When and Where Are We Now?

Introduction

This chapter explores the history or “temporal variation” of human population size and how we characterize it mathematically. We explore the concept of “exponential growth” and compare theoretical exponential growth to the actual growth of the human population. We also explore other ways to characterize human population growth that more closely capture the actual change to the human population over time. In addition, we explore the geography or “spatial variation” of the human population in terms of where we are. We discuss the importance of understanding the difference between size (e.g., total population), rates (e.g., percent of left-handed people or number of left-handed people per thousand), and densities (e.g., persons per square kilometer). We explore how cartographers and data scientists best use these to make maps, infographics, and geovisualizations of a variety of demographic phenomena.

Guiding Questions

How has the size of the global human population changed over time?

What does “constant growth” mean?

What does the term “exponential growth” mean and how is it different from logistic growth?

What does “Zero Population Growth” mean and how is it related to “Carrying Capacity?”

How many humans have ever lived and how does that compare to today’s global population?

What is “overshoot and collapse” and has the human population ever done this?

How does the human population manifest spatial variation in the world today?

How do maps, infographics, and geovisualizations of various attributes of the human population use absolute size, rates, and densities to appropriately characterize demographic phenomena?

Learning Objectives

Demonstrate knowledge of the history of human population growth, explain why it is often characterized as “exponential growth,” and explain why “exponential growth” is an increasingly inaccurate characterization of contemporary changes to the size of the global human population.

Provide a basic knowledge of the fundamental ideas presented in “The Limits to Growth” as they pertain to both past and future demographic trajectories.

Understand the difference between size, rate, and density as they pertain to mapping demographic phenomena.

Key Terms and Definitions

Exponential growth, Logistic growth, Zero Population Growth (ZPG), Carrying capacity, Limits to growth, Overshoot and collapse, The Rule of 72, The Great Population Growth Rate Spike, Cartogram, Dot density map, Choropleth map, Population size, Population rate, Population density, Volunteered Geographic Information (VGI), Constant growth

2.1 History of the Human Population

Here you are on Planet Earth—one of roughly 8 billion living souls. How many humans (Homo sapiens) have preceded you? How many will exist in the future? Will we go extinct as so many other hominid species have? An urban legend states that there are more humans alive today than have lived and died in all of human history and prehistory (Kaneda & Haub, 2020). How could that be true? Does that make any sense at all? If this is true you would have to be alive during an exponential growth of the human population that, combined with your human lifespan, enabled more people to be alive right now than have lived and died in the last roughly 200,000 years. This begs the question—How has the human population changed over time? Has it simply grown uniformly from a small group of 10–20 newly minted H. sapiens to 8 billion of us? It is helpful to examine a graph of the global human population over time to assess the feasibility of the idea that more people are alive today than have ever lived (Figure 2.1).

[figure number=Figure 2.1 caption=“The Size of the Global Human Population Through Time” filename=Fig_2.1.jpg]

Is it possible that someone born in 1926, who lived 88 years and died in 2014, had at any point in their life been on the earth when the number of living human beings exceeded the number of humans that had lived and died? The short answer is no. This false idea was popularized in the early ”1970s near the publication of Paul Ehrlich’s book “The Population Bomb,” which warned about the social and environmental consequences of continued rapid population growth. The Population Reference Bureau has estimated that a little over 108 billion members of our species have been born in the past 50,000 years. Our current population of roughly 8 billion is slightly less than 8% of the total number of us that have been alive in the last 50,000 years. Clearly, there are many assumptions that need to be made to estimate the total number of humans that have lived and died in the last 50,000 years. Many demographic parameters have to be estimated to make this kind of calculation including life expectancy and how it changes over time, infant mortality and how it changes over time, fertility (how many children the average woman gives birth to in her lifetime) and how that has changed over time (Table 2.1).

Year	Population	Births per 1,000	Births between benchmarks	Number ever born	Percent of those ever born
190,000 BCE	2	80	–	–	–
50,000 BCE	2,000,000	80	7,856,100,000	7,856,100,002	0
8,000 BCE	5,000,000	80	1,137,789,769	8,993,889,771	0.1
1 CE	300,000,000	80	46,025,332,354	55,019,222,125	0.5
1200	450,000,000	60	26,591,343,000	81,610,565,125	0.6
1650	500,000,000	60	12,782,002,453	94,392,567,578	0.5
1750	795,000,000	50	3,171,931,513	97,564,499,091	0.8
1850	1,265,000,000	40	4,046,240,009	101,610,739,100	1.2
1900	1,656,000,000	40	2,900,237,856	104,510,976,956	1.6
1950	2,499,000,000	31–38	3,390,198,215	107,901,175,171	2.3
2000	6,149,000,000	22	6,064,994,884	113,966,170,055	5.4
2010	6,986,000,000	20	1,364,003,405	115,330,173,460	6.1
2022	7,963,500,000	17	1,690,275,115	117,020,448,575	6.8
2035	8,899,000,000	16	1,758,578,889	118,779,027,464	7.5
2050	9,752,000,000	14	2,068,409,608	120,847,437,072	8.1
Table 2.1

 

An interesting thing to note in the table outlining these estimates below is that the “percent of those ever born” is actually growing in the modern era and will continue to grow through the year 2050. It is also interesting to note that birth rates are actually dropping. A common misconception is that population growth is caused by rising birth rates. This is simply not true. It is primarily driven by increasing life expectancy that results in lower death rates. Video 2.1 shows how human innovation changed biogeochemical cycles in a way that allowed for the astronomic population growth of the 20th century.

[Insert Quick Check 2.1]

2.1.1 Exponential Growth

Human population growth is often described as “exponential” or “Exponential Growth”. What does that really mean and is it actually true? If you put $100 in the bank at 5% interest, you can expect to have $105 in the account at the end of 1 year. The formula for the exponential growth of a variable x at the growth rate r, as time t takes place in discrete intervals (i.e., at integer times 0, 1, 2, 3, …), is

{\displaystyle x_{t}=x_{0}(1+r)^{t}}Xt = Xo (1+r)t

where x0 is the value of x at time 0. Money deposited in an interest-bearing account will experience “Exponential Growth” because the balance at time “t” is described by an equation in which “t” is an exponent in the formula. This formula assumes you do not add or subtract from the account and the interest is only calculated once per year (e.g., “compounded annually”—see compound interest calculator here: https://www.calculatorsoup.com/calculators/financial/compound-interest-calculator.php). If interest is calculated on a daily basis (e.g., compounded 365 times) then the formula is:

{\displaystyle x_{t}=x_{0}(1+r)^{t}}Xt = Xo (1+r/n)n × t

Where “n” is the number of times the interest is “compounded” or calculated in a given year. For a $100 deposit compounded 365 times a year at 5%—the balance at the end of a year would be $105.13. It is not a profound increase from $105.00 but certainly not insignificant, particularly if the $100 deposit were more substantial (e.g., $1,000,000). That 13 cents would then be $1,300. If you want to go back and review some calculus you can engage in calculating “continuously compounded” interest rates (e.g., compounding the interest at infinitesimally small-time steps). There are diminishing returns going this route. The total growth approaches an asymptotic limit. Your $100 deposit at 5% for 1 year with continuously compounded interest would still only be $105.13 at the end of the year. Any gains from all those extra compounding would be less than half a cent increase in a year. The formula for continuously compounded interest does help us see where the term “Exponential Growth” comes from.

