Mapping, Society, and Technology Chapter 7 Lying With Maps

. Lying With Maps Eric Deluca and Sara Nelson You Ve learned many different ways that you can represent data and modify your maps . Geographer Mark argues that all of these little of geographic features , choices about classification scheme , aggregation of data , or clever use of little He writes , To portray meaningful relationships for a complex , world on a flat sheet of paper or a Video screen , a map must distort reality . no escape from the cartographic paradox to present a useful and truthful picture , an accurate map must tell white ( 1996 ) White lies include all sorts of cartographic strategies , including symbolization , generalization , and unintentionally misleading mistakes . Then there are the other kinds of maps , advertising maps , maps for military defense and disinformation , and maps that push a particular political perspective . As a critical map reader and map maker , it is imperative that you be able to identify and understand all of the ways that maps lie . In this chapter , we are going to focus on how and why maps lie , whether innocently or not so innocently . By the end of this chapter , you should be able to critically read the maps that you encounter in your daily simple informational maps or maps with a more complex social or political understand the strategies that the have chosen to promote a particular message or highlight specific data features . In order to be a critical map reader , pay attention to the guiding questions in the box on the right . The Various mapping strategies that you Ve learned about in this course all work together to produce a specific result in the final map . The mapping choices that are made can have a big impact on the final product . As we will see a little later in this chapter , these the lies that they also have consequences for people and societies . This chapter will introduce you to guiding questions in thinking about lying maps Who made this map and why ?

What is included and what is excluded from the map ?

What is the source of the data on this map ?

Which modification strategies are at work in this map ?

What is the effect ?

132 Mapping , Society , and Technology Little lies Projection All maps inherently include white lies and subtle these white lies are fundamental to the very act of mapping ! Think back to our discussion of projection . Recall that only a object is able to preserve both shape and area , so anytime you translate a object onto a surface , you must choose how you are going to distort the object . This misrepresentation is a form of lying with projection . One example that has garnered much attention is the difference between the and projections . The projection was created by Flemish cartographer in 1569 and is used in many settings , from classrooms to Google Maps and other online services . This map was primarily made for navigation and it preserves angles and shapes well . The major drawback to this projection is that it does not preserve area , so that countries near the poles appear much larger than they should relative to countries near the equator . has only the land area of Africa , for example , but it appears to be just as large on the map . In contrast , the projection preserves area at the cost of shape ( is significantly distorted but the size of its area is correct in comparison to Africa ) Peters in the 19705 argued that the projection could introduce bias into the perception of the world , given that countries in the northern latitudes were perceived as much larger than those closer to the equator . As a result , Peters argued , the projection shows a bias and alters the world perception of the importance of the global south . He offered an alternative , the Peters projection , that better preserved area . It turns out that this project existed long before , having actually been invented by James Gall in the 18005 , but the projection is usually termed the Peters projection to recognize Peters argument .

. Lying With Maps 133 Projection Projection Lying with projections . The and projections are both correct in the sense of being good projections but present the world differently . Symbolization Because it is impossible to show or even to acquire all of the information that could be mapped in a particular area , symbolization is a common way in which lie in order to present or highlight certain information . Compare the Google Maps road map of Boston in the figure below to its satellite image equivalent . The image and map represent the same area , but in the road map , Google uses symbolization to tell

134 Mapping , Society , and Technology white different roadways using size and hue ( federal highways in thick orange , state highways in yellow , county roads in thick white , city roads in thin white ) different shapes to signify different types of highways , and different label styles for different towns and neighborhoods in the area . These forms of lying with symbolization help to fulfill one of the central purposes of Google Maps clear presentation of information . You would be to use the raw imagery to do useful things like drive around Boston or find locations like Cambridge . On the other hand , the image could be more useful to make guesses about urban land cover in the Boston region or how deep the water is in the harbor . Same ! Lying with symbolization . Google Maps View of Boston and satellite View of Boston are of the same area but use symbolization differently . are another way of lying with symbolization . are maps that distort area or distance by substituting another thematic variable . Because of the dramatic distortions that produce , you might consider them to be telling more than white lies . However , are just different ways of symbolizing the same data in order to tell a story . The big lies that we will deal with in the next section are lies that are told for very specific purposes or for eliciting particular reactions . are considered white lies here because they are just another set of symbolization choices that affect the representation of data on your map . The figure below is an example of a map showing the number of people and amount of wealth per country in 2015 . Note how the size of any given country is not displayed in accordance with the land mass of that country , but in fact corresponds to the number of people living there or its total wealth . This map gives you , very quickly and effectively , a read on which countries have the most people and most money .

