Geography 291: Geospatial Analysis with Desktop GIS

Chapter 1:

This chapter really sets the table for the idealization and tenants of how you need to have when in the mindset of making a map, both physically and mentally. These two ideas are different from each other but also very important, because as it is made known by this section, simply having data to map is not all that is needed to start on the creation of said map.  Firstly, there are different types of physical data that can be used (vector and raster), which both can be used to represent most if not all types of feature types. Being comprised of X and Y coordinates, vector data is most usefully utilized in discrete features and area bound data, while raster data is most commonly utilized with continuous numeric data like elevation maps. Despite this chapter being very wordy with its introductions of the many types of features and approaches that can be done in mapping, I found it very interesting to look through, especially with the colored illustrations to compliment the text. It seems very elementary but showing how two different approaches at showing data can be done both in words as well as with pictures (especially when the one way of mapping something is very clunky or scattered) to compare them and see which is more advantageous for the end goal of the map was a great idea. I thought the final sections about attribute values was an apt summary of how you can play around with your data to make it accessible to the viewers. Many of these reminded me of the population project in GEOG 112, where we were given big census data and allowed to play around with it as much as we wanted, but eventually made to chop it down into digestible, readable data for the viewers. Utilizing ratios and counts/amounts is perfect for this, especially when there is so much raw data that you do not absolutely want to use every last piece of.

Chapter 2:

The beginning of this chapter jumps into deciding what to map, and from this answer, how to map such idea. While this is a rather personalized question to ask, it is quite important as it is really easy to map too many things and confuse the meaning of the map. I learned this from experience, especially when you are given a bunch of types of data from a specific area and want to provide your reader with as much of it as possible. It is here where you have to keep your specialized reason for mapping in mind, as the point and execution of your map will be most clear if you keep as many extracurriculars out of the map interface as possible; you would not want to confuse your reader from your main point. The part about categorical mapping was pretty neat, especially the part where it expressed that in general you should not exceed seven categories of data, since after seven it is apparently difficult to distinguish one from another. This is probably due in part to (1) there are only so many colors that can look totally different from each other with a background color and a separate color for boundaries and (2) seven may just be the arbitrary limit of a map having too much going on in terms of categories. Scale is also very important, which I learned from the GEOG 112 project, when working with categories of data. Having smaller scaled sections of data with many categories can be tricky, as sections that are similar in numeric value but in adjacent quadrants can look the same when they are not, especially if you are using a 2-color scale to distinguish all of the categories. Using the full range of colors in a small area of boundaries, I have also learned, can be less advantageous to the reader, as it may require more looks at the legend to distinguish the different categories as opposed to a 2-3 color scale.

Chapter 3:

The start of the chapter brought up an interesting point about how maps are made with different purposes. For example, if you are making a map to explore relationships between two things, you would limit the use of information that is not relevant and try to display the data in multiple different ways to show how deep the relationship is. On the other hand, if you are making a map to explore the results of a finding, utilizing all of the data in the result is most likely the best approach to show the full extent of what was tested and what was found. It talks later in the chapter about the different methods of splitting up or classifying categories via natural breaks, equal interval, and standard deviation. The first two I have heard about and used but I was unfamiliar with standard deviation being used in this way, as “each class is defined by its distance from the mean value of all the features”. Displaying data based on its distance from the mean seems very specialized, as I cannot recall any times where I have seen this done in graphic form. When in the making maps portion, they mentioned the use of graduated symbols when measuring volumes or numeric values in area. I always thought that graduated symbols were a strange choice when expressing the size of numeric values, since the size of a specific shape can be hard to quantify in my opinion. While having to constantly check a legend or key to determine what size relates to what, it can be hard to determine such size when there are too many categories of size, especially if the area in question has a smaller scale.

Chapter 4:

One of my favorite techniques used in density maps are the contour lines. I think that showing the rate of change across a surface of the highest densities is a really cool approach and one that is unique to many of the other methods that they mentioned like dot diagrams, color gradients, etc. A use for them that I am aware of is with isobars in determining the rate of change in pressure across a weather map. In this scenario, determining the rate of change is very important as high rates of change in pressure can allow storms and bad weather to permeate in these areas, making it very crucial to be able to map and identify these areas. Additionally with the methods of displaying density information, I think dot density maps are quite neat in its simplicity and straightforward idea in showing what places has higher density values, but I do think they can make a map a fair bit cluttered at times. The book mentions that you can try to avoid this by making the size of the dot small enough so that it obstructs as little of the boundary lines as possible, but  I still feel that even if the dots are small, they can still make the boundary lines confusing to follow, especially in spaces where the scale is small or if there are small areas of high dense areas near each other, like in a density map of a U.S. state’s counties. Counties with very high density will have a hard time being distinguished from one another from an eye that is not familiar with their placement on a map. Also, I think that density surfaces are really great tools to show density while preserving boundary line integrity, but the type of very precise locational data that would be needed to accomplish this makes the actual creation of such maps really specialized for very exact data. Map density area is a lot more generalized and can be done with more simple data, but it requires much less data processing, and it is gives decent ideas of where densities are located in a larger area of land, which might just be what a person is looking for as opposed to the exact regions of density increase within boundary lines.

Chapter 1:

The first chapter acted as an introduction to the book. The process of analysis was given in a detailed manner. I liked how the chapter said framing questions was the first way to start analyzing data. Understanding data, choosing a method, processing the data, and looking at the results were explained briefly as a part of the process. I liked the different types of maps under the Geographic Features Category to show maps summarized by area, discrete or continuous phenomena.  I thought the maps co-relating businesses with areas/zip codes were interesting because I have always associated GIS maps with disaster management or weather, so this felt new. I knew that Geographic features could be represented using vectors, but this was my first time coming across the “raster” method, which is the representation of a matrix of cells in continuous space. Most of the analysis in this method occurs by combining layers to create new layers with new cell values. The book also included important tips like using the perfect-sized cell instead of too large or too small of a size for a more precise map. I will keep this information in mind when I start making actual maps on the software. Geographic attributes were divided into multiple things like categories, ranks, counts, amounts, ratios, etc and even working with the data tables seemed very math-oriented and statistical than something more social sciencey than I previously assumed. There was a lot of calculation and selection used to summarize data. Overall, different concepts for different types of analysis’ were mentioned, and I found them useful because understanding them will help me get a better idea of what kind of analysis I would like to do. Also, I’ve been trying to find an intersection between technical and social sciences, and I’m trying to see what kind of doors GIS opens up for me there. 

