Mapping, Society, and Technology Chapter 6 Analysis

. Analysis Laura and Eric Deluca Analysis is a way of interpreting what is going on in the maps that you encounter and create . Analytical tools provide ways of engaging with data , understanding spatial patterns , and giving us a vocabulary for discussing what we see when we look at a map . There are many ways to spatially analyze the data displayed in maps too many to mention here . In this chapter , we will focus on a few particular techniques for analyzing maps , and we will touch on some of the social , economic , and political implications of map analysis . This chapter will introduce you to four kinds of analysis point pattern proximity correlation These categories differ in several ways . They can differ in whether they are looking at location alone , or location and attributes at the same time . They sometimes differ in whether they look at points and areas , or just points or areas . These analytical approaches also differ in whether they are looking at just one theme ( say , just population ) or more than one theme at a time ( such as two maps of counties , one of population density per county and another of median income ) Depending on the focus of inquiry and the number of themes being analyzed , some maps can be analyzed using more than one of these methods , and other maps are best analyzed using just one . In this chapter , we will teach you the differences between these four types of spatial analysis , and ask you to use these analytical methods to understand maps . Keep in mind that although we draw distinctions between these types of analysis throughout the chapter , there are many overlaps and situations in which different analytical methods ( particularly proximity and correlation ) can be used in tandem . At the end of this chapter , you will have the basic skills to analyze and interpret maps and spatial data .

112 Mapping , Society , and Technology of Anal sis Geometric Feature Number of Themes Location Point Theme Location and Attribute Area Theme Location Themes Location and Attribute Themes Analysis . Four common methods for analyzing differ in whether they are looking at location alone , or location and attributes at the same time . Point Pattern Analysis Point pattern analysis looks at the spatial arrangement of the locations of objects or events within a single theme and does not consider how their attributes vary . In particular , this kind of analysis looks at the relationship between the locations of objects or events in space relative to the locations of other objects or events . The map below looks at the distribution of burglaries near the Thames River in London . Here we are looking at the locations of specific occur when someone enters a building illegally with the intent to steal something . Note that technically there is a qualitative attribute being considered did a burglary occur at a given location or not ?

but we are really just interested in the location of these events . We are not interested in the attributes of any given burglary itself , such as what was stolen , what was it worth , whether the thief caught , or any number of other kinds of attributes we could measure or questions we could ask .

. Analysis 113 London Burglary Locations ( 2015 ) no ' I . a ' I a . London burglaries . This map portrays Lhe distribution of burglaries near the Thames River in London . We use point pattern analysis to describe the pattern of this one particular theme of of the mapped area . Point pattern analysis can help us to see where spatial patterns of burglaries are occurring , such as if burglars are targeting a particular block in recent days . As you would guess from the name , point pattern analysis is interested in finding patterns in locations , in this case where there are any patterns in burglaries . We need a language to describe these patterns , which is what we explore next . There are three main types of point patterns or spatial of locations of entities or events in a map random , uniform , and clustered .

114 Mapping , Society , and Technology Point Patterns Random Uniform Clustered Point patterns . Three general point patterns are random , uniform , and clustered . Random . A random pattern is where locations are distributed seemingly randomly , or in other words , where the position of any one point is unrelated to the locations of other points . Marketing firms that conduct phone surveys often want a random distribution of people , for example , so they use methods to ensure that they choose random locations where people live in a city , state , or nation . Uniform . A uniform pattern is one in which locations are evenly distributed through space . Maps of fire stations in a county often display a uniform pattern because fire stations are deliberately spaced out across a city or county in order to ensure that firefighters can quickly and efficiently access fires across the area . Another example is the location of wolf packs , in that packs spread out as much as possible as each pack tries to keep a lot of space between itself and the other packs in order to reduce over game . Clustered . A clustered pattern describes when a number of locations are very close to one another , or in clusters closer than you would expect if they were randomly patterned . Burglars who target a particular neighborhood will create a cluster of burglaries on a community crime map . Disease is often clustered in space because the location of one event , such as the , makes it more likely that other cases will be nearby , as the is spread through close contact among people .

. Analysis 115 Keep in mind that you will often find multiple point patterns on the same map . The figure below shows a map of locations of hardware stores in the Midwest of the United States . Looking at this map with point pattern analysis , you could describe store distribution as uniform in northern Iowa ( circled in red ) random in central Wisconsin and the border area ( yellow ) and Clustered around Milwaukee and the Twin Cities ( blue ) Clustering in stores . Clustering comparison for the locations of hardware stores in Lhe US Midwest . You could describe store as uniform in northern Iowa ( circled in red ) random in central Wisconsin and the border area ( yellow ) and clustered around Milwaukee and Lhe Twin of Minnesota ( blue ) Analysis While point pattern analysis is concerned with the relationships among locations on the map , pertain to both the spatial distribution of location and attributes over an area . Census data , for example , are for analysis . Though census data may be collected at the level of individual households , the demographic data are ultimately and mapped over an area , rather than tied to specific household locations . looks at the relationship of one attribute to itself or in other words , is a way of analyzing the degree to which things of the same kind are related .