. Lying With Maps 135 , Population by Country ( 2015 ) Wealth by Country ( 2015 ) Lying with . Population total and wealth by country in 2015 . Note how the size of countries on the map is not their actual area but instead proportional to the attribute being measured , population or wealth . Standardization How and whether data is standardized also has a huge impact on the story that the map tells . The maps in the figure below represent poverty data from the 2000 US census . The top map data by the percentage of the population the number of people living in poverty relative to the total population of that census tract . The bottom map is based on raw numbers for how many people are

136 Mapping , Society , and Technology living in poverty in the census tract . By failing to account for poverty as a percentage of the total population , the bottom map tells a much bigger lie about poverty levels in the US . Lying with standardization . Poverty as a percentage versus poverty as a raw number . These maps represent poverty data from the 2000 US Census but differ in their standardization . Classification matters . Choices you make about how to classify your data can have a big impact on the story that your map tells . The maps here show how you can lie with classification . The three maps in the figure below each use the same data , the same number of classes , the same color scheme and the same standardization , yet each tells a very different story about poverty in the US , depending upon the classification scheme . The first map classifies the data using equal interval , the second using natural breaks , and the third using a scheme .

. Lying With Maps 137 Lying with classification . Poverty Via equal interval , natural breaks , and classifications . The three maps use the same data , the same number of classes , the same color scheme and standardization , yet each tells a very different story about poverty in the US . The figure below shows three maps of 2000 census poverty data . Each uses a classification scheme . The only difference is the number of classes . See how the number of classes impacts your perception of poverty levels in the US . Although there are some areas that demonstrate patterns of poverty across the three maps , as you increase the number of classes , you get a more nuanced picture of how poverty is distributed across the country .

138 Mapping , Society , and Technology Classes Lying with classes . Poverty Via classifications with differing number of classes . The three maps use the same data and color scheme yet tell a different story about poverty in the US . Aggregation , and the Ecological fallacy Recall that data are often . They are often collected at one scale , such as the household or neighborhood , and then reported at much broader scales , such as the census tract or county . This is the data . While aggregation can be Very helpful in terms of preserving privacy or presenting a broader and synoptic view , it can also lead to the ecological fallacy , or the assumption that a characteristic or value calculated for a group in aggregate can be applied to an

. Lying With Maps 139 individual member of that group . In other words , data make it hard to assume or guess at the characteristics of any given individual found in that area . Imagine you are comparing the income of two blocks , each being composed of five households . Block A has an average household income of while Block has an average household income of , per the figure below . In which area are you more likely to find a household with a higher income ?

If you chose Block area with the higher average income you were tricked by the ecological fallacy . In fact , there is no way to know from data alone what the situation is like for an individual within a group . The figure shows one of many possible scenarios under which four out of five households in Block have higher incomes than households in Block Block a Average Income Ecological fallacy . Consider this scenario in which Block A has a higher average income but mostly lower individual values than Block . Watch out for the ecological fallacy whenever you are interpreting maps , or you might come to false conclusions about the people who live in a neighborhood or other areas ! The danger of the ecological fallacy for the map reader is closely tied to the subtle ways that can lie with aggregation , often in combination with classification . The figure below illustrates voting returns for the 2012 US presidential election . The map on the left represents counties by their majority vote in the election . Red signifies counties where a majority of residents voted for the Republican candidate , Mitt blue signifies counties with a majority vote for the

140 Mapping , Society , and Technology Democratic candidate Barack Obama . The map on the right attempts to reveal a little more nuance in Voting patterns , by using a scale to indicate percentages of votes , while the one on the left is simpler and more prone to the ecological fallacy . These maps are both entirely correct . They differ in how they classify their underlying data into either two categories or by not classifying the data and instead using hue and value to represent fine differences among counties . US Presidential Election Results by County ( 2012 ) Lying with aggregation and classification . Lying , or rather telling different truths , about the United States presidential election in 2012 . The map on the left shows counties by their majority Vote in the election , where red means counties where a majority of residents Voted for the Republican candidate and blue signifies counties voting Democrat . From your knowledge of the ecological fallacy , you know that even at the level of the county , this

. Lying With Maps 141 map does not accurately represent the party affiliation , beliefs , or voting patterns of each person in the county . The data are to the county level and are classified differently than the data in the map on the right . This sort of aggregation and classification is useful because it allows us to distinguish between voting patterns in different parts of the variations , for instance . The map on the right is still lying of all residents in the US voted , and those who did were not necessarily voting for the endorsed Republican or Democratic it is trying to provide a more accurate picture of the data . Both maps lie in order to give a picture of the election outcome in particular parts of the country . Consider the version of the election map , below . The cartogram distorts area based on election returns , so counties with larger populations appear bigger . The cartogram lies by distorting area , but in some ways , it gives us a clearer picture of election results . In the standard county aggregation map above it appears that the US voted strongly Republican but the shows that many counties have relatively small populations , while counties with larger populations mainly voted for the Democratic candidate . US Presidential Election Results by County ( 2012 ) cartogram . This of election results distorts apparent county area by the number of returns , so counties with larger populations appear bigger .