 

Chapter 2: 

The second chapter focused more on the concept of mapping. It talked about how maps are prepared and bought into this world.  I’ve never had the chance to map stuff before, except for one data visualization project where I visualized the crime rates in each state in India. I like how the entire process is laid out and explained in a well-detailed manner. I feel like for someone with zero to little experience with mapping, this chapter can be helpful since it starts with understanding the location and what exactly we want to map and then goes on to prepare our data and how we can map single or multiple types or by category. Some sections specified how GIS can be used to make these maps more efficient. It was interesting to see the use of maps to quantify thefts, burglaries, and crime in specific locations. It also made me think about other places where we can use maps to resolve nationwide issues like this. Maps give you information that will help analyze further to find solutions.  A few things that my dorky self would enjoy doing while mapping is choosing symbols and colors(Also, the chapter makes use of pastel colors  for maps, so I find it very cute.) 

Mapping is usually looked at as something simple, however, the chapter mentions things like the usage of maps or maps that use eighteen zoning categories. The chapter also describes how ArcGIS provides base maps, which can be used as reference features for mapping. This will help me when I start working on the software. 

 

Chapter 3: 

The third chapter takes mapping into detail. Looking at the title — Mapping the Most and Least, makes me think that chapter will talk more about quantifiable skills required to make maps. I liked the business analogy used at the beginning of the chapter to explain why we need to map the most and least quantities. This chapter, just like the previous two chapters, had detailed instructions on specific map-making processes. It mentioned things like displaying areas using graduated colors while surfaces are displayed using contours or 3D view. The next page also included a splotchy green map that looked really cool. The chapter also included things that could potentially sidetrack us from the main task, like exploring data or presenting a map and how to explore data in a way to see emerging patterns and questions. Economic and statistical terms were used throughout, like counts, amounts, ratios, ranks, proportions, etc. All the terms were clearly defined, which was helpful for someone like me who has never been in an ECON class before. The chapter made use of multiple formulae to make sure that the data was accurate and precise.

The chapter noted that similar quantities should be grouped in one class together to make it easier for the student to make the map. Mitchell mentions various classification plans, namely standard deviation, quantile, and natural breaks, and their advantages and disadvantages. As I suspected before, the chapter’s primary focus was to explain how stats and math are used to create maps. 

 

Chapter 4: 

The fourth chapter focuses on mapping according to density, and similar to chapter three, it consists of various economic and statistical terms. It starts with explaining why map density is essential and can be used in multiple areas with a specific type of data. Density maps can be helpful when looking at patterns. It helps with areas with a higher concentration, so I’m assuming that people with no knowledge will also be able to decipher the maps. The author also mentions that its important to decide what to map and what kind of data will be used so that it is compatible with the style. The book mentioned two ways of mapping according to density — By defined area, where you calculate a density value for each area using dot maps, and by density surface, which uses the raster layer mentioned in the first chapter.  Each cell in the layer gets a density value based on the number of features within a radius of the cell. Different comparing methods and ways to choose them were mentioned in the book to make it easier for students when they start mapping. 

Like chapter three, this chapter also included specific class ranges and colors for ratios for shaded maps. The book instructs on creating different types of dot density maps on different scales of data. It goes further on how to calculate density values by converting density units to cell units, searching for radius, and using different calculation methods and contours. 

 

 

The first chapter in the Textbook was a serious overview of the breakdown of how GIS runs and can be run. They go through all of the different maps to create while in ARC and how they can show your information in that one map or image. They state that there are two ways to represent geographic features one being in vectors and the other rasters. I have worked with rasters before in classes using ARC desktop but I do not think that I have ever used Vectors. The book states that raster models are one easier to manipulate and two people have a wider range of use for rasters. Vectors are a set of coordinates or data that is plugged into the image to connect lines or show large number changes. Another difference between Raster and Vector models is that rasters are made up of pixels that are taken from a camera lens while vectors get all of their information from a table in the GIS software. So throughout this chapter it really shows and tells us what GIS can actually do and how it does it. I know that there are more aspects to ARC because I have done a map that was not mentioned in the first chapter. Although they didn’t mention it in the first chapter I know that Hillshades will show up later in the textbook but this first chapter was really more of how each aspect works and what it can show. Towards the end of the chapter they move into Data tables and how they can be used to show specific data. Some of the data that they show in the book are things like population change, income throughout an area, land use, people per household, ect. There are many different things arc can achieve using data tables because you are able to link these tables with specific areas in your map so the changing data does not move from where the data has come from. 

 

I would say the focus of the second chapter was to give purpose to the maps that you create and the reason that they were made. It gives just a few examples of the maps that can be created through GIS. They explain the different features that can be linked to the data you are trying to portray, one of them being coordinate pairing. By pairing coordinates the map becomes more precise so you are able to show your data in a more exact way. The book is really looking for the reason the map is being made and how it can help the people or show the people what is happening in that specific area. They also state that by creating separate maps you are able to show a wide range of information because some of the data may correlate with other sets of data. The book uses the example of residential vs commercial areas; they then were able to show assaults, burglaries, thefts and auto thefts in both areas by only using two maps. This is a great example because it gives just a glimpse of how much information can be put into one map or even 2. Another part of the chapter that was mentioned was scale and how it affects the data and the validation of the data. An example they used was how they categorize different features of land. If you are looking at 100 square miles the categories are going to be rough areas whereas if you are looking at a 25 square mile area it would be more precise instead of just showing the general areas. 

