Month: January 2026

Chapter 1:

 Rather than jumping straight into the intricacies of mapping and data analysis, Chapter 1 stresses the importance of understanding and clearly defining the topic at hand. I believe that this is an effective way to introduce new students to the basics of GIS, as it eases you into it. One major takeaway I noted is that Mitchell highlights how GIS analysis conducted with intent tends to begin with asking the right questions related to the information you need, with more specific questions helping guide your analysis. I had not realized this prior, as I just saw GIS as simply plotting data. I realized we can use GIS to address important problems by asking pressing questions that are specific to the area of interest. It’s also important to note that Mitchell presents GIS systems as accessible, inviting us to ask our own questions and come to our own conclusions and discoveries. This is an exciting revelation for me.

Chapter 1 also introduced me to the basics, like understanding and identifying geographic features as how they are presented within a GIS. My takeaway is that there are many different ways to visualize data as features, depending on what kind of data it is and the purpose of creating a visual for said data. For example, vector vs raster modeling. Vector modeling represents features as points and lines often using coordinate-based data, making it good for plotting things like roads, boundaries, and buildings. Raster modeling represents the features as a grid of cells using continuous data, which is good for plotting things like elevation, temperature, weather patterns, and land types. I found it interesting how these two forms of modeling could technically be used interchangeably for the same purpose, but they are used for whatever they are visually better suited for. 

I feel that explaining how we can all use GIS as an effective tool is the premise and the author’s main goal after reading chapter 1. It introduces GIS analysis as a system that is capable of examining and visualizing geographic data to understand specific spatial patterns, relationships, and trends in a way that I found understandable. It takes this information and directly ties it to visual features within a GIS mapping system. Question: Would reading printed park maps be considered a form of GIS analysis? Where is the line drawn for maps being purely for visualization or analysis?

Chapter 2:

Chapter 2 starts off by building on the foundation that Chapter 1 created by highlighting the importance of visualizing data using mapping. Honestly, I found this redundant. The benefits of mapping data already seemed clear to me. For example, Mitchell discusses how visualising data on maps can help us look for patterns in the distribution of the features, and make decisions based on these patterns. I thought that was obvious, but I appreciate that Mitchell is making things very understandable for new students like myself. Some things that I was unfamiliar with that chapter 2 discusses is deciding what to map, and preparing my data for said map. I learned that when deciding what to map, you need to consider the information you want to analyze and how the map containing this information will be used. This made me realize that it’s important to take into consideration the specific audience the map will be presented to. For example, a highly detailed and overly complicated map is ineffective if it was intended to be made for the purpose of sharing basic information with the general public. As for preparing data, I was highly unfamiliar with the topic. I learned that a crucial first step is to assign geographic coordinates to the feature you wish to plot. Another important thing I learnt is that you need to assign category values to features if there are differing features, or features sorted by type. The category value is a code/tag that identifies the feature type. I often see these categorizations of features when looking at maps, but I’m now realizing that this feature identification can be used for various applications, such as distinguishing areas for city planning. Chapter 2 does a good job at answering basic questions about mapping and how to create a map, as well as explaining how to proficiently analyze these maps.    Question: How can we effectively and critically evaluate data sources to identify biases/untrustworthy information before incorporating it into our GIS analysis?

Chapter 3:

When I first read the title of chapter 3, it being called “Mapping the Most and Least”, I was confused. Unlike the previous titles, what it was trying to convey wasn’t immediately clear to me. However, Mitchell explained it in an understandable fashion. Mapping the most and least means to identify where values relating to your data/criteria are highest or lowest to analyze certain aspects about the data, most often through patterns. If I were to think of an example, I would say that analyzing a low income area for care facilities scarcity is an example of analyzing where values are the least. This is something that I had not previously considered, as I was focused on the idea of mapping where things might be located, not where things might be missing or lacking. It showed me how presenting the quantities of data in different ways is an important thing to consider when deciding what I want the purpose of my map to be. This is just one method of GIS analysis that is presented in chapter 3. Other methods are given.

For example, there are different types of quantities that you can use. According to Mitchell, being aware of the quantity type your mapping can help with deciding how to present your data in the best way. Once the type is determined, a decision must be made about how to represent it on a map. This can be done either through grouping the values into classes or by assigning each value its own individual symbol. I learnt that each choice has its merits, and I now know how to apply them to my own maps. For example, grouping the values into classes is useful for maps with a large range of values to present the data in an easily readable manner. Showing overall patterns is favored over exact data using this type. This would be good for maps about concentration levels of rain or air pollution. On the other hand, assigning each value its own individual symbol is useful for maps where precision matters, and exact values are important due to the specificity of the data. This would be good for maps geared towards recording specific sampling sites, or showing how specific geographic locations may present differently from each other.     Question: What if the data is in a middle ground where it isn’t clear if I should present my data with simplicity or complexity? 

