Last summer, I worked at United Nations Headquarters, belonging to
United Nations Center for Human Settlement, a Secretariat of the last UN
Conference "City Summit, Habitat II", held in Istanbul last June, and went
there to attend the conference as an assistant for UN staffs from the
beginning,  This opportunity made me realize what current urban problems
were and what we should do in the future.  After this internship, I made a
trip to Egypt for three weeks, and I saw a lot of urban problems in Cairo,
which were the same situation as what was discussed in Istanbul.  Although
I may have nothing to do with these problems in Cairo, I felt like my own
and would like to carry researches into why a lot of people gather this
large city and what kinds of urban problems will be brought about.  That
is the reason why I selected this topic in this course.
	First of all, I will introduce what urbanization is, including
future trend and current reality.  Second, I will mention about the City
Summit, Habitat II.  After showing the objections and goal, I will add
what I did there and what I thought regarding this conference.  Third, I
want to show you the itinerary of my Egypt trip, including some pictures.
I will report from the current situation in Cairo to some tourist spots
such as Luxor, Aswan and Alexandria, and show you what I saw and thought.
Fourth, I will introduce about Egypt, Geography, Population Maps and
Economy.  After that, I will show some graphs as a demographic transition.
Finally, I want to show my analysis by using STELLA II.  Recent data can
tell us the future trend of urbanism such as population growth; however,
few people knows why these things happen.  After promoting better
understanding about this country, I try to examine available data in
various fields as mush as possible, and will find possible relationships
among them.  These analysis will allow me to create the population
dynamics model in Egypt by using STELLA II.  This model will visibly show
the relationship among the population growth and others, and bring about
better understanding about the population dynamics in this country.   I
made a simple model taken into account population factors such as birth
and death rate from the data of World Resource Institute, and linked it to
GDP (Gross Domestic Products).  It was very difficult to link them,
because they cannot be represented by an equation.  
	For example, a fertility rate is supposed to be linked with Industrial
output per capita; however, each graph never tells any relationships.
Fortunately, I found a good relationship between them by multiple
regression analysis.  I could link between each distribution of GDP and
population growth factors by a liner equation.  This STELLA model tells us
to what extent each distribution of GDP will effect on the population
growth, which can be handled by the government.  In conclusion, I will
give some findings and possible further studies in the future, based on
the results from the STELLA model.
	Urbanization is one of the critical global trends shaping the
future, reshaping the physical and social environment, as it fuels
economic growth and spurs environmental degradation.  Cities can serve as
centers of employment, growth and innovation; however, the rapid
population growth in large cities in developing counties will go beyond
its capacity.  On the table below, I show some future trend and current
reality in the world, which we are surely surprised at. 

Table 1 : Future Trend and Current Reality Future Trend By 2025, world population is expected to reach 8.3 billion, a 50 % increase over the present. Africa's population is expected to double, Latin America's to grow nearly 50 %. and Asia's to grow 40 % during that period. Current Reality Fertility rate in most developing countries are declining, but must fall mush further if even the mid-range population growth projection (8.3 billion) is not to be exceeded. Future Trend By 2025, 2/3 of the world's people will live in cities. Current Reality Only one third of the world's population was urban 35 years ago. More than 150,000 people are being added to urban population in developing countries every day. Future Trend By 2015, the world will have 33 "megacities" with population over 8 million and more than 500 cities with population of 1 million or more. Current Reality Greater Tokyo already has 27 million people; Sao Paulo, Brazil, 16.4 million; and Bombay, India, 15 million. Future Trend In coming decades, most of the world's poor will be in urban, living under conditions that can be worse than those of rural poor. Current Reality Between 1970 and 1990, the number of urban poor in Latin America alone increased from 44 to 115 million. Future Trend By 2000, the physical size of cities in developing countries is expected to be double what it was in 1980, exerting phenomenal stress on local environment. Current Reality More than 40 % of all cities with a population of 500,000 or more are in tidal estuaries or on the open coast. Half of the world's coastlines and coastal ecosystems are already at risk from development. Future Trend By 2020, energy use will increase by 50 to 100 %. Emissions of greenhouse gases that contribute to the risk of climate change will increase by 45 to 90 %. Current Reality In the past 20 years, global energy use has grown nearly 50 %. The Intergovernmental Panel on Climate Change has concluded that greenhouse gas emissions have led to a "discernible human influence on global climate. Future Trend By 2010, the number of motor vehicles could grow to more than 800 million, exacerbating urban air pollution especially in rapidly industrializing countries. Current Reality In Bangkok, 300-400 more vehicles are added to traffic jams daily. In the United States, the number of miles driven in motor vehicles climbed 40 %, largely offsetting increases in vehicle efficiency. Future Trend World food production is expected to keep up with population growth, but many people will still go hungry. By 2010, the number of African's which suffer from malnutrition is expected to increase 70 % to 300 million. Current Reality Nearly one billion people get most of their protein from fish. Overfishing has already depleted more than one-fourth of the world's marine fish stocks. Future Trend By 2050, as many as 2.4 billion people could live in countries facing water scarcity. This is nearly 1/5 of the world's projected population. Current Reality Withdrawals of fresh water from rivers and lakes have quadrupled in the last 50 years. In 1994, at least 220 million urban dwellers lacked steady access to safe drinking water. Fully 90 % of sewage in developing countries go untreated. Source: World Resources 1996-97 Press Release

