Stage 2 – Anderson, Vance, Luttrell

  1. The scope of the project (how wide an area and how much time will your map/mapping product consider?) Make sure to keep the scope of the project manageable.

The scope of the project is a north to south area focus on Dodge Street to Ames Avenue with east and west parameters of 16th street to 72nd street, with a time period of 1870 to the present. This will allow us to see racial borders to both the north and west through time, starting before the Great Migration of the 1910’s.

2) What are some of the possible sources (i.e. digital maps, online data, historical data, secondary sources) that will be employed in your digital mapping product?

Possible sources include University of Iowa’s data collection and database of Omaha’s population and nationality in 1870. More in-depth, this website also features a ‘methods’ tab that describes the sources of the data as well as some of the adjustments they have already made to the map.

Another source of data will be the national historic GIS compilation. Similar to University of Iowa’s data, the NHGIS will have adjustments and changes over time that include boundaries and classification sets that we will need to account for in our final product.

Lastly, an additional source of data that we may look to include the vast array of secondary sources, such as local newspapers and histories of Omaha’s neighborhoods. These accounts will almost certainly be subject to a different set of biases and views than the sources above but will help us put the different data in context with one another.

3) What type of final project will you create/present? Will it be a static map? A series of maps? An interactive web map? Why is this the best way to present your question/data/answer?

We believe that creating a series of maps over time will best portray our argument and account for the silences we have identified so far. In addition to presenting the data in map format, it may also be important for us to visually depict what an ‘average’ family or person looks like in this region in a given time period. Finding statistics such as income, working hours, education, life expectancy and other ‘demographic’ statistics will help paint the background of our study in the change of population.

4) A clear explanation of the value of this project as a piece of scholarship

This allows us to see how or if Omaha has changed in its racial areas since the early 1900’s. Looking at this time period should give us deeper insights into how the city responded to growing pains during the early 1900’s with the Great Migration, roaring 20’s, then crash during the 30’s and 40’s due to the Dust Bowl and Great Depression. We believe that by analyzing and accounting for the judgments, assumptions, and adjustments previous scholarship has taken at this angle that we can produce a more complete and accurate map of Omaha’s demographic change over time than what has already been created.

Mapping Rural Development – Vecera

For this week of readings and maps, the focus was on the great plains and developing of these areas for crops during the beginning of the 20th century. The reading stated that between 1870 and 1930 American farmers cultivated  more than 100 million acres of grasslands with single-species crops. The reading, written by Geoff Cunfer in 2005, focused on farmer Elam Bartholomew during this time period.

The first map was created by geographer Henry Gannett in 1900, displaying a land map of the United States. This map displays no topographical features, but displays what appears to be all major rivers, state names, some major cities, longitudinal and latitudinal lines. However, the primary features on the map was the colored portion, indicating wheat per square mile during this year. Coloring ranging from tan to green indicates the amount of bushels per square mile. The northeast region and midwest have the most wheat according to the map, while the west and south lack wheat. A lack of color indicates unsettled area, and the western region has the least amount of color overall.

The second range of maps was from the reading by Geoff Cunfer, and displayed the central region of the United States, showing the percentage of grassland during these areas over a span of years between 1880 and 1997. The states shown are North and South Dakota, west to Montana, and directly south to Texas. Areas colored dark were between 80-100% grassland, and as they lightened in color the grassland disappeared.  In 1880, a large majority of the region was between 80-100% grassland. In fact, the entirety of North and South Dakota was grassland.

This changed drastically after 1880 however, as the cultivation of these lands for farming purposes led to a sharp decrease of grassland.  By 1935, just 50 years later, 50% of these two states had been plowed for farming. 

The reading touched on this trend, and how it effected the prairies and ecosystems overall. “The decline in native grassland continued into the twentieth century, reaching a low point in 1925 at about 45 percent prairie, 55 percent cropland.” This was caused by the pushing of cattle raisers out of the grasslands so these areas could be used for farming instead. Once the farmers realized they had exhausted the land, they “plowed straight past those limits by 1935, realized their mistake, and slightly contracted cropland again by 1940.”

