Month: January 2023

Chapter 1 , Introducing GIS analysis

GIS is constantly changing due to the rapid evolution of new technology. GIS is evolving because people are finding new uses for GIS. It is becoming more than just mapmaking. GIS for analysis can be applied to many field to get the most accurate data and information. GIS analysis is defined as looking for patterns in your data and at relationships. Simply making maps is a form of analysis.  Steps important to analysis are listed: ask a question, understand the data, choose a method, process the data, look at the results. I enjoy lists and found this useful. Some parts are self explanatory. Understanding the Data requires finding information on what kind of data and how specific it is to determine how fit it is for the project. Looking at results includes stuff like deciding what information is helpful. 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.  Summarized data represents density of individual features within area boundaries. Categories are groups of similar things. Ranks put features in order. Counts are actual number of features. Amounts are a measurable quantity associated with a feature. Ratios show the relationship between 2 quantities. Categories and ranks are set number values within the given data layer. Calculating and summarizing are different. Calculating evolves assigning new values to features and summarizing involves using data tables to find a piece of data. This chapter contained a great overview of GIS and these were the points that stood out to me. I would be curious to see at the end of this course whether these concepts were the most important things in this chapter.

Chapter 2,  Mapping where things are

This sections covers the actual placing of articles on a map compared to the introduction found last chapter. Mapping the locations of individual features allows you to see the distribution of the feature as well as the patterns that may help mapping. It is important to create a map that shows features relevant to yourself and the audience. There are many things you can alter to help. Only included relevant data and have clear concise categories. Geographic coordinates and codes must be assigned; you will need to assign them if they are not in a GIS database.  You can map single features by repeating a symbol which may reveal patterns to your audience. You can choose subsets of your features to map. I enjoyed the example of  all crime vs. burglaries to help understand the concepts. Using different symbols can show different category within your data. You can adjust the size and range of your categories to adjust the way GIS displays your map. Keep in mind that the map needed to be discernable. Stick to less than 8 colors or symbols. Adjusting the grouping can help help keep the map clean and remain under 8 categories. Choose symbols with define shapes and colors. All of these are key to making patterns visible to your audience. I really enjoyed learning the different parts that go into making a map. This makes me view maps differently because I can imagine how simple it could be to manipulate data. I also am grateful for the opportunity to learn GIS somewhere where I can learn the ethics behind GIS as well as the application.

Chapter 3, mapping the most and the least

This chapter focused on mapping based on quantity associated with each feature. There was a lot of information present and was difficult for me to understand and summarize. It adds more information than mapping features. There are several options for displaying most and least depending on what type of feature you are looking at. Simply put, you can “map quantities associated with discrete features, continuous phenomena, or data summarized by area.” . Discrete features are defined as individual locations, linear features,  or areas. Next, this chapter discuses how the context you are mapping effects the look of your map.  . When exploring data you can map as many features as is helpful for you in pattern recognition. When presenting this should be streamlined and made ‘cleaner’.  Ratios are created by dividing one quantity. Averages are used to compare those with few features to those with many. Proportions show you how much of a whole each part represents. Densities show the concentration of features. Cases are created in 4 basic way natural breaks, quantile, equal intervals, and standard deviation. Which one use use depends on the data. Graduated colors and symbols are use similarly.  You can also use small chats such as a bar graph or pie chart. They can cause issues identifying patterns if not used in the right context. contour lines are used to show rate of change such as elevation or precipitations.  3d renderings can allow the audience to better understand the change of a continuous phenomena. Perspective can be influential when using 3d  models. This chapter was very definition heavy but I feel the concepts are easy to understand but hard for me to describe.

Chapter 4 mapping density

Mapping density shows you the highest concentration of a feature. By simply mapping features you could see patterns in density however density mapping allows for you to see the visual difference easier. They are most commonly used to map areas such as population. There are two ways to map density you can go based off of area or density surface.  each method has positives and negatives and it is important to pick the method based on your data. The chapter goes into detail about dot density maps and the proper way to display dots to be impactful but still effect in showing the data. I do not see the appeal in the dot density. Other methods seem better than others but hopefully going through this course I will understand the benefits of all types of modeling. Density surface is created using a raster layer. from my understanding it creates a new layer that overlays the gradient on the map. It is noted that this method takes more time. This process uses graduated colors and contour lines. The chapter goes into very specific details regarding the process behind making these density maps. I will likely use this chapter as a reference. My thoughts were quite scattered while reading this. There is a lot of information on may different aspects that I do not fully comprehend.

