Gabe Murphy: Stage 6 – Review of Sam Ellerbeck

In the maps provided, and the project as a whole, Sam showed the history of redlining practices, medical facilities, and the ties to racial inequality. He even discussed the implications it had on COVID-19, which I thought was a great touch. Overall, Sam’s argument can be summed in one quote: “In Omaha, medical deserts are appearing in the areas in which redlining was once practiced – the locations of marginalized minority communities who have been disadvantaged by historically structured racial discrimination.”

There were many approaches, from differing points of view, and the mapping products themselves were great. In particular, I thought the 30-minute Walk was a great use of technology to highlight the medical desert that northern and southern Omaha are. To begin, who would want to walk 30 minutes to a hospital when you need to see a doctor? In many cases, that walk may even be impossible. Secondly, I estimate ~60% of the map’s area is not within a 30-minute walk of the nearest hospital. Without access to a car, how in the world may people get the treatment they need? This is a real issue that needs serious change. Another strength I noticed was acknowledging the opposing side of the argument, and providing evidence for why that side was incorrect. Instead of ignoring opposing viewpoints, they were acknowledged and proven wrong: great. I also really appreciated the different styles of mapping Sam used for the project: choropleth, dot-vectors, geo-referencing historical maps. In my project, I used the text more to argue my point; but Sam’s use of mapping as one of the primary sources for evidence made it easy to see his argument. 

Going back to the 30-minute Walk map, I think color-correlation would be something that could be changed. Many of the hospitals and 30-minute radius overlapped, so using different colors that each radius could correspond to the hospital point would make the overall viewing/analysis of the map much easier for the reader. My only other critique would be along the lines of color again: keeping a consistent theme. Some maps have orange, some have blue, some have red, some have yellow. That may be nit-picky, but I like to have a consistent feel and the different colors seemed a little jarring to me.

Great project overall! The use of maps was outstanding.

Gabe Murphy: Stage 3

“Acres Enrolled in the Conservation Reserve Program – 2016.” Legislative Services Agency. December 2016. https://www.legis.iowa.gov/docs/publications/FCTA/860716.pdf

This is a short source that includes a choropleth map of CRP county enrollment of total acreage in Iowa, and a dot-density map of the same subject for the entire United States. Again, as my project will focus on Iowa, I will only use this upper map/graph to my advantage. The graph shows total CRP acreage values from 1986 to 2016, which may help me in the process of creating a connection between the two variables I have chosen to research. Further, it provides me with an example of what my CRP static map should look like for 2016, given that this source used the same USDA data that I will likely be using for my project. With this, the map can be used as a reference/checking point to ensure I have transferred all of the data correctly. I may use this first so that I can double-check my work, and then move on to the rest of my project with confidence that everything is accurately depicted. 

Allen Thomas et al. “Recreational Use & Economics of Conservation Reserve (CRP) Acreage: A National Survey of Landowners.” Southwick Associates, Inc. & D.J. Case & Associates. January 2008. https://www.fsa.usda.gov/Internet/FSA_File/national_survey_of_landowners.pdf

This source is a report for the ENTIRE United States, showing not land use statistics, but how the CRP land was used. This is an important story for my project. If CRP land is not majorly used for hunting, or habitation of deer, then my correlation makes no sense. However, this report details the hunting and recreational use of CRP. I could use the data to convince/argue my point that CRP is indeed critical for deer hunting/population numbers. In this source, CRP land was used for recreation 57% of the time, and of this 89% of the “recreation” was hunting. That means that 50.73% of all CRP land is for hunting. That helps support my argument and I can use that to tie the two variables I chose (CRP and deer harvests) together. 

“Conservation Reserve Program Acreage by County.” U.S. Geological Survey. May 31, 2023. https://catalog.data.gov/dataset/conservation-reserve-program-acreage-by-county.

This set of data has acreage of land enrolled in the CRP programs from the year 1987-2004. Interestingly, it excludes data from 1994 and 1995, so I would need to do further research to see why that may be the case. Not only this but it covers why CRP is beneficial to wildlife populations in a short introduction, that I may also use if I decide to make a final project storymap. Another interesting aspect of this site is that it allows you to update the dataset and the static map portrays changes alongside the data change. This is something I would like to do, or something of a timeline to allow the stacking of multiple static maps. I have encountered another data set with CRP acreage values, so I can use these two sources to compare and contrast the numbers and (hopefully) find that they are relatively similar if not exactly the same. This again can help me feel confident in my final project and ensure that my maps are accurate.

