Category: Course Student Work

Hi! My name is Grace Kopelcheck and I am currently a senior majoring in zoology! I currently work at the Columbus Zoo and Aquarium as a seasonal zookeeper in the  Animal Encounters region (right next to the sea lions!). I have also volunteered at the Ohio Wildlife Center for a little over a year, as well as have interned in the Asia Quest region for a semester! Currently this semester I am interning at Cosi with their animal care team!

When reflecting on the reading I had many comments and thoughts throughout the reading. Firstly when reading the first page I was shocked to learn that GIS can rationalize organ donations. I always thought that GIS was used for more land mapping, but I did not realize the full extent of the content GIS can create/explore. I also never realized the social implications of GIS and what it can do to expand on things other than land. It’s also interesting to read that GIS is considered to have an identity crisis. I think this is funny as well as interesting as I even thought of a different identity for GIS than what it has been used for. I also find it interesting how much we have utilized GIS even though it was stated in this paper that it was a newer technology. This use of newer technology becoming an upcoming feature for many companies and usage reminds me of AI and how this a new very utilized technology as well. It was also interesting to read how one challenge GIS faces is being able to draw strict boundaries represented with lines drawn from GIS. Overall this reading was interesting and gave me a better understanding of the capabilities that GIS has and can do.

For looking into sources of GIS applicable/interesting to me, I used the key subject of Virginia Opossum (as they are my favorite animal) to find GIS application. I found two interesting articles talking about the population distribution of Virginia Opossum and how they move towards human populations due to food (Beatty et al. 2016). Another article tracked the same information however with road kill found and the findings of human influence land types and the amount of roadkill found had negative correlations (Kanda et al. 2006). GIS proves useful in tracking local populations of animals! Below are some GIS maps from both these articles that show the land that Virginia Opossum were found to inhabit (both dead and alive).

 

Beatty, W. S., Beasley, J. C., Olson, Z. H., & Rhodes Jr, O. E. (2016). Influence of habitat attributes on density of Virginia opossums (Didelphis virginiana) in agricultural ecosystems. Canadian Journal of Zoology, 94(6), 411-419.

Kanda, L. L., Fuller, T. K., & Sievert, P. R. (2006). Landscape associations of road-killed Virginia opossums (Didelphis virginiana) in central Massachusetts. The American Midland Naturalist, 156(1), 128-134.

My name is Spencer Yates, and I am a microbiology and zoology major. I’m currently applying to graduate schools with areas in virology.  The reading was very interesting to me. I had no idea that GIS software was so important to modern research and engineering. I thought it was just for geographic research, but it’s much more broad than that. The creation of the GIS software is also fascinating, as it involved so many people all across the world over a large period of time. It is kind of hard for me to understand so far, as I’ve never really done mapping before. However, I am determined to master GIS by the end of this course. I think that this skill will be really useful for me in the future.

When I looked for applications of GIS that are interesting to me, I chose two categories: professional interest and personal interest. For professional interest, I looked up if GIS is useful for tracking the progression of diseases. There are a lot of examples of this, but I found a recent malaria study using GIS mapping to explore the prevalence of the disease in Nigeria to be the most fascinating. The paper used GIS mapping to show the changes in prevalence in areas of Nigeria over a 20-year period, which is incredibly useful to know.  Source: https://link.springer.com/article/10.1007/s00436-024-08276-0#Tab2

As for a more personal interest, I chose butterfly migration and conservation. In this category, monarch butterfly migration and habitat appeared to be a highly studied area. I found an interesting example of mapping the amount of suitable overwintering habitat for monarch butterflies in Mexico. While this map is only for one point in time, making similar maps in the coming years can allow for the tracking of habitat gain and loss, which can let researchers know if conservation efforts are effective or not. Source: https://creeksidescience.com/what-we-do/gis-analysis/

The blog is not letting me upload either of the maps I found, but they are in the links if you want to see them.

Hello! I’m Ariauna Hickman. I’m from Pittsburgh Pennsylvania. I’m a sophomore, majoring in Pre-Professional Zoology on the Pre-Veterinary track. I am also double minoring in business and chemistry. A quick fun fact is that I have a Great Dane who is a goofball.