Since 1940, the area of grassland has been relatively let alone, and still sits around 45% grassland for the Great Plains region. These maps do a solid job of displaying the patterns of cultivation in the Great Plains, however the Western region appears to be rather unobserved, and is an intentional silence in these maps.


Building Vector Layers – Meehan

1. Are there any features on your historical map that would have been difficult to assign a vector categorization (point, line, polygon)? Why?

Chicago Harbor would have been difficult to categorize with any one of the three vector categorization points because of both its unusual shape and that its changed significantly since the map was published. In fact, when you overlay it with a modern-day map like Open Streets or Google, they hardly look the same:

2. What other attributes (aside from name) do you think would have been
appropriate to add to your vector dataset?

Something like “unchanged today” or “changed today” in the sense that some of the features like cemeteries have vastly expanded since the map’s creation, versus features like Grant Park, which has remained its original size today.

3. Did you detect any spatial relationships when digitizing your map that you
would not have otherwise? Did you see your historical map in a new way? If so,

The green space in Chicago is almost in layers centered around the Magnificent Mile. As you first start to expand out, you find a number of parks and some branches of the Chicago River. However, the further one ventures out, the fewer parks are seen/branches of the Chicago River and the greater number of cemeteries. In addition, cemeteries are found in the far north and south sides of Chicago, but not in central or west Chicago.

Redlining and Interpolation – Meehan

1.  What patterns do you see between mortgage companies and
locations that supplied lendees in Philadelphia?

It seems that Berean (shown in blue dots) almost exclusively supplied lendees in fourth grade neighborhoods, whereas MetLife (yellow dots) had a bit more even distribution between each of the four grades. Yet, between both mortgage companies, a fair number focus on the fourth grade areas (see below):

Upon further layer mixing and matching, it seems that African Americans predominantly live in third or fourth grade areas, as seen below:

2. Which regions had the highest interest rates?

Areas deemed third or fourth grade neighborhoods had the highest interest rate (see below):

3. 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?

Given the current vector layers of data, I don’t think there’s enough information to effectively see that HOLC caused redlining. To establish this relationship, I think a vector layer depicting the number of mortgage requests before successfully receiving one from Berean or MetLife could establish a stronger relationship. That way, one can see the number of times one or both of the companies denied a house owner’s application.

4. 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?

Number of foreclosures, dollar amount proposed for a housing project vs actually spent on said housing project, and more recent census data could give stronger evidence of discriminatory housing policy/segregated urban

5. Create one clear, legible map that you think best
demonstrates the most compelling visualization of redlining
in Philadelphia.

I believe this map could best convince someone that redlining exists in Philadelphia. To be completely honest, I don’t remember all the vector layers that were here, but it appears that the HOLC layer, interpolated interest rate layer, and number of mortgages layer (vua red dots) are all depicted on this map. From this map, one can see that those that had mortgages had to pay higher interest rates, which often were from third or fourth grade neighborhoods.

Macro– QGIS Philadelphia Redlining

In Philadelphia there is a relationship between HOLC designations and trends in mortgage lending. Locations in Philadelphia that have more people of color are designated as hazardous. While areas that have a better rating contain more white people. Areas that have a low HOLC rating also tend to have the highest interest rates and highest concentration of mortgage lending especially from the Berean mortgage company.  

The highest interest rates were concentrated in locations that were deemed “hazardous” by the HOLC. This is clear as the rates almost stopped exactly where these low rating areas ended. There is a clear difference between interest rates on different sides of Millbourne Street, in Philadelphia. This is a testament to the lasting legacy the HOLC has had on creating a racialized space (not vice versa). Naturally humans don’t live in straight lines. However, that is how the HOLC drew its map and how Philadelphia was still segregated over fifteen years later. The HOLC created a systematic approach to racism that became institutionalized. This argument could be made stronger if we also compared the census data from before the creation of the HOLC’s map, measuring a change over time. Other data layers that would help argue this point are changes in socio-economic status over time (or lack thereof) categorized by race and HOLC rating.