 

In chapter three the three main things that I took away from the chapter were the classes, map creation, and looking for patterns in maps. All three of these features are needed to create a map that shows specific data. In map creation you need to first find out what you are going to portray in the map, then the why factor which is for what reason you are creating the map. This would mean what information and audience that you would want to attract. As you are gathering information for the map you are then able to see patterns throughout the area you are studying. This would mean something like income, as they used in the book to show that there is a very clear drop off in income while you exit the heart of income but towards the outskirts the change is very slow. In the chapter it says you are able to see where there is rapid or gradual change in the information portrayed. The last and biggest part of this chapter would have to be the classes because I believe it has the most effect on maps that include data and information. Classes allow you to see patterns in the map so when you take the accurate data that has been gathered you are able to generalize values and show the gradual or rapid change in values across the map. Patterns and classes can be seen in many different ways because it all depends on how precise the classes are and what type of classification you use. There are classifications for all different sets of data in order to portray them correctly on the map. By having classifications you are able to create a much stronger and accurate map because you are using reliable data with thought out classifications. 

 

Chapter 4 was a great one to follow three because it gave me the why for mapping with classes. The chapter says the classes can be used to show density if there is a color shade to them with higher values on one end. The second way the chapter says you can map density is by using a dot map or just dots. So instead of using color to show the information on the map you would use a cluster of dots to show the values. Each dot would represent a certain number in your data and then dots are placed accordingly to match with the data. I think this would be a little rough on the eyes but it is an easy way to show simple data to people and get the point across without any questions. Later on in the chapter they go on to use graduated colors to show density in the maps that they create. So instead of using dots to show numbers they are linking colors with certain numbers in the data to show a fading effect on the map. They use the color read and I believe that this is much easier to read and understand rather than trying to count the amount of dots in a certain area. I believe Graduated Colors are the better option when it comes to showing information for density on a map because people would be able to read it easier and other features would be able to be shown on the map like landmarks or roads. By being able to show roads and landmarks people can see where the map is located and really be able to relate to the map and stay interested because they have seen the area before. 

Chapter 1:

Mitchell opens by explaining how much GIS has grown with developing technologies. Mitchell proceeds to delve into the main theme of Chapter 1 by defining GIS analysis and concisely describing how to conduct such analysis in a recommended series of steps. The steps are framing the question, understanding your data, choosing a method, processing the data, and examining the results. Mitchell then explains the types of geographical features and how they are represented. The three types of features are discrete, continuous, and summarized by area. Discrete features can be easily plotted on a map because they describe whether the feature is present or not. Discrete features may be represented by dots, lines, or other methods. A discrete feature might be a dot representing the location of a well or a curved line representing the path of a river. Continuous phenomena are features such as weather and temperature. Continuous phenomena can be constantly changing. The areas between sample points (ex. weather station)  require interpolation to produce a value. Meanwhile, summarized data represents counts or density of features within an area’s boundary (ex. zip code parcels colored depending on the average number of households). Mitchell goes on to describe vectors and rasters, which are ways of representing features. In regards to vectors, each feature is a row in a table and is defined by x and y values in space. Vectors can include dots, lines, and areas (shapes). Features in a raster are represented as a matrix of cells in continuous space. Mitchell then explains the different types of geographical features in more detail, even though some are self-explanatory.  The geographical features include categories, ranks, counts, amounts, and ratios. The chapter ends with a brief summary of working with data tables. One thing that I admire from Mitchell’s writing is how he includes several examples of potential features or applications. Various maps are included throughout the text, which brings clarity and emphasis to these ideas. The writing has a typical but well-structured pattern in which information is brought forward. Once a new term is introduced, it is defined and an example of its application is typically included. The flow of information is more easily digestible with this structure and the graphics serve as a great visual aid.

Chapter 2:

The second chapter covers the topic of mapping and why it is important. There’s a lot fewer GIS vocabulary terms in this chapter as it pertains mostly to concepts. Ultimately, creating maps helps you see where features are present or absent and to recognize patterns. Sometimes finding these patterns is the goal of the map. Mitchell reminds the reader that it is important to keep the audience in mind when making a map. What data is depicted, and how the data is presented affects the overall clarity of the map. Therefore, mismanaging the presentation of data can make a map unnecessarily complicated. Making sure your map expresses the information in the most efficient and clear way possible is something I’ve put a lot of thought into when working on previous projects, so I’m glad Mitchell touches on this simple but important concept in a lot of depth. I never knew this, but Mitchell states that it is usually best to have no more than seven categories on a map. Apparently the majority of people can distinguish up to seven colors or patterns on a map, but begin to have more difficulty discerning information when there are additional categories. Logically, this makes sense as having too many categories can easily clutter a map. Mitchell makes it clear that the amount of categories or symbols used should be chosen given the purpose of the map. He also expresses that when color coding regions on a map, it is a good practice not to use random colors but to instead assign similar categories to similar colors. In a lot of cases, this can improve clarity. For example, a land use map may use light green for lightly forested areas and a darker green for deeply forested areas. Alternatively, you can combine the use of different colors, shapes, and thicknesses when appropriate. The example Mitchel uses is for a road/transportation map. In this case, it is a good idea to make freeways thicker than highways and highways thicker than local streets. It is also a good idea to use familiar symbols when possible. For instance, most people associate two parallel lines connected with a series of horizontal lines as a railroad track. It would not be good to utilize the common railroad pattern for a highway or vice versa. At the end of the chapter, Mitchell asserts that it is important to use statistical analyses when quantifying the relationship between features or patterns.  

