Author: drobinson

Chapter 4:

This chapter covers spatial and regular databases. Using the .gdb extension, which is not a file extension but merely a folder. The first section starts us off by importing data into a project. Next using the capabilities of a database in the Catalog pane. This allows us to alter or copy geodatabases. The chapter mentions the importance of columns in data tables, where most attributes are handled. 4-2 discusses modifying attribute tables, using the calculate field tool (I found this part a bit confusing but later figured it out), and joining/exporting features. The same section also shows how to calculate fields and draw out/concatenate (basically combine; e.g., “Bob” + “Bob” = “BobBob”) string fields. The section after I thought was the most interesting, where we connect data from tables to feature classes. This is done with SQL queries regarding filtering, which contain 3 parts: the attribute name, a logical operator (<, >, etc.), and an attribute value. (Haven’t heard of SQL since my Intro to Database Fundamentals class a good minute ago.) There are 2 types of queries: simple and complex. Simple queries use 1 connector, while complex queries use 2 or more. Connected by the operators an and, or, an or. Queries can be used to filter out features based on certain characteristics like age, time, (in this chapter) type of crime, and location. Section 4-4 was a short section on how to aggregate data with spatial points. Later, the chapter showed us how to use central points for shapes. I thought of this as basically adding a dot, where the dots represent the number of burglaries by neighborhood. The last section of the chapter had us create a new table to help the user simplify data by reducing duplicates. Overall, I thought this chapter was pretty interesting to work with.

Chapter 5:

In the last chapter, we learned about filing geodatabases; in this current one, we learn more about spatial databases specifically. In the first section, we used coordinates and changed world map projections. I think I spent maybe 20-30 minutes looking at all the types of projections that the GDB file had to offer. The next section had us working within the subject, but within the U.S. Not only that, but the chapter also discusses how to set reference zones to help select projected coordinates. Based on what area the user wants to study. One of the most interesting things I learned from this chapter was how to add new layers on a coordinate system. Later, we changed the map coordinate system; this process was pretty easy. I originally thought it would be harder than that, or at least more complex. The following section has us working with vector data formats. This required importing a shapefile, adding x and y coordinate data, and finally converting a KML (Keyhole Markup Language) file into a feature class. 5-5 really gets into real-life data through the U.S. Census, requiring us to download specific data sets. I never even knew a website like that could exist. I also spent some time going through the website, checking the menus and data from other cities. After all this information was downloaded, it was entered into GIS. This section took the longest because I messed up a step along the way and had to repeat the process. When all the information is gathered, the collected data is joined and turned into a choropleth map. Learning how to download geospatial data was pretty straightforward, as was adding it to the current map. When this chapter was finished I explored some agency maps, this specific chapter piqued my curiosity.

Chapter 6:

In the previous chapter, we learned to search through geographic databases, either by collecting the information ourselves or through another source, like a government agency. The sixth chapter focuses heavily on geoprocessing and its use cases to build our areas of study. The first section of this chapter had us delete features to create new ones using the Pairwise Dissolve function. This totals the attributes using statistics like count, sum, and mean. Next, we clip (a geoprocessing tool) and extract those features for the purpose of studying them. By using Select By Attribute (this part was easy) and the Select function, a bit annoying to use because I couldn’t figure out how to use it correctly. After that, we combined water features using the merge function. This tool basically combines two or more feature classes into one. Then we used the Append tool to add data from one source to another dataset. (Haven’t heard this word since my Python Class maybe a year or two ago.) Next, we intersected those features to determine their specific zones. This part of the chapter uses the Pairwise Function once again but with a new tool called Intersect. This new tool removes any input layers that do not overlap. Then we use a tool called Union to overlay the attributes and space of specific layers to create a new one. Finally, the chapter closes out with us using the Tabulate Intersection. The results show us the calculations of how map layers overlap, which it outputs in the table. Overall, I thought this chapter was a bit complex, but I learned a lot from this part of the book. The complexity comes from performing a bunch of new tasks with newer functions. As well as learning what these new functions specifically do and their uses.

Chapter 1:

Before the start of this chapter, I thought using GIS was going to be pretty straightforward. The setup process was pretty easy. When I finally opened and ran the program, it looked pretty intimidating. So many buttons, sections, and menus to click through. One common thing I kept running into was the book’s broad directions when it came to finding menus. That made finding them a bit difficult. It took a good minute to get used to everything. Overall, I thought the workflow was fun, even though it was challenging to use, especially at first. The first chapter covered a few things, from the introduction to the GIS program to working with 3d3Daps. This chapter also covered some of the topics mentioned in the Mitchell book. Some of the included terms were classes, features, maps, etc. Part 1 was just a general overview of the program, understanding some of the basic concepts of it. The next section showed us how to navigate the program, which did help trying to move around the app program a bit easier. The next section had us working with attribute data, which lets the user search for features on a map and find necessary information. Section 6 had us symbolizing maps, turning raw data into visual information that everyone can see. This section was the most interesting because it involved setting up the overall map. I was trying to get all these bits and pieces to come together and turn them into a final product. One thing I was toying with for a good minute was the 3-d maps. After doing the labs for Chapter I, I felt generally really excited about learning to use these new programs. I look forward to the rest of the chapter and what it could offer down the line.

