp. Roma
Tanah Longsor
Bank Permata
Jamsostek
Masjid Baitusyakur jodoh
PT. Mei Koji Jaya
Komp. Inti Sakti
TPS Hotel Sentosa & Batam Jaya
Komp. Seruni & Abadi Tama
Nagoya Garden Phase II
TPS Harapan Bunda
Seraya Kavling, KODIM, Seraya Garden
Villa Cemara &
Pertokoan Tg. Pantun
Planet Holiday
Komp. Sumber Agung, Happy Valley
Hotel Pacific
PT. Lautan Jaya
Pertokoan Tg. Pantun & Jaya Putra
Pertokoan Depan Novotel
H. Kenanga
Melia Panorama
Komp MCP
Psr Sukaramai
Pertokoan pasar bengkong centre
Bengkong Indah I
Bengkong Indah II
Pertokoan Green Town
Bengkong City
Bengkong Ratu
Pasar Melati
YKB
Bengkong Abadi
Komp. Bengkong Permai
Top 100 Bengkong
Bengkong Harapan I-II
Bengkong Polisi
Sarmen Raya
Bengkong Palapa
Bengkong Harapan I-II
Taman Harapan Indah
Apartement Green Town
Perum. Putra Kelana Jaya
53
NO.
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
DISTRICT/KECAMATAN
LUBUK BAJA
TRANSFER/TRANSIT POINT
Perum Bengkong Kodim
Bengkong Baru
Bengkong Mas
Pasar Harapan & Pasar Angkasa
Gudang Kayu Bengkong Ratu
Windsor Central keliling, Pujasera
Citra Mas
Sri Jaya Abadi, Keliling
Sakura Ampan, Keliling
Polaris sakti, keliling
Mercu Lautan Indah
Hotel Said Rasinta, TPS
Pujasera Nagoya Foodcourt, TPS
Pasar Lucky Estate, TPS
Kp. Utama Atas+TPS Rusun Liar
Bukit Mas
Baloi Mas Asri
Taman Kota Mas & sekitarnya
Baloi Indah
Kp. Utama Bawah
Bumi Indah Blk I-V, Kios Bumi Indah
Bisnis Cntr, Windsor Square, Hotel Sri Jaya
Jl. Sriwijaya sekitarnya
Pasar Pelita
Penginapan Pelita
TPS Seraya Atas
Perbengkelan Pelita
Perum Pelita I-VII, Rusun Pelita
Perum TNI
Perum Windsor Phase II
Komp. BTN, TPS
Rest. Fajar, Hotel 88, TPS
TPS Hotel Royal, Hotel Mutiara
TPS Hotel Kolekta, TPS Blk Kolekta
TPS Pizza Hut, Komp. Harmoni, Libra Star
TPS Hotel Formosa
TPS Hotel Centre Point
TPS Matahari
TPA A1 Pujasera
Nagoya lama keliling, TPS
TPS Windsor Rumah Susun
TPS Tapekong
Perum Happy garden
TPS Hotel Sensai
Hotel Pelita
54
NO.
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
DISTRICT/KECAMATAN
TRANSFER/TRANSIT POINT
TPS Hotel Singa
TPS Hotel 99
TPS Hotel Puri Garden
Komp. Nagoya Garden, TPS
Puja Bahari, TPS
Ramayana, TPS
Robinson, TPS
Nagoya Baru (Toss 3000)
Puja Bahari, TPS
Perum Alamanda, Marina Park
Villa Marina
Komp. Angkasa, kllg dn sktrnya
Nagoya newton, Kllg
Pasar Nagoya Baru (Toss 3000)
Rusun TNI
Hotel Vista, TPS
Pasar Nagoya Baru (Toss 3000)
Tepi Jalan Penuin (Samping Tapekong)
Pasar Blok IV
Perum Blok IV
Perum Blok V
Perum Baloi Centre
Perum Blok II-III
Perum Baloi Kusuma
Perum Baloi Impian
Pinggir jalan Bunga Raya
Perum Blok III
Perum Blok VI
Baloi Garden
Baloi Garden II
Baloi sekitarnya
Komp. Lumbung Rejeki
Jl. Imam Bonjol
Jl. Raden Patah
Baloi Mas (Bt. Batam) & sekitarnya
Jl. Imam Bonjol
Jl. Raden Patah
Perum Nagoya Permai & Batam Park
Baloi Permata
Batu Batam & Sekitarnya
Pasar Pagi Tanjung Uma, Pasar Induk
Nagoya Baru dan sekitarnya
Plaza Top 100 Blk Robinson
Square 91 TPS
Pasar Pagi Tanjung Uma, TPS Tg Uma
55
NO.
DISTRICT/KECAMATAN
262
263
264
265
TRANSFER/TRANSIT POINT
Pasar Penuin
BCS Mall
DC Mall
Top 100 Penuin
Waste collection is an activity where solid wastes were collected and
centralized at temporary or permanent landfills. Waste collection system in Batam is
divided into collection for residencials, commercial estates, institutional, industrials,
and public facilities. Dinas Pasar dan Kebersihan Kota Batam (DPK) and its
partners covered the area of residencial and public facilities, while industrial estate
were covered by DPK’s partners, in corporation with PT Surya Sejahtera that directly
take the waste to the landfills.
