Valentine’s Day is here again, and the GIS community group at Malagis is buzzing with excitement. Watching the lively discussions makes one nostalgic for youthful enthusiasm. On such a romantic day, shouldn't everything—including GIS—have its own charm? Let’s explore two GIS-inspired romantic ideas shared by the group.
Do you still remember the Bonne projection? Or have your cartography lessons faded into memory? Here's what it looks like:
Building upon our previous tutorials on 《Creating Dynamic Marathon Route Visualizations with MapPlus: A No-Code GIS Solution》 and 《Implementing Dynamic Marathon Route Visualization with Cesium》, this guide introduces a solution for users seeking pre-processed trajectory data without manual digitization.
Official Website: https://www.2bulu.com/
Following our previous article on Processing China Marathon Data for Spatial Analysis, this guide demonstrates heatmap visualization of marathon venues using QGIS.
When crafting dynamic marathon routes from official maps, directly tracing simplified route images is inefficient without proper spatial referencing. Georeferencing solves this by aligning raster images with real-world coordinates.
When processing DWG data in QGIS, exporting multiple layers individually becomes tedious. Here are three efficient batch export methods。
When our frontend team requested updated administrative division codes, I leveraged the 2024 national standard vector map dataset (Approval No. GS(2024) 0650) shared in our previous article. This official dataset contains accurate administrative codes within its attribute tables, making it ideal for extraction.
Following our previous article on the https://malagis.com/gis-data-share-2010-2020-global-forest-age-distribution-gami-data.html, this guide demonstrates netCDF visualization techniques in QGIS, eliminating the need for specialized tools like IDL.
We're introducing the Global Age Mapping Integration (GAMI) dataset—a high-resolution global forest age distribution resource for 2010 and 2020.
Following our previous article 《「GIS Data」Global Maximum Personal Income Tax Rates for 2023》, many readers requested guidance on visualizing the data. This guide demonstrates a rapid technique to acquire global administrative boundaries using native QGIS functionality - no plugins required.
Note: While remarkably simple, this approach requires boundary verification, particularly for China.
With the rapid development of AI technologies—such as in our earlier article A First Look at Using Segment Anything for GIS and the booming trend of generative AI—many GIS professionals might wonder: is there an AI tool that can help with vectorization tasks? The answer is yes. In this article, we will explore how to use an AI-powered plugin in QGIS to automate vectorization.
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