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Laura Matson 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
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.
Analysis. Four common methods for analyzing maps. They differ in whether they are looking at location alone or location and attributes at the same time. 
6.1 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 events—burglaries—which 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.
London burglaries. This map portrays the distribution of burglaries near the Thames River in London. 
We use point pattern analysis to describe the pattern of this one particular theme of interest—locations of burglaries—over 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 distributions of locations of entities or events – in a map: random, uniform, and 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 conflict 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’s crime map. Disease is often clustered in space because the location of one event, such as the flu, makes it more likely that other flu cases will be nearby, as the flu is spread through close contact among people.
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 Minnesota/Dakotas border area (yellow), and clustered around Milwaukee and the Twin Cities (blue).
Clustering in stores. Clustering comparison for the locations of Menards hardware stores in the US Midwest. You could describe store distribution as uniform in northern Iowa (circled in red), random in central Wisconsin and the Minnesota/Dakotas border area (yellow), and clustered around Milwaukee and the Twin Cities of Minnesota (blue). 
6.2 Autocorrelation Analysis
While point pattern analysis is concerned with the relationships among locations on the map, autocorrelation pertains to both the spatial distribution of location and attributes over an area. Census data, for example, are well-suited for autocorrelation analysis. Though census data may be collected at the level of individual households, the demographic data are ultimately aggregated and mapped over an area, rather than tied to specific household locations. Autocorrelation looks at the relationship of one attribute to itself; or in other words, autocorrelation is a way of analyzing the degree to which things of the same kind are related.
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 autocorrelation 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.
Burglaries by area. Reported burglaries in London aggregated over an area. 
There are three ways to describe autocorrelation patterns: negative autocorrelation, positive autocorrelation, or no autocorrelation. These descriptive terms call to mind what has been termed Tobler’s First Law of Geography: “Everything is related to everything else, but near things are more related than distant things.” The figure below offers a highly simplified example of how these autocorrelation descriptions might appear on a very stylized map.
Autocorrelation. Negative, positive, and neutral or no autocorrelation. 
- Negative autocorrelation describes a pattern that defies Tobler’s Law – the attribute is uniformly distributed across the area, intersects uniformly with dissimilar attributes, and is not concentrated.
- Positive autocorrelation corresponds with Tobler’s 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 autocorrelation indicates that there is no discernible pattern in the distribution of the attribute.
Another way of thinking about autocorrelation is to ask whether the values of an attribute in one place are likely to be similar to those in nearby places (positive autocorrelation), very different from neighboring locations (negative autocorrelation), and whether there is basically no connection between neighboring places in terms of the attribute (no autocorrelation).
Much of the demographic data that we deal with in this course will display positive autocorrelation. 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.
Burglaries by borough. London burglary rates aggregated by borough. Those with high burglary rates are generally located nearer to other boroughs with high or above-average burglary rates. 
6.3 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 health—determining how diseases spread, how to predict vulnerability to disease, and how and where to most effectively target interventions. Dr. John Snow developed one of the classic examples of public health proximity analyses. As noted in Chapter 1, a cholera outbreak had ravaged London in 1854 and left many public health advocates and policy makers alarmed and unsure of how to contain the virus. Dr. Snow interviewed residents 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 Dr. 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 pump. 
To persuade the medical community that cholera was a waterborne rather than an airborne disease, Dr. Snow created the map shown above to demonstrate the relationship between cholera cases and the Broad Street pump. Dr. Snow’s map was one of the earliest examples of proximity analysis conducted to understand spatial disease vectors, and it paved the way for significant expansions 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 time we are interested in Euclidean distance , which is the straight-line distance between two points. However, unless you are able to scale and hop across buildings, the distance between point A and point B 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 Euclidean 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 Mt. Cook are two of the most breathtaking sites, and only about 35 km from one another, which is very proximate using a Euclidean measure of distance. Still, to travel between them by car takes over 5 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!
Kinds of distance. Google Maps driving directions from Mt. Cook to the Fox Glacier, New Zealand. Travel between them by car takes over 5 hours even though they are close in terms of Euclidian distance. 
