Author: ilfry

Chapter 3: I began by finishing the remaining tutorials for this section learning how to integrate with aspects of the online GIS system which was very interesting. I unfortunately could not get my maps to share with the online program, so to accomplish these tutorials I had to use maps already shared by classmates. However, I was able to accomplish and create all the other features on my own within the web system.

Chapter 4: The first two tutorials were interesting because I had to start from scratch and open a project with the regularly set base layers and build from there using database information in the esri folders. It was really helpful to learn how to do this, and I felt like I got much closer to actually being able to create a whole map from scratch on my own, which is obviously one of my goals in taking this course. While doing the third tutorial I was able to save and import the definition query but it would not appear in the mapping system again. I could not find what error was occurring so I moved on to the other queries in the chapter, which will definitely prove helpful in the future when working with large datasets and trying to understand different variables. Tutorial four was quick, it reminded me of how to create a map using simple data and graduated colors, it was pretty basic but at the very least made me feel accomplished. The next tutorial was pretty similar, I just had to add graduated symbols to the map which was a refresher but I was able to accomplish this with no issues. Finally, in tutorial 6 I was able to join the crime type data and manipulate the symbols to make data visualization easier.

 

Chapter 5: The first tutorial taught me how to change the type of distortion that is used in a 2D map of the world in GIS to be useful for different regions and have different rates of distortion. The second tutorial was the same but exclusively for the United States. Tutorial 3 was useful to learn more about how to use the coordinate systems within the maps and changing and manipulating the the coordinate system of a map. For the fourth tutorial, I was not able to locate the data file within the chapter 5 folder, it was not a part of my initial esripress download which is stored on my external hard drive. So I moved on to the fifth tutorial, which I spent a ridiculous amount of time extracting data from the US Census Bureau, but eventually found the proper way to integrate it all into the map. Finally, I moved on to the 6th tutorial, which involved adding even more data on things like bike stations into this map.

 

Chapter 6: I began with creating the map of New York City fire battalions which taught me how to dissolve and merge boundaries within the data of the map. I found this very interesting and I was able to execute all the actions without too much issue. Next, I used the instructions in tutorial 2 to clip the streets in the Upper West Side which was relatively simple. Then, tutorial 3 had me merge all the water features in the area of Manhattan. Tutorial 4 taught me how to merge two datasets into one with the police and fire stations of New York. Tutorial 5 was useful to find intersecting features which in this example were the streets of Manhattan and their fire companies. Tutorial 6 was all about calculating and comparing geometric attributes of the mapping data which in this case was the landuse of Brooklyn. Finally, tutorial 7 was useful to learn more ways to analyze counts of data within regions in this case the amount of people with disabilities within a fire companies jurisdiction. Overall this chapter was the smoothest one I’ve completed so far, and I actually remembered to take some pictures while I was doing it.

Chapter 1: I began by successfully signing into my account on ArcGIS in SCSC 207. Then, after downloading the necessary files, I was still unable to access the proper data in the program, so I began running an update to the ArcGIS software on the desktop. This took a significant amount of time so I then attempted to switch computers. I was in the lab for over an hour and could not manage to get the map to open properly or display any of the data. Eventually, I was able to get it to show up but, I am honestly unsure what we did that got the program to actually display the data. In the first tutorial I learned how the general program works, saving my project, and using a few different tools. The next tutorial taught me how to make a bookmark, zoom, change my layers, and select an attribute. In the third tutorial, I continued to use the attribute tables. I learned how to manipulate the data stored in these tables and change the view available within them. I also learned why manipulating this data may be useful in the context of census data. I did encounter an error message that I did not understand when trying to save my changes to the municipalities attribute table, but I do not think it will significantly impact the rest of the tutorial. I also learned how to find the summarized statistics for data used in my map. In the final module, I learned how to add more feature planes to my layers.

Chapter 2: The first tutorial helped me learn to change the colors of different areas in a map, in this case based on land use. The following tutorial gave me the skills necessary to label these areas and assign pop-ups within them with additional data. The third combined these to help me manipulate the symbols used for different types of assistance organizations and manipulate the map to better find these places in Manhattan. Unfortunately, I was unable to complete most of the fourth tutorial because there was an error with the tutorial data, and the file did not contain the neighborhoods data that was necessary to complete the work. I did read this tutorial so that I now understand more about how to create a 3D map in the program and how to manipulate the view of this map to get the proper visualization of the data. For the fifth tutorial it was really interesting to see how symbols can be used to better visualize and compare data within the map by comparing the food insecurity of the two groups overlaid as differently sized circles in their residential areas. Tutorial 6 was very interesting because I learned how to assign graduated colors to values in the program, and how to duplicate this on another data set and compare the two easily. Tutorial seven was helpful in understanding the dot density plots that were discussed in the reading section from last week because I hadn’t really read too much about or used that type of map before so it was really interesting to get to make one and look at how the data appears. For the 8th tutorial, the notes about labeling and zoom were helpful, however, I could not locate an “out beyond” button in the visibility range group which made it difficult to take all the necessary steps for this tutorial.

Chapter 3: In the first tutorial I am learning to make a layout using maps that were created in the GIS program. Unfortunately, I ran into an issue with the renaming of my layout so it is just called layout, but I will try again next time on the naming issue. Learning how to add the maps to the layout, resize them, and move them around is nice to know how to do when thinking about the practical uses for GIS knowledge and creating figures from data in the future to use in real life. It was also good to have the knowledge of creating a bar graph from the data and turning it into a graphic which will be useful in the future if/when I ever need to use data from GIS in a figure for research purposes. Unfortunately, due to the delays I had with my technically difficulties in getting started this week, this was as far as I was able to make it in the tutorials, I will begin with 3.2 next week and I intend to start earlier to accommodate for the extra workload I am putting on myself next week and to allow for anything else that may go wrong in the future.