{\displaystyle x_{t}=x_{0}(1+r)^{t}}Xt = Xo × et × r

where “e” is that magical irrational number 2.71828… also known as the base of the natural logarithm. This little mathematical digression is meant to augment our understanding of the answer to the question: What is exponential growth and how does that relate to human population growth?

Let us consider our $100 investment growing over time. I encourage those of you with math anxiety or math phobia to use a spreadsheet such as Microsoft Excel to build a graphical output along with this textual description to strengthen and deepen your computing and computational skills. Build a little table like the one to the right. The Excel formula for the yellow cell for X(t) is simply: = 100 × EXP(C2 × A3) (Table 2.2).

Year	X(t)	Insert rate
0	100.00	0.05
1	105.13	0.05
2	110.52	0.05
3	116.18	0.05
4	122.14	0.05
5	128.40	0.05
Table 2.2

 

This little spreadsheet calculation shows that you have grown your deposit by more than $25 in only 5 years. How can that be? Many people think or reason along these lines—It takes me a year to “earn” $5 of interest. Therefore, it will take me 5 years to “earn” $25. It sounds “reasonable” but it is wrong. In 5 years, you have “earned” more than $25—actually $28.40. Exponential growth grows on the growth that has taken place. It is useful to graph the value of this account over time to see what “constant growth” looks like. Constant growth is growth that takes place at a constant rate of increase (e.g., in this case 5% per year). You can extend your spreadsheet to 50 rows to produce 50 years of interest earning on your initial $100 deposit. The plot of this is shown on the right (Figure 2.2). The top curve shows your $100 deposit growing at a constant growth rate of 5% per year for 50 years. The bottom curve shows your $100 deposit growing at a constant rate of 5% per year for 1,000 years. The lower curve looks much more like the “J” curve or “Hockey Stick” graph that resembles actual human population growth shown in Figure 2.1.

[figure number=Figure 2.2 caption=5% Constant Growth of $100 at Two-Time Scales filename=Fig_2.2.jpg]

Consider this: Let us assume there were 100 H. sapiens individuals on the earth 200,000 years ago. Let us also assume there are 8 billion H. sapiens on the earth now. If we grew at a constant exponential growth rate from then to now what would our annual percentage growth rate have been? The math is actually pretty easy. Solve this equation for “r”:

8,000,000,000 = 100 × e 200,000 × r

We will not belabor the algebra and cut to the answer. If the human population started at 100 individuals and grew at a constant growth rate of % 0.00909877 (less than 1/100 of 1%) for 200,000 years we would reach 8 billion in the mid-21st century. Do you think that is how we got here? All we need to do to answer that question with a “no” is check what the current growth rate of the human population is. In 2024, the global human population stands at around 8 billion. In the year 2020, we saw over 1 million deaths due to the coronavirus pandemic; however, those deaths will be vastly outnumbered by the absolute growth of the human population (total births total deaths), which will likely be around 83 million. Thus, the population growth rate of H. sapiens in 2020 was 83 million divided by 7.8 billion which is roughly 1.1% per year.

While 1.1% growth may not seem like much, it is dramatically (100 times) larger than the constant percentage growth rate needed to get H. sapiens from a couple of tribes of 100 persons to a planet full of 8 billion in 200,000 years. What does that mean? It means that the human population growth rate has NOT been constant over time. It does not take much thought to work this out. If Adam and Eve were the first H. sapiens and gave birth to a child (“Cain” according to the bible) in year “X” the population growth rate that year was a whopping 50% (Note: percentage changes relative to very low numbers can be very large). However, for most of prehistory, the growth rate of the human population was very, very low. It likely fluctuated above and below zero with more time above zero than below but with significant variation in the total global human population.

In the early days of H. sapiens’ presence on the earth (~200,000 to 125,000 years ago), harsh climate conditions may have reduced the entire human population to several thousand individuals at the southern tip of what is now South Africa (Marean, 2012). It is hypothesized that we exploited a unique set of resources along the coast of South Africa during a low sea level period of Earth’s history and that this may have triggered the onset of cooperative behavior that may have enabled our continued survival. There is also lation that the human population was almost extinguished by a gigantic volcano called “Toba,” which erupted roughly 75,000 years ago in Sumatra. This volcano cooled the earth and impacted climate, vegetation, and H. sapiens. It is hypothesized that H. sapiens may have been reduced to less than 10,000 individuals (Krulwish, 2012). There is ample evidence in genetic, archaeological, and paleontological records to suggest that the human population growth rate has NOT been constant throughout human history (Video 2.2). In the early days, it bounced up and down at rates above and below zero. It is generally agreed that the onset of the agricultural revolution is when the growth rate of the human population increased slightly to support a slow but continuous growth rate that was above zero.

[figure number=Figure 2.3 caption=Adapted from “Human Population Growth RATE versus Time” filename=Fig_2.3.jpg]

An interesting book written by W.W. Rostow titled The Great Population Spike and After provides a very provocative graph charting the global human population growth rate over time (Figure 2.3). Study Figure 2.3 and consider the question: Could that graph be accurate?

Rostow’s theoretical graph mostly consists of estimates that are fairly accurate. For most of human history, our population growth rate (e.g., the annual percentage increase in total population size) has been very low. The “spike” consists of the annual percentage growth rate rising from some level well below 0.5% to a little over 2% in about 200 years peaking in the 1960s. During that relatively short time (~200 years) the world’s total population grew from less than a billion to over 3 billion. However, recall that Exponential growth grows on the growth that has taken place. Consequently, even though the annual percentage growth rate of the global population peaked and begun to drop in the 1960s, it is operating on a much larger base (Figure 2.4). Consequently, the number of people added to the earth’s total population continues to increase even after the global population growth rate has spiked.