Habitat II Goals and Objectives As can be seen on the table 1, such urban problems were the main topics at the recent international conference, Habitat II, which was held in Istanbul last June. The overall objective of the Habitat II Conference is twofold: one is to increase the world's awareness of the deteriorating living environment, and the second is to awaken the planet to the potentials of human settlement as catalysts for social progress and economic growth -- and that can only happen if our cities, towns and villages are healthy, safe, just and sustainable. The fundamental goal is to prepare the international community for life on an urbanized earth. What I did I have worked as an intern in the United Nations Centre for Human Settlement (Habitat), New York Office, from May 22 to July 26 1996. I assisted with various tasks in the preparation of the Habitat II Conference which take place in Istanbul, Turkey. I attended the conference in Istanbul from 1 to 16 June, was to assist to the UN staff in the organization of special events including hospitality services and coordinated logistics for the Secretary-General's Advisory Group. Several opportunities to attend conferences were given to me, such as main conference, NGO Forum and other official conferences. A lot of current urban problems were reported there, which made me realize what happened in urbanizing world. What I thought My main impression about the conference is that from now on our efforts to improve the living environment must be focused on urban areas. That is where most of the world's population will live and work, where most economic activity will take place, where the most pollution will be generated, and where most natural resources consumed. In Istanbul, I had an opportunity to know some keys to urban efficiency; such as decentralization, employment generation, environmental infrastructure, land regulations, municipal finance, and promoting good governance. These might be the ways to reduce the urbanization; however, first of all, I should understand why and how such movement of human population occurs and what kinds of relationship with the global environment exists. Trip to Egypt After the internship at United Nations, I made a trip to Egypt for three weeks, visiting Cairo, Alexandria, Upper Egypt and Red Sea. Since various urban problems were discussed at the conference in Istanbul, this trip was not just sight seeing. I arrived at Cairo on the 5th of August. The current situation of Cairo is just terrible; such as a lot of people and auto-mobile, which bring about a lot of garages with sand in the streets, smog in the air and even River Nile is far from clean. The scene is surely different from the image which I had. Furthermore everything is written in Arabic and people who can speak English tend to tell a lie to travelers, so I think visitors cannot make a trip individually unless they speak Arabic. Next, I visited Alexandria, the second biggest city in Egypt, which is one of the cities faced on the Mediterranean Sea. I met a French gentleman there, and he said that Alexandria was the most beautiful in Mediterranean cities, even more than Nice or Monaco in France; however, now it becomes one of the worst cities with floating garbage in the sea. Then I headed for Aswan by a mini-bus, which took me about 15 hours to get there. I visited the Aswan High-Dam and found an interesting sign, saying "This is an Egyptian challenge against nature". In fact, this dam can control the water flow and brings about steady water supply without flood; however, it resulted in climate change around there. It used to have little rain before, but, because of the Lake Nasal, it sometimes has rain due to the vapor from the lake, causing environmental change. At Luxor, I pedaled a bicycle to visit King of Valley under hot weather. The direct sunshine brought more than 44 C(degree), and I almost died. Finally I went to Hurghada, Red Sea to go scuba-diving. Red Sea has one of the most beautiful coral reefs, which attracts all divers in the world. It is sure that a lot of world heritage still exist in Egypt, and a lot of foreign people want to see them once. However, it seems to me that a lot of people depend upon them too much. That is the reason why service per capita has been growing up recently. Frauds and crimes for visitors have increased, which makes the image of Egypt worse. Under such a current trend, nothing new will be produced. If the government thinks much of service sector especially in travel, it should commit with a positive attitude, which create repeat visitors to Egypt. EGYPT Geography and History Officially Arab Republic of Egypt, from 1958 to 1971 UNITED ARAB REPUBLIC, republic, Northeast Africa and Sinai Peninsula, Southwest Asia. It is bounded on the North by the Mediterranean Sea, on the East by Israel and the Red Sea, on the South by the Sudan, and on the West by Libya. The country has a maximum length from North to South of about 1085 km (about 675 mi.) and a maximum width, near the South border, of about 1255 km (about 780 mi). It has a total area of approximately 1,001,450 sq. km(approximately 386,660 sq. mi). The land of the Nile River, Egypt is the cradle of one of the world's greatest ancient civilizations and has a recorded history that dates from about 3200 BC. The descriptive material that follows is pertinent to modern Egypt. The History section covers Egypt from ancient times, including the Dynastic Period (3200 BC-343 BC), the Hellenistic Period (332 BC-30 BC), Roman and Byzantine Rule (30 BC-AD 638), the Caliphate and the Mamalukes (642-1517), Ottoman Domination (1082-1882), and British colonialism (1882-1952) as well as modern, independent Egypt (1952- ). Land and Resources. Less than 10% of the land area of Egypt is settled or under cultivation. This territory consists of the valley and delta of the Nile and a number of desert oases. More than 90% of the country consists of desert areas, including the Libyan Desert in the West, a part of the Sahara, and the Arabian (or Eastern) Desert, which borders the Red Sea and the Gulf of Suez, in the East. The Libyan Desert (also known as the Western Desert) includes a vast sandy expanse called the Great Sand Sea. Located here are several depressions with elevations below sea level, including the Qattarah (Qattara) Depression, which has an area of about 18,100 sq. km (about 6990 sq. mi) and reaches a depth of 133m (436 ft) below sea level, the lowest point in Africa; also found here are the oases of Siwah, Kharijah, Bahriyah, Farafirah, and Dakhilah. Much of the Arabian Desert occupies a plateau that rises gradually East from the Nile Valley to elevations of about 610 m (about 2000 ft) in the East and is broken along the Red Sea coast by jagged peaks as high as about 2135 m (about 7000 ft) above sea level. In the extreme South, along the border with the Sudan, is the Nubian Desert, an extensive region of dunes and sandy plains. The Sinai Peninsula consists of sandy desert in the North and rugged mountains in the South, with summits looming more than 2135 m (more than 7000 ft) above the Red Sea, and including Jabal Katrinah (2642 m/8668 ft), the highest elevation in Egypt. The Nile enters Egypt from Sudan and flows North for about 1545 km (about 960 mi) to the Mediterranean Sea. For its entire length from the South border to Cairo the Nile flows through a narrow valley lined by cliffs. At the Sudan border lies Lake Nasser, a huge reservoir formed by the Aswan High Dam (q.v.) . The lake is about 480 km (about 300 mi) long and is about 16 km (10 mi) across at its widest point. Population Map in 1960.