116 Mapping , Society , and Technology Recall the map of London burglary locations discussed above . The figure below demonstrates the difference between a point map ( like that above ) which is best examined using point pattern analysis , and an area map , which is better suited to an analysis . The point map above shows specific locations where burglaries were reported in London while the one below reports these data as burglary rates for specific neighborhoods , which allows us to compare burglaries among neighboring areas . These two types of maps are useful for different purposes . If you want to understand the particular houses or blocks that burglars target in a neighborhood , the point map is better for gleaning information about the spatial clustering of burglaries . If you work for the city of London and you are trying to decide how to distribute resources to various police precincts , it is more helpful to you to know where the most crime is happening across different precinct areas . In that case , knowing the locations of specific households would not be as useful as having spatial data over an area . If you are considering buying a house in the neighborhood , either of these maps may help you to understand your general risk of burglary . I AM Balm ammo Low or no Burglaries by area . Reported burglaries in London over an area . There are three ways to describe patterns negative , positive , or no . These descriptive terms call to mind what has been termed First Law of Geography Everything is related to everything else , but near things are more related than distant The figure below offers a highly simplified example of how these descriptions might appear on a very stylized map .

. Analysis 117 Negative Positive None . Negative , positive , and neutral or no . Negative describes a pattern that defies Law the attribute is uniformly distributed across the area , intersects uniformly with dissimilar attributes , and is not concentrated . Positive corresponds with law the areas nearest to each other will display similar patterns or densities of the attribute , and the areas farther away display different densities of the attributes . No indicates that there is no discernible pattern in the distribution of the attribute . Another way of thinking about is to ask whether the values of an attribute in one place are likely to be similar to those in nearby places ( positive ) very different from neighboring locations ( negative ) and whether there is basically no connection between neighboring places in terms of the attribute ( no ) Much of the demographic data that we deal with in this course will display positive . For instance , a map that shows the burglary rates for all of London demonstrates that boroughs with high burglary rates are generally located nearer to other boroughs with high or above average burglary rates , Boroughs with low burglary rates are generally nearer to other boroughs of low or below average burglary rates .

118 Mapping , Society , and Technology Lee Valley Park ' puma mi buy Potters Bar El Above Average Average Below Average I Low I er am Ki Burglaries by borough . London burglary rates by borough . Those with high burglary rates are generally located nearer to other boroughs high or above average burglary rates . Proximity Analysis Proximity analysis describes the spatial relationships and patterns between locations across two themes think of it as point pattern analysis with two different kinds of objects or events . Using proximity analysis , you can look at the relationship between houses and streets , crimes and surveillance cameras , patients and disease vectors , or stores and where people live , Beyond the spatial relationship between multiple points , proximity analysis can help us to make sense of the world over time and distance . Proximity analysis can be tremendously useful for public how diseases spread , how to predict vulnerability to disease , and how and where to most effectively target interventions . John Snow developed one of the classic examples of public health proximity analyses . As noted in Chapter , a cholera outbreak had ravaged London in 1854 and left many public health advocates and policy makers and unsure of how to contain the virus , Snow interviewed residents

. Analysis 119 and discovered that those who contracted cholera obtained their water from the Broad Street pump , as in the figure below . Soon after the handle was removed from that pump , the cholera epidemic subsided . Proximity and disease . Cholera outbreak and proximity to Broad Street water pump , London 1854 , drawn by John Snow ( pictured ) The small black dots represent cholera cases , the large green dots represent water pumps , and the red dot is the Broad Street Water Pump . The red circle is the concentration of the greatest number of cholera cases , proximate to the Broad Street . To persuade the medical community that cholera was a waterborne rather than an airborne disease , Snow created the map shown above to demonstrate the relationship between cholera cases and the Broad Street pump . Snow map was one of the earliest examples of proximity analysis conducted to understand spatial disease vectors , and it paved the way for significant of disease mapping . This information led to the reform of the entire public health system in Victorian England , which included the Nuisances Removal and Diseases Prevention Act of 1846 , and the expansion of the London sewer system in 1849 . Proximity analysis can also help us to think about different ways of measuring distance . Most of the

120 Mapping , Society , and Technology time we are interested in distance , which is the distance between two points . However , unless you are able to scale and hop across buildings , the distance between point A and point will be affected by the natural and built environment . Imagine that you are an ambulance driver , and you need to get a critically injured person to the nearest hospital . The closest hospital based on time ( speed of the roads , traffic ) or distance ( miles of road ) might not be the same as the hospital that is closest in distance . Manhattan distance , or taxicab geometry , is a measurement of distance that takes into account the grid like pattern of city streets ( as on the island of Manhattan ) and is better suited to understanding navigational proximity . Manhattan distance is not only useful for thinking about proximity in urban environments . It more generally denotes the distance you have to travel over a transportation network in order to reach someplace , or network distance . Imagine that you are planning a trip to the beautiful Southern Alps of New Zealand . Fox Glacier and . Cook are two of the most breathtaking sites , and only about 35 from one another , which is very proximate using a measure of distance . Still , to travel between them by car takes over hours because there are few places to pass through the mountains . Your travel plans must take into account network distance , or you will be in for a very long and potentially frustrating day of travel !

