Author: hgkelner

Chapter 9:

This chapter went pretty well aside from some bumps here and there.  I struggled to find stuff occasionally but other than that it was good.

Chapter 10:

Like chapter 9, this one was smooth as well.

Chapter 11:

I struggled a bit with this chapter but all in all it went decent. Usually it was me struggling to find something or I just couldn’t figure something out so I moved on. The chapter was mildly annoying but it shows some pretty cool ways to show data.

 

Chapter 4:

This chapter went pretty well and I didn’t really run into any issues. If I ran into a roadblock I sorted it out pretty quick.

 

Chapter 5:

I struggled a fair bit with this chapter. I had trouble with my data and with adding it into ArcGIS. I had some mapping and software issues as well, like for example I wasn’t able to find the Display XY Data button. All in all this chapter put me through the ringer and I’m glad I was at least able to get bits and pieces done for it.

Chapter 6:

This was a much better chapter compared to 5. Honestly it went smoothly and I had the same issues as I did in 4 of just struggling to find stuff.

Chapter 7:

Again, the chapter went smooth and really enjoyed playing around with the polygons.

Chapter 8:

Very straight forward and easy. I finished this chapter up fairly quick.

Chapter 1:

GIS is a “process for looking at geographic patterns in your data and at relationships between features”. When utilizing GIS lots of information has to be taken into account, and depending on what information is present leads to different routes to form your map or data. Next, choose a method for processing the data and analyzing the results. Features can be classified as discrete or continuous, and they can also be summarized by area. Discrete features (such as specific locations) can be pinpointed, while continuous phenomena (like temperature) can be measured at any location since every spot has a temperature. Continuous data is typically derived from a set of discrete points, and summarized data represents counts or densities of features within defined areas, like the number of households in a county. Geographic features can be represented as vectors or rasters. Vector features are represented as rows in a table with defined x and y coordinates, making them ideal for discrete data. In contrast, rasters use a grid of cells to represent features in continuous space, making them suitable for continuous numeric values. While rasters can depict continuous categories, they can also combine discrete features with other layers. Categories help organize and make sense of your data, while ranks create a relative order among features. Counts indicate the total number of features visible on a map, while amounts refer to any measurable quantity associated with those features. Ratios show the relationship between two quantities by dividing one by the other for each feature, and proportions indicate what portion of a total each value represents. Finally, densities provide insights into how features or values are distributed per unit area.

Chapter 2:

The beginning of the chapter stresses the significance of analyzing patterns among various features on a map. It illustrates how police utilize GIS to monitor crime and decide where to deploy patrols. The selection of features to display and their representation is guided by the information required and the map’s intended purpose. Before creating your map, it’s crucial to assign geographic coordinates to the features you want to include, a process mainly handled by the GIS. An optional step prior to mapping involves assigning category attributes with values to each feature. For single-type features, you can represent them all with the same symbol. The chapter notes that GIS records each feature’s location as pairs of geographic coordinates or sets of coordinates that define its shape, whether it’s a line or an area. Using a subset of features can help reveal patterns that might be obscured when mapping all at once. Mapping by category, with distinct symbols for each, can improve your understanding of how a location functions. Additionally, organizing features by type can uncover different patterns, as features may belong to multiple categories. Sometimes, creating separate maps for each category is beneficial if features are too close together, making them hard to distinguish. When mapping multiple categories, it’s best to limit the number to seven on a single map. The number of categories that can be effectively displayed may also vary based on the map’s scale and the features involved. If you have more than seven categories, consider grouping them to enhance pattern visibility. Including recognizable landmarks—such as major roads, political boundaries, towns or cities, and significant rivers—can greatly assist viewers in interpreting the map.

Chapter 3:

Chapter 4:

This chapter concentrated on mapping density, which illustrates varying concentrations of specific features and helps reveal patterns in the data. It can be applied in contexts like census maps and the frequency of robberies per square mile. GIS is a valuable tool for visualizing the density of certain points or lines, typically represented through a density surface. Density maps can be created based on the number of occurrences of a feature within a defined area or the values related to that feature. There are two main methods for generating density maps: using defined areas or creating a density surface. The first method, which focuses on defined areas, is more graphical and often employs dot maps. In a dot map, the proximity of the dots indicates the density of that feature in a specific location. To calculate the density value for each area, you divide the total number of features (or their overall value) by the area of the polygon. The second method involves creating a density surface, typically as a raster layer in GIS. Each area is assigned a density value based on the number of features within a certain radius. While this approach requires more effort, it offers greater detail than the first method, displaying the locations of features and continuous phenomena. Personally, I prefer the defined area method because it results in clearer and more understandable maps. The cell size in either mapping approach significantly influences the observed patterns; if the cell size is incorrect, the resulting patterns can vary, which can remind one of gerrymandering. To determine the cell size, you convert the density units to cell units, divide by the number of cells, and then take the square root to find the size of one side of the cell. Overall, I found this chapter fascinating. There’s much more complexity to density maps than I initially realized. I’ll likely need a textbook handy when I start working with the program this year, as there’s a lot to absorb.