Population Map in 1992.

Population Map in 2001.

Economy During the presidency of Gamal Abdel Nasser, the economy of Egypt was radically socialized. Beginning in 1961, foreign trade, banking, insurance, and most wholesale and industrial establishments were nationalized. Those sectors which remained in private hands were placed under heavy regulatory restraints. Industry was expanded and production increased according to a five year plan. Inadequate foreign investment, a sluggish bureaucracy and the disastrous 1967 Arab-Israeli War subverted subsequent programmes until a process of economic reform was inaugurated by Abdel Nasser's successor, Anwar Sadat, in the aftermath of the October War of 1973. By reversing many of Abdel Nasser's policies and opening Egypt to foreign investment, Sadat began a gradual revival of the Egyptian economy which was significantly enhanced by remittances from Egyptian working in the surrounding oil producing countries. The very slow but sure relaxation of import, currency and trade restrictions stimulated Egypt's foreign exchange economy. Tourism, which had fallen off drastically during Abdel Nasser's time due to Egypt's anti-western stance and poor tourist infrastructure, was restarted with the privatization of many nationalized tourist facilities. Sadat's dramatic peace initiative and treaty with Israel transformed the western view of the Arab leader and his country and further enhanced the country internationally, although the gesture was motivated by more practical considerations: Egypt couldn't afford another war with Israel. Despite the many advances the country has witnessed under President Hosni Mubarak, Egypt continues to suffer from the vagaries of regional instability and its exploding population. Government leaders openly admit that population growth is undermining all efforts toward developing the country's economy. This situation is further aggravated by consumerism. Servicing a foreign debt over twice the size of the national budget is another negative factor. Under pressure from the IMF and World Bank, Egypt finally began to lift price controls, reduce subsidies and begin to relax restrictions on trade and investment. Tourism represents one of the most lucrative sectors of Egypt's economy but is highly vulnerable to internal violence and regional politics. The government remains hopeful that the oil and gas discoveries in the western desert will produce significant revenues. Figure 1. Total Population. Graph.

