3 posts tagged with "crop"

Hello friends, thanks for following my journey so far. To catch you up, I'm trying to solve the problem of farmers and traders relying on weak and untimely predictions of corn yield. Weak because they are at the national level and untimely because the predictions come once a month.

So here's the deal: farmers and traders have been relying on national-level corn yield predictions that are not only weak but also painfully slow, arriving just once a month. Imagine making critical decisions based on a single data point each month.

Not ideal, right? That's exactly the issue we're tackling in this blog post series.

A raster, a vector, and an array walk into a bar…

Ok I will spare you the corny jokes.

But seriously, I was facing a problem with these three data types when I approached Fused. It felt impossible to join this information together in a meaningful way. Fortunately, I was quickly proven wrong with the power of UDFs. Let me catch you up.

Ever tried to make sense of the myriad file types in spatial data science and felt like you've wandered into a linguistic labyrinth? Trust me, you're not alone. As a data scientist who's spent more time wrangling datasets than I care to admit, I thought I'd take a casual stroll down memory lane with an old high school friend: regression models. Just a simple plot of actual vs. predicted, right? But when spatial data's involved, you can't just sit back and relax—you've got to keep one eye on the geometries.

I'm currently working on an agricultural project, and growing up on a farm gives me a personal stake in this. This blog illustrates my solution to the geometry debacle. I'll first take you to the area where I grew up: Lyon County.