“Conservation Reserve Program Statistics.” USDA Farm Service Agency. December 2023. https://www.fsa.usda.gov/programs-and-services/conservation-programs/reports-and-statistics/conservation-reserve-program-statistics/index. 

On the opposing end of the project, this source covers nearly everything I would need for CRP acreage values. It has a multitude of data sources (20+) that include land use by county, # of CRP contracts, yearly soil reports, rental rates (which I could use to help decipher the first source), nationwide annual summaries, and many more. It is very useful for creating the other half of my project in which I will compare static maps of 5-10 year increments showing the changes in CRP acres per county and how this correlates to hunting success/deer harvest in that county. 

“EWG’s Agriculture Conservation Database.” EWG Conservation Database (Iowa) Conservation Reserve Program. Accessed April 2, 2024. https://conservation.ewg.org/crp.php?fips=19000. 

This source is a useful tool in tracking land use patterns among counties and CRP payment values, as well as the amount of acreage contracted for specific CRP projects (wetlands, quail population, etc.). Using this data and contrasting it to deer harvest reports for the year, I can see what CRP projects produce a greater deer harvest overall (ie: what projects are also good for deer habitation). I could also use previous years reports to see the change in total CRP acreage and if that had impacts on the # of reported deer kills on a year by year basis. 

Harms Tyler et al. “Iowa Bow Hunter Observation Survey: 2022 Summary.” Iowa Department of Natural Resources. December 2022. https://www.iowadnr.gov/Portals/idnr/uploads/Hunting/trends/observation_2022.pdf

In this survey, the DNR asks bowhunters to report the number of deer sightings they have per 1,000 hours hunted, and splits the results into 9 geographic areas: Northwest, Northcentral, Northeast, Westcentral, Central, Eastcentral, Southwest, Southcentral, and Southeast. With these numbers, they use an algorithm to estimate the total deer population for these areas. They have reports for 2012 and up, and have split the results into different genders (for deer) and also have reports for bobcat, coyote, badger, and other species. In my project, I would focus on the deer report numbers. I would like to have data from much further back so that I could verify the trends, so I will look deeper into that. Since CRP is a main habitation area for deer, I could track how CRP acreage values in these regions have changed over time, and how that has impacted population (or rather, directly influence deer sightings which the DNR believes correlates to population numbers). There are many other factors that influence deer sightings, but the DNR itself uses this data to estimate population so that is enough confidence for me to be able to do the same. Not great data, but I would only use this if absolutely need be, as accuracy is a goal for me. 

“Iowa’s Population Trends.” Iowa DNR. Accessed March 28, 2024. https://www.iowadnr.gov/hunting/population-harvest-trends.

In this source, population surveys are recorded year by year including “​​White-tailed Deer, Wild Turkeys, Furbearers, Waterfowl, Upland Wildlife, Peregrine Falcon, Osprey, Sandhill Crane, Bald Eagle, River Otter, Bobcat, Mountain Lion, Black Bear, Gray Wolf, Trumpeter Swan, Greater Prairie Chicken.” Of most importance, white-tailed deer population numbers could be used to track how CRP affects these values. Further, if I wanted to differentiate my project I could use turkeys/eagles/bobcat populations and see how CRP affects the ecology of these species. However, I would likely focus on deer and how their population fluctuates alongside CRP and harvests for the previous year. This data may not be as convincing/argumentative as populations tend to be estimates rather than true representations. If only we could lawfully enforce an animal census.

Lyon, Kayla. “Trends in Iowa Wildlife Populations and Harvest 2020-2021.” Iowa Department of Natural Resources. September 2021. https://www.iowadnr.gov/Portals/idnr/uploads/Hunting/trends/logbook_2020.pdf. 

This source is the best comprehensive report of deer harvesting one could find. It tracks every harvest by county from 1953 up to 2021, success rates, kill per square mile, anything I could ever need for the deer harvest comparison aspect of my project. There are multiple maps that I may use in a story map, raw data I could standardize to create my own static maps, and many other options. In terms of what/why this source is useful, it could entirely make up half of my project (I will not use it as such, but it could be if needed)!