Chapter 1

Chapter one introduces GIS and how it is used to analyze geographic features. The most common analysis people do includes mapping the location, density, and change. GIS analysis is identifying and studying geographic relationships and patterns through maps and data layers. Analysis begins with a question. Your method of analysis depends on what question you have and how you are presenting the results. It is important to know what data you have and what data you need to calculate and create. Studies using approximate data are quicker, but those requiring accurate data take more time. I really like the example the chapter gives in describing the difference: if you are looking at assaults in a city, it will be a quick study, but if the information is used for evidence in a trial, you will need the precise measurements for the locations and numbers in a specific area over a period of time. The results of the GIS analysis can be displayed as a map, table, or chart. It is important to not only understand how GIS works but also what geographic data is being displayed. Discrete locations and lines do not have a distinct location, such as parcels of land value. Continuous phenomena can be measured at any location, so there is data everywhere you are mapping. If the data is not used in an area with boundaries, GIS uses interpolation on a series of points. Interpolation is assigning values to the area between the points. The third type of data is summarized data, which represents the density of certain features within a boundary, such as the number of households within each county. Geographic features can be represented by vectors and rasters. With vectors, features are defined with an x,y coordinate. With rasters, features are represented by multiple cells.

Chapter 2

Chapter two discusses how to prepare and map your data. Through observing a distribution of features, rather than individual ones, you can find patterns in the data. GIS mapping can be used to show where features are and aren’t, and the different types of features. The audience and issue determine how you present your mapped data. Every feature will need geographic coordinates and an identifying code. For individual locations, GIS will put a symbol at the given point. For linear features, GIS draws lines connecting each point. For features within an area, GIS draws an outline. Mapping subsets is common for individual locations rather than linear features because highlighting only linear features doesn’t provide any information about the surrounding areas. You can also map features by category, with each category having a specific symbol. GIS will store a value for each feature in the layer and an assigned symbol for each value. It may be helpful to have separate maps for each data set, otherwise it can get messy if there is too much data. You should keep the maximum number of categories to seven, as it can be difficult for most people to interpret if there are more. You can also group categories if you need to show a lot of data, but keep it to one map. There are three ways to group categories into detailed and general: assign each record two codes, create a table with a record for each detailed code and corresponding general code, or assign one symbol to each detailed category within the general category. It is important to include references such as major highways or rivers so the map can be more meaningfully interpreted. These references should use lighter colors so they don’t take away from the actual data.

Chapter 3

Chapter three explains the importance and process for mapping the most and least. Mapping the most and least can help people solve problems or see relationships. You can map discrete features, continuous phenomena, or data summarized by area. Discrete features are locations, linear, or areas. Continuous phenomena are defined as areas of continuous values. Data summarized by area uses shading based on its value. Maps can be used to find patterns or present patterns that have already been identified. If you want to find patterns, the data needs to be displayed in many different ways and with great detail. If you are presenting previously found patterns, you need only to create a map with generalized data. For mapping the most and least, you assign a symbol to each feature based on a quantity: counts or amounts, ratios, or ranks. A count is the number of features and the amount is the value associated with each feature. Ratios show the relationship between two quantities to even out the differences between large and small areas. Some examples are densities and averages. When summarizing by area, ratios should be used. Ranks show relative values in order from high to low. Ranks can be used, for example, when seeing which soil type in an area is best for growing crops. Classes are used when representing quantities on a map. The four ways to group data into classes are natural breaks, quantiles, equal intervals, and standard deviation. Natural breaks are set by natural groupings of data values. Quantiles have an equal number of features within each class. An equal interval has an equal difference between the high and low values. Standard deviation has features that are placed based on how much the value varies from the mean, which is calculated by the GIS.

