Geography 291: Geospatial Analysis with Desktop GIS

Chapter 1

The first chapter was able to give me a better understanding of what GIS is. It is a process looking at Geographic patterns in data and relationships between features. It was able to tell me the process I will go through when performing an analysis; the steps are as follows frame the question, understand your data, choose a method, process the data, and look at the results. It is important to look at your data and figure out how it can be analyzed before beginning in order to make the process easier. There are three geographic features they are discrete, continuous phenomena, or summarized by area .Each feature is used differently to find answers to a question. These geographic features can be represented in two different ways vector and raster. The vector method shows features of discrete locations and can even be used to pinpoint that spot of a crime. The raster model looks much different, features are represented as a matrix of cells in a continuous space. Depending on the size of cell for a raster layer it will affect the results of the analysis. I found it interesting that in each model certain features are better represented. Next the chapter talked about map projections and coordinate systems. It discussed that map projections can distort shapes of the features being displayed but generally when making maps of small areas that error is negligible. I found it interesting that the type of analysis you preform depends on the geographic attributes. The attribute values are categories, ranks, counts, amounts, and ratios.  Finally, the chapter ends by talking about an important part of GIS analysis this is the data tables that contain attribute values and summary statistics.

Chapter 2

Chapter 2 begins by talking about why map where things are.  Personally I would like to work in conservation so this next part stuck out to me. It discussed how mapping can show you places where you need to take action and when applied to conservation that could be destroyed Wetlands or protected habitats. What really helped me understand each of these chapters better was the real life examples they used. Deciding what to map actually was an easy concept for me to understand. For example how a police department may use GIS in order to figure out where the highest concentration of crimes are in an area. Each section was very detailed and helped me understand how to properly use GIS maps in the most efficient way. Additionally this chapter thoroughly listed out each step before and after making a map which helped me better understand the process. An important note was about how even basic maps that show where features are can reveal important patterns. The GIS uses the coordinates that you put in to draw the features,  individual locations, linear features, and areas all will display something different depending on what you want to see .Although this chapter makes GIS seem easy to understand I’m sure there will be difficulties when using it in the future. I am excited to start the next step of applying my knowledge to the ARGIS program.

 

Chapter 3

In chapter 3 it’s really interesting to see what goes into mapping and how it is used to scale out the variety of businesses shown as examples on the first few pages. There are an endless amount of items you can map out, from the book we can see examples such as discrete features, continuous phenomena, or data summarized. From the text, most maps that show data summarized are expressed through shading each area that is also based upon its value or using charts to show the amount of each category in each area. It is surprising to me that business maps should not be grouped by employees because block groups vary so much in size. While on the other hand larger block groups should have more workers but can be more spread out. They would need to map workers by the square miles so that you can see some distribution. There is a way to use ratios in a mapping situation, it’s fun to hear the use of ratios can even out a difference in larger and smaller areas, or also areas with many different features even with the ones with a few. Some of the more common ratios that are used in a mapping setting seem to be averages, proportions, and even some densities. When classes are being represented there are features with some similar values by assigning them the same symbol. Having these makes you see some less features while also seeing features with similar values. Classes should be made manually if you are looking for a feature that has a specific criteria or a comparing feature in a specific meaningful value. The maps that are shown on page 69 and explained on 68 were really interesting to see because the similarities in visual representation in the natural breaks (Jenks) and quantile were so fun to look at because it seems as if they are representing the same thing. Also keeping the data simple while explaining and showing expressive topics seems very hard but with the GIS system it has to be done to help successfully make a map.

Chapter 1

With GIS it is so fascinating that you are able to predict and prepare for future outcomes using any model you create. Mapping has come a long way, from inaccurate drawings when people would sail around and map the area, all the way to being able to create new maps and find patterns within these maps to more easily understand your surroundings. I like how simple and straightforward it is performing this analysis, just find the data and choose a method then see your results. It seems pretty straightforward with set rules you follow. The different types of features are simple to understand with clear directions of what they are and what they look like. The chapter explained how you can have different types of maps, like vector or raster, they can have harsh lines showing the barriers of different layers, but also soft layers blending them together. Although this can cause information to be lost because of the change in cell size and can affect results of the analysis. It’s important to have all the data layers in the same map projection and coordinate system to make sure you have accurate results. I wonder if there is a way to change the distortion you would get when mapping a larger area, if you can calculate for the curvature of the earth. The categories are good, there isn’t one word for everything but groups of words that mean the same thing and can help describe specific things to break it down or generalize it to see the bigger picture. There are also ranks, which put things in order from high to low. Counts and amounts show the actual number of features on the map, it lets you see the value to compare it to other features. Finally, ratios show the relationship between two quantities divided by one quantity, the map will be able to more accurately show the distribution of features.

