Month: January 2024

Chapter 1:
Distinguished differences in data and emphasized the importance of being as specific as possible about the question you’re trying to answer. This is because it will help you decide how to approach the analysis, which method to use and how to present the results. There two types of models in GIS; raster and vector. In a vector model, each feature is in a row in a table, and feature shapes are defined by x,y locations in space. Features can be discrete locations or events, lines, or areas. Lines such as streams, roads, or pipelines are represented as a series of coordinate pairs. Areas are defined by borders and are represented as closed polygons. They can be legally defined or naturally occurring boundaries. Discrete features and data summarized are represented in this model. With a raster model, features are represented as a matrix of cells in continuous space. The cell size you use for a raster layer will affect the results of the analysis and how the map looks. Cell size should be based on map scale. Continuous categories are usually represented as either vector or raster. Continuous categories are represented as raster. Discrete features may also be represented by raster if you are combining them with other layers in a model since raster is particularly food for this kind of analysis.

Chapter 2:
Mapping where things are can show you where you need to take action. This allows you to explore causes for the patterns you see. Look for geographic patterns in your data to map the features in a layer using different kinds of symbols. Can also use GIS to map different types of features and see whether certain types occur in the same place. Each feature needs a location in geographic coordinates. When you map features by type, each feature must have a code that identifies its type. To add a category, you create a new attribute in the layer’s data table and assign the appropriate value to each feature. Many categories are hierarchical, with major types divided into subtypes. In some cases a single code indicates both the major type and subtype. To create a map, you tell GIS which features you want to display and what symbols to use to draw them. You can map features by category, by drawing features using a different symbol for each category value. Mapping features by category can provide an understanding of how a place functions. The GIS stores a category value for each feature in the layers data table. It also stores, separately, the characteristics of the symbols you specified to draw each value. When you display the features, the GIS looks up the symbol for each feature based on its category value and uses that symbol to draw the features on the map. Features might belong to more than one category. Using different categories can reveal different patterns.
Usually, several categories are shown on the same map. However, if the patterns are complex or the features are close together, creating a separate map for each category can make patterns within a particular category and even across categories- easier to see. Displaying a subset of categories may make it easier to see if different categories are related. If you’re showing several categories on a single map, you want to display no more than seven. Because most people can distinguish up to seven colors or patterns on a map, displaying more categories than this makes the patterns difficult to see. The distribution features and the scale of the map will also affect the number of categories you can display.
If the map contains small scattered features rather than large contiguous ones, rader will find it difficult to distinguish the various categories. If the features are sparsely distributed, you can display more categories than if the features are dense.

Chapter 3:
Mapping features based on quantities adds an additional level of information beyond simply mapping the locations of features. You can map quantities associated with discrete features, continuous phenomena, or data summarized by area. Discrete features can be individual locations, linear features, or areas. Locations and linear features are usually represented with graduated symbols, while areas are often shaded to represent quantities. Continuous phenomena can be defined areas or a surface of continuous values. Areas displayed using graduated colors while surfaces are displayed using graduated colors, contours, or a s 3D perspective view. Data summarized by area is usually displayed by shading each area based on its values or using charts to show the amount of each category. Once you’ve determined what type of quantities you have, you need to decide how to represent them on the map, either by assigning each individual value its own symbol or by grouping the values into classes. Counts, amounts, and ratios usually are grouped into classes, since each feature potentially has a different value. This is especially true if the range of values is large. Use graduated symbols to map discrete locations, lines or areas. Graduated point symbols are drawn at locations of individual features, or at the centroid of an area, to show magnitude of the data value.
Use graduated colors yo map discrete areas, data summarized by area, or continuous phenomena . Usually assign shades of one or two colors to the classes. If you have less than five or six classes, use the same color and vary the shade. Different colors have different visual impacts. Reds and oranges attract the most attention; blue-green, the least. It’s easier to distinguish between shades of blues and purples than shades of other colors.
If you have more than seven or eight classes, you may want to use a combination of colors and shades, using two or even three colors to help distinguish the classes. Warm colors for higher values . Cool colors for lower values. Using two color is also good for showing data with both positive and negative values, such as percentages above or below an average value. Use charts to map data summarized by arena or discrete location or areas. With charts, you can show patterns of quantities and categories at the same time. That lets you show more information on a map rather than showing each category on its own map.