Lyon, Kayla. “Trends in Iowa Wildlife Populations and Harvest 2021-2022.” Iowa Department of Natural Resources. September 2022. https://www.iowadnr.gov/Portals/idnr/uploads/Hunting/trends/logbook_2022.pdf

If this citation looks awfully similar to the one above, well that’s because it is. Through my first round of research, the latest data I could find was for the 2021 season and before. Going back through my sources, I now found data for the 2022 season–in which I somehow missed in my first scan. Similar to the comprehensive report from 2021, this source details every kill from the season, population survey trends, graphs showing harvest numbers throughout the year, quotas, numbers of licenses sold, and many other important statistics to help me promote my argument for my final project. This source will be helpful as it is another piece of relevant and recent data to show how the trends continue to today. I would like to have reports from the most recent seasons (2023 and 2024) but I do not believe that will happen until later: likely September of this year. I will use the data and maps/graphs presented in this PDF to create static choropleth maps of standardized harvest numbers. 

“Trophy Deer Taken in Iowa.” Iowa DNR. Accessed March 24, 2024. https://www.iowadnr.gov/portals/idnr/uploads/Hunting/iowatrophydeer.pdf. 

This source is a comprehensive report detailing trophy buck kills by county, from the year 1953 and on-ward, when they were killed, during what season, and their total score (inches). If wanting to relate CRP back to trophy buck kills, I could sort this data into years, then by county, and average out their total score to see how this related to CRP land use of that given year. Or, I could average it out in 5-10 year increments and complete the same process. This would be a more niche project, though still very intriguing. After all, a lot of hunters today are after those that you hang on the wall, and not as much about just getting meat in the freezer. But that’s a whole different issue in itself. 

Secondary sources:

Allen, Arthur W., and Mark W. Vandever. “Conservation Reserve Program (CRP) Contributions to Wildlife Habitat, Management Issues, Challenges and Policy Choices.” Scientific Investigations Report (2012): 5066. https://pubs.usgs.gov/sir/2012/5066/SIR12-5066.pdf

This source itself is an annotated bibliography of hundreds of secondary sources that help to explain the ecological mechanisms behind the changes that CRP produces. There are sections on ecosystems, energy maintenance, mammals, wildlife habitats, predation, and many more topics that would again help me understand the why behind CRP, and be better able to convince the audience of my argument. Understanding why/how the CRP is crucial for deer population and hunting is a necessary aspect of my project. Even if I decide to do a project of layered static maps, I would like to include an introduction page so that my reader is not blindly thrown into a topic they do not know about. Sure, I may be able to communicate change over time through maps, but if the reader does not understand the topic/context then their conclusion is of lesser significance than it would be with the right context. I would use this source, and the many sources listed within it, to aid in that process. 

Bangsund, Dean A., Nancy M. Hodur, and Larry F. Leistritz. “Agricultural and Recreational Impacts of the Conservation Reserve Program in Rural North Dakota, USA.” Journal of Environmental Management 71, no. 4 (July 2004): 293-303. https://doi.org/10.1016/j.jenvman.2003.12.017

This source discusses the long-term effects of retiring croplands for other uses (mainly CRP). Similarly to the article by Loomis, the economic aspects of CRP are discussed. Unsurprisingly, CRP has a negative impact on agricultural business–but benefitted business surrounding hunting. With this, the research concluded that CRP should only be implemented in areas with low agricultural productivity. In an attempt to convince those who are not on the side of CRP, I could use this source to help convince them not to turn fully to CRP, but only in areas that do not have a great agricultural output.

Grovenburg, Troy W., Christopher N. Jacques, Robert W. Klaver, and Jonathan A. Jenks. “Drought Effect on Selection of Conservation Reserve Program Grasslands by White-Tailed Deer on the Northern Great Plains.” The American Midland Naturalist 166, no. 1 (2011): 147–62. http://www.jstor.org/stable/41288694.

This source describes observed deer foraging for different seasons (summer, vs. not summer). In the summer, deer often picked crops for feeding, and all other seasons picked CRP wetlands. They also noted that the most important factor was access to water. Again, I would use the data collected and discussion following to support my proposition of correlation between CRP and hunt success. 