142 Mapping , Society , and Technology Aggregation and Zonation We have looked at how data are to larger areas and how this process of aggregation can affect how data are interpreted , such as causing the potential for the ecological fallacy . In addition to data , we can also zone it differently , which is another way of saying we can draw an almost infinite array of differing boundaries for any given aggregation . We often use county boundaries in our analyses , for example , but the boundaries of counties are arbitrary . They were drawn over time according to a range of different principles , such as where a river flowed or where settlement was being encouraged . Population data is often and reported by county but they could be ( and often are ) reported via other , such as zip codes , area codes , school districts , or watershed boundaries . Importantly , the way in which data are and where the boundaries of zones are drawn can easily change data patterns and analysis outcomes . Depending on the scale at which you look at a geographic pattern , you can derive completely different results from the exact same underlying data . This is called the areal unit problem . For example , consider the rate of a disease in a population . Individuals at specific locations become sick , but health officials often want to know the broader trends of diseases . For this reason and to protect the privacy of patients they may count the number of cases by block , zip code , or another Zonation . But artificially breaking up space into these larger areas can change the apparent patterns of disease . The figure below illustrates this for deaths from respiratory problems in , Canada . Each map uses the same underlying data but groups or zones cases differently by a health department aggregation , by neighborhood , and by census tract . Each classes spatial units into , or in other words , divides the areas into four equal groups by how many people are dying in those areas . Note that these three maps all use the same exact underlying data and are just using different ways of zoning the groups . Nonetheless , these maps look very different , highlighting the challenge of the modifiable areal unit problem . Neighbourhood Census Tract Health and zonation . These maps all show data on where people die from respiratory problems in , Canada . Each map uses the same underlying data but but groups or zones cases differently . Each categorizes spatial units into . 10

. Lying With Maps 143 In addition to zoning , the modifiable areal unit problem can result from aggregation . Consider the figures below of solar potential in the lower 48 United States , Solar potential refers to the suitability of a particular place to develop solar power , These data are from the National Renewable Energy Laboratory and these maps were created by Anthony Robinson ( 2010 ) The first map shows the average annual solar potential by state . In other words , these data are by state . You can see right away that some states look better than others . Solar Potential Excellent Good Good Solar potential by state . Annual solar potential by state , which measures the suitability of a particular place to develop 11 solar power . The same underlying data to counties instead makes it look like a lot of states that were shown in one color at the state level actually include several categories of solar potential when you look at the data by county .

144 Mapping , Society , and Technology Solar Potential Solar potential by county . Annual solar potential by county , which measures the suitability of a particular place to develop solar power . The third map shows the original underlying data on which the other two maps were based . These original data calculate solar potential in 10 kilometer grid cells . You can see how the state and county boundaries compare to the raw data and how aggregation changes the apparent distribution of phenomena .

. Lying With Maps 145 Solar Potential Solar potential by grid cell . Annual solar potential given by the original data , which used a grid cell . 13 Big Lies There are other mapping lies that are not so innocent . In the remainder of this section , we are going to focus on some examples of maps that are lying more working hard to represent a Very particular story . Keep in mind , though , that there is a fine line between the intending clarity pushing an agenda . The first example addresses the ways that aggregation and zonation are manipulated to achieve a particular political end . Political Lies There are many ways that maps are modified in order to promote particular political perspectives or outcomes . Political lies in mapping take the of propaganda , campaign advertisements , and resource maps . One particularly important political lie is ,

146 Mapping , Society , and Technology the manipulation of the boundaries of an electoral constituency in order to favor a particular political party or group . Because US Congressional districts are in the years following the decennial census , political leaders in power at that time may take the opportunity to redraw convoluted favor their party continued political success . has been an important and highly contested part of the US political process for quite some time . The term was coined in the Boston Gazette in reference to the contrived redistricting schemes designed to secure the political power of Massachusetts Governor Gerry Party . In 1812 , the Gazette published the illustration seen below comparing Gerry convoluted redistricting to the image of a creature . Thus , was born . This political technique has a storied history , and is a powerful way of lying with maps works to consolidate or distribute political power , with such tactics as isolating opponents ( known as packing ) and breaking up areas of opposition ( cracking )