According to the data from World Resource Institute, the total population in Egypt will grow up as can be seen on the figure 1. Although it looks like a linear curve, I got a better result using logistic model. Basically, logistic models represent that the curve will get close to the limit, so the growth of the total population in Egypt will be declining over time. By 2050, it is expected that the total population in Egypt will reach almost 12 million. Figure 2. Urban and Rural Population.

Figure 3. Crude Birth and Death Rate.

The graph of Urban & rural population in Egypt shows urbanization clearly. According to the data, the urban population will be doubled in the next 30 years. This is one of the typical cases of urbanization in the developing countries. Crude birth rate will be still higher than death rate; however, the crude birth rate is expected to be declining over time. On the other hand, the death rate will be growing up again around the year of 2015. Since people are aging, such a trend can be expected in the future. Population Growth Analysis STELLA II About 20 years ago, Dr. Dennis L. Meadows, a professor at MIT, represented the world model in his book, "The Limits To Grows". This model was built specifically to investigate five major trends of global concern; such as accelerating industrialization, rapid population growth, widespread malnutrition, depletion of nonrenewable resources, and a deteriorating environment. Since this model is a formal and mathematical model, it has two important advantages over mental models. First, every assumption is written in a precise form so that it is open to inspection and criticism by all. Second, after the assumptions have been scrutinized, discussed, and revised to agree with the best current knowledge, their implications for the behavior of the world system can be traced without error by a computer, no matter how complicated they become. Referring to the world model and using current available data, I will create the Egypt model by STELLA II. Simple Modeling First of all, I created a simple model, taking into account population factors only, such as birth and death rate. The generation was divided by three, and each growth pattern can be seen in the graph. As a result of this simple model, I could got a similar growth patterns as can be seen on the figure 1. However, the range is a little higher than the data from WRI. Other factors such as Industry, Agriculture and Natural Resource will effect on this growth pattern. Since some of these other factors would play a significant role to decrease the population growth such as birth control, I would like to put GDP (Gross Domestic Products) into the previous model. Figure 4. STELLA II model 1 (population factors only)

Figure 5. Population growth by generation.

Link to GDP Figure 6. Distribution of GDP (percent).

Figure 7. Each category per capita.

GDP is divided three outputs such as industry, service and agriculture. As can be seen, the service sector holds about 60 % of the total GDP. With the increase of service output, service per capita is also growing up in spite of the rapid population growth. Each output will have some relationship with population growth factors such as fertility and mortality. For example, if the industrial per capita increases and people obtain better life, the birth rate would be effected by that. The service per capita also have an influence to this, because people would have better education, which would bring about birth control. Figure 8. Relationship between industry per capita and fertility rate.

Figure 9. Relationship between service per capita and fertility rate.

