Munir Ahmad Profile picture
Mar 22 7 tweets 2 min read
Do you want to learn how to create Voronoi polygons in @qgis ? You have a dataset of cellular towers locations in a city, and you want to create Voronoi polygons to visualize the coverage areas of each tower.Follow this step-by-step guide. #gischat
1- Prepare your data:

Import your cellular towers locations dataset as a point layer in QGIS.

Make sure your point layer has a unique identifier for each tower.
2-Create Voronoi polygons:

Go to "Vector" > "Geometry Tools" > "Voronoi Polygons".

Select your point layer as the "Input layer".

Choose an appropriate name and location for your output Voronoi polygon layer.

Click "Run" to generate the Voronoi polygons.
3-Styling and analysis:

Style the Voronoi polygons layer as desired, for example, by coloring each polygon based on its corresponding tower identifier.

Use the Voronoi polygons layer for analysis, such as determining which areas of the city have the best coverage.
Real-life applications of Voronoi polygons:

In urban planning, Voronoi polygons can be used to analyze the accessibility of public services such as hospitals or schools.

In ecology, Voronoi polygons can be used to study the distribution of species or habitats.
Real-life applications of Voronoi polygons:

In retail, Voronoi polygons can be used to analyze the market areas of stores and determine where to open new stores.

In transportation, Voronoi polygons can be used to analyze the catchment areas of transit stops or terminals.
Real-life applications of Voronoi polygons:

In meteorology, Voronoi polygons can be used to study the spatial distribution of weather stations and estimate weather patterns in areas with no data.

Start creating Voronoi polygons in QGIS and explore the endless possibilities.

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More from @MunirSpatial

Mar 22
Compressed image formats are a great way to reduce file size without sacrificing image quality. There are several compressed image formats that are commonly used in GIS (Geographic Information System) applications. #gischat #geospatial #gis
ECW (Enhanced Compression Wavelet) is a proprietary compressed image format developed by ERDAS. It's commonly used for compressing large raster images, such as satellite imagery and aerial photography. ECW files can be handled by software ArcGIS, Global Mapper, and ERDAS Imagine.
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Mar 21
Here is a more comprehensive list of plugins for land use and land cover (LULC) analysis in QGIS. The selection of plugins depends on the specific research question and the type of data available. #gischat #geospatial #GIS #LULCanalysis #QGIS
Semi-Automatic Classification Plugin (SCP): This plugin provides a user-friendly interface for supervised and unsupervised classification of remote sensing images. It includes a wide range of classification algorithms.#gischat #geospatial #GIS #LULCanalysis #QGIS Image
Land Cover Classification System (LCCS): This plugin allows users to classify land cover based on a standardized classification system developed by the FAO. It includes a set of rules and guidelines for classifying land cover.#gischat #geospatial #GIS #LULCanalysis #QGIS Image
Read 10 tweets
Mar 13
Remote sensing (RS) software tools can help you analyze and interpret satellite or airborne data. If you're on a tight budget, don't worry - there are plenty of great free options out there!#gischat #geospatial
#QGIS is a powerful and user-friendly open-source #GIS software that includes RS tools. You can use it to visualize, analyze, and process your RS data. It supports a wide range of file formats and has an active community.#gischat #geospatial
qgis.org/en/site/foruse…
Sentinel Application Platform is a free, multi-platform tool for processing and analyzing data from various RS sensors, including Sentinel-1, Sentinel-2, and Sentinel-3. It includes a user-friendly GUI and a powerful toolbox. @esa #gischat
step.esa.int/main/download/…
Read 7 tweets
Mar 11
Machine Learning (ML) algorithms have revolutionized spatial analysis, providing new insights into geographic data. Here's a list of popular ML algorithms used in #geospatial #DataScience
#DataScience #MachineLearning #SpatialAnalysis #gischat
K-means clustering is a popular unsupervised ML algorithm used for clustering and segmentation of spatial data. It's used to identify spatial patterns, group similar geographic features, and create thematic maps.
Random Forests is a supervised ML algorithm used for classification and regression tasks in Geospatial Data Science. It's used to predict land use, soil properties, and other geographic variables.
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