Mitchell Chapter 1:

This chapter introduces GIS and explains that it is a process for looking at geographical patterns in data between features. It emphasizes the fact that it is more than just maps and has many real world applications. The basic elements like points, lines, and polygons seem straightforward, but once they are placed in space, their arrangement can form clusters, dispersed patterns, or something that looks random. This makes sense because many times in my experience when looking at maps or graphs they seem to be random or hard to interpret until you actually break it down piece by piece. Scale also becomes a major theme, because the same data can look completely different depending on the extent or level of aggregation. This could also lead to misinterpretation if someone is not familiar to how the map was constructed. The chapter also highlights that patterns reflect processes, which raises questions about how confidently someone can link a visible pattern to a real-world cause. A linear pattern might suggest transportation routes or environmental constraints, but without context it is hard to know which explanation fits. There is also and emphasis on the steps of inspection which is important because with a different way of looking a graph a whole new interpretation can be made. The chapter seemed decently basic just with a few important vocabulary words. Many maps were used in this chapter which also helped me to understand how things are or what they mean or look like. Overall, Chapter 1 feels like a reminder that GIS analysis starts with careful observation and a willingness to question what the map is showing rather than jumping straight into technical methods. 

Chapter 2:

This chapter focuses on how GIS moves from simply noticing patterns on a map to actually measuring and identifying them. The chapter explains that raw point maps can be misleading, so tools like density mapping, kernel density, and nearest neighbor analysis help reveal whether features are truly clustered, dispersed, or randomly arranged. It also introduces different distribution shapes, such as linear or circular patterns, and discusses how these shapes can hint at underlying processes. It first starts out by introducing areas on a map that are important when interpreting it like clusters or blank areas which seemed very basic. The chapter then talked about distributions such as random, clustered, and uniform. It also keeps coming back to the idea that raw point maps only tell part of the story and that you often need tools like density mapping or distance measures to actually see what is going similar to chapter 1. I keep noticing how density surfaces can completely change how a pattern feels because they smooth out the noise and highlight where activity is really concentrated. It makes me wonder how often people rely too much on the raw points and miss the bigger structure underneath. Overall, the chapter pushes me to move beyond just eyeballing a map and start using methods that actually measure the pattern, while still reminding me that none of these tools give a perfect answer on their own. It shows how analytical methods make pattern recognition more objective and reliable, even though interpretation still plays a role.

Chapter 3:

This chapter focuses on how GIS helps you move from simply noticing where things occur to understanding why certain patterns and conditions appear together in space. The chapter explains that once you identify where features are located, the next step is to look at how those features relate to other layers or conditions. The chapter discusses proximity, which looks at how close features are to each other and how distance might influence a pattern. Some other key concepts are counts, amounts, ratios, and rates when looking at maps. It also covers how to choose a classification scheme, how to deal with outliers, deciding how many classes and more when making a map. I feel like this would be challenging trying to portray your data the best you can. The rest of the chapter gives many samples of maps and things you may use or see when viewing or making a map. One thing that stands out is how much the chapter relies on comparing layers to understand where conditions overlap. It makes me realize how important it is to choose the right layers in the first place. I liked thinking about the possibilities of GIS after reading this chapter because of the types of graphs you can make. I really enjoy fishing so in the chapter one of the maps I thought was cool was the one with line thickness ranking the fish habitat from excellent to poor. Overall, Chapter 3 is about using GIS tools to study how features relate to each other in space and how those relationships can help explain real‑world patterns.

Chapter 1 Notes- GIS use has grown enormously. Spatial data has become more abundant, and we now have new ways to access it using LiDAR and drones. Scientists are discovering that GIS is not just for map-making and geodatabases. By learning to use GIS for analysis, you can obtain more accurate, up-to-date information. By using GIS, you can better understand the place you are studying and make more informed decisions about its future. In the upcoming decades, it is said that GIS will continue to grow and expand our understanding of the world. Chapter 1 also breaks down many different kinds of maps, what they are, and what they show. They also show what they are used for and how to use them. Besides talking about GIS and how it worked, it also gave us websites like ArcGIS, which were pretty interesting to look at. As well as. Living Atlas of the World is another GIS website to look at, which I may spend some time exploring the data they have on their site. Some of the import definitions they gave were for GIS, which, in simple terms, involves analyzing geographic patterns in your data and the relationships between them. They also list the five types of attribute values, describe them, and provide maps showing how each is used. The five attribute values include categories, ranks, counts, amounts, and ratios. Categories are groups of similar things that help you organize and make sense of your data. Rank was described as a feature, ordered from highest to lowest. They also tell us when we should use ranks when direct measures are difficult to obtain. Counts and amounts show you the total number of your data. The count is the actual number that appears on the map, and the amount is a quantitative value associated with the feature of the number. Ratios show the relationship between two quantities. 

