A Cellular Automata Model to Predict the Growth
Trends in Tripoli-Libya
Adel Zidan
Maysam Abbod
School of Engineering and Design
Brunel University, London
Uxbridge, UK
e-mail:adel.zidan@brunel.ac.uk
School of Engineering and Design
Brunel University, London
Uxbridge, UK
e-mail: maysam.abbod@brunel.ac.uk
Abstract— The world population is rapidly increasing due to
uncontrolled growth especially in developing countries, however
governments still hold the responsibility to provide civilized and
modern life which includes infrastructure, housing and
healthcare system. Random settlements are the main problem
associated with this issue. To solve these problems governments
must have advanced spatial plan to deal with this growth and
demand on people desires. Achieving sustainable development
which will reduce many of the problems in the future predict
population and size of area required for this inhabitants and the
establishment of appropriate projects within each region. This
paper is considering a case study based on a real data of the
Libyan capital Tripoli. The study has considered three sceneries
increasing, constant and decreasing in population prediction. The
study covered a period of 60 years from 1980 to 2040.
Keywords-population; growth; government; CA; sceneries;
prediction; increasing; Tripoli;
I.
INTRODUCTION
The Using of advanced technology which performs the
ideal results within short time, that will save time and money
which is what the research is trying to achieve. To make a
change in people's life, governments should invest in the
available technology and develop it in the way that is
beneficial to humanity. Applying cellular automate
programmer for extending cities can predict which places are
faster and cheaper to develop depending on some factors
inside each city for example natural constraints rivers,
mountains, slopes, the country’s policy or people’s preference
in terms of living near the sea or avoiding windy areas. Cities
are recognized as complex systems; a fuzzy cellular automata
urban growth model can be helpful to handle these
complexities [6]. Dynamic urban expansion models are useful
tools to understand the urbanization process and there are
several socio-economic indicators which affect the urban
expansion [3]. A cellular automata (CA) model of land cover
change is great method to provide protection of
environmentally sensitive areas and urban growth projections
[11]. Population growth is one of the most important engines
of change in any urban system and determines the shape of the
city in future for that this factor must be the initial part of any
model [9]. A number of factors contribute to the challenges of
spatial planning and urban policy in mega-cities, including
rapid population shifts, less organized urban areas, and a lack
of data with which to monitor urban growth and land use
change. To support sustainable development in cities, CA
model is used to predict how growth trend will continue in the
future [10].
II.
CASE STUDY
The main study area is Tripoli, the capital of city of Libya,
it has a population of about 1,600,000 people [7] and is
suffering from several problems such as crowding, traffic jams,
slow movement many environmental issues [8]. As a
consequence, the city became expensive which led to delays in
the marriage age that resulted in a drop of 2% in the population
growth rate compared to the average rates in the last decade
after reaching the peak in 1973 of 7% [12]. All of these
negative impacts were due to the fact that the city did not
expand for a long time. All expansion plans were stopped
including Tripoli Second Generation Plan. People were
encouraged to build houses in open areas out of the competent
authority control, later on when the government decided to
implement the third generation plan faced many problems and
forced to demolish areas totally in some places and partly in
others, these actions were very costly both to home owners and
the authorities . This research provides a solution through the
use of a model which gives decision makers opportunities to
plan and develop areas in advance. Controlling the growth and
direct it to the right way and also preserving the natural
resources.
III.
METHODOLOGY
A cellular automata model was designed for urban
growth to simulate the process of urbanization in a hypothetical
region. This model consists of a set of rules that describe the
spatial interaction of cells and a set of parameters that lead to
explore different urban forms. Furthermore exploring how the
different results of the model can be evaluated throughout
fractal analysis and the estimation of the fractal dimension.
significant connection is noticed between the parameters of the
model and the value of the fractal dimension, there are few
important factors were used into the model to make it
applicable in the examination of real urban patterns are
landscape constraints, transportation network, protected areas
and physical geography [1]. Simulate the urban growth over
the last three decades for Indianapolis city, Indiana firstly
Urban growth simulation for an artificial city has been done.