Chapter 3:

Chapter 3 pertains to mapping the most and the least and is the most comprehensive chapter thus far. Mapping the most and least of something is a good way to visualize relationships between places. This includes discrete features, continuous phenomena, and data summarized by area. Mitchell makes note that data is generalized to create patterns. To expand on this, I believe since maps are a reflection of the real world, data is almost always generalized to some degree. Moreover, there is a lot of reiteration in the beginning of the chapter. I appreciate this approach as it helps you remember and retain what was learned in chapter 1. Mitchell reviews counts and amounts, ratios, and ranks specifically, which are important terms to keep in mind as they can all be grouped into classes. I actually went back and discovered that the explanation on ranks is verbatim to that from chapter 1, and the same map is used as an example as well. Mitchell then goes on to explain classes and how to use them both manually or with a standard scheme. The four most common schemes (natural breaks, quantile, equal intervals, and standard deviation) are defined and mapped for an easy comparison. Each type of scheme has its advantages and disadvantages, but none are necessarily better than another because each scheme should be used depending upon the distribution of data itself. For example, if the data is heavily skewed, then an equal interval will provide a misleading result. Next, the ways in which quantities can be expressed are covered. As with the different scheme types, each quantitative type has its purpose and its own advantages and disadvantages. Graduated symbols vary in size, which is good since people naturally associate symbol size with magnitude. Graduated colors accomplish a similar thing, but with areas and continuous phenomena. Colors are not always associated with magnitude, but people tend to assume ‘dark’ means more and ‘light’ means less. I am most unfamiliar with charts, but the text describes the use of charts very well. Charts make it easier to read patterns or feature values, but they may obscure patterns by compromising visuality. Contours, or contour lines, make it easy to see the rate of change over an area, but individual feature values may be harder to determine. Contour lines are great for depicting air pressure gradients or changes in elevation. Additionally, 3D perspective views grant a high visual impact and can depict elevation very well. However, this style of map makes reading individual feature values more difficult. 

Chapter 4: 

Chapter 4 deals with mapping density. While density maps are not very good for examining the location of individual features, they are reliable for looking for patterns and for mapping areas of different sizes. While it is true that you can examine the location of features by plotting the location of all the features on the map, it may be difficult to truly differentiate the different concentrations within the map accurately. Density maps utilize a uniform areal unit, such as square miles or hectares, which allows a clearer more precise image of the distributions. Potential applications for density mapping include census data analysis, crime analysis, or plotting the distribution of businesses. There are two approaches to density maps. The first technique involves basing your density map on features summarized by a defined area(s). The second involves creating a density surface. A density surface is typically created as a raster layer with each cell in the layer being assigned a density value. This provides more detail, but at the cost of more effort. Under defined area(s), a density map can be produced using a dot map or by calculating density values for each area of interest. For a dot map, each dot represents a specific number per feature. For example, a single dot can represent 5 businesses or 100 people. The dots are distributed randomly in their area, but the density can be observed by how close or far dots are relative to each other. The rest of the reading instructs how to create a dot density map, creating a density surface, and explaining the calculations GIS does for these procedures. Mitchell explains how to use the appropriate methods and when, including potential real life examples along with corresponding maps. 

Chapter 1

In this first chapter, Mitchell introduces GIS analysis as a whole, explaining the process one needs to follow to use GIS programs like ArcGIS efficiently and the best way to present your data. You need to know what question you’re asking, what information is required to answer your question, understand the data you have as well as choose a method to map your data that represents your findings the clearest. Your results can either be mapped as discrete or mapped as continuous, or even mapped as a summary of areas. However, while all three approaches are using the same data, the end results of both methods will be different, making it important for you to choose the method that will convey what you’re presenting the clearest. Mitchell also walks us through the differences between vector models and raster models, saying that with vector models, “each feature is a row in a table, and feature shapes are defined by x,y locations in space”, and those areas are defined by borders and are represented by closed polygons. From there he explained that with the raster model, locations aren’t defined by specific coordinates but rather with matrices of cells in continuous space and that the sizes of the cells can be altered to fit the data that you have, which he goes further in-depth later on. From there he lists different types of attribute values such as categories, ranks, counts, amounts, and ratios. Categories are groups of similar things, ranks put features in order from high to low, counts and amounts show total numbers, and ratios show the relationship between two quantities. This was a lot of information to try to retain, especially for this being my first time diving deep into GIS analysis, but in the following chapters, each definition and feature are further explained, aiding me in my understanding of it all.

Chapter 2

Chapter 2 talked about mapping where certain things are, such as crimes, businesses, employees, etc. When asked the question of “why” it seems obvious for the reason to look at a map is to see where a certain feature is. While that still remains true, mapping where things are helps make patterns noticeable and from there you can decide where you need to take action. An example of this in the textbook is when you map where certain crimes (burglaries, theft, auto theft, assault) occur in a specific area. From there you can see where they all have occurred, and see patterns in where there are more crimes in one area in another, as well as where certain crimes are more likely to occur. This is made possible by starting with a basic map with all of the same symbols, from which you can move to divide the feature into different categories, making your data points more specific by either using different symbols or different colors. Mitchell then dives into how you use your map and states that it is paramount when creating a map, to make sure that the map is appropriate for the audience you’re addressing as well as the issue that you are addressing. If your audience isn’t familiar with the area you’re representing, it’s good to add reference locations such as major roads, lakes, or administrative boundaries to provide more context to your map. Not only are reference locations important, but so is the amount of categories you decide to use. If you use too many categories the patterns in the map can become too complex to see, however, if you include too few categories, essential information can be lost. The same thing goes for symbols, it is easier for people to discern between different colors than different symbols if there are enough points of data that are clustered together. The end goal for your map is to convey the patterns and information you desire in the clearest and most efficient way possible. 

Chapter 3

This chapter focused primarily on quantitative data and was by far the most when it comes to information overload for me. Early in this Mitchell states that mapping the most and the least allows you to compare places based on quantities, which can help bring out patterns and a better understanding of the relationships found in your data. From this basic understanding, the next step is to understand the three features and what they each entail, where discrete features are individual locations, linear features, or areas, continuous phenomena as defined areas, and data summarized by shaded areas. The theme of the audience is revisited in this chapter and the discussion of how the appearances of maps differ between the exploration and the presenting of the data you study. If you are simply exploring your data, then your map should be more detailed as well as mapped in various different ways. If you are presenting the map and data, your map should obviously be more specific with the relationship you’re attempting to prove to your audience. From there the chapter dives back into the different attribute values, where I learned more about the use of ratios. Ratios in this chapter were very important when it came to displaying the highest and lowest values of data, and especially important when it comes to shaded areas on a map. Ratios help generate the differences between large and small areas. This can be especially useful when it comes to finding proportions and densities, which are talked about in chapter four. Counts, amounts, and ratios are usually grouped into classes because each feature in your map can have different values, especially when the range of values you have are larger. When creating classes it’s important to know where each feature will lie in your classes, because if you change the classes of your map, the map can look very different from the one before. We then go into the different kinds of class breaks. Natural breaks are where classes are based on natural groupings of data values, quantile is where each class contains the same amount of features, an equal interval is where the difference between the high and low values is the same for every class, and standard deviation is where features are placed in classes based on how much their values vary from the mean. They all operate differently, meaning that they all have their advantages and disadvantages which can make it difficult to decide which class type will be most effective in appropriately displaying your data.

