This is my undergraduate thesis defense presentation. The study adopted network analysis along with different spatial analysis in ArcGIS to determine the accessibility of fire vehicles in Khulna city corporation.

1. ASSESSING THE ACCESSIBILITY OF FIRE
VEHICLE IN KHULNA CITY
PRESENTED BY
ARIJIT BISWAS ARGHYA
ROLL:1417011
Supervised by
Palash Chandra Das
Assistant Professor
Department of Urban & Regional
Planning
Department of Urban and Regional Planning
Khulna University of Engineering & Technology
Khulna-9203, Bangladesh

2. PRESENTATION OUTLINE
❑INTRODUCTION
❑LITERATURE REVIEW
❑METHODOLOGY
❑ANALYSIS
❑FINDINGS & CONCLUSION
❑LIMITATIONS

3. INTRODUCTION

4. 1.1 Background
❑Urban fire is one of the intense issues in developed and developing countries.
❑It is a threat to human lives and habitats. Sometimes fire becomes so destructive
if any effective measure has not been taken (HOSSAIN, June 2016)
❑The urban area is more populated than the rural area. As the urban area is more
densely populated and it is more prone to fire. ( Rahmawatia, et al., 2016)
❑Fire can expand in its volume within a minute and it can be said that more travel
time or response time results in more losses (ESRI, 2007).
❑ Recently the fire incident of Dhaka chalk bazar killed 78 people.
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5. Table 1:Year-wise number of fire incidents and economic loss in Bangladesh
Year Economic loss
(Billion)taka
Death
2010 3.25 719
2011 2.93 1385
2012 4.82 759
2013 7.79 1385
2014 3.59 210
2015 8.56 216
2016 2.40 247
2017 2.57 269
2018 3.85 664
Source: Annual report of BFSCD 2018
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Source:BFSCD Annual Report,2018

6. 1.2 Objectives
•To identify the vulnerable areas in terms of accessibility.
•Determining the response coverage of existing fire stations and site suitability
analysis for future fire station location.
•To develop a GIS-based system to identify the optimal path from the fire station
to the incident spot.
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7. LITERATURE REVIEW

8. SL
NO
Title Author Year Objectives Methodolgy Results
1.
GIS Based Fire Emergency
Response System
Eric K. Forkuo and
Jonathan A.
Quaye-Ballar
2013
To establish a GIS
(Geographic Information
System) based fire
emergency response
services where GNFS can
identify the optimal route
from its location to any fire
incident.
Used Network analysis extension
for optimal path and geospatial
analysis for suggesting new
locations for fire hydrants
Provides a visual representation
in GIS interface and shows the
optimal path between Fire
incident and fire station and
identify the closet facility
.
Urban Fire Risk Assessment
Using GIS: Case Study On
Sharjah
M.M Yagoub and
Ahmed
2014
To measure the emergency
time
response of the fire
vehicles in the city and
weighted overlay analysis is
utilized to propose new
suitable
locations for fire stations
They used the AHP model for a
suitable location for the new fire
station based on Criteria they have
selected., the weights of the
criteria were given by using the
AHP model. Finally, weighted
overlay analysis was performed to
find out the suitable location of the
newly proposed fire station.
Sharjah was not well served by
existing fire stations which
actually covered 36 % area
within 6 minutes. They
proposed 6 new stations fire
station which will cover 75%
area of Sharjah city within 6
minutes
3
Manifestation of an Analytic
Hierarchy Process (AHP)
Model
on Fire Potential Zonation
Mapping in Kathmandu
Metropolitan City, Nepal
Sachin Kumar
Chhetri and Prabin
Kayastha
2015
To prepare the
fire potential zonation map
of Kathmandu
Metropolitan City, Nepal
They used AHP method for scoring
the weight of the criteria. Expert’s
opinion survey was conducted for
the AHP model. Then in order In
order to generate fire potential
index map, the linear weighted
combination is used
FPI value varies between 0.05-
1.03. It has been found that
58.04% area is low potential
zone. About 32.92 % area is
moderate and Rests belongs to
the high-risk fire potential zone.
4

9. STUDY AREA

10. 3.Study Area
Name X- Coordinate Y-Coordinate
Daulatpur fire
station 22°52'20.90"N 89°31'8.93"E
Khalispur fire
station 22°51'28.26"N 89°32'50.47"E
Boyra fire station 22°49'52.04"N 89°32'44.76"E
Tootpara fire
station 22°48'19.03"N 89°34'14.42"E
Table 3.1: Khulna Fire stations names and locations
• Khulna is the third largest city of Bangladesh
• It covers a total area of 59.57 km²
• 34.04% area is residential(KCC,2012)
Figure 1: Study area map
5 Source: Google Earth

11. Table 3.2:sources Of Data
SL Name of data Source
1 Road network data KCC and open street map
2. Average vehicle speed of fire
vehicle
Fire service and civil defense
Khulna
3.
Population data, population
density
BBS 2011
4
Fire station and police station
location
Google earth
5. Waterbody KCC
6.
Fire incident location Fire service and civil defense
Khulna
6

12. METHODOLOGY

13. Data collection
Road Network data
Suitable Location
• Distance from road
• Distance from
waterbody
• Distance from fire
stations
• Incident density
Vulnerability map
Criteria selection for
suitability analysis
• Roads
• Police Stations
• Existing Fire Stations
• Population density
• Incident density
Topology Check
Road network dataset
Service
coverage
Optimal route from
closest facility
O-D matrix
Average speed of
vehicle
Previous incident
location
Study area selection
AHP model
Criteria selection for
Vulnerable zone
Incident pattern
analysis
Average neighborhood
method
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Network analysis