Urban sprawl determinants such as the size and shape of
neighbourhood besides examine different types of constraints
on urban growth simulation were assessed. The results were
showing that circular type neighbourhood are easer but faster
urban growth as compared to nine-cell neighbourhood as well
as the definition of CA rules is critical phase in simulating the
urban growth pattern in close behaviour to existing growth.
Then includes running the developed CA simulation over
classified remote sensor historical image in developed GIS
tool. A set of crisp rules are defined and calibrated based on
real urban growth pattern. To tasting the accuracy of the
simulated results as compared to the historical ground truth
images examination is performed. Were obtained encouraging
results and the percentage of accuracy was excellent, reaching
an average accuracy of more than 80% with the need to amend
CA growth rules over time to go with the growth pattern
changes
to achieve accurate simulation [2]. The
implementation of Cellular Automata model in France. In
France, the management of urban growth depends upon the
housing policies that are designed by municipalities by the
department of region or the state. Like many parts of the world,
sustainable development in urban areas is crucial. In order to
manage these problems, the urban planners use varieties of
tools as conceptual models, like computer aided drafting and
geographic information system. As these are traditional tools
and have lost their efficiency, new strategies have been
developed by researchers. Cellular Automata is an effective
tool to improve explanation and forecasting of urban growth
and its outcomes on the population. This is the model that
describes heterogeneity of the produced results and envisages
an interesting debate regarding growth management and
territorial intelligence [4]. A model was designed using Fuzzy
Cellular Automata (FCA) in MATLAB after digitizing land use
map type to a city that can be done by giving numbers to the
different areas on the map, each area indicating particular
sector for instance residential area, industrial area and
agricultural area etc. Also there is open area surround the
existing city out of the built zones, this area is able to develop
which is mean the area will become either new residential or
new industrial area depending on the old area nearby and will
stay as it is if it located far of the existing residential or
industrial areas, also the time needed to be develop depending
on its location how far from the old area. Parameter has been
used which determines whether there is chance to expand in the
open area or no, this parameter was giving the result in initial
model from 1980 to 2010 and the same parameter is used in
prediction model. To compare the previous growth in the past
years, old maps were digitized to identify any changes between
the past and present growth. The model presents the areas as
cells and allows some areas to grow (residential and industrial)
if they have a chance to do so (open area nearby) and other
areas (public facilities, business, sports and leisure areas) will
be added to the final map as public services area (20% to 25%
of total area).In the prediction model, the fuzzy logic file
consist of two inputs were used, population and area inputs and
nine rules made into it. The outputs are five which are (small
(S), small-medium (SM), medium (M), medium-high (MH)
and high (H))
TABLE 1. FUZZY LOGIC MEMBERSHIP RULES.
Population
Area
Small
Medium
High
Small
Medium
High
S
SM
M
IV.
SM
M
MH
M
MH
H
RESULTS
A. Initial Model
This model designed to test the period from 1980 to 2010
and it has resulted in a land size of 615 km2 and population
1,600,000 which is consistent with the official governmental
[5], the population growth rates for the 30 years have been
used into the model(colours indicating different type of area
for example residential, industrial. etc).
Figure 1. Tripoli land use map in 1980.
Figure 2. Digitized Tripoli Land Use Map of 1980.
Figure 3. Tripoli Land Use Map of 1980 by Model.
Figure 4. Tripoli land use map of 2010.
B. Prediction Model
This model was designed for the next 30 years from 2010 to
2040 using fuzzy logic file with two inputs (population and
area) has been used to obtain accurate results. Additionally the
model was made of three sceneries decreasing, constant, and
increasing in population growth prediction. The growth rate at
2% presumed as constant scenario for the reason that the
average growth rates for the period between 2000 to 2010
were 2% whereas the increasing scenario represents a growth
rate of 0.2% every ten years and finally the decreasing
scenario represents a minus growth of 0.2% every ten years.
The three sceneries have provided almost the same results
with predictive calculation.
𝑃2020 = 𝑃2010 × (1 + 𝑖)n
(1)
Figure 5. Digitizing map of 2010.
n
𝑃2030 = 𝑃2020 × (1 + 𝑖) (2)
𝑃2040 = 𝑃2030 × (1 + 𝑖)n (3)
where i is the growth rate, n is number of years.