PT. Surya Sejahtera has started its work in Febuary 2009, after winning the
tender with the government in Januari 2009. As the one in charge of waste
management, the company has to report to Dinas Pasar dan Kebersihan Kota Batam
(DPK). PT. Surya Sejahtera collected the waste in all districts daily by utilizing 75
ttucks, each of them has 4 tons capacity. The trucks collected waste from 265
transfer points without categorizing household waste, commercial waste, industrial
waste, or any other kinds of wastes, as long as it is not categorized as hazardous
waste. They do not have consistent record on each pick up, however the next figure
shows the daily collection from each district in February-June 2009 (Table 4.3)
Table 4.3 Waste Generates
No
1
District
Batuaji
Waste Generates
Total per a month
Average / day
920.47
30.682
Transfer Point
17
56
2
Sekupang
1087.524
36.251
14
3
Sagulung
948.86
31.629
19
4
Sei Beduk
475.49
15.85
7
5
Batam Kota
1439.88
47.996
38
6
Nongsa
481.18
16.039
6
7
Batu Ampar
1756.37
58.546
50
8
Bengkong
914.31
30.477
35
9
Lubuk Baja
2224.59
74.153
89
9248.674
341.62
265
Total
Source : PT. Surya Sejahtera (Febuary-Juni 2009)
4.4
Database Development
Criteria setting and model analysis process was developed after preparing
criteria database. This database development level is the main processes which need
to be emphasized. This is because the database used will affect analysis results.
Identification data process is predicated and model analysis was developed. Model
form determined for each criteria whether in polygon form, line or point (Table 4.2).
This model is dependent research scale. This will form one database that describes
the current situation in Kota Batam.
Table 4.4. Data modeling in research
Component
Data
Form
57
Administration
Landuse
Network
Transit Point
Municipal’s Boundary
Polygon
District Boundary
Polygon
Public Amenity
Polygon
Mangroves Forest
Polygon
Urban Forest
Polygon
Catchment Area
Polygon
Commercial
Polygon
Special Area
Polygon
Recreation Area
Polygon
Fishery
Polygon
Industrial
Polygon
Residential
Polygon
Agriculture
Polygon
Reservoir Plan
Polygon
Costal Line
Polygon
Reservoir and Lake
Polygon
Artery Road
Line
Collector Road
Line
Collector Secondary Road
Line
Other
Line
Transit Point (TPS)
Point
Whereas for that data preparation there is data format that has been used.
From this case study details information that has been used is:
Data Format : Shapefile (*.shp)
Data Type : Vector And Raster Data
Most of the data found in secondary resource. The data was taken from:
1.
Local Plan (RTRW) or Landuse Plan Municipal of Batam, 2008-2028.
2.
Slope Layer from www.ESRI.com
3.
Transit Point Waste Collection
58
Data format (Landuse Plan) that was developed were in the form
Tabel (tab), it need to convert to shapefile(*.shp). This format is used because
it easy to use it in ArcGIS software. Data format used was adequate with need of
research analysis. Meanwhile data type that was developed is in vector form. Vector
data conversion to raster data would be made during research analysis.
Further data source of necessity have to identified before that collection and
database development had been made. There are two methods how database
development should made namely of primary data and secondary data. Primary data
measurement involves process directly which made in study area. In this research
information there were some need to be taken through field research and it entails
spatial information and non spatial information. Between example of information
development through field research are such as transit point position, the logest
transit point in each district and others.
4.5
Using Dijkstra’s Algorithm in Network Analysis
The algorithm breaks the network into nodes (where lines join, start or end)
and the paths between such nodes are represented by lines. In addition, each line has
an associated cost representing the cost (length) of each line in order to reach a node.
There are many possible paths between the origin and destination, but the path
calculated depends on which nodes are visited and in which order. The idea is that,
each time the node, to be visited next, is selected after a sequence of comparative
iterations, during which, each candidate-node is compared with others in terms of
cost (Stewart 2004). The following comprehensible example, which is an application
of the algorithm on a case of 6 nodes connected by directed lines with assigned costs,
explains the steps between each iteration of the algorithm (Figure 4.2). The shortest
path from node 1 to the other nodes can be found by tracing back predecessors (bold
arrows), while the path’s cost is noted above the node.
59
Figure 4.2 An example of Dijkstra’s algorithm (Orlin 2003).
Each node is processed exactly once according to an order that is being
specified below. Node 1 (i.e. origin node) is processed first. A record of the nodes
that were processed is kept, call it Queue (Table 1). So initially Queue={1}. When
node k is processed the following task is performed: If the path’s cost from the origin
node to j could be improved including the vertex (k,j) in the path then, an update
follows both of Distance[j] with the new cost and Predecessors[j] with k, where j is
any of the unprocessed nodes and Distance[] is the path’s cost from the origin node
to j. The next node to be processed is the one with the minimum Distance[], in other
words is the nearest to the origin node among all the nodes that are yet to be
processed. The shortest route is found by tracing back predecessors.
60
CHAPTER 5
RESEARCH METHDOLOGY
5.1
Introduction
This
the
chapter
selection
process
discusses
for
the
the
methodology
landfill
site
and
utilized
in
determine
analyzing
the
nearest
facility with the best route. Analysis in making decisions is a systematic set of
procedures to solve problems. Data geography play a role in producing
alternative
sites
and
routes
in
accordance
with
criteria
determined
Alternatives formed is determined by the analysis depends on the management of
spatial data.
