Got featured in #RWeekly under R in Academia! 🗞️

In the article, I discuss the limitations of standard spatial models in capturing a structural network for archipelagic nations like the #Philippines, and how #ArchipelagoEngine (now on #CRAN) addresses this gap.

Huge thanks to the @rweekly team, especially Jon Calder for the review and Jonathan Carroll for merging my last-minute revision!

🔗 Check it out @ https://rweekly.org/#RinAcademia

#RStats #SpatialEconometrics #OpenScience #AcademicMastodon

@njtalingting @rweekly I've often had disconnected adjacency matrices for spatial analysis and my favoured solution is to try and add the connections that can be justified by real-world connections. Conversely it may be justified removing connections between adjacent regions where the border is a rarely-crossed river or mountain range, for example. Ideally if you can get i-j travel data, use a weighted connection matrix.
@geospacedman - Spot on, Barry! Manual weighting is the gold standard for high-res local models. v0.1.1 offers a systematic baseline for large-scale archipelagoes where travel data is missing. But I'm currently working on v0.1.2 where I'll integrate satellite human footprint data to automate those 'real-world' connections you mentioned. Thanks for the feedback!

@njtalingting @rweekly

Add the link to the article and package itself.

Addressing a Decade-Old 'Continental Fallacy' in Spatial Econometrics