Chapter 4

Chapter four is completely on map density, which in the beginning states what it is and what it does: mapping density can show the highest concentration of a feature you’re examining, it can be more efficient than just mapping locations, and it’s good for census tracts and counties. However, there is a difference between the two methods of mapping density, by defined area and by density surface. When you go by a defined area, you can use a dot map or calculate a density value for each area, which allows you to see density graphically. When calculating the density value of each area, “you divide the total number of features, or total value of the features, by the area of the polygon” and from there each area is then shaded based on its density value. When mapping density value it’s best to use different shades of a color, typically the lightest shade indicating the lowest density and the darkest shade representing the highest density value. When mapping by density surface it is usually created in the GIS as a raster layer, which we learned in chapter one as matrices of cells in continuous space. The benefit of mapping by density surface is that it provides the most detailed information in comparison to mapping by defined area, however, requires more effort to do. It was nice that there were tables included in this chapter that stated when to use one or the other; you should map density by area if you have data already summarized by area, or lines/points that you can summarize, and you should map density by surface if you have individual locations, sample points, or lines. I feel that most of the time when mapping density it would make more sense to map the densities by shaded areas rather than graphing dots because, for me personally, it’s easier to distinguish the difference between color shades as opposed to clusters of dots, because the clusters can then further skew the true value of density being portrayed on your map. Cell size and search radius also play roles in how your map and presented patterns appear. If your cell sizes are smaller, you’ll have a smoother display, and if your cell size is larger you’ll get a coarser image. The typical range of cell sizes to use is between 10 and 100 cells per density unit. With a search radius, the larger the search radius, the more generalized the patterns in the density surface will be, while a smaller search radius shows more local variation, but you have to be careful because if your search radius is small enough, most cells will have very low-density values, creating, “broader patterns in the data may not show up.” In all of this reading, I have learned that many factors in GIS mapping are a range or a scale, and it’s up to you to find the right proportions that will bring the most fruition to your map.

Chapter 1:

Chapter one was a good introduction and foundation for concepts the book explains more in-depth later in Chapters 2, 3, and 4. Some of the important terms were: discrete (the feature’s actual locations can be pinpointed), continuous (the features blanket the entire area you are mapping and aren’t pinpointed to one location), and summarized by area, categories (groups of similar things), ranks (features in order from high to low – you only know where a feature falls in the order, don’t know how much higher or lower), counts/amounts (counts – the actual number of features on the map, amounts – any measurable quantity associated with a feature), and ratios (show you the relationship between two quantities). Discrete and continuous are considered types of features, while categories, ranks, counts/amounts, and ratios are considered types of attribute values. Furthermore, categories and ranks are not continuous values, they are a set number of values in the data layer, and counts, amounts, and ratios are continuous values, each feature could have a potentially unique value in the range (highest to lowest). Thus, each of these types of attribute values can be classified as a certain feature type. Another key part of chapter one was the difference between calculating and summarizing in your data tables. Calculating allows you to assign new values to features in your table and summarizing allows you to take the values for certain attributes to get statistics. An example of summarizing would be calculating the total average mean. This is important as it is the basis of how you get your data values to work with in the GIS.

These features seem to be the foundation of GIS and classifying data because if you know how to determine which of these terms your data falls under then you will know the best ways to represent it when it comes to mapping. Ideally, Mitchell explains that understanding each of these terms and correctly classifying your data will lead to maps that better represent and display the patterns in which you are trying to see.

Chapter 2:

Chapter two takes the terms from chapter one and dives a little deeper into the why, what, and how you should map. In terms of what you should map, Michell discusses 3 key things. Knowing where your locations are, being appropriate for the audience, and being appropriate for the issue. This means things like not providing too much or too little detail and paying close attention to adding reference features like roads your audience may know to give them context. The next portion of the chapter discusses how to get your data ready to map (assign it coordinates and category values) and then the mapping itself. It goes over two types of mapping (mapping a single type and mapping by category). When mapping a single type, Mitchell recommended using the same symbol to represent all features, but you can also show a subset of features with category values. When mapping by category a different symbol should be used to represent each category. Creating a separate map or subset for each category may make patterns easier to see as well. I think the most important thing I took away from this part of the chapter is that the way you chose to represent the features can alter the patterns. For example, you should use no more than 7 categories because patterns become harder to distinguish. Additionally, using too small or too big of an area relative to your features can obscure patterns as well. Mitchell concludes by talking about how symbol colors and size as well as reference features can change and affect the look of your map as well. Overall, I thought this discussion of aesthetics was important because looking at some of the “wrong” examples in the book, you could tell they didn’t display the data as well as the “right” examples.

Chapter 3:

Chapter three was a long chapter, but it was pretty straight forwards. I found that in many spots it often just elaborated on the concepts and terms we had learned in previous chapters. It went back over discrete, continuous, data summarized by area, counts/amounts, ratios, ranks, and classes. Then it goes on to talk about different schemes you can use to determine your distribution values. There are 4 types: Natural Breaks or Jenks (a natural grouping of data values – breaks where there is a large jump in values), Quantile (each class contains an equal number of features), Equal Interval (difference between high and low values is the same for every block), and Standard Deviation ( based on how much their value varies from the mean). Each of these distributions creates a very different map because certain data points fall differently into the categories depending on which distribution you use. Mitchell then continues to show how you can use a bar chart to visualize the distribution and determine which classification scheme is best. He then talks about outliers and how they can skew the data so you should make sure that they are not a mistake and then group them into their own category or in with the rest of the data. The chapter then moves into an in-depth discussion of ways to show quantities on a map. These are Graduated Symbols, Graduated colors, Charts, Contours, and 3D Perspective Views. The book thoroughly discusses all of these and their appropriate uses, advantages, and downfalls. I personally didn’t like the way any of the chart maps looked. I felt like they displayed too much information and the charts were so small it was hard to read. The chapter was finalized with a discussion of what patterns to look for in maps. These included highest, lowest, clusters, scattered, and even distribution.