Chapter 2 

In Chapter 2 we learn to map where things are, we decide what to map, and analyze geographic patterns. We were able to find what we’re looking for and where to take action. I can use a map to identify individual features or look for patterns in the distribution of the features, the maps are able to identify and help find patterns in any information you input into it. Mapping where things are can help one see a visual concentration of the data they are using and solve their problem, or find out if it was just random occurrences and then you would need either more information or change the method you are using. It is important for the map to be appropriate for the audience you are showing it to, if it is for someone who is just learning about this it should be short and not clouded with information that isn’t necessary to them. If it is someone who needs to know a lot of information in one spot it should be well organized with a good system to identify what they are looking for. The chapter talks about making sure to have geographic coordinates input and assign them to your data, you can put it into the GIS database, or it might already exist in another program or in the database. Basic maps can use the same symbol, this can help someone notice patterns easily and solve their problem or question. GIS can draw symbols to identify places or draw linear features that describe streets, rivers or sidewalks, it can also fill plots of land with color or patterns. When you map an area that is large in comparison to the size of the features, it’s best to not use more than 7 categories because it will make the patterns difficult to see, if there are less categories it will make the patterns clearer. The chapter states how it is important to use statistics to measure and find the relationships between your features. 

Chapter 3 

In chapter three, we learn about comparing places based on quantities so we can see which places meet our criteria. We learned what we need to map, understanding quantities, making a map, and looking for patterns. When we map based on quantities and add a level of information past mapping the locations and features, we will be able to see patterns much clearer and realize what we need to do. Learning how to map features it is important to know what they are called. Discrete features can be individual locations, linear features, or areas. Continuous phenomena can be defined areas or surface continuous values, like areas displayed using graduated colors. Data summarized by area is displayed by shading each area based on its value or using charts to show the amount of each category in each area. In this chapter they talk about the importance of making sure your map is intended for the audience in mind and how the information on your map should represent that. Changing the way the question is framed and how you present your data will help reveal specific patterns. It is also important to understand quantities, the amounts ratios or ranks on your map. Counts and amounts will show the total numbers. Ratios show the relationship between two quantities, and are created by dividing one quantity by another. The most common ratios are averages, proportions, and densities. Averages help compare places that have few features and many features, you can create an average by dividing quantities that use different measures. Proportions show what part of a whole each quantity represents, to create a proportion you divide quantities that use the same measure. It’s important to note that proportions are often presented as percentages. Finally, densities show where features are concentrated, to calculate a density you divide a value by the area of the feature to get a value per unit of area. You can create ratios by adding a new field to the layers data table and calculating new values by dividing two fields containing the counts or amounts. 

Chapter 1: Introducing GIS Analysis

Chapter 1 discusses what GIS analysis is along with the types of geographic features and how they are used.  “GIS analysis is a process for looking at geographic patterns in your data and at relationships between features” (Mitchell 22).  GIS analysis is used to figure out why things are where they are and allows for data to be processed in a way that allows you to visually see how it correlates.  There are three types of geographic features, those being discrete, continuous phenomena, and summarized by area.  These geographic features have an effect on the analysis process.  Discrete features allow for the actual location to be highlighted.  Continuous phenomena is continuous data that are values such as precipitation or temperature that can be measured at any part of the map.  Features summarized by area is data that is summarized in a way that counts the total number of specifics such as the total population for example.  There are two ways of representing geographic features.  Those two ways are using different models called vector and raster.  The vector model appears to be more organized with each geographic feature being in a row that is in a table.  The raster model on the other hand the features are represented in a flush and continuous way.  Each geographic feature has a specific attribute that gives information as to what the feature is.  The different types of attribute values according to the book are categories, ranks, counts, amounts and ratios.  Categories allow data to be organized in groups which helps to condense the information.  Ranks put features in order from high to low and are specifically used when it’s hard to get direct measures.  Counts and amounts both show a total number and allows you to see what actual value of a feature is.  Ratios show the relationship between two quantities by dividing one by another.  An important part of GIS analysis is data tables.

