History, People, Methods, and Patterns

Introduction

This chapter explores demography, the science of population. Who uses demography as an analytical tool? A lot of people. Political scientists, insurance companies, population biologists, geographers, urban planners, school boards, epidemiologists, advertisers, medical researchers, and the list goes on and on. Demographic analysis typically studies the size, age structure, gender ratios, ethnic makeup, income, and other behavioral aspects of people. In this chapter, we will provide a brief history of demography and its evolution from the broader area of inquiry known as statistics. We will define some of the more commonly used statistics that demographers use and explore maps and infographics of some of the demographic patterns that are manifesting in the world today.

Guiding Questions

When and why did the study of demography take place?

What do demographers measure and how do they measure these things?

What is a “population pyramid” and what information can be gleaned from one?

How does reputation bias and/or confirmation bias perpetuate racial and gender inequality?

Why is the regular administration of a census of the population essential to good governance?

What benefits do businesses and other nongovernmental organizations derive from census data?

Is it possible for Mexico to have a lower life expectancy and a lower death rate than the United States?

What are the differences between direct and indirect methods of data collection for a census?

Learning Objectives

Demonstrate knowledge of the history of demography and be able to explain how demography provides vital information, insight, and understanding to governments, businesses, and nongovernmental organizations.

Provide a basic knowledge of the fundamental measurements used by demographers and demonstrate an awareness of how these measurements manifest spatially in the world today.

Develop an awareness of the people and institutions that are directly or indirectly involved in gathering, storing, analyzing, and disseminating demographic information.

Key Terms and Definitions

Demography, John Graunt, Thomas Robert Malthus, Reputation bias, Census, Basic Demographic Equation, Crude birth rate, Total fertility rate, Natural increase, Life expectancy, de facto vs. de jure census, Stable population, Stationary population, American Community Survey (ACS), Population pyramid, Dependency ratio, Cohort, Longitudinal study, Infant mortality rate, Age-specific fertility rate, Maternal mortality

Demography—The Essential Social Science

Demography is essentially the science of population. This book on population geography draws heavily on what is more broadly known as demography (the word “demography” comes from the Ancient Greek demos [the people] and—graphy [writing, description, or measurement]). Demography is most generally the statistical study of populations. It is suggested that demography emerged from the broader field of statistics in the second half of the 19th century (Demography, n.d.). While most people assume that demographic analysis consists of analyses of human beings it does not necessarily have to be. For example, studies of coastal oak trees in central California explore the idea that the age distribution of trees is skewing toward older and older trees, suggesting that recruitment of new generations of trees may be impaired for a variety of reasons including human activity (Tyler et al., 2006).

Demographic analysis, whether it be of people, trees, birds, or bees can be an incredibly powerful way to identify, characterize, understand, and in some cases, control, important ecological, social, economic, and political phenomena. The same can be said of the more general field of statistics. Consequently, the practice of both statistical and demographic methods has been absorbed and incorporated into numerous academic disciplines including anthropology, sociology, geography, political science, economics, health sciences, and history to name a few. In fact, most universities have departments of anthropology, sociology, history, economics, and biology. However, few universities have departments of demography. Does this suggest that demography is becoming increasingly irrelevant, or, does it suggest that a demographic perspective is so essential to so many other disciplines that much of the field has been incorporated into those cognate disciplines? This population geography textbook suggests the latter. Demography, like statistics, provides essential tools for understanding population-related processes and phenomena in many avenues of inquiry.

Demography and statistics are vibrant areas of intellectual inquiry in and of themselves while their established methods continue to be useful and relevant in myriad related fields. The University of California at Berkeley is one of the few true “Departments of Demography” that exist in the United States today. Princeton University has an Office of Population Research (OPR) that is the oldest population research center in the United States (founded in 1936). There are many centers for population studies at a variety of universities around the world that vary to the extent that they focus on formal demography or broader cognate areas. Formal demography focuses on the measurement of population processes (e.g., fertility, mortality, and migration), whereas cognate fields such as social demography and population geography are often more interested in relationships and interactions between economic, environmental, spatial, and social processes that influence populations. The vitality of “demography” in its theoretical and applied practice is demonstrated by the large numbers of research institutes (e.g., OPR), international agencies (e.g., United Nations Population Fund), national associations (e.g., Population Association of America), and NGOs (e.g., Committee for International Cooperation in National Research in Demography) that are dedicated to demographic inquiries.

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3.1 History of Demography

Thomas Robert Malthus’s 1798 Essay on the Principle of Population is perhaps the most often mentioned historical writing related to demography. However, Malthus was not the first person to think about demographic phenomena, patterns, and consequences. The ancient Greeks wrote about demographic phenomena. Herodotus (c. 425 BCE) is embraced by both History and Geography as one of their earliest practitioners. Herodotus was interested in the origins of the Greco-Persian wars. Understanding war and war strategy, particularly in ancient times, likely involved attempts to count the number of people on each side. Plato’s last dialogue (Laws) suggested that the ideal size of a city should have 5,040 (or 7!) citizens. Many Romans wrote of demographic ideas (e.g., Cicero, Seneca, and Cato). Ibn Khaldun, a famous Arab scholar of Islam, is considered by many to be one of the greatest philosophers of the Middle Ages (Japanese Internment Camps (History.com), n.d.). Khaldun’s thinking influenced fiscally conservative arguments for lower taxes as a pathway to higher revenue. Ibn Khaldun’s ideas likely influenced the development of the “Laffer Curve,” suggesting that optimal revenue occurs somewhere between 0% and 100%, which was used to justify tax cuts during the Reagan administration in the United States (1980–1988).

Demographic studies of the modern period include John Graunt’s Natural and Political Observations upon the Bills of Mortality (1662) (see inset box). This work is the foundation of life tables used by insurance companies to set life insurance policy rates. Actuarial Science is the discipline that uses statistics and demography to assess risk in the insurance industry. Benjamin Franklin’s essay “Observations Concerning the Increase of Mankind, Peopling of Countries, etc.” was intended to encourage the British to increase their population and power by expanding across the Americas. Perhaps ironically it is suggested that Franklin’s essay influenced Malthus’ 1798 essay suggesting that the exponential growth mentioned by Franklin may present some problems with respect to our ability to feed ourselves. The contested and controversial nature of the Malthusian questions of overpopulation will be explored in greater detail in Unit III of this book. Prominent modern and contemporary demographers continue the tradition of bringing a demographic perspective to many important social, political, environmental, and economic issues of the world today.

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3.2 Influential Modern and Contemporary Demographers and Population Geographers

A list of all the prominent and influential modern and contemporary demographers is beyond the scope of this text. Nonetheless, it is worthwhile to be aware of some of these names and their contributions. John Bongaarts (https://www.popcouncil.org/research/expert/john-bongaarts ) published a seminal article in 1994 titled “Population policy options in the developing world” and now serves as vice-president of the Population Council (https://www.popcouncil.org/ ). Johnathan Weeks (https://geography.sdsu.edu/People/Emeritus/weeks.php ) is Director of the International Population Center at San Diego State University and is noted for integrating satellite imagery with the practice of demography. Ansley Coale (https://en.wikipedia.org/wiki/Ansley_J._Coale ) made major contributions to our understanding of the demographic transition. Hans Rosling (https://en.wikipedia.org/wiki/Hans_Rosling ) is famous for his brilliant TED talks using data visualization techniques he developed with the Gapminder Foundation. Graeme Hugo (https://scholar.google.com.au/citations?hl=en&user=o4c2-mYAAAAJ ) was director of the Australian Migration and Population Research Center at the University of Adelaide and was a leader in facilitating the integration of GIS technology to demographic analysis. Wolfgang Lutz (https://scholar.google.com/citations?hl=en&user=QRH1wRYAAAAJ ) has made noted contributions to demographic analysis and population projection and serves as director of the World Population Program at the International Institute for Applied Systems Analysis (IIASA; https://iiasa.ac.at/web/home/research/researchPrograms/WorldPopulation/Research/Research_Intro.html. Paul Demeny (https://hungarytoday.hu/dr-paul-demeny-honored-with-the-highest-hungarian-state-award/ ) is a Hungarian demographer who founded the East-West Population Institute, served at the Population Council and was founding editor of Population and Development Review. Nathan Keyfitz (https://en.wikipedia.org/wiki/Nathan_Keyfitz ) was a pioneer of mathematical demography and taught at the University of Chicago, Berkeley, and Harvard serving as director of the Harvard Center for Population and Development. Richard Easterlin (https://scholar.google.com.au/citations?hl=en&user=pBYzJL8AAAAJ ) is a seminal thinker in an area of inquiry associated with happiness and wellbeing—his noted “Easterlin paradox” suggests that the correlation between happiness and income weakens with time. Douglas Massey (https://scholar.google.com/citations?hl=en&user=T758DcgAAAAJ ) teaches at Princeton University and has made noted contributions to our understanding of international migration and racial segregation.

This list of demographers in the preceding paragraph is exclusively composed of White men. Race and gender bias in demography, science, and academia in general has existed for most if not all of recorded history. In addition, this kind of bias is self-perpetuating in that “lists” of prominent people in a discipline often are derived from historical records, well-established prejudices, stereotypes, and reputation and confirmation biases (Video 3.1). For example, Thomas E. Brennan, a Michigan Supreme Court Justice, conducted a survey of 100 of his fellow lawyers asking them to list the best law schools in the country. The list he asked them to rank included Harvard, Yale, the University of Michigan, Penn State, and several lesser-known law schools. Brennan’s summary: “As I recall, they ranked Penn State’s law school right about in the middle of the pack. Maybe fifth among the 10 schools listed.” At the time, Penn State didn’t even have a law school. While the aforementioned demographers are truly outstanding in their field, it is very difficult for women and people of color to make these kinds of lists for a variety of reasons including biases and prejudices. It is worth noting that younger cohorts of demographers and population geographers are more diverse and will likely be considered as prominent as the likes of Ansley Coale and Kingsley Davis in the not too distant future.

