Entirely too ambitious--but at least I joined some tables and shapes!

This process of trying to create maps has been a humbling one (in a good way), and as I spent this afternoon in our classroom trying to create a map that would shed light on a question I have had about the Lowcountry, I realized that my baby skills in ArcGIS Pro and even in navigating the NHGIS are still in their very infancy.  The question I had hoped to explore this week was the following: we know narratively that the 1954 Brown v. Board of Education of Topeka, KS Supreme Court decision that led to the integration of public schools in America (and which actually originated in SC, not KS!) was not implemented until the 1960s in most of SC.   I was hoping that we could look at census data around school enrollment by race in 1950, 1960, and 1970 to see if the integration process had any relationship with school enrollment trends for children of diverse races and, if so, what these might look like on a map--perhaps looking at one or a few counties.

My first stumbling block in creating beautiful maps to illuminate these issues for us was more basic than I expected: using the filters in the NHGIS datastore, I actually could not find any educational data disaggregated by race for 1960 or 1970.  In fact, I was unable to find much educational data at all for 1960--only two datasets ("Percentage of Children in Elementary School Attending Private School" and "Percentage of Persons 14 to 17 Years of Age in School," both fairly limited in focus and neither disaggregated by race).  Lesson number 1 for this assignment, then, was not to imagine that the data we hope to find will be easily pulled up from old censuses run by a government that did not have substantial interest in collecting data about school enrollment by race.  This little exercise was a reminder about how contested the battles even to generate such data must have been.

My NHGIS search to find information on education data disaggregated by race for 1960

So I pivoted to 1980 when I thought surely the data would be in a stronger place.  Not so fast.  I downloaded a dataset whose title promised more ("1980 Census: County Population by Age, Sex, Race & Spanish Origin") but, once opened, provided only a single code: total population.  I was also excited to see an education-related database called "1980 Census: STF 4Pb - Sample-Based Detailed Population Data with Race/Ethnicity Breakdown," only to realize upon studying the columns and the codebook that there was not, in fact, any race/ethnicity breakdown.

Sample from the codebook whose title hinted at a "race/ethnicity breakdown" that was not to be

My hopes were not yet dashed.  I came to realize that my dreams lay fairly well beyond me capacities at this point, and I returned to the exercise to see what I could do with the data at-hand.  I successfully brought these folders into the map, completed two joins between the .csv files and the shape files for the counties.  I made these joins permanent in a new geodatabase file--baby steps that will help me do more sophisticated things down the line, perhaps.

Now, for actual maps.  I offer you two built from the tabular join I was able to create.  Neither one includes any information about race since I did not locate any data about race.  But there is a temporal story here: since I had one column in the education database for men aged 18 to 24 who had never been to school and another for men 25+ who had never been to school, I was able to roughly match the Brown v. Board of Education dates (25 year-old men would have been born one year after the decision, and men 18-24 would have been born in the first seven years after the decision).  I normalized these data against total population, and we can see that men born before 1955 living in South Carolina were, generally speaking, more likely never to have gone to school than those born between 1956 and 1962.

We can see the general trend towards fewer men who have never been to school fairly easily--the second map, which is for the younger men, just has less color overall.  But there are still questions: why were there more men who had never been to school in Laurens county in the seven years after 1955 (the only one to go from middle to high), and why did three new counties that were previously "low" move to "middle" (Union, Newberry, and Dorchester)?

Three final thoughts: first, I realize that by creating the categories of "low," "middle," and "high" without any percentages behind them, I am definitely lying with my map!  (I just don't really know how to play with the legends yet).  But second and third: I created a legend, and I exported these maps.  Two more baby steps.