Chapter 2: Mapping Where Things Are

Chapter 2 focuses on how maps are made, used and analyzed.  Maps allow for people to be able to see where or what something is.  By looking at these individual features it reveals patterns that can then further be analyzed.  By using these patterns it can answer questions related to individual features.  GIS can be used to map the location of different types of features to see whether certain types of these individual features occur in the same place.  The most important thing about maps is that they must use certain keys and categories to allow for the person viewing to be able to easily process the information that is being presented.  Using specific locations is also important when mapping in case someone viewing it is unfamiliar with the location.  This chapter further explains how to prepare data for creating a map and lists the most important steps being to assign geographic coordinates, and category values.  The geographic coordinates and category values allow for the information to be more identifiable to the GIS system.  For creating maps you have to use this information and tell the GIS which features you want to be shown and what symbols to attribute to them that will be drawn on the map.  For mapping a single type you use the same symbol; this is often seen on basic maps that just focus on a specific feature and what patterns can be revealed.  The GIS uses the geographic coordinates to draw the features at the coordinate that is listed.  To map areas the GIS uses these coordinates to create an outline which is then filled with a specific color or pattern.  Another way of mapping is using a subset of features which allows for all types of a specific category values to be shown which can reveal patterns because not all features are shown.  While this is helpful for mapping features in a very specific location sometimes this isn’t helpful as more context is needed.  The book uses an interesting example of types of zoning and how it might be beneficial to see all the different zones in a lighter color to be able to see the areas around in order to highlight how much area of the map this zone takes up.  To map larger areas you would have to take into consideration the scale of the map and the amount of categories that would be used and how you can group them in order to maintain the easy readability of the map.

Chapter 3: Mapping the Most and Least

Chapter 3 focused on explaining why you have to map the most and least.  This chapter also gives further information about mapping.  The reason for mapping the most and least is to be able to reveal a relationship between places.  Mapping the amount can be crucial as it gives a bigger picture besides just showing where something is which is what mapping the locations of a feature is.  Going back to the idea presented in Chapter 1 related to geographic features you can map quantities that are associated to discrete features, continuous phenomena and the data summarized by area.  By taking these geographic features into account it helps for deciding how to present a map.  I feel as though this chapter is very statistics based and focuses on how to properly document and map a specific quantity of a feature.  The chapter then further talks about how it is important to understand quantities and how when mapping the most and least you should assign specific symbols that correlate with the features that are based on the specific attributes of these quantities.  Relating back to Chapter 1, the specific attribute values can also be used here to map the most and least.  Count or an amount can be used to see the value of a feature.  Ratios can be used to help identify and reveal relationships between two quantities.  Ranks are helpful towards putting features in a specific order which is from high to low.  Taking these quantities into consideration allows for the next step which is to decide how you want this data to be displayed on the map.  I think to be able to determine which attribute value would be useful you would have to take into consideration the data you have.  Once you figure out how to classify and organize the data then a map can be created as it places the information that is given and in turn reveals patterns related to what was inputted.

Chapter 1:

The first chapter is very similar to our reading from last week, it’s obviously designed to provide a solid introduction to GIS for beginners–like myself. The chapter breaks down the core concepts of GIS, discussing how spatial data is analyzed and represented visually through maps. The goal is to highlight the uses of GIS in understanding complex geographic patterns and relationships in an easily comprehending way.

One thing I took away is the distinction between the different types of data that can be handled in GIS. These include discrete, summarized by area, and continuous data. Discrete data represents specific features like buildings or roads, while summarized by area data aggregates information, such as population density in a region. Continuous data, like temperature or elevation, is represented as a gradual change over space. The chapter really emphasizes how GIS can handle this wide range of data types, making it a versatile tool for many types of analysis.

Another concept introduced in this chapter that I find to be important is the difference between vector and raster data models. Vector data uses points, lines, and polygons to represent objects that have clear boundaries, like roads or property lines. Conversely, raster data breaks the map into a grid of cells, ideal for representing continuous phenomena like weather patterns or land elevation. The chapter taught me that understanding these models is crucial when choosing how to map and analyze data effectively.

This chapter also includes the importance of map projections, it highlights how distorting the Earth’s curved surface onto a flat map often leads to inaccuracies. Lastly, it covers how GIS combines different data sources to reveal relationships and patterns, such as linking demographic data to geographic features, which enhances the value of maps as tools for various types of analysis. Overall, the chapter sets the stage for deeper exploration into GIS analysis, emphasizing its role in visualizing and interpreting a variety of spatial data.

Chapter 2:

Chapter 2 dives into the practical process of mapping and analyzing geographic patterns, it emphasizes how the choice of data and map design can influence the clarity and effectiveness of the message conveyed through the map. The chapter discusses the importance of selecting the right amount of information based on the map’s purpose and its intended audience. For example, urban planners might need a map that categorizes different road types, while a tourist map of a park should keep information simple for easy navigation. Too much detail can overwhelm the viewer, while too little can obscure key insights.