Chapter 2:

Chapter 2, just like Chapter 1, still felt like moving into a new world. Throughout the process, I was still trying to get used to the menus. Knowing where to find things, which sections they are in, etc. Chapter 2 goes into more detail about designing a map. A good chunk of the information on how to make a good map (depending on the data you’re presenting) was talked about previously. That gave me a decent understanding of what to expect in this chapter. Chapter 1 had us symbolizing qualitative attributes, learning how to symbolize features based on their code. The next section, my favorite section so far, was labeling features and how to configure pop-ups. Finding out what pop-ups are in GIS caught me a bit off guard. Regardless, it was one of the easiest sections of the chapter. Section 3 was the most interesting one, showing us how to filter information by defining queries. The next one discussed how to make choropleth (I’d never heard this word before) maps for quantitative attributes, but this section was the most enjoyable. Section 5 made us create maps with dot-to-scale references and gradients. Making dot density maps was the most interesting, and I spent maybe 30 minutes toying around with some of the features. Just to see what the end result would look like. My attitude toward GIS changed by the end of chapter 2. Searching through menus has gotten easier (the book was hit or miss), making the process of going through these labs much quicker. When working through this chapter, I started clicking through the menus more, trying to figure out each one’s function. Most of that time was spent working on section 3. Regardless, I had a fun time working with this chapter, and everything that I had to do in it.

Chapter 3:

Chapter 2, just like Chapter 1, still felt like moving into a new world. Throughout the process, I was still trying to get used to the menus. Knowing where to find things, which sections they are in, etc. Chapter 2 goes into more detail about designing a map. A good chunk of the information on how to make a good map (depending on the data you’re presenting) was talked about previously. That gave me a decent understanding of what to expect in this chapter. Chapter 1 had us symbolizing qualitative attributes, learning how to symbolize features based on their code. The next section, my favorite section so far, was labeling features and how to configure pop-ups. Finding out what pop-ups are in GIS caught me a bit off guard. Regardless, it was one of the easiest sections of the chapter. Section 3 was the most interesting one, showing us how to filter information by defining queries. The next one discussed how to make choropleth (I’d never heard this word before) maps for quantitative attributes, but this section was the most enjoyable. Section 5 made us create maps with dot-to-scale references and gradients. Making dot density maps was the most interesting, and I spent maybe 30 minutes toying around with some of the features. Just to see what the end result would look like. My attitude toward GIS changed by the end of chapter 2. Searching through menus has gotten easier (the book was hit or miss), making the process of going through these labs much quicker. When working through this chapter, I started clicking through the menus more, trying to figure out each one’s function. Most of that time was spent working on section 3. Regardless, I had a fun time working with this chapter, and everything that I had to do in it.

Chapter 4:

In the previous chapter, we learned how correlations can be shown when using data sets to map the most and least of this information. This chapter focuses on mapping density, its importance, and the various ways we can use and build it. This is crucial because it shows viewers where high concentrations of features are located. GIS allows you to map features or feature values; each will result in different outputs (based on what the user inputs). To make either useful, we need to create a density map. One way to do that is to define an area. This can be done graphically with a dot map or by calculating each value per area. Using calculations requires a couple of things. First, by adding a new field for a density table based on the coverage area of the polygon. Then, assign density values and finally divide the values by the polygon area. This method may need a conversion equation if the density units don’t equal area units. This creates a map by total amount or counts, while noting what each dot signifies (e.g., 10 or 10,000 people). Another way is by density surface, created through a raster layer.  It divides the total amount of features by the search radius field. When it comes to calculating density sizes, a few things are required: cell size (to define patterns, its size is given by the length of the cell), searching for a radius (used to find objects on a map), calculation methods (which have 2 methods that can be used: simple and ring series), and units (measurement). How do we display a density surface? We can display it using gradient colors and contours. (This topic was discussed in Chapter 3.) The results will depend heavily on how the density surface is created.