This map compares 1950s interest rates and the 1937 HOLC ratings. The stark similarities (out of all the maps) most clearly demonstrates the explicit connection between HOLC ratings, and an institutionalized system of keeping minority populations at a large disadvantage.

Weekly Blog Post

The Gannet map and the Cunfer reading display how the Great Plains have changed over time. The Cunfer reading itself shows how agricultural practices developed over time and became more technologically advanced. Before steam plows were invented and widely used, farmers could only use horses to plow their fields, which took much longer to do and resulted in very little land development.  Cunfer describes plowing, even with just horses, as equivalent to clear cutting a forest. Therefore, horse plowed fields had less of an impact on environmental change than more mechanized ways used later on.

In this first picture you can see very little of the plains has been destroyed.

There is a big difference in comparing the amount of existing plains with maps depicting the percentage of plains remaining in later years.

Cunfer notes that farming was largely profitable and that people of other professions often left their jobs to plow fields. The profits and technological advances of plowing had a large impact on the environment and the amount of actual plains left.


Blog Post 5 – Vance

The maps for this week both focus on communicating changes in land use patterns in the Midwest. As Geoff Cunfer explains in “On the Great Plains”, it took several decades for changes in scenery (ie. the shift from grassland to farmland) to be visibly obvious, and most farmers would leave large sections of their land unplowed because so much effort was required to clear the land. I think that Cunfer makes interesting arguments about how farmers needed to meet the demand for crops but were limited by the natural capabilities of the land, and I agree with a lot of what he said, but I also think he projected his conclusion onto his maps. Of course, any map is influenced by the cartographer’s perspective, but I think that Cunfer especially, created a lot of silences by not including factors that might have influenced the changes in topography (ie. he left out specific uses of the land, a more detailed breakdown of ownership, influence of weather, etc.). In some ways this is helpful, as it offers the audience an easy-to-understand, simple message. I think Cunfer’s method of showing the same landmarks over time–so that the only clear change represented is the amount of grassland–is easy to interpret.

A section of Cunfer’s maps

However, I believe this also creates a lot of questions, answered more by his written explanation than by his maps.

Gannett’s method is to explain land use by showing what was produced in each section of land at different times, while Cunfer’s method was to show how the land was categorized over time. Gannett’s map appears to have a little more specific details, and I think it has more practical use for different groups of people.

Screenshot of part of Gannett’s map

It appears more accurate that Cunfer’s map due to a greater attention to detail, although I think there are inconsistencies in both maps. One way to resolve this (for both Gannett and Cunfer’s maps) would be to create smaller categories for the breakdown of data (ie. have more shades on the maps for different percentages of land.

Casper–Blog Post 5

After analyzing this week’s readings and maps, my knowledge of the agricultural history of the United States has truly expanded. Being from California and living in the midwest for the past four years, growing up I was not exposed to this side of American history.

Cufner’s argument in On the Great Plains seeks to explain the expansive agricultural boom in the United States in the late 1800s to the early 1900s. He does this by focusing on the people who inhabited the area (farmers), the environment they lived in (grasslands), and the ways and means they achieved the process of agriculture (tools). He achieves this argument through historical sources of farmers, specifically Elam Bartholomew, and additional maps depicting the concentration of grassland and wheat in the Midwest during this time. That being said it is clear that Cufner’s target audiences are ecological historians or the broader farming class.

Percent of Grassland in the Midwest between 1880-1920

The natural ecology of this area, or the “native grass” as Cufner likes to describe it, was densely grassland. Farmers such as Bartholomew saw an opportunity to access these great plains and enjoy the bounty they provided. Through practices such as plowing, millions of farmers were able to achieve something no one else in the past was able to do–large-scale farming and agriculture production. From the map above, the audience can see how quickly this area went from dense grassland to a plethora of farming homesteads. This is further supported by the map below. This map, created by the United States Census Office in 1903, depicts the concentration of wheat production in the United States.