Chapter 2 – Chapter 2 mostly discusses how to use maps in GIS and make them readable for everyone. In the first paragraph of the chapter, it asks why we should use maps and why they are important. The example they used was about the police, where crimes are in the area, and what they were. As they tell us, some helpful things to look for when gathering information for mapping. The book’s example was to see where customers were from, so they could put an ad in that area and attract more customers, which led to more business. As well as telling us when we are mapping, that we should have the absolute location of the area by Gps or coordinates. So we can get the right area, we are mapping it out. One of the points they brought up was to make sure that we give Category values to what they are. To bring it back to the first example, when they were mapping the crimes in the area of the business, what kinds of crimes were they, and where exactly were they happening? When making the map, we can place all the figures on the same layer, but make sure they do not overlap. They point out that when we are mapping, we use different symbols for different categories to avoid confusion. The example they used was roads, where a darker, thicker line indicated a main road and a lighter, thinner line indicated a residential road. The chapter also points out that when we put it all together, the layers may reveal a new pattern between the categories. It says we can use up to 7 categories; anything more might be a little confusing to others when they look at the map. 

Chapter 3-in chapter three, they ask why mapping the most and least is important. They explain that having more qualities helps understand and find patterns, and recognizing these patterns helps associate the map with better directions and answers. To map the most and least, you map features based on a quantity associated with each. In the example in this chapter, they give us a company trying to sell children’s clothing. The Company would try to find areas where young families have set up so they could sell children’s clothes to them. Knowing the quantities helps them determine which areas to put ads in or to reach out to more people. This chapter also talks about the things we will be mapping and what they represent. Counts and amounts are one of thing they talked about for mapping. Counts are the actual number of features on the map. Ratios are another type; they show the relationship between two quantities and are expressed as the ratio of one quantity to another. Ranks are the last ones; they are listed in order from high to low. They show relative values rather than measured values. It also talks about creating a class. Once you know your quantities, you can decide what they represent on the map. By mapping individual values, you can make a more accurate map, though it may take more effort and time. Once you know how to classify your data values, you will want to create a map to inform viewers. The chapter shows us the difference between the symbols in maps. Graduated symbols, Graduated colors, Charts, Contours, and 3D perspective views are the. Based on these, you can have a better understanding of what the map is.

Chapter 1

Chapter 1 introduces GIS as a tool for identifying and making locations and patterns. Overall, this chapter felt pretty uneventful because it mostly reviewed ideas that seem straightforward, such as using maps to understand where things are and why they are there. Mitchell also focuses on the idea that GIS is not just about making maps, but about using spatial data to answer questions and support decision making. Some vocab words were vector and raster data, which are two basic ways GIS represents spatial information. Vector data uses points and lines to show exact locations. Raster data represents space as a grid of cells, with each cell holding a value, and is used for things like elevation or temp. While vector data focuses on precision and defined features, raster data is better for showing continuous patterns across an area. While this distinction is important, much of the chapter felt like setup rather than new information. Even though the chapter was not very exciting, it did help establish the foundation for the rest of the book. It clearly explains why spatial thinking matters and how GIS can be used to identify patterns and relationships that might not be obvious in other data.

Chapter 2

In chapter 2, the focus shifts to how identifying locations and features helps explain the patterns you start to notice on a map. A lot of this chapter is about making choices, like deciding what information is actually worth mapping and how that choice affects what the map shows. GIS can take things like addresses or latitude and longitude points and turn them into mapped features, which helps give structure to otherwise scattered information.

I found it interesting how much emphasis was placed on categorizing features, since mapping by category can change how a place is understood. At the same time, the chapter points out that too many categories can make a map hard to read, which is why it suggests keeping the number fairly limited. ArcGIS basemaps also came up as a way to give context to your data. When looking at geographic patterns, features can appear clustered, evenly spaced, or random, and mapping the highest and lowest values adds another layer of meaning to the patterns you see.

Chapter 3

In Chapter 3, it really builds on the earlier chapters by showing how much interpretation and decision goes into making a map. The chapter made it clear that maps are not neutral, since every choice really illustrates something to the observer or maker. The purpose of the map shapes how information is presented, whether you are trying to simply observe relationships or highlight a specific pattern. This chapter also discusses different types of quantities, such as counts, ratios, and ranks, and explains that each needs to be represented in a different way than the other.

I liked that this chapter emphasized having a clear question before choosing an analysis method. It showed that GIS is a step by step process rather than just experimenting with tools. This chapter connected the ideas from earlier chapters to real applications and made GIS feel more concrete and useful. Overall, this chapter was the most helpful in terms of understanding how GIS can be applied.

