Smithsonian Internship 2

In the past few months, I have been working on a data visualization of damaged and destroyed cultural sites in Syria. I managed the data and tried to find good ways to present them on the interactive maps. The main programming language is JavaScript and according to my research, JavaScript is the mostly used programming language to make interactive maps, so, my focus has been on trying JavaScript and D3.js to bring data to life. D3.js is a JavaScript library for manipulating documents based on data.

What I am enjoying the most during this experience is furthering my understanding of how to use a programming language—JavaScript in this case—to produce dynamic and interactive data visualization in web browsers. I meet the director, Dr. Daniels, once a week and besides presenting and discussing my ideas and data visualization work, I can learn about some new styling and visualization effects under his guidance or suggestions and then explore how to make them. Sometimes I use Kepler gl if Kepler happens to make great visualization without the need for much customization. But most of the time, I need to code and make visualization on mapbox which enables you to customize the map to your needs. Coding is challenging but very fulfilling experience. Codes are beautiful when they work and drive you crazy when they don’t. However, the problem-solving process is the fastest way to deepen my understanding of JavaScript and interactive data visualization in web browsers. There are example codes on mapbox’s website, but this does not mean you just copy and paste the code and it is done. Instead, it is a bone frame that needs you to add flesh and blood. Based on different datasets, the codes may or may not work. For example, a block of codes, I call it Block A, works, and Block B works but Block A+Block B doesn’t work. Sometimes your code is exactly like the example code mapbox gives but it does not work. By figuring out what was going on in these problems, I furthered my understanding of JavaScript and interactive map making. I have saved on file the customized codes of the visualization effects I successfully made so that in the final project, there is less customization workload and faster completion of data visualization.

Thanks to the internship, I have the opportunity to work out data visualization on maps and use a programming language I used to build websites only. I solve problems faster and faster because of my increasing knowledge of JavaScript. My curiosity and passion in data visualization prompted me to have signed up for a Python for Data Science certificate of Cornell University. I am very excited about and look forward to what data visualization Python can create. I have knowledge of Python but this systemic training can strengthen my toolkit in data visualization.

My work style preference, based on the experience in the past few months, is to focus on mapping in one day or two instead of working three hours a day. Since I have other responsibilities to juggle, to contribute a whole day to only one type of work helps me better focus on the work and finish it. It takes time to work back and forth between coding and dataset management and one day or two are the ideal time length for such work.

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