Mapping Disease

The piece by Steven Johnson was a retelling of how the societies worked, especially how waste was removed. The ending of his piece connected exactly with the story behind John Snow’s article. John Snow’s piece was a firsthand account of his predictions and findings about the spread of Cholera. Both of these sources give a narrative background to the two maps. John Snow’s map showed how the Cholera outbreaks were concentrated around the Broad Street water pump. This was a really important revelation for someone to make at the time because the means by which Cholera was spread was unknown. It may seem straightforward today to trace the illness back to a common, contaminated water source, but at Snow’s time this was a novel idea. Johnston’s map was from around the same time period and took a broader look at the spread and concentrations of illnesses throughout the world. He too looked for larger trends and how different epidemics spread across the world. He also incorporated things like temperature in July in Africa. Across the bottom of the map he has various graphs as well that seem to draw more conclusions from the outlines he created. He was trying to connect all of the contributing factors of the various illnesses. As the title says, he is connecting the distribution of health and disease to natural phenomena. While the map is very informative, it is also artistically pleasing with the different shadings and arrows and color coded lines.. Considering how old this map is, I’m curious how he was able to gather all of this information. He cites different diseases all over the world, so he could not have seen all of them on his own.

Redlining and Interpolation- Week 7-Eric Howard

  1. The pattern between mortgage companies and lenders in Philadelphia depends on the company. Metlife was white-owned and compared to the black-owned Berean loan company there is a clear difference. The following two maps show a heat map of mortgages overlayed on top of the black population. In the Metlife map, the loans are not present in predominantly black areas while the rest of non-black Philadelphia have loans. Furthermore, Berean has the most loans in black communities showing the role race played in deciding to give a loan.
Metlife Loans
Berean Loans

2. I wan unable to get a fully working interpolation overlay as the QGIS program would repeatedly fail to work. However, the interpolation still has the data but not in a clear cut format. I have included the base map of the black population alongside my interpolated map depicting which areas had the highs loan rates. The highest concentrated area with African Americans also had the highest loan rates, indicating again financial discrimination.

Base Map with African American populations
The interpolated interest rate map

3. These maps can be used to argue HOLC discrimination based on race, due to the difference between the two loan companies. If there were no HOLC maps Metlife might be more inclined to lend to all equally. As opposed to Berean loans which clearly show loans in the black community with some in non-black communities. This does establish that there is certainly something that is happening but not clearly discrimination. To further these connections more HOLC maps will need to be gathered and overlayed with mortgages, demographic data, and loan companies. Doing so will allow more information to be gathered and thusly establish a stronger connection on HOLC discrimination.

4. Some additional layers that will help show discrimination could be how many mortgages were paid off. This will show that the HOLC was not wholly basing their decision on property values. If black communities were paying their mortgages off then it would be fair to say that it would be a safe investment. Another possible layer of discrimination is having a black-owned and white-owned business layer. This can also show what companies banks loaned to adding further depth into how deep HOLC maps affected lenders in their decisionmaking process.

5. This map includes the base HOLC map in 1937 with Metlife loans shown as dotes across Philadelphia, with an opaque layer showing where African Americans lived. This is the most compelling Redline map at clearly shows the lenders favoring “safe’ zones over other areas. Companies such as Metlife consciously avoided “hazardous” areas that included African American homeowners arguably showing race-based discrimination.

Redlining and Interpolation of Philadelphia, PA

Correlation between mortgage companies and locations that supplied lendees in Philadelphia, PA

There is a very distinct correlation between the two mortgage companies and the populations that they lent to the most. The Berean Savings and Loan Association (in purple) lent primarily to non-white communities. The dots that show their mortgage loans are nearly all in “D” rated areas that were primarily inhabited by non-white ethnic minorities. The MetLife company on the other hand (in pink) disproportionately lent to “A” and “B” rated areas. I was surprised, however, that MetLife also lent to some “C” rated areas.

Correlation between redlined regions and higher interest rates

This map shows a strong correlation between “C” and “D” rated areas and higher interest rates, in contrast to lower rates in “A” and “B” rated areas. I turned the opacity of the HOLC map down so the viewer can see the darker areas (higher interest rates) that are located nearly entirely in “D” and “C” rated areas. On the other hand, the lighter grey (lower interest rates) are nearly all located in “A” and “B” rated areas. This map proves that there is a direct correlation between the HOLC maps and redlining. The designation of “A”-“D” ratings of certain areas due to race heavily affected the availability and interest rates of loans. I do think that the placement of the mortgage heat map could also help the argument towards the HOLC’s involvement in redlining. I have a map that includes all the factors below.

