Spatial Big Data Analysis for Environmental Scenarios

This course introduces advanced approaches for analyzing large and complex spatial datasets in environmental research. Students explore methods for integrating, processing, visualizing and interpreting spatial and spatiotemporal data using modern GIScience and data-analysis techniques. The course emphasizes practical applications and demonstrates how large geospatial datasets can support the investigation and modeling of environmental processes and scenarios

Course at a Glance

Focus: Spatial Big Data, databases and geospatial data analysis
Approach: Hands-on, computational and GIScience-based
Core Skills: Database design, SQL querying, spatial data management, data analysis
Key Technologies: Relational databases, SQL, Python, server-side and cloud-based solutions

Why Spatial Big Data?

Environmental research increasingly relies on large, complex and rapidly evolving spatial datasets. Understanding how to organize, query, analyze and integrate these data is essential for transforming raw information into meaningful geographic knowledge. This course provides the practical and conceptual foundations needed to work effectively with modern spatial data infrastructures and analytical workflows.

Course Objectives

  • Understand the fundamental  principles of Big Data
  • Develop practical skills in database design with hands-on experience in querying and manipulating data using SQL
  • Apply Python-based tools and libraries for Big Data analytics
  • Integrate Big Data analysis with GIScience approaches, emphasizing spatial data handling and spatial decision-making
  • Become familiar with server-side and cloud-based solutions

View / Download Syllabus → (currently not available)

Course Content

Explore the core concepts, technologies, programming tools and workflows used to manage and analyze complex spatial data:

Lesson 1: Introduction to big data and GIScience

This lesson introduces the fundamental concepts of Big Data and Geographic Information Science (GIScience), with an emphasis on their growing convergence in modern spatial analysis. Students will examine the characteristics of large-scale spatial and temporal datasets and explore how GIScience provides frameworks for extracting meaningful information from complex data sources. The lesson also presents the course objectives, structure, and assessment requirements.

Students will be introduced to the principles of data storage, organization, and management within Relational Database Management Systems (RDBMS). The lesson covers key concepts such as tables, records, attributes, primary keys, and relationships between datasets. Particular attention will be given to the role of database systems in supporting spatial and non-spatial data management.

This lesson focuses on the principles of designing and constructing relational databases using Microsoft Access. Students will learn how to create database schemas, define relationships between tables, and implement normalization techniques to improve data consistency and efficiency. Practical exercises will demonstrate the process of developing databases that support GIS applications.

Students will learn how to manage, organize, and retrieve information stored within relational databases. The lesson covers querying techniques for both tabular and spatially related datasets using Microsoft Access. Emphasis will be placed on data integrity, efficient data retrieval, and the integration of database systems with GIS workflows.

This lesson introduces Structured Query Language (SQL) as the standard language for managing and querying relational databases. Students will become familiar with the Microsoft SQL Server environment and its role in enterprise-level data management. The lesson establishes the foundation for developing SQL skills that support advanced GIScience applications.

Students will learn the core components of SQL, including data selection, filtering, sorting, aggregation, and table joins. The lesson emphasizes the practical use of SQL for extracting and analyzing information from large datasets. Through hands-on exercises, students will develop the skills required to efficiently query and manage relational databases.

This lesson explores the application of SQL within GIScience workflows and spatial data management environments. Students will use SQL queries to support data preparation, analysis, and integration tasks involving geographic information. The lesson also includes dedicated time for interim project development and instructor-guided feedback.

Students will examine how GIScience integrates data, analytical methods, and computational technologies to support spatial decision-making. The lesson introduces conceptual frameworks for analyzing spatial relationships, patterns, and processes. Practical examples will demonstrate how integrated GIS environments support complex geographic analyses.

This lesson introduces Python as a tool for analyzing spatial and temporal data within GIScience applications. Students will learn how to work with topological structures, automate analytical processes, and perform computational analyses on geographic datasets. The lesson highlights the growing importance of programming skills in modern geospatial workflows.

Students will be introduced to the principles and applications of Geoanalytics for large-scale spatial data analysis. The lesson covers key analytical methods, including kernel density estimation, hotspot analysis, spatial aggregation, and proximity analysis. Through practical examples, students will explore how these tools reveal patterns and trends within geographic datasets.

Building on the previous lesson, students will apply advanced Geoanalytics techniques to complex real-world scenarios. The lesson focuses on designing analytical workflows that address challenges in areas such as urban planning, environmental monitoring, transportation, and public health. Students will evaluate the strengths and limitations of different analytical approaches when working with large and diverse datasets.

This lesson examines current developments and future directions in GIScience and Big Data research. Topics include artificial intelligence, machine learning, spatial data mining, cloud-based analytics, and predictive modeling. Students will critically assess how emerging technologies are transforming the collection, analysis, and interpretation of geographic information.

The final lesson is dedicated to student presentations of their course projects, providing an opportunity to demonstrate the application of concepts and methods learned throughout the semester. Students will present their analytical approaches, findings, and conclusions while engaging in peer discussion and feedback. The course concludes with a review of advanced GIScience and Big Data topics and their relevance to future research and professional practice.

Tools & Technologies

The course provides hands-on experience with technologies used to manage, query and analyze large and complex datasets. Students work with relational database management systems, Microsoft Access, Microsoft SQL Server, SQL, Python-based analytical tools and GIScience workflows. Server-side and cloud-based approaches are also introduced to demonstrate how modern spatial data infrastructures support scalable data management and analysis.