Chapter 5:

Previously, we learned about map density, importance, and their use cases. Within this chapter, discussing the importance of mapping its inner contents. Helping the reader compare fewer and more areas (reviewed in the previous chapter). To define our evaluation, there are a couple of things the user wants to know. How many areas do you have, and whether they are inside one area (e.g., a shopping mall) or have multiple areas (e.g., zip codes)? Each area must be identifiable by a unique name. Drawing areas and features like surfaces and lines works well when the user is trying to find what’s inside and outside a given area. It’s easy to use, but it relies heavily on visual information. Selecting features in a region is useful if you want a summary or list of what’s inside that region. Its downside is that it works for only one area, not multiple. Overlaying has the benefits of both drawing and selection (only within), but it requires more processing power. The only time a user should choose overlaying is if they have multiple areas (examples: zip codes, disjuncts: pieces of land that are separated, or nested: smaller areas inside larger ones) and want a summary of each area; a single area (examples: a shopping mall, buffer, an administrative or natural boundary, manually drawn area, or a result of a model) and want a list of discrete features; or a single area and want a summary of continuous values. Overlaying with discrete features is used when the user wants to know which features are inside or outside a given space. A use case is finding the number of bears in a specific park. With continuous data, we blend gradients to find patterns. A pretty common example is varying temperatures or rainfall across a certain region.

Chapter 6:

In the prior chapter, we learned about mapping inner contents. This chapter covers mapping nearby areas. People define distance as a measurement (think inches, feet, and miles), while others measure it by cost. The best method for your analysis entirely depends on a couple of things. Maybe you want to use list, count, or summary (discussed in previous chapters). Or you might want to know how many distance ranges you’ll need. There are 2: inclusive rings, used to find the total increase as the distance rises. The other includes distance bands, used for when you want to compare distance to different characteristics. How do you find what’s nearby, though? There’s straight-line distance, used to create boundaries or select features around an area. Cost over a network, which finds what’s within travel distance. Then there is cost over a surface, which not only measures distance but also calculates the area within a given range. Buffers create a boundary line to show what’s far away. To create one, the user defines the source feature (or features) and the buffer distance. The aftermath lets people see details in a given area. Special ones are used for finding multiple features. To find objects in several distance ranges, create multiple proximity zones. To pick features close to many objects, the user selects and tags the feature with a code. For features within several distance ranges, you select each object once for each distance. Feature-to-feature is great for tracking distance from a source object, which has its own use cases. There are several options when making a map for this case. Out of the many maps used in this chapter. The one that caught my attention the most was the spider diagram (not really a map, but close enough). It looks like a firework going off in multiple locations.

Chapter 1:

The first chapter discusses a general introduction to GIS analysis. A multi-step process when it comes to viewing the relationships and patterns between multiple features. It starts with framing a question to determine what information is needed. Who it will be used for and how also comes into play. Which is really important. Once the information is gathered, we need to understand the data. Using certain data types and features will help us better determine which method to use. What approach should be used? That depends entirely on the question and how the results will be used (Remember this: “Who it will be used for and how… ”). After that, we need to process that data and look at the results. Then display them in various ways (charts, tables, maps, etc.).

There are many types of geographic features.  They play a role in our analysis process. Discrete features represent precise locations, whether the feature is there or not (ex: legal boundaries or barriers). Continuous phenomena (like rainfall or temperature) can be measured anywhere, with no gaps. Data represented by density or counts of multiple features  shown in a given area. For example, the number of parks or schools in a place. These features can be shown in 2 ways. By a vector model, as a row contained in a table with x, y coordinates. Their features are represented as lines or events. The other way is the raster model, where features are shown as a group of cells that create layers. (Vectors remind me of C++, which is used to alter arrays in a data set.)

The features in question have values attributed to them. Some include categories that help organize and group similar data together. Ranks assert the importance of multiple factors or provide a direct measurement when it is hard to capture. These are noncontinuous values. Counts give us the total numbers. On the other hand, amounts are quantifiable associations within features. Ratios can show us the connection between 2 numbers. They are divided by another one for each feature present. The last 3 are known as continuous values. We put these values into data tables to help us characterize and summarize data. 3 operators are used for tables. They are selecting (assigning values), calculating (pretty self-explanatory), and summarizing (summing specific traits to get data).

 

Chapter 2:

In the last chapter, we got a general introduction and learned some GIS concepts. This part of the book focuses on mapping: deciding, preparing, making, and analyzing a map. Describing how certain features on maps can be used to show patterns of correlation. Note that it should always represent the intended audience. (You don’t want to make a map representing customer data and show it to a bunch of zoologists.) 

Each feature needs a coordinate and a code to assign a type (one or more categories). Groups of features with similar characteristics are called categories. To map (features) them, we simply draw them, represented by a marker. The same applies to categories, but with colors or patterns. The downside is that users are limited to seven. The reason why is that most people can’t differentiate 7 patterns or colors on a map. This has something to do with our cognitive ability to recognize patterns. (This topic interested me; I spent some time looking into it.) If a person tries to use more than that, it’ll make the map harder for the user to read. It is recommended to try to group these together. 