6.4 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 aggregated over an area, it can also be used to talk about the relationship between an aggregated 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’s look at a typical example of correlation analysis: the spatial relationship between income and education.
Across the world, many governments, NGOs, and media outlets herald the relationship between higher education and increased income. The figure below shows the average incomes of those earning $75,000 or more and the percentage of people with a bachelor’s 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.
Correlation analysis. Income and educational attainment are correlated in the Los Angeles Area. 
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’s 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 9301.01, 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 $75,000 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.
6.4.1 Potential mistakes
When performing a correlation analysis, you need to be careful to avoid two common pitfalls: 1) correlation does not necessarily mean causation, and 2) data are sometimes not interoperable.
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 relationship among your variables.
The correlation/causation 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 Income.” (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 cannot make a claim about causation for certain places or spatial scales without introducing additional peer-reviewed 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 the complexity of these relationships 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: Interoperability oversights
Make sure that the data you are correlating are actually comparable. You’ll want to verify that the maps you are comparing—and the data that are displayed—are based on similar aggregation units, categories, and temporalities. You can only draw effective correlations if your maps and data are interoperable. The figure below shows an example of how correlation analysis can be used 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.
Correlation and interoperability. Spatially interoperable maps that show correlations in demographic shifts over time. 
Using what you know about census data, take a look at the temporalities, aggregation units, spatial extent, and attribute categories. Are these maps properly interoperable? 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 censuses), 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 interoperability bases and can be effectively analyzed using correlation. If you have additional questions about interoperability, refer back to the chapter on Data for a more in-depth discussion.
6.5 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 Anaheim, California.
Mixing kinds of analysis. Locations of fatal police shootings, percent Hispanic demographic data, and Median household income, Anaheim, California, United States. 
Using this figure, we can perform a point pattern analysis by observing the distribution of fatal police shooting sites. We could conduct an autocorrelation analysis on the top map by looking at the relationship between the density of Hispanic residents across neighboring tracts (relatively positive levels of autocorrelation), 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, the percentage of Hispanic residents, and/or 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 aggregated attributes of percent Hispanic population and median household income.
Let’s 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, Pepperdine 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 30-minute drive, from either Dole Foods or Health Net, the corporations located respectively to the northwest and northeast of campus.
Proximity analysis. Proximity analysis between corporations and universities in the Los Angeles area. 
In this chapter, we examined a few methods for analyzing maps. We have narrowed our focus to four general categories of analysis: point pattern, autocorrelation, 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.
For more information about analysis:
- ESRI (the world’s largest GIS company) looks a few ways of using maps
- ESRI’s chief scientist looks at ‘ story maps ‘ and spatial analysis
- Eduardo Porter. 2014. “ A Simple Equation: More Education = More Income .” The New York Times.
- CC BY-NC-SA 4.0. Steven M. Manson, 2015 ↵
- Map generated with map interface at The London Telegraph (2015) http://www.telegraph.co.uk/finance/newsbysector/constructionandproperty/11328658/Crime-map-Is-your-home-in-one-of-Londons-burglary-hot-spots.html ↵
- CC BY-NC-SA 4.0. Laura Matson, 2015 ↵
- Public Domain. Metropolitan Police Service (2015) http://news.met.police.uk ↵
- CC BY-NC-SA 4.0. Steven M. Manson, 2005 ↵
- Public domain. Metropolitan Police Service (2015) http://news.met.police.uk ↵
- Public Domain. John Snow; Published by C.F. Cheffins, Lith, Southampton Buildings, London, England, 1854 in Snow, John. On the Mode of Communication of Cholera, 2nd Ed, John Churchill, New Burlington Street, London, England, 1855. https://commons.wikimedia.org/w/index.php?curid=2278605 ↵
- CC BY-NC-SA 4.0. Laura Matson, 2015. Google maps. ↵
- CC BY-NC-SA 4.0. Sara Nelson 2015. Data from SocialExplorer 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 2, 2012). A Divided City. ↵
- CC BY-NC-SA 4.0. Laura Matson, 2015. Google maps ↵
Mapping, Society, and Technology by Laura Matson and Eric Deluca is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License , except where otherwise noted.
