Geography 291: Geospatial Analysis with Desktop GIS

Chapter 1
This chapter was extremely helpful in offering a step-by-step breakdown of the process I would need to go to in order to properly perform an analysis. Map-based analysis can be really daunting, so having it broken down into smaller, easy to follow steps is definitely a benefit of this chapter. Frame the question, understand the data, choose a method, process the data, and look at the results- these all help to break a large task into something easily digestible. The textbook also does a good job at explaining the phenomena we would be working with. Continuous phenomena will be found everywhere, with no gaps across the area of the map. This can be helpful for measuring elements such as rainfall or temperature. Summarizing data can represent the density of features within a boundary, which can be useful for analyzing things such as number of businesses or amount of streams within a set area. Another thing I hadn’t known before was the different ways data can be represented. There are two main ways to represent geographic features; vector and raster. The vector model takes feature shapes and defines them by their x and y locations within the location. Basically, the GIS places dots and connects them to draw lines and outlines. These spots can also be represented as points with geographic coordinates. The raster model, however, is made up of groups of cells in a continuous space. Each layer of cells usually represents one attribute, and more layers are combined in order to analyze the map. While raster maps are personally more easy for me to see and understand, it seems that they also lose some of the detail that vector maps can display. They definitely seem to have their own unique benefits, though I imagine I will need to use them myself before I am fully able to understand them.

Chapter 2
This next chapter explains the practice of choosing what to map and how to present it. It explains that mapping features of an area can be extremely useful for recognizing patterns within an area. I can see this being helpful in measuring things such as animal population, or potentially the amount of deforestation in a specific area. It’s also helpful to know that analyzing with GIS does not necessarily need to be complicated. Sometimes, all we need to know are two different variables in order to sufficiently identify a pattern or problem. The chapter also explains the importance of understanding your audience in order to make the most helpful map. For example, if the audience is unfamiliar with the area, it can be helpful to include data such as geographic features, as well as city-specific zoning (industrial, commercial, etc.). Creating a map is definitely a daunting process, as you sometimes have to manually input the location of each feature via coordinates or address. Adding value codes also seems confusing, although that could simply be a result of having not practiced it yet. Either way, it was certainly very interesting to learn what the GIS does with the data I input and how it can be such a beneficial tool for identifying patterns and analyzing them. It’s also very helpful for looking at specific subsets, such as if I want to identify the population of a specific animal from within a larger group. There is also definitely a dilemma in choosing to feature a smaller or larger map. Larger maps can show more categories and details, but they can also be overwhelming to unfamiliar audiences. Smaller maps have the opposite problem, with important information being potentially left out in order to compartmentalize categories.

Chapter 3
This chapter, while a bit repetitive, goes into more depth about quantities (being counts, amounts, ratios, and ranks. It also begins by explaining the “most and least”, which is when you map features based on the quantities associated with them, then use that to either find places that meet certain criteria to take action in, or to see the relationship between those places. The book once again describes continuous phenomena and summarizing data, this time featuring many example graphs in order to better showcase exactly what they are talking about. I find these graphs very helpful, although it can be difficult to understand some of them due to the amount of information they are detailing. My personal favorite graph is the one ranking rivers based on the quality of their fish habitats. While some of the lines can be too similar to know exactly which rank they are, it is a relatively simple and easy to understand map. This, along with the several other maps are excellent for showing the range of both use and complexity that GIS can be used for.
While I am not personally the best with numbers, this chapter does a good job at breaking down what each quantity type means, and how they might be used. They also feature a few maps for each type, which makes it easier to understand what they are talking about. The book also does mention that there is often a trade off between showing accurate amounts and generalizing the values in order to see patterns on the map. One way to get around this is by mapping individual values. However, this takes a lot more work since the values are no longer grouped together. It is, however, possibly the best way to do it if you are unfamiliar with the data or area being mapped, or want to see the raw data. This map can then help you figure out how to group the values.

