Tabular Joins

 For this assignment, I imported 1930 census data from NHGIS. As for my visualizations, it displays the number of homes, by percentage of county population, that are either rented or owned. I decided to narrow down results even further to see if there was a drastic difference between homes owned within the former Confederacy and post-1860/pre-1870 Union decades after the war and the readmission of the rebelling states. Up until the Great Depression, many of the former Confederate states employed the practice of tenant farming as a means to keep their former cash crops profitable after the fall of slavery as an institution. When the Great Depression hit, tenant farming also took a hit. So, the goal of this data is an attempt to see a general view of the population of tenant farmers through rentals in correlation with those owned their properties around this time in relation to the regional practice.

Overall, the general consensus, based on the data, is that the states of the Union had higher rates of ownership, compared to rented, than the former Confederacy (Note: population differs drastically between the higher density counties up north than south). However, to achieve a sturdier argument on this basis, I would have to import tenant farming data alongside pulling information on formerly enslave community concentrations per county. This way, with the additional data, a much stronger argument could be made.

The most difficult part of this process was attempting to separate the maps based on state concentrations (i.e. the former Confederacy and pre-1880 Union). In fact, I was unable to figure out how to take the original data set and further confine it to just the states of the Union pre-1880.

Owned Homes 1930

Owned Homes, Former Conf 1930

Rented Homes 1930

Rented Homes, Former Conf 1930

Farm to Table: Cattle Drives & Demographics in the 1870s/1880s

Fig. 1

One of the most well-known, and famous, professions of the 19th century that still proliferate American minds is that of the "cowboy." Composed hodgepodge of ethnic and national groups, many early cowboys stemmed from Hispanic communities along the border of modern-day Mexico. However, with the increase in profit margins due to established cattle trails, many European immigrants got in on the trade. In addition to those from Mexico, many German and Irish immigrants took to the prairies of the Midwest to find their fortunes. 

Within Fig. 1, the image depicts the populations of German-born immigrants during the 1880s (with higher densities displayed in a darker blue) and the counties in which they resided. Overlaid on the map are also the developed rail lines of the century (colored red) alongside existing cattle trails from 1870s-1880s. Each trail, represented by its own respective color with the Goodnight-Loving Trail in green, the Western Trail in orange, the Chisholm Trail in blue, and the Shawnee Trail in purple, all demonstrate some of the most lucrative routes used by the early cowboys.

Fig. 3

Fig. 2

Upon further investigation, one can easily see the ethnic German population concentrating within central Texas and along the final stages of the cattle trails. At the end of these cattle trails were usually economic centers, such as St. Louis and Kansas City. At the time, there was likely a connection between German-owned/German-populated businesses, especially in St. Louis, within these centers and connections to German-born cowboys. However, the same can be said for Irish-born immigrants as well, who also took up residence in these "cattle counties" and population centers at the ends of the famous trails. As displayed by Fig. 3, there is a potential link between the Irish populations within "cattle counties" in central Texas and the population centers at the ends of Western and Chisholm Trails.

Another important connection to note are the locations of the end of trails in relation to existing railroad routes of the era. All of the displayed trails siphon off along rail routes that led to major cities as cattle cars then transported the livestock to processing facilities, and later, major cities for distribution purposes. With the existence of higher densities of German and Irish born immigrants within these cities, it is likely there was an economic connection between them and the cowboys that shared their home country. However, this is hypothesized and can only be further confirmed by the usage of business ledgers and tracking transactions between the cattle drivers and the facilities at the ends of the famous trails. In addition to this, the maps do not effectively label what percentage of their populations, in relation to place-of-birth, were cattle drivers. However, this can easily, but tediously, fixed by examining professions within the 1870s/1880s census records.

2010 Census & Redlining

The creation of this map required that I overlay several layers pertaining to 2010 census data, redlined districts, and historical maps of the city of Columbia, South Carolina. Initially, population areas were highlighted with varying sizes of dots that needed to be altered to accommodate a proper rendering of population density (ergo, using color blocks to display the higher densities of African American populations in 2010). 

Ultimately, the map shows racial discrimination in loan acquirement through the display of African American/Black population density within Columbia overlayed with areas deemed "unfit" for funding. With addition of an 1850s map and the potential for utilizing prior census data, one has the ability to see how redlined districts have changed depending on whether or not the areas used to house different demographics. 

The map effectively displays how there is likely a racial incentive when it comes to loans and investment through its display. But, what the map ultimately lacks is further research that can display how the redlining has changed over time and how it has depended upon the racial population living within these centers. Essentially, to raise more questions through the incorporation of more data, one must look to acquiring further census data and compile (and display) info pertaining to redlining throughout the past several decades.