How to Perform Spatial Analysis
Most data and measurements can be associated with locations and, therefore, can be placed on the map. Using spatial data, you know both what is present and where it is. The real world can be represented as discrete data, stored by its exact geographic location (called “feature data”), or continuous data represented by regular grids (called “ raster data ”). Of course, the nature of what you’re analyzing influences how it is best represented. The natural environment (elevation, temperature, precipitation) is often represented using raster grids, whereas the built environment (roads, buildings) and administrative data (countries, census areas) tends to be represented as vector data. Further information that describes what is at each location can be attached; this information is often referred to as “attributes.”
In GIS each dataset is managed as a layer and can be graphically combined using analytical operators (called overlay analysis). By combining layers using operators and displays, GIS enables you to work with these layers to explore critically important questions and find answers to those questions.
The idea of stacking layers containing different kinds of data and comparing them with each other on the basis of where things are located is the foundational concept of spatial analysis. The layers interlock in the sense that they are all georeferenced to true geographic space.
In addition to locational and attribute information, spatial data inherently contains geometric and topological properties. Geometric properties include position and measurements, such as length, direction, area, and volume. Topological properties represent spatial relationships such as connectivity, inclusion, and adjacency. Using these spatial properties, you can ask even more types of questions of your data to gain deeper insights.
Anatomy of an Overlay Analysis
GIS analysis can be used to answer questions like: Where’s the most suitable place for a housing development? A handful of seemingly unrelated factors—land cover, relative slope, distance to existing roads and streams, and soil composition—can each be modeled as layers, and then analyzed together using weighted overlay, a technique often credited to landscape architect Ian McHarg.
The true power of GIS lies in the ability to perform analysis. Spatial analysis is a process in which you model problems geographically, derive results by computer processing, and then explore and examine those results. This type of analysis has proven to be highly effective for evaluating the geographic suitability of certain locations for specific purposes, estimating and predicting outcomes, interpreting and understanding change, detecting important patterns hidden in your information, and much more.
The big idea here is that you can begin applying spatial analysis right away even if you are new to GIS. The ultimate goal is to learn how to solve problems spatially. Several fundamental spatial analysis workflows form the heart of spatial analysis: spatial data exploration, modeling with GIS tools, and spatial problem solving.
Spatial Data Exploration
Spatial data exploration involves interacting with a collection of data and maps related to answering a specific question, which enables you to then visualize and explore geographic information and analytical results that pertain to the question. This allows you to extract knowledge and insights from the data. Spatial data exploration involves working with interactive maps and related tables, charts, graphs, and multimedia. This integrates the geographic perspective with statistical information in the attributes. It’s an iterative process of interactive exploration and visualization of maps and data.
Smart mapping is one of the key ways that data exploration is carried out in ArcGIS. It’s interesting because it enables you to interact with the data in the context of the map symbology. Smart maps are built around data-driven workflows that generate intelligent data displays and effective default ways to view and interact with your information to see things such as your data’s distribution.
Smart mapping allows you to choose multiple attributes from your data, and visualize the patterns from each attribute within a single map using both color and size to differentiate (also referred to as bivariate mapping). This can be valuable for exploring your data, and allows you to tell a story using one map instead of many.
Combining Interactive Charts and Graphs with GIS Maps
Visualization with charts, graphs, and tables is a way to extend the exploration of your data, offering a fresh way to interpret analysis results and communicate findings. Typically you might begin by browsing through the raw data, looking at records in the table. Then maybe you’d plot (geocode) the points onto the map with different symbology and begin creating different types of charts (bar, line, scatter plot, and so on) to summarize the data in different ways (by district, by type, or by date).
Next, you can begin to examine the temporal trends in the data by plotting time on line charts. Information design is used to arrange different data visualizations to interpret analysis results. Combine a series of your strongest, clearest elements such as maps, charts, and text in a layout that you present and share.
