Demography of Human Populations
Introduction
Over the past 200 years, dramatic changes have occurred in human society which have enabled more
and more people to live longer and in many cases better quality lives. Factors such as improved food
supplies, vaccinations, improved health care, and antibiotics have led to this trend. One way to study the
impact that these factors have had on the life expectancy of a population is to collect information on
birth and death dates for a group of people. This data can be obtained by visiting a cemetery, collecting
obituaries from a newspaper, or by searching records on the internet. The study of the characteristics of
human populations, such as size, growth, density, distribution, and vital statistics is called demography.
One technique that demographers use to study life expectancy is to create survivorship curves. A
survivorship curve is a graphical representation of the chance that an individual will survive from birth to
a particular age. Usually the survivorships of many individuals in a certain group are plotted together.
This group is called a cohort. What constitutes a cohort is usually determined by the demographer. The
demographer might want to compare men and women, people from different cultures, people from
different locations, or two groups of people that were born at different times.
Whether studying humans or other species, survivorship curves usually fit one of three general shapes.
(Figure 1). Human survivorship typically fits a Type I curve. Especially interesting though is that slight,
but distinct, differences can be seen when curves from separate communities are compared, or when
cohorts from different decades for a single community are compared.
Figure 1: Three types of survivorship curves:
Type 1 shows low initial mortality and many individuals living to an old age.
Type 2 shows a steady death rate where mortality is not dependent upon age.
Type 3 shows high juvenile mortality, but those that survive this period usually live to an old age.
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Objectives
At the conclusion of this laboratory you will be able to:
1. Construct survivorship curves for two cohorts in a community using data collected from birth
records and death records.
2. Compare and explain the differences in survivorship curves for two cohorts in a community.
3. Hypothesize on future changes in the demography of a particular community based on current
socio-political trends.
Procedure
1. Select two specific cohorts. These may be based on birth dates, death dates, geographical
factors, gender or other known variable. As an alternative your instructor may provide data that
has been collected with a specific criterion in mind.
2. Visit a cemetery in your community (or using the provided data) select approximately 100
individuals in each cohort (i.e. 100 men and 100 women) at random. Calculate the age at death
of each individual by subtracting the birth year from death year and record this value as the age
at death. Place these values in a table like the sample below. You should collect data on at least
100 individuals in each cohort.
a. Data can be collected from cemetery databases found at www.interment.net
b. Data should be collected at random from within the cohort that you have determined. It
may be necessary to visit or view data from larger cemeteries to ensure a random
sampling as well as a large enough sample.
Table 1. Sample Data Sheet
A complete version of this table is attached at the end for your use.
Indiv.
#
Birth
Year
Death
Year
Age at
Death
Gender
Indiv.
#
1
95
2
96
3
97
4
98
5
99
6
100
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Birth
Year
Death
Year
Age at
Death
Gender
Data Analysis
1. For each individual calculate the age at death by subtracting the birth year from the death year.
Record this value in your table. Do not be concerned with the month or day in any dates.
2. You will now group your data into age classes of ten year intervals. For the 0-9 age class, count
all of the individuals who died at an age of 9 or less. Place this value in Table 2 under the column
“Mortality - # Deaths per age interval”. The number of deaths for an age interval or ‘age class’
is commonly abbreviated “dx”. Continue to record the number of deaths for each of the age
classes listed in Table 2. When you reach the bottom of the column, determine the total number
of deaths and record this number in the indicated space. This number should be equal to the
total number of tombstones or death records that you included in your cohort.
3. The next column in Table 2 is for survivorship data (lx) and the calculations of these data are
cumulative. Under the column “# Surviving from Birth” you will see the number “100” entered
in the first age class row (0-9). This number represents the number of people alive at the
beginning of the age interval. Of course, for the first age class, this would be equal to 100.
4. Subtract the number of deaths in the first age class (0-9) from 100 and place this value in the
next age class row (10-19) under “# Surviving from Birth.” Continue in this manner for the
remainder of the column. If you have done the calculations correctly, when you reach the
bottom of the column your last entry should be a zero (0). (See Table 3 for an example of data
determined in this manner.)
5. Standardize the survivorship data to per 1000 (S1000) to allow you to compare your data with
other students and other cohorts. Use the following equation to make the necessary
calculations.
S1000
= #surviving from birth x
total # of records
1000
To check your calculations for correctness, the top line should be 1000 and the bottom line zero.