Among these newer faces with perhaps an eye toward population geography rather than demography are Ellen Kraly (https://www.colgate.edu/about/directory/ekraly) who holds the William R. Kenan Jr. Professor of Geography and Environmental Studies at Colgate University; Kavita Pandit (University of Georgia) and Qingfang Wang (https://scholar.google.com.au/citations?hl=en&user=bxHqxHgAAAAJ) examine relationships between ethnicity, entrepreneurship, and immigration; Rachel Silvey (https://scholar.google.com.au/citations?hl=en&user=K1z6IlgAAAAJ) studies migration and development with a focus on Indonesia; Emily Skop (https://scholar.google.com.au/citations?hl=en&user=lgCPUIoAAAAJ) was Chair of the Geography Department at UCCS and founding director of the UCCS Global Intercultural Research Center; and Deborah Balk (https://scholar.google.com.au/citations?hl=en&user=b7FyapUAAAAJ) who is at the CUNY institute for Demographic Research and engages in research that relates spatial demography to climate change and urbanization science.

Jane O’Sullivan (https://uq.academia.edu/JaneOSullivan) studies the environmental, social, micro- and macroeconomic impacts of population growth, and the efficacy of policy and program options addressing it. A recent article by Jane O’Sullivan suggests that projected declines in the global population are a “Demographic Delusion” based on the idea that there are naive assumptions about fertility decline associated with increasing affluence that are overcome by declines in support for family planning that will have the larger and opposite effects (O’Sullivan, 2023). This “Demographic delusions” article is just one example of how contested the ideas, theories, and debates about the causes and consequences of population growth remain.

In Unit III of this book addressing “problematics” at the intersection of demography, social justice, and sustainability science there are many prominent women whose ideas and contributions will be discussed in more detail including Elinor Ostrom, Gro Harlem Brundtland, Margaret Sanger, Donella Meadows, Waangari Maathai, Jane Goodall, Sylvia Earle, and Rachel Carson to name a few. The next section of this chapter will explore some of the demographic methods that virtually all the aforementioned demographers and population geographers use in their respective avenues of inquiry and research.

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3.3 Demographic Measures

The scientific method undoubtedly manifested from our instinct to ask questions. Not all questions lend themselves to being answered by the scientific method (e.g., What is your favorite flavor of ice cream?); however, many questions are well addressed via the scientific method (e.g., What country is most likely to have the largest population increase in the next decade?). Making observations is almost always the first step of both the data acquisition and the question generation elements of scientific inquiry (note: there are exceptions such as the “thought experiments” that Einstein was famous for). Establishing protocols for actually making observations is part of the development of the methodology of scientific inquiry. How do we weigh things? How do we measure time? How do we count and characterize people? The basic observations of demography are typically the data you would find in a census of the population.

The word “census” is not limited to counting human populations, although most of us strongly link the two. In general, a census is a complete enumeration of a full set of entities that belong to a given population, system, or universe. Typically, these entities are all measured at a specific point in time with respect to one or more characteristics (e.g., number of antennae, sex, income, age, make, mass, color, and citizenship). We can do a traffic census or study the demography of kangaroos. This book focuses on censuses conducted on human populations. As you can imagine, empires and nations have taken regular censuses for quite some time—usually for the purpose of taxation or the recruitment of soldiers to serve in military campaigns. Historically, soldiers were typically men, which may be a contributing factor to the fact that there is a gender bias toward men in historical censuses in particular and the practice of demography in general. Perhaps ironically, the study of gender bias is a contribution of demographic methodology to our understanding of the world.

The Population Reference Bureau (PRB; https://www.prb.org/) is a great source of demographic data and reports on demographic trends and phenomena. The PRB’s report on Milestones and Moments in Global Census History (https://www.prb.org/milestones-global-census-history/) is a prime example. This document outlines some human population census milestones including:

3800 BCE Babylonian empire’s enumeration of livestock and agriculture products.

2500 BCE Egypt conducted a census to assess labor capacity to build the pyramids.

2 CE China’s Han Dynasty counted 57.7 million people living in 12.4 million households with the largest city (Chengdu) having a population of 282,000.

1804 The Domesday Book—William the Conqueror’s survey of English landowners and their holdings was used as a basis for taxation.

1250–1270 Mongol Empire (post Genghis Khan) census of captured territories.

1400 The Incas used Quipu (a system of knots and strings made from animal hair) to record census data.

1665 Quebec, Canada (then “New France”) is regarded as the first modern census.

1790 the first U.S. census conducted as proscribed by the constitution.

There are many others not on this list. Suffice it to say that most nations have come to the conclusion that conducting a census of their population is a good (albeit expensive) idea that serves their interests.

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3.3.1 The Basic Demographic Equation

The most fundamental census data is “Total Population.” Of course, we also want to know how many women? How many men? How many children? Ethnicity and race proportions, average age, income distribution, life expectancy, and so on. But total population is a good place to start. The fundamental equation describing how “Total Population” changes with time is quite simple:

Population (t+1) = Population (t) + Natural Increase (t) + Net Migration (t)

Where “Natural Increase” is simply given by:

Natural Increase (t) = Births (t) – Deaths (t)

And “Net Migration” is simply given by:

Net Migration (t) = Immigration (t) – Emigration (t)

This basic demographic equation is true by definition; however, the practice of measuring the necessary events (e.g., births, deaths, immigration, and emigration) and the “initial” enumeration of the total population size can be challenging, to say the least (Video 3.2). For example, the 2020 U.S. census cost roughly $16 billion to conduct which works out to almost $50 per person enumerated. Clearly, collecting the data in the process of conducting a census is a significant investment on the part of most nations. A census of the population is one of the most direct methods of obtaining demographic data. There are several methods of data collection that are broadly categorized into “Direct Methods” and “Indirect Methods.”

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3.3.2 Direct Methods of Demographic Data Collection

Direct methods are typically more exhaustive than indirect methods. Most countries take the measurement of vital statistics very seriously. Birth certificates and death certificates are formal documents issued in most nations of the world. These are matters of life and death after all. Registries of vital statistics are typically official government agencies that record births, deaths, marriages, divorces, and legal places of residence (for voting and taxation purposes). Typically, the vital statistics provided by these registries are the best way to get the Births (t) and Deaths (t) data needed to calculate Natural Increase (t). Quite often these registries also record changes to legal place of residence which in some cases can enable the acquisition of Immigration (t) and Emigration (t) information also.

An actual census of the population is another direct measure. An actual census of the population provides Population (t) (from the last census) and Population (t+1) at the current point in time. Governments of the world’s countries and nations typically make an attempt to count every person in their country on a regular basis (typically every 5 or 10 years). The aforementioned “Direct Measures” seem to account for all the pieces of the puzzle that is the “Basic Demographic Equation.” Does counting the population of a country seem like a simple thing to do in theory? Does it seem simple in practice? Let us ask some questions to help clarify why the practice of demography can be a little complicated and messy.

Who should be counted? Should a census of the population include noncitizens? Should it include legal residents who are not citizens (e.g., people with green cards or people here on H1B visas)? Should it include foreign students in the country temporarily? Should it include undocumented immigrants? At what point in a human life does one’s existence warrant being counted? At conception? At first trimester? At birth? Where should a student at Ohio State University be counted if they still live with their parents in Atlanta, Georgia? What demographic characteristics should be measured?: Age, sex, gender identity, income, religion, language, country of birth, immigration status, employment status, level of education attained, blood type, address, political party, and so on? The simple idea of conducting a census of the population gets complicated pretty quickly. Simply defining what questions to ask raises a lot of questions and we have not even touched upon how the questions are asked.

3.3.2.1 The U. S. Census as an Example of a Direct Method

The U.S. census is conducted every 10 years as mandated by the U.S. Constitution. The 2020 U.S. census form only asked seven questions about each person in a household. The surveys were sent to physical addresses to be filled out by a “head of household” The term “head of household” is not used in the census form per se. We use the term here to describe the person at a physical residence who actually fills out the census form. The head of household is instructed to count all people, including babies, who live and sleep at this physical residence most of the time. This means that the U.S. census is not strictly a de jure or a de facto census of the population but one of “place of usual residence” (see inset box on de jure vs. de facto censuses). Counting homeless persons is a challenge. One way this is addressed is by instructing the head of household to include anyone who does not have a permanent address who stayed at their residence on census day (April 1, 2020). They are instructed to NOT include persons of the household who may be at college, in the Armed Forces, in jail, in prison, in a nursing home, or in a detention facility. The census form is mailed to, and associated with, a particular address. The head of household is asked if this is a house, apartment, or mobile home and whether they own or rent the structure. They then proceed to fill out the form for all of the people that live at this residence. The seven questions characterizing each person in the household and their limited answer choices are displayed in Figure 3.1. Filling out the census form is required by law. Those who fail to fill out the form can be fined for refusing to participate.

[figure number=Figure 3.1 caption=2020 Census Form Questions for Second Person in Household filename=Fig_3.1.jpg]

3.3.3 Indirect Methods of Demographic Data Collection

Demographic data derived from direct methods is not always available for a variety of reasons. Indirect methods often involve samples from populations in which inferential statistics are used to estimate the kinds of things that are directly measured by censuses. Many countries of the world do not have the resources to conduct high-quality censuses of their populations. Historical demography often relies on incomplete information which is often derived from indirect methods. Nonetheless, many institutions, agencies, and NGOs need reasonable population data for a variety of reasons including for the formation and execution of economic development policies, distribution of vaccines and other health interventions, and urban and regional planning. The United Nations has produced technical manuals to estimate total populations and project the total size, age, and sex structure of current populations into the future.

In the 1960s, there was a growing concern among many governments about rapid population growth and its impacts on social and economic development. Many countries of the world were interested in implementing policies with the objective of reducing fertility rates in order to slow population growth. Indirect methods of demographic data gathering would be necessary to assess the effectiveness of these policies. Ansley Coale and Paul Demeny (at the request of the United Nations) were charged with the creation of a classic volume on “Indirect Methods” of demography Manual IV: Methods for Estimating Basic Demographic Measures from Incomplete Data. This document presented a variety of methods for estimating demographic data and modeling population processes such as models of mortality, models of fertility, population momentum, and population projections. Many other kinds of records can be used as sources of indirect demographic data such as driver’s license records, car registrations, phone bills, Volunteered Geographic Information (VGI) from cell phones, and church records (e.g., baptisms, tombstones, and marriages).