. Analysis 121 Fox I , Cook National Park National ( Distance Mount Asp ir a ' Pork , Network Distance , Kinds of distance . Google Maps driving directions from . Cook to the Fox Glacier , New Zealand . Travel between them by car takes over hours even though they are close in terms of distance . Correlation Analysis Correlation analysis involves analyzing the spatial relationship between multiple attributes or themes . In other words , correlation analysis attempts to measure the degree or extent to which two or more different attributes are spatially related . Although correlation is generally a good method for looking at multiple attributes over an area , it can also be used to talk about the relationship

122 Mapping , Society , and Technology between an attribute and a specific point . In this way , sometimes there are overlaps between proximity and correlation analysis . We will deal with these overlaps more comprehensively later . For now , let look at a typical example of correlation analysis the spatial relationship between income and education . Across the world , many governments , and media outlets herald the relationship between higher education and increased income . The figure below shows average incomes of those earning or more and the percentage of people with a bachelor degree or higher for census tracts in the city of Los Angeles . Looking at these two maps side by side , we can see a general correlation between certain areas with relatively high incomes and higher levels of educational attainment . The places where the correlations between the two attributes are strong are highlighted with a circle or oval .

. Analysis 123 Income and Education in Los Angeles ( 2010 ) More than 2010 we 51 514 Population 25 Years and over Bachelor degree or more 2010 Year Estimates IE is za ) so ea ac Correlation analysis . Income and educational attainment are correlated in the Los Angeles Area . 10 There are other areas on this map where the correlation is not so clear , as noted by the squares and rectangles . For instance , in downtown Los Angeles ( the square to the upper left of the circle in roughly the center of the map ) we see little correlation between education and income . It important to read your maps carefully when assessing correlation take a look at the long diagonal census tract in the upper right of each map . This is Los Angeles County Census Tract , and we can see from the maps that though there is a large population of educated residents , there is insufficient data as to how many of them are making or more per year . This may be because only 119 people live in this whole area of the San Gabriel mountains . Although a number of areas on the map seem to support the proposition that higher education correlates to higher incomes , the map also demonstrates that there are areas that do not adhere to this pattern , and that the situation is likely more complicated in reality .

124 Mapping , Society , and Technology Potential mistakes When performing a correlation analysis , you need to be careful to avoid two common pitfalls ) correlation does not necessarily mean causation , and ) data are sometimes not . Mistake One Correlation Causation As with the income and education example above , just because you see a correlation in the map does not mean that you have sufficient information to determine causation . Looking back at our maps of income and education in Los Angeles , we do not have adequate information to claim that higher education causes higher incomes , or that higher incomes cause higher education . All we can see from the map is that the two are correlated . If you wish to make an assertion about causation when doing a spatial correlation analysis , you must consult and cite other robust academic literature that supports your analysis . In short , you must develop a candidate theory or concept that explains the relation among your variables . The fallacy is perpetuated throughout the popular media . For instance , in 2014 , the New York Times Economy section posted an article with the headline A Simple Equation More Education More ( Porter 2014 ) Now this proposition may be true in certain areas , and at certain levels of aggregation , but we know even from a simple glance at our map that in the Los Angeles area , there are almost as many places where there is little correlation between income and education . We simply do not have enough information to understand why some tracts do not display a correlation between education and income when other nearby tracts do . We can not make a claim about causation for certain places or spatial scales without introducing additional data , and even then we must be very careful about causal assertions . Similarly , the proposition in the New York Times article , which argues that higher education does have a causal relationship to higher income , does not help us to explain these low correlation tracts either . Though there may be widespread correlations between educational attainment and income at the state and county level , a critical reading of our map of income and education demonstrates complexity and the fallacy of a simple correlation equals causation argument . For these reasons , academic literature is required to clearly state its research methodology and is more transparent about how data are collected and how conclusions are drawn than popular media sources . Generally , academic resources are more useful if you are interested in making causal arguments . Mistake Two oversights Make sure that the data you are correlating are actually comparable . You want to verify that the maps you are the data that are based on similar aggregation units , categories , and . You can only draw effective correlations if your maps and data are . The figure below shows an example of how correlation analysis can be used