Chapter 5:

This chapter explores the “Finding What’s Inside” technique, a flexible tool for mapping areas to deepen understanding of their dynamics. It also facilitates comparisons between different regions, making it a valuable resource. The first method involves overlaying a boundary on the area’s features. The second method selects features located within this boundary, while the third combines these two approaches to produce summary data. This technique can uncover patterns within a specific area or across multiple regions. Categories can be either discrete or continuous, with continuous data potentially coming from earlier GIS maps. Area graphs can help determine whether a particular feature is present within an area, compile a comprehensive list of features in that area, or count the number of features in a specified region or set of regions. Since some linear or discrete data may extend both inside and outside an area, you can choose to include only data that is fully contained, completely outside, partially inside but extending beyond the area, or just the portions that are within the area. The first approach to “finding what’s inside” involves drawing boundaries over features. The next method selects features within a designated area, and another option overlays both areas and features. Different methods are more effective for different problem-solving contexts. When creating these maps, using bold lines or shading to define areas can enhance clarity. After developing these maps, statistical analysis can provide valuable insights into the data and highlight visual patterns. The chapter also outlines how to create various types of maps and the associated steps involved.

Chapter 6:

Mapping the area around a feature can be advantageous in numerous ways, such as estimating travel time from home to a store or tracking logging activities near a river or property line. The concept of “nearby” can be interpreted in different ways. For example, it could refer to a specific distance, like identifying all trees of a particular species within a mile of a river, or it might involve travel routes, such as how quickly a fire truck can reach a fire. When assessing proximity, measurement units can include more than just distance; considerations like time, cost, and effort are also significant. It’s also essential to decide whether to factor in the Earth’s curvature when calculating distances. There are three main methods for determining what’s nearby: straight-line distance, distance or cost over a network, and cost over a surface. While I’ve already discussed the first two, I’ll explain cost over a surface. This approach is especially useful for assessing travel costs over long distances, as it employs a raster surface to represent the costs associated with moving away from a feature across the map. Additionally, GIS allows you to select features within a given distance. By entering a distance from a source, it highlights all features within that range and provides a list, count, or summary of those features without the need for a defined boundary. However, when dealing with multiple sources, it’s crucial to label each feature to indicate which ones are near each source. Fortunately, GIS comes equipped with a built-in street network, so you don’t have to input any extra data when measuring distances or costs over a network.

Chapter 1:

I personally found the start of this chapter monotonous but that comes with learning a new program. Once I got the hang of it I was able to get through the tutorials fairly quick. The biggest problem I have with ArcGIS is finding stuff and I sometimes just sat for 5-6 minutes looking for something, quite literally, right in front of me. Other than that this chapter went pretty smoothly.

Chapter 2:

Chapter 2’s tutorials also went well aside from the data being corrupted in Tutorial 2-3 I believe so I ended up skipping it. It was a pretty cool getting to see how  much thought goes into fonts and text colors when naming the neighborhoods and water ways. I did struggle to find the white halo effect for the neighborhood text but otherwise things went well.

Chapter 3:

I struggled with the online sharing and may need to look at it later but I really enjoyed making the map and legend page. It was also cool to see the other side of GIS with making the bar graph and how diverse the system can be with showing data. Other than the online issue, things went well but I still get lost and can’t find stuff for a bit.

Hi! My name is Hayden Kelner and I’m a sophomore Environmental Science and Zoology double major. I’m in Chi Phi, the Entertainment Director for CPB (Campus Programming Board), the Secretary of the Rock Climbing Club, I play bass drum in the marching band, and I work as a tour guide and student curator at the campus’s Natural History Museum. I enjoy playing videogames, board games, building Legos and model kits, and embroidering on occasion.

I had heard of GIS from my uncle’s cousin who works for the North East Ohio Regional Sewer District. He works as a field biologist and collects water samples and catches bugs to help determine quality of water as well. However, there is a whole department that works hand and hand with them that uses GIS. Similar to how the chapter mentions uses for GIS, they help by analyzing water runoff and  highlight potential problems areas for other departments to survey. When I had heard that a light went off in my head that this was an important tool to have under my belt for my career ahead. While reading I was surprised to see how many uses GIS has. It’s used in ways I didn’t even know were possible like with epidemiologists. Whether it be with environmentalists, or even sales departments, it seems like GIS has use in every field. It’s uses in graphing, mapping, making models and other tools cover such a wide array of needs that it can really be molded into any way necessary. It was also really cool to read about GIS’s uses in farming. Agriculture is such an overlooked field of work and not many people give it much thought. Seeing the behind the scenes of it in a sense and seeing what processes and issues can be identified and dealt with was really interesting.

This semester I’m taking entomology, so getting to see how GIS is used in that field was interesting. In this article, researchers used GIS to identify how forest dwelling insects have been disturbed over the years in Grand County, Colorado. By using GIS they were able to identify how certain beetles may react to logging and other habitat factors. By using previously gathered data they were able to use that to replicate the effects through GIS.

Source: https://www.sciencedirect.com/science/article/pii/S0304380017302053