CH 1

This book (site it) defines GIS analysis as “a process for looking at geographic patterns in your data and at relationships between features.” This chapter walks you through exactly what GIS analysis looks like. Sometimes it’s just creating the map, while other times it is a lot more complex by adding layers and different data that’s been previously collected. Frame the question, understand your data, choose a method, process the data, and look at the results are the steps the book suggests a person should take when running the analysis. Different methods could make the information easier to gather but slightly vague or more tedious to gather and precise. It is up to the creator to decide which is better for the question that they are trying to answer. The book also walks the reader through the types of maps and the types of data that can be shown through mapping. These include categories, ranks, counts, amounts, and ratios. Categories group similar things together and determine if there is a geographical pattern. Ranks order the data from highest to lowest. An example of this could be precipitation in a given region or libraries within each school district. Counts and amounts are expressed on a map as the actual value. This might be universities in a state or grocery stores in a county. These allow the viewer to understand the scale at which the feature is prevalent in the space. Ratios show a direct relationship between two pieces of data. Continuous values would be projected using ratio counts and amounts. These features will have a range of data so it is considered continuous. Noncontinuous values are usually quantitative. These would be categories or ranks. GIS analysis allows you to individualize the data by selecting and calculating the exact type of data that you want to express. 

CH 2

GIS analysis allows you to layer information to identify possible correlations between two variables. This chapter is entirely about mapping and how to use mapping to identify important pieces of information. It is so interesting to me that so much information has already been entered into the GIS database. Each time I read something else I realize that GIS is so big and it’s something I have not been privy to at all. When creating a map there are so many possibilities and GIS allows you free reign to adapt and curate the perfect map for the information that you are trying to display. You can map a single type which would be something like roads or forests. You can type and subtype layers of your map. The example in the book is all crimes are a type that would be entered into GIS. Then you can subtype the crimes into different categories. This makes the map more or less detailed based on what is needed. Category mapping is using different colors to differentiate within a category. This could be stored with the subcategories being which stores. The book uses roads and crime as an example. The book says that you should not display more than seven categories because otherwise it could become blurred and people may have a hard time distinguishing between different shades of colors. Mitchell goes on to discuss that it is important to use scale when creating a map. Using too many or too few categories can make the map confusing or too vague for the viewer. Categories can be grouped in different ways. One way is to give each record a general and detailed code that can be used when creating the map. The second option is to create a table for each detailed code within the general codes. This one is more tedious but it is easier to edit once it has been input. Assigning symbols to each piece of data is the third option. This is the least invasive for the dataset, but it does not save in the dataset itself. The biggest thing to consider when picking symbol designs is that color is easier to distinguish than shape, and using a variety of widths and colors will make reading the map easier.

CH3 

Chapter three begins by describing different types of maps and the different figure types. “Discrete features can be individual locations, linear features, or areas.” These discrete features are represented with different levels of a single dot. The example given is smaller and bigger dots representing the locations of businesses by number of employees. Continuous features can be an area or surface with continuous values. This might look something like a COVID-19 infection map. Continuous maps usually have shades of color to display the values that they are trying to represent. The person creating the map must know what type of map they want. What question are you trying to answer? It is also important that the numbers that are being represented are accurate and the values are understandable to a viewer. Mitchell goes on to describe how to create ranks and classes in the GIS analysis. Standard classifications are natural breaks, quantile, equal interval, and standard deviation. The best scheme will be evident based on the type of data and the goal of the map. Natural breaks can be determined by viewing a chart of the data and seeing a jump in the intervals. That would be a good time to split the data. Quantiles might cause the data to present in a deceiving manner if this is not the best classification. It displays the data and then identifies the quartiles within the data. Equal intervals break down the range into four even groups to display where the majority of the figures are. The standard deviation is decided based on the intervals away from the mean. Standard deviations would be good for seeing which features are outliers and which are above and below the mean. This type does not show the actual values which would not be beneficial in certain situations. Outliers can be dealt with in different ways depending on what they might represent. In some cases you can use a different symbol to identify outliers, another example would be to group them in their class or a class with other outliers. Mitchell goes on to outline a way to determine the best map that is available. This job is subjective, but it’s important to know the pros and cons of the different types.

Chapter 1

• A bit of review because I took GEOG 292
• GIS has evolved from simply making maps to analyzing some of the world’s most pressing issues
• Beginning of chapter focuses some of the creation of GIS maps 
  • Framing a question and why we would even create the map in the first place
  • Understanding the data that has been collected for the map. Allows you to think and produce a map in a way that the viewer can absorb the information well and possibly think critically about the information.
  • This is a part of the “choose a method” phase it talks about in order to adequately show the data.
  • Also talks about processing the data and looking at the results. All apart forming the basis of a map.
• Understanding geographic features in a map and how the data plays into that. 
• Types of features
  • Discrete features
    • Location, lines, and actual location pinpointed
  • Continuous phenomena
    • Blanket entire area of mapping-no gaps
  • Features summarized by area
    • Represents the counts or density of individual features within area boundaries
  • Two ways of representing geographic features
    • Vector Model: each feature is a row in a table and feature shapes are defined by x,y locations in space
    • Raster model: features represented as a matrix of cells in continuous space
• Map projections and coordinate systems
  • All data layers being used should be in the same map projection and coordinate system.
    • Ensure accurate results when layers are combined to see relationships
• Types of attribute values
  • Categories
    • Groups of similar things, helps organize and make sense of data
  • Ranks
    • Puts features in order from high to low. Used when direct measures are difficult or if the quantity represents a combination of factors
  • Counts and Amounts
    • Show total numbers
    • Counts is the actual number of features on a map and an amount can be a measurable quantity associated with a feature
  • Ratios
    • Show a relationship between two quantities and are created by dividing one quantity by another for each feature