Grovenburg, Troy W., Robert W. Klaver, and Jonathan A. Jenks. “Spatial Ecology of White-Tailed Deer Fawns in the Northern Great Plains: Implications of Loss of Conservation Reserve Program Grasslands.” The Journal of Wildlife Management 76, no. 3 (2012): 632–44. http://www.jstor.org/stable/41418526.

This source monitored 81 white-tailed deer fawns and how their movement changed over the course of changing CRP land uses. They saw that fawns located within land dedicated to CRP moved significantly less than the deer who did not have the cover from CRP. This is due to the available vegetation and protection from predators that the CRP allows for. As for areas that tended to have crops (corn/wheat), fawns were tracked to move at a much greater extent: likely showing that the food sources were more scattered and predation was of greater consequence. Fawns are the largest contributor to future growth/management (as nearly ~25% of the entire deer population is killed in the hunting season) so learning how to increase their likelihood of survival is critical. This source could be used to show just that, and how CRP needs to be here to ensure a stable future of the species. 

Kaminski, Dan J., Tyler M. Harms, and Jim M. Coffey. “Using Spotlight Observations to Predict Resource Selection and Abundance for White-Tailed Deer.” The Journal of Wildlife Management 83, no. 7 (2019): 1565–80. https://www.jstor.org/stable/26776710.

This source discusses the relation between quality of habitat and species distribution (of white-tailed deer). Instead of simply willing the reader to believe me in saying that CRP leads to an increased deer population, I can use this journal entry to help show evidence/reasoning why deer tend to choose natural vegetation for bedding and home regions. 

Loomis, John, and Michelle Haefele. “Economic Contribution, Impacts, and Economic Benefits of Deer, Waterfowl and Upland Game Bird Hunting in North and South Dakota: Relationship to CRP Lands.” Department of Agriculture and Resource Economics at Colorado State University. September 19, 2015. 

In an interesting turn, this source talks about the economic implications/benefits that CRP land has. The study area was the entirety of North and South Dakota, and they found that CRP and the hunting that occurs on it creates $448 million in revenue for both of the states combined. This is a huge number. In an effort to convince my audience that we need more CRP and not less, I would use this staggering data point and the many other economic benefits it lists to my advantage. 

McCoy, Timothy D., Eric W. Kurzejeski, Loren W. Burger, and Mark R. Ryan. “Effects of Conservation Practice, Mowing, and Temporal Changes on Vegetation Structure on CRP Fields in Northern Missouri.” Wildlife Society Bulletin (1973-2006) 29, no. 3 (2001): 979–87. http://www.jstor.org/stable/3784426.

Many of these secondary sources will be used to set the background information needed for the reader, or help to convince the reader of my argument. This source does the former. In order to understand why CRP works, you must understand the management and influence this has on wildlife. This source talks about how CRP land changed in its vegetation once enrolled in the program, and how this may affect species living within the ecosystem. I would use this information to help support my argument of CRP increasing deer populations. 

Nagy-Reis, Mariana B., Mark A. Lewis, William F. Jensen, and Mark S. Boyce. “Conservation Reserve Program Is a Key Element for Managing White-Tailed Deer Populations at Multiple Spatial Scales.” Journal of Environmental Management 248 (October 15, 2019): 109299. https://doi.org/10.1016/j.jenvman.2019.109299. 

In an effort to convince my audience as well as gain insight for my own benefit, this paper details how CRP and wetlands influence populations: most notably, white-tail deer population. This survey occurred outside of my mapping borders in North Dakota, but I could use the evidence and knowledge to apply to the state of Iowa. In an effort to introduce my topic and my argument, I must convince the readers that CRP does have an unquestionable effect on deer population–in turn leading to greater harvests. If more numbers are seen, more hunters will have an opportunity to shoot and kill the animal they are after. This source shows exactly how CRP affects population numbers, and presents data to support this argument. The ecological aspect behind this is rather unknown to me at this moment, so reading more about why CRP leads to an increase in white-tailed deer population specifically will be beneficial. 

Walter, W. David, Kurt C. Vercauteren, Jason M. Gilsdorf, and Scott E. Hygnstrom. “Crop, Native Vegetation, and Biofuels: Response of White-Tailed Deer to Changing Management Priorities.” The Journal of Wildlife Management 73, no. 3 (2009): 339–44. http://www.jstor.org/stable/40208533.

