Locating Bins using GIS - IJENS IJET-IJENS.pdf · Locating Bins using GIS I.A.K.S.Illeperuma1, Dr....

International Journal of Engineering & Technology IJET-IJENS Vol:10 No:02 75

106502-8181 IJET-IJENS © April 2010 IJENS I J E N S

Locating Bins using GIS I.A.K.S.Illeperuma

1, Dr. Lal Samarakoon

2.

1Senior Lecturer dept. of CPRSG, Faculty of Geomatics

2Director GIC Asian Institute of Technology, Thailand

Abstract-- In today’s world solid waste management is a

global environmental issue which creates significant health and

environmental risk. This is a crucial problem in Sri Lanka too

due to the lack of a proper solid waste management system. This study was conducted to improve the present solid waste

management system of Maharagama Urban Council, Sri

Lanka using GIS.

Sample survey was done to collect the data about amount of

waste generated from a house, number of people and income of a family and the

households’ attitude towards the waste from randomly selected

houses. GPS survey was carried out to find out the sensitive

locations.

Model was created to estimate the amount of waste generated from each house. GIS was used to identify the locations for

bins and estimate the required capacity of them. It could be

found that 1006 bins with 100m service area are required to

cover entire area.

Index Term-- Urban Solid Waste Management (USWM), Bin

location, Geographical Information System (GIS), Service

area, Global Positioning System (GPS).

1. INTRODUCTION

Solid Waste Management (SWM) is a function of

combination of various activities such as collection,

transportation and disposal of solid waste. It also includes

processing and treatment of the solid waste before

disposing. (Robinson, 1986). The purpose of SWM is to

create uncontaminated environment for people without

disturbing natural resources (World resource Foundation,

1996; McDougall et al., 2001) and a proper SWM helps safe

disposal, reduction of final waste and increase re-use and

recycling. On the other hand a poor management system, on

the contrary, leads to a filthy environment affecting the

well-being of the people residing therein.

At the present all over the world, due to the

industrialization, urbanization and uncontrolled urban

sprawl and improvement of living conditions and population

growth, SWM become a monumental problem. Waste

collection, transportation and disposal methods may vary

from place to place over the world. SWM system has

improved with the help of new technology in developed

countries.

In Australia urban households have been given a bin to

put their waste and those bins are emptied weekly by the

local council. (ISWA, UNEP, 2002).

Basic measures taken in recent years to control waste

management in Japan include: pollution prevention, reuse

and recycling, and waste incineration with air pollution

control. (Sakai et al., 1996).

Netherland government has implemented high land

filling tax to make it less interest by the people and

incineration of waste is the favored method of waste

treatment to reduce environmental risk (Bartelings, 2003).

The most popular method of waste disposal in Canadian

urban centers is curbside collection. But in rural areas

people have to carry their waste to the transfer stations.

Then waste from this transfer station is transported to

landfill site (ISWA, UNEP, 2002).

Studied carried out by Visvanathan et al., 2001 shows

that in Asia waste disposal is a serious problem due to

uncontrolled and unmonitored urbanization, and lack of

financial and human resources trained in SWM system.

According to this study the per capita generation of waste in

Asian cities rang from 0.2kg/day to 1.7kg/day. Also it

highlighted that in Sri Lanka waste generation per capita

rang from 0.4 to 0.85kg/day/person due to increased

consumption patterns as well as the movement of the people

from the rural areas to urban centers.

In Thailand people are encouraged to waste segregation

at the source of waste generation. Therefore wastes are

sorted into 3 types: recyclable, food and toxic and dispose

them into 3 different dustbins. (Bui Van Ga, 2004).

Similarly in many Indian cities and towns, solid waste

is normally disposed in an open dump. (Mufeed, 2006).

Although collection and disposal of the municipal waste

have been improved in Vietnam, there is no safely disposed

method. Recycling and reuse in Vietnam is an actively

implemented by informal waste pickers (Vietnam

Environment Monitor, 2004).