14. ANALYSIS

15. 5.1 Incident Pattern Analysis
• The distribution of fire
incidents in the Khulna city for
2016-2017 was spatially
clustered with R-value 0.73
• It is found that most of the
incidents from ward no
16,17,19,21
Figure 5.1.1: Incident location Figure 5.1.2: ANN report
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16. Criteria
Accessibility
by roads
Distance
from the
fire station
Distance
from
waterbody
Fire
history
Accessibility by roads 1 3 4 5
Distant from fire station 0.33 1 4 3
Distant from waterbody 0.25 0.25 1 2
Fire history 0.20 0.33 0.50 1
Eigen values 0.530376 0.275299 0.114036 0.080288
Consistency ratio 6.4%
Principal Eigenvalue 4.174
Table 5.2.1:Pairwise comparison matrix of criteria of vulnerability map
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17. 5.2.2 Vulnerability Map
•Most of the area of ward no 23,22,29,30,31 area
highly vulnerable.
•Khulna shipyard area, notun bazar, rupsha area,
south-central road etc are situated in the highly
vulnerable zone..
•Important structures including 8 schools,5 colleges,
bazar, clinic, church etc are situated in the high
vulnerable zone.
Figure 5.2.2: Vulnerability map11

18. Category Area(acres) Percentage
Low vulnerable 4222 37.84%
Moderate
vulnerable
3498 31.35%
High vulnerable 3425 30.70%
Table 5.2.3: Area percentage and percentage
12

19. 5.3 Service Area
25.23
47.17
64.85
0
10
20
30
40
50
60
70
4 minutes 6 minutes 8 minutes
PERCENTAGE OF SERVED AREA
Figure 5.3.2:Service area of existing fire stations
Figure 5.3.1: Percentage of served area
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20. Table 5.3.3: Land Use Wise Area Coverage
land use
In 4 minutes(%) In 6 minutes(%) In 8 minutes(%)
Commercial 66.91 86.72 89.65
Community Service 24.35 73.13 76.21
Education and Research 45.25 66.03 79.69
Government Services 44.63 77.55 68.38
Manufacturing and Processing Activity 4.62 16.16 41.57
Miscellaneous 37.03 76.55 85.11
Mixed Use 31.93 90.01 88.38
Non-Government Services 9.39 64.70 50.20
Recreation Facilities 27.41 83.56 93.46
Residential 32.14 65.27 77.69
Restricted Area 39.49 59.55 66.52
Service Activity 37.87 77.43 88.15
Transport and Communication 59.89 92.30 98.72
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21. Table 5.3: Area percentage and
percentage
Criteria Distant from roads
Distant from fire
station
Distance from
police station
Population density
Incident
density
Distance from
roads
1 2 8 7 6
Distance from fire
station
0.50 1 7 6 3
Distance from
police station
0.12 0.14 1 0.50 0.20
Population density 0.14 0.17 2 1 0.33
Incident density 0.17 0.33 5 3 1
Eigen values 0.487043 0.29214 0.037283 0.05617 0.127364
Consistency ratio 4.2%
Principal
Eigenvalue
5.188
Table 5.4.1:Pairwise comparison matrix of criteria for site suitability
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22. 5.4.2 Suitable Site For Locating Future Fire Stations
Figure 5.4.2: Site suitability map
The most suitable area is near sher-e bangla road,
Uttar para road, mujgunni area ,B.I.D.C road. Old
Jessore road, MA Bari sharok
17

23. 5.5.1 Origin-destination Cost Matrix
• Here origins are selected as the exiting fire stations
and destinations are selected as the high vulnerable
area generated from previously mentioned
vulnerability map.
• Among 143 destinations, 96 destinations can be
reached by the existing fire stations in 8 minutes by
the four fire stations
Figure 5.5.1: Origin destination cost map
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24. 5.5.2 Origin-Destination Cost Matrix
19
From the origin destination cost matrix it is
found that the maximum time required for
fire vehicle to reach the destination from the
closest fire station is 17.40146 minutes and
minimum times required is 0.2 minutes

25. 5.6.1 Optimal Path From Closest Facility
❑Figure 5.6.1.2 path required 2
minutes more than the figure A
Path though the 1.5 mile away
from the facility.
Figure 5.6.1.1 : Optimal path based on
travel time
Figure 5.6.1.2: Optimal path based on
distance20
❑ For Figure 5.6.1.1 the optimal
path is required 5 minutes to
reach the location and it is 1.7
mile away from the facility

26. Direction Of Optimal Path
Figure 5.6.1.3.: Direction of optimal path based on time
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Figure 5.6.1.3.: Direction of optimal path based on distance

27. 6.Findings & Conclusion
•From the ANN method in ArcGIS found that the incident pattern of fire incidents is
clustered.
•30.70% area are in high vulnerable zone in terms of accessibility.
•About 69% of roads are accessible for fire vehicle
•Only 25.23 % area can be covered by existing fire station with in 4 minutes
•From the origin destination cost matrix it is found that to cover the high vulnerable area
from the closest fire station it required 17.4014 minutes.
•Road network dataset which has been prepared can be used for finding the optimal path
from the closest fire station to incident spot
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28. 7. Limitations
•Real-time travel time is not considered. here average speed of the vehicle has
been used according to the road category.
• Traffic congestion is not considered as it is not constant all the day long for all
the roads
•Road condition was not considered, whereas vehicle speed depends on the
condition of the road.
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