Mitchell Ch. 1,2,&3 readings

Chapter 1 seemed to talk a lot about the types of ways data can be used and how it is categorized. This included discrete features, which are data with precise location, and continuous phenomena, which are data that can not be pinpointed at one location and take up a range of areas like weather. Data can also be summarized by area, which is where counts or exact data is summarized by combining it based on different locations such as by households, towns, counties, etc. I was surprised to learn that there were many more options for mapping besides just x, y, and z coordinates. The x, y, and z coordinates are utilized in vector models to show precise locations while raster models use cells to show abnormal shapes of similar areas. These are two ways that geographic features can be represented in GIS. Additionally, the attribute values are beneficial for presenting data in different ways. Attributes included counts and amounts which showed the exact numbers of something. Ranks that would provide a numbered rank for things but not show the numeric difference between the ranks, just that they are in different ranks. Ratios show the average number of things per something, like the average number of pets per house. Lastly, categories allow for similar things to be categorized together such as rivers, streams, and waterways. For example, trying to show the exact number of animal shelters in a certain state would be better displayed using counts. Trying to show the average number of animals per shelter would be better displayed using ratios. For working with data in tables, how the data is selected is important. For example, to find a specific characteristic of something within a category, you would select the category and then add “and X <8” where x is the specific characteristic you want to look at. For looking for things that fall in either/or category, you would include “or” between the categories listed. Tables can also be used to calculate things such as rank or ratio or to summarize data. 

Chapter 2, had a lot of good points about what information should be shown on the map and how to present it to make the purpose of the map clear. When mapping a single type, you would just plot all the data points using the same symbol, which can show the data distribution. You can break the data down into subsets to get more specific data to compare. An example of this may be instead of just stores, you can break them down into subsets of grocery stores, clothing stores, and gas stations. These more specific data points can help reveal distributions or patterns in the data that might reveal that 8/10 of the grocery stores are clustered between ⅖ of the towns on the map, making it more difficult for further away towns to get groceries. Different categories may be shown on a map to demonstrate where the data is found, however, the book warns that no more than seven categories should be used. I think that this is a rule because too much data can become very overwhelming and make it hard to see and read the data. If the map is hard to read or understand, the viewer is less likely to try to figure out what it is trying to show. Additionally, mapping by category can make it easier to read and understand the map and where the different data points are about different landmarks or roads.  The overall conclusion of that chapter seemed to be that the amount of data listed on the map and how it was displayed, like what colors and shapes to use, depended on the purpose of the map, and that reference features are helpful to better view and understand the map. 

Chapter 3 talks about mapping the most and least values as this can help find where certain things may be more popular or available like the number of bakeries within a state or where something is lacking like the number of dentists in different areas within a state. Again, the purpose of the map is important to keep in mind. Using a map to show a specific pattern would require fewer data to be displayed than trying to look for possible patterns that may be present. This chapter talks about the 4 different classes known as natural breaks, quantile, equal interval, and standard deviation. Natural breaks are grouped based on groups that have similar values. This is useful when the values are not evenly distributed but can make it difficult to compare to other maps. Quantile is where the data is grouped so that each group has an equal number of features. This is useful when the areas are approximately the same size and mapping data is evenly distributed but may make it so that data points seem more different from each other than they are. Equal intervals are grouped so that in each group the difference between the highest and lowest values in each group is the same. This type of class allows data to be displayed so it is easily understood but clustered data could lead to too many or no features in each class. Standard deviations are grouped based on how far from the mean the values deviate. This can be useful for easily identifying the values that stray above or below the average but doesn’t provide precise values for the features, just the difference of the actual value from the mean or average. I thought it was interesting that you can tell if you chose the right classification scheme based on if there is a significant change in the data when the number of classes is changed. The chapter talks about how to assign colors to classes and mentions that most people think of greater values in association with darker colors. This makes sense to me and I’ve seen this pattern in maps I’ve seen. Charts can also be used to display more info, quantities, and categories in different locations, but can make it more difficult for readers to interpret. Contour lines are lines commonly used to show changes in elevation or pressure on a map. When the lines are closer together, there is a higher rate of change, while lines further apart represent a lower rate of change.

Chapter 1:

I find these readings to be in a format that makes them much easier to read than the previous week’s readings. As I stated in the first blog post, it’s interesting to see how much GIS and the associated software have grown over the past two decades or so. Prior to college and maybe up until sophomore year, I had never heard of GIS until it was mentioned to me by academic advisors. Even then, I still had no clue what GIS entailed until last week. Chapter 1 is very useful in breaking down the basics of GIS into an easy to understand format, such as listing out the steps from making a question to getting results. As someone who does a lot of fishing, I like to think I’m quite familiar with maps and geographical formations and features as understanding these things play a large role in the activity. Chapter 1 also lists several common geographical tasks such as mapping the location of things, mapping density, and mapping change. The chapter then goes into different types of data, such as continuous data. While the explanation between ‘discrete’ and ‘continuous’ data does clear some things up, the explanation of what entails discrete data could definitely go a bit more into detail. Being that I’m also very ADHD, having pictures and charts to explain things rather than walls of text is also very useful in understanding the reading. Reading can only get me so far though and once it goes into things such as counts, ratios, values, and data tables it starts to lose me. While I understand what things such as ratios are of course, getting into the numerical and data aspect of things is a bit rough.

