I am writing this in the dark. A massive thunderstorm swept through my area this evening, and knocked out the power lines! I have no internet access right now, so I’ll be posting this once it comes back. Summertime means the sun is going down late, and between that, the candles I’ve lit, and the LEDs of this screen, I should at least have enough light to finish writing this. This week, Anirudh and I met with Oscar to learn more about GIS. Our goal was to learn to use it to "interpolate." To collect measurements about the Earth, real people need to carry heavy equipment to remote places. Even then, there’s a reasonable limit to how many sites can be set up. That means that we’ve got to be able to infer what conditions at the spaces between our sites look like in order to get a full picture. A practical example of this is weather stations. Even though every neighborhood or town doesn’t have one, meteorologists are able to estimate what is happening miles away from their stations. They observe at each station, and use mathematical algorithms to make educated guesses about what's happening in between. We’ve all seen that this is not a perfect science! Limitations in hardware and software translate into imperfect predictions.
Since we’re studying gravity, we’ll be collecting information about the field at individual sites to estimate what the overall field in a larger area probably looks like. There are a few different methods to do this, but none are a catchall. We’ll need to answer certain questions about our own data to choose the right one for the situation. Are our collection sites evenly distributed, or sporadic? Does the method we use create a map with ugly inconsistencies? Based on what we do know, which method generates the most plausible fit? For next week, Ani and I are using our own hometowns and Geographical data available to us in order to test out these interpolation techniques. Hopefully by then, the sun will have come out!