However, there is no equation to link between them. To what extent the increase industrial per capita effects on total fertility? How about service output? In order to solve this problem, I created graphs in the previous page, showing the relationship between each category of GDP and fertility rate. As can be seen, the fertility rate will be declining over time, while each output changing randomly. That means I cannot have the relationship, because the fertility rate is not determined my them. However, let me think like this. The difference of the fertility rate each year may be effected by the actual results of industry per capita or service one. So I summed it up year by year, and look into the relationship by using Multiple Regression. Multiple Regression Fertility Rate Equation Number 1 Dependent Variable.. FERTIL Block Number 1. Method: Stepwise Criteria PIN .9800 POUT .9900 SUMIND SUMSER SUMAGRI Variable(s) Entered on Step Number 1.. SUMSER Multiple R .99805 R Square .99610 Adjusted R Square .99592 Standard Error .03889 Analysis of Variance DF Sum of Squares Mean Square Regression 1 8.50350 8.50350 Residual 22 .03328 .00151 F = 5621.96550 Signif F = .0000 ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig T SUMSER -3.41548E-04 4.5552E-06 -.998049 -74.980 .0000 (Constant) 5.506274 .012850 428.511 .0000 ------------- Variables not in the Equation ------------- Variable Beta In Partial Min Toler T Sig T SUMIND -.126370 -.191485 .008950 -.894 .3814 SUMAGRI -.145369 -.255322 .012025 -1.210 .2397 Variable(s) Entered on Step Number 2.. SUMAGRI Multiple R .99818 R Square .99636 Adjusted R Square .99601 Standard Error .03849 Analysis of Variance DF Sum of Squares Mean Square Regression 2 8.50567 4.25283 Residual 21 .03111 .00148 F = 2871.05761 Signif F = .0000 ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig T SUMSER -2.92100E-04 4.1109E-05 -.853557 -7.106 .0000 SUMAGRI -1.18900E-04 9.8253E-05 -.145369 -1.210 .2397 (Constant) 5.527124 .021414 258.108 .0000 ------------- Variables not in the Equation ------------- Variable Beta In Partial Min Toler T Sig T SUMIND .023096 .021745 .003230 .097 .9235 Variable(s) Entered on Step Number 3.. SUMIND Multiple R .99818 R Square .99636 Adjusted R Square .99581 Standard Error .03943 Analysis of Variance DF Sum of Squares Mean Square Regression 3 8.50568 2.83523 Residual 20 .03109 .00155 F = 1823.75924 Signif F = .0000 ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig T SUMIND 1.32982E-05 1.3671E-04 .023096 .097 .9235 SUMSER -2.94550E-04 4.9072E-05 -.860716 -6.002 .0000 SUMAGRI -1.31929E-04 1.6755E-04 -.161299 -.787 .4403 (Constant) 5.528470 .025937 213.147 .0000 End Block Number 1 POUT = .990 Limits reached. In this multiple regression, independent variables; such SUMSER as sum of service per capita, were entered on step number, meaning that the most relative independent variable to the dependent variable of "fertility rate" was selected to be in the equation at first. As can be seen in the R square, the fertility rate is totally effected by service per capita (R Square = .99610). The other two variables could not give significant change in the R Square (R Square = .99636). This seems to mean that there is little link between the fertility rate and the industrial or food per capita. It is sure that this discovery will decisively effect on the model, and I can put an equation into it based on the result of the multiple regression as follows; "Fertility rate" = .000013982 * SUM(industrial_per_capita) + (-.00029455) * SUM(service_per_ capita) + (-.000131929) * SUM(food_per_capita) Mortality Rate Equation Number 1 Dependent Variable.. MORTAL Block Number 1. Method: Stepwise Criteria PIN .9800 POUT .9900 SUMIND SUMSER SUMAGRI Variable(s) Entered on Step Number 1.. SUMAGRI Multiple R .99574 R Square .99150 Adjusted R Square .99111 Standard Error .28094 Analysis of Variance DF Sum of Squares Mean Square Regression 1 202.48991 202.48991 Residual 22 1.73634 .07892 F = 2565.61570 Signif F = .0000 ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig T SUMAGRI -.003984 7.8645E-05 -.995740 -50.652 .0000 (Constant) 17.985729 .103648 173.527 .0000 ------------- Variables not in the Equation ------------- Variable Beta In Partial Min Toler T Sig T SUMIND .993537 .713548 .004385 4.667 .0001 SUMSER .406696 .483664 .012025 2.532 .0194 Variable(s) Entered on Step Number 2.. SUMIND Multiple R .99791 R Square .99583 Adjusted R Square .99543 Standard Error .20146 Analysis of Variance DF Sum of Squares Mean Square Regression 2 203.37397 101.68698 Residual 21 .85228 .04058 F = 2505.54368 Signif F = .0000 ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig T SUMIND .002798 5.9949E-04 .993537 4.667 .0001 SUMAGRI -.007949 8.5162E-04 -1.987096 -9.335 .0000 (Constant) 18.469929 .127621 144.724 .0000 ------------- Variables not in the Equation ------------- Variable Beta In Partial Min Toler T Sig T SUMSER .134039 .195267 .003230 .890 .3838 Variable(s) Entered on Step Number 3.. SUMSER Multiple R .99799 R Square .99599 Adjusted R Square .99538 Standard Error .20246 Analysis of Variance DF Sum of Squares Mean Square Regression 3 203.40647 67.80216 Residual 20 .81978 .04099 F = 1654.14658 Signif F = .0000 ------------------ Variables in the Equation ------------------ Variable B SE B Beta T Sig T SUMIND .002477 7.0200E-04 .879610 3.529 .0021 SUMSER 2.24357E-04 2.5197E-04 .134039 .890 .3838 SUMAGRI -.008028 8.6035E-04 -2.006650 -9.331 .0000 (Constant) 18.501888 .133183 138.920 .0000 End Block Number 1 POUT = .990 Limits reached. In comparison with the fertility rate, the most relative independent variable of SUMAGRI (food per capita) to the dependent variable of "mortality rate" was selected to be in the equation at first. As can be seen in the R square, the mortality rate is totally effected by food per capita (R Square = .99150). The other two variables could not give significant change in the R Square (R Square = .99599), either. This seems to mean that there is little link between the mortality rate and the industrial or service per capita. I can also put an equation into it based on the result of the multiple regression as follows; "Mortality Rate" = .002477 * SUM(industrial_per_capita) + .000224357 * SUM(service_per_capita) + (-.008028) * SUM(food_per_capita) Curvefit Analysis Since the multiple regression is used a liner model, there might be another estimation such as logistic model in representing the relationship. I found strong relationships between the fertility rate and the service per capita, and the mortality rate and the food per capita as I noted earlier. In these two cases, I compared the linear model with the logistic one below. The results tells us that each of the linear models has a slightly better R-Square than that of the logistic model. It means that I had better use a linear model in the multiple regression rather than a logistic one. Curvefit fertility with service per capita CURVEFIT /VARIABLES=fertil WITH sumser -> /CONSTANT -> /MODEL=LINEAR LGSTIC -> /PLOT FIT. Independent: SUMSER Dependent Mth Rsq d.f. F Sigf bound b0 b1 FERTIL LIN .996 22 5621.97 .000 5.5063 -.0003 FERTIL LGS .995 22 4021.98 .000 . .1798 1.0001 Figure 10. Curvefit fertility with service per capita