Bangladesh is also experiencing the problems of solid

waste management. Less than fifty percent of whole waste

generated in Dhaka City was collected by Dhaka City

Corporation and bins are not located sufficiently along the

road. So it can be seen that waste are scattered over the area

(Syed, 2006).

Similar to most of developing nations, in Sri Lanka,

solid waste, especially Urban Solid Waste (USW), is a

critical problem and it becomes severe due to absence of

proper solid waste management systems in the country. At

present recyclable, reusable and organic waste are collected

together and being dumped in environmentally very

sensitive places like road sides, marshy lands, low lying

areas, public places, forest and wild life areas, water courses

etc. causing numerous negative environmental impacts

(Hazardous Waste Management Unit, 2004).

There are no sufficient infrastructure and resources for

the SWM in many Urban Councils of the country, and there

are no enough and suitable services to dispose most of the

solid waste from households and industries. (Levien et al.

2000).

With the introduction of new policies for rapid

economic changes during the last two decades it can be seen

that rapid urbanization and also it is more difficult to find

lands for disposal or waste treatment facilities in urban areas

than in rural areas. Therefore people in those areas

compelled to dispose their waste in improper manner

creating environmental and health hazards. In contrast

western province is highly urbanized and densely populated

compared with the other provinces in the country. So the

waste management problem is more severe in the western



province (42 Sri Lanka, 2001). Thereby Colombo is the

most severely affected area with the disposal generation of

around 1500 tons per day (Perera, 2003). This problem is

quite significant in Maharagama Urban Council (UC) which

is in Colombo district. To minimize environmental and

health hazards it is necessary to locate bins along the roads

so that people can find a bin to dispose their waste easily.

Therefore this

study aims to identify the proper locations for bins along the

roads using GIS in the Maharagama UC area.

2. STUDY AREA

Maharagama UC is one of the largest Urban Council in

Sri Lanka lies in the Colombo district in Western province.

It is situated at 6.8460 North latitude and 79.928

0 East

longitude and is subdivided into 41 GN divisions for

administrative purpose (Fig 1). It covers an area of 3775

hectares. Principal towns of the area are Maharagama,

Mirihana and Kottawa and it has a population of just over

177000 people. There are about 28000 households in the

area. The UC officers were estimating per capita waste

generation is around 2.5kg in the area. Rukmale West,

Makumbura South and Kottawa East GN divisions and the

Wijerama, and Pragathipura GN divisions are the lowest and

highest populated GN divisions respectively. Most of the

commercial lands and industries are found along main

roads. There are more residential lands and relatively less

agricultural lands in the area. (Table I)

T ABLE I

LANDUSE DATA OF MAHARAGAMA UC

F

rom

pers

onal communication made with Officials in UC regarding

urban solid waste management in Maharagama UC area, it

could be known that UC provide polythene bags to

householders to collect disposal materials and to deliver

these bags to the vehicle at the time of collection or place

them by the side of the road closer to their house or put

them into the bin located along the road for the cleaners to

collect these bags when they come to collect waste. From

the UC officers, it was found that four compactors and two

tippers are used in collecting waste along the main streets

and ten tractors are used in lanes and small streets where

trucks can not approach. Due to the unjustifiable command

area of the existing dustbins located along the road, those

bins are not used by most of the householders to dispose

their waste and instead they use drains, roadside, water

bodies or any other improper things. This creates poor

sanitary conditions in the area due to animals: goats, dogs,

cows, cats, crows etc. foraging for food. Further, this waste

may causes to block the drainage system and creates flood

during raining seasons making significant inconvenience to

people and also stagnant and harmful water pools may form

making a better environment for sources of many diseases

such as flies, cockroaches, mosquitoes and rodents. When

these wastes are rotten and decomposed neighborhood make

dirty, bad smelling. Lighter waste materials are observed to

have been scattered by animals, wind and vehicles adding

unpleasant outlook to the area.

All the wastes collected from households and other

places by UC were transferred to open dump site located at

Navinna GN division of the Maharagama UC area.

