rubenstien ch 2 population powerpoint

Unit TwoPopulation and Migration

Geographical Analysis of Population

Worlds Population Distribution - 7 billion mostly in Less Developed CountriesPopulation Concentrations Sparsely Populated Regions (related to climate) Population Density

The Worlds Population IncreaseNatural Increase Fertility Mortality

Population growth and decline over time and space Historical Growth of the Human Population (in billions) Demographic Transition ModelPopulation Pyramids graph of population structure Demographic Transition and World Population Growth World overpopulation problem? World Health Threats disease increase CDR Africa Epidemiologic Transition Model linked to the Demographic Transition Model

Worlds Population Distribution

World Population

World Population CartogramFig. 2-1: This cartogram displays countries by the size of their population rather than their land area. (Only countries with 50 million or more people are named.)

The world as a Village of 100

World Population Density

Worlds Population Distribution - 6 billion mostly in Less Developed CountriesPopulation Concentrations East Asia 20% (China, Korea, Japan, Taiwan)South Asia 20% (India, Pakistan, Bangladesh)Southeast Asia 500 million peopleEurope almost 50 countries, mostly urbanizedOther Clusters Eastern USA, Southeastern Canada, and West Africa

World Population Distribution & Climate ZonesFig. 2-2: World population is unevenly distributed across the earths surface. Climate is one factor that affects population density.

Climate Zones (simplified)

Worlds Population Distribution - 6 billion mostly in Less Developed CountriesPopulation Concentrations East Asia 20% (China, Korea, Japan, Taiwan)South Asia 20% (India, Pakistan, Bangladesh)Southeast Asia 500 million peopleEurope almost 50 countries, mostly urbanizedOther Clusters Eastern USA, Southeastern Canada, and West Africa Sparsely Populated Regions (related to climate) dry lands not enough water for agriculture wet lands too much water strips nutrient from soil cold lands polar regions covered in ice no farming high lands mountain can be covered in snow and ice

Arithmetic Population DensityFig. 2-4: Arithmetic population density is the number of people per total land area. The highest densities are found in parts of Asia and Europe.

Physiological DensityFig. 2-5: Physiological density is the number of people per arable land area. This is a good measure of the relation between population and agricultural resources in a society.

Measures of Population Density

Worlds Population Distribution - 6 billion mostly in Less Developed CountriesPopulation Concentrations East Asia 20% (China, Korea, Japan, Taiwan)South Asia 20% (India, Pakistan, Bangladesh)Southeast Asia 500 million peopleEurope almost 50 countries, mostly urbanizedOther Clusters Eastern USA, Southeastern Canada, and West Africa Sparsely Populated Regions (related to climate) dry lands not enough water for agriculture wet lands too much water strips nutrient from soil cold lands polar regions covered in ice no farming high lands mountain can be covered in snow and ice Population Density arithmetic density number of people divided by land area physiological density measures number of people in certain types of land - eg, arable (farming) land we can see relationships of people to resources agricultural density ratio of farmers to available farm land shows economic differences

Population Increase

World Population Growth1950 - 2005Fig. 2-6: Total world population increased from 2.5 to over 6 billion in slightly over 50 years. The natural increase rate peaked in the early 1960s and has declined since, but the number of people added each year did not peak until 1990.

Crude Birth RatesFig. 2-8: The crude birth rate (CBR) is the total number of births in a country per 1000 population per year. The lowest rates are in Europe, and the highest rates are in Africa and several Asian countries.

Crude Death RatesFig. 2-12: The crude death rate (CDR) is the total number of deaths in a country per 1000 population per year. Because wealthy countries are in a late stage of the Demographic Transition, they often have a higher CDR than poorer countries.

Natural Increase RatesFig. 2-7: The natural increase rate (NIR) is the percentage growth or decline in the population of a country per year (not including net migration). Countries in Africa and Southwest Asia have the highest current rates, while Russia and some European countries have negative rates.

The Worlds Population IncreaseNatural Increasecrude birth rate (CBR) total number of births per 1,000 people per year crude death rate (CDR) total number of deaths per 1,000 people per year natural increase rate (NIR) difference between CBR and CDR world NIR is 1.2 % today (2.2% in 1963)Europe has negative NIR, Sub-Saharan Africa 3% doubling time refers to how long it takes the worlds population to double (54 years today, 35 in 1963)

Total Fertility RatesFig. 2-9: The Total fertility rate (TFR) is the number of children an average woman in a society will have through her childbearing years. The lowest rates are in Europe, and the highest are in Africa and parts of the Middle East.