One of the applications for GIS that I find most fascinating is discussed in this chapter; the use of GIS to map crime rates in a city, helping law enforcement allocate resources more effectively. This highlights how GIS can be applied in unexpected areas like public safety, showing its versatility in various fields. The chapter also introduces the concept of finding the “center” of a cluster of features, using measures like the mean center or median center to identify patterns. Which reminds me of the use of GIS discussed in our reading from last week to find the source of contamination for a Cholera epidemic. However, the chapter also notes that outliers can skew these results, especially when there are fewer data points, emphasizing the need for accurate data input and careful organization.

Another key takeaway from this chapter is the power of layering data in GIS. Combining demographic data with environmental information on a single map gives the opportunity for deeper analysis and insight. This capability shows how the same dataset can be regrouped and analyzed from different perspectives. The chapter also touches on the technical side of mapping, including coding challenges, though it acknowledges that some of the more technical aspects can be difficult for beginners. As a whole, the chapter provides a solid foundation for understanding how GIS can reveal patterns and relationships, while also highlighting the importance of accuracy and data organization.

Chapter 3:

The third chapter takes a deeper dive into how GIS can be used to map and analyze numerical data in order to uncover trends and patterns. This chapter builds on the concepts introduced in the first two chapters, focusing on how the different types of data—discrete, continuous, and summarized—impact the way maps are created and interpreted.

The chapter revisits the distinctions between these data types but goes into more detail. For example, continuous data, like rainfall, is best mapped using gradient colors to show gradual variations across a bigger area. Discrete data, like car accidents, is represented with specific points on the map. Summarized data, such as average income in a neighborhood, creates a broader view by grouping data into bigger categories, making it useful for seeing patterns across areas.

Another major focus of this chapter is on grouping data into classes to make maps easier to understand. This is done through classification methods like equal interval, quantile, or natural breaks. Choosing the right classification method is pretty much crucial, and it can significantly affect how clear and useful the map is. This process is especially important when trying to communicate complex data in a visually simple way, which is a main function of GIS.

The chapter also touches on the design elements of map-making, such as the use of colors, symbols, and even 3D effects to make maps more engaging and informative. However, it stresses the challenge of balancing aesthetics with clarity—maps need to be visually appealing but still easy to interpret.

The chapter concludes with practical tips for designing maps that suit specific analysis purposes. It ties together the concepts of data types, classification, and map design, reinforcing that good map design is essential to effective GIS analysis. One key takeaway is that how you design a map—from selecting data to choosing visual elements—is what makes GIS such a powerful tool for communicating ideas and insights.

In Chapter 1 of the Esri Guide to GIS Analysis, primarily the fundamentals of GIS are explained. GIS analysis is looking at geographic patterns within specific data and looking at the connection relationships. The steps to GIS analysis include asking a specific question, choosing the method that works for the data you are trying to discover, processing the data, and reading the results. Similar to GIS analysis I’ve participated in biological studies where it is better to get a more specific question when doing an experiment to get specific data. When reading the results at the end, there are specific types of features to look out for on the map. Those include discrete, continuous phenomena, or summarized by area. To me I would think discrete would not mean any specific location, but in fact that is quite the opposite. To me, I feel the most common feature is the features summarized by area. I also feel they are the easiest to read because of the clear area boundaries that they have. The two models that represent features are vector and raster models. I prefer the vector models because I prefer having hard boundaries when reading a map in most instances.

In Chapter 2 it features the actual mapping process. It emphasizes the need to carefully select the amount and type of information included in a map, depending on its intended purpose and audience. For example, urban planners may require a map with categorized road types to inform their decision-making, while a tourist map of a park should prioritize simpler information to aid navigation. Including too many categories or too little can either overwhelm the user or make the map difficult to use. The chapter also covers various methods for analyzing geographic distributions, such as finding the “center” of a cluster of features, which can be defined using different statistical measures like mean center, median center, or central feature. These centers help understand patterns like crime distributions or the most central locations in a set of data points. For example, a crime analyst may use GIS to track changes in crime patterns by comparing the center of auto thefts during different times of day. A key takeaway is that outliers can skew the results of these calculations, especially when there are fewer data points. Additionally, the chapter discusses how GIS maps rely on coordinate systems and data tables to assign locations and generate visualizations. The complexity of a map should align with its objective, balancing enough detail to convey meaningful patterns without overwhelming the viewer. Proper map scaling and categorization are essential for clarity, as too much detail or too broad a focus can obscure the main message the map is meant to communicate.