In general, this study was to determine the location of landfill in Municipal
of Batam involves some major rankings. In conducting this analysis, a design model
used to interact directly with the data base and information in operational systems.
This study involved 3 main stages which are data preparation, site selection for waste
management area and network analysis. The research methodology for this study is
shown in Figure 5.1.
61
Data Preparation
Problem statement
Research objectives
Preparing data in ArcGIS
Available Site Selection
Identify constraints and potential in study area
Network Analysis
Creating a shape file based network dataset
Finding the best route using a network dataset
Calculating service area
Result
Figure 5.1: Research Methodology to find the best route for the trucks to the best
site
5.2
Data Preparation
In order to make this study effectively performed, it was imperative that the
information was accurate and have been representative of the Batam Municipal. This
study is using ArcGIS 9.2 software and the data was supplied in shapefiles (.shp)
form. Data preparation is very important in generating the analysis in ArcGIS. Based
on the problem statement that had been discussed before and the objectives for this
study, the related data had been setup in the GIS database. GIS database design is the
most important part of the entire application (Mohammed, 2008).
62
It deals with the compilation and development of the spatial as well as nonspatial data according to the user requirements. For this study, GIS database is
developed by combining 9 main shapefiles (refer Table 5.1). The database then will
be used and manipulate to run the analysis in this study.
.
Table 5.1: Database structure for the study
Component
Data Layer
Attribute
Administration
Batam Municipal
District
Sub district
Physical
Elevation
< 10◦
10◦ – 20◦
20◦ – 30◦
> 30◦
Transportation
Airport
Location
Residential
Location
Road
Minor Arterial
Collector
Minor Collector
Local
Other
Environmental
Transit point
266 transit points
Starting point
10 starting points
Sensitive Sensitive area
Area
Puddle
Urban Forest
Mangrove
Restricted Forest
Reservoir
Coastal Line
Tourism Area
Tourism
Recreation Area
Tourism Place
63
5.3
Available Site Selection
A site selection criterion for waste disposal area is based on the guideline and
standard from the Indonesia National Standard. For this stage, this study will
determine the alternatives suitable site for waste disposal area in the Batam
Municipal. The data which had been prepared in the ArcGIS database is important to
identify and addresses the best location proposal for the waste disposal area by taking
into account the strengths and obstacles that occur at the site. This is aimed to ensure
minimum impact and also give less interference to people and environment. The
buffer, union and erase operation in the ArcGIS had been applied in selecting the
suitable site (will be explained in the analysis chapter).
a)
Potential Area
Industrial area, high accessibility area
b)
Constraint Area
Environment sensitive area, slope more than 30◦
5.4
Network Analysis
Geographical Information System (GIS) is used for modeling of road
networks offering algorithms to analyze and find the shortest or minimum route
through a network. GIS can be used to calculate distance between sets of origin and
destination, whereas location-allocation functions determine site locations and assign
demand to sites (Imtiyaz, 2006). A GIS optimal routing model is proposed to
determine the minimum cost/distance efficient collection paths for transporting the
solid wastes to the landfill (Ghose et. al, 2006).
64
Transportation, the process by which humans, vehicles and other
commodities moves from one geographic landscape to another, is the essential part
geographic phenomena, and one of the key area of Geographic Information System.
GIS plays a very important role in tracking, navigating, routing and scheduling
vehicles, and for many other tasks.
Using ArcGIS Network Analyst is provides network-based spatial analysis
including routing, travel directions, closest facility, and service area analysis (ESRI,
1995). The NETWORK module (ESRI, 1995) of Arc/Info GIS software is used with
the planned infrastructure to find the shortest or minimum impedance path through a
network. The speed of the vehicle is taken as the arc impedance and no turn
impedance is being used.
Selecting the best route through an area is one of the oldest spatial problems.
But lately, this problem has been effectively solved with the help of GIS and Remote
Sensing technologies. Since last few years, many attempts have been made to
automate the route-planning process using GIS technology [Yildirim, Nisanci.R et
al,2006] Using the GIS Software’s buses can be routed to give the best service for
the maximum number of students and routes can be planned to give the most
economical operation of buses with distance and road conditions being the major
criteria for economical routing.
65
CHAPTER 6
OPTIMAL ROUTE AND CLOSEST ANALYSIS
6.1
Introduction
Planning and development in various development plan level was able to be a
tool to preparing assessment and related development analysis in the future. In this
study GIS was used as a tool to model a spatial analysis to selecting suitable landfill.
Model analysis generated within grid environment. This chapter will analyze in the
study area to selected alternative suitable landfill based on criteria and to know the
closest landfill. Determination of suitable landfill in the study area is one of the
objectives to achieve in this research. Furthermore this research would found the
closest landfill by assess the best route. This analysis used Network Analysis and
applied GIS to aid in achieving decision (Figure 6.1).
66
Research Analysis
Using
Suitable Landfill Area
GIS
Site Selection
Process
Analysis
Application
Using
Closest Landfill
Network Analysis
Best Route
Further Analysis
Service Area of Landfill
Figure 6.1. Conceptual Framework of Analysis
6.2
Identifying alternative landfill sites
Criteria to determine suitable Landfill in GIS Processing are :
1.
Suitable land fill is calculated only in main land of Batam
2.
Slope ≤ 20%.
3.
Distance from road buffer is 1000 M
4.