3.3.4 The American Community Survey

The U.S. Census Bureau uses the American Community Survey (ACS; https://www.census.gov/programs-surveys/acs) to help communities, NGOs, businesses, and local, state, and national governments understand and respond to the changes taking place in their communities Video 3.3). The ACS has a significantly higher temporal resolution in that it is administered monthly. The ACS asks more questions than the regular decennial census; however, it is administered to a subset of the total population. The ACS is administered to roughly 300,000 people every month or roughly 1% of the total population per year (3.5 million). Participating in the ACS is mandatory and those who refuse to participate can be fined $5,000. The ACS asks questions about ancestry, citizenship, educational attainment, income, language proficiency, migration, disability, employment, and housing characteristics. Because of its limited sample size, it is not particularly useful for characterizing small geographic areas; nonetheless, it is very useful to numerous public sector, private sector, and not-for-profit stakeholders for myriad purposes including allocating funding, planning for emergencies, and school enrolment planning. The United Kingdom has several interesting longitudinal surveys of its population: the 1946 National Survey of Health and Development, the 1958 National Child Development Study, the 1970 British Cohort Study, and the Millennium Cohort Study. These are “longitudinal” studies because they follow the individual lives of a large sample of people for many years. Contrasting these studies helps us understand very interesting ways the health, education, attitudes, fertility, mobility, and employment of these four distinct generations are distinct from one another and how the British people have changed over time.

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3.3.5 Trust and Faith in the Census

One of the reasons there is a “Trust and Safety Team” at the U.S. Census Bureau is the violation of privacy that was promised by the Census Bureau during World War II (WWII). Census data was used to establish the Japanese internment camps during WWII. During WWII, the U.S. Census Bureau assisted these internment efforts by providing specific individual census data on Japanese Americans (https://www.washingtonpost.com/news/retropolis/wp/2018/04/03/secret-use-of-census-info-helped-send-japanese-americans-to-internment-camps-in-wwii/). The Bureau denied this for years. In 1988 President Ronald Reagan made a formal apology to survivors of the internment camps and provided a $20,000 reparation to all surviving victims https://www.npr.org/sections/codeswitch/2013/08/09/210138278/japanese-internment-redress).

Our responses to the U.S. census are legally supposed to remain “private” or anonymous for 72 years. Technically ensuring this promise has an influence on the minimum size of the geographic areas (Census Blocks) the census can delineate. Historical demographers dig into the treasure trove of 72-year-old census data every 4 years and often discover fascinating details of human lives in the not so distant past. The Trump Administration (2016–2020) went to great but unsuccessful efforts to add a question to standard census asking “Is this person a citizen of the United States?” (https://www.npr.org/2019/05/22/719159163/has-the-u-s-census-ever-asked-about-everyones-citizenship-status). There are many reasons to be concerned about the inclusion of such a question including (1) Will noncitizens avoid completing the form for fear of being identified? (2) Will the government use this information to engage in immigration enforcement? (3) Will there be attempts to subtract noncitizens from the population numbers used for congressional redistricting? The 2020 U.S. census was likely impaired both by the COVID-19 pandemic and by the talk of including a citizenship question on the form. Accurate census data is vital to our ability to engage in rational governance. It is impossible to gather accurate data if the citizens have lost faith in the institutions conducting the census (Video 3.4).

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3.3.6 Basic Demographic Observations

Fundamental demographic observations are associated specifically with births and deaths. The list grows when we add mobility (e.g., migration), ethnicity, education, income, sex, etc. The most common fundamental measures are the following:

Crude birth rate (CBR): the number of live births per 1,000 people in a given year.

General fertility rate (GFR): the number of live births per 1,000 women of childbearing age (typically ages 15–49) in a given year.

Age-specific fertility rate: the number of live births per 1,000 women broken down by age range (e.g., 15–19, 20–24, 26–30 . . . .45–49).

Crude death rate (CDR): the number of deaths per 1,000 people in a given year.

Infant mortality rate: the number of deaths of children less than 1 year of age per 1,000 live births.

Life expectancy (aka expectation of life): the number of years that an individual at a given age can expect to live at present mortality rates.

Total fertility rate (TFR): the statistically anticipated number of live births per woman completing her reproductive life assuming all the age-specific fertility rates at that point in time held for the duration of her childbearing years.

Replacement level fertility: the average number of children women must have in order to replace the population for the next generation.

Gross reproduction rate: the number of daughters who would be born to women completing her reproductive life at current age-specific fertility rates.

Net reproduction ratio: the expected number of daughters, per newborn prospective mother, who may or may not survive to and through the ages of childbearing.

These fundamental observations seem relatively straightforward in their meaning and interpretation; however, the age structure of a population can complicate what may seem to be a reasonable interpretation of these sorts of statistics. For example, consider the fact that Mexico has a lower CDR than the United States (US: 8.3, Mexico: 5.4). In other words, more Americans die than Mexicans in a given year as a fraction of their respective populations. This occurs even though the United States has a higher life expectancy (US: 78.54 years [in 2020], Mexico: 74.99 years [in 2020]). How can that be true? Mexico has a younger population than the United States. A population pyramid (aka age–sex pyramid) is a useful graphical device for characterizing the age and sex structure of a population.

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3.3.7 The Population Pyramid

A population pyramid is a kind of mirrored histogram or mirrored horizontal bar chart that displays the relative frequency or relative abundance of men and women as a function of their age. Population pyramids are a good example of how a picture can be worth a thousand words (Figure 3.2)

[figure number=Figure 3.2 caption=U.K. Population Pyramid (2018) filename=Fig_3.2.jpg]

Typically, women are arranged by increasing age (from bottom to top) on the right and men are similarly arranged by increasing age on the left. The longer the bar the more people in that age cohort. This can be expressed in raw numbers (e.g., millions of people) or as a percentage of the total population. The numbers of both men and women typically narrow as we get older and older (going up) because fewer and fewer of us make it to the oldest categories. Women tend to outnumber men at the top of the pyramid because women have a longer life expectancy than men. Bulges in the pyramid can be an indicator of “baby booms” or migration. Indentations can be indicative of wars, famines, or other effects. Countries with high life expectancies have taller pyramids. Countries with rapidly growing populations and/or increasing fertility typically have broad bases that narrow rapidly as age increases. If a country has a “Stable Population” the shape of its population pyramid does not change because its crude birth rate and CDR remain constant. A “Stationary Population” is a special case of a stable population in that the crude birth rate equals the CDR so the total population remains constant as does the shape of the population pyramid.

An article in The Economist magazine suggests that we are fast approaching “the end of the population pyramid” and start referring to it as the “population dome.” Current good faith estimates of the evolution of the global human population pyramid suggest we are moving to a stationary population with a dome shape (Figure 3.3).

[figure number=Figure 3.3 caption=Evolution of the Population Pyramid filename=Fig_3.3.jpg]

It should be noted that these figures of the global population pyramid do not all look this way throughout the world. Population pyramids vary from one region to the next (Figure 3.4).

Figure 3.4 associates a simple map with three population pyramids and scales the true relative sizes of the Asian, African, and European population pyramids with color coding for education levels. This well-designed infographic clearly shows that Europe has the smallest population which is highly educated and shrinking in size from 2000 to 2050, whereas Africa has a rapidly growing population with a significant proportion of the population with no formal education. The sheer size of the population of Asia (minus Russia) relative to Europe and Africa is quite dramatic as portrayed in the population pyramids. The implicit population projection suggested by this figure suggests that Asia’s population growth rates are slowing.

[figure number=Figure 3.4 caption=Infographic of Population Pyramids for Europe, Africa, and Asia filename=Fig_3.4.jpg]

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3.3.8 Dependency Ratios

As we go through life, we start entirely dependent on our parents or other caregivers as children, become increasingly self-sufficient (hopefully) at some point in or around adolescence, and stay self-sufficient until we are too old to work to support ourselves. Hopefully, we save enough money to not be too much of a burden on others in our retirement years. Dependency ratios are a very broad-brushed way of thinking about what fractions of the population are in what parts of this aging process for a population of people. Typically, the “Labor Force” is regarded as people from age 15 to 64. People under 15 are regarded as “Children” and people over 64 are regarded as “Aged.” Of course, these terms and the age cutoffs are not “true” for all individuals and can be seen as loaded with a variety of stereotypes and prejudices. Nonetheless, all children and many aged people need to have working adults support their existence for significant parts of their lives. We use the following simplifying dependency ratios to characterize these aspects of populations:

(# of persons aged 0–14) + (# of persons aged 65 and older)

Total Dependency Ratio = 100 × ————————————————————————

Number of persons aged 15–64

(# of persons aged 0–14)

Child Dependency Ratio = 100 × —————————————————————-

Number of persons aged 15–64

(# of persons aged 65 and older)

Aged Dependency Ratio = 100 × ————————————————————-

Number of persons aged 15–64

We think about and use the concept and measurement of dependency ratios for a variety of reasons. As a population ages (as is currently happening in much of the developed world including the United States), a smaller number of working-age people exist to support a growing number of elderly people and a shrinking number of children. This can present government and society with challenging questions of resource allocation and planning (see boxed inset: Aging Japan). Allocation of both public and private resources to build and/or maintain schools and aged care facilities can clearly be informed by looking at population projections and dependency ratios. These sorts of analyses can take place at national scales (e.g., for assessing the financial solvency and planning taxation levels for things like the social security program) and at local scales for anticipating how many primary school teachers need to be hired at individual schools in a given school district.

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3.4 Spatial Representation of Demographic Data

The very word “statistic” is rooted in the word “state.” The word “statistics” is said to have been derived from the German Statistik, introduced by German political scientist Gottfried Aschenwall (1719–1772). The word meant “science dealing with data about the condition of a state or community.” The word “statistics” now has a much broader meaning; nonetheless, demographic data is fundamentally “data about the condition of a state or community.” Section 3.3 mentioned several historical censuses that have taken place over the past millennia. As you can imagine, the means by which these censuses are executed have evolved dramatically over the years. Gathering data for the Domesday Book was organized by William the Conqueror after his 1,066 conquest of areas now in the United Kingdom. The methods are briefly described here (Video 3.7):

Most shires were visited by a group of royal officers (legati), who held a public inquiry, probably in the great assembly known as the shire court. These were attended by representatives of every township as well as of the local lords. The unit of inquiry was the Hundred (a subdivision of the county, which then was an administrative entity). The return for each Hundred was sworn to by 12 local jurors, half of them English and half of them Norman.