Chapter 4 centers around the concept of density and mapping it. Mapping density makes it easier to understand which areas are the most concentrated in some type of resource or landmark, for example in the book it references small businesses. Instead of simply plotting each location on a map where they could become overlapped and difficult to understand, you map with darker colors in areas of high density and include a key so that density can be better visualized. Mapping density rather than simply the location of features on a map gives you a measure of their density per area. Density can be mapped using a graph, a dot map, or calculating density for each designated area. Creating a density surface in GIS is usually preferable but it requires the most data input and more individual data on locations rather than data separated by region or county lines. GIS can also take a map density by area map and use the data to construct a dot density map to represent density graphically.
When using GIS to create a density surface there are many factors to consider including cell size, search radius, calculation method, and units of measurement. Another thing to consider when creating a density surface is that data that is summarized by defined areas can be used to make these types of maps but it must be generalized by the centroid of each defined area. This means that the summarized data is assigned to the point at the center of the defined area for which it is summarized. Additionally, for these maps a graduation of colors is assigned to each value so that the density can be visualized. The results of creating these types of maps in GIS are almost entirely dependent on the choices made with the many variables that can be manipulated in the program, meaning the same data can look different in final products where different visualization choices were made.

Chapter 5 discusses the need to map what is inside an area. This is necessary because the bonds for these areas can be “within 1000 feet of a school” or something like that to impose stiffer punishments on crime. This is an example of finding what is inside one area but you can also use mapping to find what is inside several areas such as each district in a city. In either case, you first have to know the boundaries of your area(s). Then, the discrete or continuous nature of the features you are measuring has to be taken into account. You can also use GIS to list features, count them, or get a summary. You also have to consider if the features being measured are completely in the area because discrete features can easily be partially in or out of a defined boundary.
There are three ways to find what is inside the area. First, drawing both the areas and the features, this way you can see the boundaries of your area and what features are inside it. This is specifically good if you only need to know which features are inside and outside of an area. Second, you can specify the area and the layer that contains your features so that GIS selects a subset of the features which is inside the area. This is best for getting a list of the features inside an area. Finally, you can overlay the areas and the features to create a new layer which compares the two layers and summarizes the statistics for each area. Which is best for doing both at the same time, as it is the most flexible.
Using the results of these summaries can be tricky. Some ways it can be used include: the count of a total number of features in an area, the frequency of a number of features with similar values in an area, or to summarize a specific numeric attribute such as the sum of certain features. These similar principles can be applied to much more complicated data, overlaying layers onto each other and creating understandable visualizations of complicated data over a range of areas.

Chapter 6 focuses on mapping what is nearby to a feature. This is important because some features may require notifying anyone living within a set distance. However, “nearby” is a concept that ranges in distance, within GIS you must define the distance which is being considered as nearby. Sometimes it is just straight distance away, or in some cases distance has to be measured using networks of transportation such as roads. Distance and cost can both be used to measure what is nearby, cost can include the amount of time it takes to get somewhere from your location. You also must consider the information you require from your analysis, sometimes it may be a list of everything “nearby”, a count of the total number of restaurants nearby, or a summary statistic for the area.
To determine “nearby” you can use GIS to set an inclusive ring based on straight-line distance, which is best for defining the area of influence around the feature. To do this you have to create a buffer in GIS at a certain distance from the feature you are discussing. Additionally, using this method you can use GIS to find the distance between two features, or to create a spider diagram with your chosen feature at the center. Another option is using distance or cost over a network (such as roadways), which is best used when measuring travel over a fixed infrastructure is necessary. GIS includes a ready-to-use street network which can be used to find whats nearby in terms of distance; however, this is not the only possible network you may want to use so custom networks can be built in the program. GIS will start at your feature and check the distance to the nearest junction in relation to your specified distance, and it will repeat this until a definition of everything “nearby” has been reached. You can also specify more than one center in this type of mapping. Finally, measuring cost (of time or another variable) over a surface is most helpful when you need to measure overland travel and calculate how much area is within your range. This has to be done using a raster layer of continuous distance from your feature.

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.

I’m Izzy Fry! I am a Freshman Environmental Science and Zoology major, and I’m on the field hockey team. I’m from Dublin, OH.

It’s really cool to learn about all the applications of GIS because I didn’t know it had so many uses in various fields. I also did not know that the technology and concepts were developed in the 1960s. It is also interesting to learn that in the early days manual maps were preferred for the process of overlaying information even into more modern times. It was really interesting to get a little bit of a window into the history of cartography while learning about GIS. I had also not thought much about how the availability data and scale of a GIS based project can impact what is actually able to be produced. It was also very interesting to learn about how GIS can be used in terms of public health, specifically in the example of a cholera outbreak and locating the cause. Additionally, agricultural uses for GIS are very interesting because of the possibility of considering so many variables together. I’m excited to learn more about the possibilities of GIS.

I learned that GIS can be used in zoology for a variety of projects including gaging habitat suitability. This particular map is from “Modeling habitat suitability for endemic Grizzled leaf monkey (Presbytis comata) using geospatial machine learning approach” performed in Indonesia in 2022, by a team of researchers. This is very interesting because without GIS technology it would be very difficult to visualize this data especially in a comparative manner.

I also learned GIS can be used for resources that are important to humans including water quality. This map is from “GIS Mapping for Distribution of Ground Water
Quality in Udaipur” also performed in 2022. Which is interesting in terms of global health, public safety, and animal habitats.