Chapter 4:

Chapter 4 was a lot shorter than chapter 3, but it went a lot more in-depth. Particularly it focused on density mapping. Mitchell discusses two ways to map density. The first is by defined area. In this method, when using a dot map each dot represents a feature and is distributed randomly in the given area. These dots DO NOT represent the exact locations of a feature. You can also graph the density value for each area. In this case, using too large or too small areas can skew your graph making patterns hard to see. The other method of mapping uses density surfaces. This uses a raster layer as discussed in chapter one and each cell gets an individual value. This process is much more detailed but takes longer. The chapter then goes into depth on dot density maps. The most important takeaway I got from this section was that the more each dot represents the more spread out they will be, and dots should not be so big as to obscure patterns. After this discussion concludes, the chapter then moves into the specifics of creating a density surface. This discussion includes what cell size to use, how large the search radius should be, and 2 calculation methods. The simple method counts only features within the search radius of the cell, while the weighted method uses mathematical functions to give more importance to features toward the center of the cell. Ultimately, the weighted method results in a smoother surface that is easier to interpret. The chapter then moves into how you should display the data and brings back the distribution models discussed in chapter three (natural breaks, quantile, equal interval, and standard deviation). They are applied to density in a similar way as discussed in chapter 3. The chapter discusses contour lines and how adding them to your density surface can provide clear labels and show variance across a region. This helps make patterns and feature clearer to the audience. The chapter finalizes on an important note that you should map the features on which you based the density with the density surface or on a separate map. I believe this is important in that it provides context to the viewer.

Chapter 1:

The most common GIS analysis tasks are mapping where things are, mapping the most and the least, mapping density, finding what’s inside, finding what’s nearby, and mapping change. These are all analysis tasks that we can do in GIS, with a plethora of different datasets. GIS analysis is the process for looking at geographic patterns in your data and at relationships between features. There are four types of geographic features, discrete features, continuous phenomena, features summarized by area, features summarized by area. The two distinct methods or models used to represent geographic features in GIS are vector and raster. A vector model involves each feature as a row in a table, and each feature represents either discrete locations, events, lines, or areas. Differently, the raster model represents features as a matrix of cells in continuous space. I have some experience working with both vector and raster, and have never fully understood what the difference is and what is actually going on within the computer. Even after reading this section of the chapter I still feel somewhat confused about what is really going on. Each individual layer represents one attribute and almost all analysis happens by combining layers to create new layers with new cell values. Attribute values are important for understanding geographic attributes. Attribute values are categories, ranks, counts, amounts, and ratios. Very simply, categories are groups of similar things which allow you as the researcher to organize data. Any feature with the same value in a category is alike in some way, and different from a plethora of other features with other values. It is a sorting tool. Ranks are just what they sound like, they rank features in order from high to low and are often used when direct data isn’t available. Counts and amounts show total numbers. Count being the actual number of features on a map, and an amount being any measurable quantity associated with a feature. Ratios show relationships between quantities and are made by dividing one quantity from another. Ratios are important when disparities exist between features or areas, such as large and small geographic areas.

 

Chapter 2:

I think the idea of altering or catering a map to specific stakeholders is very interesting and plays into the discussion on making a map. I’m excited to see what the chapter has to say about this section. When thinking about deciding what to map, it is important to recognize what information you are ultimately wanting to display or understand through your analysis of the map and to think about how you will use your map. These questions guide what features are displayed or contrasted and ultimately the answers to these questions shape the choices of the mapmaker and ultimately the impact of the map. I’m confused about the section titled “making your map”, it uses the phrase “you tell GIS which features you want to display and what symbols to use to draw them”. I don’t understand how the symbols and drawing function and what this does or does not do to a map. Using a single layer (mapping a single type) draws all feature types with one single symbol, which can still reveal patterns, even though they solely show where features are. Conversely, using a subset of features allows you to map all features in a data layer based on a category value. This allows you to map all crimes, select burglaries and map only those, even selected commercial burglaries from there. This layering allows the discovery and illustration of more and more patterns. I’m starting to have some more clarity about symbols and drawing, I think reading about different ways to map is helping me better understand the role of layers. You can also map using categories. You would therefore be drawing features using different symbols for each category value. This can potentially provide insights about how places work at a deeper level than simply single groupings, like crop type (simple) and species (complex). It is important to choose the symbols you use to display your map categories because they help to reveal patterns within the data. This is a section I will definitely dog-ear and come back to when it is time to pick symbols. Ultimately, if you do all the stuff listed above, you should be able to potentially recognize some geographic patterns within the maps you create. This will come to fruition if the map presents information clearly. Things to look out for are features that appear to be clustered, uniformly spaced, or randomly distributed.

 

Chapter 3:

This chapter begins by discussing the ability map makers have to add more meaning and value to their maps by mapping “most and least”. This involves mapping features based on quantities which can add another level of information (more than just the locations of features as discussed in the previous two chapters). In order to achieve this, you must map the patterns of features with similar values. In order to do this it is essential to know the types of features you are mapping, and again, the purpose of your map so that you can present the patterns.The types of features that you can map are discrete features, continuous phenomena, or data summarized by area. The discrete features are individual locations, linear features, or areas. Continuous phenomena are areas or a surface of continuous values. Data summarized by area is usually shown through shaping each area based on its value or charts that show the amount of each individual category in each area. I am still somewhat confused about what each of these three features look like in practice. I had a hard time contextualizing the examples the book gave. Quatinies, such as counts or amounts, ratios or ranks, can also help you decide the best way to present data. After ascertaining the quantities you have, you generate classes in order to best show them on the map. The essential part of generating classes is that there exists a trade off between accurately presenting data values and generalizing the values to see patterns on the map. Swinging too far in either direction decreases the accuracy of the map and can shift the impact of the data. I appreciate the example maps which show how different the same data can look based on different class schemes. I remember learning about how the natural breaks, quantile, equal interval, and standard deviation classes are different and alter your map projection. I appreciated reading about these and learning more about how they work. I feel like the “making a map” and “choosing a map type” will be more easily understood if I have a map of my own that I am trying to make and can more easily compare the different options to. Currently it is somewhat difficult for me to get through all that information without a baseline map to compare the different options to.