It has 11 excluded factors based on Municipal’s landuse planning, they
are: gas lines, commercial, coastal lines, tourism forests, city forests,
agriculture, dams and lakes, public facilities, port facilities, conserved
forest, genangan (puddle).
67
6.2.1
Digitizing Area Boundary and Road
Data needed in the selection process of landfill alternative are landuse layer,
layer slope, and road network layer. After creating newshape file boundary (Bdy),
layers can be added. Layers of data to be added included landuse_spatadj,
waduk_batam, roadbufferJalan, Reservoir_batam, roadarteri_batam. All layers then
converted into polygon format to get the boundary shapefile. It could also be done by
digitizing the new shape file to get the boundary of the area. Then create shapefile
road. Digitize road from the existing information / data. Road created based on the
main hierarchy: artery, kolector, local, non/other. (shown on figure 6.2 and 6.3)
Figure 6.2 Digitizing the new shape file to get the boundary of the area
68
Figure 6.3 Road made base on main hierarchy : artery, kolector, local, non/other
6.2.2 Creating Slope from Elevation
Initial data on the can is in the form of layer elevation, it is necessary to
change the layer on the slope. Using arctoolbox, surface changes can be made so that
data can be in the form of elevation make Scopé layer. The process can be performed
on the figures 6.4 below.
69
247
-59
Figure 6.4 Elevation Layer
Figure 6.5 Slope Layer
70
After the layer slope in the can, then the classification should be done and
then do the convert or merger to form layer shapefile to obtain the slope which will
be included in the analysis in the process of selecting site analysis (Figure 6.6).
Classification slope based on slope : < 10,
10 – 20, 20 – 30 and > 30
Figure 6.
6.6
Classification Slope
Figure 6.7. Slope Layer after Classify
71
6.2.3
Environment Sensitive Area
According to SNI criteria, some areas are not meant to be developed as
landfills namely, mangrove forests, urban forest, conservatory forest, dams and lakes,
coastal line, and recreation area. There are some area which are not included in the
analysis, namely residential, fisheries, tourist attractions and forests, and airport.
Extract from landuse to create shapefile ESA. It will combined from type of landuse
that categorized sensitive area that could be done by selecting attribute like shown
Figure 6.8 and Figure 6.9.
Figure 6.8. Extract from landuse, create shapefile ESA
72
Figure 6.9 Reselect landuse that out from the analysis.
73
6.2.4 Site Selecting Process
This process started by creating buffer. There are two buffer processes should
be done, namely road buffer (according to SNI is 1000 meters), airport buffer (3000
meters), and residential
ial buffers (1000 meters). (Figure 6.10).
Road Buffer
Buffer
Airport Buffer
Residential Buffer
Figure 6.10. Buffer Process
74
Every buffer result might be done by arcToolbox, and finished by dissolve
process. (Figure 6.11). Dissolve is used to create a simplified coverage and to merg
merge
adjacent polygons, lines, or regions that have the same value in the buffer layer
layer.
Dissolve maintains linear data belonging to different buffer in the same coverage and
maintains all section subclasses.
Figure 6.11. Dissolve Process
75
After the dissolve process, the next operation is to ‘Union’ all the area that
are not supposed to be established for landfill (Road buffer, Buffer airport and Buffer
residential) to erase step from landuse to get the suitable area. It combined from type
of landuse that categorized sensitive area ; mangrove puddle, urban forest,
conservation forest, reservoir and lakes, reservoir plan, coast lines, recreation area,
residential, airport. (Figure 6.12). Then 3 landfill alternative sites came up. (Figure
6.13).
Figure 6.12 Union Process
76
3
1
2
Figure 6.13 The result of alternative suitable landfill
From the analysis using GIS, then the alternative site was found 3 that
match the criteria in the SNI as a landfill site in the City Batam. This can be input to
Government of Municipal of Batam
Batam City to determine the future landfill site.
site
77
6.2.5
Site Selecting Model
80
6.3.
Number of Waste Generated and Truck Capacity
The data obtained from the PT. Surya Sejahtera, the average amount of solid
waste in Batam about 341.62 tons / day. Based on the number of fleet of 75 trucks
with 4 tons of capacity and the transfer of 265 points, it can be known how much
truck for each district with the following formula :
n=
i
(y x 4 tons)
i = Average Waste Generates (Tons/day)
y = The Number of Trucks (per district)
n = The Number of trip/day
The total numbers of trucks are 75 trucks. To allocate the number of trucks of
each district, the total numbers of trucks divide the total average waste multiplied by
the number of average waste of each district (Table 6.1)
Table 6.1 Allocate The Number of Trip
No
District
Average Waste
(Tons/day)
(i)
Transfer
Point
Truck
(y)
1
Batuaji
30.682
17
7
2
Sekupang
36.251
14
8
3
4
5
6
7
8
9
Sagulung
Sei
Beduk
Batam
Kota
Nongsa
Batu
Ampar
Bengkong
Lubuk
Baja
Total
6.4 Network Analysis
31.629
19
15.85
7
47.996
38
16.039
6
58.546
50
30.477
25
74.153
89
341.62
265
The
number of
Trip
(n)
1
1
7
3
1
1
10
1
4
13
1
1
7
15
1
1
75
9
81
In order to determine the optimum route, which is the shortest and less-time
consumption, it takes a network analysis applying arcInfo version 9.2, where the
analysis was setup. Because this study is related to the collection and transport of
wastes, then 265 transit points are included to be assessed in this analysis.