Year
1980
1995
2000
2010
2020
2030
2040
1) Declining Growth Scenario
In this scenario the growth rate is -0,2% every ten years and
the prediction are 832km2 total area and 2,163,300
population.
TABLE 2. POPULATION PREDICTION.
Decreasing
Constant
Increasing
784,000
784,000
784,000
997,000
997,000
997,000
1,100,000
1,100,000
1,100,000
1,600,000
1,600,000
1,600,000
1,912,500
1,950,400
1,989,000
2,241,500
2,377,500
2,521,300
2,575,800
2,898,200
3,259,100
Figure 6. Expanded map on 2040 based on declining growth scenario.
Figure 7. Total area using declining growth scenario.
Figure 8. Population prediction based on the declining growth scenario.
Figure 10. Total area based on fixed growth scenario.
Figure 11. Population prediction based on fixed growth scenario.
2) Fixed Growth Scenario
3) Increasing Growth Scenario
The population growth rate is fixed in this scenario with
2% for the thirty years, the gained results is 1,109 km2 area
and 2,885,900 population which almost equal the predictive
calculation of the population prediction.
In this case the growth rate goes up by 0,2% every
decade, the results are 1,150km2 area and 3,002,900
population.
Figure 12. Expanded map on 2040 based on increasing growth scenario.
Figure 9. Expanded map on 2040 based on fixed growth scenario.
authorities the potential to plan ahead in advance to control the
expansion and avert the troubles of unplanned growth. The
model results are similar to predictive calculation that gives
evidence the model is reliable. The Libyan urban planning
agency is therefore encouraged to use this model so that
expansion of cities can be based on factual data and which will
to an improvement in the quality of life of citizens.
ACKNOWLEDGMENT
I would like to thank the Libyan government for the
financial support.
REFERENCES
1.
Figure 13. Total area based on increasing growth scenario.
Figure 14. Population prediction based on increasing growth scenario.
V. CONCLUSIONS
The study showed the importance of using the CA model
for future urban planning. This model provides to authorities
with easy, efficient and accurate methods for planning based
on actual population growth rates. It also provides the
L, Apostolos, Urban Growth Simulation Through Cellular Automata: A
Way to Explore the Fractal Nature of Cities, Science Regional, 2010.
2. S. Alkheder, J. Shan, Cellular Automata urban growth simulation and
evaluation-A case study of Indianapolis, Proceedings of the 8th
International Conference on Geo Computation. 1-19, 2005.
3. D. Wu, J. Liu, S. Wang, Simulating urban expansion by coupling a
stochastic cellular automat a model and socioeconomic indicators.
24, 235-245, 2010.
4. P. Antoni, P. Frankhauser, C. Tannier, S. Youssoufi, Modelling
Urbanization to Simulate Prospective Scenarios, Territorial Intelligence
and Governance Participatory action-research and Territorial
Development, 14-58. 2012.
5. A. Alshrief, Population, education and manpower in Libya Dar Alketab
Benghazi Libya, 2010.
6. K. Al-Ahmadi, A. Heppenstall, L. Hogg, Modeling urban growth
dynamics using cellular automata and GIS. 2010.
7. Urban Planning Agency. Regional Development Plan. Tripoli Region,
2007.
8. Urban Planning Agency 2nd Generation Plan Evaluation. Tripoli
Region, 2007.
9. A. Mavroud, Simulating city growth by using the Cellular Automata
Algorithm MSc AAC, 2007.
10. H. Shafizadeh, M. Helbich, Spatiotemporal urbanization processes in
the megacity of Mumbai, India: A Markov chains-cellular automata
urban growth model. 40, 140-149, 2013.
11. D. Mitsova, W. Shuster, X. Wang, A cellular automata model of land
cover change to integrate urban growth with open space conservation.
0169-2046, 2010.
12. The World Bank. Libyan population growth 1960 – 2012[online]
available at http://data.worldbank.org/ [accessed on 21/11/2012].