CURVEFIT /VARIABLES=mortal WITH sumagri -> /CONSTANT -> /MODEL=LINEAR LGSTIC -> /PLOT FIT. Independent: SUMAGRI Dependent Mth Rsq d.f. F Sigf bound b0 b1 MORTAL LIN .991 22 2565.62 .000 17.9857 -.0040 MORTAL LGS .989 22 1954.93 .000 . .0537 1.0003 Figure 11. Curvefit mortality with food per capita.

Gross_Domestic_Products(t) = Gross_Domestic_Products(t - dt) + (capital_investment - capital_depreciation) * dt Initial Gross_Domestic_Products = 40000000000 INFLOWS: capital_investment = (agricultural_output*.3+industrial_output*.7+service_output*.5)*(investment_rate/ 100)+official_development_assistance OUTFLOWS: capital_depreciation = Gross_Domestic_Products/average_lifetime_of_capital/100 population_1__#0_to_14#(t) = population_1__#0_to_14#(t - dt) + (births - deaths_0_to_14 - maturation_1_to_2) * dt Initial population_1__#0_to_14# = 23924000 INFLOWS: births = population_2__#15_to_64# * 0.48 * fertility_rate - total_population * infant_mortality OUTFLOWS: deaths_0_to_14 = population_1__#0_to_14#*mortality maturation_1_to_2 = population_1__#0_to_14#*(1-mortality) Modeling linked with GDP Figure 12. STELLA II model linked with GDP.