Chapter 1: 

 

Throughout this first chapter, we get an introduction to the world of GIS through various definitions and skill sets that we will apply when using the arcGIS database later in the course. 

GIS is defined as a process of looking at geographic patterns in data as well as the relationship between features. GIS analysis in turn helps you see patterns and relationships in geographic data with results that give you insight into a place, helps focus actions, or helps you choose the best option. We also learn that spatial data is more abundant than ever and even has new sources such as Lidar and drones which were brought upon by Gis being shared more openly leading to advances in Gis software. Through GIS, you are able to employ spatial analysis and address pressing issues throughout the world. You can figure out why things are the way they are through accurate and up-to-date information (GIS allows you to create new information). The information that you find and create then helps you gain a more distinct understanding of a place, make the best choices, or be able to prepare for future events and conditions. In order to do GIS analysis effectively, you need to know how to structure your analysis and you have to be able to understand tools to use for specific tasks. You need to understand how to frame the question, understand your data, choose a method, and process the data.  To aid in this there are different types of features including discrete features, continuous phenomena, interpolation, features summarized by data, representing geographic features with sub sections for vectors and rosters. When doing map projections and coordinate systems, all data layers should be in the same projection and coordinate system. This ensures accurate results when combining the layers in order to see relationships. We need to consider geographical attributes such as categories, ranks, counts, amounts, and ratios. Adding to this, when you work with data tables, you need to understand selecting, calculating, and summarizing. 

chapter 2:

In the second chapter we learn more about how to look for locations and features and how that helps you begin explaining the cause for the patterns that you found and observed. When deciding what to map, you interpret your information based on the features you need to display and the understanding that you need to display them based on the information you need and how the map is used. You can use GIS to map the location of information like a street sign, address, or latitude/longitude values- these are read by GIS and then appropriately assigned category values. GIS is able to store the location of each feature of geographic coordinates as a set of coordinate pairs that are able to define it’s shape. You can map all features in a data layer ar a subset based on a category value. This is more commonly done for individual locations, sharing a subset of continuous data leaves the feature without a context. Through mapping by category, you can provide an understanding of how a place functions. Connecting with this, you can not display more than seven categories because anything over seven will be confusing and hard to understand when people are interpreting your maps/graphs. If you do use more than seven categories, you can make the graph easier to understand and differentiate by using symbols to display categories. ArcGIS has basemaps that you can use for mapping reference pictures in your own work. When analyzing geographic patterns, you may be able to see patterns in data. In Single categories, features may seem clustered, uniform, or randomly distributed. When mapping the most and least, you map features based on a quantity that is associated with each- this adds an additional level of information. 

Chapter 3:

Chapter three is almost a reiteration of information from the past chapters, explaining how to do data processes that we will be doing once we personally begin mapping on GIS. We again learn about mapping most to least and the idea that you need to map features based on a quantity that is associated with each number or group. You can map quantities associated with discrete features, continuous phenomena, or data summarized by area. We also learn again why we need to map and how mapping features and patterns with similar values helps you see where the most and least (as referred to earlier), are. Discrete features can be seen as individual locations, linear features, or areas. With locations and linear features, they are usually represented with graduated symbols with areas shaded to represent quantities. When referring to continuous phenomena, it can be seen as defined areas or a surface of continuous values. These area as portrayed using graduated colors, contours, or even a 3D perspective view. When you are summarizing data by area, it is usually displayed by shaded area based on its value. You can als ouse charts and shows the amount of each category that is in each area. You are able to summarize individual locations, linear features, or areas. While remembering the purpose of your map and what you are intending to show, you need to decide how to present the information that is being displayed on your map. When you map the most and least, you assign symbols to features that are based on a character or attribute containing a quantity. These quantities can be counts, amounts, ratios, or ranks. After deciding the quantities you want, you then need to decide how to represent them on the map. 

GIS Week 2 HW

 

CH 1: 

I found this chapter very introductory and not super informative as it was introducing the basics of GIS a lot of which I’m already generally familiar with. It focused on how to structure and approach GIS projects and gave steps on how to process your data within a given project which will be helpful I imagine for this course. I found the section labeled “Types of Features” a little hard to understand as its wording was very technical and lacked some background explanation of what these different feature types were. I found the part about Discrete Features especially confusing but hopefully these are things that I will sort out as I work through projects. The section on Vector and Raster made a little more sense to me as they are two different models used for different GIS projects depending on the data group you are trying to represent but I also think this section lacked a little verbiage or examples that would help break down the concepts. Another large focus of this chapter was on types of attribute values such as ratios which help represent the relationship between two quantities and these are made by dividing one quantity by another. The final part of this chapter talked about how to interact with data tables by selecting, calculating, and summarizing them. I again found this section a bit dry and hard to understand but I imagine this will make sense once we start using them. 