Maharagama UC officials said that then these wastes are

sold to the private company. Company people sort them out

at the site and bring to their place.

In some of the areas wastes are collected by UC very

frequently while in some other areas wastes are not

collected at all by the UC. If the UC vehicle comes to

collect the waste almost all householders are prepared to put

their waste into the vehicle. Only the householders of those

areas where the UC does not collect waste adopt alternative

methods to solve their problem of waste disposal.

Followings are the disposal methods used by those people to

dispose their waste.

1) Collect and Burn.

In this method all types of wastes together collect and

burn.

2) Dispose waste into a hole in the garden.

People who have enough space to dispose their waste,

prepare a hole in their garden and dispose their all waste

into this hole.

3) Collect all types of waste under the tree.

Landuse Area (m2)

Barren 197016.88

Cemetry 17706.30

Commercial 820892.91

Industry 393868.83

Marshy land 1013882.06

Other agricultural land 1316884.96

Paddy 4958619.18

Playground 38522.72

Public 867372.81

Religious land 223419.15

Residential land 26514910.94

Scrub 345844.38

Water bodies 327383.73

Fig. 1. Map of Maharagama UC Area



4) Plastic / paper/ polythene burn and other waste dispose

into a hole in the garden.

In this method plastic, paper and polythene waste were

separate from household waste and they were burned.

Then rest of the waste was disposed into a hole in the

garden.

5) Put all waste into the UC vehicle when it comes to collect

waste.

Inquiries made from officials of the Central

Environmental Authority and Maharagama UC, it revealed

that government offices and schools have their own

procedures to collect waste and they do not use bins located

along the roadside to dispose their waste. Everyday UC

vehicles go to those places and collect those wastes. Further

they stressed that commercial waste too is separately collect

by the UC. Therefore in this study consideration was limited

only to the residential buildings.

3. METHODOLOGY

Methodology followed in this study is included

conducting questionnaire survey to collect data and GIS

based analysis to find proper location for bins along the

roads. Procedure of the study can be summarized as in Fig.

2.

3.2 Data collection

For this study, data from different sources were

collected and were integrated to create database for the

study area. Digital maps of Land use/Land cover, road

network of the area, streams, water bodies, population

density map and foot print of buildings over the area were

collected from Road Development Authority of the country.

Digital map of building foot print with height attribute was

collected from Survey Department of the country. Few

questions were prepared to collect the data about amount of

waste generated from a house, number of people in a house,

income of a family and to have an idea about the peoples

attitudes towards the waste. Then using this questionnaire,

householders from randomly selected ten houses in each GN

division of the Maharagama UC were interviewed.

Altogether four hundred and ten households were used for

this questionnaire survey. Same time GPS survey was

conducted to find the location of these houses. Two sample

bags which can be filled with one kilogram and half

kilogram of waste were used to estimate the weight of waste

generated from these households. Showing these bags,

householders were asked how many bags of waste are

generated from their house. Further to get the location of

sensitive areas such as school, religious places etc. where

bin should not be located at the close proximity of them,

GPS was used. Locations of bus stops over the area were

surveyed too.

3.3 Allocation of bins along the road

Procedures conducted in this process mainly divided

into two. Firstly analysis of sample survey data was done to

create models to estimate the number of people in a house

and amount of waste generate from a house per day and

income of a family. Allocation of bins along the road is the

second and main part of this process. Fig. 3 summarized the

work flow.

GPS Survey Questionnaire Survey

Identify the

sensitive

areas

Models to

estimate amount

of waste

generate from a

house

Road

Network

Identify the

locations for

bins & calculate

service area

Determine

capacity of bin

Fig. 2. Procedure of the study



Generally it could be said that amount of waste

generated from a house mainly depend on the number of

people in that house, education level and income of the

family. But household wise information was unavailable to

collect. Also it is out of scope to conduct a field survey to

gather information from each household in the area as time

Centroids are within

sensitive area

Sample Survey data

Model formation

Approximate

number of people in

each household

Approximate income

of each household

Landuse

data

Building

Layer

Identify households

in residential area

Rasterization

Waste density

map

Estimation of waste generate

from each household

Polygonization

Identify centroids in

high density area

yes

Exclude points

Centroids are on the

road No Yes

No

Shift the points to

the closest point on

closest road

Consider

centroids as bin

location

Calculate service

area of initial

bins

Network data

set (Road)