Chapter 2:

The second chapter goes into more detail about mapping and the process behind it. Given that when you’re analyzing data, you need to be able to see where things are, showing how mapping may be useful in the context of GIS. It also further highlights the usefulness of GIS as a tool and the many applications for it as outlined in the first blog post. Maps can also be broken down into various categories and groups such as assigning color and coordinates as a way of making the map easier to read and understand, along with how the map is intended to be used. Mitchell does also warn of adding too many categories to the map as the more categories there are the more difficult the map will be to read. Mitchell outlines a rule of seven, with seven being the most categories any map should have. The factors which play into the categories needed or desired generally stem back to the scale of the map in question and the features on it. For example, a large map with many features may need more categories of which the seven category rule may then restrain and make things more difficult. Then of course there are different types of maps depending on what and how much data needs to be visualized. For example, single type maps being the most basic maps display data using only a single symbol. The chapter ends with various ways of deciphering and analyzing maps just by looking at them, for example using symbols, landmarks, references, and patterns.

Chapter 3:

Chapter 3 is by far the longest chapter of the first three chapters and is incredibly intimidating. That’s not to say that chapter three isn’t interesting though. Each section presents a question to the reader that guides them through a sort of map making process. Chapter 3 also re-elaborates many of the ideas and concepts discussed in the previous two chapters, such as choosing which data points to use, counts and amounts, ratios, and ranks. By using certain data points, among other factors, you can get the most out of the relationships between the larger data sets and the smaller data sets. Maps are not limited to just data points but the other aforementioned concepts and factors such as rank can also be used. Another main idea from the chapter is if you are presenting a map to answer certain questions, or if you are creating a map to analyze data. Using classes on a map allows readers to more quickly compare areas and is useful in displaying data such as poverty rates. Regarding the making of the map itself, chapter 3 also goes over details such as graduated colors and symbols, charts, contour lines, and 3D perspectives. For example on page 83, a map outlining fish habitat is detailed using graduated colors to show the ideal habitat for fish compared to surrounding waterways. Contour lines are something that have always managed to confuse me somewhat. I understand how they work in terms of showing the rate of elevation change, but not the verticality of said change. Overall the data presented here is a mouthful, but it still manages to be interesting in some parts.

Chapter 1: 

Chapter one was the backbone of what GIS is and certain criteria that is needed for the application and data collection. GIS is used to see geographical patterns within data and relationships. Through GIS you can compile various datasets to create the results that are catered to your question you’re proposing. There are different layers to GIS that will create your data set such as features that will represent your data. These features are: discrete, continuous phenomena, and summarized by area. These features will create different maps based on the question you’re proposing and working with. GIS is about layering maps and information to create an end result. This means that on top of the features there are two different ways to represent the information which is vector and raster. On top of this information there are different ways of presenting the data points such as with percentages, numerical, pinpoints and more. A point was made in the book that it’s important to make sure the projections and coordinate systems are the same to ensure the data is presented correctly. All in all, GIS has many different factors that can be collected and compiled which makes each map and creation be different from one another even if they are focused on the same question. 

This was a really important chapter and with all the definitions and examples I am understanding more thoroughly what GIS is and how it can be used. The use of visual examples really helped me understand the content a bit better and I feel more prepared to work with GIS with this new information.  Some of the parts were confusing for me like the understanding of the difference between vector and raster. To me I just see that Raster is less clear and Vector has more definitive boundaries but I am not sure if there is more to the difference and use of them. 

Chapter 2: 

Chapter two was talking more about the in-depth understanding of the maps in GIS. There was information about putting your data into GIS and if a certain location isn’t already in the GIS platform, you do have to input it yourself manually. This means that you have to know the location information such as the street address or geographic coordinates. In this chapter there were descriptions about how you should designate your values, how they should be set up, the variations that come with that as well as the limitations. One of the limitations that was described was that a single map should not have more than seven categories. This resulted in being able to combine things that might be harder to put together which can change the outcome of the perception on the dataset. This limits the dataset because some areas shouldn’t be grouped together therefore it caused a disturbance in the creation of the dataset. Through this chapter it also talked about map skills which can really determine what catches the audience’s eyes such as having a zoomed in image of a map or a further away picture. In regards to the limitations of having only seven categories it was also talked about that sometimes having fewer categories can make the understanding easier but also have a more broader result. The final part of the chapter talked about analyzing Geographic patterns, which was really interesting to read about and seeing the variations that Maps can present and what data can be pulled from the data set. 

