I’m just wrapping up a week in Berkeley and San Francisco. I came for two reasons, both have been fantastic, and I’m definitely feeling now the post-high blues that good academic interaction tends to induce.

I spent the first few days at the Berkeley Institute for Data Science, where the good folks of PySAL organised the Spatial Data Science Summit. This was a meeting to discuss opportunities and overlaps across the spatial data science community, and beyond, including the wider Python ecosystem for scientific computing. These days, I’m much less active (and that’s a kind way of putting it) in the development of PySAL. But it was nevertheless a fantastic experience. PySAL 1.0 shipped in 2009 and I was there to see it. Since then, it’s been a project and a community that has brought me much of what I am and who I’ve become. And, as the cliché goes, many of the collaborators have become close friends. Clichés are clichés for a reason.

Then I hopped on the BART to San Francisco’s Tenderloin, where geographers were descending left and right to discuss all things Geography at the AAG. I was part of a session brilliantly put together by Elizabeth Delmelle and Geoff Boeing on “problem-driven methods” (as opposed to the seemingly more common “method-driven problem development”) in the context of cities. I presented on satellite embeddings. When I was preparing the slides, I felt a bit uncomfortable because, arguably, I was about to engage in precisely the type of behaviour the sessions sought to avoid: “reaching for this year’s shiny new tool” instead of addressing cities' ‘wicked problems’”. Elizabeth and Geoff prompted us to include a slide at the beginning stating the “big urban problem” we were tackling. I’m so glad they did because it really nudged me to spell out why I think embeddings are more than this year’s shiny tool. My framing revolved around two key arguments. First, there is much more we can (and should!) do to tailor the data we use to the problems we tackle. Second, there is a lot of untapped data to help in that tailoring. In this context, satellites are one of those underutilised sources that can help provide better empirical fit to the questions we care about. And embeddings lower the barrier to access satellite data, making it cheaper to ask questions and explore ideas. I think, in the end, it went well and was well received.

I also participated in a panel organised as part of the discussion we had started in Berkeley earlier in the week around open source in spatial data science research. Serge Rey prompted us to think about existing gaps, low-hanging fruit, and surfaces of overlap. We covered quite a bit of territory, and Serge structured the conversation so there was clear and constant interaction with the audience, which became an “additional panelist”. A lot of fun.

Besides the strictly “work” things, this week also had plenty of space for fun. We (i.e., Rachel, Levi and your truly) attended the AAG Awards ceremony to pick up our Media Achievement award for GLaD. To celebrate it, that afternoon we hijacked a boardroom, stuck a hand-written note in the door that read “recording in process”, and taped the first episode in a long while where we were physically together. And, after all that flurry of activity, the day after, I managed to convince Geoff Boeing and Martin Fleischmann to join me on a walk around very tall trees north of the Golden Gate. Long walks are underappreciated ways to exchange ideas.

With that, the week came to an end. It’s been so much compressed in such a small amount of time and space. I know it’ll take me a few days to unpack, literally and figuratively. This is conferencing at its best: more brain cycles in less days, all away than your usual routine. Academic life the way it should be.