Chapter 2

• Better to look at distribution of features rather than individual to gain better understanding
• Different features for different layers
• Cater map towards audience
• Each layer needs geographic coordinates and map features must have a type of category value to identify each easily
• Map features of as a single type must all be using the same symbol
  • Easily shows patterns even within the simplest of maps
• GIS is able to put the data, location, and feature types all together in order to make a cohesive map 
• Using a subset of features allows you or the user to narrow down the the category value to something more specific or even make the range more broad
• Mapping features by category can provide understanding on how a place functions
• Features may belong to more than one category, using different categories within the map can reveal different and addition patterns on the data
• Too many categories within the same map is detrimental. 
  • Display no more than seven different categories
  • When mapping an area that is large relative to the size of the features, using more than seven categories can make the patterns to hard to determine(map scaling)
  • In smaller areas that are being mapped, individual features are easier to distinguish, so more categories will also be easier to distinguish
    • Using too few categories can cause important info to be left out

Chapter 3

• People map where the most and least are to find places that meet their criteria and take action or in order to observe relationship between places and data
• To map the most and least you map features based on a quantity associated with each
  • Adds an additional level of info beyond mapping the locations of features
• By mapping the patterns of features with similar values you’ll see where the most and least are
• You can map quantities associated with discrete features, continuous phenomena, or data summarized by area
• Must keep the purpose of the map and the intended audience in mind in order to help decide how to present the info on your map
• Once you determined what type of quantities you have, need to decide how to represent them on the map
  • Either assigning each individual value its own symbol or by grouping the value into classes
• Mapping individual values you present an accurate picture of the data since you don’t group features together
  • May require more effort on the part of map reader to understand the info
• To decide which scheme to use, need to know how the data values are distributed across their range
  • Create bar chart and set horizontal axis to be attributed values and vertical axis the number of features having a particular value
• Look at outliers closely as they may be result of an error in the database or anomalies based on small data samples or may be completely valid
• Once decided how to classify data values, you’ll want to create a map that presents the info to map readers as clearly as possible
  • Keep map simple and present only the info necessary to show patterns in data

Chapter 1: This first chapter highlighted the various ways in which GIS is used, as well as the different types that are utilized. This chapter also shows how patterns are used to map out the geography of a space and how different data sets can be shown on one map. This book so far does a decent job at giving visuals to help the reader better understand what exactly GIS maps are and the types of information the maps convey. This chapter details the specific terms of GIS features, those being discrete GIS data, continuous phenomena, and area summarization. Each of these components have the potential to be used at once to map out specific areas. The ability to keep record of several key companies with GIS can help researchers monitor environmental changes in a variety of ways. 

     These maps help scientists map out large areas with coordinates. Measuring a large area can be difficult, so using GIS coordinates can help solve part of that problem. I was not previously familiar with the terms “vector and raster” before, but this first chapter helped me understand what these two words are and what they mean in the context of GIS data (vector being exact geographical points and lines and raster being a mix of “cells” that represent certain information). 

 

Chapter 2: The second chapter builds off the first and goes into detail about how the maps function and how they work. It outlines how to set up the maps and how to appropriately apply collected data to said maps. It shows the difference between smaller maps as opposed to larger ones, as well as what can be used to mark points of interest. One thing I found interesting about this chapter is the fact it suggests that no more than 7 data sets should be represented on the map. I can understand why they suggest this, but after doing some research on GIS maps, I feel like I’ve seen maps with a lot more than 7 points on them. I’m sure they suggest the smaller number to make the map easier to understand for those that are new to GIS, but once you learn how to use them and how to read them, using a few more data categories isn’t too big of an issue. 

