Here, we see a slight increase in cultivation of the Great Plains prior to the Dust Bowl. This hurts Cunfer’s argument of over cultivation in the Great Planes contributing to the Dust Bowl because a very significant amount of the land has not been cultivated. On paper, it is a theory that may make sense, but when looking at the data and mapping it, you realize that this was not a causation of the Dust Bowl.
Together, these two maps help to show Cunfer’s argument about cultivation on the Great Plains before the Dust Bowl. In 1880, even the most cultivated areas only had about half of their land cultivated. By 1900, the percentages of cultivation in the most farmable areas were higher, but much of the western plains was still largely uncultivated. THis data shows that it was not over cultivation that caused the dust bowl because there was very little cultivation in the areas that were hit hardest by the dust bowl.
Focusing on Kansas, what I found to be the most interesting about this state was how in 1880, the percent of land cultivated was concentrated along the eastern border. Twenty years later, the state of Kansas was less dense and was starting to cultivate pretty evenly around the state. Granted the western counties still had not plowed the land, but the Kansas farmers were cultivating a large portion of their state by the twentieth century.
Looking south we see Oklahoma next. Oklahoma had no land cultivated in 1880 but had started to see some cultivation by 1900. To me, this makes sense for a couple of reasons although it’s a large size gap between Kansas and Texas. Kansas and Texas became states in 1845 and 1861, respectively. Whereas Oklahoma did not become a state until 1907. This distinction is one that I think is important to make because we do not see county lines in Oklahoma, (because there were no counties in the state yet). Without Oklahoma being an official state in the United States, the Homestead Act might not have attracted farmers to Oklahoma yet, although that is just a prediction I have. I suppose a good research question for another time would be what was the relationship between farmers and the area of Oklahoma before Oklahoma became an official state?
Looking to the most southern state, we see Texas. Texas had massive growth in the state (geographically). The farmers were expanding further south and further west from 1880 to 1900 in terms of percent of land cultivated. The area that we know as the Dallas metropolitan area became the densest part of land cultivated in Texas after 20 years of continuous cultivation from 1880.
These maps show a very distinct increase in land use for crop cultivation. While initially starting in two distinct areas in 1880, land used for crops would steadily increase until it covered the majority of the eastern half of these states. Cunfers data does in fact show a steady change over time through an increase in farm territory. With fewer farms encroaching on the farther west regions potentially due to unfavorable geographic and climatalogical conditions. On the other hand regions where land has prevalent crop use may show that these are areas with abundant water and nutrient resources as well as good geography and climate conditions.
These three maps show the percentage of cultivated land in each county in Colorado, Kansas, New Mexico, Oklahoma, and Texas. Each map has data from different years, with the left map being from 1880, the middle map being from 1990, and the right map being from 1940. Looking at the three maps, we can see that the percentage of cultivated land has increased over time. It has increased both in the number of counties cultivated and in the amount of land cultivated in each county. This increase in cultivation most likely occurred due to advancements in farming techniques and technologies that allowed the more arid/desert regions of Colorado, New Mexico, and Texas to be cultivated.
Another good use for a heat map or Thiesen polygon map could be tracking animal populations within a specific area. By tracking endangered species this way, and combining it with temporal data, we can see how the location of these animals have changed over time and how humans have an impact on where endangered species live. This could be useful for me to use in my final project because I am also planning on looking at animal populations. Seeing how human developments have impacted local wildlife populations over time is one of the main research questions for my final project.
Heat maps and Voronoi maps seem to be incredibly useful in determining and visualizing data within certain areas. These kinds of maps could be used to potentially map out votes in an election map or general demographic data in a population map. Concentrations of a particular race or vote choice could be represented with darker colors in the heat map and larger polygons in the Voronoi map. These are useful tools in more easily visualizing numbers and concentrations within a given area. For a final project topic I could use these mapping strategies to potentially mark out concentrations of sugar cane plantations workers on Kaua’i. Sugar cane plantations were a huge part of the early Kaua’i economy and integral to its development. I could even take a wider look and map out regions of high plantation density in general. I could also use it as a method of just mapping out concentrations of certain races island wide.
Thiessen polygons help to show areas of influence beyond political boundaries. Political boundaries are not always the best way to visualize areas of influence. These polygons help to give new ways that the world functions. Maps of the US if states were created around the largest cities are fairly common. These polygons could also be used to map grocery stores or other services within a city. Thiessen polygons are a useful tool to describe the areas of influence for any feature that extends beyond typical political boundaries.
- The larger portion of lendees live in middle to south Philadelphia. Not as many in northwest
- The Northeast region by the looks of the last two maps.
- Using the color coded HOLC it makes it clear which areas are shown as good places to loan to but we don’t have much context with that map. It isn’t specific enough to draw the conclusion of redlining.
- Maybe just a vector that goes into more depth about loans given to separate races. Then the discrimination would become even more evident.
- I think the last map, #4, is the most compelling one that shows evidence of redlining. You get all the components such as the mortgages and the likely locations with the heat map.
- What patterns do you see between mortgage companies and
locations that supplied lendees in Philadelphia? The pattern that I see between mortage companies and locations that supplied lendees in Philadelphia is that when there is a large presence of one, the other followed suit. You can see on the three major focused areas that they are very heavily concentrated areas for mortage companies and the lendees.
- Which regions had the highest interest rates? The southwest section of Philadelphia had the highest interest rates. When you look at this map you can see very clearly that the darkest shades of the city are in the southwest corner of the map.
- What indication do you see (if any) that HOLC maps caused redlining (as opposed to
mapping preexisting discrimination). If none, what additional historical evidence do you
think you might need to establish this relationship? I could see HOLC maps having a role in causing redlining judging by the fact that the lendees with the highest interest rates are very concentrated to very specific areas of Philadelphia. By grouping lendees together, it could be plausible that redlining was either just starting or already underway.
- What additional data layers do you think might supply evidence of discriminatory housing
policy/segregated urban development that you don’t have access to in this exercise? I think some variables that would be interesting to see would be average wait time for a loan application response, ethnicity percentage at this current time and over time, and median income per area.
- Create one clear, legible map that you think best demonstrates the most compelling
visualization of redlining in Philadelphia.