Chapter 3 of The Esri Guide to GIS Analysis, Volume 1 focuses on mapping quantities to reveal patterns and relationships between features. The key idea is that mapping the most and the least of something helps identify areas that meet specific criteria or require more resources. The type of data being mapped—whether counts, amounts, ratios, or ranks—determines how it should be represented. Once the data is classified, the map can use different symbols or group values into classes to make the patterns easier to visualize. To map quantities effectively, a standard classification scheme such as natural breaks (Jenks), quantile, equal interval, or standard deviation is used to group similar values. This helps identify patterns like clusters or trends in the data. Visualizing the data with bar charts can also aid in selecting the right classification scheme. Several mapping techniques are discussed in the chapter based on the type of data and features being mapped. Graduated symbols are ideal for mapping discrete locations, lines, or areas, while graduated colors are better suited for discrete areas or continuous phenomena. Charts are used to map data summarized by area, and contour lines show the rate of change in values across a spatial area. For visualizing continuous data, 3D perspectives are employed, where the viewer’s position and other factors like the z-factor are manipulated to provide a detailed view of the surface. The chapter stresses the importance of selecting the right map type and classification method based on the data’s characteristics and the map’s purpose. A well-designed map will clearly highlight where the highest and lowest values are, providing valuable insights into the distribution of the data.

Chapter 1

This first chapter gave me a very similar vibe to that of the first reading we were assigned. It very much had an intro style intended to introduce new GIS’ers such as ourselves to key ideas, such as understanding the basics of data tables and different identifiers on a GIS map.  To be honest, as much as I’m “understanding” the concepts that this chapter discusses, I’m definitely feeling some weird gap between hearing and seeing. By this I am meaning that without seeing more active examples its a bit harder to interpret the exact usage of any given features or attributes. However, despite my confusion I am seeing how when diving into the GIS application these concepts may come easier since I’ve already seen them. One notable thing that started to make me think this way is the difference between raster models and vector models. I think I sorta understand the general idea of what separates the two, but without using it and having to deal with the actual software I feel like I’m missing some pieces of the puzzle. Another good example would be continuous and discrete features, once again giving me a good general idea, but leaving a few foggy spots in my head. I think what I really took from this chapter is the mindset for acquiring the data needed to be able to make a map in the first place. It really sent it home for me that without data this whole application and process can be kinda useless.

Chapter 2

This chapter really drove home the idea of GIS being a tool for optimal human visualization. Pretty much everything talked about in this chapter at some point mentioned the way in which what is being map is going to be interpreted by another human being. When it comes to roads on a map it is important to have some sort of distinction between the different types of roads in a way that is easily perceivable among the other layers you’ve implemented. This specific example does not always apply though, the information that you want your map to have on it heavily depends on the audience that you intend to see and use the map. With the road example it would be very convenient for an urban planner to have all the roads on a map categorized in order to properly plan around them, but if your map is made to help tourists navigate some sort of park the addition of road categorization may take away from the information they need to not get eaten in the woods. It is important to be able to find the correct amount of information to feed into your map depending on what your are trying to convey and the space that you physically have to apply categories and coordinates to. If you do not have enough information layered on your map it could become very easy for the user  to be unable to locate the information they need. It is also equally easy to include too many categories to your map making it far too hard for anyone to reasonably navigate and use it. It is imperative to apply the correct data, and correct amount of the correct data to your map to make it legible.

Chapter 3

I’m immediately having issues figuring out how to properly write 300’ish words for this chapter, its about putting numbers on a map. However, I see the importance and value that goes along with knowing more about the process and ways that numbers can be visually interpreted through a map. One of the biggest factors relating to displaying numbers in your map is the generalization of those numbers. It is possible to be very specific with your data or generalistic and still properly convey the information. Within this idea of trying not to overcomplicate or undersell the information that your are mapping we can use similar ideas to that of the past chapters and integrate more systems of categorization. Using what are referred to as classes it can be easy to solve the problems surrounding the possible comprehension of your work. Further, there are even more labels for identification within the class system that can be used to organize, such as Natural Breaks or Equal Interval. At this point I can say that my mind is confidently jumbled with all of this information. Once again I think I’m understanding the descriptions and ideas being displayed to me, but without using the program yet I’m still kind of unsure how to properly implement all of these tools. The chapter moves on to talk about Graduated Symbols and Colors and I cant entirely tell if they are supposed to be within the class system or not, same for the charts. From this whole book I’m seeing the importance of organization and keeping things legible, but its hard to separate all of the different tools and situations I’ve been learning about without trying to use them. A lot of these ideas are blending together in my brain as all of them are just different ways of categorizing and organizing information to make it an interpretable as possible for the intended viewer. For example I understand the difference between Charts and Classes, but I doubt I could figure out how to use them in GIS. I may be thinking about this a but too deep for the time being though, I assume the class will dive deeper into the actual application of these ideas in the future, but I still feel some sense of confusion for sure.