[figure number=Figure 2.4 caption=A Deeper Dive into the “Great Population Growth Spike” filename=Fig_2.4.jpg]

Figure 2.4 is close to reality but, in fact, is a “smoothed” model of reality. The question raised in the figure (“Has the author experienced increasing or decreasing population growth rates in his lifetime?”) is nonetheless a poignant example of how our lack of clarity with language can obscure significant mathematical truths. Clearly, having been born in 1960, I have experienced an overall decline in the annual percentage growth rate of the human population (i.e., it went from roughly 2% per year in 1960 to roughly 1% per year in 2020). During my lifetime, the actual rate of growth has bumped around a bit (Figure 2.5); however, the annual increase in the total global population has mostly gone up with a peak at 84 million in 2015 (Table 2.3). My experience has

Year (July 1)	Population	Yearly % change	Yearly change	Median age	Fertility rate	Density (P/km2)	Urban population %	Urban population
2020	7,794,798,739	1.05	81,330,639	30.9	2.47	52	56.2	4,378,993,944
2019	7,713,468,100	1.08	82,377,060	29.8	2.51	52	55.7	4,299,438,618
2018	7,631,091,040	1.10	83,232,115	29.8	2.51	51	55.3	4,219,817,318
2017	7,547,858,925	1.12	83,836,876	29.8	2.51	51	54.9	4,140,188,594
2016	7,464,022,049	1.14	84,224,910	29.8	2.51	50	54.4	4,060,652,683
2015	7,379,797,139	1.19	84,594,707	30	2.52	50	54.0	3,981,497,663
2010	6,956,823,603	1.24	82,983,315	28	2.58	47	51.7	3,594,868,146
2005	6,541,907,027	1.26	79,682,641	27	2.65	44	49.2	3,215,905,863
2000	6,143,493,823	1.35	79,856,169	26	2.78	41	46.7	2,868,307,513
1995	5,744,212,979	1.52	83,396,384	25	3.01	39	44.8	2,575,505,235
1990	5,327,231,061	1.81	91,261,864	24	3.44	36	43.0	2,290,228,096
1985	4,870,921,740	1.79	82,583,645	23	3.59	33	41.2	2,007,939,063
1980	4,458,003,514	1.79	75,704,582	23	3.86	30	39.3	1,754,201,029
1975	4,079,480,606	1.97	75,808,712	22	4.47	27	37.7	1,538,624,994
1970	3,700,437,046	2.07	72,170,690	22	4.93	25	36.6	1,354,215,496
1965	3,339,583,597	1.93	60,926,770	22	5.02	22	NA	NA
1960	3,034,949,748	1.82	52,385,962	23	4.90	20	33.7	1,023,845,517
1955	2,773,019,936	1.80	47,317,757	23	4.97	19	NA	NA
Table 2.3
Adapted from “World Population Growth”

been mostly one of more people being added to the earth’s population every year, while the percentage increase has been dropping. What did I experience—a decreasing or an increasing rate of population growth? Of course, it depends on what you mean by the word rate? If rate is expressed as “added persons per year” it has been going up, if rate is expressed as “percentage increase of the existing population” it is going down.

[figure number=Figure 2.5 caption=Actual Data Relevant to the “Great Population Growth Rate Spike” Model filename=Fig_2.5.jpg]

Mathematical reality and our everyday use of language do not always play well together. The late Al Bartlett, a much-beloved professor of Physics at the University of Colorado at Boulder, suggested our inability to understand exponential growth was “The Greatest Challenge” facing humanity. Let us return to the question: Has the human population grown exponentially? There is no simple answer. Many would argue that exponential growth involves a “constant rate” of growth. The rate (e.g., annual percentage increase) of growth of the human population has changed (it spiked rather rapidly between the onset of the industrial revolution and today). Historically, the “J” curve (Figure 2.1) certainly looks like exponential growth but detailed scrutiny reveals some nonsmoothness (Figure 2.5) that suggests more complexity including the fact that the population growth rate has been decreasing since the 1960s. Unfortunately, this allows some people to make statements such as “Global population growth is dropping. The world is slowing down in population growth. Nothing to worry about.” But what does “slowing down” mean? When I was born in 1961, the earth added roughly 50 million people to the human population. Today in 2020, the earth is adding roughly 80 million to the human population. Does that seem like population growth is “slowing down?” Perhaps Al Bartlett’s quote captures it best (Video 2.3):

The greatest shortcoming of the human race is our inability to understand the exponential function.

[Insert Quick Check 2.2]

2.1.2 Doubling Time and the “Rule of 72”

One way to improve our ability to think “exponentially” is using a simple rule about “doubling time.” Doubling time is the amount of time it will take for something growing at a constant percentage growth rate to double in size. Using the $100 example, doubling your initial deposit of $100 to $200 does not take the 20 years that one might think (e.g., $5 per year for 20 years is $100, which doubles the original $100). In actuality, it only takes about 13.8 years to double something that grows at 5% per year. The simple way to calculate doubling time is to divide the interest rate (e.g., 5%) into the number 69 or 72. Note that 69/5 = 13.8, while 72/5 = 14.4. The number “72” is usually used rather than “69” because 72 divides by 1,2,3,4,6,8,9, and 12 easily. For continuously compounded interest, the mathematically accurate number is 69.3. This is related to the fact that e⁰.693 = 2. For a good explanation of this, see the Wikipedia site (https://en.wikipedia.org/wiki/Rule_of_72).

A simple application of the “Rule of 72” can be applied to your life and the current global human population by answering this question: How long will it take for the global human population to double from today? Well, currently the global population growth rate is roughly 1% per year. Divide 72 by 1: 72/1 = 72. Thus, if the global population continues to grow at 1% per year for the next 72 years, we will have doubled the global population from 8 billion to roughly 16 billion people. Virtually no projections of the global human population suggest we will reach that number. Why? Because we do not expect the 1% per year global population growth rate to continue. We expect it to continue to decline. How much will it decline and how fast are the questions associated with population projections.

The fact is that we are not going to increase exponentially in the future. Rostow is right. The absolute growth rate of the human population (in persons/year) started slowing around 2015, just about 50 years after the annual percentage growth rate of the human population peaked. What will this look like going forward? Will the global human population stabilize at some point?