The Worlds Population IncreaseNatural Increasecrude birth rate (CBR) total number of births per 1,000 people per year crude death rate (CDR) total number of deaths per 1,000 people per year natural increase rate (NIR) difference between CBR and CDR world NIR is 1.2 % today (2.2% in 1963)Europe has negative NIR, Sub-Saharan Africa 3% doubling time refers to how long it takes the worlds population to double (54 years today, 35 in 1963) Fertility total fertility rate (TFR) average number of children a woman in a society will have measures behavior of women in changing culturesTFR is two or under in Europe and over six in Africa

Infant Mortality RatesFig. 2-10: The infant mortality rate is the number of infant deaths per 1000 live births per year. The highest infant mortality rates are found in some of the poorest countries of Africa and Asia.

Life Expectancy at birthFig. 2-11: Life expectancy at birth is the average number of years a newborn infant can expect to live. The highest life expectancies are generally in the wealthiest countries, and the lowest in the poorest countries.

The Worlds Population IncreaseNatural Increasecrude birth rate (CBR) total number of births per 1,000 people per year crude death rate (CDR) total number of deaths per 1,000 people per year natural increase rate (NIR) difference between CBR and CDR world NIR is 1.2 % today (2.2% in 1963)Europe has negative NIR, Sub-Saharan Africa 3% doubling time refers to how long it takes the worlds population to double (54 years today, 35 in 1963) Fertility total fertility rate (TFR) average number of children a woman in a society will have measures behavior of women in changing culturesTFR is two or under in Europe and over six in Africa Mortality infant mortality rate (IMR) number of infants under 1 who die per 1,000 life expectancy how long you will live life expectancy and IMR tell us about health care

Population Growth and Decline Over Time and Space

Population growth and decline over time and space Historical Growth of the Human Population (in billions)Demographic Transition Model explains changes in the natural increase rate in economic and industrial terms Population Pyramids graph of population structure Demographic Transition and World Population Growth World overpopulation problem? World Health Threats disease increase CDR Africa Epidemiologic Transition Model linked to the Demographic Transition Model

Population Growth

Earths Population History1 billion reached circa 18302 billion reached 1930 (100 years later)3 billion reached 1959 (29 years later)4 billion reached 1974 (15 years later)5 billion reached 1987 (13 years later)6 billion reached 1999 (12 years later)Source: Kuby, HGIA

Expansion of the Ecumene 5000 BC - AD 1900Fig. 2-3: The ecumene, or the portion of the earth with permanent human settlement, has expanded to cover most of the worlds land area.

Ecumene, 5000 B.C.

Ecumene, A.D. 1

Ecumene, A.D.1500

Ecumene, A.D.1900

Population growth and decline over time and space

Historical Growth of the Human Population (in billions)1 billion reached in 18302 billion reached in 1930 (130 years later)3 billion reached in 1959 (29 years later)4 billion reached in 1974 (15 years later)5 billion reached in 1987 (13 years later)6 billion reached in 1999 (12 years later)

The Demographic TransitionFig. 2-13: The demographic transition consists of four stages, which move from high birth and death rates, to declines first in death rates then in birth rates, and finally to a stage of low birth and death rates. Population growth is most rapid in the second stage.

Demographic Transition Model

DTM only predicts changes in birth/death rates over time

Observed changes in RNI correlate to changes in economic development

Thus, DTM implies:The greater the wealth,the lower the RNI ... but use caution describing this relationship

Stages in Classic 4-Stage Demographic Transition Model (DTM)(Some books show a 3-stage model; others mention a new 5th stage)

Stage 1: Pre-Industrial

High birth rates and high death rates (both about 40)

Population growth very slow

Agrarian society

High rates of communicable diseases

Pop. increases in good growing years;declines in bad years (famine, diseases)

No country or world region still in Stage One

Stage 2: Early Industrial

High birth rates (over 30) but death rates decline (to about 20)

RNIs increase sharply (pop. explosion); growth rate increases thruout Stage Two

Growth not from increase in births, but from decline in deaths

MDCs = starts early 1800sLDCs = starts after 1950s

TRANSITION TO STAGE TWO IN CLASSIC DTMKnown as the Epidemiologic Transition

Agricultural technology

Improvements in food supply: higher yields as agricultural practices improved in Second Agricultural Revolution (18th century)

In Europe, food quality improved as new foods introduced from Americas

Medical technology

Better medical understanding (causes of diseases; how they spread)

Public sanitation technologies

Improved water supply (safe drinking water)

Better sewage treatment, food handling, and general personal hygiene

Improvements in public health especially reduced childhood mortality

Stage 3: Later Industrial

Birth rates decline sharply (to about 15)

Death rates decline a bit more (to about 10 or less)

Note growth still occurs, but at a reduced and declining rate

MDCs = starts in late 1800sLDCs = starts after 1980s*

* Or hasnt started yet

TRANSITION TO STAGE THREE IN CLASSIC DTMKnown as the Fertility Transition

Societies become more urban, less rural

Declining childhood death in rural areas (fewer kids needed)