Chapter 1: Introduction to GIS Basics

Chapter 1 gives a basic overview of Geographic Information Systems (GIS) and how they’re used to analyze spatial data and create maps. Since I don’t have much experience with GIS, this chapter was a great starting point to understand what it’s all about.

One of the main things I learned is that GIS can represent data in three ways: discrete, summarized by area, and continuous. Discrete data is about specific things like buildings or roads. Summarized data looks at groups, like population in a city. Continuous data, like temperature or elevation, shows gradual changes over a whole area. This helped me see how flexible GIS is.

The chapter also explains two main ways to show geographic data: vector and raster. Vector data uses exact coordinates to map things with clear boundaries, like property lines. Raster data breaks the map into a grid, which works better for stuff like weather patterns.

I also found it interesting how mapping large areas can cause distortion because the Earth is round, but maps are flat. Choosing the right map projection is a big deal to avoid these issues.

Another cool part was learning how GIS combines data. For example, you can link a table of population stats to a map of neighborhoods to see patterns. This connection between data and visuals is what makes GIS so powerful.

Chapter 2: The Importance of Mapping Locations

Chapter 2 talks about why mapping locations is so useful and how it can show patterns and connections you might not notice otherwise. For example, mapping crime data helps police know where to focus resources, and mapping health data can highlight areas that need more support.

One thing I found really interesting was how GIS can layer data. For example, you could map income levels and air pollution on the same map to see how they’re related. This layering makes GIS super versatile.

The chapter also points out how mistakes in data can mess up your results. If coordinates or other details are wrong, it can throw off the whole analysis. That’s why being careful with data is so important.

There’s a section on the technical side of GIS, like coding and making sure different data formats work together. Some of it was a little hard to follow, but it shows how much precision GIS needs.

Another thing I learned was about scale and resolution. A small-scale map shows a big area but with less detail, while a large-scale map focuses on a smaller area with more detail. Knowing this helps you pick the right map for your goal.

Chapter 3: Mapping Quantities

Chapter 3 dives into how GIS can map numbers to spot trends and patterns. It builds on what was covered in the first two chapters and gets into the details of how different types of data affect the maps you make.

It went over discrete, continuous, and summarized data again, but in more detail. For example, if you’re mapping rainfall, you’d use continuous data. If you’re mapping car accidents, you’d use discrete points. Summarized data, like average income in a neighborhood, gives a bigger picture.

A big focus was on how to group data into classes to make maps easier to read. You can do this manually or use methods like equal interval, quantile, or natural breaks. Picking the right method makes a big difference in how clear and useful the map is.

I also liked the part about using colors, symbols, and even 3D effects to make maps more engaging. But it’s tricky to balance making the map look good and keeping it easy to understand.

The chapter ends with tips for making maps that fit your purpose. It ties everything together and shows how to use what you’ve learned to make maps that really communicate your ideas. A key takeaway for me is that good map design, from picking data to deciding how it looks, is what makes GIS so powerful.

Chapter 1: Introduction to GIS Basics

Chapter 1 introduces the foundational concepts of GIS, mapping, and spatial analysis. Since my prior knowledge of GIS is minimal, this chapter served as a helpful primer. I was surprised to learn about the broad range of features that GIS can map and the various methods of representation. One concept I found particularly engaging was the differentiation between “discrete,” “summarized by area,” and “continuous phenomena.” Each type serves a unique purpose, enabling GIS to handle diverse applications. The chapter also explains the two primary methods for representing geographic features: vector and raster models. Vector models utilize x and y coordinates to create tables, resulting in clearly defined borders and shapes. In contrast, raster models employ grids of cells, creating a smoother, layered representation. The side-by-side visual comparisons of vector and raster maps clarified when and why to use each model.Another fascinating aspect was the issue of distortion when mapping large areas due to the Earth’s curvature. This challenge highlights the complexity of GIS at scale. The chapter concludes with an overview of attribute values and their applications, offering practical examples and guiding the reader through data table integration within GIS systems.