Finding the signal in the noise. Visualizing data through charts helps uncover patterns, trends, relationships, and structure in data that may otherwise be difficult to see as raw numbers. Depicting violent crime statistics from Chicago, a combination of chart and map styles work together to unlock patterns and meaning from what started out as pure tabular data.
This post is excerpted from The ArcGIS Book, Second Edition: 10 Big Ideas about Applying The Science of Where , by Christian Harder and Clint Brown . The twin goals of this book are to open your eyes to what is now possible with Web GIS, and then spur you into action by putting the technology and deep data resources in your hands. The book is available through Amazon.com and other booksellers, and is also available at TheArcGISBook.com for free.
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- spatial analysis
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Design a web map to analyze residential racial segregation in the U.S.
Read this article
Geographic skill 4.
Analyzing geographic information involves seeking patterns, relationships, and connections. As students analyze and interpret information, meaningful patterns or processes emerge. They can then synthesize their observations into coherent explanations. Students should note associations and similarities between areas, recognize patterns, and draw inferences from maps, graphs, diagrams, tables, and other sources. Using basic statistics, students are able to look for trends, relationships, and sequences.
Geographic analysis involves various thinking processes. It is sometimes difficult to separate the processes involved in organizing geographic information from the procedures used in analyzing it; the two processes go on simultaneously in many cases. But in other instances, analysis follows the manipulation of raw data into an easily understood and usable form. Both activities involve the use and development of students’ spatial skills.
Students should scrutinize paper and digital maps to discover and compare spatial patterns and relationships. In addition, they can study tables and graphs to determine trends and relationships between and among items; probe data through statistical methods to identify trends, sequences, correlations, and relationships; and examine texts and documents to interpret, explain, and synthesize characteristics. All students need to develop these analytical skills.
Digital tools provide additional ways to analyze spatial data. For example, a GIS spatial display can be used to analyze georeferenced data. Multiple data layers may reveal relationships or trends as a part of the analysis. These analytic processes then may lead to answers to the questions that first prompted an inquiry and to the development of geographic models and generalizations.
Being able to analyze geographic information enables students to engage in doing geography by using analytical methods to interpret and evaluate geographic information. Employing accurate analysis techniques and methodologies is essential in geographic inquiry.
The student knows and understands:
1. The process of analyzing data to identify geographic relationships, patterns, and trends
Therefore, the student:
A. Analyzes simple graphs, tables, and maps using geographic data to identify relationships, patterns, and trends, as exemplified by
- Constructing a graph representing geographic information from a data table to identify trends (e.g., comparing social or economic indicators between two or more countries).
- Analyzing various maps to identify relationships or similarities between countries or regions based on the data represented (e.g., variations in climate related to latitude, population densities related to climate, railway networks in relation to elevation or topographies).
- Analyzing the relationships and patterns between political boundary lines and features on maps to describe possible trends (e.g., boundaries aligned to rivers, mountain ranges, or other physical features, boundaries aligned to lines of latitude or longitude or other mathematical formulations).
1. The process of analyzing data to describe geographic relationships, patterns, and trends
A. Analyzes graphs, tables, and maps using geographic data to describe relationships, patterns, and trends, as exemplified by
- Analyzing two or more maps or satellite images to describe changes or identifying trends that may be evident based on the data (e.g., satellite images of a city or region before and after a tsunami, earthquake, or flood, satellite images of forests where logging is taking place, maps of census data showing changes in population).
- Analyzing map legends to better understand the nature of the representation of data on the map (e.g., classification values and break points of a choropleth map, methods for determining different classification values, review the histogram of the data to see how data are represented in another form in addition to the mapped version).
- Analyzing a GIS to describe the relationships and patterns resulting from the overlay of multiple data sets (e.g., describe the relationship of tornado occurrences with population density and state boundaries).
1. The process of analyzing data to explain geographic relationships, patterns, and trends
A. Analyzes and explains geographic relationships, patterns, and trends using models and theories, as exemplified by
- Constructing a GIS model to analyze data from multiple locations and comparing the model results to identify patterns or relationships in those locations.
- Analyzing population data as represented in the demographic transition model to explain the changes through time in populations of countries.
- Analyzing a US city using a concentric zone model to explain the historical evolution of the commercial downtown.