Increasing urbanization changes traditional values about having children

City living raises cost of having dependents

Women more influential in childbearing decisions

Increasing female literacy changes value placed on motherhoodas sole measure of womens status

Women enter work force: life extends beyond family, changes attitudetoward childbearing

Improved contraceptive technology, availability of birth control

But contraceptives not widely avail in 19th century; contributed little tofertility decline in Europe Fertility decline relates more to change in values than to availability of any specific technology

Stage 4: Post-Industrial

Birth rates and death rates both low (about 10)

Population growth very low or zero

MDCs = starts after 1970sLDCs = hasnt started yet

Stage 5 (?): Hypothesized (not in Classic DTM)

Much of Europe now or soon in population declineas birth rates drop far below replacement level

Key Population Indicators for Selected Countries

Demographic Transition in EnglandFig. 2-14: England was one of the first countries to experience rapid population growth in the mid-eighteenth century, when it entered stage 2 of the demographic transition.


Historical Growth of the Human Population (in billions)

Demographic Transition Model explains changes in the natural increase rate in economic and industrial terms Stage 1: Low Growth high birth and death rate Stage 2: High Growth countries enter industrial revolutions, keep high birth rate, improve health care (medical revolution some LDCs today enter stage 2 without industrialization) Stage 3: Moderate Growth CBR drops as socio-political life improves, people have fewer children Stage 4: Low Growth natural increase drops to zero as society changes and women enter work force Stage 5: Hypothetical population decreases

Understanding Population Pyramids

Population Pyramids for Britain

Population Pyramids

http://ecp3113-01.fa01.fsu.edu/lively_introduction/fig7.gif

What will the pyramid look like in 2025? 2050?

Age Dependency Ratio

12345

38139

-2.35480528822.2162278129

-2.4522163172.3074242866

-2.47472350562.3385317125

-2.66965644462.5313890906

-3.24724797173.1145194894

-4.08522190753.8939636469

-4.28060010464.0751975374

-4.12461078683.9798845922

-3.55223934743.50499175

-3.24516548073.2832480387

-3.29064417353.3906366596

-2.76356777972.9364249281

-2.79042706263.0881816955

-2.45102434222.879357398

-2.08423911732.6866611444

-1.55416892612.3406401913

-1.34059925190.0001178115

Germantown, 38139 (Houston High)

Males(%)

Females(%)

AGE

PERCENT

43210

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

43210 Columbus, Ohio

Males(%)

Females(%)

AGE

PERCENT

Kotzebue, Alaska

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island, FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Buffalo County, SD

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Nashville

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Nashville, TN

Males(%)

Females(%)

AGE

PERCENT

Chattanooga

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Chattanooga, TN

Males(%)

Females(%)

AGE

PERCENT

West Memphis, AR

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

West Memphis, AR

Males(%)

Females(%)

AGE

PERCENT

Olive Branch, MS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Olive Branch, MS

Males(%)

Females(%)

AGE

PERCENT

Loretto, TN

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Loretto, TN

Males(%)

Females(%)

AGE

PERCENT

Downtown Memphis, 38103

-1.25081433221.0684039088

-0.46905537460.5732899023

-0.44299674270.5342019544

-3.27035830620.5081433225

-8.62540716614.8729641694

-11.51791530946.1368078176

-8.53420195444.1433224756

-6.76221498372.7752442997

-5.21172638442.8403908795

-4.14332247562.4234527687

-3.86970684043.1270358306

-2.52768729642.1498371336

-1.86319218241.6286644951

-1.08143322481.1726384365

-0.93811074921.3159609121

-0.59934853421.1986970684

-0.31270358310.9511400651

-0.14332247560.651465798

-0.07817589580.2866449511


Males(%)

Females(%)

AGE

PERCENT

38125 Southwind HS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Southeast Shelby County, 38125 (Southwind High School)

Males(%)

Females(%)

AGE

PERCENT

Italy 2000

-2.35480528822.2162278129

-2.4522163172.3074242866

-2.47472350562.3385317125

-2.66965644462.5313890906

-3.24724797173.1145194894

-4.08522190753.8939636469

-4.28060010464.0751975374

-4.12461078683.9798845922

-3.55223934743.50499175

-3.24516548073.2832480387

-3.29064417353.3906366596

-2.76356777972.9364249281

-2.79042706263.0881816955

-2.45102434222.879357398

-2.08423911732.6866611444

-1.55416892612.3406401913

-1.34059925192.6715622184

Italy, 2000

Males(%)

Females(%)

AGE

PERCENT

Sheet1

Males(%)Females(%)Males(#)Females(#)

