Author: ttweber

Chapter 9

This chapter was quick and easy to follow. It focused on buffers, which help analyze proximity and find what’s near a location. I can see how they’d be useful for businesses and city planning by helping with location-based decisions. Another part of the chapter introduced scatterplots and the Multivariate Clustering tool to analyze data. I can see how these tools could be very useful to analyze data.

Chapter 10

This chapter covered rasters and was pretty short and simple. It was my first time working with raster datasets, and I learned how to import them, create hillshade maps, and generate elevation contours. It also showed how there are multiple ways to display the same data, depending on what you want to highlight. The second tutorial introduced the Kernel Density tool to create a density map. The last section focused on ModelBuilder, which was a bit tricky but well explained.

Chapter 11

This was a short but interesting chapter introducing 3D modeling. The first tutorial covered navigating 3D scenes, and then we learned about triangulated irregular networks (TINs). One of my favorite parts was creating 3D trees with z-enabled features. There were a ton of options for symbolizing different tree types, which I didn’t realize ArcGIS had. Later, I worked with LAS Datasets, which I found confusing but still managed to complete. The last tutorials focused on 3D buildings. I struggled with getting the correct Z height, but got the idea. The final tutorial let me create an animation.

 

Chapter 4: Working through Chapter 4 felt much smoother compared to earlier sections. Importing data wasn’t difficult, though it required a bit of patience. The process took some time, but I can see how repetition helps reinforce these skills. One of the biggest improvements I’ve noticed is my ability to navigate ArcGIS Pro more efficiently. I’m no longer spending extra time searching for tools like the Catalog Pane or the Toolbox—they’re becoming second nature. It’s rewarding to see how practice is translating into better workflow efficiency. While some steps felt a bit tedious, they’re definitely helping to build a solid foundation for more advanced tasks ahead.

Chapter 5: Chapter 5 was an eye-opener when it came to working with world map projections. I hadn’t realized just how many different ways a map could be projected, and it was fascinating to see how even at a continental scale, state shapes and sizes could shift depending on the projection used. It really put into perspective how map distortion works.This chapter definitely reinforced the importance of choosing the right projection and understanding how data interacts with spatial features.

Chapter 6: Chapter 6 focused on constructing a neighborhood map, which was an engaging and practical exercise. One of the highlights was working with fire department and police station layers—it was interesting to see how these essential services are mapped and analyzed within a community. Being able to visualize and manipulate these layers added a real-world element to the tutorial, making it more than just a technical exercise. This chapter reinforced how GIS is used for urban planning and public safety, which made the work feel especially relevant.

Chapter 7; Chapter 7 was by far the most interesting and enjoyable for me. I really liked working with the different tools to create maps—it felt both creative and practical. The hands-on experience made the concepts click in a way that previous chapters hadn’t. That said, I was left with some lingering questions. While I now know how to use these tools, I’m still wondering about the best scenarios for applying them. When should I choose one tool over another? What are the real-world implications of these choices?

Chapter 8:Chapter 8 was fairly straightforward. It felt very short and fast. In 8-1, I ran into a few minor hiccups when trying to locate certain buttons, but overall, it wasn’t too challenging. After a little searching, I was able to get everything working without too much trouble. 8-2 went even smoother—I didn’t encounter any major issues, and the steps felt intuitive. It was nice to have a chapter that flowed easily, reinforcing skills without too many obstacles.

Chapter 1: 

In the first tutorial, I learned how to change base maps and add features to a map. This was an important first step because choosing the right basemap helps give context to the data. By the end, I felt more comfortable navigating the interface and working with maplayers. It then showed me how to explore the map more efficiently and adjust its features for better visibility. I got the hang of zooming, panning, and managing layers to highlight key data without making the map look too cluttered. One of the most useful things I learned was how to access and use the attribute table, it made it easier to find specific locations and filter information quickly. I got better at using the attribute table to pull out useful data. I practiced sorting and filtering to spot patterns, like areas with high population density. I also learned about customizing map symbols. I learned how to change colors, shapes, and labels to make the map easier to read. I also experimented with toggling labels and feature classes to reduce clutter. 

Chapter 2: 

In this tutorial, I learned how to adjust symbology by customizing map features with different colors, shapes, and symbols, making it easier to distinguish between data layers. I also explored the labeling tab and practiced modifying pop-ups, allowing users to click on features to see important details like names and statistics. I worked with definition queries to filter and display only the data that met specific conditions, which helped refine the map’s appearance. I also experimented with different ways to classify and display data, such as using quantile and defined intervals. Additionally, I practiced importing symbology and adjusting it to compare datasets, like income levels versus population density. Other key skills I learned included creating a dot density map to visually represent data and controlling when labels appear based on zoom levels. This helped keep the map clean while still showing important details when needed.

Chapter 3:

This tutorial was packed with valuable information and gave me a solid introduction to several key ArcGIS features. One of the most useful things I learned was how to compare two maps on the same sheet, which made it easier to analyze and contrast data. This was especially helpful for spotting patterns, like comparing population density with infrastructure distribution. There were a few challenges along the way, but overall, it was a great experience that helped me understand how different layers of information can be visually connected.  I ran into an issue with inserting the legend in the map below and the program crashed multiple times. I was not able to work through some of the final steps, but I feel I know how to do them just from the reading. Overall, this unit gave me a much stronger grasp of ArcGIS. I feel more confident in managing, analyzing, and presenting geospatial data.

Chapter 4:

Chapter 4 is all about mapping density, which helps us see where things are more concentrated instead of just plotting individual points on a map. This makes it easier to spot patterns and understand areas of high and low activity. One way to show density is by using different shades of color, where darker areas mean higher density. GIS has a few ways to do this, like graphs, dot density maps, or creating a density surface, which is the most detailed but also requires more data.