B. Analyzes data using statistics and other quantitative techniques, as exemplified by
- Constructing a scatter plot of data to identify possible relationships or trends in the data.
- Analyzing a histogram for data to determine the best method for displaying the values on a map.
- Analyzing data using descriptive statistics such as average, median, mode, and range to determine the characteristics of the distribution in the data set.
Perform analysis (Map Viewer)
When you look at a map, you may start turning that map into information by finding patterns, assessing trends, or making decisions. This process is called spatial analysis.
Some patterns and relationships aren't obvious by looking at a map. There may be too much data to sift through to present coherently on a map. The way you display data on a map can change the patterns you see. Spatial analysis tools allow you to quantify patterns and relationships in the data and display the results as maps, tables, and charts. Using spatial analysis tools, you can answer questions and make decisions using more than a visual analysis.
- Feature analysis
Feature analysis is performed on vector data sources, which is coordinate-based data that represents geographic features using points, lines, and polygons.
Feature analysis can be used to summarize features based on geographic location, measure distances around or between features, and quantify spatial patterns.
If you're a developer, you can access these tools through the ArcGIS REST API Spatial Analysis service and the ArcGIS API for Python arcgis.features.analysis module .
- Raster analysis
Raster data consists of a matrix of cells (or pixels) organized into rows and columns (or a grid) where each cell contains a value representing information, such as temperature or elevation. Rasters can be digital aerial photographs, satellite imagery, digital pictures, or scanned maps.
Imagery and raster data contains information that can be used to identify patterns, find features, and understand change across landscapes. To extract information from imagery, you can process or analyze the data. For example, you can calculate a vegetation index to get an understanding of vegetation coverage from a multiband image or find suitable locations to build solar power plants using statewide elevation and land cover raster data.
Raster functions are not currently supported in Map Viewer . Raster functions can still be accessed in Map Viewer Classic .
If you're a developer, you can access these tools through the ArcGIS REST API Raster Analysis service and the ArcGIS API for Python arcgis.raster.analytics module .
- Run the analysis tools
To access and use analysis tools in Map Viewer , complete the following steps:
- Confirm that you are signed in and that you have the required privileges to perform analysis.
- In Map Viewer , open the map containing the layers you want to analyze or add the layers directly.
If you do not see the Analysis button in Map Viewer , contact your ArcGIS administrator. You may not have the privileges required to perform analysis.
Learn more about licensing requirements for spatial analysis
- Click a toolbox to expand the tools. Alternatively, use the search box to search for a tool or keyword.
The tool pane appears.
Hover over a parameter to view the help. You can also click Learn more to open the tool help topic.
- Click Environment settings to view and update the environments that are used by the tool.
- Click Estimate credits to calculate the number of credits that will be consumed when the tool is run.
- Click Run .
The tool runs and the output datasets are added to the web map. Information about the tool operation, including unsuccessful runs, is added to the analysis history for the web map.
- Supported data
One or more input datasets are required for analysis tools. Some tools only work with certain data types. For example, Aggregate Points requires an input layer containing point features, and the Aggregate Multidimensional Raster tool requires a multidimensional imagery layer. The data must be added to the map to be used in analysis tools.
The following data types are supported for feature inputs:
- Feature service
- Hosted feature layer
The following data types are supported for raster inputs:
- Image service
- Hosted imagery layer
- Deep learning package file ( .dlpk )
Feature services, map services, and image services must be publicly accessible; that is, the URL to the service must be a public URL, not one only accessible behind a firewall.
ArcGIS Server feature services that you add to ArcGIS Online must contain fewer than 100,000 features to be used in analysis. As the complexity of the features in the service increases, the number of features you can analyze decreases. For example, if the service contains polygon features that have thousands of vertices each, you may only be able to analyze a few hundred features. If the number or complexity of features exceeds what the tool can support, you receive an error message.
- Analysis outputs
Analysis tools produce one or more outputs. When a tool runs successfully, the output will be added to the Layers pane. When the tool produces a table output, it will be added to the Tables pane. The results can also be accessed from the analysis history on the Results tab of the analysis details .
