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An Investigation into the Potential Role of GIS in Emergency Planning
Abstract
The aim of this research paper was to investigate the potential role that a Geographic
Information System could play during emergency planning. The paper investigated an
emergency scenario in which a terrorist incident on the tidal barrier in Belfast resulted in the
flooding of central Belfast. The methodology was aimed at solving the following problems:
locating the nearest health facility for the immediate evacuation of the casualties; predicting
the extent of the flood inundation if the water level rose by 2m; identifying possible effects on
the population and the road network; and finally, identifying two schools as temporary shelter
for the displaced population. The GIS was used to prepare the data for analysis, analyse the
data, and present the results.
Conetta A.
Introduction
Despite the premature prediction of the
“demise of GIS” (Allinson, 1993), it continues
to grow and has evolved into a system utilised
extensively by many different disciplines;
perhaps none more important than
emergency planning. It is undoubtedly this
wide ranging utility that has ensured its
survival. The last ten year have seen the
technological sophistication increase (Enders
and Brandt, 2007), this coupled with the
increase in computational power and storage
capacity, and the reduction in cost of
computers has made GIS move away from
specialisation to the mainstream.
In the aftermath of an emergency the term
‘confusion reigned’ is often heard, this
however does not need to be the case in an
age where we can communicate at the touch
of a button from almost anywhere on the
earth. The primary concern of a government
is the safety of its people; in the event of an
emergency a well prepared plan can save
time, money, not to mention lives. In this age
of uncertainty the one thing that is certain is
that now, more than ever, an emergency may
just be around the corner. Two of the four
major risks to UK Security identified by the UK
Government in the Strategic Defence and
Security Review of 2010 were terrorism, and
natural disaster, both of which lead to
emergencies. Tackling these emergencies
requires a cohesive approach from many
disparate organisations. GIS is the key to
unlock this problem as it ‘provides the ability
to spatially coordinate resources from
separate systems’, (Enders and Brandt, 2007).
But it is more than this; the tools provided in
the GIS can be used for tackling many more
than just geographic issues (Carr and
Addinson, 2010).
The integration of GIS into planning
department is no new phenomena, as early as
1999 a survey investigating its uses in
planning departments found encouraging
results (Gill, et al. 1999). This importance is
not lost on society, with organisations such as
Map Action providing GIS mapping support to
disaster relief around the world.
This paper is based around a scenario of a
terrorist attack on the tidal barrier on the
River Lagan in central Belfast during the
month of October (wettest). This project is
concerned with the planning of a reaction to
this emergency but will also discuss the
benefits of during and post event. During any
emergency or disaster, planners should be
primarily concern with the evacuation of the

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population to safety (De Silva and Eglese.
2000), this guides the methodology used.
Methodology
Data
The key to achieving accurate results from any
GIS analysis is the quality of the data that is
used as the basis for the analysis. The
research outlined in this paper required the
use of a number of vector datasets from a
variety of sources. The UK Census of
Population data was downloaded from
CasWeb to provide approximate numbers of
the population in the study area. All of the
remaining dataset were gathered from the
Land and Property Services at a scale of
1:2,500, and included: Roads, Infrastructure,
Hydrology, Pointer, and a 10m Elevation (txt).
Data Preparation
All data preparation was carried out using the
ArcGIS 10.1 software package. To begin the
data preparation a polygon was created to
mark the extent of the study area, and a point
to represent the location of the terrorist
incident. To reduce the processing time of
future analysis all of the data sets were
clipped to the study area using the GIS. The
most important dataset to this project was
the elevation data which came in a text file
containing x y and z coordinates covering 10m
post spacing. To create a DTM, two tiles of
the elevation points were converted to
shapefiles and merged into a point cloud
covering the whole of the study area. The
point cloud was then converted to a surface
using the IDW algorithm; this would form the
key dataset for the creation of the flood
polygon.
Flood Polygon
The DTM was interigated to determine the
average elevation value for the pixels known
to be water. The flood levels were created at
2m, 3m and 4m above the normal level, this
would allow for a comparison. The 2m flood
level was use for all of the analysis in the
remainder of this project. The raster
calculator within the ArcGIS was used to
classify the data as those pixels with a value of
less than or equal to 2m above normal, and
all other values. The reclassified flood layer
was then converted to a polygon, this created
two classes of polygon. To ensure that this
layer could be used as a barrier in the network
analysis the 2m flood class was exported to a
new shapefile containing only the required
class. This process was repeated for the
values 3m and 4m. On completion of the
process a visual check was carried and some
small polygons that represented anomolies
were deleted. This would ensure that the
route selection to be done later in the process
would not be stop impeeded by small
insignificant polygons.
Network Dataset
A network layer was created to enable the
identification of the closest health facility for
the evacuation of casualties. The roads layer
was converted to a network layer using
ArcCatalogue.
Health Location
The buildings dataset consisted of a polygon
layer that contained all building types in the
same layer. The buildings attributed as Health
related were selected by their attributes and
exported to create a new shapefile containing
only health related building. This shapefile
could then be used in the analysis of available
routes to health location for the evacuation of
casualties.
Spatial Analysis
To assess the number of buildings that would
be affected by the flood, the flood layer was
used to create a selection based on the points
from the Pointer database that were within
the flood layer. The identified buildings were
then exported as a new shape file and a table
created summarising the count for each type
of building.
The census data attribute tables that contain
the total population per Output Areas was