This chapter was mainly focused on zeroing in on mapping and points in which there were limitations benefits to certain areas as well as the orientation that map should have. I thought this was a really good continuation of the first chapter which really helped me understand the ideas behind GIS and what can be achieved from GIS analysis. 

Chapter 3: 

Chapter three elaborates on the ideas that were talked about in chapter two and dives into the comparison between most and least data sets. As well as focusing on why maps are important and what you can get out of that by summarizing certain data points. Another point that was talked about in chapter three was the idea of exploring the data or presenting a map. Which goes into why you’re creating this therefore understanding whether you need to accommodate it for other audiences or just tailor it to yourself. This chapter touches on just like chapter two did about the counts and amounts, ratios, and ranks, which are all quantities that can be utilized to illustrate information. Further in the chapter there was discussion about dealing with outliers, which is understanding the data set and getting to the most clear and concise map that you can have. It is possible that data sets will have outliers that will result in skewed data therefore they need to be analyzed and adapted to. The part about using charts was really surprising to see the visuals on page 90-91. The data sets were pinpointed as bar charts or pie charts. I had never seen anything like this before so that was something that caught my eye and I thought it was really interesting to see that you can make that type of dataset. This chapter also ended with the ideas of looking at patterns throughout GIS mapping which is really important.

I really thought this chapter connected very well with chapter two  and elaborated more on the points that were discussed in chapter two. It was also really  interesting to see the further understanding of GIS and how there are so many avenues that GIS can be used for in many different areas.

Chapter 1:

This chapter reiterates the usefulness of spatial analysis and how employing to deepen understanding of an area can allow more accurate predictions. I feel that the step-by-step process that they provide is incredibly useful, not just for GIS (or so I presume) but also for any other method of data analysis. Often, trying to figure out what information is needed and how to interpret/gather it is the most difficult part, and it can be overwhelming when examining large amounts of data without fully understanding it. This part of the chapter is something I feel I will return to. 

Interpolation is the assigning of data to points that aren’t measured between points that are measured. Since measuring tools are only so efficient, and the landscapes that people are measuring are often rather large, they can only take so many measurements. This means that space between measurement points can vary greatly. In order to fill these gaps, they typically apply a continuous connection between points, even if they’re not continuous, because it’s generally accurate enough to be accepted. If more accurate data is needed, they can do more precise measurements and simply manually edit data.

A similar issue to inaccurate measurements is cell size in the Raster model. When measuring, it has to be done in a timely manner, but also be accurate enough, which is where compromises and interpolation come into play. Cells that make up the space in GIS can vary in size, and it quickly becomes a problem of balancing storage space and time to measure/render, and being precise enough. 

The vector model is different from the raster model in that it is based on coordinate points that are linked together to make lines and polygons.

The attributes that can be assigned to points/cells are Categories, Ranks, Counts, Amounts, and Ratios. Some of these seem slightly redundant, and I’m still not quite sure what the difference between “counts” and “amounts” are.

 

Chapter 2:

This chapter is primarily focused on mapping and how to assign data from a conceptual viewpoint, rather than practical; as in what to do to make your maps decipherable (via data values, map type, scale, color coordination, etc.) rather than what buttons to press. It also discusses what types of map may be more useful for certain applications.

The section about the different uses about mapping expands on the week 1 readings from Schuurman, and it really reinforces how versatile GIS is as a tool, and proves that it’s more than the sum of its parts.
I find it very interesting that 7 seems to be the sweet spot of categories on a map. I can imagine that that may cause issues when considering large scale maps with lots of varied categories, because detail would have to be sacrificed. Of course, that does explain why simply having more maps of different scales or categories split into groupings would be so useful in these situations. 

It appears that there never seems to be an “ideal” way to indicate points on a map. Even if it is the best in general, there is always the issue of accessibility for people with certain vision issues. I don’t have the greatest vision, and my eyes hurt when looking at maps with small symbols, as my brain has a hard time differentiating between them. So personally, I prefer colours to indicate different types of things on maps. However, color blind people would have a significantly harder time with that and so they would need to use symbols or some other indicator.

The end of this chapter is more about deciphering and interpreting maps based on what you can determine by simply looking at it. Often you can find quite a bit, and it can be used for simple things with fine accuracy, but of course more complex maps require complex calculations.