- Tool comparison
The following table lists new tools, tools that have been renamed, and tools that are not yet supported in Map Viewer . Tools that are not listed are supported in both Map Viewer and Map Viewer Classic under the same name.
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Types of Maps
Maps are generally classified into one of three categories: (1) general purpose, (2) thematic, and (3) cartometric maps.
General Purpose Maps
General Purpose Maps are often also called basemaps or reference maps. They display natural and man-made features of general interest, and are intended for widespread public use (Dent, Torguson, and Hodler 2009).
Thematic Maps are sometimes also called special purpose, single topic, or statistical maps. They highlight features, data, or concepts, and these data may be qualitative, quantitative, or both. Thematic maps can be further divided into two main categories: qualitative and quantitative. Qualitative thematic maps show the spatial extent of categorical, or nominal, data (e.g., soil type, land cover, political districts). Quantitative thematic maps, conversely, demonstrate the spatial patterns of numerical data (e.g., income, age, population).
Cartometric Maps are a more specialized type of map and are designed for making accurate measurements. Cartometrics, or cartometric analysis, refers to mathematical operations such as counting, measuring, and estimating—thus, cartometric maps are maps which are optimized for these purposes (Muehrcke, Muehrcke, and Kimerling 2001). Examples include aeronautical and nautical navigational charts—used for routing over land or sea—and USGS topographic maps, which are often used for tasks requiring accurate distance calculations, such as surveying, hiking, and resource management.
In theory, these map categories are distinct, and it can be helpful to understand them as such. However, few maps fit cleanly into one of these categories—most maps in the real world are really hybrid general purpose/thematic maps.
Advancements in technology and in the availability of data have resulted in the proliferation of many diverse types of maps. Some, as shown in Figure 1.2.5, are embedded into exploratory tools intended to inform researchers and policy-makers.
Other maps are intended for a wider audience but share the goal of uncovering and visualizing interesting relationships in spatial data (Figure 1.2.6).
Maps also are not limited to depicting outdoor landscapes. Some maps, such as the one in Figure 1.2.7, are designed to help people navigate complicated indoor spaces, such as malls, airports, hotels, and hospitals.
For a map to be useful, it is not always necessary that they realistically portray the geography they represent. This map of the public transit system in Boston, MA (Figure 1.2.8) drastically simplifies the geography of the area to create a map that is more useful for travelers than it would be if it were entirely spatially accurate.
Maps that show general spatial relationships but not geography are often called diagrammatic maps, or spatializations . Spatializations are often significantly more abstract than public transit maps; the term refers to any visualization in which abstract information is converted into a visual-spatial framework (Slocum et al 2009).
Though there are many different types of maps, they share the goal of demonstrating complex spatial information in a clear and useful way. Rather than attempt to place maps into discrete categories, it is generally more productive to see them as individual entities designed to suit a particular audience, medium, and purpose. We will discuss this more in the next section.
- Chapter 1: Introduction to Thematic Mapping. Dent, Borden D., Jeffrey S. Torguson, and Thomas W. Hodler. 2009. Cartography: Thematic Map Design. 6th ed. New York: McGraw-Hill.
- Wood, D., and Fels, J. (1992) The Power of Maps. New York: Guilford.
1.6 What are Scales of Analysis?
6 min read • january 7, 2023
AP Human Geography 🚜
- Scales of Analysis in Human Geography
- What Are Scales of Analysis and Why are they Important?
- What are Map and Cartographic Scales?
- Four Types of Scales of Analysis
- Local (Ex: City with supermarkets highlighted)
- Environmental impacts of a proposed development or land use change in a particular community
- Access to health care services in a particular neighborhood
- Crime rates and public safety in a particular area
- Quality of schools and educational opportunities in a particular community
- National (Ex: A country color coded based on religion)
- Economic policies and their impact on different sectors of the economy
- National security and defense issues
- Healthcare policy and access to healthcare services
- Environmental regulations and their impact on the environment and industries
- Education policy and access to educational opportunities
- Regional (Ex: Map of schools across a state)
- Transportation and infrastructure needs in a particular region
- Environmental impacts of resource extraction or industrial development
- Access to healthcare and other services in a particular region
- Economic development and job creation in a particular area
- Global (Ex: World Map of COVID-19 Cases by variant)
- Loss of biodiversity and the impacts on ecosystems
- Global economic inequality and poverty
- Global governance and international relations
- Access to healthcare, education, and other services in developing countries
- What Do Scales of Analysis Reveal?