This source captured and observed 351 deer, tracking how management variance led to behavioral changes. Interestingly, this study showed that home ranges for deer did not change even when primary food sources were removed by the team. The studied deer preferred native grasses/natural forage over crops, but would occasionally shift towards using crops for food only if absolutely necessary. Further, the study noted a 77% overlap of home ranges for the entirety of a deer’s life, showing that they are very territorial. Changing the landscape did not push away deer, instead it just increased/decreased their survival. For this reason, shifting towards CRP would help deer populations as the study noted their preference for natural forages. I would use this source as evidence of such an argument. 

Weber, Whitney L., John L. Roseberry, and Alan Woolf. “Influence of the Conservation Reserve Program on Landscape Structure and Potential Upland Wildlife Habitat.” Wildlife Society Bulletin (1973-2006) 30, no. 3 (2002): 888–98. http://www.jstor.org/stable/3784244.

This source talks about the necessary balances needed between CRP and crops. Obviously, you can not eliminate crops completely due to the economic value they hold; but endless ranges of croplands are also not sustainable. Overall, the journal entry focuses on vegetation and the benefits CRP has with this–and makes a point that every field will have a different need. Some may have no CRP, others 20%, others 55%. It is not about sheer acreage values, but rather what the landscape needs. I could use this source, like many of the other secondary sources, to help my introduction/context section.

Gabe Murphy: Blog 8

With any argument, publication, presentation, or comment (online or in person), ethics should be considered. In terms of mapping, one should value honesty, fairness, and an unbiased representation of the proposition they are trying to make. With any set of data, numbers and visuals may be skewed/edited in order to promote a certain agenda–which is something all readers should be aware of. Look into map cartographics and classifications. Look into the data sources. Look into the map-makers history. All of these things can guide you towards the reliability of the map, and if its methods are ethical. History itself is much more than just numbers, but unreliable storytellers and map-makers can use data to enhance a story that is far from the truth. Remember the example of the vehicular crashes map of Southern California that was presented in class; it appeared as if certain areas had unsafe roads, bad drivers, or a mix of both? However, it was more of a representation of population numbers than anything else. More people means more drivers, which means more crashes. The author used this to their advantage to show an “increased danger associated with driving.” Yet that was far from reality. Those with bad intentions can do similar things with all sorts of topics and maps–so be aware of what is presented, how it is presented, and who is presenting it. When creating maps of your own, remember these values and uphold standards that you want others to adhere to.

Now, let’s take a dive into this week’s assigned readings. The two maps given are much more than sheer numbers: they are deep maps with associated readings and stories. In Monroe’s map, each lynching is tied to a name, gender, when they were lynched, where they were lynched, and why they were lynched.

Monroe & Florence Work Today. 2016. “Map of White Supremacy Mob Violence.” PlainTalkHistory. https://plaintalkhistory.com/monroeandflorencework/?u=2

It uses point vectors to associate deaths and certain coordinates (I presume), a very effective and efficient way to differentiate this map from a typical choropleth map. The tie to personal stories creates a much darker effect for the reader–instead of promoting people just as numbers. 

Monroe & Florence Work Today. 2016. “Map of White Supremacy Mob Violence.” PlainTalkHistory. https://plaintalkhistory.com/monroeandflorencework/?u=2

Further, a timeline along the bottom expertly shows change over time (a common struggle for cartographers). I would like to use/create something like this for my final project. 

Instead of using maps TO tell a story like Monroe, EJI tells a story and uses maps to AID in this. It first delivers personal anecdotes, quotes, memories, and a short film to show how slavery progressed into a looming issue of lynching, and now into mass incarceration. Monroe focused on the issue of lynching, while the EJI went back to the root issue (slavery) and showed how this progressed as laws/society changed. An important quote guiding me towards this realization was from Anthony Ray Hinton, an inmate who was wrongly put on death row. 

Equal Justice Initiative. “Lynching in America.” EJI. https://lynchinginamerica.eji.org/explore

Clicking on this then takes you to a short film about his circumstances, and further down the page two maps are presented. One shows the migration of African Americans out of the south using a decade-by-decade choropleth maps of state AA % population, and another is a choropleth map county-wide for lynching data. 