Chapter 2: The Importance of Mapping Locations

Chapter 2 explores the significance of mapping locations and how this can reveal patterns and relationships. For example, mapping crime rates in a city helps law enforcement allocate resources more effectively. I found it fascinating to see how GIS is applied in unexpected fields, such as public safety. One takeaway from this chapter is the potential for human error to impact GIS accuracy. The text emphasizes the importance of meticulous data input and organization. Additionally, the ability to layer data on a single map—such as combining demographic and environmental information—underscores GIS’s versatility. This capability enables the same dataset to be regrouped for different analytical purposes. The chapter also touched on coding and the technical challenges associated with mapping. While I’m still grappling with some technical details, I appreciate the book’s effort to clarify common questions and explain the functions of various GIS features.

Chapter 3: Mapping Quantities

Chapter 3 narrows its focus to mapping quantities and understanding spatial relationships. This approach is particularly useful for identifying trends, such as areas with the highest or lowest rates of a given phenomenon. For instance, mapping plague deaths per capita can reveal critical hotspots. Key concepts include the types of data—discrete, continuous, and summarized by area—and how they inform map design. Discrete data involves specific points, lines, or areas, while continuous data represents broader surfaces. Summarized data, on the other hand, uses categorized shaded regions. Understanding these distinctions is essential for accurate representation. The chapter introduces data classification methods and their importance in creating effective maps. Classes group similar features, which can be represented manually or through classification schemes. Comparing schemes to find the optimal fit for a given dataset was particularly enlightening. The use of colors, symbols, and 3D visualizations adds depth to maps but also poses challenges in balancing clarity and detail. A key takeaway from this chapter is the “making a map” section, which provides practical guidelines for designing maps tailored to specific purposes. This chapter synthesizes concepts from Chapters 1 and 2, offering a more comprehensive understanding of GIS capabilities.

 

Chapter 1:  Chapter 1 was meant to explain the basics of GIS, mapping, and spatial analysis. I know very little about GIS, so I found this chapter to be extremely helpful in clarifying what it is meant for and how it can be used in my field. I did not realize how many different things you could map and that there were multiple different methods of mapping something using GIS. I found learning about the different features, “discrete,” “summarized by area,” and “continuous phenomena,” to be the most interesting because each of them is unique and has its own methods and distinct uses. The chapter then dives into the two methods of representing geographic features. The first is vector models, where each feature is put in a table using x and y coordinates. Vectors have harsher lines, and each area is defined by a border. On the other hand, rasters are defined by their cells. There are no harsh lines, and they are often layered. I found the example maps showing the difference between vectors and rasters the most beneficial part of this chapter. Seeing a side by side comparison helped me understand when it is best to use what and emphasized the differences between them. One thing I had not considered or realized would be a problem is trying to map large areas. Due to Earth being spherical, large mapping systems can become distorted, and misshapen. This was interesting to read about and I will definitely keep it in mind when mapping throughout the semester. One of the last things chapter one talked about was attribute values. The book gave examples of each value, when they are used, what they are best used for, and what mapping them should look like. Lastly, the chapter starts to explain how to work with data tables in the GIS system.

Chapter 2: Chapter 2 starts by asking why it is important to map where things are. It explains that mapping individual features can be useful, but mapping an entire area is important for learning more information about the area as a whole. They gave the example of mapping an entire area based on crime rates so that police know which areas need the most attention. I found this interesting because I had not considered that police and other law enforcement jobs would use GIS to help them. The purpose of this entire chapter was to answer common questions about mapping and help clarify when it is best to use different parts of the GIS system. Something I noticed while reading was that most of the problems that arise when using GIS come from human error rather than problems with programming. I am glad that this is something that is acknowledged because it helped me understand how important it is to take your time when inputting data, assigning values, assigning coordinates, and so on. I was also impressed by how many different things you can do with GIS and the fact that you can layer things so that one map provides multiple types of information. Reading about how regrouping the same data in different ways was not only interesting but also a testament to how many things you can do with GIS. Something I am still struggling with is what the codes mean for information. I feel that I am still confused about some of the technicalities that come with mapping. Overall, I found this chapter extremely helpful because it provided a lot of information and went more in-depth with different features you can utilize while mapping.