 

 

The royal officers (legati) who executed the Domesday Book are now represented by institutions like the U. S. Census Bureau (officially the “Bureau of the Census”) which is part of the U.S. Department of Commerce. The U.S. Census Bureau employs over 4,000 full-time employees and has an annual budget of roughly 3.8 billion dollars (comparable to the budget of the National Park Service). National census methods vary from country to country; nonetheless, all methods involve obtaining, tabulating, and recording “conditions of states or communities.” These “states or communities” are typically spatially delineated areas that are mapped or georeferenced to the surface of the earth (note: This does not have to be the case—we could produce statistics for all people that are members of Greenpeace—a “community” that is not defined spatially). The “conditions” attributed to these communities are typically measured and derived from surveys of the populations that are linked to those maps.

Geographic Information Systems (GIS) are the hardware, software, and human capital that modern census bureau’s use to store, manipulate, display, analyze, and disseminate spatially referenced data (linking survey data or “attributes” to geographic features or “areas”). In fact, the need to bring census data into the digital domain was a significant driver of the development of GIS. The lab exercises that accompany this book will provide you with experiences that familiarize you with basic GIS data structures, visualization tools, and analytic functions.

3.4.1 The Spatial Framework Provided by the U.S. Census Bureau

Mapping a hierarchy of administrative boundaries is essential to establish the geographic foundation of a census. A census does not simply count people; it counts people associated with delineated spaces. Geography is fundamental to executing and recording a census of the population. The U.S. Census Bureau maintains unique geographic areas that are used in myriad ways by other local, state, and federal agencies. Census data is also used by NGOs, businesses, and the public at large. Appropriate use of census data requires an understanding of the geographic relationships between delineated areas and the attributes associated with them that are often derived from paper census forms or online surveys (e.g., The American Community Survey).

[figure number=Figure 3.6 caption=Hierarchy of Census Enumeration Areas filename=Fig_3.6.jpg]

There is a spatial hierarchy of these delineated areas called the Standard Hierarchy of Census Geographic Entities (Figure 3.6). The backbone of the Standard Hierarchy, from largest to smallest area, is Nation, Regions, Divisions, States, Counties, Census Tracts, Block Groups, and finally Census Blocks. Nations, States, and Counties may delineate other areas that may or may not use the boundaries provided by the backbone. Zip Code boundaries are defined at the national level and can “violate” the spatial hierarchy by crossing state or county lines. Zip Code boundaries are drawn to optimize the efficiency of the Post Office rather than for political purposes. States have the authority to delineate congressional district boundaries that cannot cross state lines but almost always cross county lines. Recall, the fundamental authority for conducting the U.S. census is the decadal reapportionment of congressional districts based on changes to the spatial distribution and growth of the U.S. population.

Urban areas are delineated at the nation level. Urban areas do not have to conform to place, county, or even state boundaries. Urban geographies of the U.S. census have become more complex with Metropolitan Statistical Areas, Consolidated Metropolitan Statistical Areas, Primary Metropolitan Statistical Areas, and even Urban Clusters (Domesday Book, n.d.). Counties are necessarily part of and within state boundaries. Delineating county boundaries in growing urban areas can be a very political phenomenon that results in some strange geographical delineations. The Denver metropolitan area provides some interesting examples (Figure 3.7). In 2001, Broomfield County in Colorado became Colorado’s 64th county (and its smallest). The City of Broomfield strove to become the city and county of Broomfield to avoid complexities related to its overlapping existence in four existing counties, which made them have to deal with four different court districts, county seats, and sales tax bases.

[figure number=Figure 3.7 caption=Violations of the Spatial Hierarchy in Colorado filename=Fig_3.7.jpg]

Census tracts generally fit inside and exist within county boundaries and therefore within states. However, there seem to be a few exceptions in which there are lost “islands” of one county inside another county (Core Based Statistical Areas, n.d.). Block groups, in turn, must stay within each census tract, so they also have to stay within a county and state. Census Blocks are the smallest spatial units and fall within everything else. They are the building blocks for all other geographies and therefore nest within all other geographies. The standard hierarchy provides a coherent way for people to see how the different spatial delineations at the Census Bureau relate to one another.

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3.4.2 Mapping Basic Demographic Observations

Section 3.4.1 describes the spatial framework for mapping demographic phenomena. The specific questions on the census form (Figure 3.1) enable the mapping of all the basic demographic observations (3.3.4): CBR, GFR, age-specific fertility rate, CDR, Infant Mortality Rate, Life Expectancy, TFR, Replacement Level Fertility, Gross Reproduction Rate, and Net Reproduction Ratio. This can be accomplished as long as each census form is associated with a spatial feature in the standard hierarchy. This is certainly a lot of information, but it does not include many things that many of us are interested in. Many researchers in areas cognate to demography want additional measures of phenomena such as individual income, household income, diversity, inequality, quality of life, political party affiliation, commuting distances and times, vehicle ownership, and measures of environmental sustainability. Federal laws prevent the Census Bureau and its employees from sharing data with anyone including other government agencies like the police, the IRS, and Immigration and Customs Enforcement. Here “sharing” means providing details which would allow Census data to be “linked” with things like income tax records, police records, or immigration records.

Fortunately, the Census Bureau conducts many surveys beyond the actual Census of the Population that gather other information such as income, poverty, marital status, educational attainment, employee benefits, work schedules, school enrollment, health insurance, noncash benefits, migration, and more. The Current Population Survey’s Annual Social and Economic Supplement (CPS ASEC https://www.census.gov/programs-surveys/cps.html) is an annual survey conducted in coordination with the Bureau of Labor Statistics that provides detailed annual data that is associated with the standard spatial hierarchy of census data (i.e., mappable at the zip code, county, tract, or block group level). The ACS (https://www.census.gov/programs-surveys/acs) asks similar questions to the CPS ASEC but has a larger sample size (~295,000 addresses every month) and higher temporal resolution (monthly). Results from these two surveys can differ for several reasons. Income questions in the CPS ASEC are more detailed than the summary questions asked in the ACS. The CPS ASEC survey is conducted via in-person or over the phone interviews, while the ACS is completed by the respondents online or by mail. There are many other surveys conducted by the Census Bureau including surveys of time use, housing, business, probation, wholesale trade, manufacturing, and the list goes on (https://www.census.gov/programs-surveys/surveys-programs.html). Many of these surveys can be linked to one or more of the geographic features of the standard hierarchy of geographic features.

The Census Bureau is not the only source of data that can be associated with the geographic features of the standard hierarchy (states, counties, tracts, etc.). Voter registration data can be mapped. Privately conducted surveys can be mapped if the respondents provide addresses or other spatial references (e.g., latitude and longitude). Address matching is a function of GIS that enables the “spatialization” of survey data if there is a way to locate the survey respondent in space. Government census data throughout the world is typically collected based on links to a spatial hierarchy of geographic features (e.g., nations, states, and counties).

Data is often “aggregated” to these spatial units as counts, averages (aka means), medians, and percentages. These aggregations force the hand of many cartographers when mapping phenomena. In many cases, this is not a problem and does not significantly distort the information being presented; however, when significant spatial variation takes place at spatial scales that are much smaller than the administrative boundaries at which the data is collected things can be problematic. We will address some of these issues in the section on state-of-the-art global mapping of population (Section 3.7). For now, let us explore some of the patterns that are discernable at a global scale when basic demographic measurements are mapped at national and subnational scales.

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3.5 Demographic Patterns in the World Today

A wonderfully designed and executed website/paper titled World Population Growth has been developed by Max Roser, Hannah Ritchie, and Esteban Ortiz-Ospina. The website is here: https://ourworldindata.org/world-population-growth. The reader is encouraged to explore this delightful website in detail for several hours at a minimum. This is a really good way to get a sense of what the demographic state of the world is today, how it got here, and where we think it is going. Although this is a wonderful website, it behooves all of us to be skeptical when encountering and using data. Section 3.5.1 looks at phenomena like “poverty,” “diversity,” and “inequality” with a critical eye that includes a link to an article by Jason Hickel that critiques some aspects of the Our World in Data (OWID) website. The following is a sampling of maps, diagrams, and charts derived from this website that I believe are valid in how they capture some of the salience of demographic patterns of the world today.

[figure number=Figure 3.8 caption=Geographic Breakdown of Annual Births by World Region filename=Fig_3.8.jpg]

Each figure used here is accompanied by some comments and interpretations. This figure of “Annual number of births by world region” is quite interesting (Figure 3.8). You may recall that we anticipated the population of the earth to grow by something like 80–85 million people in the year 2020 despite the COVID-19 pandemic. This graph suggests that around 140 million people will be born in 2020. Thus, we can conclude that we anticipated 55–65 million deaths to take place in 2020. Roughly 1.8 million people that died of COVID-19 in 2020 represent roughly only 3–4% of all the deaths that took place in 2020. Also, it is striking that Asia and Africa are easily account for more than 75% of the births currently taking place on the planet. Asia has the largest share, but it is shrinking while Africa has the second largest share and is growing.

[figure number=Figure 3.9 caption=Birth Rate vs. Death Rate for Selected Countries filename=Fig_3.9.jpg]

Figure 3.9 plots birth rates on the y-axis with death rates on the x-axis. The light dashed line represents where birth rates equal death rates. Russia is right on the line with birth and death rates just a little under 13 per 1,000 persons. In fact, Russia’s birth rate is just a little lower than the death rate, which means its total population is decreasing. African countries in purple have birth rates that are much higher than death rates (e.g., Niger CBR= 48.14, CDR = 9.59). The two largest circles represent the countries with the two largest populations: India and China. Countries with significantly declining populations are below the dashed line such as Japan, Croatia, and Ukraine.