Chapter 3:

When I saw what this chapter was about, the first thought that popped into my head was using derivatives to determine local min/max. Of course that would only work for more advanced calculations and determining exact locations rather than general ones. For getting the general min/max, there are helpful tools through gis that allow you to just look at the map to determine. While I understand the point of making maps more presentable, I think that when presenting to certain audiences, one should share the map in multiple degrees of detail. Some detail is hidden with general maps, and some detail is hard to determine with too precise maps. Additionally sharing the maps with messier data do allow show your train of thought and how you came to what conclusions you came to. I believe this is especially important because it allows second opinions on your thought process, which can reinforce your conclusions or disprove them.

The relativity of the ranks is also something that I feel needs more than just a map to understand. Providing some explanation to the map when being presented would be much needed context. 

Mapping classes is a good way to immediately mark all data that falls within a certain group on the map. This is useful for examining similarities and differences between data points with certain qualities. The remainder of this chapter discusses varying features of GIS and how to implement them, along with their uses. I noticed that based on how the classes can be made and categorized, it seems rather easy to lie or warp conclusions. When making the classes, if the data groupings are not evenly distributed, (ex. 1-100 being 1-10, 11-30, 31-90, 91-100) it can be used to seriously warp the presentation of data. People would assume without looking at a legend that it would be gradual and evenly spaced.

Chapter 1: Chapter one is a basic overview of the various ways to sort, analyze, and display information in GIS software. It covered the process of selecting and understanding your data, as well as all the ways to manipulate and display said data. I found the different “types of features” particularly interesting because they made intuitive sense to me and it was cool to see the different ways various types of data can be displayed. I was already vaguely familiar with the concept of vector and raster images, having used a number of different digital art programs, but it was interesting to see how that difference was represented in the much more analytical GIS software. I understand the concepts behind the various types of Geographic attributes, but I don’t fully comprehend all the ways in which these can be combined to produce different results. Presumably understanding will come with practice and experience, and I look forward to that. I worry that I will not remember all the different ways data tables can be used, as there are quite a few specific functions listed in the chapter. I understand the theory behind each of the various operations but I am sure that I will not remember them well enough to apply them to GIS without further study.

Chapter 2: Chapter two is about the process of mapping itself. It covers the basics of how to prepare the data and create the map, as well as various tips and strategies you can use to make your maps easier to understand. The chapter discussed the various uses of highly detailed maps vs more general ones. The chapter explained how unnecessarily over-detailed maps can in fact reduce clarity and make the map harder to understand, so in situations where granularity is not needed, it is best avoided. The explanation Geographic coordinates and how GIS uses them was clear and understandable, as well as the demonstration of using various types and categories to display different related types of information. What I most appreciated about this chapter was the advice it gave on how to create good maps. Advice about how detailed and granular to make the map, how many categories to include, how large of an area to cover etc. was all very helpful and interesting to learn about.

Chapter 3: I was initially confused about the concept of “the most and least” but the book did an excellent job of explaining it. The term itself was kind of confusing but I understand it now as simply including quantity in the analysis process, which makes a lot of sense and I can think of many ways that might be helpful. Additionally its use in summarizing data to be more easily understandable at a glance is also very helpful in the creation of usable maps. The various uses for counts, amounts, and ratios are now also clearer to me, as is the use of classes to simplify maps into more easily understandable divisions. Yet again it comes down to a question of granularity. In some cases, individual values may be necessary to provide detailed information for precise areas, but for other situations like public discussion or presentation, that kind of granularity is unhelpful and confusing. The standard classification schemes and their various uses are also a very helpful tool to know about. The section about choosing a classification scheme is particularly helpful in this regard and likely something I will refer back to many times during the course.

Going into the first chapter of Mitchell, I knew very little about GIS. However, by reading this chapter of the book, I became a lot more familiar with the concept. Chapter 1 was able to help me get a better understanding of what GIS is as a whole and also the breakdown of the software. One thing that I learned is that GIS has many different tasks and things that you are able to use it for. Before reading, I knew that there was a wide use of GIS. However, on page 13 of Mitchell, the book states a list of what GIS is most commonly used for. The list goes, in no particular order, “mapping where things are, mapping the most and least, mapping density, finding what’s inside, finding what’s nearby, mapping change.” This list is helpful to me because it gives me a more narrow idea of what GIS is used for instead of the “wide use” mindset that I previously had. Furthermore, the most interesting thing I learned is that in GIS there are different operations that you are able to perform on data tables. These caught my attention because they are different, useful tools that I did not know that GIS was able to do. The first operation is selecting; you are able to assign a value to a feature. The second operation is calculation; you can calculate attributes’ values in order to give new values to the features. The third operation is summarizing; you are able to get different statistics for specific attributes. I think that it is really interesting when you are able to find shortcuts or codes in different software in order to use it more efficiently. I believe that later on in my GIS learning and use, these operations will become very useful. 