. Lying With Maps 147 . The term was coined by in the Boston Gazette in 1812 . The newspaper published an editorial cartoon that showed the elongated shape of the areas in the form of a salamander . 14 In the figure below , there are fifty precincts ( the smallest area in which votes are tallied ) that can be distributed among five districts , At the precinct level , there are proportionally more green voters than yellow ( 60 versus 40 ) and so all other things being equal , you would expect districts to reproduce a proportionate outcome . Out of the five total districts , you would expect three to vote majority green and two majority yellow , which corresponds to the 60 versus 40 split of the underlying precincts , Grouping precincts into districts yield different outcomes if principles of are used , namely packing and cracking . Under A , the number of districts

148 Mapping , Society , and Technology won by each party is proportional to the number of voters for each party 60 for green and 40 for yellow . cracks the yellow precincts in a way that allows green to dominate all districts , yielding 100 for green and for yellow . packs and cracks green in a way that lets yellow eke out an upset win with three districts , which is the reverse of the underlying voting patterns of the precincts . 50 Precincts Districts A 40 of districts districts districts voters 40 60 Green has Green wins Green Yellow wins small majority as expected dominates unexpectedly Packing and cracking . Overall , at precinct level , are proportionally more green voters than yellow ( 60 40 ) Three different ways of grouping precincts into districts yield different outcomes . Under A , the number of districts won by each party is proportional to the number of voters for each party . cracks the yellow precincts in a way that allows green to dominate all districts . packs and cracks green in a way that lets yellow gain an upset win with three districts . 15

. Lying With Maps 149 is alive and well today . It is widely practiced in the US , as you can see by some of the more outrageous examples of congressional in the figure below . This type of redistricting and reapportionment can have very real consequences for people who live in these areas , limiting their representation , protecting incumbent seats , and compromising access to federal funding . Some have argued that is akin to legal election rigging . North Carolina District 12 ii , Illinois District Ohio District in the United States . These maps show examples of districts in the United States these are just a few of many . North Carolina congressional district in 2016 was example of packing , with predominantly residents ( top ) Illinois congressional district packs two Hispanic areas while meeting the requirement for contiguity running along Interstate 294 ( middle ) Ohio is an example of cracking where the urban population of Columbus , Ohio is split off into more conservative suburbs . 16 For incarcerated populations and those who live in districts where prisons are located ,

150 Mapping , Society , and Technology may have an even bigger impact . In order to understand how works , you have to think back to what you learned about how census data is collected , analyzed , and . For certain data categories , the Census Bureau counts incarcerated people as residents of the town where their prison is located , rather than the town where they resided prior to their imprisonment . Because census data are currently used for redistricting at all levels of federal , state and local governance , the particular cities and census tracts where people are counted is very important for ensuring representation and . In the US , the prison population has risen significantly in the past few decades . Advocates for ending argue that counting prisoners based on their incarceration when those prisoners are barred from voting in 48 exponential political power to the small population in the area , and their representatives . When this happens , advocates argue , the practice siphons political representation and funding from other districts in the those districts that bear the greatest costs of crime . The prison dynamic is evident in existing census maps . Let return to the Los Angeles area income and education maps that you encountered in chapter . The figure below contains details about Los Angeles County Census Tract . The map shows us that income and education are not correlated in this area . Looking at the data , we see that only of the population in this tract has a Bachelor degree , but has annual income of . Only when we dig a little deeper does this particular tract begin to make a little more sense . Census Tract is home to the Twin Towers Correctional Facility and the Men Central Jail . The and census currently tabulate all of the prisoners who live in the Twin Towers and Men Central jail as residents , and takes into account their education level . However , the census does not include incarcerated populations in household income or poverty calculations , so the map on the right only takes into account the incomes of the surrounding community . You can see how this kind of data collection method might make it very difficult to adequately account for population , ensure reasonable representation , and manage appropriations in communities , as well as the communities from which incarcerated populations come .

. Lying With Maps 151 Census tract , Los Angeles County , California Households 232 Households More than 133 ( Census Tract , Los Angeles County , California Population 25 Years and Over . Population 25 Vears and Over . Bachelors Degree or More 398 ( Prison . This detail of Census Tract shows income and education ( Left Income More than Right Education Bachelor Degree or More ) These maps show us income and education are not correlated in this area . 17 Geopolitical Lies Contested Territories There are other types of lies that have more to do with navigating the complexities of international geopolitics , For instance , in many parts of the world there are territories that are contested between two or more states , Depending on the political considerations of the and the audience , regional maps may represent these territories Very differently . ammu Kashmir is a territory in the