[Insert Quick Check 2.3]

2.1.3 Carrying Capacity, Zero Population Growth, and Logistic Growth

Hopefully, it strikes you as reasonable to see that the human population cannot grow indefinitely. Surprisingly, there are many prominent scholars, economists, and politicians who cannot fathom this or at least cannot fathom that we are anywhere near a limit to the growth of the human population. Let us put this idea to rest by extrapolating the exponential growth of the human population with a few real numbers and some basic assumptions. Suppose the human population grows exponentially until the entire mass of planet earth is converted to human beings (obviously this is impossible but let us use this as a limit). How many people would this be? How long would it take to get there if we continued to grow exponentially at a fixed rate of 1% per year? Let us assume the average human is 75 kg (~165 lbs) and that the mass of the entire earth is 5.972 ×1024 kg.

How many people would this be?

5.972 × 1024 kg / 75 kg = 7.9627 × 1022 people

How long would it take to get there?

7.9627 × 1022 = 8 billion × et×0.01 (solve for “t”) t = 2,992 years

This cocktail napkin calculation suggests that we would convert the entire mass of the planet to human beings if the population could grow at 1% per year for a mere ~3,000 years. Clearly, we will not even come close to doing that. There are limits to growth that will come into play way before the sheer mass of the planet limits our growth. A more detailed discussion of the “Limits to Growth” and the variety of opinions as to what they are will take place in Part 3 of this book.

Suffice it to say that most estimates of the “carrying capacity” of the planet for a sustainable human population fall within the range of 2–20 billion people. The Carrying Capacity of an environment is the maximum population size of a biological species that can be sustained in that specific environment. In this case, the environment is the earth, and the species is H. sapiens. One of the reasons we have diverging “opinions” as to the carrying capacity of the planet with respect to humans is varying ideas as to what level of per capita resource consumption is assumed and how is it accomplished. For example—one way to estimate the carrying capacity of the planet for humans is to measure the amount of arable land on the planet and calculate the maximum sustainable caloric production of those lands and assume that humans need “X” calories per day to live (typically about ~2,000 calories/day). The variety of assumptions that are made to estimate carrying capacity are the source of many debates. Do we assume a plant-based diet? Do we set aside some potentially arable land to preserve biodiversity? There are many contested questions to answer regarding this problematic. Suffice it to say—The Earth has a finite carrying capacity that will ultimately limit the size of the human population living on it.

Acknowledgment of a carrying capacity or a limit to growth necessitates a change to the exponential mathematical models of population growth. The simplest model that is typically used produces an “S”-shaped or “sigmoid” curve that can be seen in Figure 2.6 (in this case the “carrying capacity” is 11 billion). This “S” curve is produced by the logistic growth function. Exponential growth has a constant growth rate, whereas with logistic growth the growth rate gets smaller and approaches zero as the population approaches carrying capacity (Figure 2.6).

[figure number=Figure 2.6 caption=The Impact of Carrying Capacity filename=Fig_2.6.jpg]

Exponential growth is clearly not sustainable because it relies on infinite resources (which only exist in the minds of those who believe in the easter bunny, the tooth fairy, or neoclassical economics). Exponential growth can happen for a while, with small initial populations and many resources. However, when the number of individuals gets large enough, resources become limited and the environment’s ability to absorb waste diminishes, growth rates typically slow down. Eventually, the growth rate reaches zero and the population versus time curve will flatten out making an S-shaped curve. The population size at which it flattens out to, which represents the maximum population size a particular environment can support, is called the carrying capacity. It is not important to dive into the math too deeply for this class; however, this math is “baby differential equations” and is very useful in many fields. I encourage the student to explore Saul Khan’s explanation of the logistic growth function and perhaps realize that this math is not as scary as it may seem (Khan Academy, n.d.). The important takeaway from this is to see that carrying capacity puts a ceiling on exponential growth which is theoretically modeled by the logistic growth function—the “S”-shaped or sigmoid curve representing a population “leveling off” as a result of limits to growth (Video 2.4).

[Insert Quick Check 2.4]

2.1.4 Overshoot and Collapse—This Might Not Work Out

The logistic growth function shows a nice smooth “S” curve describing a population’s growth over time and ”its smooth slowing of growth to an asymptote at a carrying capacity. This is a nice and comforting theoretical curve that has been observed in practice (empirical observations) on some occasions. However, as Yogi Berra said “In theory there is no difference between theory and practice. In Practice there is.” We have shown that human population growth cannot exhibit pure exponential growth because of resource limitations and carrying capacity. We have shown that “in theory” a population could be modeled with a logistic growth function in which it levels off and stabilizes at carrying capacity. This has been observed empirically for things like yeast growing in a test tube. Can we expect the human population to exhibit a smooth “S” curve of logistic growth settling at a finite carrying capacity?

[figure number=Figure 2.7 caption=Overshoot and Collapse filename=Fig_2.7.jpg]

A famous 1972 study titled The Limits to Growth (Meadows, 1972) suggests that there are other possibilities including “overshoot and collapse” (Figure 2.7) and “overshoot and stabilize” (Figure 2.8). Overshoot and collapse occurs when a species exists in an abundant environment with no predators but nonetheless exceeds the sustainable resource consumption level. “Overshoot and collapse” has also been empirically observed in nature (e.g., Reindeer introduced to Saint Matthew Island in 1944 grew from 29 individuals to over 6,000 in 1963. They then proceeded to a population crash and die off back to a population of 42 in 1966; Klein, 2003). The Limits to Growth study consisted of an integrated systems model (World3) that used mathematical relationships (typically modeled using differential equations) to characterize the interactions between five main global systems (the food system, the industrial system, the population system, the nonrenewable resources system, and the pollution system; Figure 2.8) (Video 2.5).

[figure number=Figure 2.8 caption=World3 Model Output Showing “Overshoot and Maybe Stabilization?” filename=Fig_2.8.jpg]

The World3 simulations of the future consisted of different “scenarios” (e.g., “Standard Run” was a typical “Business as usual” scenario). Most of these scenarios exhibited “Overshoot and collapse” behaviors; however, some exhibited “Overshoot and stabilize” in which the population exceeded a carrying capacity and then dropped to a new, lower carrying capacity because of resource depletion influences. We will discuss the lively debate around the “Limits to Growth” in Unit 3 of the book. For now, we just want to emphasize that there are many possible ways in which the future trajectory of the human population will unfold on the earth. All of them result in Zero Population Growth (ZPG). We could have ZPG at carrying capacity, which is reached via something like a logistic growth function. Alternatively, we could achieve ZPG via an overshoot and stabilize situation, or we could achieve ZPG by overshooting and going extinct. It is becoming increasingly likely that technological and cultural evolution will determine what path we take more than the forces of biological evolution.

Thinking abstractly is a challenging and useful exercise of the mind. We have just explored the past, present, and future temporal variation of the total size of the human population. This abstract reasoning provides many interesting and cautionary ideas regarding the future trajectory of the ongoing human experiment on this planet.