. Analysis 125 to interpret attribute shifts over time , These figures , produced by the New York Times , look at correlations in demographic shifts between black and Hispanic populations in South Los Angeles between 1990 and 2010 , Shifting Demographics in South Los Angeles Since the Los Angeles riots 20 years ago , the share of Hispanics in South Los Angeles has increased while the share of blacks has dropped . The area is now ' Hispanic . Related Article to Percent Hispanic 1990 47 2010 67 60 45 30 Percent black 1990 51 2010 31 60 45 30 Correlation and . Spatially maps that show correlations in demographic shifts over time . 11 Using what you know about census data , take a look at the , aggregation units , spatial

126 Mapping , Society , and Technology extent , and attribute categories . Are these maps properly ?

In this case , the answer is yes . These maps cover the same time frames ( 1990 2010 ) in years when racial categories remained consistent ( Black and Hispanic have the same meanings in the 1990 and 2010 ) both maps aggregate data by census tract ( across years when the spatial boundaries of census tracts remained consistent ) and focus on the same spatial extent ( South Central Los Angeles ) These are maps that cover all of the bases and can be effectively analyzed using correlation . If you have additional questions about , refer back to the chapter on Data for a more discussion . Combining Analyses As mentioned above , there are occasionally overlaps between these different analytical methods , and the distinctions are not always so clear . Sometimes you can use these multiple methods to analyze one map . The figure below was published by the New York Times in 2012 as part of a series on fatal police shootings in , California . I Site of fatal police shootings Percent Hispanic 25 50 75 Median household income I 50 70 90 thousand Mixing kinds of analysis . Locations of fatal police shootings , percent Hispanic demographic data , and Median household income , California , United States . 12

. Analysis 127 Using this figure , we can perform a point pattern analysis by observing the distribution of fatal police shooting sites . We could conduct an analysis on the top map by looking at the relationship between the density of Hispanic residents across neighboring tracts ( relatively positive levels of ) or the bottom map by comparing median household incomes across tracts . We can perform a correlation analysis on the two maps side by side , by attempting to determine whether there is a relationship between the percentage of Hispanic residents and the median household income . Finally , we might use these figures to understand the proximity between fatal shooting locations , percentage of Hispanic residents , median household incomes . Because there is often overlap between correlation and proximity , you could use either or both analytical methods to understand the spatial relationships between fatal shootings and the attributes of percent Hispanic population and median household income . Let look at another example . In the figure below , proximity analysis is called for , since the focus of inquiry is the location of universities and Fortune 500 companies to one another in the Los Angeles area . In general , we see a relatively high degree of proximity between universities and Fortune 500 companies . Of course , there are some exceptions . For instance , University , nestled in the Santa Monica Mountains on the left side of your map , seems relatively isolated at this scale however , in Manhattan or network distance terms , it is less than 20 miles , and around a drive , from either Dole Foods or Health Net , the corporations located respectively to the northwest and northeast of campus .

128 Mapping , Society , and Technology Fortune 500 Companies and Universities in Los Angeles ( 2010 ) I ! Leg . i , i , i i ' I ! LUV i Proximity analysis . Proximity analysis between corporations and universities in the Los Angeles area . 13 Conclusion In this chapter , we examined a few methods for analyzing maps . We have narrowed our focus to four general categories of analysis point pattern , proximity , and correlation . These categories differ in key ways , particularly in terms of whether they look at location alone , or location and attributes at the same time , and whether they are looking at just one theme or more than one theme at a time . They also sometimes differ in whether they look at only points , only areas , or both points and areas . Regardless of approach , it is important to not lose sight of the bigger picture , to remember that you can sometimes use multiple forms of analysis with the same map , and to remain critical of causal claims based only upon correlation . Resources For more information about analysis

Notes 10 . 12 . 13 . Analysis 129 ( the world largest GIS company ) looks a few ways of using maps chief scientist looks at story maps and spatial analysis Eduardo Porter . 2014 . A Simple Equation More Education More The New York Times . Steven Manson , 2015 Map generated with map interface at The London Telegraph ( 2015 ) Steven Manson , 2015 . Laura , 2015 Public Domain . Metropolitan Police Service ( 2015 ) Steven Manson , 2005 Public domain . Metropolitan Police Service ( 2015 ) Public Domain . John Snow Published by . Lith , Buildings , London , England , 1854 in Snow , John . On the Mode of Communication of Cholera , Ed , John Churchill , New Street , London , England , Laura , 2015 . Google maps . Sara Nelson 2015 . Data from and US Census Fair use . New York Times ( April 24 , 2012 ) In Years Since the Riots , a Changed Complexion in South Central Fair use . New York Times ( August , 2012 ) A Divided City . Laura , 2015 . Google maps