0-4 yrs.-2.35480528822.216227812913591781279192

5-9 yrs.-2.4522163172.307424286614154031331830

10-14 yrs.-2.47472350562.338531712514283941349785

15-19 yrs.-2.66965644462.531389090615409081461101

20-24 yrs-3.24724797173.114519489418742901797680

25-29 yrs-4.08522190753.893963646923579632247570

30-34 yrs.-4.28060010464.075197537424707342352177

35-39 yrs.-4.12461078683.979884592223806982297163

40-44 yrs.-3.55223934743.5049917520503292023058

45-49 yrs.-3.24516548073.283248038718730881895069

50-54 yrs.-3.29064417353.390636659618993381957053

55-59 yrs.-2.76356777972.936424928115951131694885

60-64 yrs.-2.79042706263.088181695516106161782478

65-69 yrs.-2.45102434222.87935739814147151661946

70-74 yrs.-2.08423911732.686661144412030091550723

75-79 yrs.-1.55416892612.34064019138970561351002

80+ yrs.-1.34059925192.67156221847737851542008

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Analysis of Italys Population Pyramid1. Decline in Birth Rate2. Baby Boom3. Fewer men due to World War I and II 4. More women due to: a. longer life expectancy and b. World Wars (I and II) 5. More 75-79 yrs than 0-4 yrs. Signs of a future worker shortage and an overall declining population.

Aging populationdeclining birth rate

38139

-2.35480528822.2162278129

-2.4522163172.3074242866

-2.47472350562.3385317125

-2.66965644462.5313890906

-3.24724797173.1145194894

-4.08522190753.8939636469

-4.28060010464.0751975374

-4.12461078683.9798845922

-3.55223934743.50499175

-3.24516548073.2832480387

-3.29064417353.3906366596

-2.76356777972.9364249281

-2.79042706263.0881816955

-2.45102434222.879357398

-2.08423911732.6866611444

-1.55416892612.3406401913

-1.34059925190.0001178115


Males(%)

Females(%)

AGE

PERCENT

43210

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737


Males(%)

Females(%)

AGE

PERCENT

Kotzebue, Alaska

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island, FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Buffalo County, SD

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Nashville

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Nashville, TN

Males(%)

Females(%)

AGE

PERCENT

Chattanooga

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Chattanooga, TN

Males(%)

Females(%)

AGE

PERCENT

West Memphis, AR

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

West Memphis, AR

Males(%)

Females(%)

AGE

PERCENT

Olive Branch, MS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Olive Branch, MS

Males(%)

Females(%)

AGE

PERCENT

Loretto, TN

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Loretto, TN

Males(%)

Females(%)

AGE

PERCENT


-1.25081433221.0684039088

-0.46905537460.5732899023

-0.44299674270.5342019544

-3.27035830620.5081433225

-8.62540716614.8729641694

-11.51791530946.1368078176

-8.53420195444.1433224756

-6.76221498372.7752442997

-5.21172638442.8403908795

-4.14332247562.4234527687

-3.86970684043.1270358306

-2.52768729642.1498371336

-1.86319218241.6286644951

-1.08143322481.1726384365

-0.93811074921.3159609121

-0.59934853421.1986970684

-0.31270358310.9511400651

-0.14332247560.651465798

-0.07817589580.2866449511


Males(%)

Females(%)

AGE

PERCENT

38125 Southwind HS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737


Males(%)

Females(%)

AGE

PERCENT

Italy 2000

-2.35480528822.2162278129

-2.4522163172.3074242866

-2.47472350562.3385317125

-2.66965644462.5313890906

-3.24724797173.1145194894

-4.08522190753.8939636469

-4.28060010464.0751975374

-4.12461078683.9798845922

-3.55223934743.50499175

-3.24516548073.2832480387

-3.29064417353.3906366596

-2.76356777972.9364249281

-2.79042706263.0881816955

-2.45102434222.879357398

-2.08423911732.6866611444

-1.55416892612.3406401913

-1.34059925192.6715622184

Italy, 2000

Males(%)

Females(%)