population_2__#15_to_64#(t) = population_2__#15_to_64#(t - dt) + (maturation_1_to_2 - maturation_2_to_3 - deaths_15_to_64) * dt Initial population_2__#15_to_64# = 36361000 INFLOWS: maturation_1_to_2 = population_1__#0_to_14#*(1-mortality) OUTFLOWS: maturation_2_to_3 = population_2__#15_to_64#*(1-mortality) deaths_15_to_64 = population_2__#15_to_64#*mortality population_3__#65_and_over#(t) = population_3__#65_and_over#(t - dt) + (maturation_2_to_3 - deaths_65_and_over) * dt Initial population_3__#65_and_over# = 2646000 INFLOWS: maturation_2_to_3 = population_2__#15_to_64#*(1-mortality) OUTFLOWS: deaths_65_and_over = population_3__#65_and_over#*mortality/mortality agricultural_output = Gross_Domestic_Products * agricultural_capital _output _ratio /100 birth_control = (.000013982*SUM(industrial_per_capita) -.00029455 * SUM(service_per_capita) - .000131929 * SUM(food_per_capita)) * 5 fertility_rate = perspective_birth_rate+birth_control food_per_capita = agricultural_output/total_population health_service = (.002477*SUM(industrial_per_capita) + .000224357 * SUM(service_per_capita) - .008028 * SUM(food_per_capita)) * 5 industrial_output = Gross_Domestic_Products*industrial_capital_output_ratio/100 industrial_per_capita = industrial_output/total_population mortality = (perspective_death_rate+health_service)/1000 service_output = Gross_Domestic_Products*service_capital_output_ratio/100 service_per_capita = service_output/total_population total_death = deaths_0_to_14+deaths_15_to_64+deaths_65_and_over total_population = population_1__#0_to_14# + population_2__#15_to_64# + population_3__#65_and_over# agricultural_capital_output_ratio = GRAPH(TIME) (0.00, 17.0), (1.00, 16.5), (2.00, 16.0), (3.00, 15.0), (4.00, 12.0), (5.00, 11.0), (6.00, 9.50), (7.00, 8.50), (8.00, 8.50), (9.00, 6.50), (10.0, 7.50), (11.0, 9.50), (12.0, 10.0) average_lifetime_of_capital = GRAPH(TIME*5) (0.00, 30.0), (1.00, 28.5), (2.00, 28.0), (3.00, 27.5), (4.00, 26.5), (5.00, 26.5), (6.00, 25.0), (7.00, 24.5), (8.00, 22.5), (9.00, 22.5), (10.0, 22.0), (11.0, 21.0), (12.0, 20.0) industrial_capital_output_ratio = GRAPH(TIME) (0.00, 28.0), (1.00, 27.0), (2.00, 26.0), (3.00, 25.5), (4.00, 26.0), (5.00, 26.0), (6.00, 27.5), (7.00, 30.5), (8.00, 33.5), (9.00, 36.5), (10.0, 38.0), (11.0, 39.0), (12.0, 40.0) infant_mortality = GRAPH(TIME) (0.00, 0.054), (1.00, 0.043), (2.00, 0.035), (3.00, 0.03), (4.00, 0.025), (5.00, 0.02), (6.00, 0.017), (7.00, 0.014), (8.00, 0.012), (9.00, 0.009), (10.0, 0.008), (11.0, 0.008), (12.0, 0.007) investment_rate = GRAPH(TIME*5) (0.00, 1.00), (1.00, 1.85), (2.00, 2.35), (3.00, 2.75), (4.00, 3.05), (5.00, 3.45), (6.00, 3.70), (7.00, 3.95), (8.00, 4.35), (9.00, 4.60), (10.0, 4.75), (11.0, 4.75), (12.0, 5.00) official_development_assistance = GRAPH(TIME*1000000) (0.00, 2000), (1.00, 1950), (2.00, 1785), (3.00, 1695), (4.00, 1635), (5.00, 1500), (6.00, 1365), (7.00, 1305), (8.00, 1185), (9.00, 1125), (10.0, 1065), (11.0, 1035), (12.0, 1000) perspective_birth_rate = GRAPH(TIME) (0.00, 3.00), (1.00, 2.95), (2.00, 2.90), (3.00, 2.85), (4.00, 2.85), (5.00, 2.80), (6.00, 2.75), (7.00, 2.70), (8.00, 2.65), (9.00, 2.65), (10.0, 2.55), (11.0, 2.50), (12.0, 2.45) perspective_death_rate = GRAPH(TIME) (0.00, 8.00), (1.00, 7.20), (2.00, 6.80), (3.00, 6.65), (4.00, 6.20), (5.00, 6.10), (6.00, 6.00), (7.00, 6.15), (8.00, 6.45), (9.00, 6.65), (10.0, 7.20), (11.0, 7.50), (12.0, 8.00) service_capital_output_ratio = GRAPH(TIME) (0.00, 55.0), (1.00, 56.5), (2.00, 58.0), (3.00, 59.5), (4.00, 62.0), (5.00, 63.0), (6.00, 63.0), (7.00, 61.0), (8.00, 58.0), (9.00, 57.0), (10.0, 54.5), (11.0, 51.5), (12.0, 50.0) Assumption Each initial number; such as GDP and population is based on the data in 1995 from WRI. In capital investment, an inflow of GDP, it is on the assumption that each distribution of current GDP would effect on that of the next year by the following ratio; such as agriculture 30%, service 50% and industry 70%. Deaths of each generation is based on mortality, and the rest of them mature to the next generation. Birth control and health service, which effect on fertility and mortality rate, is the results from the multiple regression analysis. Some factors have (*5) at the end of equation, because 1 time on graphs equals 5 years. Each distribution of GDP capital output ratio makes up to 100%, and it is on the assumption of the prospective trend the government might think much of the industrial output in comparison with the service one over time. Average life time of capital would be improved 50% in the model. Other factors are overall based on the data of WRI, and added some reasonable assumptions. Results (Graphs) From the next page, some graphs are shown; such as Total Population, Fertility, Mortality and the distribution of GDP, which were brought about by running the STELLA II model. In comparison with the previous model, the total population became closer to the perspective of WRI, which means that the distribution of GDP would surely effect on the population growth, and the linear equations by the multiple regression analysis brought about a better connection between the distribution of GDP and the fertility or mortality rate in the model. Figure 13. Total Population.