 

The second chapter of Mitchell is all about mapping. Mapping is important because when you map things, you are able to look and see where you could potentially need to take action or what areas are able to meet your criteria. One fun thing I learned about mapping from Chapter 2 is that it is typically helpful to add different categories to your map. Categories split up features into different subtypes and aid the readability and fluidity of a map. Without categories, it might be hard to determine certain uses for your map and it may be difficult to read under certain contexts. Mitchell states that “mapping features by category can provide an understanding of how a place functions.” Mitchell then gives the example of how using a black line for road types only shows where the roads are, but if you categorize them into types of roads, the hierarchy of the roads would be visible along with regional traffic patterns. This shows that categories are extremely helpful when creating a map. In this example, adding categories gave the map a new function and made it easier to read. However, adding categories can be tricky to do properly. Mitchell says that there should never be more than 7 categories because the more categories you use, the more difficult reading your map will be. A few factors play into the 7 category rule: scale of features and map scale relative to features. One thing to keep in mind is that the scale of the features on a map can greatly influence the number of categories used. If the features on a map are small, it will be difficult to distinguish which categories are which if there are too many used. Also, if the map scale is large in scale to the features, the more categories used, the more difficult the categories are to see.

 

I found the third chapter of Mitchell particularly interesting because it was all about creating a map. I liked how this chapter was set up compared to the other chapters. This chapter was formatted by walking the reader through creating a map by telling them what questions to ask themselves in the process. These questions were the headings for each section, and the section would walk the reader through what to consider in order to answer the questions properly. My favorite section of this question system was about classes. Classes allow the person mapping to group together different values into classes, and they are typically utilized when the map will be presented for discussion instead of for individual analysis. I think the concept of classes is cool because they let the mapper create groups of data and then assign the different groups a symbol. Another thing I thought was interesting about classes is that they can be made manually or by the GIS program. Mitchell states that creating classes manually is usually used when the person creating the map is looking “for features that meet specific criteria or comparing features to a specific, meaningful value.” When creating classes using GIS software, the person mapping will use a standard classification scheme. Mitchell states that the person mapping will want to use a standard classification scheme when they “want to group similar values to look for patterns in the data. You can choose from several schemes.” I found standard classification schemes particularly interesting because there are multiple different ones that are available. There are natural breaks, quantile, equal interval, and standard deviation. In my opinion, Mitchell did a really good job breaking down the different types of classification schemes by stating how each one works, what it is good to use it for, and the disadvantages of using it. 

Mitchell Chapter 1-

I found this reading much easier to understand than the previous week’s reading. Its format, how it breaks down the information, makes me feel more confident about the information presented. I especially appreciated the step by step guide on the process of GIS analysis- frame the question, understand your data, choose a method, process the data, and look at the results. I learn best when steps are clearly laid out for me to re-write to help memorize them. Also, geographic features are broken down into discrete- pinpointed locations, continuous phenomena- values assigned between points or enclosed boundaries, and summarized by area- a data value applied to an entire area instead of any specific location within it (ex. demographics). You also represent geographic features in GIS through either a vector- features are rows on a data table, or raster model- features are a matrix of cells in continuous space. There are subsections of the geographic features I previously mentioned, which are called geographic attribute values. The types of attributes are categories- groups of similar things represented using numeric codes or text, ranks-which put features in order from high to low based on feature attributes, counts and amounts- which shows the actual number of features on a map or any measurable quantity associated with some feature, ratios- show the relationship between two quantities, created by dividing one quantity by another for each feature, and common ratios are proportions and densities, continuous (not including categories and rank attributes) and noncontinuous values- which is a way to know how the values are distributed to help group them. The last part of the chapter shows how to use the data tables in the GIS software with a step by step process. The common operations you use in data tables are selecting- choosing features to work with a subset of them or assign a new attributed value to those features, calculating- to assign new values to features in a data table, and summarizing- to summarize the values for specific attributes to produce statistics.