AGE

PERCENT

Sheet1


0-4 yrs.-2.35480528822.216227812913591781279192

5-9 yrs.-2.4522163172.307424286614154031331830

10-14 yrs.-2.47472350562.338531712514283941349785

15-19 yrs.-2.66965644462.531389090615409081461101

20-24 yrs-3.24724797173.114519489418742901797680

25-29 yrs-4.08522190753.893963646923579632247570

30-34 yrs.-4.28060010464.075197537424707342352177

35-39 yrs.-4.12461078683.979884592223806982297163

40-44 yrs.-3.55223934743.5049917520503292023058

45-49 yrs.-3.24516548073.283248038718730881895069

50-54 yrs.-3.29064417353.390636659618993381957053

55-59 yrs.-2.76356777972.936424928115951131694885

60-64 yrs.-2.79042706263.088181695516106161782478

65-69 yrs.-2.45102434222.87935739814147151661946

70-74 yrs.-2.08423911732.686661144412030091550723

75-79 yrs.-1.55416892612.34064019138970561351002

80+ yrs.-1.34059925192.67156221847737851542008

57719337

&A

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Sheet3

&A

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Sheet4

&A

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Sheet5

&A

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Sheet6

&A

Page &P

Sheet7

&A

Page &P

Sheet8

&A

Page &P

Sheet9

&A

Page &P

Sheet10

&A

Page &P

Sheet11

&A

Page &P

Sheet12

&A

Page &P

Sheet13

&A

Page &P

Sheet14

&A

Page &P

Sheet15

&A

Page &P

Sheet16

&A

Page &P

38139

-1.92157390161.8045507319

-1.95274712281.8341768508

-2.073305511.9482836439

-2.30186230392.1622567762

-2.54999083032.3968899475

-2.66630980172.47715788

-2.89926605652.6348467212

-2.99197304672.6973194213

-3.19821244612.8978683296

-3.71017347593.3699603567

-3.37755182733.4801851679

-2.83655506713.0139774642

-2.86412371783.1697422081

-2.51575719192.9554027505

-2.13928497522.7576172868

-1.59521534982.4024577373

-1.37600518752.7421195902


Males(%)

Females(%)

AGE

PERCENT

43210

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737


Males(%)

Females(%)

AGE

PERCENT

Kotzebue, Alaska

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island, FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Buffalo County, SD

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Nashville

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Nashville, TN

Males(%)

Females(%)

AGE

PERCENT

Chattanooga

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Chattanooga, TN

Males(%)

Females(%)

AGE

PERCENT

West Memphis, AR

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

West Memphis, AR

Males(%)

Females(%)

AGE

PERCENT

Olive Branch, MS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Olive Branch, MS

Males(%)

Females(%)

AGE

PERCENT

Loretto, TN

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Loretto, TN

Males(%)

Females(%)

AGE

PERCENT


-1.25081433221.0684039088

-0.46905537460.5732899023

-0.44299674270.5342019544

-3.27035830620.5081433225

-8.62540716614.8729641694

-11.51791530946.1368078176

-8.53420195444.1433224756

-6.76221498372.7752442997

-5.21172638442.8403908795

-4.14332247562.4234527687

-3.86970684043.1270358306

-2.52768729642.1498371336

-1.86319218241.6286644951

-1.08143322481.1726384365

-0.93811074921.3159609121

-0.59934853421.1986970684

-0.31270358310.9511400651

-0.14332247560.651465798

-0.07817589580.2866449511


Males(%)

Females(%)

AGE

PERCENT

38125 Southwind HS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737


Males(%)

Females(%)

AGE

PERCENT

Italy 2000

-1.92157390161.8045507319

-1.95274712281.8341768508

-2.073305511.9482836439

-2.30186230392.1622567762

-2.54999083032.3968899475

-2.66630980172.47715788

-2.89926605652.6348467212

-2.99197304672.6973194213

-3.19821244612.8978683296

-3.71017347593.4372859786

-4.3571716364.1053051683

-4.29028169234.1553814894

-3.8853285683.9393615586

-3.10017951193.3429038501

-2.53246945282.9541952994

-2.15621952092.7685768169

-2.81134441355.0452302455

Italy, 2025

Males(%)

Females(%)

AGE

PERCENT

Sheet1


0-4 yrs.-1.92157390161.804550731910805811014774

5-9 yrs.-1.95274712281.834176850810981111031434

10-14 yrs.-2.073305511.948283643911659061095601

15-19 yrs.-2.30186230392.162256776212944331215927

20-24 yrs-2.54999083032.396889947514339661347871

25-29 yrs-2.66630980172.4771578814993771393009

30-34 yrs.-2.89926605652.634846721216303781481684

35-39 yrs.-2.99197304672.697319421316825111516815

40-44 yrs.-3.19821244612.897868329617984881629592

45-49 yrs.-3.71017347593.437285978620863851932929

50-54 yrs.-4.3571716364.105305168324502192308584

55-59 yrs.-4.29028169234.155381489424126042336744

60-64 yrs.-3.8853285683.939361558621848822215267

65-69 yrs.-3.10017951193.342903850117433601879854

70-74 yrs.-2.53246945282.954195299414241131661267

75-79 yrs.-2.15621952092.768576816912125321556886

80+ yrs.-2.81134441355.045230245515809362837143

56234163

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38139

-2.02796432721.9038202562

-2.08294167871.9556402252

-2.16721858682.0354757619

-2.23364864362.0961685511

-2.29506181612.1530411259

-2.975554142.764463972

-3.23552916152.9404419038

-3.33898850763.0101603218

-3.56914799553.2339693233

-4.14048736533.83594979

-4.86252576194.5814472802

-4.78787777884.6373315605

-4.33595732124.3962571741

-3.45974499113.7306210194

-2.82619069983.296829216

-2.40630249263.0896823032

-3.13741017765.6303868869


Males(%)