Locate other

bins

Calculate service

area of bins

Determine number of

houses in each service

area

Calculate

capacity of bins

Fig. 3. Work flow for allocation bin along the roads



consuming. Therefore regression analysis was done using

sample data to estimate number of inhabitant in a house,

income of a family and amount of waste generated from a

house per day and Minitab statistical software was used for

the analysis.

Generally it can be assumed that number of people in a

house depends on the education level of the family, size of

the house and number of storey in a building. During field

survey it was noticed that there were no housing complex in

the area and no multi storied houses. Although there are two

storied houses, one family with three or four members are

living in most of those houses. Therefore a number of

storeys in a building were not considered when estimating

the number of people in those houses. Since education levels

of each and every household of the study area was not

available only the size of the house was considered to

estimate the number of inhabitant of the family. Regression

analysis was done to find out the relationship between

Number of people and area of the house. Following equation

obtained with P value zero.

Then this equation was used to estimate the number of

people in the house when analysis the whole dataset.

With the available data, income of the family is

estimated by using the area of the house. Regression

analysis was done to find out the relationship between

income and the area of the house. Following equation was

got with the P value zero and it was used to approximate the

income of a family when considered whole dataset.

Finally to create the equation to estimate the amount of

waste generated from a house, regression analysis was done

following relationship was created.

In this calculation it is assumed that all people in the house

generate equal amount of waste though it depend on various

factors.

Normally people use a road to go to the bin to dump

their waste. Hence the service area of a bin which is a region

including the households that dispose waste to the bin in

consideration can not be a circular area. In GIS software

Network Analyst function facilitate to find service area of a

particular distance around any location on a network. A

network service area is an area that covers all accessible

roads which are passing through that location and have

specified length. As an example, in Fig. 4-B brown colored

area is a 100m service area of a bin calculated using

network analyst function of ARC GIS software without

using trim length. This area covers all road sections which

are passing through the bin location with 100 meter length

from the bin and service area polygon is created by joining

end point of these roads. Therefore this service area polygon

may exclude some householders who can reach to this bin

by walking maximum distance of 100 meters or less than

100 meters. In Fig. 4-A service area of a bin was calculated

same as in Fig. 4-B but using trim length. Therefore this

polygon covers more householders who can reach to this bin

by walking 100 meters or less than 100 meters. Therefore

this method was used to calculate the service area of a bin in

this study.

As a first step of determining service area polygons of

bins, Network data set which is made of network elements:

edges, junction and turn has to be created. Then service area

analysis layer has to be created to determine the service area

polygon of each bin. Fig. 5 shows input and outputs of

service area analysis layer.

Number of people = 0.0315 * Area of the house

Amount of Waste = 0.174*Number of people

in a house + 0.000021*Income

100m

Fig. 4. 100m service area polygon

Income = 208 * Area



Fig. 5. Input and outputs of service area analysis layer

Impedance which is cost attribute of traversing along road,

polygon break which is extent of the service area to be

calculated and trim polygon length is a length that trims the

edges of the polygon to a specified distance are input of the

service area analysis layer.

From questionnaire survey data it could be found that

98%of the householders’ maximum preferable walking

distance to the bin to dispose their waste is 100m. Therefore

bins were located at the maximum preferable walking

distance of 100 meters by computing service area of the

each bin, considering road network data. 20m buffer zones

were created around schools and religious places and 30

meters buffer zones were created around water features to

avoid locating bin at the close proximity of them. Though

people requested to keep a bin near to the bus stop, four

meter buffer was created around bus stop to avoid locating

bin very closer to them.