6.4.1
Creating Topology for Road Data
Before creating topology of the existing road network, 265 transit points
should be created. They do not necessarily located above the road, because topology
process would find the nearest route. After the transit point determined, the
furthest points are determined. The furthest point means the furthest place where the
garbage trucks will go. It based on the number of the districts, which are nine.
However, in the district of Nongsa there are two furthest points.
These
furthest
points served as transit points, which would be passed by the trucks, started from the
station in PT Surya Sejahtera before heading for the landfill. Topology is very
important to avoid any digitizing-mistake, such as jagged lines or doubled lines in a
layer.
In arcInfo, Geodatabase topology is a data object created and stored in a
geodatabase. A geodatabase topology defines a set of rules about the relationships
between feature classes in a feature dataset. Geodatabase topology is created in
ArcCatalog and can be added to ArcMap as a layer, just like any other data. After
editing has been performed on the feature classes, validate the geodatabase topology
to see if the edits break any of the topology's rules. Any errors can be fixed or
marked as exceptions. An ArcEditor or ArcInfo license is required to create, edit, or
validate geodatabase topology. To use geodatabase topology when you edit data
from a geodatabase, the geodatabase topology in which the data participates must be
in the map.
To create road network topology and transit point in this study can be done in the
following way (Figure 6.14).:
82
1.
Converting road shapefile to personal
personal geodatabase ; using in ArcCatalog
And navigate to the Network Analyst.
2.
Creating new feature dataset in the personal Geodatabase ; can be done in
the ArcCatalog tree in the feature dataset. Then New Feature Class
obtained.
3.
Converting personal geodata
geodatabase
base to topology ; the existing data is in the
form of geodatabase. This data would have to be topologized in order to
minimize the effects of the road network mistakes. This process can be
done by doing feature dataset to create New Topology Wizard inviting
inviti
editing process.
4.
The next process is to convert the geodatabase about road network with
transit points (TPS = Temporary Landfill) and unionized in one layer
topology.
In the map of topology the feature classes have to be participated.
Unanticipated Layer in geometric network cannot be categorized in the topology
Figure 6.14 Create road network topology
map. This is important, considering that the entire road network has to be
83
interrelated. Then a cluster tolerance for the map topology should be set. The default
cluster tolerance is the minimum possible cluster tolerance. Increasing the cluster
tolerance may cause more features to be snapped together and considered coincident.
6.4.2
Creating New Network Dataset
Topologies road networks have to be given some rules. The rules are namely
not supposed to be overlapped, not supposed to be intersected, and not to have any
dangles. This should be done in order to identify mistakes so the topologies roads can
be-re-edited until there is no more mistakes can be found. The next step is to
convert road layer from shapefile to network dataset. It can be done using arcTool
catalog.
Figure 6.15 Data attributee in road topology layer
Parameter checking is important. The road networks have data as secondary
artery, collector, secondary collector, and others, each have value of 100 km/hr, 80
84
km/hr, 60 km/hr dan 40 km/ hr. Values can be added to data attribute by filling the
‘in the field’ icon as shown Figure 6.15 above.
Before topology road has been set up (Figure 6.16), it has to look at to specify
the attribute for network dataset. Specification of the roads based on hierarchy,
speed, length and direction should be done in the network dataset. The usage of
specify attribute network data set are :
1. Hierarchy Hierarchy Data Type Integrer
2. Speed Descriptor
Descriptor Data Type Booelan
3. Length Cost Data Type Double
4. Direction Restriction Data Type Booelan
Figure 6. 16 Topology Road
6.5
Identifying Service Area
85
It is concluded that site 1 is the most appropriate site due to its distance, timetime
span, and covered area. This is supported by another method of network analysis to
determine the covered area.
Area covered in every landfill alternative were assessed based on the distance
from and to the landfill, by assuming the time and coverage area. In this study, it is
assumed tat there are 3 alternatives of time
time-spans
spans namely 5 minutes, 15 minutes, and
30 minutes. The coverage areas are assumed to be as big as 20000 meter (20km) and
allow U-turns
ns dropdown
dropdown.. These assumptions are applied to all of the landfill
alternatives.
And its results as shown Figure 6.17
6. below. It is found that site 1 covered larger area
than the other 2 alternatives.
Site 1
5 minutes
15 minutes
30 minutes
Site 2
86
5 minutes
15 minutes
30 minutes
Site 3
5 minutes
15 minutes
30 minutes
Figure
6.17.
Service
Covered
areas
wereArea
determined based on the road covered instead of the
width of the land covered. From the three sites, the data then exported to calculate
the service area covered.
d. The results are as shown in Table 6. 2 and Figure 6.22 :
87
Table 6.2.. Comparison Covered Area
Coverage Area (hectare)
Time (min)
Site 1
Site 2
Site 3
0-5
637.43
1.60%
888.77
2.23%
321.13
0.81%
5 - 15 min
12419.00
31.18%
9023.29
22.66%
7266.10
18.25%
15 - 30
26538.77
66.64%
27901.98
70.06%
30597.20
76.83%
Area (Hectare)
45000
40000
35000
30000
25000
20000
15000
15 - 30 min
10000
5 - 15 min
5000
0 - 5 min
0
Site 1
Site 2
Site 3
Based on the covered
cov
areas, the transit point and waste generates can be
calculated per time and per area thar shown on the table below.
