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WBG's Guideline on Geospatial Open Data Collection

Draft v20250616
BI, CI, CMH

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1. Introduction

Spatial open data is a powerful resource for understanding and improving the environments in which people live and work. It reveals patterns, disparities, and relationships across geographic space, enabling evidence-based decisions in areas such as public health, urban planning, environmental management, economic development, and social services. When made openly available, this data can support transparency and foster accountability by allowing stakeholders to monitor developments, assess needs, and inform more inclusive policies. The ability to extract and use this data empowers a wide range of users—from government agencies and researchers to civil society organizations and citizens—to analyze spatial dynamics, generating deeper insights into complex societal challenges and opportunities.

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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.

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🔧 Requirements

• Python 3.8+
• geopandas

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Delete EarthEngine Assets

WARNING:
This code permanently deletes the specified asset folder and all its contents. Please double‑check the asset path before running.

1. Authentication and Initialization:
   The script begins by calling ee.Authenticate() (only when needed) and ee.Initialize().

2. delete_folder_contents Function:

• Uses ee.data.listAssets({'parent': folder}) to list all children assets.

bennyistanto

CHIRPS3 Rolling Accumulation Processor

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
• Python 3.x
• Required Python packages:
• rasterio (version > 1.3)

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GEEST2 on QGIS for Windows, LTR 3.34.4-Prizren

Tab: Project

Suggestion

1. 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.
2. 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.
3. 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.

bennyistanto

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).

bennyistanto

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:

1. Clipping - Clips the global NTL raster data to the boundary of a specified country or region.
2. Classification - Classifies the clipped NTL data into 6 classes using the GEEST Safety Classification standard.