MalaGIS

Sharing GIS Technologies, Resources and News.

On September 10, 2025, the National Forestry and Grassland Administration (NFGA) announced Notice No. 12 of 2025, officially approving the establishment of the Huangyan Island National Nature Reserve. In accordance with the relevant provisions of the "Regulations of the People's Republic of China on Nature Reserves," the area, scope, and functional zoning of the Huangyan Island National Nature Reserve are hereby published. The reserve is located in Sansha City, Hainan Province, covering a total area of 3,523.67 hectares. This includes a core zone of 1,242.55 hectares and an experimental zone of 2,281.12 hectares. The primary conservation objective is the coral reef ecosystem. The main areas are shown in the figure below:

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In the previous article titled "Displaying Selected Layer Count in QGIS with PyQGIS - MalaGIS", the author introduced a method to quickly view the number of selected layers using QGIS's Python API. However, if you need this functionality, you must open QGIS, then open the Python Console, locate the saved Python script file, and run it every single time. Although this process is simple, it can become tedious after repeated use. So, is there a way to automatically run a Python script when QGIS starts up? The author searched online and finally found a temporarily viable method to share today.

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Preface: This article is compiled based on the author's experience with internet mapping services. While consumer map applications offer automatic location detection, professional GIS tools like QGIS lack this functionality natively. This guide demonstrates how to implement this feature.

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When working with numerous layers in QGIS, it is often necessary to select multiple layers simultaneously for operations. However, with a large number of layers, it becomes difficult to quickly discern exactly how many are selected—don't worry, you can easily solve this by writing a small tool with PyQGIS!

Today, we share two methods to display the count of selected layers in real-time. These methods are straightforward to implement, with clear code, and can be tried by those in need.

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In a previous article titled "「GIS Data」Chinese Geographic Region Partition Vector Data", we shared vector data on geographic region partitions. Recently, some users in our group requested geomorphological data. After searching, we actually found a 1:1,000,000 geomorphological type spatial distribution dataset from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences (CAS), and are sharing it with everyone.

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When working with large raster datasets in QGIS, especially Float64 GeoTIFFs with hundreds of millions of cells, you may want to locate and zoom into the pixel that holds the maximum value. This can be useful in terrain analysis, remote sensing, or any context where the peak value matters.

A common approach involves converting the raster into a point or polygon layer. However, this is resource-intensive and often impractical for large datasets. This tutorial introduces an efficient alternative using PyQGIS and NumPy to directly zoom to the maximum value without raster reclassification or vectorization.

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QGIS provides two methods for creating geometries using expressions: Geometry Generator (introduced in QGIS 2.14) and Geometry by Expression (introduced in QGIS 3.0). Both leverage QGIS's expression engine but serve fundamentally different purposes. This tutorial clarifies their differences and appropriate use cases.

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In a previous article "Geo Hound: A Tool to Automatically Fetch Website Map Service Addresses", I introduced the powerful Geo Hound tool. Follow-up articles like "Get Hidden Government Map Data in 2 Steps (Geo Hound + Wuhan One Map)" and "Shanghai One Map Data (Trial)" shared practical experiences. Many readers subsequently asked: Can this map data be downloaded for offline use? While various methods exist, I'll share my two most-used approaches.

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Recently in the Mala GIS group, someone raised an interesting question: Can GIS be used to analyze which province in China has produced the most leaders? Previously, you might have had to search via a search engine, but the data might not be up-to-date, and quantitative analysis would be cumbersome, requiring tedious data cleaning. However, in the AI era, we can easily accomplish this task using AI + GIS.

P.S.: This article is purely technical, and the data may not be entirely accurate. Corrections are welcome if any issues are found.

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Extracting river/canal centerlines is a common task in GIS data processing. Users of ArcMap or ArcGIS Pro likely find it straightforward. Recently, many colleagues have started exploring QGIS, someone in My GIS Group asked about this, making it a good time to document the process.

PS: The author will focus on introducing methods to achieve ArcGIS-equivalent functions in QGIS. If you have specific needs in this area, feel free to leave a comment.

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