Figure 6.22 Comparison S
Site
88
Table 6.3. Waste Carried, Transfer Point Covered in Each Sites.
Time
(Min)
SITE 1
Amount of
Amount
Waste
of
SITE 2
Size Covered
Amount
Amount
of Waste
of
SITE 3
Size Covered
Amount
Amount
of Waste
of
Transfer
Transfer
Transfer
Point
Point
Point
0-5
15.85
1
637.43
1.60%
99.509
3
888.77
2.23%
5-15
5663.114
111
12419.00
31.18%
1039.521
32
9023.29
15-30
6290.298
127
26538.77
66.64% 11896.284
207
27901.98
Total
11,969.262
239
39,595.20 99.42% 13035.314
242
351.276
Size Covered
6
321.13
0.81%
22.66% 11,527.23
192
7266.10
18.25%
70.06%
1358.684
47
30597.20
76.83%
37,814.04 94.95% 13,237.19
245
38,184.43 95.88%
From the table above, it can be seen that the site number 3 covered transit point more than the other sites. The amount of waste that can be
carried
more
than
the
others
even
the
covered
area
for
site
number
3
smaller
than
the
number
1
site
89
In the present, Dinas Pasar dan Kebersihan Kota Batam covered 75% of the
total waste management service in Batam. It takes 2 hour drive to the landfill
existing., Site 3 shown that it has proposed service area can be covered 38,184.43
hectares (95.88%) from the total area 39825.12 hectares, but this Site have more
transit point covered and could carried most the number of waste than the other sites
. Even though site 1 covered wider area, site 3 covered more transfer points.
Therefore, selecting site 3 would achieve higher effectiveness in term of waste
management compared to the site 1. The experimental results demonstrate that
significant savings compared to the current practice can be obtained with the use of a
computerized software optimization. In this scenario, an optimization service area
covered is succeeded. Thus, the implementation of the newly proposed collection
route design by means of GIS would be conducive to both cost, time savings wide
area covered.
.
6.6.
Identifying the Optimal Route and Closest Facility
In order to determine the optimum road in ArcInfo network analysis is the
first allocation modeling technique. Network analysis procedures perform allocation
modeling, accessibility of a single location given the attractiveness of the other
locations and the levels of interaction between pairs of locations accounting for
location properties. The accessibility and interaction are performed by gravity
modeling concepts. Several ARC/INFO network analysis commands depend on an
implementation of a shortest-path algorithm. There are several algorithms that can
efficiently find least-cost paths through a network.
The best-known and simplest path-finding algorithm is the one generally
credited to Dijkstra (1959). ARC/INFO network analysis procedures use Dijkstra’s
algorithm. The ArcInfo Network Analyst Tool (NAT) was used to compare routes
generated by Dijkstra’s routing algorithm with the routes in terms of road
classifications, distance, speed and journey time of the route selected i.e. destination
90
planning, a term used here to describe the shortest optimum route based on road
class, road length, road speed and route journey time.
Data utilized in the network analysis process to determine the optimum route
in this study are as follows (Figure 6.17) :
1. Data layer landuse
2. Data layer district
3. Data layer topology of road
4. Data layer alternative landfill that has been analyzed before
5. Data layer transit point and 10 starting point ; for the district of Nongsa,
there are 1 additional starting point.
Figure 6.18 Data utilized in the network analysis process
91
In this process, the calculation has been done by the arcInfo system itself.
Hence, it is important to determine the Original Destination (OD) as a place where
the trucks started to move, and headed to 10 furthest points in every districts, and the
end points would be in the landfill alternative.
Original Destinations are the
locations of PT. Surya Sejahtera at Tanjung Uma.
This analysis process should be done for each alternative landfill side as
shown Figure 6.18 below :
Transit Point 1
Transit Point 2
Transit Point 3
Transit Point 4
Original Destination
(Starting Point)
PT Surya Sejahtera
Transit Point 5
Transit Point 6
Alternative Landfill 1
Transit Point 7
Transit Point 8
Transit Point 9
Transit Point
10
Figure 6.19 Process to find best route for every point and alternative site
Afterwards, some essential restrictions were taken into account, such as the
streets’ directions, no U-Turns rules (with the exception of the dead-ends) and also,
the fact that the truck should follow true-shape route (i.e. it mustn’t pass over the
squares). Moreover, Network Analyst was asked to show the results in meters, as the
distance criterion was selected, and to reorder the stop-points in order to find the
shortest route. It is worth mentioning that, in the special case where some piece of
refuse causes traffic problems, Network Analyst can be asked to find the shortest
route starting from this certain point, so as to relieve the traffic. In this network
analysis process, there are several supporting factors or ArcInfo in determining the
92
route. In properties, it could be set that the tolerance area is 2000
200 meters,
meters it can be
seen in the next figure (Figure 6.19) :
Figure 6.20
6 Set up the
search Tolerance
Finally, pushing the “solve” button of Network Analyst, the closest route for
the large items collection was produced. This proposed route is illustrated in Figure
Figure 6.20 :
Site 1
93
Site 2
Site 2
Site 3
Figure 6.21.. The best route from each sites
94
From the three sites, the data then exported to calculate the length of the route
passed. The results are as shown in Table 6. 1 :
Table 6.4. Result length and time from each
site
Site
Site 1
Transit
Site 2
Site 3
Length
Time
Length
Time
Length
Time
(km)
(min)
(km)
(min)
(km)
(min)
Point 1
37.78
63.1
32.42
55.6
36.26
63.4
Point 2
16.04
23.5
24.27
27.1
8.44
15.8
Point 3
24.44
37.8
35.20
45.5
11.05
23.5
Point 4
20.36
33.0
31.94
40.5
11.60
24.4
Point 5
28.18
54.9
60.73
57.0
29.64
56.2
Point 6
47.65
46.7
40.91
65.0
52.90
51.1
Point 7
36.83
37.1
49.93
47.4
42.07
41.3
Point 8
29.67
35.0
42.83
45.0
42.54
46.7
Point 9
34.67
40.1
38.66
41.2
48.42
54.3
Point 10
31.39
52.0
26.31
45.5
45.12
66.2
Total
307.01
423.2
383.19
469.9
328.04
443.0
Point
As it was mentioned above, Network Analyst calculates the optimal route by
means of Dijkstra’s
Algorithm. In particular, ArcGIS Network Analyst’s route
solver attempts to find a way through the set of stops (transit point) with minimum
cost determinedly the shorthest path. It first computes an asymmetric origin
destination (OD) cost matrix holding the travel times between the stops using the
95
Dijkstra’s algorithm. It then, applies an insertion algorithm to construct an initial
solution. At each step, the insertion algorithm inserts the least-cost unvisited stop
into the current partial solution (Rice, 2006).
