Data Sources: STRI GIS Portal

Last week, I introduced the fact that my mum is British-Guyanese. On top of that, my father is Panamanian. He comes from Bocas del Toro, an area where Chiquita bananas (owned by the United Fruit Company) were produced. In January, we visited his hometown together for the first time; he was surprised at the changes. Entering the port via water taxi, we passed massive ships carrying cargo. Cranes could be seen on the land lifting containers up and down. My father could barely recognize where he was; he remarked that Chiquita must have paved the roads to make transportation easier for themselves.

I thought of Panama not just because of my experience there earlier in the year, but because of the climate issues that it is facing. Gardi Sugdub, an island off the coast of Panama, is sinking (due to rising sea levels). Primarily a fishing town, it’s home to an indigenous community. This past summer, Panamanian officials relocated locals.

Initially, I wanted to create maps tracking the changes in climate and sea level over Gardi Sugdub and surrounding islands. Unable to find the specific files that I wanted to use, however, I decided to begin by making basic maps of the general country within QGIS.

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QGIS is a free, open-source software used for mapping and geographical information systems (GIS). Without some sort of tutorial or how-to, it can be confusing. Because I’ve used QGIS before to map NYC census data, I treated my map-making as a “refresher”. However, even with the previous experience, it is not as “common sense” as it seems. My first time using it, I didn’t understand the meaning of a “Vector Layer”. Additionally, I had to google “how to make a map QGIS” to get from the “project” mode to the “layout” mode. After using the software a couple of times for basic map-making, the process becomes almost automatic.

I used data from the Smithsonian Tropical Research Institute: GIS Data Portal. Because the chosen data is so organized, I didn’t have to do any JSON edits or cleanup. I made four distinctive maps: Provinces of Panama (2016), Areas in Panama With Electricity (2010), Areas in Panama Without Electricity (2010 – Modified Legend), and Areas in Panama Without Electricity (2010 – Original Legend).

Provinces of Panama (2016) showcased the general administrative areas set by the government of the provinces within the country. I have previously never seen Panama’s indigenous provinces or areas mentioned on a map or in general conversation, and so when turning on the “labels” of the different areas, I was happily surprised. However, with the labels, I found it difficult to position them exactly the way I wanted; it was either they hovered over the areas and obstructed outlines, or they overlapped with one another – making it confusing to read.

With population information from the 2010 census, Areas in Panama With Electricity (2010) displayed the general areas in Panama where there was electricity. STRI did not specify whether it was people, households, or entire neighborhoods that had electricity; the distinction is important as the count would drastically change. Moreover, it was on this map that I was not entirely sure whether it was my skills, the information data, or the spread of the population that was leading the map to look the way it did. The two areas where electricity seems at its highest were near the capital, Panama City, and Chiriquí, where many power plants are located. To modify the legend, I changed the styling to my “population” layer from a single symbol to graduated, with an equal interval for my VPOCLUEL (with electricity) value.

I treated Areas in Panama Without Electricity (2010 – Modified Legend) and Areas in Panama Without Electricity (2010 – Original Legend) as experiments, due to the different highlighted areas. After changing the legend values, there were no areas on the map displaying any sort of color values. This would suggest that all areas seemingly had electricity. However, in the original legend, the values for those without electricity were low, with the last spread ranging from 317 – 397. Having this sort of legend spread didn’t make sense considering the last legend contained values in the thousands.

Ultimately, I want to continue exploring QGIS features, such as the plugins, the Python and SQL servers, etc. Ujaval Gandhi makes great step-by-step tutorials ranging from basic to advanced and with time, I’ll be following those.

Google Slides: https://docs.google.com/presentation/d/1oJx6LofrL0rzZkqGGs_qOxHh42Nr073Mqq2xcvScrAM/edit?usp=sharing