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used to create a thematic layer showing
population density. This was created using
Jenks natural breaks with 5 classes. The flood
layer was place on top of the population
density layer and a visual comparison carried
out using the swipe tool to see if the flood
affect areas with high population.
Having created the flood layer and identified
the health facilities in the study area, it was
now possible to locate the closest health
facility to the incident location, and a route
was created using the flood layer as a barrier
to movement. The flood layer was then used
to identify which roads were submerged by
flood waters. The roads that were submerged
were exported as a new layer enabling the
creation of statistics on the length in metres
of each road class that were submerged.
Multi-criteria analysis was used to select
locations for Emergency Evacuation Points
(EEP). It was decided to use schools with a
ground are greater than 3000m2, not located
in the flood area. Again the flood layer was
used to identify which schools would be
affected by the flood waters. The schools
layer was then modified to remove the
schools that were affected. A search of the
building that met the 3000m2 criteria was
then carried out; these exported to a new
shapefile and would be used to pick the EEP.
Results
Flood Level
The creation of the flood Layer showed a
substantial inundation of central Belfast with
only a 2m rise in the water level there was a
flood area of 452 hectares. The direction and
extent of the flood water is determined by the
elevation and topography in this project. In
figure 2 to 4 below, the level of inundation at
2m-4m can be seen, this demonstrates the
adaptability of the analysis. The result of this
analysis will only be as good as the elevation
data used. The 10m post space leaves room
for error, as does the fact that it does not
include features such as building or
vegetation that will affect the localised
movement of water.
Figure 2: Flood level 2m (Source: LPS 1:1,250 data)
Figure 3: Flood level 3m (Source: LPS 1:1,250 data)
Figure 4: Flood level 4m (Source: LPS 1:1,250 data)
The inundation at the 2m flood level
enveloped a number of different types of
building. The count for each type of building
can be seen in the table 1. A large number of
these buildings are domestic in nature, this
kind of analysis when carried out as an

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assessment could be used to inform people of
the potential flood risk in their area. The
Pointer data provides address data for the
buildings in the affected by the flood.
Ser Classification Count
(a) (b) (c)
1 Domestic- Apartment 896
2 Domestic- detached 34
3 Domestic Other 623
4 Domestic Semi 298
5 Domestic Terrace 2107
6 Education 2
7 Entertainment 22
8 Health 1
9 Hospitality 32
10 Industry 36
11 Legal 6
12 Office 298
13 Other 534
Table1: Flood level 5m (Source: LPS Pointer Database)
The map at figure 6 clearly shows two large
clusters of building that will be affected by
inundation. The area to the west is a
commercialised shopping area that will be
busy with shoppers at many times of the day
this would have been factored into any
evacuation plan. The cluster to the east is in a
residential area, the canalised nature of the
Victorian properties would affect the
progression of any inundation.
Figure 5: Properties Inundated (Source: LPS Pointer
data)
The results from the population density show
that the areas that have been inundated by
flood water are amongst the most densely
populated parts of the city. The flood layer
has been left off in figure 6 for clarity, but
glancing between figures 5 and 6 provides a
comparison.
Figure 6: Population Density (Source: CasWeb)
The roads in central Belfast will be heavily
affected by the inundation of the flood
waters. The map at figure 7 shows the extent
to which the roads will be affected by the
predicted flood. The GIS stores information
on the lengths of the roads, this enables the
rapid extraction of information such as the
length of each road. Immediately it becomes
apparent that many of the major roads
accessing the Belfast will not be affected
because they are raised, but that many A and
B class roads, and unclassified roads will be
inundated.
Figure 7: Roads Inundated by flood water (Source: LPS
1:1,250 data)
Information on the combined length of each
class of road that will be submerged can be
seen in table 2. It is evident from table 2 that