Females(%)

AGE

PERCENT

43210

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737


Males(%)

Females(%)

AGE

PERCENT

Kotzebue, Alaska

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Fisher Island, FL

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Buffalo County, SD

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Males(%)

Females(%)

AGE

PERCENT

Nashville

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Nashville, TN

Males(%)

Females(%)

AGE

PERCENT

Chattanooga

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Chattanooga, TN

Males(%)

Females(%)

AGE

PERCENT

West Memphis, AR

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

West Memphis, AR

Males(%)

Females(%)

AGE

PERCENT

Olive Branch, MS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Olive Branch, MS

Males(%)

Females(%)

AGE

PERCENT

Loretto, TN

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737

Loretto, TN

Males(%)

Females(%)

AGE

PERCENT


-1.25081433221.0684039088

-0.46905537460.5732899023

-0.44299674270.5342019544

-3.27035830620.5081433225

-8.62540716614.8729641694

-11.51791530946.1368078176

-8.53420195444.1433224756

-6.76221498372.7752442997

-5.21172638442.8403908795

-4.14332247562.4234527687

-3.86970684043.1270358306

-2.52768729642.1498371336

-1.86319218241.6286644951

-1.08143322481.1726384365

-0.93811074921.3159609121

-0.59934853421.1986970684

-0.31270358310.9511400651

-0.14332247560.651465798

-0.07817589580.2866449511


Males(%)

Females(%)

AGE

PERCENT

38125 Southwind HS

-3.69717571033.5125267531

-4.15460153593.9405766083

-4.38960929964.4357715389

-3.59645809733.5125267531

-2.12346300732.0143522599

-3.1474254063.6803894414

-4.21755004414.7505140795

-4.75471064675.3338369214

-4.74631751235.3674094591

-4.2972848214.6959587058

-3.84825212983.7013722775

-2.29132569562.0689076336

-1.43522598511.3261152377

-0.86029627760.847706576

-0.62948508120.8393134416

-0.41126358640.6210919468

-0.18045238990.2853665701

-0.04616223930.1678626883

-0.01678626880.0545553737


Males(%)

Females(%)

AGE

PERCENT

Italy 2000

-2.02796432721.9038202562

-2.08294167871.9556402252

-2.16721858682.0354757619

-2.23364864362.0961685511

-2.29506181612.1530411259

-2.43093047272.2423071349

-2.61840675392.3479673214

-2.83368625832.5108374523

-3.09964661332.754616749

-3.30041724082.956224847

-3.23614634942.9232360547

-3.24895647122.9759331846

-3.13698945792.9438890272

-3.12225037583.0419961039

-3.28401710973.4135213882

-3.32732345793.7488667607

-5.27996704738.2708853953

Italy, 2050

Males(%)

Females(%)

AGE

PERCENT

Sheet1


0-4 yrs.-2.02796432721.90382025621021888959332

5-9 yrs.-2.08294167871.95564022521049591985444

10-14 yrs.-2.16721858682.035475761910920581025673

15-19 yrs.-2.23364864362.096168551111255321056256

20-24 yrs-2.29506181612.153041125911564781084914

25-29 yrs-2.43093047272.242307134912249421129895

30-34 yrs.-2.61840675392.347967321413194111183137

35-39 yrs.-2.83368625832.510837452314278901265207

40-44 yrs.-3.09964661332.75461674915619071388047

45-49 yrs.-3.30041724082.95622484716630751489637

50-54 yrs.-3.23614634942.923236054716306891473014

55-59 yrs.-3.24895647122.975933184616371441499568

60-64 yrs.-3.13698945792.943889027215807241483421

65-69 yrs.-3.12225037583.041996103915732971532857

70-74 yrs.-3.28401710973.413521388216548111720068

75-79 yrs.-3.32732345793.748866760716766331889048

80+ yrs.-5.27996704738.270885395326605674167686

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http://marketplace.publicradio.org/display/web/2007/11/22/why_italian_men_wont_leave_the_nest/#Mammoni: Mamas Boy

Population Pyramids at Different ScalesCountryStateCounty (Borough)

http://www.aleutianseast.org

http://dsc.discovery.com/fansites/deadliestcatch/deadliestcatch.html

ACTIVITYforSelected Population Pyramids in the United States

Helpful Hints

A. Ann Arbor, MI - University of Michigan

B. Buffalo county, SD - Crow Creek Indian Reservation, one of the poorest counties in the United States

C. Punta Gorda, FL - retirement community

D. Leavenworth, KS - United States Penitentiary

E. Manhattan, NYC - wealthy downtown, few large families

F. Nothhamton, MA - Smith College, an all girls college

G. Fort Bragg, NC - United States Army Fort

H. Springfield, IL - average American city

Answers1. B2. A3. E4. C5. G6. H7. F8. D

Population Pyramids for Selected Countries

Guest Workers, mainly from South Asia

http://fusions.wordpress.com/2007/08/06/migrant-workers-in-dubaiGuest Workers from India in the Persian Gulf Countries