 

CH 2: 

Chapter two starts to go a little more in depth in why we use maps to represent data and how this can and should be presented to your given audience. It starts off by discussing why we map where things are and the main reason why we do this is to see if there are any patterns or trends that overlap and relate to each other such as a relationship between assault crimes and auto theft. We also map things where they are to see if correlation equals causation as a data point’s physical location can affect what it represents and why it represents it. Another interesting part of this chapter is the small section on how you present you maps to your target audience as there are effective ways to represent your data and results and very ineffective ways so choosing the right map size, boundaries, and reference points can change how the info is comprehended. The next half of the chapter becomes a bit more technical with how GIS forms maps and what it does with the data points its given. It talks about very basic one layer maps that businesses can use for customer data too maps with many layers to represent multiple data points can possible correlations between them. It also discuses mapping by categories which allows for more specific data representation such as types of roads instead of simply roads. Categorizing data again allows for more accurate mapping and data representation. 

 

CH 3: 

Chapter 3 seemed the most interesting to me. I’ve never thought about the very basic use of numbers for understanding things as it seems so simple but this chapter discusses how having it or not makes a huge difference in what you represent. I found all the maps at the start of the chapter showing various symbols used to represent numbers very interesting as they all subconsciously convey their point but looking at it from this perspective helps understand 1) how simple yet complex these decisions of data representation are and 2) how you can represent the same data set in two different ways and have the understanding be completely different. I think the question of “Are you exploring data or representing a map” is a very key point for GIS and something I need to keep in the front of my mind when working on GIS projects. I think this question shows the two sides of mapping which are maps that you create strictly for analysis of numbers and data, and then maps that you create to represent and communicate social issues or other things that numbers alone can’t represent. This chapter also features some technical topics such as classes and outliers which are things you need to understand to make sure you create an effective map. Overall I liked this chapter the most because it branched away from the technical side of GIS and began discussing the importance of map representation and presentation which are the aspects of GIS that I am most interested in. 

Chapter 1:

 

The first chapter I found to be rather uneventful. Merely being a basic rundown of the different features of GIS software and general GIS terminology students should be aware of. I personally found the way the textbook categorized the terminology to be somewhat confusing at best and forcing me to re-read the same pages several times at worst. The way I understood the textbook’s definitions of different GIS uses was that they are divided into two categories: Discrete locations which represent individual points, lines and areas which are easily separable and can be viewed in a vacuum, and continuous phenomena which describe much more intertwined data that relies on the geography surrounding it. (Such as ground elevation or surface temperature.)

Other features discussed in this chapter include how GIS software can present geographic attributes relative to the different points/regions/whatever put on the map. Although this is at first glance a very useful feature, I have general concerns as to the level of data these charts can present. The textbook categorizes the types by value but I know for a fact I would want to put more “general” notes on the maps I create (yeah, I am that guy) and my experience using different specialty software to create generalized notations have varied widely and if I do pursue the geographic analysis of the Whitechapel murders in this class I know for a fact I would be adding notes pretty much everywhere and the examples of the data tables in the textbook shows very little capacity for my notes and whatnot.

 

Chapter 2:

 

The beginning of the second chapter talks about the practical applications of geography and cartography. I think given how much people still rely on maps, even in the digital age, the points given are rather obvious. Therefore most of my notes will be on the reminder of the chapter. 

This is a small thing but I love the emphasis on geographic coordinates the textbook stresses. Reading and writing coordinates is a very useful but sadly dying skill and I’m glad that it will play an important role in this class and that we will be learning the exact coordinates of the points of data plotted on our maps.

I’m also very excited to use the subsets on my maps. I personally love organizing things and reading this chapter I was absolutely blown away by the amount of options the textbook referenced how the various data points could be organized by and how you have the option to only view data points under a certain classification in your analysis or even displaying the features by type. (It’s probably dangerous for me to have this power.) The chapter also discussed the visual science of analyzing a map, such as organizing data points into broad categories to avoid confusion and labeling the data points in different colors, but no more than seven colors. 

