Original Summary

Hi all,<p>I wanted to share a recent library I made for myself since I continually have to work with large-scale point data spanning millions of rows. I generally found existing IDW tools are insanely slow and unreliable with the datasets I commonly work with.<p>ppgrid is an open source GIS tool and it is intended to create beautiful visualisations that don&#x27;t take an absurd amount of time to process. It uses a modified Inverse Distance Weighting (IDW) approach: instead of having to traverse every single point for every single grid O(N*M), it precalculates which points intersect which grids. This means that we essentially avoid the multiplicative scaling costs associated with traditional IDW.<p>For perspective, ppgrid is significantly faster than IDW from the likes of gdal_grid or GRASS, in the order of 17x faster. I say in the order of as I couldn&#x27;t really effectively test IDW on the crappy laptop I use beyond 1M points, where as ppgrid happily chewed through 16mil points.<p>My goals&#x2F;intention for this post is twofold:<p>- looking to share it as a tool for anyone who may find it relevant or useful<p>- looking to stress-test it with different types of point data and try to surface any quirks, bugs or random edge case failures<p>Important note which I discuss more in my write-up: ppgrid is not yet stress-tested or validated in terms of statistical correctness, as my current use case prioritises visualisation.<p>AI Disclosure &amp; Creds: I&#x27;ve been pair-coding this with my local Qwen3.8 27B agent, I discuss this in my write-up further. I have ~9YoE in GIS work and ~10YoE as a fullstack.<p>GitHub Repo: <a href="https:&#x2F;&#x2F;github.com&#x2F;marzukia&#x2F;ppgrid&#x2F;" rel="nofollow">https:&#x2F;&#x2F;github.com&#x2F;marzukia&#x2F;ppgrid&#x2F;</a><p>Any other feedback&#x2F;comments are welcome, please let me know if this has helped as I&#x27;d love to see it in action.

  • 情报分类：技术学习与提效
  • 分类依据：内容涉及技术、AI、软件工具或工程实践
  • 信息来源：Hacker News 新项目
  • 发布时间：2026/9/27 15:22:03