Using money as a specific and tangible example to explain phenomena like exponential growth can be a very useful way to explain things for many of us. Let us think about the idea of “Sustainability” using a “trust fund baby” and their financial endowment. Let us call our trust fund baby “John.” John inherits $1 million at the age of 18 and a nice bank promises to pay him 10% interest on his money. John could live “sustainably” on $100,000 per year (10% of 1 million). That is what his financial capital provides to him on an annual basis. If John spends more than $100,000 a year to the point where he only has $500,000 in the bank at the age of 30 he has “overshot” the sustainable yield of his financial capital. If he continues to overshoot his financial capital, he may very well be bankrupt before he is 40 years old. If he lives on $50,000 a year from the age of 30 onwards he has “overshot and stabilized.” The good people at the Global Footprint Network (https://www.footprintnetwork.org/ ) used the same idea of “overshoot” when they proposed the idea of “Earth Overshoot Day” (Global Footprint Forum, 2021) (Video 2.6). In 2020, Earth Overshoot Day occurred on August 20. That is the day that the global human population used up the interest on the Earth’s Natural Capital and started depleting our natural endowment. That day is moving to earlier and earlier dates every year suggesting that human civilization has some serious challenges ahead with respect to achieving ZPG and a sustainable and desirable future.

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2.2 Geography of the Human Population

We have explored how the global human population has varied temporally with best estimates of global population size from the past to the present with projections of several different potential futures. The nature of our spatial distribution is also very interesting. We are nowhere near being uniformly distributed spatially (e.g., across the surface of the earth). Cities are densely populated, some deserts have virtually no human beings in them, and many rural areas are sparsely populated. International variation in population density is also striking (Figure 2.9). China and India are the only two countries with a population greater than 1 billion people. It is likely that India and China will be the only two countries in the world to ever reach a population of a billion. The circle in Figure 2.9 that encompasses more than half of the world’s human population not surprisingly includes both China and India. India’s 2020 population is 1.380 billion, whereas China’s population is 1.439 billion. China’s one-child population policy (1979–2015) reduced their population growth rate relative to India. Consequently, India’s population is passed China’s in 2024. This is just one example of how dynamic the global population situation is. The 10 most populous countries in 2020 (Chart 2.1) are not the same as the 10 most populous countries in 2000 (Table 2.4). Nor do we expect the 10 most populous countries in 2020 to have the same population or rank in 2050 (Table 2.5). Note that

[figure number=Figure 2.9 caption=Ten Most Populous Countries in the World in 2017 and 2050 filename=Fig_2.9.jpg source=Source: https://www.forbes.com/sites/niallmccarthy/2017/06/22/the-worlds-most-populous-nations-in-2050-infographic/?sh=51c9054439f6 ]

Rank	Country	Population 2000
1	China	1,242,612,300
2	India	1,040,000,000
3	United States	281,421,923
4	Indonesia	206,264,595
5	Brazil	170,000,000
6	Russia	147,000,000
7	Pakistan	140,000,000
8	Bangladesh	130,000,000
9	Japan	127,000,000
10	Nigeria	110,000,000
Table 2.4

 

Japan holds the rank of ninth most populous country in the year 2000 (Table 2.4) but does not even make the top 10 in the year 2020. Japan has been experiencing a population decrease for almost 10 years. Japan’s population likely peaked in 2011 at around 127 million. The only country in the 10 most populous countries of the world with a declining population in 2020 is Russia. Russia’s population decline is more recent, starting around 2017. Japan’s population decline is primarily associated with a declining birth rate, whereas Russia’s population decline is associated with both a lower birth rate and higher death rate. In Unit II of this book, we will look at the

Rank	Country	2020	2050	2000	Growth population %
		Population	Expected population	Population	2000–2020
1	China	1,439,323,776	1,301,627,048	1,268,301,605	13.40
2	India	1,380,004,385	1,656,553,632	1,006,300,297	37.10
3	United States	331,002,651	398,328,349	282,162,411	17.30
4	Indonesia	273,523,615	300,183,166	214,090,575	27.70
5	Pakistan	220,892,340	290,847,790	152,429,036	44.90
6	Brazil	212,559,417	232,304,177	174,315,386	21.90
7	Nigeria	206,139,589	391,296,754	123,945,463	66.30
8	Bangladesh	164,689,383	193,092,763	128,734,672	27.90
9	Russia	145,934,462	129,908,086	147,053,966	0.80
10	Mexico	128,932,753	150,567,503	99,775,434	29.20
Top 10 countries		4,503,002,371	5,044,709,268	3,597,108,845	25.10
Rest of the world		3,293,613,339	4,329,774,957	2,547,898,144	29.20
Total world population		7,796,615,710	9,374,484,225	6,145,006,989	26.90
Table 2.5

 

processes that drive these sorts of demographic changes. Population stabilization and decline is described by many economists as an alarming phenomenon suggesting economic collapse and social stagnation. Many thousands of scientists have stated that population stabilization must be the long-term goal for any policy associated with achieving a sustainable and desirable future (Union of Concerned Scientists, 1992; Ripple et al., 2017; Lidicker, 2020). Most economists do not agree with most scientists on questions of population stabilization and the viability of perpetual growth. Scientists have produced a widely publicized “World Scientists’ Warning to Humanity,” which clearly makes the case for population stabilization (Figure 2.10). Sadly, it has been mostly ignored by economists, policymakers, and the public. These problematics will be explored further in Unit III of this book.

[figure number=Figure 2.10 caption=2008 Presidential Election Results for the United States at the County Level filename=Fig_2.10.jpg]

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2.2.1 Population Size, Population Density, and Population Rates

Human beings seem to have some built-in disabilities with respect to thinking rationally about statistical and probabilistic phenomena (Kahneman, 2011). This is particularly true of many demographic phenomena. One example of this is our inability to use background information (e.g., population rates) when deciding what categories something is more likely to fall into. Consider this: Jane is an American who loves books and ties her hair in a tight bun. Jane spends much of her day politely asking people to be quiet. Is Jane a Librarian or a School Teacher? Many people have a stereotypical image of a librarian who loves books, is a woman who ties her hair in a bun, and “shushes” people in a library. Of course, Jane could easily be a schoolteacher who loves books, ties her hair in a bun, and encourages her students to be quiet during some time in her classroom. What is the “background rate” relevant to thinking about this question? Well, there are roughly 350,000 librarians in the United States and 3.5 million schoolteachers. Schoolteachers outnumber librarians by 10 to 1. Does knowing the relative number of schoolteachers and librarians change how you might guess the answer to the question: Is Jane a schoolteacher or a librarian? It should, but even when provided with this information many people do not use it. There are many interesting ways this kind of flawed reasoning manifests with gender and racial stereotypes also.