- Does a map of bitcoin servers in Italy tell us about bitcoin servers in Australia? No, a map of bitcoin servers in Italy would not provide any information about bitcoin servers in Australia. Bitcoin servers, also known as nodes, are distributed across the world and there is no central organization that maintains a comprehensive list of all of the nodes that exist. In order to get a sense of the distribution of bitcoin nodes in a particular region, you would need to gather data about the nodes that are present in that region.
- Does a map of poverty in your city reveal trends of poverty in your state? It is possible that a map of poverty in a city could reveal trends of poverty in the state in which the city is located. However, it is important to consider that poverty can vary significantly from one location to another, even within the same state. In order to fully understand the distribution and trends of poverty in a state, it would be necessary to analyze data from across the state, rather than just from one city. This could include data from a variety of sources, such as the Census Bureau, local government agencies, and non-profit organizations.
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Analyze a Map
Meet the map.
- What is the title?
- Is there a scale and compass?
- What is in the legend?
Observe its parts.
- What place or places are shown?
- What is labeled?
- If there are symbols or colors, what do they stand for?
- Who made it?
- When is it from?
Try to make sense of it.
- What was happening at the time in history this map was made?
- Why was it created? List evidence from the map or your knowledge about the mapmaker that led you to your conclusion.
- Write one sentence summarizing this map.
- How does it compare to a current map of the same place?
Use it as historical evidence.
- What did you find out from this map that you might not learn anywhere else?
- What other documents or historical evidence are you going to use to help you understand this event or topic?
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.
This can be valuable for exploring your data, and allows you to tell a story using one map instead of many. Combining Interactive Charts and Graphs with GIS Maps. Visualization with charts, graphs, and tables is a way to extend the exploration of your data, offering a fresh way to interpret analysis results and communicate findings.
A. Analyzes simple graphs, tables, and maps using geographic data to identify relationships, patterns, and trends, as exemplified by Constructing a graph representing geographic information from a data table to identify trends (e.g., comparing social or economic indicators between two or more countries).
Run the analysis tools. To access and use analysis tools in Map Viewer, complete the following steps: Confirm that you are signed in and that you have the required privileges to perform analysis. In Map Viewer, open the map containing the layers you want to analyze or add the layers directly. On the Settings (light) toolbar, click Analysis .
1.4K views 3 years ago World History Commons: Analyzing Maps World History Commons is a free, digital resource with high quality, peer-reviewed content in world and global history for teachers,...
Cartometric Maps. Cartometric Maps are a more specialized type of map and are designed for making accurate measurements. Cartometrics, or cartometric analysis, refers to mathematical operations such as counting, measuring, and estimating—thus, cartometric maps are maps which are optimized for these purposes (Muehrcke, Muehrcke, and Kimerling 2001).
The local scale of analysis refers to the level of a particular community or neighborhood. It is a relatively small scale that focuses on issues and problems that affect a specific place or group of people. At the local scale, issues and problems may be related to the physical environment, such as access to clean water and air, or to social and ...
Analyze a Map Download the illustrated PDF version. (PDF) Español Meet the map. What is the title? Is there a scale and compass? What is in the legend? Type (check all that apply): Observe its parts. What place or places are shown? What is labeled? If there are symbols or colors, what do they stand for? Who made it? When is it from?
18 Line-of-Sight Buffers Add Intelligence to Maps 19 Identify and Use Visual Exposure to Create Viewshed Maps 20 Visual Exposure Is in the Eye of the Beholder 21 Use Exposure Maps and Fat Buttons to Assess Visual Impact Links to Further Reading Hands-on Experience Map Analysis May 2007 nline description with hyperlinks to related