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Landcover annual transitions in hectares by country
Landcover Zonal Statistics & Transition Analysis
This repository provides two complementary workflows for deriving landcover area summaries and full transition matrices from MODIS IGBP or ESA CCI landcover datasets. Both workflows produce per‑year class area CSVs and comprehensive inter‑annual transition tables, then optionally split those tables by ISO3 code.
This script processes CHIRPS (Climate Hazards Group InfraRed Precipitation with Station data) version 3.0 dekadal (10-day) rainfall data to create rolling accumulations over various time periods. It's particularly useful for climate monitoring and drought analysis.
Prerequisites
CHIRPS3 dekadal data (.tif files) downloaded from CHIRPS3 website
Almost missed the folder icon on the right side 😃. I think it's better to put it on the left side and below the label Working Directory.
After setting the directory, whenever we close and open it again, the last Working Directory has been set, this is inline with what Tim explain about setting in JSON file.
I think it would be good if we have a "Reset" button on this page. The function is to make everything like a fresh start and to prevent users from using unwanted folders.
We need to add a NOTE label (with the "i" icon also fine) above the 'Study Area Layer" with the text: Below combo-box will active if you have a polygon (it can be an administrative boundary or just a bounding-box polygon), active as a Layer. If you haven't done so yet, you can add it from the menu editor Layer > Add Layer > Add Vector Layer.
HarvardX-PH125.8x Data Science Machine Learning, Titanic Exercises, part 2 Q7: Survival by fare - Loess
HarvardX-PH125.8x Data Science Machine Learning
Question 7: Survival by fare - Loess
Set the seed to 1. Train a model using Loess with the caret gamLoess method using fare as the only predictor.
What is the accuracy on the test set for the Loess model?
Note: when training models for Titanic Exercises Part 2, please use the S3 method for class formula rather than the default S3 method of caret train() (see ?caret::train for details).
PyQGIS NTL Clipping, Classification, and Visualization
Overview
This script is designed to run within the PyQGIS console and provides a complete workflow for processing Night Time Lights (NTL) data. The script performs the following tasks:
Clipping - Clips the global NTL raster data to the boundary of a specified country or region.
Classification - Classifies the clipped NTL data into 6 classes using the GEEST Safety Classification standard.
The existing color scheme in the image uses a predominantly orange-red palette for most of the map, with some yellows, greens, and blues appearing minimally. While this scheme does show variation, it has several limitations. The heavy use of similar orange and red hues makes it difficult to distinguish between different levels of enablement and population, especially in the low to moderate ranges. Additionally, this color scheme may not be easily interpretable for individuals with color vision deficiencies.
I propose changing to a more effective color scheme based on the ColorBrewer RdYlBu (Red-Yellow-Blue) 5-class diverging color palette.