Bringing knowledge of population size, population density, and population rates to our thinking about so many social, economic, and environmental questions is vital to effective decision making and policy development. The ideas of population size, population density, and population rates are introduced here to support your interpretation and absorption of Chapter 3 on demographic characterization using a variety of measures that are typically taken in a census. The following examples will hopefully shed light on the importance and utility of these distinctions.

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2.2.1.1 Population Size and Population Rate as Important Background Considerations

Imagine a TV news anchorperson presenting the following editorial on gun violence: “Who says that southern republican states have problems with Gun Violence? Consider these facts: Arkansas is a southern, typically republican state. There were only 541 firearms murders in Arkansas in 2016. In that same year California, an elite, west-coast, democratic state had 3,184 firearms murders. Can we put to rest this notion that southern republican states are the source of firearms violence? The facts presented by the new anchor are indeed facts (Firearm Death rates in the United States by State, n.d.). Is there anything wrong with the reasoning? California’s population size is much larger than Arkansas.” A “fairer” way to compare firearms murders between the two states is to use population rates. For many phenomena population rates allow us to make “Apples to Apples to Apples’” comparisons that are meaningful. To calculate a population rate, one typically divides the phenomena in question (e.g., firearms deaths) by the population in question. When we compare the relative rates of firearms murders in California and Arkansas, we find that California’s rate is 7.9 firearms murders per hundred thousand people, whereas Arkansas’ firearms murder rate is 17.8 (more than twice California’s). In fact, California is below the national average for 2016 (11.8 firearms murders per hundred thousand people), whereas Arkansas is significantly above the national average. This is directly opposite to the argument made by the TV news anchorperson. Perhaps you can engage in a personal assessment of your own stereotyping: What News Network do you think would be most likely to present the editorial above: BBC, CNN, or FOX News?

Mark Twain is oft quoted as saying “There are three kinds of lies: Lies, Damn Lies, and Statistics.” Statistics are an important and useful tool that have undoubtedly saved lives and enabled humanity to understand a complex world; however, when used with intent to deceive they can certainly obfuscate our ability to see the truth. I would argue that it is important to build a base of demographic knowledge akin to your familiarity with your times tables, the alphabet, and a map of the world. Basic statistical knowledge and basic demographic knowledge are vital tools for understanding myriad important phenomena such as political polling, risk assessment, and economic policies.

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2.2.1.2 Population Density as an Important Background Consideration

Maps of presidential electoral outcomes are a fascinating demonstration of how the Electoral College can override the popular vote in the election/appointment of the president of the United States, clarifying the importance of understanding population density as it relates to these kinds of maps. A map of the 2008 presidential election at the county level with red for counties in which the republican candidate won (Senator John McCain) and blue for the counties in which the democratic candidate won (Senator Barack Obama) illustrates the point about population density (Figure 2.10). Clearly the counties mapped as red (e.g., Republican victory) visually overwhelm the counties mapped as blue (e.g., Democratic victory) in terms of areal extent. It might be hard to believe that the blue candidate (Democrat Barack Obama) won

[figure number=Figure 2.11 caption=The Earth as Seen From a Composite of Nighttime Satellite Images filename=Fig_2.11.jpg]

this election at the electoral college level 365–173 and in the popular vote 53%–46%. This map clearly does not capture the fact that the blue counties have much higher population densities. Population density is another kind of population rate but in this case, the population is in the numerator and the area in question is in the denominator. These kinds of maps are often used to explain the idea of an urban–rural divide that closely matches a democratic–republican divide in the United States today. In actuality, all of the counties are purple (e.g., a mix of democrats and republicans) and this map oversimplifies what is a much more complex and nuanced reality. As we progress through this book, we will overlay other demographic patterns such as race, ethnicity, income, religion, fertility, and age. Hopefully, your existing background demographic knowledge enables you to engage your mind’s eye with this map already.

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2.2.1.3 Cartography and Geovisualization of the Spatial Distribution of Population

Imagine trying to explain or describe to someone where all the people are on the earth. This is a challenging problem of cartography or what is increasingly called “geovisualization.” As you can imagine, there are many ways to do this. Nighttime satellite images are often used to in some sense depict the human population distribution in movies, TV news, and graphic art. Consider Figure 2.11.

[figure number=Figure 2.12 caption=Choropleth Map of the World’s Nations by Population filename=Fig_2.12.jpg]

This image is derived from satellites that observe the earth at night. It should be clear to you that this image is necessarily a composite or fusion of many separate images because we know that the earth is a sphere rather than a rectangle and the sphere on which we live is always half bathed in sunlight. In fact, a great deal of image processing was necessary to produce this image. The images need to be georeferenced to a cartographically distorted mathematical model of the earth (Video 2.7), clouds have to be eliminated from the imagery otherwise many of the city lights would appear blurred out, forest fires have to be screened out using a variety of time series analysis techniques and spectral characterizations, in addition lights that are not from “cities” such as lightning, boats, airplanes, and oil rigs need to be removed. To what extent do you think the resulting image (above) of city lights is a good way to explain the spatial distribution of the human population on the surface of the earth to someone?

In many respects, the image above is a pretty good answer to the question. Densely populated urban cores such as New York, Tokyo, London, and Paris are brightly lit, whereas barely populated areas such as central Australia and the Sahara Desert are dark. In some very significant ways, this does appear to capture the distribution of at least the urban fraction of the world’s population. What aspects of the human population distribution are not well characterized by this image? For one it does not communicate the absolute size of populations very well. Consider the United States (population ~330 million) and India (population ~1.3 billion). It appears that more light is emanating from the United States than from India. There are three reasons for this: (1) The United States has a higher fraction of its population living in urban areas, (2) the brightness of the city lights does not capture the highly variable population density both within and between the two countries, and (3) the United States produces more light per capita than India. Despite the fact that nighttime satellite imagery is not a direct proxy measure of population or population density, it is often used as an input to be combined with other census and satellite information to produce models of a variety of socioeconomic phenomena including population density. Can you imagine how nighttime imagery might be used to map economic activity, energy consumption, ecological footprint, or emissions of Carbon Dioxide? Nighttime imagery has been used to inform estimates of all of these phenomena.

