POINT 001: Foundation of Spatial Data Science
Online Course, Plaform for Optimizing Urban Intelligence, 2024
This module provides a comprehensive introduction to the principles and practices of spatial data science and geographic information systems. We will learn to leverage fundamental tools such as python and R effectively to collect, process, analysis, and visualize spatial data.
This module delves into the specialized management of spatial data within database systems. Students will explore the design, implementation, and optimization of spatial databases, learning to handle complex queries and large-scale geospatial datasets using Structured Query Language (SQL).
This module offers a foundational overview of statistical techniques and quantitative analysis used in various fields. Students will learn key concepts such as probability, hypothesis testing, association, and data visualization, applying these methods to interpret and analyze data.
It introduces us to the application of machine learning techniques in the analysis of spatial data. We will explore algorithms and models tailored for spatial systems, such as land cover classification, spatial pattern recognition, and predictive modeling using geospatial datasets.
This module provides an in-depth exploration of remote sensing technologies and their applications using Google Earth Engine. Students will learn to process and analyze satellite imagery, extract meaningful information, and monitor environmental changes over time in the planetary scale platform.
The module teaches us how to create compelling presentations and professional portfolios showcasing our spatial data projects. Participants will learn to use Xaringan for dynamic, interactive slide decks and GitHub Pages for hosting and sharing our work online.