When making a density map, things like cell size, search radius, calculation method, and units of measurement matter a lot. A challenge is that data is often summarized by area, meaning it gets assigned to the center of a region, which might not always be accurate. The way we choose to display data can change how it looks, so different settings in GIS can give different results. The flexibility of GIS allows for different approaches, but it also means results can vary widely based on how data is processed. Another factor to consider is how data is collected, smaller datasets may not show accurate density trends, while too much data can lead to an overly complex representation.

Some questions I have: How do you decide the best search radius for a density map? How does interpolation affect the final results? How do different density visualization methods compare in terms of accuracy and clarity?

Chapter 5:

Chapter 5 talks about mapping what’s inside a certain area. This is useful for things like zoning laws or analyzing crime rates. GIS helps with this by letting you identify, count, and summarize features inside a set boundary. The ability to determine what falls within a boundary can help city planners, businesses, and law enforcement make better decisions.

There are three main ways to do this. First, you can just draw the boundaries and see what’s inside, which works well for simple visualizations. Second, GIS can select features that fall within the boundary and list them, which is useful for identifying all features within an area. Third, you can overlay the area and features to create a new layer that combines the data, which is the most flexible option and allows for deeper analysis.

Some things to keep in mind are whether the features you’re analyzing are continuous or discrete and whether they completely fall within an area. Some features might only partially exist within a boundary, which can lead to challenges in classification. GIS tools can help refine these classifications by weighting how much of a feature falls within a boundary or by assigning partial values based on overlap. These methods help summarize data across different regions, like neighborhoods or districts, allowing for deeper insights into how features interact with specific areas.

Some things I’m wondering: What are the limitations of overlay analysis? How does GIS handle features that only partially fall within an area? How could boundary analysis be improved to ensure more accurate data representation?

Chapter 6:

Chapter 6 focuses on figuring out what’s nearby. This is important for things like emergency planning, business locations, and public services. But “nearby” can mean different things, it could be a straight-line distance, a route along roads, or even the time it takes to get there. Understanding the right way to define proximity is key to making GIS analysis useful.

GIS offers several ways to analyze proximity. You can create buffers around a feature to set a specific distance, which is useful for defining areas of influence. Another approach is making spider diagrams that show connections between locations. Road networks can be used to measure real travel distances, while cost-based distance analysis helps measure things like travel time or terrain difficulty. These different methods allow for flexible applications, whether determining emergency response times or measuring accessibility to public spaces.

Choosing the right distance threshold is key. A 10-minute drive and a 10-mile radius might give completely different results. That’s why understanding how distance works in GIS is important. Road networks can change over time, and factors like traffic congestion can affect how “nearby” something actually is. GIS allows for adjustments based on real-world conditions, making its insights more practical.

Some questions I have: When is it better to use straight-line distance versus road networks? How does GIS factor in things like traffic when measuring distance? What are the best ways to incorporate real-time data into proximity analysis?

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.

My name is Trey Weber. I am a Junior on the Lacrosse team here. I’m a Finance major and I’m minoring in Economics. I am from Denver, Colorado. In my free time I like to ski and work on cars with my dad. 

Chapter 1 of Nadine Schuurman’s GIS: A Short Introduction introduces Geographic Information Systems (GIS) in a straightforward and relatable way. She explains that GIS is more than just software for creating maps; it’s a powerful tool for analyzing and understanding spatial data. From city planning to tracking environmental changes, GIS plays a role in solving everyday problems and answering big questions about the world around us. Schuurman gives a brief history of GIS, explaining that it emerged in the 1960s in different parts of the world. This simultaneous development shows how widespread the need was for tools to manage and analyze spatial information. She also highlights the difference between mapping and spatial analysis. While mapping visualizes existing data, spatial analysis uncovers new insights by examining patterns and relationships within that data. For example, mapping might show where hospitals are located, but spatial analysis can reveal gaps in healthcare coverage. A key takeaway from the chapter is the distinction between GISystems and GIScience. GISystems are the tools and software, like ArcGIS or Google Maps, while GIScience is the theory and research that guide how those tools are used. This distinction helps readers understand that GIS isn’t just technology, it’s a way of thinking and solving problems. Another important point is how data representation in GIS impacts understanding. Choosing symbols, colors, or map designs can influence how people interpret the information. Schuurman stresses the importance of clear and standardized design to avoid confusion. In summary, Schuurman’s first chapter lays the foundation for understanding GIS as both a practical tool and a scientific approach. It’s a great introduction that shows how GIS can help us navigate and make sense of the complex and interconnected world we live in.

My first search was “crime gis applications”. Here I found crime info for the city of Denver. It breaks down the type and location of certain crimes around the city. This gives an idea of what areas may be dangerous or have lots of theft. This can help people to plan where they may want to live. It also can be used by law enforcement to survey different areas and allocate officers strategically. 

Source: https://www.arcgis.com/apps/dashboards/17dcc405627742ad8f48988310b5a4d0 

My second search was “wolf telemetry gis applications”. I found that wolves are key to keeping ecosystems balanced. With GPS collars and GIS (Geographic Information Systems), scientists are uncovering the secrets of wolf behavior and movement like never before. GPS collars show where wolves roam, helping identify migration routes and hunting areas. GIS maps reveal what wolves need to thrive, guiding conservation efforts. Also, by mapping wolf activity near human areas, we can predict and prevent problems. GIS helps outline pack territories, revealing how wolves interact and share space among each other.