I show the correlation between the HOLC map and redlining clearly here by using an interpolated clipped mask of an interest rate map, loans that were granted by certain companies, and the HOLC map. The correlation between high-interest rates and low-grade areas are obvious, as well as the loans that were granted from Berean. It also shows the correlation between high-grade areas and low-interest rates, along with the loans granted by MetLife. This shows that the HOLC was directly involved in the redlining that would cause predatory loans to target low-grade areas, and affect multi-generational wealth through insanely high-interest rates on short term maturity loans.

I do wish, however, that I could have used tools that showed smaller intervals between the interest rates. I think that it could be interesting to see how the rates went up from a “D” to “C” rated area and so on. I also wish that I could have used a tool to examine more predatory loan companies vs. large banks- and the number of loans they distributed throughout the city and where the people who they were distributed to lived.

Choropleth Maps of the South


Corn seemed to have been grown everywhere in the South regardless of slave distribution. A considerable amount was grown in areas heavily saturated with slaves, but more was grown in lower-density areas like Missouri and the upper South.

The majority of wheat was grown in the upper South, in areas of low slave density, however, Virginia grew a significant amount of wheat in high slave concentration counties.

Tobacco was almost exclusively grown in Missouri, upper Tennesee/Kentucky, and Virginia. These regions did not have the highest slave concentrations like the deep South, although they still had significant slave populations.

Cotton was produced in the heavily concentrated slave areas of the deep South, particularly along the Mississippi River.

Sugar was virtually only produced in the Southern tip of Louisiana, a region with a fair slave concentration.

The relative importance of each crop can be surmised by its prevalence in areas of high slave concentrations, as well as the geographic distribution of the crop. Also (as I understand it) the size of the circles gives you an idea of how the amount of the crop produced, so larger circles indicate high crop yields. Cotton was important for sure, but I didn’t realize that corn was grown to such a high extent in the South. Cotton may have been king (because it made them a handsome profit), but corn fed the South (along with wheat, to a lesser degree).

1850 United States Census

Percent Non-White Slave
Corn Production

The amount of corn production staggers throughout the counties with high slave populations, as well as some with little to no slave population. I believe this is due to climates that grow corn more readily- for example, southern Kansas and northern Missouri are known for their high corn production. On the flip side, some counties produced extremely high amounts of corn because they had an enhanced amount of labor that was working on the production of corn.

Wheat Production

The correlation between slave population and wheat production is far less than other crops, however, it can be observed that counties that produce wheat and have slaves overall produce more than counties that produce wheat without slaves. The production of wheat is heavily reliant on climate, and the southern climate largely does not support its growth.

Tobacco Production

The correlation between tobacco production and the slave population is similar to the relationship between wheat and the slave population. While the product exists in counties with slaves and counties without, the production is far higher in the counties with increased amounts of slave labor.

Sugar Production

The production of sugar is secluded to a few counties in the deep south. These counties also have high slave populations. This can be attributed to the intensive labor that the production of sugar requires alongside the strong confederate ties in this region and the proximity to the original slave ports where the slaves with imported into the United States.

Cotton Production

The correlation between cotton production and counties with a high slave population is one of the strongest that we see between all of the crops. Wherever there is high cotton production, there is a high population of slaves. This, similar to sugar, is due to the intensive labor that cotton production requires alongside the proximity to the deep south.

Cotton and sugar are the main crops that the confederacy relied on for economic prosperity. Each of these crops is extremely labor-intensive and are sold for very high amounts of money. The cycle of sugar and cotton production and sales sets up these plantation owners to continuously increase their labor force, and therefore increase their cotton/sugar production.

1860 Census Map

Map of Slavery in the Southen States
Cotton Distribution
Corn Distribution
Wheat Distribution
Tobacco Distribution
Sugar Distribution

Although all of the crops have different degrees of intensity in each map, the dots signifying their location of production are always most prominent along the hot spots of slavery in these southern states. The West Virginia region (still part of Virginia then) and eastern Kentucky region shows that there were relatively few slaves in that region, and consequently, the crop maps show very little being grown in that general area, wheat apparently being the only small exception. In general, no matter the crop being grown, all the crops seem to be produced in regions with dense slave population.