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2.2.1.4 Population Mapping Using Cartograms, Choropleth Maps, and Dot Density Maps

A common cartographic technique is the choropleth map. A choropleth map is a kind of thematic map that depicts predefined areas (e.g., countries, states, etc.) with a range of colors that represent a statistical variable such as population density or per capita income. Choropleth maps can be useful for mapping rates and densities, but they are not so good for mapping absolute size. Consider the choropleth map of total population size for the nations of the world below. In one sense this choropleth map communicates that India and China have larger populations than the United States (something that the nighttime satellite image composite does not). Nonetheless, the map reader cannot compare the relative population sizes of India and China (the darkest category mapped) nor can one distinguish the differences between the United States, Brazil, Indonesia, Nigeria, and Japan.

[figure number=Figure 2.13 caption=Cartogram of the Population of the World’s Nations filename=Fig_2.13.jpg]

Cartograms are a better way to communicate absolute population size. A cartogram (aka anamorphic map or value-area map) is another type of thematic map using a predefined set of features (e.g., countries, states, etc.) that have their areal extents distorted to be proportional to a variable such as population, Gross Domestic Product, or biomass. Figure 2.13 is a cartogram using population as the variable to which the countries’ areal extent is proportional to.

[figure number=Figure 2.14 caption=The True Size of Africa filename=Fig_2.14.jpg]

Inspection of the map above shows clearly and quickly that China and India have by far the largest populations in the world at over 1 billion people each. Note that all the nations of Africa combined seem to be roughly equivalent to the size of India. It is interesting to note that when many people imagine an “overpopulated” country, they either think of “Africa” which is a continent and not a country or they think of a country in Africa. This is particularly surprising when one considers the true size of Africa (Figure 2.14). From the perspective of “carrying capacity” or “Ecological Footprint,” the nations of Africa are not anywhere near as “overpopulated” as the United States, China, India, or most of the nations of Europe. In Unit III of this book, we will discuss the issue of “land-grabbing” and the idea that the lifestyles of the rich and famous (i.e., those people who live in the developed world) are dependent on the exploitation of resources in the less developed world particularly in Africa and South America.

[figure number=Figure 2.15 caption=Dot Density Map of Blacks and Hispanics in the United States by County filename=Fig_2.15.jpg]

Another popular way of mapping population distribution is the “Dot Density Map” or “Dot Distribution Map.” A dot density map is yet another type of thematic map that uses point symbols to visualize the geographic distribution of many phenomena. Consider the dot density map of the Hispanic and Black population of the United States in Figure 2.15. The dots are constrained to the county of residence of these populations because that is the source of the data.

[figure number=Figure 2.16 caption=Dot Density Map of the United States Population in 2000 filename=Fig_2.16.jpg]

This dot density map shows a distinct spatial pattern that either raises or answers many demographic questions about the demography of the United States in the past, present, and future. Some dot density maps simply use a dot to represent a fixed number of people (e.g., each dot represents 7,500 persons) without using color or symbology to characterize any other attributes. Consider the dot density map of the U.S. population for the year 2000 created by the U.S. Census Bureau (Figure 2.16). Note how similar this cartographic product is to a nighttime satellite image of the United States. In many respects these two representations of population distribution in the United States (dot density and nighttime satellite imagery) have the same cognitive impact; however, the cartographic products do not have the same capacity or function as actual census data or modeled population density. One of the reasons for this is the limited cognitive capacity of most human minds.

For most mapping applications it is recommended that no more than seven color levels or classes be used because it is difficult for the human eye to distinguish the differences between a lot of slightly different colors (Pimpler, 2020). In addition, it is difficult for the human brain to do a lot of quantitative reasoning as well—ask a friend to explain to you the difference between millions, billions, and trillions. I suspect most people cannot do it and even fewer could produce or imagine cartographic devices that communicated these differences effectively. Actual census data and modeled demographic data in a raster or pixel-based format that we typically use in Geographic Information Systems are easy to store, manipulate, analyze, and display. The science of demography and its increasing use in multidisciplinary applications has been dramatically improved and increased as a result of the ease with which computers, databases, and geographic information systems have developed in the last 30 years.

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2.2.1.5 The State of the Art of Population Mapping

It is no secret that mapping has gone digital. We use our cell phones to help us find our way around street networks in almost every corner of the world. The GPS in our cell phones works both ways. Simultaneously we are providing “Volunteered Geographic Information” (VGI; Goodchild, 2007) about our own whereabouts to a variety of companies including Google, Facebook, Uber, Lyft, Snapchat, and Instagram to name a few. Providing our VGI for free to others who can sell our VGI has made many people incredibly wealthy. The ride-sharing company “Uber” has a “God View” application that allows them to see all of the Uber vehicles in a city and all of the Uber users waiting for a car (Hill, 2017). The drivers and users can of course be easily identified and potentially stalked by someone with access to this information. It raises serious questions about privacy, ownership of data, and how technology outpaces our ability to keep up both ethically and legally.

We can certainly imagine a future state-of-the-art census of the population that is derived from cell phone data. Cell phones can identify the individual who owns them, track their location in space, and document numerous other activities including how fast you drive. Insurance companies are using cell phones to provide “good driver discounts” (Rainie & Duggan, 2016). Will the census of the future be accomplished via cell phone? It is easy to imagine a government-issued cell phone provided to every U.S. citizen—perhaps even at birth. This cell phone could be used for traditional census purposes, voting, carrying your driver’s license, and numerous other applications. It is interesting to ask how likely is it for the government to actually conduct a census by cell phone? A cell phone-based census would have unprecedented quality in terms of spatial resolution, mobility, and timeliness. Imagine an animated dot density map where each dot is a single human with a cell phone with all the information that cell phone contains. Clearly the basis for dystopian novels and movies. This could look like Big Brother Godzilla on steroids.

There are undoubtedly numerous benefits that VGI provides to individuals, companies, societies, and governments. VGI is essential to the utility of cell phone applications that helped us monitor the COVID-19 pandemic. The OpenStreetMap (https://www.openstreetmap.org/) application has used VGI to develop one of the best maps of the world’s transportation network ever made. Numerous citizen science products such as eBird, iNaturalist, and Globe at Night enable the collection, storage, and dissemination of scientific data that informs and enhances our understanding of our world in myriad ways.

