SQL

I have not updated the blog about my project in a while. Its scope has been pared down significantly – as Ethan has said from the start that it would. The main change is that in order to make the creation of a database manageable within the time parameters of the program, given my total lack of experience with SQL, I have had to identify one highly uni-variant form of data within the corpus of economic and environmental statistics that exist in relation to the Lake Victoria basin. I selected tables relating to the production and consumption of electricity in East Africa, for historiographical and methodological reasons. The generation of hydropower at Owen Falls is an emerging point of emphasis in the historiography of East Africa, and will likely have considerable significance within the context of my dissertation. Therefore, I think that data from Owen Falls and other sites in East Africa offers a useful point of focus for this exercise.

It also offers a relatively accessible point of entry into writing SQL, because the information consists mostly of simple X-Y tables with recurring categories, e.g. Power (Horsepower) produced, Light (Kilowatts) consumed. Still, I have had to do some data cleaning, because these observations were not necessarily made to be compared with one another and were not produced in a standard form like Blue Books (at least, I haven’t digitized any relevant Blue Books). This data includes some tables that are pre-grouped. The largest bodies of information among these groups are a time-series that charts power and light production at sites across Kenya and Uganda across a decade, and a set of revised projections for the demand for electricity based on a revised estimate for the cost of power generation. These groups of tables seem to offer the most low-hanging fruit for the linking of tables through SQL – and the most historically-sound use of the language in this context, given the fact that the creators of these tables intended to group them.

The tables in these groups have also categories in common with other tables outside their own groupings, and so through the use of SQL these data can reveal an integrated picture of electricity production and consumption. This can give researchers increased access to the history of the region, but can also impose an ahistorical image onto the hydropower industry in East Africa, because historical actors did not necessarily see the industry in the ways that a database might present it. Then again, this tension can also be valuable in trying to understand the historical trajectory of hydropower.