The importance of a crop for the confederacy could be determined by looking at both the density that the map shows the production of the crop as well as the density of the slave population in the counties that the crop is being grown. Cotton clearly seems to have the most important crop for the confederacy considering the large area and intensity of the dots on the map. Also, in all the counties cotton is shown to have been grown, there seem to be more intense numbers of slaves in those counties. Tobacco seems to have held strong importance in the states of Virginia and Kentucky, and Missouri to a lesser extent. Sugar seems to have been the cash crop of the Louisianna counties. Corn and wheat seem to have held the least amount of importance in the confederacy, although they were still grown rather broadly across many of the counties, but not with the same intensity as the other crops.

Census Map

Original Map of South
Sugar overlay
Tobacco overlay
Corn overlay
Wheat overlay
Cotton overlay

The general assumption that can be made from almost all places where crops are grown is that there is a higher concentration of slaves. There is also a greater amount in the areas that show greater slave population based on quantity as shown by darker red. Crops of different in many places overlap in where there is high concentration of slaves and places that show little to no slaves have few to none overlap.

The importance can be looked at in various ways such as general area a crop covers, the concentration of slaves for it, the general location, or overall quantity grown among others. The area a crop covers is important because they had limited land to grow on due to how fertile the land is or other factors so deciding to grow in large quantities shows it is important. Amount of slaves concentration is important to look at because they were valuable labor and higher amounts means greater production or the good was harder to process therefore it must be important for them to focus on. Finally the location and quantity show significance in many ways such as the ratio of each crop means on was more important to use land on which could be easily used on another. Also like I said before there was most likely land that could not be used to grow crops so the land must have been used wisely.

Week 7 Mapping Inequality

The sad history of Omaha is really on blast with the maps this week. The first thing I couldn’t help but do was find Omaha on the racial dot map and compare our two subjects. On the Omaha side, the racial makeup of today is incredibly similar to the red lined sections of historic Omaha. However, Council bluffs is in a very different situation. Though today it is almost entirely white, most of the neighborhoods from the historic map are B or D not having the normal distribution of Omaha. I wonder what caused this. I have three theories. First, It may be a climate thing. The areas most apt to be D in the Bluffs are very near the river, maybe indicating flood risk or something. Second, maybe the demographics have just naturally shifted. Finally, it may just be an aspect of human error in that different people worked each area and came to different conclusions. I was also struck by the dramatic segregation of Omaha. I had heard the usual “Omaha is one of the most segregated cities in America,” but to see it so clearly displayed in the data is shocking to say the least. It makes me yearn for the influx of data our 2020 census will bring us. Does that data become available quickly, or will it take a year or two? I am curios for the future.

Council Bluffs’ oddly skewed grading graph.
Omaha’s more normally distributed grading

Weekly Post – Masters

When looking at redlining it is very interesting to be able to see how redlining in some areas especially the Omaha area semes ot really coincided with race. Looking at the two topographical maps below you can see the correlation between the two. The top map showing the redlining district and the second one showing the racial differences between the different areas.

Looking at these two maps it really shows not only how racially divided the greater Omaha area truly is. Through not only looking at the racial divided but then also focusing on how this fits into the redlining districts and seeing how those interconnect into that divide. It also then is interesting to look at other cites on the map to see if this race separation is particular to Omaha or if this is something that this a theme in big cities across America? I think that in a way looking at other redlining districts across the country would be a very interesting thing to look at.

Choropleth Week 6

1a. Corn has a well defined spatial relationship with slavery. It is most poignant in the band of slavery found in middle Missouri. As well as the crescent running through Alabama. There is a striking correlation between counties with higher percentage slave population and corn output.

1b. The correlation with wheat is less exaggerated, but still visible in its map. In the northeast of the South, the more slave owning sections produced more wheat. I think the lack of evidence in other sections come from this being a less prevalent crop in other climates.

1c. Tobacco follows very similar trends to wheat, with more of a center in Richmond. Again, the more slaves in proper climate places, the more more of that area’s important crops.

1d. With Cotton, we see very similar sights to our first corn map. Again this crescent crossing through Alabama displays high slavery percents and high yield of important cash crops. This map also displays the heavy correlation between the Mississippi and slavery (also higher crop output).

1e. Finally sugar, which seems to be tied to different factors and doesn’t show similar findings to the other four. Sugar seems locked to the wet, hot climate of southern Louisiana, and not to the slave percentage. This may be a culture thing (this area having culture deriving from sugar cane growing peoples) or just a climate thing.

2. I think to properly judge the importance of different crops for the confederacy, different sets of stats would be important. I think the prevalence of corn across the whole of the South instead of in only regions speaks to its importance, however I don’t know which crops were being bought or supported by their government.