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the unclassified roads will be the hardest hit
by the floods. This information combined
with the road names contained in the
attribute tables could be used to inform the
authorities as to which roads will require to
be closed. Using the information in the table
below it could be possible to better target
resources in the clean-up operation.
Ser Road Classification Length Submerged
(a) (b) (c)
1 A Road 9689.50m
2 B Road 247.97m
3 Unclass 22104.54m
Table 2: Roads Submerged by flood water (Source: LPS
1:1,250 data)
The selection of the nearest facility resulted in
a route that required to detour from the most
direct route due to the barrier of the flood
water. This is when the lack of accuracy of
the flood layer can have a detrimental effect if
the are lots of small polygons restrict the
movement. A visual examination of the layer
should be done prior to the analysis.
Figure 8: Casualty Evacuation Route (Source: LPS
1:1,250 data)
The last part of the analysis involved the
selection of the schools to be used as the
emergency shelter. There were only two
schools that could not be used because of
location (under water). There were six
schools that had the required 3000m2, of
which two were chosen. The EEPs can be
seen in the map at figure 9.
Figure 9: Emergency Shelters (Source: LPS 1:1,250 data)
Discussion
The use of GIS within government department
is not new. Many of the problems
encountered when dealing with emergency
situations originate from a lack of a common
operating picture. The use of a GIS can
provide this either on a computer screen, as a
paper map, or published on the internet.
During emergency situation there are many
different types of data produced by various
agencies, a GIS has the ability to collate these
in one overarching software package, the
providing authoritive answers from one
source. This enables the visualisation of
datasets that may not often be seen together;
often unseen patterns and relationships
emerge.
As well as the clear benefits described above
there are also a number of fringe benefits. If
for example we take the identification of the
roads that were submerged by the flood, this
information could be used by: the police,
providing them with information on which
roads needed to be closed; the Ambulance
service for routing ambulances to the
incident; emergency worker travelling to the
incident area or by the Local News stations to
make the public aware of road closures. It
also has benefits for the deployment of
resources during any subsequent clear-up
operation.

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As with any introduction of a new system
there will be a cost for new equipment and
software. To get the best from the system
there will also be a training requirement for
staff; these costs are not insurmountable.
The use of flood prediction naturally suggests
that much of the work is carried out before
the flood occurs. This again shows one of the
strengths with the modelling capability of a
GIS. Providing that the datasets are available,
and the expertise there to operate the GIS,
emergency scenarios can be exercised before
the situation arrives in real-time. This could
lead to identification of weaknesses and a
revised plan being created. An example good
example for the advantages of pre planning is
the identification of the emergency shelters.
The two schools in question could be kept
stocked with basic provisions, blankets etc,
saving time in the event of a real flood.
Data is a key issue when considering the
implementation of a GIS. There are limitation
on the data and how well it can represent the
real world. The GIS and the various models
and analysis used are only as good as the data
that it is based on. It is important that that
the right conceptual model is used when
selecting or creating the data to be used.
Getting the right data and ensuring that staff
are aware of its limitation is important. When
the wrong scale of data is use the GIS will still
provide an output. A common error when a
GIS is implemented is staff believing that all
results are inherently correct, this is far from
the truth and in emergency situations could
cost lives.
The above project could be improved on by
using LIDAR data as the elevation data. This
would provide a higher resolution dataset to
use in the analysis, however this is expensive.
Conclusion
Emergencies all have one thing in common;
they all happen somewhere. It is this spatial
nature that makes the GIS invaluable to
provide situational awareness, and aid
decision support in the event of an
emergency. This paper has explored the
complexities of only one emergency scenario,
but has tried to demonstrate the flexibility of
the system. It has shown that given the right
datasets GIS can be pivotal to emergency
planning providing an extensive toolset that
can be utilised to provide answers to critical
questions in a timely fashion. This paper has
demonstrated the benefits of a GIS in dealing
with the complexities of an emergency
situation caused by a flood, with this in mind
it is recommended that a GIS be implemented
in the Emergency Planning department.

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References:
ALLINSON, J. (1994) The Breaking of the third wave: the demise of GIS, Planning Practices and
Research, 8(2), pp. 30-33.
CARR, B.G. and ADDYSON, D.K. (2010) Geographic Information Systems and Emergency Care
Planning, Academic Emergency Medicine 2010, 17, pp. 1274-1278.
DE SILVA, F.N. and EGLESE, R.W. (2000) Integrating simulation modelling and GIS: spatial
decision support systems for evacuation planning, Journal of the Operational Research Society
(2000), 51, 423-430.
ENDERS, A. and BRANDT, Z. (2007) Using Geographic Information Systems Technology to
Improve Emergency Management and Disaster Response for people With Disabilities, Journal
of Disability policy Studies, 17:4, pp. 223-229.
GILL, S. et al. (1999) GIS in Planning Departments: Preliminary Results from a Survey of Local
Authorities in Wales, Planning Practice & Research, 14:3, pp. 341-361.
Response of Feedback
During this project I have tried to standardise the maps throughout. I have also ensured that
any fill colours are no placed over other colours. I have ensured that all of the Maps tables and
figures are labelled correctly.