We can combine them in various ways. Assigning a code, using a linked table, or assigning categories by using symbols.  All this data is stored in a table, including the description of the shape you gave it. (Which I found was cool.) When mapping features, they have to be the right size for viewers to visualize the categories. Scaling also plays an important role, as long as the map’s characteristics are defined and easy to view. Symbols and lines, including line width, have their own criteria for user visibility. Text labels can also help distinguish categories. Using distinguishable landmarks like rivers, roads, buildings, and more can make a map unique.

Chapter 3:

The last chapter focused on mapping where things are. This chapter discusses why mapping the most and least of the datasets can show correlations between locations. Including the features that help put everything together. A potential map should include the following features: discrete features, represented by lines or symbols; continuous phenomena, shown with 3-D perspective (view), colors, or contours. Or data summarization; displaying category amounts by shading in a given area. 

To plot this data, it must be given a quantity. Some include continuous or noncontinuous data. (These types were discussed in chapter 1 but also mentioned in this chapter.) Once the quantities are given. It’s time to either group them together with continuous data. Or plot them individually with the usage of ranks. You can group similar values with their features, or manually create groups under certain conditions. You can do this using standard classification schemes based on how the data is laid out. 

There are 4 common types. Natural breaks indicate jumps in values when data is not evenly distributed. Quantile, in which each class is used and has the same number of features. Equal interval, showing the gap between the highest and lowest values in every class. The last type is standard deviation, where it adds or subtracts the standard deviation from the mean. This creates the class breaks. With all this data at hand, it’s time to create a map. Using graduated symbols, colors, charts, contours, or even 3d perspectives to display our data. Each has its use cases, upsides, and downsides. (This includes the use of standard classification schemes.) As long as the data matches the visual map. We can easily compare and contrast different areas within it. For example, we can see where certain parts of a map contain lacking, concentrated, or scattered values.

*GEOG 291 Quiz completed*

Introduction:

My name is Darren Robinson, born and raised in Columbus, Ohio. Currently taking my first semester at OWU after completing the Associate of Science program at CSCC (Columbus State Community College). I’ve always had a curious mind and a love of learning. During college, I took filler courses in history and science, which made me fall in love with the subjects even more. Outside of college, I enjoy watching a lot of informational content on YouTube (examples: Biographics, CPG Grey, PBS Eons, h0ser, Real Science, Kurzgesagt, and more) and being outside, walking around parks and trails, and enjoying the scenery.

About the reading:

Reading Shuurman Chapter 1 has been an interesting read. This section discusses what made GIS so successful. It can be implemented in various fields for many purposes. Its origins trace back to McHarg (a landscape architect), who wanted to lay out a highway route for suburban development in 1961. Learning about the first computerized cartography system and its usefulness in describing certain data in a given area. Understanding the arguments about the introductory principles of GIS and the scholarly domain of GIScience. With the importance of understanding visual data and why it matters. (Delved into a rabbit hole learning about this topic specifically.)

Before reading this, my knowledge of what GIS is was very little to none. After reading this text, I understand some of the concepts, history, background, and wide-scale integration. It’s a system about as old as my father (for context, he’s 64). Another thing in the text caught my attention. The Quantitative Revolution was one of them, and it led me down another rabbit hole of research into the subject. It was a period in the 1950s and 60s that shifted geography toward a more systematic methodology. This, combined with newer computer technologies, played an important role in developing tools used not only to examine data but also to display it.
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This plays a big role in GIS; these programs create maps and models that we interpret based on given information (raw data). The text talks about the use of visual information as “a seemingly ‘unscientific’ method.” This is truly not the case. Shuurman mentions that humans understand visual information (compared to text) better. Why can visual information be processed better than words? To sum it up, a large part of our brain is used for visual processing. Homo sapiens evolved to conceptualize visual patterns before written language came into play. Finally, reading requires more brain power compared to looking at an image. Geographic Information Systems rely on this. They have a complex history, but the products they’ve produced are important for the modern world

Application 1:

One of the applications GIS can be used for to help asses habitat quality due to human intervention. In this case, resource extraction.

Source: Remote Sensing of Forest Structural Changes Due to the Recent Boom of Unconventional Shale Gas Extraction Activities in Appalachian Ohio, by Yang Liu (2021)  (https://www.mdpi.com/2072-4292/13/8/1453)

Application 2:

Another way GIS can be used to help asses which districts need more funding for social services. Based on median income and poverty levels.