[figure number=Figure 3.10 caption=Population Growth Rate for Countries of the World in 2020 filename=Fig_3.10.jpg]

Figure 3.10 is a map of the population growth rates of the nations of the world. This is a choropleth map that provides a single number (annual rate of natural population growth) for each country. Countries in sub-Saharan Africa show the highest rates of population growth, while much Europe, Russia, and Japan show declining populations (negative rates of population growth). Population growth rates are influenced by fertility rates, life expectancy, and migration. This map is just a map of “natural increase” that consists of births minus deaths. Russia’s population is shown as declining in this map; however, what is not shown in this map is the fact that in migration to Russia in recent years has brought Russia back to a slight positive rate of population growth, although the war in Ukraine is changing that picture.

[figure number=Figure 3.11 caption=Changes to Life Expectancy 1800, 1950, 2015 filename=Fig_3.11.jpg]

Figure 3.11 shows the dramatic changes to life expectancy that have taken place since 1800. At some point in time around 1800, the world’s total population reached 1 billion. At that time the global average life expectancy was only 29 years. By 1950 the global population had more than tripled to over 3 billion and inequality of life expectancy between the developed world and the developing world had manifested to a significant extent. In 2015 the entire world experienced increased life expectancy but the developed world still has higher life expectancies. Increasing life expectancy is one of the driving factors of overall population growth. We will discuss this in more detail in the context of the “Demographic Transition,” which is one of the processes we consider in Unit II. Tying the patterns of these figures to the processes we discuss in Unit II is how we hope you will build a demographic perspective for assessing the problematics of Unit III Problematics.

[figure number=Figure 3.12 caption=Maternal Mortality filename=Fig_3.12.jpg]

Figure 3.12 shows the annual number of maternal deaths by country. This is a function of both the number of births each year and the probability that a mother will die as a result. Note that this is NOT a rate of maternal mortality. A rate of maternal mortality would provide you with an idea of the percentage of mother’s who die as a result of childbirth. The five countries with the highest number of maternal deaths in 2015 were Nigeria (58,000); India (45,000); the Democratic Republic of Congo (22,000); Ethiopia (11,000); and Pakistan (9,700). This figure is a good example of how it is important to understand the relationships between population size, rate, and density. In this case, the highest numbers are a function of both population size and rates of maternal mortality.

[figure number=Figure 3.13 caption=Number of Births per Woman (TFR) filename=Fig_3.13.jpg]

Figure 3.13 is a map of the TFR (children per woman) of the countries of the world. TFR is the average number of children born per woman in a country. If a country consists of 10 women, 9 of whom have two children and one woman who has three children, then the TFR of that country is 2.1 (e.g., (9 × 2 + 1 × 3) / 10 or 21/10 = 2.1). The TFR of the United States is currently about 1.7. The population of the United States is still growing due to in migration. The highest TFR values are in sub-Saharan Africa with some areas of the Middle East, Latin America, and Asia having some higher values. Much of Europe shows fertility levels that are below replacement levels. Replacement fertility is not exactly 2.0 because not all children reach reproductive age; however, it is roughly 2.1 for developed countries. The “Our World in Data” website allows you to explore how these numbers change over time. While the United States may have a relatively low fertility rate today, it is interesting to explore the time series data. Fertility rates in the United States during the 1800s was often higher than six children per woman. It has taken some time to develop an understanding of what drives changes to the TFR of a country or region. Understanding the drivers of changes in fertility rates has been a major concern of demographers for decades. There are many drivers that can interact in complex ways, which will be discussed in Unit II: Processes. One of the major drivers is the “empowerment of women” for which “women’s educational attainment” serve as a proxy measure.

[figure number=Figure 3.14 caption=Relationship Between Women’s Education and Fertility filename=Fig_3.14.jpg]

Figure 3.14 portrays the “trajectories” of the countries of the world from 1950 to 2010 with regard to changes in “Average years of schooling of women in reproductive age” (x-axis) relative to “children per woman” or TFR (y-axis). The overwhelming trend of these trajectories indicates that there is a profound negative correlation between TFR and the average years of schooling of women in reproductive age. We must always be careful about assuming that “correlation is causality.” Does increasing the education of women reduce fertility? The data certainly suggest that. We could also reverse the causality and ask: “Does reduced fertility allow women to get more education?” What if increasing levels of economic development caused reduced TFR because it caused improved access to birth control and increased levels of women’s schooling because of greater investment in education by governments? If that were the case, we could think of “economic development” as the true cause of both of these changes. If this were the case, we would regard “economic development” as a confounding variable. Questions of causation in social science can be very complex and difficult to prove. Nonetheless, the idea that improving the status of women reduces the number of children women have throughout their lifetime is almost universally acknowledged now. These issues will be discussed in greater depth in Unit II: Processes. For now, let us look at Figure 3.12 more closely.

Figure 3.14 is a great example of how a geographic perspective provides a mechanism for untangling variables to improve our understanding of complex, multivariate processes. Most countries in Figure 3.14 show very similar patterns; however, there are a few outliers. Niger, the Democratic Republic of Congo, and Pakistan are bucking the trend with increasing fertility as women’s education levels increase. What may be going on in these countries that changes the dominant relationship between women’s education and fertility over time? A geographic perspective enables us to think in a more nuanced way about many phenomena.

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3.5.1 Measuring and Mapping Complex and Contested Demographic Concepts

Standard surveys conducted by the Census Bureau and other entities and institutions do not always ask the questions that we might want to answer. In some cases, the phenomena of interest cannot be asked of individuals but must be derived from an aggregation of individual responses (e.g., measurements of diversity or inequality). In other cases, the very concept of what you wish to measure and map is contested and/or difficult to define (e.g., measurements of social justice and human well-being). In addition, mapping the ecologically sustainable behavior of the people of a given country or state would typically require the integration of nondemographic data such as land cover and energy consumption. Here, we will explore a few examples and leave others as exercises for the student.

3.5.1.1 Poverty

Most of us would agree that poverty is bad. Bill Gates, the World Health Organization, The United Nations, and just about any nonsociopathic individual would agree that we should try to create a world with minimal if not zero levels of poverty. How might one map “poverty?” Poverty must be defined in order to map it and the devil and neoliberal agendas can manifest in these details. Poverty has been defined as the state of being in which one cannot meet their basic needs (e.g., needs for food, shelter, water, clothing, and many other things). It is usually translated to a level of income (usually $/day) that presumably enables one to buy these basic needs.

The World Bank defines poverty as those living on less than $1.90 per day. In the year 2000, the World Health Organization participated in the establishment of the Millennium Development Goals. The first goal was to eradicate extreme poverty and hunger. Why might the World Bank use $1.90 per day instead of $2 per day as the standard for poverty? Figure 3.24 suggests the world has made dramatic progress in reducing the number of people in extreme poverty. Note how different the $2 per day definition is from the $1.90 a day. How accurately do you think anyone could measure the fraction of people living on $1.90 versus $2 a day? The responses you get from individuals you survey will likely have modalities at $1 per day and $2 per day. Everyone who responds $2 per day will be categorized in the relatively happy status of “not living in extreme poverty.” This brings to mind Mark Twain’s quote about “lying and statistics.” Figure 3.24 suggests that less than 10% of the world’s population was living in poverty in 2015. How does that jibe with the fact that 29% of the world population does not have access to clean water (https://www.who.int/news-room/fact-sheets/detail/drinking-water)? Does not it seem that access to clean water is necessary to not be in “extreme poverty?” This “access to clean water” figure comes from the same World Bank that defines “extreme poverty” at $1.90 per day. According to the Food Aid Foundation, 821 million (1 in 9) people do not have an adequate food supply to function normally (https://www.who.int/news-room/fact-sheets/detail/drinking-water), that is more than 10% of the global population.

[figure number=Figure 3.15 caption=Changes to the Percentage of the World’s Population Living in Poverty filename=Fig_3.15.jpg]

Bill Gates and other neoliberal billionaires who like to meet at Davos to solve the world’s problems seem to be very enamored of telling the world that we are making great progress in reducing poverty. They point out that less than 10% of the world lives in extreme poverty. They often use figures like Figure 3.15 to make their case. These arguments are of course made at the same time as there are billionaires like Jeff Bezos who can spend a million dollars a day for the rest of his life and not run out of money. Jason Hickel makes a very cogent argument that the Davos crowd could not be more wrong about the world succeeding in “ending poverty as we know it.” Hickel’s article in The Guardian titled Bill Gates says poverty is decreasing. He couldn’t be more wrong (https://www.theguardian.com/commentisfree/2019/jan/29/bill-gates-davos-global-poverty-infographic-neoliberal) explains how some of the data presented on the World Population Growth website can be misinterpreted. Hickel provides an appropriate and critical perspective on the nature of data and its presentation and interpretation.

3.5.1.2 Technological Optimism and Our World in Data (OWID)

OWID, while offering a valuable resource for global statistics and trends, carries an implicit technological optimism that warrants critical examination. The maxim: “Follow the Money” may be relevant here. OWID is funded by Bill Gates, Elon Musk, the Pritzker Innovation Fund, and other progrowth neoliberal libertarians that do not acknowledge limits to growth (https://www.influencewatch.org/organization/our-world-in-data/). The website often presents an overwhelmingly positive narrative regarding the impact of technology on various aspects of human life and development, neglecting to adequately address the associated challenges and potential negative consequences. Naomi Oreskes argues in this piece in Scientific American that the scientific data regarding the state, trends, and drivers of social and environmental conditions are in direct contrast to the tone of the techno-optimists at OWID.

One key concern is the selective framing of data, which tends to highlight positive trends while downplaying or overlooking the adverse effects of technological advancements. For instance, the website may showcase the increasing access to information technology globally, but it might not delve into the digital divide, where marginalized communities lack equal access to these transformative tools. By accentuating the positive aspects without providing a nuanced perspective, OWID may inadvertently contribute to a skewed understanding of the complex relationship between technology and societal progress that contributes to the evolution of technological optimism morphing into a blind faith that looks like technological fundamentalism. Technological fundamentalism (https://robertwjensen.org/articles/technological-fundamentalism-why-bad-things-happen-when-humans-play-god/) refers to an unwavering and uncritical faith in technology’s ability to address all challenges, often leading to an overreliance on technological solutions and a neglect of broader ethical, social, and environmental considerations (Jackson & Jensen, 2022). This mindset can result in a dogmatic belief that technology alone holds the key to progress, potentially disregarding the unintended consequences, ethical dilemmas, and societal disruptions that may arise from unchecked technological development. It underscores the importance of maintaining a balanced and critical perspective on technology, acknowledging both its benefits and potential risks to ensure responsible and thoughtful integration into society.