Post War Baby Boom and Declining Birth Rate

http://www.economist.com/world/displaystory.cfm?story_id=9539825http://kotaku.com/gaming/only-in-japan/strange-japanese-game-center-name-226261.php

Declining Birth Rate, Emigration, War

http://www.dw-world.de/dw/article/0,2144,1817206,00.htmlMourning the dead.

Stable Population Growth

http://www.airninja.com/worldfacts/countries/Argentina/fertilityrate.htmSlow Decline

High Birth and Death Rates

http://atlas.7jigen.net/photo/?n=Comoros&ln=enAlthough the Comoros is a poor country, extreme manifestations of poverty such as famine or homelessness are rare. The great majority of people have access to adequate food, clothing, shelter, and, to some extent, water.http://poverty2.forumone.com/library/view/8688/

Slightly Increasing Population

Increased prosperity as a result of the Celtic Tiger's economic boom in the wake of its 1973 EU membership has led to major changes in Ireland which is no longer traditionally a country of emigration and is receiving substantial numbers of immigrants to fill jobs.http://www.religiousconsultation.org/News_Tracker/Ireland_population_to_jump_to_five_million.htmhttp://en.wikipedia.org/wiki/Image:Vilnius_at_Dublin.jpg

Extremely High Birth and Death Rates

http://www.biyokulule.com/February_%201990s(4).htmhttp://www.islamic-relief.com/submenu/appeal/somalia_crisis.htmPersistent Poverty and Violence

Percent of Population under 15Fig. 2-15: About one-third of world population is under 15, but the percentage by country varies from over 40% in most of Africa and some Asian countries, to under 20% in much of Europe.

Elderly Shoppers in Russia

Population Pyramids in U.S. cities Fig. 2-16: Population pyramids can vary greatly with different fertility rates (Laredo vs. Honolulu), or among military bases (Unalaska), college towns (Lawrence), and retirement communities (Naples).



Demographic Transition Model explains changes in the natural increase rate in economic and industrial terms

Population Pyramids graph of population structure Age Distribution dependency ratio younger than 15 and older than 65 do not work (need to be supported) LDCs have 33% of population under 15 MDCs have elderly populations that need support Sex Ration women live longer than men older populations have more women younger populations are male



Demographic Transition Model explains changes in the natural increase rate in economic and industrial terms

Population Pyramids graph of population structure

Demographic Transition and World Population Growth most countries around the world are in stage 2 or 3 advances in health care led to drop of death rate globalization changes model as international groups help cut down CDR, but societies do not reduce CBR

Census taking in China

Rapid Growth in Cape Verde Fig. 2-17: Cape Verde, which entered stage 2 of the demographic transition in about 1950, is experiencing rapid population growth. Its population history reflects the impacts of famines and out-migration.

Moderate Growth in ChileFig. 2-18: Chile entered stage 2 of the demographic transition in the 1930s, and it entered stage 3 in the 1960s.

Low Growth in DenmarkFig. 2-19: Denmark has been in stage 4 of the demographic transition since the 1970s, with little population growth since then. Its population pyramid shows increasing numbers of elderly and few children.

Will the World Face an Overpopulation Problem?

Will the World Face an Overpopulation Problem?Malthus on overpopulationPopulation growth & food supplyMalthus criticsDeclining birth ratesMalthus theory & realityReasons for declining birth ratesWorld health threatsEpidemiological transitions

Thomas Malthus

Food & Population, 1950-2000Malthus vs. Actual Trends Fig. 2-20: Malthus predicted population would grow faster than food production, but food production actually expanded faster than population in the 2nd half of the 20th century.

Fuel Wood Collection in Mali

Crude Birth Rate Decline, 1980-2005Fig. 2-21: Crude birth rates declined in most countries during the 1980s and 1990s (though the absolute number of births per year increased from about 120 to 130 million).

Use of Family PlanningFig. 2-22: Both the extent of family planning use and the methods used vary widely by country and culture.