152 Mapping , Society , and Technology northwestern region of South Asia along the borders of India , Pakistan and China . Since the 1947 partition of British India created the contemporary states of India and Pakistan , the two countries have been engaged in a territorial dispute over the region . and Kashmir are geopolitically significant , and are the source of the River and its tributaries , which sustain India and Pakistan water supplies . Kashmir . Kashmir is a territory in the northwestern region of South Asia along the borders of India , Pakistan and China . 18 India and Pakistan both claim and Kashmir . The figure below shows how Google Maps images of the region appear differently whether the search is conducted in the US ( which considers

. Lying With Maps 153 the two states territorial claims on ammu and Kashmir to be unsettled ) or India ( which its claim to ammu and Kashmir in the map ) United States . Biosphere Reserve . Park ' Devi Bios here ' I I Jim Co ( Park Contested Kashmir . Google Maps will show the contested Kashmir region differently depending on whether the user is in the United States or Different media organizations have also favored different representations of ammu and Kashmir , as seen below .

154 Mapping , Society , and Technology Fox PAKISTAN ADMINISTERED KASHMIR Kashmir in news . Competing media representations of Kashmir . Different media organizations favor different representations of and Kashmir . 20 Commercial Lies Advertising In 2009 , AT filed a lawsuit in federal court against . The complaint ?

AT alleged that a ad campaign featuring coverage maps of each carriers cellular data service misrepresented the actual reach of AT wireless service and misled customers . AT sued over a lying AT was concerned that the map would make customers think that its entire wireless network was reflected in the spotty map below , while contended that the map accurately reflected the differences between the two carriers networks . AT dropped the suit about a month after it was filed , but it raises some important questions about lying maps in advertising .

. Lying With Maps 155 Wireless AT More Coverage ( base mare ( not suitable . Lying competition . AT alleged a ad campaign There a Map for That featured coverage maps of each 21 carrier cellular data service that misrepresented the actual reach of AT wireless service and misled customers . For mobile service providers , maps that demonstrate extensive coverage are becoming increasingly important as companies like and AT compete for larger shares of the marketplace . However , many consumers have expressed frustration over what they perceive as inaccurate coverage maps in the advertising materials of mobile phone service providers . In recent years , a number of apps and websites have popped up to mobile phone coverage data across the US and paint a more accurate picture of mobile network coverage . The figure below shows a map from one such site ,

156 Mapping , Society , and Technology Mobile Network Mapping A , on . A . IF I , nan So , Weak Signal Open mapping . This mobile network coverage map from is arguably less biased than those offered by companies because firms have a vested interest in making their product seem better than competing offerings . 22 Conclusion In making maps , we tell many lies . Small lies include all sorts of standard cartographic strategies , including projection , symbolization , standardization , classification , aggregation , and zonation . Then there are the other kinds of , propaganda maps , maps that push a particular political perspective , and misleading advertising maps . As a critical map reader and map maker , it is imperative that you be able to identify and understand the ways that maps lie . Resources For more information about lying with maps Mark . 1996 . How to Lie with Maps ( University of Chicago Press ) Prisoners of the Census For more perspectives on Michael , A attempt that went hilariously awry , Los Angeles

Notes ! Lying With Maps 157 limes ( 31 , 2015 ) Christopher , congressional districts , Washington Post ( May 15 , 2014 ) John Sides Eric , Isn Evil Why independent redistricting won save us from political gridlock , Politico ( June 30 , 2015 ) Christopher , How to steal an election a visual guide , Washington Post ( March , 2015 ) Adapted from Daniel Own ?

Steven Manson , 2015 . Benjamin . Steven Manson 2005 Data from and US Census . Steven Manson 2005 . Data from and US Census . Steven Manson 2005 Data from and US Census . Sara Nelson 2015 BY . Newman . 2012 Maps of the 2012 US presidential election results . BY . Newman . 2012 Maps of the 2012 US presidential election results . BY . 2011 ) The modifiable areal unit problem ( in the relationship between exposure to NO and respiratory health . International journal of health geographics , 10 ( Adapted from Anthony Robinson . Maps and the Revolution , Adapted from Anthony Robinson Maps and the Revolution . Adapted from Anthony Robinson . Maps and the Revolution . Public domain . By ( Originally published in the Boston , from Steven File How , to , Steal , an , Election , Public domain . Adapted from . Accessed 2013 . Sara Nelson and Steven Manson 2015 Public domain . CIA and of Texas kashmir ,

158 Mapping , Society , and Technology 19 . Sara Nelson and Steven Manson 2015 20 . Fair use . Steven Manson 2015 Maps from , 21 . Fair use . Steven Manson 2015 22 . Steven Manson 2015