Similar to the Economist, Scott Cunningham who said, “my path to economics was not linear”, my path to economics research in agriculture using geospatial data was a combination of randomness and deliberate interest, and none of it was linear.

I got to study agriculture during my bachelor’s because I put “Agricultural Extension and Rural Development” on the admission form, but only as a random fallback course of study if I did not get a slot in my preferred courses. Having landed in the faculty of agriculture and realizing there’s no way to pivot to a more interesting faculty without starting all over, I was forced to begin to really appreciate agriculture. While at this, economics piqued my interest. Therefore, I planned to do a master’s in Agricultural Economics after my bachelor’s in Agricultural Extension and Rural Development.

While working on my master’s thesis in Agricultural Economics, I found it interesting to create study area maps. Discussing this with a friend, he told me how easy it is to create these maps and volunteered to teach me how to do this with QGIS. While practicing on my own, I met ArcGIS and the array of learning opportunities provided by ESRI. Having gotten into the world of GIS, I was happy to later learn from one of my professors that GIS skills have become increasingly relevant for Agricultural Economics research. Soon, she forwarded me a PhD vacancy requiring this skill and well, I got the job and took the opportunity. As someone who is naturally fascinated by geography, space and astronomy, working with geospatial data fits right into my chambers, and working in this domain has not bored me since I began.

Besides my PhD project’s intense use of vector geospatial data, my economic research experience over the years has involved both qualitative and quantitative research methods and I find both means of knowledge discovery valuable, even though I must admit that quantitative research allows for larger and broader sample coverage while still providing high-quality learning. Also, the array of plots for visualizing quantitative data is just always fun to see. Further, there is the endless list of statistical and economic formulas and functions, all of which I certainly do not know but which quantitative data allows one to use for answering research questions.

Long story short, I am an economist who loves to work with geospatial data and whose economic analysis experience has been within the domain of agriculture. I was once deeply in love with econometrics. Always, I have been in love with science or art that addresses women. Today, I am deeply in love with economic history and academic/literary works that uncover hidden or untapped perspectives ( I call these “feminist perspectives“). I tend to overthink and be very slow at pivoting to trending topics except if already coincidentally there. This is why even though I find Machine Learning very interesting, I have yet to apply it in my work. I am widely curious about how societies function and how different stakeholders behave and I think it is simply common sense that we continue to uncover and adopt ways in which the earth can operate more sustainably.

For the near future, I am interested in harnessing multiple types of spatial data for exploring sustainability topics relating to forests and arable lands, climate change and other planetary issues on which geospatial data allows us to uncover knowledge and solution-relevant information.