Women Using Family Planning

Family Planning Methods used in three countries

Promoting One-Child Policy in China

Population growth and decline over time and space Historical Growth of the Human Population (in billions)Demographic Transition Model explains changes in the natural increase rate in economic and industrial terms Population Pyramids graph of population structure Demographic Transition and World Population Growth World overpopulation problem? Thomas Malthus (1766-1834) on Overpopulation (1798) - said population grew quicker than food supply Neo-Malthusians 200 years later LDC are in stage 2 but do not have the socio-economic development to cut CBR not just food not enough essential resources Malthuss Critics humans can choose alternatives Declining Birth Rates since 1950, food production was higher than CBR reasons include economic development, education and health, and distribution of contraceptives has reduced CBR

5 Stages of Epidemiologic Transition

or a short history of contagious diseases and mankind

The 5 Stages of

The 5 Stages of Epidemiologic Transition

Stage 1 (infectious and parasitic disease) spread globally (like Black Death)

Stage 2 local receding pandemics (London Cholera) smaller area medicine and sanitation limits spread

Stage 3 (human created diseases, cardiovascular and cancer)

Stage 4 - science helps extend life or cures diseases

Stage 5 infectious & parasitic return

The Bubonic Plague aka Black Death

Cholera in London, 1854Fig. 2-23: By mapping the distribution of cholera cases and water pumps in Soho, London, Dr. John Snow identified the source of the water-borne epidemic.

Tuberculosis Death RatesFig. 2-24: The tuberculosis death rate is good indicator of a countrys ability to invest in health care. TB is still one of the worlds largest infectious disease killers.

Avian Flu, 2003 - 2006Fig. 2-25: The first cases of avian flu in this outbreak were reported in Southeast Asia.

HIV/AIDS Prevalence Rates, 2005Fig. 2-26: The highest HIV infection rates are in sub-Saharan Africa. India and China have large numbers of cases, but lower infection rates at present.

Population growth and decline over time and space World Health Threats disease increase CDR Africa Epidemiologic Transition Model linked to the Demographic Transition ModelStage 1 (infectious and parasitic disease) spread globally like the Great Plague Stage 2 (in overcrowded cities) locally Cholera receding pandemics smaller area Stage 3 man created diseases (cardiovascular & cancer) due to industrial revolution Stage 4 where science helps extend life or cures disease, most affect the elderlyStage 5 infectious & parasitic return diseases have evolved to fight science poverty helps spread disease improved transportation helps spread of diseases AIDS Sub-Saharan Africa - huge HIV problem

The End

Population growth and decline over time and space Historical Growth of the Human Population (in billions)1 billion reached in 18302 billion reached in 1930 (130 years later)3 billion reached in 1959 (29 years later)4 billion reached in 1974 (15 years later)5 billion reached in 1987 (13 years later)6 billion reached in 1999 (12 years later) Demographic Transition Model explains changes in the natural increase rate in economic and industrial terms Stage 1: Low Growth high birth and death rate Stage 2: High Growth countries enter industrial revolutions, keep high birth rate, improve health care (medical revolution some LDCs today enter stage 2 without industrialization) Stage 3: Moderate Growth CBR drops as socio-political life improves, people have fewer children Stage 4: Low Growth natural increase drops to zero as society changes and women enter work force Stage 5: Hypothetical population decreases Population Pyramids graph of population structure Age Distribution dependency ratio younger than 15 and older than 65 do not work (need to be supported) LDCs have 33% of population under 15 MDCs have elderly populations that need support Sex Ration women live longer than men older populations have more women younger populations are male Demographic Transition and World Population Growth most countries around the world are in stage 2 or 3 advances in health care led to drop of death rate globalization changes model as international groups help cut down CDR, but societies do not reduce CBR World overpopulation problem? Thomas Malthus (1766-1834) on Overpopulation (1798) - said population grew quicker than food supply Neo-Malthusians 200 years later LDC are in stage 2 but do not have the socio-economic development to cut CBR not just food not enough essential resources Malthuss Critics humans can choose alternatives Declining Birth Rates since 1950, food production was higher than CBR reasons include economic development, education and health, and distribution of contraceptives has reduced CBR World Health Threats disease increase CDR Africa Epidemiologic Transition Model linked to the Demographic Transition ModelStage 1 (infectious and parasitic disease) spread globally like the Great Plague Stage 2 (in overcrowded cities) locally Cholera receding pandemics smaller area Stage 3 man created diseases (cardiovascular & cancer) due to industrial revolution Stage 4 where science helps extend life or cures disease, most affect the elderlyStage 5 infectious & parasitic return diseases have evolved to fight science poverty helps spread disease improved transportation helps spread of diseases AIDS Sub-Saharan Africa - huge HIV problem

**DISCUSSION:* What is the present world population? [get an up-to-date estimate at http://www.census.gov/]*DISCUSSION:* Are there countries that do not fit this model?*DISCUSSION:* Are there any countries still in stage 1 of the demographic transition?* What other countries not shown would you expect to find in stages 2, 3, and 4?11111111111111111111111111111111111

rubenstien ch 2 population powerpoint

Documents

climate population density

human population

taiwansouth asia

bangladeshsoutheast

parts of asia

climate dry lands

populated regions

agriculture wet lands