 

Chapter 4:

Chapter four focuses on how mapping density can elevate a map and allow you to elevate your maps. A density map allows you to use a uniform aerial unit, like hectares or square miles to measure the number of features and clearly see distribution. This is good for things like the number of people in a census or in a county. I think we did this in the previous GIS class with our state county maps. The two ways you can create a density map are by shading defined areas based on a density value or by creating a density surface. Point or line data is typically mapped using a density surface, which looks kinda like a topographic map with varying bands of intensity. When mapping density by defined area, you can use a dot map to represent the density of individual locations. This doesn’t show the specific density centers, just the individual points. Mapping by density surface, as stated above, allows you to see the density centers. I feel a bit overwhelmed by the math that goes into creating a density surface. Does the GIS do this math or is it something done by hand? I remember doing some very basic math for generating classes for the county data we used in the other class, so I feel like it could go either way. In order to display the density surface you generate, you can use either graduated colors or contours. Each class you pick (from chapter three) will show the graduated colors differently, and therefore change the way the map looks. So it is important to understand what you want to show, and which class illustrates it the best. Using contours is a bit different and does a good job showing rate of change across the surface. If the contours are more closely spaced, this means the rate of change is higher. This one actually looks more like a topographical map with differently spaced lines. In the density surface the lines are blended and not defined.

Chapter 1:
Since the book was published the use and abilities of GIS have grown, from making maps to analysis that can solve problems. GIS analysis is finding patterns within your data, starting with a question. To decide what method to use to obtain your data you will need to understand the data you are working with. To understand GIS analysis you need to understand the geographic features you will be working with; discrete, continuous phenomena, or summarized. Discrete locations can be pinpointed with their exact location. Continuous blanket the entire area of your map, these can either be continuous or non-continuous data. If the data is non-continuous the software will use interpolation to fill in the gaps. Summarized data represent data within a given area, not specific points given as the density of individual features. These features can be shown using vectors which are defined as a location within a table that can be connected to create areas or a raster model which uses a matrix of cells that can be increased or decreased in size. A map projection system converts data from global or rounded data to a flat or 2D plain so it will distort some features. To do the analysis you need to separate your data into categories, ranks, counts and amounts, or ratios. When working with data for a GIS analysis you will need to create tables for your information these come as selecting, calculating, and summarizing.

Chapter 2:
When doing GIS analysis mapping is a large part of the work because it allows you to see patterns forming. Knowing your audience is important when deciding how complex a map should be or the data that is being mapped. Before creating your map you will need to assign values to your data for their location and code on the map. Creating the map requires you to tell GIS what feature you want to be present in the final product. Often used in this process is single-type mapping where the data is only shown by one symbol on the map. Once you have done the above steps GIS then takes the data and symbols with the geographical location and creates a map that matches your inputs. Instead of having all data present on one layer, you can create subsets so that there are categories within that correspond with different symbols. When creating different categories you can also change the size of different symbols based on key values within your table. If you are doing this though you will want to keep in mind your audience because most people cannot distinguish more than 7 different categories. One key thing is included features that are recognizable for people unfamiliar with the area such as street names. Even if you have found a pattern in your map you still need to find if it is statically backed.

Chapter 3:
When mapping man people want to find the most and the least amount found in the patterns. To decide how to map these you will need to know whether your data is continuous or non-continuous. To understand your data you will want to keep in mind your audience and whether you are presenting the data. If you want to find a deeper analysis you will have to explore your data further than the patterns on the map. Counts show how many of a thing are found on a map whereas amounts are the value associated with each item. Ratios are used to smooth out data and are useful when summarizing data within an area. Ranks are used when the mapped data is hard to quantify and show relative values. Once you understand the data you are working with you will need to assign values and symbols to the items which come with the trade-off of showing things accurately or generalizing. Individual items when mapping lead to complex data that is hard to read for certain audiences, whereas creating classes generalizes that data allowing for an easy analysis for some. When creating classes you can either do it yourself which is normally for more specific data or use previous breaks mentioned these include, natural breaks, quantile, equal interval, or standard deviation. To decide on which of these methods you need to use you will typically create a bar chart to see how the data is distributed across the x-plain. In doing these methods outliers may be found that can cause a problem with your analysis these can be a problem with your data set, wrong, or involved. The final step in creating classes is how many are you going to include and how will you show these on your map. If you have properly done the correct method in separating them this step should be easy and some GIS software will automatically make the classes continuous. Now that you have quantified your data you will need to map it, the normal reaction is to make the mapped complex but that should not happen. Graduated symbols are typically used for volumes or rank in linear systems, whereas graduated colors are mapped to show continuous data within the specified area. Charts are used typically for a quick study of the area and not complex data. Contour lines show the rate of change from one set of data to the next and are typically used for spatial data. The most complex form of this data visualization is the 3D form which is typically used to allow the audience to better visualize the surface of the data. The most important part of 3D data is the viewer location since larger sets of data will block the view of other data and allow for worse analysis. The next two z-factor which exaggerate the data for easier visual separation of data and the light source which is the location of the source help the audience better view the patterns.