Chapter 3: The last chapter this week starts by examining a more narrow topic than the two previous chapters. It focused on mapping the most and least of something. It is most useful to map the most and least to understand relationships between places, and see if a specific place meets your specific criteria. Through all three chapters this week I found the example maps to be the most useful to me. Reading about mapping is helpful but being provided a visual helped me understand the content significantly better. Mapping quantities was a term that I found very important. Quantities are the amounts or numerical values you need to be able to map something correctly. The next important topic in Chapter 3 was classes. Classes are when similar features are assigned a matching symbol so it is easier to see what the map will look like. You can do this manually or with a classification scheme. It is also beneficial to compare different schemes to find out which one would be best for your specific map. The section that compares the different schemes and explains each of their uses helped me a lot with understanding classes as a whole. A lot of the information in this chapter reminded me statistics. The classes, quantities, and outliers all reminded me of graphing for stats. The most important takeaway I took from the assigned reading this week came from the “making a map” section of this chapter. It laid out numerous examples, the advantages and disadvantages of each type and what each type is most used for. Since there are so many terms and things to remember when it comes to GIS, I think I will end up utilizing this chapter throughout the semester.  This chapter incoorporated the terms introduced in the two previous chapters and brought it all together.

Chapter 1

I find it very intriguing how scientists are finding new ways to employ GIS aside from simply putting together maps alongside analyzing the space in and around those maps. Additionally, with the proliferation of remote sensing via drones and other contraptions, more spatial data in those harder to reach, ecologically sensitive, or other remote areas where new or different information can be recorded and applied in GIS software. 

With all of the details concerning how types of data and phenomena exist in GIS, I do wonder about the process to collect, record, and input the spatial data in order for it to be fit to analyze within the software. Is there a generalized set of data for most areas that can be accessed freely with the proper software– such as the Delaware Data from the Delaware County Auditor and co? Does the same thing exist for more precise topography around the globe? Also, who is accountable for updating data in areas lacking government use of mapping systems for tax purposes, etc.? 

Moving past that, there are the two types of representations for GIS features in vector and raster- which pique my interest in their differences. Vector models consist of XY coordinates, while raster models consist of expressions that somehow become continuous shapes. Each respective model takes up different amounts of shape and can be used for representing different types of data, but I wonder if there is another way to represent spatial data- especially since one model appears to be exponentially more complex than the other. Could there be any other way to represent continuous numeric data that would make doing so more accessible?

Chapter 2

One of the most critical aspects of mapping is that maps depict locations. Going a step further, is to use features within maps to analyze the various patterns within them in relation to locations and to each other. You could use these features to map out anything, really: bears, criminals, school zones, soybean fields, sewage leaks, etc. These features are given their own unique layer to be easily accessed and assessed. 

From that point, features are used for various purposes. If I wanted to assess the yield of soybeans, I would collect data- or review already collected data, compile and input information, then review. Once I know the yield of soybeans, I could compare previous yields and report to the Ohio Soybean Council or to the farmers directly and let them know how their soybeans are doing. Maybe there is also a pattern between the manner in which the soybeans are cultivated, such as with no-till or with limited chemical use, then I could analyze that information and communicate accordingly. Another possibility is that there is a relationship between soybean yield and location, then, I could record the coordinates of soybean fields in a particular area. I do wonder if the Ohio Soybean Council uses extensive GIS to strategically plant their monoculture fields. 

Shifting away from soybeans, GIS has a wide range of features that can be used to arrange, record, and track data. A major application of GIS mapping and analysis is for land-use and parcels, but there are many other possibilities- as established. GIS allows for easy assessment of distribution patterns from just taking a look at a zoomed-out map or through analyzing statistics for a statistically significant relationship.

Chapter 3

A highly important manner of GIS analysis is through mapping the most and least. An example of this would be mapping the amount of bubonic plague deaths per 10,000 people to detect hotspots where the bubonic plague is taking the most lives. There are three types of data: discrete, continuous, and data that is summarized by area. Discrete data represents bits of data including points of interest, lines, and areas. Meanwhile, continuous data represents entire areas or surfaces with continuous values- whereas discrete data is less encompassing. Summarized data represents shaded areas that are categorized- which can include discrete or continuous data.

The technicalities of GIS and map-making, in general, require an understanding of evaluating data and having the ability to apply that to a map. The many bits and pieces are the building blocks of GIS which allow users to visualize and express data. There are also many ways of quantitatively classifying data. Each means of classification, like the types of data, have their benefits and drawbacks that make them useful for different scenarios. Statistics play an important role in how many types of data within GIS are used and organized from standard deviation to outliers, and GIS has computing power to some extent for data classification. When creating a map, there are many options of the manner in which data is visually represented including symbols, colors, charts, contour lines, and 3D which, similarly to the ways of classifying data, have their pros and cons. The chapter provides a rudimentary guide for employing the various details that it discusses, but it is a bit difficult to retain every piece of information without something concrete to apply it to at the moment. There has been a lot of planning to shape GIS into what it is today.