Chapter 2-

This chapter focuses on mapping where things are and beginning to understand why things are the way they are. The first subsection is “why map where things are” describes the benefits of looking at a distribution of features on a map, which help you more easily identify patterns, in comparison to looking at just individual features. Mapping where things are can show you where on a map you need to take action, or the specific areas that meet your criteria, and explore causes for the patterns you see. The next subsection, “deciding what to map” states that in order to look for patterns in your data you need to map the features in a layer using different types of symbols. What information you need from your analysis will help you display the features, like where they are and are not, map the location of different types of features and if certain types occur in the same place. You should use the map based on your intended audience for the issue you’re addressing. The next subsection is “preparing your data,” which is making sure your features have geographic coordinates assigned to them- either using the databases or mapping it by hand, and that your features have assigned category values- a code that identifies its type, and can be divided into subtypes as well. “Making your map” is the next subsection, which describes the features you tell the software that you want to display, the symbols to use to draw them, and that you can map all your features in a layer as one type or show them by their categorical values. It also describes what the GIS does for each way to map features. This subsection is very in depth and I will most likely refer to it fairly often. The last subsection is “analyzing geographic patterns,” and describes multiple ways features in a category can be presented as, such as a clustered, uniform, or random distribution, for example. Patterns can be the result of multiple factors, and any patterns that you can’t see just by looking usually need statistics to measure and quantify the relationship. 

Chapter 3-

This chapter describes what mapping the most and the least entails, how to do it, and its benefits. The first subsection, “why map the most and the least,” explains that people map where the most and the least are to see the relationships between places or to find places that meet their criteria, by mapping features based on a quantity associated with each. “What do you need to map” is the chapter’s next subsection, describing what you need to do to decide how to best present the quantities to see the map’s patterns. You can map quantities associated with the geographic features listed in chapter one, and make sure to remember the purpose of your map and its intended audience when deciding how to present your information. The next subsection, “understanding quantities,” describes how you need to assign symbols to features based on an attribute that contains a quantity- amount, ratio, or rank. Counts and amounts show total numbers and allow you to see the value of each feature and its magnitude compared with others, ratios show the relationship between 2 quantities and can even out differences between small and large areas, or areas with many or few features, so the map more accurately shows the features’ distribution, and ranks put features in order from high to low and care useful when direct measurements are difficult or if a listed quantity represents a combination of features. “Creating classes” helps you decide two to represent your quantities on a map, either by assigning each value its own symbol or grouping values into classes, typically based on which feature you choose to map your data. The 4 most common classification schemes, natural breaks, quantile, equal interval, and standard deviation are also explained and compared to each other in depth. “Making a map” is the next subsection, and describes the options GIS has for creating maps to show quantities- graduated symbols, graduated colors, charts, contours, and 3D perspective values, along with creating the view, z-factor, light source, and perspective view very in depth. “Looking for patterns” tells you to either look at the transition between the least and most are, whether values cluster or not, to see how the phenomena behaves.

Chapter 1: I think it’s interesting how GIS has become more accessible over time with an increase in social media and tech use. Before coming into college as an ENVS major, I had honestly never even heard of GIS as a field. I like how this book begins with the basics of GIS and explains what it is and how to use it before getting into more of the actual map-building concepts. I think building up a base level of knowledge on GIS will come in handy later when we are presented with more complex topics. I also think it’s interesting how the book explains there are various ways to display the same data. Some methods are just more in-depth and are useful for certain scenarios whereas other methods may be more useful for quick looks at patterns. The explanation of the difference between discrete and continuous data was helpful, and I had not realized there was a distinction between these two kinds of data, but when the book presents the pictures, it makes sense that they are two different things. It is also very helpful that the book includes pictures next to the concepts being introduced so that once we begin map building, we can visually recognize these terms. Interestingly, single-point locations like businesses look very similar as both a vector and raster, but lines on a map, like highways, look much different as a vector compared to a raster. Areas appear to have the most loss of detail when expressed as a raster compared to a vector. The portion of the book explaining the various attribute values was slightly confusing at first because each type seems like they have similar functions (or at least the words all seem similar to me). However, once I went back through and read it a couple more times and studied the maps closer, it began to make more sense. I also suspect that once we begin making our own maps this will be a little more intuitive in practice. 

Chapter 2: The first second chapter begins by explaining that you can either map things to identify individual features or to look for patterns in the distribution, though it’s interesting how when looking at the two different maps, they are actually the exact same. I also think it’s very cool that by identifying patterns on a map, a range of different professions from police officers to biologists can determine plans of action based on the data. It is slightly unfortunate that smaller maps cannot show as much information as large maps so they don’t become overcrowded, because sometimes maps may need to be in a small format. However, if there is too much information on the map to be able to read or identify anything, it would defeat the purpose of it entirely. I like how the book explains what the user does to input data for a map versus what GIS does when making the map. When I use R in my biology classes, we also use subsets a lot to uncover hidden patterns. Usually, we’re working with a very large dataset and it’s a lot easier to understand the data with subsets, and I like how that also translates to maps! Like the last chapter, I think a lot of information is being presented here and it’s a little confusing/overwhelming now, but in a couple of weeks when we begin implementing all of these terms, it will all come together. One interesting thing I did not know that this chapter said was that people can typically only distinguish up to 7 factors on a map, which does make sense. I think that it’s helpful that the book includes directions on what not to do- basically things that may make a map look confusing or hard to see data points. This part will be good to look back on when we make maps. It also brings up ways to make maps with lots of data less confusing, such as using text labels. I like that this chapter introduces ways to analyze the data. I think it will be important to discover the trends in the data being mapped and not just simply to make a map to look at for no reason. 