Furthermore, the website’s optimistic tone may inadvertently foster complacency or a lack of critical inquiry. By consistently emphasizing the positive outcomes of technological innovations, it may discourage users from questioning the potential downsides or engaging in meaningful discussions about the ethical implications of rapidly advancing technologies. This lack of critical scrutiny can hinder our ability to anticipate and address the challenges that come with technological progress, such as issues related to privacy, algorithmic bias, and the socioeconomic impacts of automation.

Another point of critique lies in the often implicit assumption that technological solutions alone can address complex global issues. While technology has undoubtedly played a significant role in advancements across various sectors, the website sometimes underplays the importance of comprehensive, multidimensional approaches that involve social, economic, and political considerations. Relying solely on technological optimism may divert attention from the underlying structural issues that perpetuate inequalities and hinder sustainable development.

While OWID provides valuable insights into global trends, its implicit technological optimism should be approached critically and perhaps even be described as technological fundamentalism. Acknowledging the limitations and potential drawbacks of technological advancements is crucial for fostering a more comprehensive understanding of their impact on society. Embracing a more nuanced and balanced perspective will enable individuals and policymakers to make informed decisions that consider both the benefits and challenges associated with the rapid pace of technological change.

3.5.1.3 Inequality

Inequality is another complex and contested economic and demographic idea that is garnering increasing attention in the world today. The growing consensus is that economic inequality is too high, and it is growing. “Occupy Wall Street” was a movement that began in Zuccotti Park in New York City in 2011 (Hernandez, 2018). The “Occupy Movement” was a protest against economic inequality. Many have argued that the “Occupy Movement” did not accomplish anything. Many would counter that argument by noting that the “Occupy Movement” put the idea of economic inequality into the global collective consciousness (no mean feat). We now use the idea of “the 1%” and “the 99%” to make an economic classification of people. Academics have followed up with research on questions of inequality. If not for the “Occupy Movement” Thomas Piketty’s book Capital may not have garnered as much attention as it did (Piketty, 2014). Capital is a data-rich exploration of economic inequality that suggests the following: (1) The ratio of wealth (money in the bank and other assets) to income (money earned from working) is growing, (2) this trend will continue, (3) if it continues the income earned by wealth will exceed the income earned by work, (4) taxing wealth is the only way to avoid returning to a 19th-century economy that looks like serfdom for most people.

How do we measure economic inequality? The idea of inequality presupposes “difference.” A small town in which all citizens have identical below-poverty-level incomes would theoretically have zero inequality. We have to ask about the spatial scale and sample size at which we might want to measure something like inequality. Many will argue that there is a “sweet spot” of economic inequality that is essential to motivate people to work. Piketty has made a very cogent argument that we are way past any “sweet spot” and that economic inequality throughout most of the world today is too high and is economically inefficient.

One of the earliest measures of economic inequality was invented by an Italian statistician named Corrado Gini (Gini, 2008). The GINI coefficient is best explained with pictures rather than words. It involves using a sorted frequency distribution of the cumulative percentage of the population versus the sorted cumulative percent of income. The actual values are compared to a uniform distribution of income and calculating the area between these two curves (a kind of Lorenz curve). Again, Saul Khan of the Khan Academy does a great job explaining the GINI coefficient (Video 3.8).

I encourage you to conduct a web search for images of global maps of income inequality or the GINI coefficients of the nations of the world. Note how variable the status of the United States is in these maps. The “truth” is out there but it is not always easy to find. One of many global maps of the GINI coefficient for the nations of the world is presented here that shows the United States as having a significantly higher GINI coefficient (more inequality) than many of the countries of Europe, Australia, New Zealand, Canada, and even Mexico (Figure 3.16). For many years the United States had a lower GINI coefficient than Mexico, but inequality has been growing in the U.S. Statistics like this provide evidence to ideas like “The Middle Class is disappearing in the United States” and likely contribute to motivating reality behind movements like “Black Lives Matter” and “Occupy Wall Street.”

[figure number=Figure 3.15 caption=GINI Coefficient of Wealth Inequality filename=Fig_3.15.jpg]

Choropleth maps are good for comparing nations; however, sometimes we want a more nuanced spatial perspective. One way to get a sense of the spatial manifestation of inequality is to build a Lorenz curve of population versus nighttime satellite intensity on a pixel-by-pixel basis (Figure 3.16). The spatial Lorenz curve shown in Figure 3.17 explains in some detail how to do the arithmetic to calculate a GINI coefficient. This work did not demonstrate that light and population distributions can be used as a proxy measure of a GINI coefficient; however, it did prove to be a very useful proxy of the Human Development Index that we will discuss in greater detail in Unit III: Problematics.

[figure number=Figure 3.16 caption=Spatial Lorenz Curve to Map Inequality Based on Nighttime Satellite Imagery from “The Night Light Development Index (NLDI): A Spatially Explicit Measure of Human Development from Satellite Data” filename=Fig_3.16.jpg]

The GINI coefficient is one of the figures that “goes beyond the mean” by accounting for the actual distribution (varying incomes) of the phenomena in question. Building your statistical and demographic perspective is an important learning outcome for this course. Appreciation of how spatial variation and statistical distributions manifest and are measured and communicated is a challenging and rewarding body of knowledge to achieve. There are other measures of economic inequality. One suggested by the Ice Cream Company Ben and Jerry’s was the 5 to 1 rule which later became the 7 to 1 rule. The CEO’s salary at Ben and Jerry’s was limited to seven times the salary of the lowest-paid employee. Sadly, this corporate policy was abandoned in 1994 (Edwards, 2011). This ratio of incomes is another potential measure of income inequality. Measuring inequality is challenging. Averages, bar charts, and even maps capturing spatial variation cannot always convey the information in a satisfying way. The animated infographic titled “Wealth Inequality in America” is a brilliant use of animation to convey distributions of values. It is also a very interesting exploration of what we think reality is, what we think reality should be, and what the actual distribution of wealth in America is. Videos on topics like this did not exist prior to “Occupy Wall Street,” and they provide an excellent public service for those who are interested in finding out more about what the “Occupy Movement” is all about.

3.5.1.4 Diversity

There is a growing consensus that “diversity” is good. We want to save biological diversity (aka biodiversity), and we are increasingly recognizing that human diversity in most, if not all, its manifestations is something to preserve, sustain, and celebrate. There is a mounting body of literature on the benefits of demographic diversity that include (1) demographically diverse executive boards generate better returns, (2) age diversity of groups leads to better problem-solving by those groups, and (3) racially diverse juries are more deliberative in their decision making (Core Based Statistical Areas, n.d.). What exactly, does diversity mean? How can we measure it?

A traditional measure of biodiversity is species richness that is simply the number of species in a given area. This simple measure can be problematic. Consider an area “X” with 28 individuals of species “A,” one individual of species “B,” and one individual of species “C.” Area “X” will have the same species richness as another area “Y” with 10 of “A,” 10 of “B,” and 10 of “C.” Species diversity considers both species richness and species evenness. The species diversity of area “Y” will be higher than that of “X” because it has a greater species evenness. The most used index of biodiversity is “Simpson’s diversity index” (Video 3.8). Simpson’s diversity index varies from values of 0 (no diversity) to 1 (high diversity). Simpson’s diversity index involves a little math but makes intuitive sense and combines richness and evenness.

How might these concepts translate to demographic diversity? Are race, gender, ethnicity, income, age, religion, and education all to be considered as elements of something we call demographic diversity? Should all these attributes be given equal weight or consideration when measuring diversity? Often, particular aspects of demographic diversity are identified as something to change (e.g., increasing the racial diversity of congressional representation or changing the gender diversity of scientists). A website titled Visualizing the U.S. Population by Race (Hernandez, 2018) used infographics to characterize racial diversity over time (with bar charts; Figure 3.17) and space (with a map-like mosaic of bar charts; Figure 3.18).

[figure number=Figure 3.17 caption=America’s changing racial composition 2000 to 2060 filename=Fig_3.17.jpg]

[figure number=Figure 3.18 caption=America’s Spatially Varying Racial Diversity filename=Fig_3.18.jpg]

It is interesting to note that Simpson’s diversity index was not used to characterize the racial diversity of the United States in these infographics. Simply presenting numbers that varied from 0 to 1 for each of the states or showing how a number from 0 to 1 increased over time would not provide anything remotely close to the richness of information in the Visualizing the U.S. Population by Race infographics. It is said that “a picture is worth a thousand words.” The digital revolution of the last few decades has dramatically reduced the effort needed to make maps, diagrams, histograms, and scatterplots that communicate frequencies, numeric distributions, averages, change over time, and spatial distributions in ways that bring demography and population geography to life in new and delightful ways.

The National Equity Atlas is a good example of this. This Atlas is an interactive website that provides several ways of characterizing diversity including Race/Ethnicity, Nativity and Ancestry, People of Color, Population growth, Racial generation gap, Diversity index, and Median age (https://nationalequityatlas.org/). This website is worthy of some exploration. An example of the sorts of things you can create with the website is a chart of percentage of the population that are “working poor” in Denver, CO broken down by race and charted over time (Figure 3.19). The website allows you to choose your index, your geographic region, etc.

[figure number=Figure 3.19 caption=National Equity Atlas Chart of Working Poor in Denver, CO filename=Fig_3.19.jpg]

Statistics have a profound ability to help our minds understand large sets of numbers. We often need to use statistics to understand and monitor phenomena that we want to change through agency or to establish policy (e.g., policy to increase diversity and policy to mitigate climate change). Indices such as Simpson’s diversity index can perhaps hide realities that can be seen in Figures 3.16, 3.17, and 3.19. These infographics can make the reality that these numbers represent more real and personal. The GIS software company ESRI has developed its own demographic diversity index on the following idea: What is the likelihood that two persons, chosen at random from the same area, belong to different race or ethnic groups? (ESRI Diversity Index, n.d.) This index draws on the standard race and ethnicity data gathered by the U.S. census. The index can be calculated down to the block group level and ranges in value from 0 (no diversity) to 100 (complete diversity).

