Previously I recommended historical GIS resources include:
Recently discovered OldMapsOnline - a globally-oriented historical mapping solution.
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.
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.
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.
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.
In a previous article "Practical Application of Geo Hound: Extracting Wuhan's Unified Planning Map Services for GIS Analysis", I demonstrated using Geo Hound to access Wuhan's planning map data in QGIS. A key objective was comparing land-use planning attributes against reality to identify discrepancies. While I mentioned using layer swipe for this comparison, many readers inquired about implementing swipe functionality in QGIS.
Following my previous article on extracting Wuhan's planning map services using Geo Hound, the original Wuhan data source implemented access restrictions. For a recent leadership demonstration, I identified an alternative: Shanghai's Detailed Planning Map service.
Note: While workarounds exist for Wuhan's restricted data, this article focuses on ethically accessible sources.
Shared by members of the MalaGIS community (special thanks!), this official platform provides comprehensive urban planning data:
https://shanghai.tianditu.gov.cn/xg/map.html
Author: An anonymous GIS professional specializing in astronomy, geography, remote sensing, and geomancy.
Tianditu (National Platform for Common Geospatial Information Services), hosted by the Ministry of Natural Resources, provides public online geospatial services and promotes geospatial data sharing. For GIS professionals, a common requirement is loading various online basemaps. Tianditu offers convenient services for desktop software like ArcMap and QGIS. While QGIS has mature plugins, this guide focuses on ArcMap users.
This article reviews prominent water detection indices - NDWI, MNDWI, NDMI, AWEI, and WRI - highlighting their distinct formulas, applications, and limitations in remote sensing analysis.
Normalized Difference Water Index
Developed by McFeeters (1996) to identify open water bodies using spectral reflectance differences:
Formula:
NDWI = (NIR - Green) / (NIR + Green)
Principle:
As Mac gains popularity among professionals, many GIS users inquire about its suitability for geospatial work. While Windows remains the preferred platform for comprehensive GIS workflows, Mac + QGIS handles basic to intermediate geospatial tasks effectively.
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