In the present, the municipality of Batam decides on the large items collection
route empirically. Everyday, the truck covered almost 40 km-50 km, according to
the Dinas Pasar dan Kebersihan Kota Batam, Site 1 shown that it has the shortest
path and the shorthest time, while the Network Analyst’s proposed route was just
30.7 km long. The experimental results demonstrate that significant savings
compared to the current practice can be obtained with the use of a computerized
software optimization. In this scenario, an optimization of 30%-50% is succeeded,
comparatively to the empiric method that the Dinas Pasar dan Kebersihan Kota
Batam has used so far. Thus, the implementation of the newly proposed collection
route design by means of GIS would be conducive to both cost and time savings.
Table 6.5 Amount of Waste in each Route – SITE 1
No of Point
Point 1
Point 2
Point 3
Point 4
Point 5
Point 6
Point 7
Point 8
Point 9
Point 10
Total
Number
of Transit
Point
13
8
31
9
26
23
15
27
15
2
6
1
7
17
12
212
Amount of Waste
(per Transit Point)
Districts
2.589
1.263
0.833
1.263
0.833
1.171
1.219
1.171
1.263
2.673
1.263
2.673
2.264
1.805
1.646
Sekupang
Batam Kota
Lubuk Baja
Batam Kota
Lubuk Baja
Batu Ampar
Bengkong
Batu Ampar
Batam Kota
Nongsa
Batam Kota
Nongsa
Sei Beduk
Batu Aji
Sagulung
Amount of
Waste
33.657
10.104
25.823
11.367
21.658
26.933
18.285
31.617
18.945
5.346
7.578
2.673
15.848
30.685
19.752
280.271
96
Table 6.6 Amount of Waste in each Route – SITE 2
No of Point
Point 1
Point 2
Point 3
Point 4
Point 5
Point 6
Point 7
Point 8
Point 9
Point 10
Total
Number
of Transit
Point
Amount of Waste
(per Transit Point)
9
4
34
30
2.589
1.646
0.833
0.833
16
1
25
1.171
1.219
0.833
3
8
8
3
30
2
5
10
2
6
17
19
233
1
1.263
2.589
1.171
1.219
2.264
1.263
2.589
2.673
1.263
2.673
2.264
1.805
1.646
Districts
Sekupang
Sagulung
Lubuk Baja
Lubuk Baja
Batu
Ampar
Bengkong
Lubuk Baja
Batam
Kota
Sekupang
Batu Ampar
Bengkong
Sei Beduk
Batam Kota
Sekupang
Nongsa
Batam Kota
Nongsa
Sei Beduk
Batuaji
Sagulung
Amount of
Waste
23.301
6.584
28.322
24.99
18.736
1.219
20.825
1.263
7.767
9.368
9.752
6.792
37.89
5.178
13.365
12.63
5.346
13.584
30.685
31.274
308.871
97
Table 6.7 Amount of Waste in each Route – SITE 3
No of Point
Point 1
Point 2
Point 3
Point 4
Point 5
Point 6
Point 7
Point 8
Point 9
Point 10
Total
Number
of Transit
Point
Amount of Waste
(per Transit Point)
Districts
14
56
33
26
24
14
24
9
4
8
2
6
7
17
19
263
2.589
0.833
0.833
1.171
1.171
1.219
1.263
1.219
2.673
1.263
2.673
1.263
2.264
1.805
1.646
Sekupang
Lubuk Baja
Lubuk Baja
Batu Ampar
Batu Ampar
Bengkong
Batam Kota
Bengkong
Nongsa
Batam Kota
Nongsa
Batam Kota
Sei Beduk
Batuaji
Sagulung
Amount of
Waste
36.246
46.648
27.489
30.446
28.104
17.066
30.312
10.971
10.692
10.104
5.346
7.578
15.848
30.685
31.274
338.809
Alternative sites 1 and 3 have no significant difference. Based on the transfer points
locations, site 3 is considered to be more effective due its closeness to the transfer
points and residential area (Table 6.8). The situation occurred because the
development in Batam is focused on the district of Lubuk Baja. Site 2 located too
distant from the district of Nongsa. However, in the future, the development would
be focused in the city district and Nongsa; hence, site 1 is considered the best choice,
because it is located at the center of the island.