After reading Schuurman ch. 1, it is clear to see how important GIS is in many different career choices. I like how it can be used for many different things. The part where it mentions how municipalities use it for things like affects on highways, however, confuses me a bit. How are they able to tell how it affects those areas? Now that I read the next page, my question was answered. It is to see how it would work with landscapes and housing. It is crazy to think about how more than 50% of our brains neurons are used for visual intelligence. No wonder GIS is so important. It is like a visual way of seeing geographical locations. The visuality is used as a means to be able to make it more accessible to see the patial awareness of areas.

After looking at a few different ways that GIS is used. I found how it can be used for video games.  For example, Watch Dogs 2 usesGIS in the way that it is open world. They made it highly detailed and realistic with the urban planning, architecture, and digital infrastructure. GIS is also used in Pokemon GO. People go around using a geographical map that matches their location to go and catch different pokemon. It is a blend between the physical and digital world.

Geographic Information Systems, Intersections, Iowa Computer Science Education Week 2023, Technology, Video Games, Video Games and GIS, Wael Alhaj

Don’t drown in Geog 291!

All you have to do is one thing. Keep up.

Any impediments to keeping up and turning things in on time (every Friday): talk to me.

Just about everything you need is on this lovely website.

What to do by the end of the day Wednesday, Jan. 15

1) Read over the class web material here carefully: the Syllabus, Schedule, Data Inventory and Final Exam tabs above.

2) Accept the invitation to post on this site from WordPress. Thanks to those of you who already did this.

3) After reading over the web materials, set up an appointment with me the first week here.

After that, what to do the rest of the week:

1) Log in to this site and post this week’s work by midnight Friday, Jan. 17. It’s an easy one.

2) Watch for an email from ESRI / ArcGIS for your ArcGIS Online Account. You need this to use the ArcGIS Pro software in the GIS lab.

3) Get the books for the course or plan to use the ones in the drawer in back of 207 SCSC (top, nearest door). Don’t take those books out of the lab.

I just sent in a request for you all to get 24/7 access to SCSC and emailed you (earlier) the access code for the GIS lab (207 SCSC).

Email or catch me around or set up an appointment if you have questions.

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.

 

Chapters 9 and 10 went fairly smoothly for me. Definitely more complicated than the previous chapters, but I also found it to be very interesting and helpful. I ran into a few issues with the Chapter 11 tutorials, but if it took too long trying to figure things out, I would move on and it went pretty well. I liked that these chapters showed more intricate ways to represent and symbolize data. These are the kind of maps you see a lot, so it was cool to be able to make them myself and see how much I had improved with the intricacies and details of the software. Attached are screenshots of a few of my tutorials

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.

Final

I chose to do option 1. Selecting and Classifying Land Uses for my first half of the final. I first created a map and inserted the data file. Then I opened the attribute table of the Parcel layer and used the select by attributes tool to create queries of all of the different land uses. I used the provided land use codes to select each section and then used the create new layer with selected attributes button to create a new layer for each of the land uses. I created 5 separate layers of land use, as Delaware does not have any land that fits the mineral land use classification. I then color coded these layers. I then did the same process to map agricultural uses. Excluding Tobacco and vegetable farms, as well as greenhouse farms as we do not have these in Delaware.

 

For my second option I chose to do 2. Making New Shape Files from Existing Shape Files. I decided I would make a map showing the proximity of roads to different land use types. I did this by finding and imputing a data set that showed all of the roads in Delaware, I then used the same Parcel data set from selecting and classifying land uses, and went through the same process. I opened the attribute table of the Parcel layer and used the select by attributes tool to create queries of all of the different land uses. I used the provided land use codes to select each section and then used the create new layer with selected attributes button to create a new layer for each of the land uses. I believe this map could be useful for land use planning. Land use planning often involves analyzing the proximity of development to roads and infrastructure. Selecting specific types of land use near major roads can help assess urban sprawl or development impacts. Urban planners could use this map to assess land development patterns and ensure sustainable growth or identify areas where development could cause traffic congestion. To make the map a little more clear I also added in a hydrology layer to delineate where waterways are present between the roads.

 

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.