ESRI’s Diversity Index varies from less than 15 to over 60 across the 3,006 U.S. counties with high values along the coasts and U.S.–Mexico border and low values in the mid-west and along the Canadian border. The diversity index for the entire United States in 2014 was 62.6. The diversity index for the United States is increasing over time (2010—60.6, 2014—62.5, 2019—65). Diversity, of course, is only one measure. Diversity is increasing in the United States; however, so is wealth and income inequality and those changes to wealth and income inequality are disproportionate in that younger people and people of color are much more likely to experience decreasing or unchanging wealth and income.

Systemic racism in most if not all our institutions, including those in law enforcement, corrections, and the judiciary is increasingly recognized as not just a moral failure of our civilization but one that reduces economic efficiency. This has motivated many laws and policies aimed at improving “diversity and inclusion.” The business community seems to be evolving from hiring people for “cultural fit” (which can be a “dog whistle” for racist) to more legitimate ideas of diversity. This can be a complex endeavor because it is challenging to establish measurable, meaningful, and valid metrics for these goals (Menzies, 2018). Measures of diversity presented here can be used at school, business, and local levels or at broader state and national levels to improve our understanding of what the levels of diversity are at a range of scales and to monitor progress on cocreating a more just, diverse, and sustainable world.

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3.6 Integration of Demographic Data With Satellite Imagery

Demographic data has historically been gathered and recorded as numbers associated with administrative boundaries (e.g., numbers of persons in nations, states, counties, and census tracts). The digital age has brought us digital cameras, digital images, and of course, digital satellite imagery. In these datasets, numbers are associated with pixels or cells (usually squares). The two primary ways GIS store spatially referenced data are Vector (e.g., points, lines, and polygons characteristic of the Standard Hierarchy of the U.S. Census Bureau) and Raster (the pixels or cells characteristic of digital photographs and satellite imagery). In the early days of GIS (~1980s and 1990s), there was a saying that “Vector is correcter but raster is faster.” As technology has advanced and the spatial resolution of imagery has become finer (meaning data files are larger), the saying is becoming less and less true. Vector data is used much more now because of the speed and ease with which it can be delivered over the internet. In any case, there is a growing demand for the provision of demographic data in raster format for a variety of reasons including easier integration with other raster datasets that are often derived from satellite imagery, providing the data in a structure that is more amenable to building spatially explicit models that use both demographic and other raster-based datasets, and improving and simplifying geovisualization of a variety of phenomena that are derived from these studies.

3.6.1 State-of-the-Art Raster Representation of Demographic Data

As GIS technology improved so did our ability to represent demographic data in raster format. One of the challenges associated with changing the data structure from vector to raster is creating smooth edges between rasterized polygon data. This is referred to as pycnophylactic interpolation (Figure 3.20; Tobler et al., 1995). The National Center for Geographic Information and Analysis was a pioneer in the development of raster-based representations of the world including The Global Demography Project (Budiman et al., 2018).

[figure number=Figure 3.20 caption= Pycnophylactic Interpolation or “Smoothing” of Rasterized Vector Data filename=Fig_3.20.jpg]

Several institutions built upon these ideas and are now producing state-of-the-art representations of the human population and other spatially explicit demographic datasets in raster format. The following is a partial list of some of those institutions and data products:

1. Oak Ridge National Laboratory (ORNL)

ORNL produced the LandScan (https://landscan.ornl.gov/) global population database, which has a spatial resolution of approximately 1 km × 1 km (30″ × 30″). The cells in this dataset represent “ambient” or time-averaged population density which attempts to account for human mobility. Many census data products produce population density maps of where most of us sleep at night. LandScan has significant population density at places like airports, which typically do not have high population density for data products derived from census data.

[figure number=Figure 3.21 caption=LandScan Population Data Product filename=Fig_3.21.jpg]

1. Center for International Earth Science Information Network (CIESIN)

CIESIN is a research center at Columbia University that produces a wide variety of data products including the Global Rural-Urban Mapping Project (GRUMP; https://sedac.ciesin.columbia.edu/). Among these datasets are settlement points, urban extent polygons, and grids of population counts and population density.

[figure number=Figure 3.22 caption=Human Footprint Data Product from CIESIN filename=Fig_3.22.jpg]

1. WorldPOP

WorldPOP (https://www.worldpop.org/) is a research center located at the University of Southampton that works to develop methods for the construction of open and high-resolution geospatial data on population distributions, demographic phenomena, and dynamics. Their focus is on low and middle-income countries. One of the analytical approaches they use to capture population dynamics is mobile phone data (Video 3.11).

3.6.1.1 The Global Human Settlement Layer

The European Commission has established a program at the Joint Research Center (JRC) in ISPRA, Italy called the Global Human Settlement Layer (GHSL; Figure 3.23; https://ghsl.jrc.ec.europa.eu/). The vision of the GHSL is to provide open and free data and tools for assessing human presence on the planet. The GHSL was developed at the JRC with collaboration from many academics, NGOs, government agencies, and people from organizations like CIESIN, WorldPOP, and ORNL. The GHSL leverages new spatial data mining technologies for the automatic processing of myriad datasets (e.g., satellite imagery, census data, and VGI) to develop novel analytic methods and data products from massive quantities of heterogeneous data. Guiding principles of the GHSL are that the information products are reproducible, scientifically defensible, synoptic, fine resolution, complete, global in coverage, and cost-effective.

One rationale for the GHSL is moving investments in human capital and intelligence away from information gathering and toward the analysis of information to produce knowledge and wisdom. The spirit of this program is the facilitation of information sharing, multilateral democratization of information production, and collective knowledge building. The data products of the GHSL are essential for evidence-based modeling and understanding of the impacts of human activities on ecosystems, human access to resources, and human and physical exposure to threats such as environmental degradation as well as disasters and conflicts.

[figure number=Figure 3.23 caption=The Global Human Settlement Layer (GHSL) filename=Fig_3.23.jpg]

There are many data products and tools developed by the GHSL that are free to download or use including a built-up area grid (derived from Europe’s Sentinel Satellite), a built-up area grid (derived from U.S. Landsat satellite data), a population grid derived from the gridded population of the world (GPW 4.10), and a settlement model layer (GHS-SMOD). The GHS-SMOD uses two inputs (Figure 3.24) to produce seven human settlement classifications: (1) a built-up area grid and (2) a population density grid. The built-up area grid is derived from high-resolution satellite imagery to identify building footprints. The population density grid is derived from census data. The GHS-SMOD has seven categories of settlement: (1) Urban Centre, (2) dense urban clusters, (3) semi-dense urban cluster, (4) suburban, (5) rural cluster, (6) low density rural, and (7) very low density rural.

[figure number=Figure 3.24 caption=Logic of the GHSL Settlement Model filename=Fig_3.24.jpg]

The logic for classifying the GHS-SMOD uses binary logic with thresholds of population density, thresholds of percent build up, and spatial adjacency rules to map the seven types of settlement (e.g., population density greater than 1,500 person per square kilometer and greater than 50% built up is an urban center pixel or cell).

[figure number=Figure 3.25 caption=From Data to Information to Knowledge to Wisdom filename=Fig_3.25.jpg]

Data products from the GHSL are increasingly recognized as standards by the United Nations, OECD, EU, World Bank, FAO, UN-Habitat, and other organizations. Periodic releases of discoveries and insights derived from the GHSL are published in Atlases of the Human Planet (https://ghsl.jrc.ec.europa.eu/atlasOverview.php) which is part of the GEO Human Planet Initiative (https://earthobservations.org/geoss_wp.php). The GHSL is eager to have anyone use their data products for analysis that leads to wisdom.

There is growing interest in developing indicators for the Sustainable Development Goals that are derived from Earth Observations because they are inexpensive to measure and allow for “apples to apples to apples’” comparisons from one country or region to another. The GHSL will likely be the standard spatial framework for many of these efforts. The GHSL may be establishing a new global standard hierarchy for demographic data comparable to the U.S. Census Bureau’s national Standard Hierarchy for Geographic Entities (e.g., nation, states, counties, and tracts; Figure 3.6).

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3.7 Patterns of Human Migration

People move around the world. Some of this travel is for migration, some is for tourism, and some is for other reasons. We will explore the technical definitions associated with the process of migration in greater detail in Unit II. In this section, we will simply provide a broad picture of some migration patterns of the past and present and discuss why measuring and mapping human migration is challenging. Historic migration patterns have contributed to some of the patterns we have explored so far. The dot density map of blacks and Hispanics in the United States by county (Figure 2.16) has the dark shadows of the forced migration of the trans-Atlantic slave trade (Video 3.12), evidence of subsequent migration of blacks to major urban centers in the West, and evidence of migration of Mexicans and other Latin Americans to the southwest.

The map of total fertility (Figure 3.13) shows the United States with a value between 1.5 and 2, while Mexico has a value between 2 and 2.5. There would undoubtedly be a larger difference between the United States and Mexico if migration from Mexico to the United States were not taking place. This is because migrants tend to be younger and have children at the destination of their migration. In 2018 there were roughly 45 million foreign-born immigrants living in the United States (Universal Declaration of Human Rights, n.d.). Immigrants tend to raise the fertility of the country they migrate to and lower the fertility of the country they leave. Migration is one of the key elements of the Basic Demographic Equation (Section 3.3.1) and is fundamentally geographic. Prehistoric migration patterns are not derived from census data but from fossil records and theories to make sense of them. Mapping prehistoric migration is consequently a very generalized and speculative endeavor (Video 3.14).

Accessing and visualizing migration data can be challenging. The International Organization for Migration (IOM) is one of the specialized agencies of the United Nations. IOM gathers and disseminates a great deal of migration data. IOM’s Global Migration Data Analysis Centre runs the Global Migration Data Portal, which serves as a unique access point to timely, comprehensive migration statistics and reliable information about migration data globally.