6. To standardize your data for graphing, calculate the logarithm to the base ten (10) of each
number in the “Survivorship per 1000” column. Place your answers in the “Log10 (S1000)”
column. When plotted on linear coordinates, this data will produce a curve that resembles the
type of demographic pattern that exists in the data. Once again to check your calculations, the
number in the top row should be 3.00 and the bottom line will produce an error (log 0 is
undefined) on your calculator. For this value, “0” has been entered.
7. Plot the “Log10 (S1000)” calculated data on a graph with the x-axis representing time (age class)
and the y-axis representing the log of the proportion of individuals surviving. Figure 2 on page 4
provides an example. Use the graphing space provided on page 7.
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Table 2. Data calculations for survivorship curve.
AGE CLASS
YEARS
0-9
Mortality –
# Deaths per age
interval (dx)
# Surviving from
Birth
(lx)
Survivorship per
1000 (S1000)
LOG10
(S1000)
100
1000
3.00
10-19
20-29
30-39
40-49
50-59
60-69
70-79
80-89
90-99
100-109
110+
TOTAL
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“0”
Table 3. Example of survivorship table summarizing data collected for 160
individuals born in the 1830s from a cemetery in Newberry, South Carolina.
AGE CLASS
YEARS
# Deaths in Class
(dx)
# Surviving from
Birth
(lx)
Survivorship per
1000 (S1000)
LOG10
(S1000)
0-9
8
160
1000
3.00
10-19
7
152
950
2.98
20-29
13
145
906
2.96
30-39
9
132
825
2.92
40-49
13
123
769
2.89
50-59
21
110
688
2.84
60-69
23
89
556
2.75
70-79
39
66
413
2.62
80-89
22
27
169
2.23
90-99
4
5
31
1.49
100-109
1
1
6
0.78
110+
0
0
0
“0”
TOTAL
160
Figure 2: Survivorship Curve for Newberry, South
Carolina 1830s age cohort
3.50
Log10 (S1000)
3.00
2.50
2.00
1.50
1.00
0.50
0.00
Age Classes
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Graphical Analysis
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Analysis Questions:
Answer the following questions on a separate sheet of paper and attach it to this packet.
1. How do your graph(s) compare with Figure 1: survivorship curves for different types of
populations? Do your populations follow Type I, II, or III survivorship? Why might this be?
2. Compare the two cohorts that you investigated? Are there any differences between the two? To
what might you attribute these differences?
3. One problem with studying survivorship curves is that a birth cohort must be followed through
until death of the last individual. How would a 1940’s survivorship curve for some group of
people in the United States be different from those of the 1800’s?
4. How could you modify the age classes used in this data analysis to better show the effects of
infant mortality in a population?
5. What shifts/changes in survivorship would you expect if:
a.
b.
c.
d.
Budget cuts reduce social programs for prenatal and infant care.
AIDS continues to increase and no cure is found.
Medical advances continue to eliminate most diseases.
Pollution increases causing an increased incidence of environmentally related diseases
(i.e cancer.)
6. What assumptions do demographers need to make in order to develop survivorship curves for a
particular town or location?
References
Flood, Nancy & Charles N. Horn. “Cemetery Demography.” presented at the 1991 Ecological Society of
America workshop.
Keilman, Tammy. “Human Population Ecology: Demography.” 2008.
Weisstein, Eric W. "Survivorship Curve." From MathWorld--A Wolfram Web Resource.
http://mathworld.wolfram.com/SurvivorshipCurve.html
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Table 1. Data Sheet
Indiv.
#
Birth
Year
Death
Year
Age at
Death
Gender
Indiv.
#
1
26
2
27
3
28
4
29
5
30
6
31
7
32
8
33
9
34
10
35
11
36
12
37
13
38
14
39
15
40
16
41
17
42
18
43
19
44
20
45
21
46
22
47
23
48
24
49
25
50
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Birth
Year
Death
Year
Age at
Death
Gender
Table 1, continued: Data Sheet
Indiv.
#
Birth
Year
Death
Year
Age at
Death
Gender
Indiv.
#
51
76
52
77
53
78
54
79
55
80
56
81
57
82
58
83
59
84
60
85
61
86
62
87
63
88
64
89
65
90
66
91
67
92
68
93
69
94
70
95
71
96
72
97
73
98
74
99
75
100
Birth
Year
Death
Year
Age at
Death
Gender
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