Chapter 4:
Mapping density rather than individual locations better allows you to see concentrations. You can either map points and lines or summarized data with density maps. You can either map density features such as businesses or feature locations such as employees per location. Density defined by area is where you map each location then divide and then summarize the data within a given polygon size to create density or by a surface that uses a raster model to create density per cell. A dot density map is a method of density defined by are but rather than a shaded color within an area, it uses dots to represent your data which better allows for a generalized area to be more accurately shown specifically population density. In a raster model cell size is the most important decision and that does not change is density surface. Along with cell size, another thing to keep in mind is the search radius which defines how many features will be calculated within each cell. There are two types of calculations used in the GIS simple method which uses rings around the cell and the weighted method which uses a mathematical function. Areal units are what define the legend of the map rather than cell units. When graphing density surface it can either be shown in graduated colors or contours.

Cox-Week 2 Mitchell Notes PDF

Eliza Richardson 

27 January, 2023 

ArcGIS Week 2 Blog Post

 

Chapter 1 

Mitchell begins the first chapter with laying out some necessary information needed for GIS mapping, and how to decide the best way to represent your chosen data. You first need to understand the data, choose a method, process the data, and then contribute results. These results could either be discrete (location of businesses in the state of Ohio) or continuous (counties color coded by number of businesses). They would both display the same data, but with a different application and meaning of the data. Discrete data can be represented with a vector model, which is better for individual points, and continuous data can be represented with the raster model, which is better for widespread data. Mitchell then describes the types of attributes that you can describe your data with; categories, ranks, counts, amounts, or ratios. An example of categories could be types of employment in Franklin county. You could categorize each household on what kind of field they are employed in. Ranks may relate to things like the redlining maps; which neighborhoods are more likely to be accountable for a loan and pay a mortgage on time. Then be sorted into ranks of A – D. Counts and amounts refers to any measurable quantity, such as the amount of employees at various businesses. Ratios can be separated into proportions and densities. For example, you can map the average number of people per household by county to see which counties have the most people living in each home. 

A lot of the information covered in this chapter are things that I have heard discussed and have used when doing GIS projects in other courses, but it is very helpful seeing them all spelled out in front of you so that you can maximize the efficiency of your data. In other courses, I felt like I wasn’t presenting my data in the most optimal scenario, and I could have used a better consensus of the way to maximize the presentation of it. 

 

Chapter 2

GIS is so important in the development of so many different fields. Using GIS, “police can map where crimes occur each month, and whether similar crimes occur in the same place or move to other parts of the city.” and “wildlife biologists studying the behavior of bears may want to find areas relatively free of roads to minimize the influence of human activity.” (Mitchell 24) In order to create a map that represents your data, you first need to link your data to geographical coordinates. You can then link the subset of your desired data to the geographical coordinates in order to create the image you want to portray.  However, if you don’t want to portray too much information to digest in your figure, then it will be too difficult for the reader to understand the purpose of the map. If you have a detailed description, you could categorize them into smaller portions in order to make the message of the figure clearer. Mitchell states that no more than 7 categories should be used on a map or else the message of the data will be lost. The fewer categories you can evaluate in one image, the easier it makes for the reader to understand and compare each subset. In addition, the scale of the map and the amount of categories can make the patterns difficult to see for the reader, and can get lost in the figure. Keep It Simple Stupid. 

However, if you need to include all of the categories in order to portray the message of the data  completely, one way to do this is to create multiple maps of the same geographical area, but with different data that it is showing. If you want to show 15 data points, you can subset them into categories, and then make another map for each category that you subset the data into. This makes it so that you can still provide all of the data necessary to reach a full conclusion, but still allow for the maps to be decipherable. 

 

Chapter 3

When exploring data, it is important to keep in mind the purpose of the figure, and what the figure should be telling the reader. Are you evaluating the data? Or are you trying to find a pattern? Or an answer? Based on the goal of the data, then you can choose the way you want to graph your data. One way that you can evaluate one data point is through classes. This is very similar to rankings, but you can assign an upper and lower limit to which values fall within the rank, to represent the entirety of the region. For example, if you are trying to evaluate soil quality, you can assign a value of 8 to the top 15% of soil, and separate all soil regions based on the following 15% to show which areas have the best soil quality. You could also classify a region into percentiles, and represent the bottom 25th percentile, middle 25-75 percentile, and top 75th percentile separately. Mitchell goes through the various ways that you can section data. Keep in mind that not all of these sectioning strategies will be optimal with your data, and you must choose depending on the layout of your data.  Natural breaks (jenks) which is when data is separated based on where there is a jump in values. Natural breaks are good for mapping a data set that is not evenly distributed and has clusters of information. Quantile contains an equal number of features. Quantile is good for comparing areas that are roughly the same size, and for data points that are relatively equally distributed. Equal interval is when the difference between the high number and the low number are the same for each section. Is good for data that does not have a large variance in value and for presenting nontechnical information like precipitation and temperature. Standard deviation is when “features are placed in classes based on how much their values vary from the mean.” (Mitchell 68) This is good for seeing which values are above and below the mean, and how far above and below they are. 

 

Chapter 4

Mapping density is very important when trying to distinguish trends in the area. There are two ways to create a map by density: “based on features summarized by a defined area, or by creating a density surface.” (Mitchell 109) By using a defined area to create a density map, you can use predetermined boundaries, such as counties or countries, to determine the density of that area as a whole, compared to another section of the region. By using a density surface, you can see the variation in density across the area as a whole, not simply where boundary lines occur. You should use a density map if  “you have data already summarized by the area, or lines or points you can summarize by the area”. This is easier than creating a density surface, but can cause some inaccuracy, especially if you are analyzing a large area. You should use a density surface if “you have individual locations, sample points, or lines.” This requires more data processing on the authors part, but is more precise in the long run. Another way you can add to a density map is to add a layer of the density points overtop of the map. That way you can see where the individual points are coming from, but also the overall trend for boundaries in the data. Another way to change the variability of the density map is to change the cell size. If it is hard to distinguish where the dense places are on the map, try making the cell sizes larger so that you get a closer look at parts of the data. To distinguish non dense areas between dense areas, you should use a color gradient to make a visual representation of how the density fades between geographical regions. However, if you have too many gradient points, it will be difficult to distinguish where the points are falling on the graph, and the difference between each region. Again, you don’t want to include too much information so that it is hard for the reader to comprehend.