As a guide to locate initial bins, waste density map is

prepared to identify the high density waste generation area

and first bins were located at the centroids of the high

density area. First step of doing this, waste generation point

map is converted to raster map with cell size 100m and cell

value of this raster map calculate as bellow.

Cell Value = Sum of the attribute of all the points within

the cell

Where attribute is amount of waste generate from the point.

Then waste density map was prepared using the

following equation.

Waste Density = Cell Value / Area of the cell

To identify the centroids of the high density areas this

density map was polygonized and polygons with their

centroids at the high density areas were shown in the Fig. 6.

Then centroid of this high density area was considered as

location of the bins and check whether they are within the

buffer zones of sensitive area or not. Centroids which are in

buffer zones were excluded. However bin should be located

along the roadside. Therefore to check whether the other

centroid points are on the road, they were overlay with the

road network.

If a road crosses over the centroid points then centroid

location is considered as a bin location. If not firstly locate

the point at centroid then it is shifted to the closest point on

the closest road of that point. It was done by drawing a

Network data

set

Network location

(Bin locations)

Service area

polygons

Roads within each

service area

polygon

Input

Outputs

Fig. 6. Polygons with their centroid over the high waste density area.



perpendicular line from the centroid to the closest road.

Then the intersection point of that line and road was

consider as the location of the bin since it is the most closest

point on the road to that particular centroid.

Thereby service areas of these bins were calculated by

using network analysis. To locate the next bins trial and

error method is used with the aim of avoiding much

overlapping of the service areas, cover more areas and all

the sections of the road network by service area. If these

points produce satisfactory results, then proceed to find the

location of the next bin. Self judgment will be applied to

select a location for the bin. This way all the points will be

located (Fig. 7.).

Fig. 7. Location of bins along the roads

After locating bins, amount of waste generated within

service areas of each bin which is the capacity of bins to

collect the waste within a day can be easily determined with

ARC GIS software. This is the capacity of bins to collect the

waste within a day. There by considering present waste

collection frequency by UC, capacity of bins were

determined.

4. Results and discussion

From questionnaire survey data analysis it could be found

that mainly three methods are used to dispose the household

waste in this area (Fig. 8).

65.4%

21.7%

12.9%

Category

Burn

Open dumping

Put into the UC vehicle

Disposal methods practice in the Area

Fig. 8. Disposal methods

All these methods create environmental and air pollution

and create an inviting environment for such pests as flies,

mosquitoes, cockroaches, rats etc. Therefore the danger of

spreading diseases like Dengue, Malaria, Brain fever,

Pylaria etc. is there too. People in this area adapted to these

disposal methods since there is no proper waste collection

procedure by the UC. Hence it is necessary to locate bins

along the road so that people can find the bin easily to

dispose their waste. Using Network Analysis function in

ARC GIS software 1006 bins were located to cover entire

area (Fig. 9). Thereby amount of waste generated within

service areas of each bin were determined. Fig. 10 bellow

shows the amount of waste generated within each of the

service area per day. According to the Fig. 10, amount of

waste gathered into a bin per day range from three

kilograms to hundred kilograms in the UC area. Bins with

same capacity can be located along the roadside. Then there

might be some bins which get filled within a day or even in

a less time while some bins get filled in two days or take

even more time. So capacity of the bin determines the waste

removal frequency of the bin too. Then when deciding the

capacity of the bins it is better to consider the frequency of

waste removal from bin and optimum path of the UC

vehicles to transport the waste from bin to landfill site too.



Fig. 9. Location of bins along the road

Fig. 10. Amount of waste generated within service area polygon per day

From the questionnaire survey it could be seen that in some

of the areas wastes are collected by UC very frequently

while in some other areas wastes are not collected at all by

the UC. Table 2 given bellow shows that the percentage of

households of different frequencies of waste collection by

the UC.

Fig.11 shows the frequencies of household waste collection

by the UC in different GN divisions.