Chapter 5 of the book talks about mapping “what’s inside.” This is useful because it can help to monitor those occurrences, compare those occurrences with others, and conclude when and how to take action. Areas are defined by drawing boundaries and data can be found in a single area or several and discrete (identifiable) or continuous (seamless) features. Which method is used can be determined by examining what kind of information the user needs from the actual analysis. Some examples of this information may be lists, counts, summaries, or feature views. This chapter discusses three different ways of finding the things inside of an area. The first way is drawing a map that shows the area boundary and the features to see what features are within the boundary. The second way is by specifying the area boundary and features for a summary. The last method is separating the area boundaries and features into different layers, overlaying them, and having the system combine and compare to make summaries. This chapter next discusses how to actually draw the maps, talking about using/mapping locations, lines, discrete areas, and continuous features. Then, it talks about choosing the features that the user wants to use within a boundary, which can be done by the GIS. These results can then be compiled into a report or spreadsheet in many kinds of summaries, including the popular ones of count, frequency, and numeric attributes (sum, mean, median, standard deviation, etc.). Lastly, this chapter talks more about the method of overlaying, discussing the two big categories of using this method with discrete areas and continuous categories. The continuous way uses one of the two methods: vector (compares cross areas) or raster (compares cells). After reading this chapter, I feel more comfortable with the concept of using areas and their contents to draw conclusions and summaries when mapping. 

Chapter 6 of the book talks about finding what’s around a feature, which is used to determine the area that is impacted by an event. First, the book explains the importance of specifically defining the analysis, which includes possibly determining distance, travel to and from, cost, and planes (whether it includes earth curvature or not). Next, the user must ask what information is needed from the analysis, which could be a list, count, summary, or distance/cost ranges (may need inclusive rings that show relationships between totals and distances or district bands that compare distances to other attributes). Then, the chapter explains the three ways of discovering what’s nearby, which include straight-line distance (creates a boundary/selects characteristics at a set distance), distance or cost over a network (finds what’s within a manageable distance/cost), and cost over a surface (finds overland travel cost). This chapter then goes very deeply into using straight-line distance, talking about the potential of making and using buffers, just choosing features within a certain distance and the different ways to do/use that, using the GIS to find the distance between features and how to obtain/utilize the results, or creating a distance layer in the program and using it to create specific distance buffers. Next, the chapter talks about measuring over an entire network in the GIS, which means that the system finds all lines in a network within certain parameters. To do this, the user may have to specify the layer for the network, assign segments to centers, set travel specifications, choose surrounding features, and make the final map. Lastly, this chapter talks about finding cost in a geographic surface, which involves specifying cost, personalizing cost distance, collecting the information, summarizing the results, and creating the final map. This chapter was a bit more abstract for me, but I’m sure the concepts will be more clear once we’re using these skills. 

Chapter 7 of the book talks about mapping changing conditions or movement in order to predict future happenings, choose a method of action, and interpret the effects of some kind of policy. The first topic in this chapter is defining the analysis, which then goes into the types of change that can be mapped, which include change in location (discrete features and events) and change in character/magnitude (discrete features, data summarized by area, continuous categories, and continuous values). The chapter then moves on to the concept of measuring time, which can be mapped in one of three patterns: a trend (shows increasing/decreasing or direction of movement), before and after (shows impact of event or action), and a cycle (shows patterns in feature behavior). Time mapping can consist of showing the locations at multiple times or the data can be summarized. In both of these methods, the user must choose how many/which dates to include. Then, the user must determine what information they need from the analysis, whether that ends up being how much or how fast the data changed. Next, the chapter talked more about the methods of mapping change which are using a time series (uses snapshots), a tracking map (shows feature movement), or just measurements (shows difference in a characteristic). The book then explains these methods further, talking about ways in which each of them can be used to show change in both location and magnitude/character, methods in constructing the map itself, and how to examine/use the results. Finally this chapter concludes by talking about ways to report the results and methods in summarizing. This chapter was one that felt very straightforward. I found it useful that it showed ways to use each method as well as showcasing the pros and cons of each, which will be helpful when choosing what way to do things.

Chapter 1:

This chapter had 3 main topic, what is GIS analysis, understanding the geographic features and understanding the geographic attributes. It explained that GIS analysis is the “process for looking at geographic patterns within the data and the relationship between features.” This is done by framing the question or what information is needed. The question poised that creates the need for a map often decided how to approach the analysis. So you need to understand your data and then choose a method. From there process the data and then look at the results. This last step can help decide whether the information used is valid or whether you should return to step one and re-run the analysis with different data or a different method. For understanding the geographic features, the type of feature can affect the steps of the analysis process. The types of features are discrete, such as lines and locations that can be pinpointed. Continuous phenomena, which is like a temperature or precipitation and is given a value. Features summarized by the area are the counts/density of individual features such as population and number of things in a region. There are also 2 ways of representing geographic features, vectors and rastor. A vector model has a feature in a row on a table and the features are given a address with a x and y location in space. these features can be discrete, events lines or areas.  For a rastor model, the features are a matrix of cells in a continuous space, with each layer representing an attribute. Any type of feature can be represented using either vector or a rastor model but discrete and data summarizations by the area are usually represented through a vector model. For understanding the geographic attributes, the values need to be known. Categories, ranks, counts amounts and ratios are all attributes.  Categories group similar things together, ranks put features in order from high to low and are used when direct measures are hard or represents a combination of factors. Counts and amounts show a total number and is the actual number of features on a map. ratios shows the relationship between two qualities and rare made by dividing one quantity by another for each feature. For continuous and noncontiguous values, categories and ranks are noncontiguous while counts amounts and ratios are continuous values.

Chapter 2:

Chapter 2 focuses on why map thins, deciding what to map, how to prepare your data, making the map and how to analysis geographic patterns. Pertaining to the first question, mapping things can show you where action is needed, or what locations meet a criteria. For deciding what to map, you need to decided on what information you need for an analysis. Such as the location of the features in comparisons to a deciding factor like the example the book had, crimes compared to the police departments location. how the map will be used is also important because some features are not relevant to a topic and can muddy a maps purpose. For preparing the data, assigning geographic coordinates is something usually done via the data brought in, the same for assigning a category for the values. Making the map, there are many different types of maps, such as mapping only a single feature, such as only showing the roads or buildings. Knowing what GIS does with the locations of each feature and how it stores the location within the map. using a subset of features, this is more commonly done for individual locations. Mapping by category and displaying a feature by the type can be used. Choosing the symbiology of a map is also important as if you’re mapping individual locations using a single marker in a different color for each category of the locations can break of the map to be more legible. Changing eh locations to all have the same color but different shapes is harder to read and thus wouldn’t be recommended for this type of map. taking in to account how the map will be viewed also can help with the symbiology such as is it digital or a physical map on a poster. Analyzing geographic patterns is to ensure that the map presents the information clearly.

Chapter 3:

Chapter 3 focuses on why to map the greatest and lowest values, what needs to be mapped, how to understand the qualities within the map, creating classes, making the map with these in mind and looking for patterns. Mapping the extreme values can help people find what meets their criteria and to take action. or to see the relationships between locations. What to map is based on knowing what features you’re mapping as well as the purpose for the map. these factors will help decide how to present the features and qualities to see patterns in the map. Feature type and what is being explored within the data or presented in the map also help with what to map. Understanding the quantities of thing like counts and amounts which show total numbers are important for qualitative maps. Ratios for these maps show the relationship between two quantities and are useful when summarizing by area, with the most common ratios being averages, proportions and densities. Ranks are useful when direct measures are difficult or the quantity represents a combination of factors. Creating classes within a map groups values into their own symbols or being in the class. this requires a trade off between the presentation of the values are the generalization of said values. Mapping individual values present an accurate picture of the data when the features are not grouped together. but this requires the readers to understand more information especially if the map contains lots of values. Using the classes to group similar values features helps by assigning them the same symbol. you can do this by creating classes manually. Classes can also be created using a classes based on a larger set of features such as a population census. Making the map discusses that the GIS program gives you different options for creating your map such as graduated colors, graduated symbols, charts, contours and 3D perspectives. each have advantages and disadvantages based on the information they provide and the limitations of using such a option.  Map type is also important as it may show discrete lines or area and whether or not you have spatially continues phenomena’s that are used. Creating 3D perspectives are used most often with continuous phenomena and help viewers visualize the surface of an area such as the height and magnitude of the area. Looking for patterns helps to present that map more clearly and can be used to compare different parts of the map. The relationships between locations of features such as the highs and lows of values help understand how the phenomena behaves.

Chapter 4:

Chapter 4 features on a maps density, deciding what to map, the two ways of mapping density, mapping density for defined surfaces and creating a density surface. Map density show the highest and lowest concentrations of features and where. Deciding what to map helps to decide what method to use based on the information needed for the map. Two ways of mapping density show that you can map by defining the area or by density surface. Defined areas can be a dot or calculated a density of each surface. Density surface is usual created in GIS as a raster layer with each cell layer getting its own value providing more detailed information but requires more effort by the creator. Mapping density for defined areas, based on the two methods for mapping density. Calculation the density value for each defined areas. Creating the dot density map is a method where each area is mapped based on the total count/amount and each dot must be specified on its representation. The dots don’t represent actual locations of features. If there are individual features but want to map density summarized by defined areas, GIS can summarize features for each polygon area. Creating a density surface are raster layers that GIS calculates a density value for each cell layer.

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.