Visualizing data: Maps, graphs, and the coffee Industry

Overview

As one of the world’s leading agricultural industries, coffee is not only relevant to many people, from a variety of socioeconomic classes worldwide, but also to discussions on globalization and environmental issues. Geographical Information Systems (GIS) have many pertinent uses in this context, from creating useful data visuals and analyzing spatial densities, to bolstering producer agency in global supply chains. Data visuals are an important element in the move to comprehend and minimize sustainability issues within the coffee industry. The first paper in this Plan seeks to illustrate a variety of ways in which the global and Indonesian coffee industries can be interpreted, and visually demonstrate the usefulness of GIS-based thematic maps in providing accessible representations of the coffee industry. I argue that through a combination of government/NGO financial support efforts, certification schemes, data transparency movements, and GIS-based data analysis, it is possible to shift the current imbalance of power along the coffee supply chain. This Plan also includes a paper on using remote sensing to tackle the coffee rust epidemic, a paper on creating an online trail map for Marlboro College, a map portfolio, and exams in pure mathematics, statistics and discrete mathematics, and calculus.

Excerpts

As global recognition for region specific Indonesian coffee products grows, it is reasonable to expect general demand for Indonesian coffee to grow as well. Arafin (2010) suggests that increased demand for, and price of Indonesian coffee would provide growers with the proper incentive to focus on honing the quality of their product. He also discusses statistics of the major deforestation issues in the Sumberjaya region of Lampung, Sumatra and the increased use of agroforestry methods in coffee farming. These two elements could be combined to create a line graph where one line depicts the total forest coverage each year and another shows the hectares of land dedicated to coffee agroforestry systems over the same time period. This graph would not only make the stated trends more apparent but would also highlight any impact coffee agroforestry might be having on major deforestation issues; the expectation being that agroforestry would have a damping affect on deforestation. The use of graphs in this circumstance would reinforce Arafin’s argument by making the data more readily digestible for reader.

With the intensification of climate change, devastating crop diseases, such as coffee rust, will become increasingly prevalent. Coffee rust has the potential of destroying the crops of millions of smallholder coffee farmers worldwide. Although preventative measures are being taken in some locations, monitoring for changes in landscape or weather patterns that might increase the susceptibility of other potentially endangered areas is important. With the monetary assistance of governments and/or other outside programs, steps can be taken to reduce the likelihood of infection. Remote sensing can be used in this monitoring process and in the identification of less disease-prone regions where coffee growth might be encouraged. However, considering the lack of consistent imagery obtained by Landsat satellites and the site-specific nature of aircraft-based remote sensing, it is necessary that there be a more reliable data source before accurate temporal trends can be identified.

The purpose of this section is to explore data breaks and how they effect the viewer’s perception of a data set. Figure 1 lays the groundwork for this comparison by representing the farmers’ market location data in a dot density map. Although in some ways the dot density map is the least adulterated format through which to view the spread of farmers’ markets throughout the United States, the concentration of markets in urban areas is the only trend obviously present in this map.

Reflections