 

Chapter 3:

 

This chapter summed up the critical thinking aspect of mapmaking quite well and stressed the idea of finding relationships in the data across geographic and numerical lines. (or as the textbook referred to it, “mapping the most and least”) This is also where the textbook begins discussing the mathematical values within geography. Many instances reminded me of my high school statistics class.

Luckily for me, most GIS softwares (at least according to the reading) can aid in calculating the stats and preparing them for analysis. 

However, the classification schemes look rather difficult to deal with. Midway through the chapter, there are several tables displaying the advantages and disadvantages of using each one. (Nothing worth doing is easy I guess) Given the rather point focused data I will be experimenting with in this class, I wonder if any of these classification schemes are applicable, or even worth doing in the first place since many of the examples in the textbook using them represented maps analyzing continuous phenomena and/or maps with different data values per region that are being analyzed. 

Moving towards the realm of the types of digital maps presented by the textbook, they show a similar story to the classification schemes in regards to practicality in terms of the situation at hand. I will admit I found the 3D maps to be a somewhat corny feature at best and an outright terrible way to display data at worst. Though it is undoubtedly the most versatile of the map types discussed in the textbook in terms of what can be marked, it still (in my opinion) shows very little point by point data, can be very confusing to read, and honestly just looks tacky.

I know for a fact that the Graduated Symbol map would be my go to on the more independent projects for they look ideal for the kind of geographical criminal profiling I want to do in this course.

Chapter 1: Geographic Thinking and GIS Analysis

Chapter 1 really opened my eyes to the bigger picture of GIS. I had always thought of GIS as just software for making maps, but Mitchell emphasizes that it’s really a way of thinking about the world spatially. The chapter introduces geographic thinking, which is about considering location, proximity, and spatial relationships when analyzing any data. It made me realize that the “where” is often just as important as the “what.” GIS is not just a tool; it’s a framework for asking meaningful questions, and the software is just one way to explore the answers.

Another key point was the idea of scale and how it affects patterns. What looks like a cluster at one scale can appear completely different at another. This made me reflect on how careful we need to be when interpreting maps—seeing a pattern doesn’t automatically mean something significant is happening. I also appreciated the discussion on vector and raster models, even though raster still feels a little tricky to wrap my head around. Vector models, using points, lines, and polygons, felt more intuitive, especially for plotting discrete events like crime locations or schools.

Overall, this chapter helped me see GIS as more than just technical steps; it’s a mindset. Thinking geographically forces me to consider relationships I might otherwise ignore, like how environmental factors relate to population density or how distance influences access to resources. I’m curious to see how this perspective will shape the way we approach actual map creation in class, and I wonder how geographic thinking can help tackle complex problems when data is incomplete or messy.

Key Concepts: Geographic thinking, GIS analysis, spatial patterns, scale

Questions: How do analysts avoid bias in interpreting spatial patterns? How does scale influence the conclusions drawn from GIS data?

Chapter 2: Understanding Geographic Data

Chapter 2 shifted my focus from thinking about GIS conceptually to thinking about the actual data that feeds it. Mitchell explains that understanding data is just as important as knowing the software because poor data choices lead to misleading results. The distinction between vector and raster data was useful. Vector data feels more tangible—points, lines, and polygons that represent features like roads or buildings—while raster data is more abstract, representing continuous surfaces like elevation or temperature. I think I’ll need to practice with raster more to feel comfortable using it in analysis.

Attribute data also stood out to me because it shows that location alone isn’t enough. For example, plotting all the schools in a city is informative, but adding enrollment numbers or funding data allows for meaningful comparisons. I was surprised at how many factors affect data quality—accuracy, resolution, completeness—and how each one can influence the results. It made me appreciate how critical it is to assess the data before running any analysis.

I also liked the practical examples in this chapter about choosing the right data for a map’s purpose. A city council zoning map needs different detail than a map showing air pollution trends, and understanding these differences is key to making effective, useful maps. This chapter made me think more critically about the data we’ll use in GIS assignments and how important it is to know both the strengths and limitations of each dataset.

Key Concepts: Vector data, raster data, attribute data, data quality

Questions: How do analysts decide which data model works best for a project? How can low-quality or incomplete data be handled responsibly in analysis?