Chapter 3: This chapter seemed slightly daunting at first because of its length, but luckily it was a lot of tables and pictures! I feel like the beginning of this chapter was slightly repetitive, as it introduced some of these concepts earlier. However, it’s nice that they go more in-depth with each of the ideas and show more examples of maps demonstrating certain concepts. I think I’m slightly confused about classes and the four different schemes, but as I’ve been saying I’m sure it will make more sense in practice and I can come back and look at this part of the book for reference. I do like how the chapter compares the different classification schemes by listing the pros and cons for each and also giving a general explanation of how they work. I think displaying concepts like this in a textbook really helps me understand the material. I think this chapter will be extremely helpful to look back on later when choosing a map type. The 3D perspective maps are really interesting and I didn’t realize that GIS was capable of doing this! I don’t have as much analysis or reflection on this chapter since it was mainly maps, but again I think showing the maps is obviously a very good way of introducing ideas and comparing different terms. 

Chapter 1- 

The first chapter starts out by saying that more and more people are starting to use this software and spatial analysis. GIS analysis is a certain process that looks at geographic patterns and finds relationships between features. It’s important to ask good questions in order to understand and collect the right set of data. Posing the original questions correctly is the key to correct GIS analysis. Selecting the correct method is also important. There are two different models that GIS can be used with: vector and raster. In my opinion, the raster model is easier to see, but the vector model gives more data for the area. It’s important to mention that maps usually distort what they are portraying. With small areas and towns, the distortion is barely noticeable. Data tables are also very crucial with the use of GIS. Three common methods used with data tables are selecting, calculating, and summarizing. Selecting is just selecting the features you will be working with. Calculating is measuring the data and putting it into certain areas for reference. Summarizing is combining all the data and looking it over. 

Overall, I liked this chapter because it gave a simple intro to GIS and why it’s commonly used. I learned about different features: discrete, continuous phenomena, and summarized. All three of these show different types of information collected. 

I also learned that there are five different ways to describe a feature. The easiest one for me to see is ratios. This attribute value shows the relationship between two quantities.  A commonly used ration is population (density) and proportions. It’s also very easy to see the data collected using ratios because everything is color coordinated and in separate boxes and lines. 

Chapter 2- 

It’s very crucial for us to be able to map where things are so we can navigate, communicate, and educate ourselves about a certain area. Police use maps to be able to look and see where the most amount of crimes are. In those areas, more police officers can be dispatched to help keep the neighborhood safe. Deciding which map to use is also difficult because it needs to portray the information correctly and match the needs of the viewers. A good point the book makes is about an audience that is unfamiliar with the area. If viewers don’t know what kind of map they are looking at, it might cause some confusion or just straight bewilderment. Landmarks or certain roads or boundaries might relieve some of this confusion. Different colors (green for forests, or yellow for cities) might also be a solution. 

This chapter went into more detail about how GIS works in relation to our everyday lives. I liked looking at all of the photos because they gave examples of the different types of maps that can be created by using GIS. A lot of the map examples in this chapter show streets, neighborhoods, and crime rates. The maps about neighborhood location with family size and industry really shows how cities are broken up, and where people tend to live. Office buildings and industries are downtown, with housing being more in the suburb areas. 

Chapter 3: 

I’ve never really thought about this before, but maps are usually mapping most and least data. This is quantity data. Mapping lets people see where the most and least things are, for example, businesses. Discrete features are a good way to determine simple most and least data. How thick the line is or how big the circle is determines how many of something there are in a certain area. Always keeping the purpose of the map in the back of your head is an important way to determine what needs to happen to the data collected. In other terms, is it about the data or presenting a map? 

When constructing a map, patterns are good to look at. Patterns represent different features on a map, such as land use or vegetation. Patterns also aid in visual representation on different areas on the map. This chapter was focusing on how to turn the raw data into a pleasing map for viewers and other scientists. The map needs to be scientifically accurate but also simple to understand. Having a key to explain what the data is, is also a critical part because without a key, then people have no idea what the map is talking about.