Currently, the major collectors and sellers of VGI are in the private sector (e.g., Google, Facebook, Amazon, etc.). These companies are making significant profits on information they own and control that was provided to them for free. Many studies related to serving the public good (e.g., traffic analysis studies conducted by departments of transportation) may be better served by traffic data provided by cell phones because it is cheaper, more accurate, and more timely to acquire. This kind of data has been historically gathered by government agencies. The disruptive technology of the interacting phenomena of cell phones, GIS, GPS, and the internet may be unintentionally privatizing what have historically been government functions (e.g., conducting a census, monitoring traffic, household economic surveys, etc.). At this point, we seem to be voting with our VGI that we trust Google, Uber, and Amazon more than our government with respect to this information—which raises the question: Will government–sponsored censuses of the population become obsolete? Chapter 3 explores current patterns in these sorts of demographic data that have historically been obtained primarily by government and/or international agencies such as the United Nations. Knowledge of these demographic patterns and understanding the processes that produce them provide us with a demographic perspective that dramatically improves our understanding of the world.

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Chapter Summary/Key Takeaways

This chapter explored the idea of exponential growth and its relationship to human population growth. We looked at how the global human population has grown over time and used exponential and logistic mathematical functions to model population growth. We also explored the spatial distribution of the human population around the globe. You should now understand the significant differences between population size, population rates, and population density in addition to having a familiarity with a variety of population mapping techniques including dot density maps, choropleth maps, and cartograms. GIS, GPS, cell phones, social media, and VGI are all changing the nature of how demographic data can be acquired, stored, represented, and analyzed. These technologies are challenging our society with myriad issues of privacy, ownership of data, and other government functions related to providing public goods.

In Chapter 3, we will take a closer look at the broader discipline of demography and how demographic tools and methods are used to characterize the state of the human population in the world today. Where do most of us live? How many children do we have? How long do we live? What do we eat? What gods do we believe in? What languages do we speak? What are our migration patterns? What is the distribution of wealth and income? In other words—where and who are we now?

Comprehensive Questions

• Did the coronavirus pandemic of 2020 stop the human population from growing? Explain your answer with numbers.
• Did the author of this book experience increasing human population growth or decreasing population growth in the first 60 years of his life? Explain why this is an ambiguous question.
• Explain the “Rule of 72” or “The Rule of 69.” Why are there two names for the same rule?
• List the five most populous countries in the world today. Name two countries of the world that are experiencing a decrease in population size. What country do we expect to be the most populous country of the world in 2030?
• What is “The Limits to Growth” and “World3?” What sort of population projections resulted from this effort sponsored by The Club of Rome?
• Define the term “Zero Population Growth” or ZPG. Do you think the earth will ever achieve ZPG? Why or why not?
• Define the term “Carrying Capacity” and relate it to the idea of the human “Ecological Footprint.” Using these terms explain the idea of “Earth Overshoot Day.”
• What does “Overshoot and Collapse” mean? Has it been observed in nature? Do you think the human population could do this?
• The human population has likely gone up and down in the past. Describe three events in the historic or prehistoric record that we believe actually drove the global human population down.
• Explain how the idea of carrying capacity changes an exponential growth model to a logistic growth model.
• Describe three phenomena that are easily misunderstood when the distinctions between population size, population density, and/or population rates are confused.
• Name the three most populous countries in the world from largest to smallest. Do you expect this to change by 2050? How many countries have reached a population of more than 1 billion? Do you expect any other countries to do this?
• In what ways is a composite of nighttime satellite imagery a good way to represent the human population distribution on the earth and in what ways is it not?
• How could cell phones be used to make the most sophisticated representations of the global human population distribution ever made? What are the drawbacks to doing this?
• What did the “World Scientists Warning to Humanity” say about sustainability and the human population?

References

Firearm death rates in the United States by State. (n.d.). In Wikipedia. https://en.wikipedia.org/wiki/Firearm_death_rates_in_the_United_States_by_state

Global Footprint Forum. (2021). Earth Overshoot Day. https://www.footprintnetwork.org/

Goodchild, M. F. (2007). Citizens as sensors: The world of volunteered geography. GeoJournal, 69, 211–221. https://doi.org/10.1007/s10708-007-9111-y https://link.springer.com/article/10.1007/s10708-007-9111-y#citeas

Hill, K. (2017). ‘God View’: Uber allegedly stalked users for party-goers’ viewing pleasure, Forbes, October 3, 2014. Retrieved September 1, 2017, from https://www.forbes.com/sites/kashmirhill/2014/10/03/god-view-uber-allegedly-stalked-users-for-party-goers-viewing-pleasure/?sh=6d7fd

Kahneman, D. (2011). Thinking, fast and slow. Farrar, Straus and Giroux.

Kaneda, T., & Haub, C. (2020). How many people have ever lived (2020). Population Reference Bureau Publication. https://www.prb.org/howmanypeoplehaveeverlivedonearth/

Khan Academy. (n.d.). Exponential and logistic growth. https://www.khanacademy.org/science/ap-biology/ecology-ap/population-ecology-ap/a/exponential-logistic-growth

Klein, D. R. (2003). The introduction, increase, and crash of reindeer on St. Matthew Island. The Journal of Wildlife Management, 32, 350–367. https://web.archive.org/web/20110709032911/http://dieoff.org/page80.htm

Krulwish, R. (2012). How human beings almost vanished from Earth in 70,000 B.C., Krulwich Wonders, NPR. https://www.npr.org/sections/krulwich/2012/10/22/163397584/how-human-beings-almost-vanished-from-earth-in-70-000-b-c

Lidicker, W. (2020). A scientist’s warning to humanity on human population growth. Global Ecology and Conservation, 24. https://doi.org/10.1016/j.gecco.2020.e01232.

Marean, C. W. (2012). When the sea saved humanity. Scientific American. https://www.scientificamerican.com/article/when-the-sea-saved-humanity-2012-12-07/

Meadows, D. H. (1972). The limits to growth: A report for the Club of Rome’s Project on the predicament of mankind. Universe Books, 1972. https://en.wikipedia.org/wiki/World3

Pimpler, E. (2020). Creating graduated color maps in ArcGIS Pro. GeoSpatial Training Services. https://geospatialtraining.com/creating-graduated-color-maps-in-arcgis-pro/

Rainie, L., & Duggan, M. (2016). Scenario: Auto insurance discounts and monitoring. https://www.pewresearch.org/internet/2016/01/14/scenario-auto-insurance-discounts-and-monitoring/

Ripple, W. J., Wolf, C., Newsome, T. M., Galetti, M., Alamgir, M., Crist, E., Mahmoud, M. I., Laurance, W. F., & 15,364 scientist signatories from 184 countries. (2017). World scientists’ warning to humanity: A second notice, BioScience, 67(12), 1026–1028, https://doi.org/10.1093/biosci/bix125

Union of Concerned Scientists. (1992). World scientists’ warning to humanity. https://www.ucsusa.org/sites/default/files/attach/2017/11/World%20Scientists%27%20Warning%20to%20Humanity%201992.pdf