T ABLE II

FREQUENT OF WASTE IS COLLECTED BY THE UC AND PERCENTAGE OF HOUSEHOLDS

Frequent of waste collect % of Households

Every other day 4.88

Once a week 53.17

Twice a week 7.32

Not collected by UC 34.63



Fig. 11. Frequency of households waste collection by the UC

(Households shown in the figure are houses used for questionnaire survey)

It is necessary to make an arrangement to extend the present

waste collection procedure to cover entire area. Further

waste cannot keep in the bin for long time it better to collect

waste from bin twice a week. With this

waste collection frequency required capacity of each bin to

accommodate waste dispose by the people within the service

area polygon each bin is shown in the Fig. 12.

Fig. 12. Capacity of bin

5. CONCLUSION

Service area of a bin can be calculated accurately using

Network Analysis function in GIS software instead of

creating circular buffer around it. Therefore it can be

conclude that GIS can be used to locate bins along roads

accurately based on road network. Further in this study

amount of waste generate within the service area of a bin

was determined with the help of GIS. Also it can be

conclude that GIS based computation for waste generation

estimation can ensure accurate design of capacity of bins.



REFERENCES [1] Bui Van Ga. “Urban Solid Waste Treatment and Landfill Siting

for Danang City.” Environmental Protection Research Centre, Danang Unversity.2004

[2] Bartelings H.” Municipal Solid Waste Management problems, an applied general Equilibrium Analysis.” PhD Thesis.

Wageningen University. 2003 [3] Hazardous Waste Management Unit, Technical Guidelines on

Municipal Solid Waste Management. Environmental Pollution Control Division, Central Environmental Authority. 2004

[4] ISWA, UNEP. Industry as a partner for Sustainable Development: waste management. United Kingdom.2002

[5] Levein van Zon and Nalaka Siriwardana.2000. Garbage in Sri

Lanka. Integrated Resources Management Programme in Wetlands, Sri Lanka, Free University of Amsterdam, the Netherland.

[6] Mufeed Sharholya, Kafeel Ahmad

a , R.C. Vaishya

b and R.D.

Guptab . 2006. Municipal solid waste characteristics and

management in Allahabad, India. aDepartment of Civil

Engineering, Jamia Millia Islamia (Central University), Jamia Nagar, New Delhi 110025, India,

bDepartment of Civil

Engineering, Motilal Nehru National Institute of Technology (Deemed University), Allahabad 211004, Uttar Pradesh, India

[7] McDougall, F.R., White, P.R., Franke, M., Hindle, P. “Integrated Solid Waste Management:” A Lifecycle Inventory ,

Blackwell Science, London. 2001 [8] Perera K.L.S. “An Overview of the Issue of Solid Waste

Management in Sri Lanka” in proceedings of the third

International Conference on Environment and Health, Chennai, India. December 2003. pp 346-352.

[9] Robinson, W.D. 1986. The Solid Waste Handbook: A Practical Guide, John Wiley & Sons, Chichester, .

[10] Sakai, Shinichi, Municipal Solid Waste Management in Japan. Environment preservation centre. Kyoto University, Japan 1996.

[11] Syed Mahmood Anwar. 2006. Solid waste Management and GIS. The Mphil Thesis available at http://www.sma-

bd.com/swmandgis.htm (Retrieved on 21.05.2007) [12] Vietnam Environment Monitor, 2004. Report on Solid Waste

prepared by the Ministry of Environment and Natural Resources (MONRE), the World Bank, and the Waste-Econ Project

(funded by CIDA) 42 Sri Lanka, 2001. STATE OF THE ENVIRONMENT 2001 available at

[13] http://www.rrcap.unep.org/reports/soe/srilanka_waste.pdf (Retrieved on 24.08.2007)

http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6VFR-4K5HWCD-1&_user=1402360&_coverDate=12%2F31%2F2007&_rdoc=1&_fmt=&_orig=search&_sort=d&view=c&_acct=C000052592&_version=1&_urlVersion=0&_userid=1402360&md5=e70aaf173095c3a18eaadb7350695d35#aff1




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http://www.sma-bd.com/swmandgis.htm

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