Wheat production per square mile, 1903

This map shows the wheat production during the middle of this agricultural boom. From this, the audience can see how concentrated wheat production is in the Midwest or this area formerly known as the Great Plains. The only thing to make this maps argument stronger would be the addition of another map from another time in order to show longitudinal change (change over time). By having an additional from before 1903 and after 1903, the audience would be able to better analyze the beneficial and adverse effects of plowing and wheat production throughout this area.



Anderson – Week 7

Although Cunfer does an exceptional job of characterizing the grasslands of the plains for nearly a century of its history, I think there are crucial points he misses. When Cunfer talks about the ‘normalization’ of the plains, back to their environmental limits from an overflowing of the land, I think he talks about this time too gracefully. There is a very high probability that this overplowing and over utilization of the land caused the Dust Bowl, one of the greatest natural disasters ever witnessed in the United States. Additionally, I think Cunfer could have dove into more depth around the time period that this naturalization occurred – the trough of the Great Depression. I agree with everyone of Cunfer’s points dealing with human change of the land and how we can’t overpower everything we want to, but the heartbreaking story of the 1930’s has just as much appeal to me as the ‘get rich quick’ stories of the early 1900’s

Turning my attention to the map, this compliments Cunfer’s narrative extremely well. Seeing that the map was created from data in the Census of 1900, it makes sense that expansion and exploitation of the prairie grasslands had not taken its complete effect yet. In terms of who this map was created for, I think data like this would be extremely helpful to the US government and the farm bureau. Cunfer’s piece noted that the government still controlled much of the land of the great plains at this point, but they had a dilemma as to how much to sell each plot for. Looking at data like this, I would think, should help them assess which areas of land are valuable and which are not, though I hope they would have known of this information strictly looking at the differences in landscape (sandy vs rich soiled vs rocky) to determine a rough price. Overlaying a map of the average price per square acre may reveal some insights as to what prospectors thought of the area and how closely a high average price correlated to high wheat production. Lastly, I think one of the most notable silences of this map is population centers and towns. Embarking out to the prairies took courage,though towns often built up around major agricultural and processing centers. It would be interesting to see if major cities are located around historically high producing areas.

Blog Post Week One_Stang

It is extremely interesting to me how two maps of the same place can be so different. Each map depicts Chase County, Kansas in a unique way. Upon analyzing these maps you begin to realize that the fact that both maps depict the same place is one of the only similarities between the two. Evert’s map is far more topographical than Heat-Moon’s map, but that doesn’t mean that his map is a better or worse map than Heat-Moon’s. It’s important to understand that both of these maps were made for different purposes and different audiences. Heat-Moon’s map is considered a “deep-map” because it goes beyond topographical mapping and incorporates the history of the county. Not only does it go beyond topographical mapping, it makes the history of Chase county the priority of the map. For instance, Heat Moon tells a story about a physical location from the county (Roniger Hill) instead of showing only it’s physical location within the area.

Stories like this make it clear that this map was written for an audience who desires to know more about the history of the area than the physical locations and dimensions of the area. For me, the combination of the little snippets of topographical map and the stories are what makes this map affective. The stories are an easy read and effectively get across the history that is intended to be shared.


Evert’s map looks basically like what you would think of when you hear the word map. It is topographical in nature and splits the county into five main section (presumably separate towns within the county). If you look within those sections you can see that this map is actually broken down by the acre. Also, I thought it was interesting that someone (maybe the author) verified the accuracy of the map in the bottom left corner.


This leads me to believe that the author of the map was probably commissioned by Chase Country to create an accurate depicting of the county. I believe that the purpose of this depiction was to outline properties and show the names of the family who owned the property. If this is the case, this map is effective because it is easy to see who owns what property and where their property begins and ends.