 

Chapter 3: This chapter seems to me like it’s testing your knowledge of the first two chapters. The first two chapters served as a tutorial of sorts to introduce you to what GIS is, what it’s used for, and how to read it. This chapter poses questions to the reader to take you through the processes of creating your own GIS map. This section also makes the reader think about what data they’re putting on their map and what matters more. The types of data that’s available on the maps make it easier to compare and contrast different regions about what that area may lack compared to another. 

     This chapter goes into more detail about how to color code categories and classes of information on a map. These colors can be used to differentiate waterways from roads as well as show man made landmarks vs. natural landmarks. Contour lines can also be used to show elevation and pressure changes within a given area. 

Ch. 1 

Chapter 1 laid some groundwork for understanding and using GIS Analysis. This section focused on geographic features and data. Some of these topics were: how to use data and decide how you want to represent your data, understanding geographic features and ways to represent them, utilizing data tables, summarizing, etc. Another topic that was addressed that I am excited to learn more about is coordinate systems. It seems that they are very important to understand that your data will appear accurately. 

Something I found important was the different types of geographical features. Understanding that there is not just one type of feature used in GIS helps me comprehend part of why GIS is so advanced and useful. The 3 different types of features (discrete, continuous phenomena, summarized by area) are something I feel like everybody knows but it sits at the back of your head and it’s not really something you’re ever conscious of.

It was interesting to see (through images and writing) how GIS allows you to visualize different data sets together using overlaying. It seems like such a simple and common thing and I’ve never thought about how data is layered to create maps like these. 

Reading about the different geographic attributes and when to use them was very valuable. It’s definitely something I’ll come back and refer to when we start using the software. 

I found the data table information difficult to understand. It took me a few read-throughs to make sense of the information. Data is obviously a crucial part of GIS so I made sure to grasp the information.

Ch. 2

Chapter 2 focused more on the actual mapping and how to use different mapping techniques to achieve different outcomes. It also addressed how maps should be presented with the audience in mind. An audience familiar with the area wouldn’t need as much detail and as many references points as an audience that isn’t familiar with the area. Also, not all maps will have the same amount of details, depending on size (although this seems to be more for aesthetics than practicality). 

This section also addressed how inputting data and creating the map will look. I learned about using a subset of features. This is useful when you want to be able to separate different features on a map. Although I learned more about subsets and when to use them/what to use them for, I think I am still a little confused on what they actually are. But, as I read on, I did understand how mapping by category can be more logical than mapping with subsets. Mapping by category makes different features distinguishable.

Displaying features by type is a productive way to display large amounts of data. It allows you to combine a lot of data onto one map in a way that is not cluttered. It also offers an option to make multiple maps in order to separate large amounts of data. Another way to simplify the data is to group categories together. The text provided multiple ways in which to group categories effectively. Of course, it notes that a con of this is that important information can be lost. In this case, if it is really important, you could just split the categories up onto multiple maps.

Lastly, this chapter describes how to interpret geographic data patterns. The 3 types of patterns were clusters, uniform, and random. Looking at geographic patterns is useful when observing maps in order to understand why a community works a certain way or when planning a visit. 

Ch. 3

This chapter addressed mapping quantities and the different ways that can be useful. When using quantitative data and summarizing by area, ratios should be used so that the distribution of the data is accurately represented. The text mentioned the most common ways to find ratios in GIS: averages, proportions, and densities. It talked about when each of these are useful and how to calculate them. 

Ranks is another feature that was addressed. Ranks put things in order and don’t show exact values, but values in relation to each other. This is useful when the feature you’re using is hard to get an exact measure on or if it takes multiple data factors into account. I learned why it is wise to group values together because if you didn’t, the map would be confusing and would not convey any valuable information.

Classes should be used when comparing data to a specific value or trying to see which data meets a certain criteria. Classes allow you to group data based on your needs and what you are trying to gain/understand. 

A new topic to me, standard classification schemes, was introduced in this chapter. These are valuable when looking for patterns in your data. There are four common schemes. Natural breaks is one where classes are broken up by the natural grouping of the data values. There can be an uneven number of features in each group using this method. Another common grouping, quantile, contains an equal number of features in each group. The next scheme was equal interval. The way I understand it is that the max and min of each interval are the same distance apart. I am not sure if this is completely correct though. The last one is standard deviation. This one, I am familiar with. This organized features based on how far their value is away from the mean. The text then went into depth about these and when to use each as well as combating problems that may arise.

This chapter was long, so it covered quite a bit more material throughout the rest of the chapter. This included how to choose, use, interpret, and find patterns in different map types. I anticipate this information to be extremely helpful once we start to create our own maps.

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.