For both of the projects above, original reports and sources are listed so that readers are able to read and come to their own conclusions if need be. I like their acknowledgment of this issue and the transparency aspect that it provides. A great solution for a possible ethical dilemma. 

In the last assigned source, an article going over both of these projects and the associated ethics behind historic visualizations is given. Similarly to what I prefaced earlier, it highlights the fact that the author uses intentional silences to promote their cause (ie: EJI and systemic racism).

Hepworth, Katherine & Christopher Church. 2018. “Racism in the Machine: Visualization Ethics in Digital Humanities Projects.” Digital Humanities Quarterly, 12(4). https://digitalhumanities.org/dhq/vol/12/4/000408/000408.html

This again shows the importance of digging into the data/sources used, the map itself, and the individual/group that made the map. Different symbology, scales, classifications, colors, and other cartographic elements can be used to alter data in a way that it should not be. While the sole point of maps is to create and maintain a proposition, they should not be used to falsely promote a point for argumentation’s sake. Mapmakers themselves should adhere to ethical standards, but in the fact that this is not always how it works, readers need to be given access to original sources and be aware of tricks/deceptions used to portray data.

Gabe Murphy: Stage 2

In this project, I would like to consider the entire state of Iowa as hunting is prevalent in all 99 counties. With state-run programs and databases, there are harvest numbers and kills per county for every year after 1953 (the inaugural season of deer hunting). Instead of going all the way back to 1953 though, I would likely focus on the last 20-30 years. Finding other data for comparison for every year from the 1950s and up may be impossible. I would like to take two variables and see how they affected each other–but I am yet to decide on what that second variable is. I originally intended to see how deer population affected hunting for each season; however, the true population of deer is nearly impossible to figure out. Instead, the DNR simply estimates deer populations every now and then (seriously, there was not a trend in the years it just looked like they would randomly decide on when to estimate population numbers). Because this did not seem feasible or reliable to me, I will change that second variable. I was thinking something along the lines of urbanization, farming, other things that would directly impact habitat and other critical factors of ecology. For certain, I will be using the DNR assessment of TRENDS IN IOWA WILDLIFE POPULATIONS AND HARVEST that lists every counties harvest numbers, broken down into doe/buck kills, tags sold (measuring success rate), harvest per square mile, which season they were killed in, and many other important data points and figures. It is a gold-mine for this project. I was very excited to find something that encompasses my entire research question and have it run by a reliable, state-funded program. Further, the data is as accurate as can be as not reporting is illegal with major legal consequences. In this 192 page document, there are tables, graphs, maps, and everything else I could possibly need. Perfect. With a possible 2nd variable of urbanization, I would hopefully find more raw data or already created maps of urban area % in each county in Iowa–and similarly for farm land. What is the percentage of each county that is being used for either urban/farming, whichever route I choose. Contrasting this with a year-by-year comparison of harvest success rates or kills per square mile, I think I could create a great argument/proposition for whatever correlations I find. In terms of the final project, I would like to create an animation/video showing change over time. I think year by year for the deer (30 something slides in the animation; all screenshots of maps created using QGIS/arcGIS) using a choropleth map is something that would look great as well as be very functional. While viewing the DNR report, choropleth mapping was something they took advantage of. Conversely, for farming/urbanization I would like to do a dot-density map that would help argue a space component, as the biggest detriment to these deer is their surrounding environment. As urbanization/farming grows, less habitat is there for the deer to survive which I believe would lead to a less successful hunt. Placing these two different maps side by side and then turning on the animation allowing for examination/observation of the changes over time (year by year) for the two corresponding variables is what I picture currently as my final product; but that may change. I feel like this is the best way to represent my data as it would be very easy to see my maps’ proposition as well as show the trends over time. Change over time is something we have wrestled with already throughout the semester, and I feel as though an animation/video year by year does it the easiest and most efficient. I am hoping to find a relevant and state-wide connection between success rates/sheer kill numbers of the hunts and land use within the counties. As a piece of scholarship, this could be a possible tool for those looking to buy land (or sell it!) specifically for hunting to use as a talking point. If I knew certain counties had high urbanization, and this made hunting poor, then I would avoid it! And vice versa. Obviously I would need to research many more variables to ever use it as described–but this could be the first step.

