I can oddly relate because I’m taking courses right now that deal with these data sets, GIS is a great field with lots of opprotunities that pays well even in entry positions. If anyone is curious, they should look it up. It can be used in soooo many different applications and fields, it’s very versatile.
Everytime I hear this I can’t help but imagine some researcher out in the wild taking a twig or something off of a magpie, the swoopy boi retaliates and the researcher is like “corvids understand the concept of zero?!”
What a weird idea that there is any sort of rivalry between two data types used in the same science. I have a hard time believing that any geospatial science professor is in a war over this. Analogously this would be like two carpenters having a war over saws vs hammers. They are both indispensable tools in carpentry. Sometimes you saw something and then use a hammer on the next step, sometimes you hammer something and saw something next. It’s… You use both. You’ll always use both.
But someone making about as many spelling mistakes as there are words in the post should be the first red flag. This reads like someone who slept through an intro to GIS course, or someone who hates ESRI (based?) and wanted to simultaneously send their entire board of directors into seizures.
I really hope you’re obfuscating the real thing they are fighting over because it makes zero sense that anyone would fight over which is “better”. Like, what? You… You have to use both like… All the time. I’ve never had a project outside of a class assignment that didn’t require both.
My domain is more bioinformatics than GIS, but the way I imagined it was that if one was arguing that [thing] data is better, they’re arguing that if more people recognised the innate benefits of [thing], we wouldn’t have to rely on software that uses [other thing] so much, and that to properly utilise [thing], it would take a bit of radical reworking of workflows, but there would be significant long term net benefit.
Basically, I think arguments like this tend to be more grounded in the socio-cultural practices of a research field than the absolute technical merits of an approach. Like in my domain, a DNA sequence is just a long sequence of 4 different letters (A, T, G & C), but there’s a bunch of ways we can encode that data into a file, many of which have trade-offs (and some of which are just an artifact of how things used to be done)
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