98
Table 6.8 Amount of Transit Point, Waste, Length and Time
SITE 1
Amount
of
Transit
Point
212
Amount
Amount
of
Length
Time
Waste
280.271
of
Transit
Point
307.01
SITE 3
SITE 2
423.2
233
Amount
Amount
of
Length
Time
Waste
308.871
of
Amount
of
Transit
469.9
263
Time
328.04
443.0
Waste
Point
383.19
Length
338.809
Based on the table above, site 3 has less area covred than the site 1 but site 3 covered more transit point and generated more
wastes than the other alternatve landfill.. Hence, site number 3 becomes the right choice to become landfill.
99
6.7. Conclusion
From the analysis in this chapter is known that the site number 3 is more
suitable in landfills made. The best alternative to landfills in the analysis by using
GIS Covered Area Network Analysis and Best Route Analysis. Site number 3 is
more has advantages because it covers more transit points and could generates more
waste. Implemented network analysis was capable to identify covered areas and to
find out the best route the nearest facilities. This can be made to produce the best
landfill that the wide area covered and the best route based on the transit point
covered and waste generates. This study also could be made inputs to the
Government of the Municipal of Batam and as a guideline to control development
plans in study area.
100
CHAPTER 7
DISCUSSION AND SUGGESTION
7.1 Introduction
This chapter discusses about the findings of the process of finding the most
suitable landfill. Furthermore, this chapter discusses about the goal achievement,
objective and discusses issue and problem arose in the process of database design, model
development and implementation.
This study also discussed about the most appropriate route economically, which
means that it might minimize the cost. The use of arcInfo GIS version 9.2 and network
analysis supported the process. Suitable landfill and the best route identified might be
proposed for the next development of Batam. This study as well shown that the
application of Djikstra algorithm might be helpful to identify the best route, but in the
arcInfo, the algorithm is setup.
101
7.2. Research Findings Formula
GIS use of suitable landfill model helped to identify the areas which required for
suitable landfill. This study is about determining suitable location for landfill area.
Model analysis was designed to guide and provide information of the landfill; it
provided lots of help in determining the landfill. It found that land use factor have much
influence in model production conservation determination this area.
Current situation analyses enabled the researcher to understand about the
development status in study area. It also enabled to specify the criterion of the landfill.
Planning factors models such as accuracy and effectiveness measure model were done in
order to evaluate selected suitable landfill area model. The findings showed that landfill
identification is closely related to the road networks, land usage, area sensitive, and
slope.
Also added in this study, another way to identify the optimal or the most suitable
route, it is the Network Analysis. GIS in ArcInfo, without any extension, might do the
work. Djikstra algorithm is setup inside the ArcInfo, hence the result.
7.3.
Issues and Problem of Study
GIS helps to enhance the quality and accuracy for the decision-makers in order to
produce a better policy. Furthermore, other emerging issues might be solved by applying
spatial analysis. In general, the researcher found three major problems during the whole
process such as lack of accurate visual data that leads to problems with database design,
integrating one analysis model over the others based on the criteria from SNI, and model
implementation stage, where specific algorithm might not be described due to its being
setup in the software.
102
7.4. Suggest Landfil Area and Route For Collection
7.4.1
Suggest Suitable Landfill
Based on the findings, the needs of suitable landfill based on SNI, landuse, and
through site selection process in GIS, there are 3 alternative sites (Figure 6.1)
7.4.2. Best Route and the Closest Landfill.
Using network analysis. determining (Original destination) as the starting point,
station of PT. Surya Sejahtera at Tanjung Uma. Then determine the farthest point 9 as a
transit point to illustrate that the truck had passed the farthest point.. 9 furthest points
divided based on the numbers of the district, as mentioned before, the district of Nongsa
has 2 transit points, hence there are 10 transit points.
After setting the tolerance at 2000 m, which means that in the radius of 2000 m
the truck can still cover the wastes from the road passed, then the trip should be ended at
the alternative landfills. The same process applied to every landfill alternative. Then the
result obtained was that the most suitable landfill alternative is the alternative 3, due to
its distance, coverage, time-span, transit point covered, and wastes generate.
7.4.3. Service Area
Using network analysis can also calculate its area coverage. Landfill Alternative
3 covered most transit point and could most waste generates with less time span,
assuming that time consumed to the landfill in 5, 15, and 30 minutes, with 20,000 meter
of service area.
103
LEGEND
LANDUSE PLAN
SUITABLE LNDFILL
SUITABLE LANDFILL
IN BATAM
Faculty of Build Environment
Universiti Teknologi Malaysia
2009
104
LEGEND
ROUTE 1
ROUTE 2
ROUT 3
ROUTE 4
ROUTE 5
ROUTE 6
ROUTE 7
ROUTE 8
ROUTE 9
ROUTE 10
BEST ROUTE AND CLOSEST
LANDFILL IN BATAM
Faculty of Build Environment
Universiti Teknologi Malaysia
2009
105
LEGEND
5 MINUTES
15 MINUTES
30 MINUTES
SERVICE AREA LANDFILL
SITE 1 IN BATAM,
Faculty of Build Environment
Universiti Teknologi Malaysia
2009
106
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