Figure 14. Distribution of GDP.

The total population would increase over time, which looks like a logistic growth. It would reached about 13 million in 60 years. Since WRI expects about 12 million in 2050, this result is very close to it. On the other hand, GDP would gradually grow up, and the distribution clearly reflects the assumptions which I gave. Each of these output effects on the fertility and mortality rate on the basis of the equations, resulted by the multiple regression. Table 2. Input Data of Each Sum of Distribution of GDP per capita and Population Growth Factors.

Spreadsheet, first part.

Spreadsheet, second part.

Spreadsheet, third part.

Spreadsheet, fourth part.

Figure 15. Fertility Rate.

Figure 16. Mortality Rate.

The fertility rate would gradually be decreasing and reach around 2.10 children per women. According to the equation from the multiple regression, the sum of service per capita would strongly effect on it. Since service per capita would be still high for a while, the fertility rate would be surely influenced, and would be the same rate that WRI estimated. On the other hand, the mortality rate would be increasing in about 30 years. The sum of food per capita would have a significant role for the mortality rate in the equation of multiple regression results. Because of the low share of the agricultural output, the mortality rate might not depend upon the linear equation. Conclusion In this research, I found very strong relationships between the sum of service per capita and the fertility rate, and the sum of food per capita and the mortality rate by multiple regression analysis. Each R Square is over .99, which means the dependent variable changes as the almost same as the independent variable does. By using these results, the new STELLA II model would reflect the population dynamics more realistically in comparison with the previous simple model. However, it is sure that a lot of factors; such as pollution, nonrenewable resources and agriculture, should be included into this model in order to make it more realistic. Furthermore, most of other students created maps and graphs among countries in the world. It is sure that such maps and graphs make us easily understood to what extent of the category in the country is situated in the world. I could include other neighbor countries into the STELLA model, too. Actually, such a relationship is surely important for the population dynamics, though the data might be difficult to obtain. I hope this research would provide further studies regarding the population dynamics and that this STELLA model would be endlessly improved by adding more factors. References Donella H. Meadows, Dennis L. Meadows, Jorgen Randers, William W, Behrens, The Limits To Growth, 1972 Donella H. Meadows, Dennis L. Meadows, Jorgen Randers, William W, Behrens, Beyond The Limits, 1992 William D. Drake, Towards Building a Theory of Population-Environment Dynamics, 1992 World Resources Database, World Resource Institute, 1996 World Resources, A Guide to the Global Environment 1996-97, New York: Oxford University Press, 1996 The Institute of National Planning, UNDP Egypt, Human Development Report 1995, 1995 UN ECOSOC Special Session 1993, Programme Planning and Implementation Fifth Country Programme for Egypt, 1993 High Performance Systems, STELLA II Applications, 1994 High Performance Systems, STELLA II An Introduction to Systems Thinking, 1994 Guy Aubert, Le Courrier Du Cnrs Cities: Habitat II Istanbul, 1996 Claude Abraham, Urbanism May - June 1996, 1996 The World Bank, Sustainable Transport: Priorities for Policy Reform, 1996 United Nations, Habitat II Agenda: Istanbul Declaration, 1996 United Nations DESIPA, Urban and Rural Areas 1994, 1995 UNDP, Choices: Cities on the Edge, 1996 United Nations Conference on Human Settlement (Habitat II), The Future of Human Settlements: Good Policy Can Make A Difference, 1996 Ellen Kitonga, UNCHS (Habitat II), Countdown to Istanbul, 1996 Bob Catterall, CITY: Featuring Habitat II, The Right to a Sustainable City, 1996