Chapter 3: Exploring Geographic Patterns

Chapter 3 felt the most practical and immediately applicable of the three. Mitchell dives into identifying and interpreting geographic patterns, like clustering, dispersion, and trends. What really stood out to me was the idea of “most and least”—using maps to show where the most or least of something occurs. This seems simple, but I can see how it would be incredibly powerful in fields like public health, urban planning, or environmental monitoring. I was also struck by how often statistics are intertwined with map-making, which reminded me of my high school stats class and the maps we used to analyze datasets.

A major takeaway was the difference between maps designed for analysis versus maps designed for communication. Analytical maps might include more detail for exploring data, while presentation maps should simplify the information to prevent overload. I thought this was a helpful reminder that GIS isn’t just about plotting data; it’s about thinking critically about your audience and how information is presented. The chapter also emphasized the importance of revising maps and being selective about what to include, which makes me realize how iterative the map-making process really is.

I found myself reflecting on the ethical implications of maps. Since patterns can suggest relationships that aren’t necessarily causal, it’s important to be honest about what a map can and cannot show. This chapter made me excited to start creating our own maps while keeping in mind both accuracy and clarity.

Key Concepts: Clustering, dispersion, trends, exploratory spatial analysis

Questions: How can uncertainty be effectively communicated on maps? What ethical responsibilities should map creators consider when visualizing sensitive data?

Chapter One

The most important part of this chapter for me was the statement about what GIS analysis is, “lets you see patterns and relationships in your geographic data.” However, I find it difficult to read all of this and not have practice with it right away. There is so much to this. I truly feel like I do not understand what this is all about. One point the text made is that using analysis will give you insights to focus your study of different areas. GIS helps with trends and patterns. I liked the example of the process that is followed. It is similar to the scientific method: Framing the question, understanding the data, choosing the right analytical method, processing the data, then examining and interpreting the results to create your conclusions. The vector vs raster representations confused me a little. Discrete values which are specific points or lines are usually represented with vector data. Continuous data are usually represented with raster. However the text notes that any feature can be represented using either method. Discrete values that are in layers may use raster as well. However, when looking at some of the examples, I didn’t see much of a difference. The various attributes section was much easier to understand. These descriptors give meaning to different patterns. I think overall, my problem with reading through all of this is that there is a disconnect between the reading and actually experimenting with the software. It is clear however, that GIS isn’t just playing around with maps, it is a methodological approach to understanding the data provided by the maps. 

 

Chapter Two

Mapping Where Things are helped explain how we will make our own maps. One point that the book made that stood out to me is that we should only put on the map what needs to be displayed so it doesn’t take away from the overall effect of the map. What is actually mapped is decided on the purpose of the map and who will be seeing it. When preparing the map each feature will need to have coordinates. Sometimes these coordinates are in the software, but sometimes they have to be hand entered. When different features are mapped by type, you create a symbol for each type. If that map only has one type of data, all the points will use the same symbol. However subsets help show more patterns. Sometimes it may be necessary to create two maps so that it is easier to discern what is being shown. One thing I really took away from this chapter is that there should be a limit of seven categories. More than that gets confusing for viewers. However, too few categories may not make the point you are trying to show with your map. Another point the text made was that the creator needs to be careful that the map is portraying what they are after. This chapter did help clear some things up for me and help with the feeling of being overwhelmed. The emphasis on preparing the data seems tedious, but choosing the symbols and categories seems interesting. I also like that the chapter talked about how different colors and widths of lines can be very helpful. I thought it was interesting that it said that printed maps are easier to see than those on a screen, but I would argue that with many screens today, it may be easier to view on a screen. 

 

Chapter Three

 

Chapter three builds on Chapter two significantly. The purpose of what you are doing helps you determine how to present the information of a map. For example, you may just be looking at the given relationships, but you may be trying to show a specific pattern. When mapping quantities such as the most or the least of something, this helps to not only see relationships, but also to make decisions based on those relationships. There are different quantities to consider in mapping, depending on what you are trying to portray. There are counts, rations, and ranks. These need to be represented in different ways. I liked how the book points out that knowing what you are mapping helps you present it. The new concepts to this chapter are about classification. Natural Breaks, Quantile, Equal Interval, and Standard Deviation are discussed. Jenks was a new word for me. Each of these classifications are important, but selecting the one that fits what you are presenting is important. Symbolization and cartographic displays are also a part of this chapter. This is the concept of choosing the elements that help to visually represent the data how you want it represented. Once a map is mad, patterns should be looked for as well as outliers. I like how the chapter helps me understand how the maps are up to interpretation. However, the choices made for a map communicate many things. Making the map visually pleasing is extremely important because it weighs heavily in how viewers are able to interpret the data.