IOM data paints this picture of the migrants in the not-too-distant past. In 2019, there were 272 million international migrants (people residing in a country other than their country of birth). Women are almost half of these migrants (~48%). It is estimated that 38 million of these migrants are children, and three out of four international migrants are of working age (20–64 years old). Of them, 164 million are migrant workers. Most of these migrants are in North America, Europe, and Asia. People migrate for a variety of reasons. The magnitude of some of this migration globally ranges from leisurely tourism to desperate attempts to escape poverty (Figure 3.26). The United Nations High Commission on Refugees estimates that almost 30 million of these international migrants are forcibly displaced people (26 million refugees, 3.5 million asylum seekers) with another 41 million forcibly displaced persons that are inside their country of birth. Both the numbers of international migrants and the numbers of refugees in the world have been increasing dramatically in the past decade. Many of the indicators for the Sustainable Development Goals relate to the status of these migrants.

[figure number=Figure 3.26 caption=Movements of People: Migration and Tourism filename=Fig_3.26.jpg]

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Migration data is complex for many reasons. Imagine attempting to characterize internal migration in the United States. What temporal scale is appropriate? What spatial scale is appropriate? Let us assume we only want to characterize interstate migration over a 5-year interval of time. This would mandate a matrix of 50 × 50 = 2,500 numbers. A table with 50 rows (one for each state) and 50 columns (one for each state) would be needed. Each cell in this table would have the number of people that migrated from the state in Row “x” to the state in Column “y.” How does one communicate the 2,500 numbers that are needed to characterize interstate migration in the United States for a single step of time? Pretty challenging. Animated maps may be the best way to communicate such a large quantity of information in a way that our minds can grasp. The world migration map (Video 3.14) attempts to capture annual migration flows between the countries of the world—a dataset similar in structure to the interstate migration data for the United States.

Immigration to the United States has long been a contentious and polarizing topic, encompassing debates around cultural identity, economic impact, and national security. The terms “illegal immigrant” and “undocumented worker” contribute to the controversy, reflecting divergent perspectives on the status and contributions of individuals entering the country without proper authorization.

The term “illegal immigrant” is often criticized for its perceived dehumanizing nature, reducing individuals to their legal status and potentially fostering negative stereotypes. Critics argue that this label oversimplifies a complex issue by focusing solely on the legality of migration without considering the myriad reasons individuals may choose to enter the country without proper documentation, such as fleeing violence, seeking economic opportunities, or reuniting with family.

On the other hand, proponents of strict immigration enforcement argue that the term “illegal immigrant” accurately describes the violation of immigration laws and emphasizes the importance of upholding legal frameworks. They contend that precise language is necessary to distinguish between those who have followed legal channels for entry and those who have not, emphasizing the rule of law as a foundational principle of the United States.

The term “undocumented worker” is often used as an alternative to “illegal immigrant,” aiming to shift the narrative toward the economic contributions of migrants. Advocates argue that this terminology places a focus on the labor and skills these individuals bring to the country, highlighting the complexities of their status and the potential benefits they provide to the workforce.

The controversy surrounding immigration terminology mirrors deeper societal divisions over how to address immigration policy. Striking a balance between acknowledging legal frameworks and recognizing the human aspects of migration remains a persistent challenge. As the United States grapples with immigration reform, the discourse around terms like “illegal immigrant” and “undocumented worker” reflects the broader complexities and ethical considerations inherent in shaping a comprehensive and compassionate immigration policy.

[figure number=Figure 3.27 caption=Latin American Second Immigration Wave filename=Fig_3.27.jpg]

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3.7.1 Immigration as a Political Issue

Migration and immigration are loaded political issues. Far-right populist politicians rail against global migration in many parts of Europe and the United States. Many moderate governments feel pressure to “be tough on immigration.” Sadly, short-term strategies dominate the debates, while many of the fundamental drivers of migration go unaddressed. Europe’s far-right populists, like France’s Marine Le Pen, Italy’s Matteo Salvini, and Hungary’s Viktor Orban continue to inflame anti-immigrant sentiment. Anti-immigrant sentiment was central to U.S. President Donald Trump’s winning 2016 presidential campaign, which succeeded in redesigning American security policy around stopping illegal immigration and building giant border walls. Trump’s anti-immigrant rhetoric was focused on Muslims. The spread of religion often is related to migration but not necessarily. Video 3.16 provides a nice animation of the geographic diffusion of the world’s major religions.

Political debates over immigration are dominated by short-sighted strategies like closing borders rather than looking for ways to address root causes that include poverty, conflict, and war. There has also been very little attention paid to future drivers of migration, including climate change and other potential causes of environmental refugees. All humans deserve to be treated with dignity and afforded the rights outlined in the Universal Declaration of Human Rights (20). Humane policies aimed at improving the lives of international migrants and displaced persons will undoubtedly benefit from accurate, up-to-date, spatially explicit demographic data.

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Chapter Summary/Key Takeaways

This chapter explored the history and practice of the science of population which is commonly known as demography. The basic measurements that demographers make were presented along with the basic demographic equation. We then proceeded to explore how to represent demographic data in a spatial framework. The standard hierarchy of geographic features of the U.S. Census Bureau was presented as a traditional vector (point, line, and polygon) framework and juxtaposed with the newly developed Global Human Settlement Layer spatial framework which is in structured a raster or cell-based format. We explored patterns of demographic phenomena in maps, charts, infographics, and animations. The challenges associated with measuring and mapping some complex and contested phenomena such as inequality, diversity, and poverty were presented. The mapping and political challenges with respect to international migration and the refugee crisis were also addressed.

This chapter completes Unit I: Patterns. In Unit II: Processes, we will take a deeper look at the mechanisms that create many of these patterns including the demographic transition, economic development, and urbanization. Our understanding of these processes is still developing and will be needed to inform solutions to the social, economic, and environmental challenges addressed in Unit III: Problematics.

Comprehensive Questions

• Present an argument that suggests demography is an essential social science.
• Why are there so few demography departments in the world’s universities?
• Who was John Graunt and why might he be regarded as the father of actuarial science?
• How can reputation bias produce gender discrimination?
• What is a census of the population and why are they carried out?
• What is the Basic Demographic Equation?
• What is the difference between direct and indirect measures of demographic data?
• Why does the U.S. Census Bureau have a “Trust and Safety” Team?
• What is a population pyramid and what kind of information can be gleaned from one?
• What is the Standard Hierarchy of Census Geographic Features?
• Describe the spatial variation at the global scale of three basic demographic variables (e.g., total fertility rate, life expectancy, infant mortality rate).
• Why is it challenging to map things like diversity, inequality, and poverty?
• What is the Global Human Settlement Layer and how is it used?
• What are some major patterns of migration in the world today?

References

Budiman, A., Tamir, C., Mora, L., & Noe-Bustamante, L. (2018). Facts on U.S. Immigrants. Pew Research Center. https://www.pewresearch.org/hispanic/2020/08/20/facts-on-u-s-immigrants/

Core Based Statistical Areas. (n.d.). U.S. Census Bureau. https://www.census.gov/topics/housing/housing-patterns/about/core-based-statistical-areas.html

Demography. (n.d.). In Wikipedia. https://en.wikipedia.org/wiki/Demography

Domesday Book. (n.d.). In Wikipedia. https://en.wikipedia.org/wiki/Domesday_Book#Survey

Edwards, J. (2011). Occupy Wall Street: Why Ben and Jerry’s endorsement rings hollow. https://www.cbsnews.com/news/occupy-wall-street-why-ben-jerrys-endorsement-rings-hollow/

ESRI Diversity Index. (n.d.). https://www.esri.com/content/dam/esrisites/sitecore-archive/Files/Pdfs/library/whitepapers/pdfs/diversity-index-methodology.pdf

Gini, C. (2008). In The Concise Encyclopedia of Statistics. Springer. https://doi.org/10.1007/978-0-387-32833-1_168

Hernandez, E. (2018). If you think Denver’s weirdly shaped, wait’ll you see the islands of not-Denver in Denver. Denverite.com. https://denverite.com/2018/12/16/if-you-think-denvers-weirdly-shaped-waitll-you-see-the-islands-of-not-denver-in-denver/

Japanese Internment Camps (History.com) (n.d.). https://www.history.com/topics/world-war-ii/japanese-american-relocation

Khaldun, I. (n.d.). In Wikipedia. https://en.wikipedia.org/wiki/Ibn_Khaldun

Marter, A. (2017). Seven studies that prove the value of diversity in the workplace. https://blog.capterra.com/7-studies-that-prove-the-value-of-diversity-in-the-workplace/

Menzies, F. (2018). Meaningful metrics for diversity and inclusion. Culture Plus Consulting. https://cultureplusconsulting.com/2018/10/16/meaning-metrics-for-diversity-and-inclusion/

Occupy Wall Street. (n.d.). In Wikipedia. https://en.wikipedia.org/wiki/Occupy_Wall_Street

Piketty, T. (2014). Capital in the twenty-first century. The Belknap Press of Harvard University Press.

Statistics (etymology online). (n.d.). https://www.etymonline.com/word/statistics

Tobler, W. R. (1979). Smooth pycnophylactic interpolation for geographical regions. Journal of the American Statistical Association, 74(36)7, 519–530, https://doi.org/10.1080/01621459.1979.10481647

Tobler, W., Deichmann, U., Gottsegen, J., & Maloy, K. (1995). The Global Demography Project (95-6).

Tyler, C. M., Kuhn, B., & Davis, F.W. (2006). Demography and recruitment limitations of three oak species in California. The Quarterly Review of Biology, 81(2), 127–152. https://www.journals.uchicago.edu/doi/abs/10.1086/506025

Universal Declaration of Human Rights. (n.d.). United Nations. https://www.un.org/en/universal-declaration-human-rights/

New References

Jackson, W., & Jensen, R. (2022). An inconvenient apocalypse: Environmental collapse, climate crisis, and the fate of humanity. University of Notre Dame Press.

O’Sullivan, J. (2023). Demographic delusions: World population growth is exceeding most projections and jeopardising scenarios for sustainable futures. World, 4, 545–568. 10.3390/world4030034.