Gabe Murphy: ArcGIS Activity – Cultivation

Percent cultivation of land through years 1880, 1900, and 1940. 

Most notably, from 1880 to 1900, Oklahoma gained county borders but I find this interesting as it was not a recognized state until 1907. Because of this, it is hard to note a change from 1880-1900 because there would have been 0 data for the region in 1880. It could have increased in cultivation, it could have decreased–there is truly no way to tell. Setting this aside, as years progress the percent cultivation steadily increases. As people move west, more land needs to be allocated for agriculture to feed the increasing population. This makes sense. From 1880 to 1900, there were few differences in land cultivation but the change is still relevant and noticeable. However in the 40 years from 1900 to 1940, there is abundant change. The darkest blue regions (highest percent cultivation) nearly double in sheer number. I maintained the same symbology so that it would be easier to note and decipher the change instead of creating three different legends for “readers” to use. County values for percent cultivation in 1880 stay relatively similar, ie: dark blue regions in 1880 tend to stay dark blue through 1940. The critical change comes from expansion. More and more areas west begin to be used for agriculture.

Gabe Murphy: Blog 7

In the US today, there is little to no land that is not currently serving a purpose. By this, I mean that nearly every acre is allocated to something: farming, homes, parks, state parks, cities, and all in between. Even land that has seemingly no function, is usually placed within a conservation easement of some sort; therefore, serving a purpose. However, in the time of Elam Bartholomew this was not the case. The prairies served an endless abundance of farmland, once rightfully plowed. Areas they determined were bad for farming, were mowed or left for grazing/hay. This was common:

Geoff Cunfer, “Pasture and Plows,” On the Great Plains, 2005; 19

Across the entire Great Plains, this transformation of natural grasses to pastures and farming was occurring: in what one described as the “most important ecological action” (Cunfer 17). Most importantly, the farmers discovered the natural limits of nature: something I believe science takes advantage of today. From 1880 to 1920, you can see the slow and tedious process of turning grassland into agricultural land:

Figure 2.4. Percentage of total county area not plowed, 1880-1920.

The eastern regions were targeted first, with subtle differences in large 20 year increments. Due to increased farming, demand for land, improved technologies, and an improved economy, the next fifteen years created immense change.

Figure 2.5. Percentage of total county area not plowed, 1925-1940.

When viewing the 1935 map, the change is greatly shown. The dark black map from the 1880s is now ~⅓ white, denoting 0-25% grassland in these areas (in other words a ~75% decrease in grassland!). By 1940, the resistance/limit set by the land is shown as the grassland returns in some areas. The author uses a choropleth map to maintain his argument, contrasting white and black which allows for an easy interpretation and visual of the change occurring. Sharing a similar argument, the 1903 map of wheat/square mile shows that eastern land was developed first and the Great Plains remained largely untouched until the 20th century. 

Henry Gannett, Wheat/sq. mile, 12th census of the US, 1903.

The dark green regions show the most bushels per mile2, while the white/unshaded regions represent the lowest amount of wheat production. Again, the mapmaker uses simple contrasting choropleths to show the argument. If this map were continued into the 1930s to 1940s, I presume it would show the same changes that Cunfers’ maps did–expansion and plowing of the Great Plains. The unshaded regions would become darker as the years passed and farming became more prevalent.

Gabe Murphy: QGIS Activity – Heat Maps and Voronoi Diagrams

  1. Aside from mortality data during an epidemic, can you think of any other situations where a heat map or Voronoi polygons would be useful ways to analyze spatial data? How might they be useful in this context of your final project (if at all)? 

What came to mind first for me is response/action for troops, paramedics, firefighters, police, and other response teams where time is crucial. Using Voronoi polygons, it could help mark what station is closest given a certain address or location within the city, state… whatever the area may be. This would allow for the quickest time to arrival–and for many of these occupations, an increased rate of survival or deescalation of a situation. In terms of my final project, I could use heat maps to mark reported deer kills within the state of Iowa, which would allow an easy visual of where most deer are killed; I could further change this to two different heat maps of doe vs. buck reported harvests and see the differences in this. In fact, I believe this would be a great addition to my final project and would help visually/spatially support an argument I am trying to make with my map.

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