Spatial Big Data analyses for environmental scenarios

The “GIScience & Big Data” course offers an in-depth exploration of Geographic Information Science (GIScience) integrated with Big Data analytics to address spatial and temporal challenges. Designed for students and professionals interested in mastering cutting-edge tools and concepts, this course combines theoretical foundations with practical applications to bridge the gap between GIS and data science.

Participants will gain insights into Big Data concepts, database management, and SQL programming, learning how to manage and query tabular and spatial data. The course also introduces Python programming for analyzing spatial and temporal datasets, emphasizing automation and advanced analytics. Students will explore geospatial analysis frameworks, including Geoanalytics tools for functions like kernel density, hotspots, aggregation, and proximity.

Practical sessions will focus on database design using Microsoft Access and SQL Server, as well as hands-on experience in Python scripting for GIScience tasks. The course also delves into enterprise-level GIS solutions, offering students exposure to server-side and cloud-based architectures.

By the end of the course, students will have a solid understanding of how to integrate Big Data analytics with GIScience approaches, making them capable of handling complex spatial data challenges in various industries, including urban planning, environmental management, and logistics.

Syllabus

Lesson 1: Introduction to big data, GIScience, and their integration for spatial and temporal analyses.

Lesson 2: Basics of data storage and management using Relational Database Management Systems (RDBMS).

Lesson 3: Principles of designing and building RDBMS using Microsoft Access.

Lesson 4: Managing and querying tabular and spatial data in RDBMS with Microsoft Access.

Lesson 5: Introduction to SQL (Structured Query Language) and Microsoft SQL Server.

Lesson 6: Fundamentals of SQL and its applications in querying data.

Lesson 7: SQL queries in GIScience, including interim project work.

Lesson 8: Exploring GIScience’s integrated framework for analyzing spatial data.

Lesson 9: Using Python for spatial and temporal analysis of topological-based structures.

Lesson 10: Basics of Geoanalytics functions: Kernel density, hotspots, aggregation, and proximity analysis.

Lesson 11: Advanced implementations of Geoanalytics tools for real-world scenarios.

Lesson 12: Future trends in GIScience and Big Data: AI integration, machine learning applications, and spatial predictive analytics.

Lesson 13: Final project presentation and discussion of advanced GIScience and Big Data topics.

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