Introduction to GIS

Concept Overview

Geovisualization and Geographic Information Science (GIScience) form the foundation for understanding how spatial data represents real-world phenomena and how we communicate geographic information effectively. Unlike traditional data, spatial data includes location, enabling us to analyze patterns, relationships, and trends across geographic space. This week introduces the conceptual framework that distinguishes GIS from simple mapping—moving beyond 'what is where' to 'why is it there' and 'what does it mean'—while establishing the technical literacy needed to work with geospatial software and

Concept Overview

Coordinate systems and map projections are the foundation for accurately representing Earth's three-dimensional surface on flat maps and computer screens. Because Earth is spherical but our maps are flat, we must use mathematical transformations (projections) that inevitably distort shape, area, distance, or direction—choosing the right projection depends on your analysis purpose and geographic region. Understanding spatial reference systems ensures that your GIS layers align correctly and measurements are accurate, building directly on Week 1's foundations by adding the mathematical precision

Concept Overview

Spatial data models are frameworks for organizing and representing the real world in a GIS. Vector data models use discrete geometric objects—points (locations like wells or fire hydrants), lines (roads or rivers), and polygons (parcels or lakes)—each linked to attribute tables that store descriptive information. This week builds on your understanding of coordinate systems (Week 2) by showing how geographic features are actually stored and structured within those coordinate spaces, forming the foundation for all spatial analysis and map-making you'll do throughout the course.

Concept Overview

Raster data models represent geographic space as a continuous grid of cells (pixels), where each cell stores a value representing a phenomenon like elevation, temperature, or spectral reflectance. Unlike the discrete vector features studied in Week 3, raster data excels at representing continuous surfaces and imagery from satellites or aerial sensors. This week you'll learn how raster resolution, cell values, and data formats influence analysis capabilities, and gain hands-on experience processing elevation data and satellite imagery to create meaningful terrain visualizations and extract geog

Concept Overview

Cartographic design principles and symbolization form the foundation of effective visual communication in GIS. While previous weeks taught you how to store and manipulate spatial data, this week focuses on how to present that data so viewers can quickly understand patterns, relationships, and insights. You'll learn that good map design isn't just about making maps look attractive—it's about applying color theory, visual hierarchy, typography, and symbolization strategies to guide your audience's attention and ensure accurate interpretation of geographic information.

Concept Overview

Spatial analysis methods and geoprocessing are the computational techniques that allow you to answer geographic questions by manipulating and analyzing spatial data. While earlier weeks focused on displaying and symbolizing data, this week you'll learn to create new information by combining datasets (overlay analysis), measuring proximity (buffers and distance calculations), and joining attributes based on spatial relationships. These tools transform GIS from a mapping system into an analytical platform for solving real-world problems like site selection, environmental impact assessment, and r

Concept Overview

Python programming enables automation, reproducibility, and scalability in geospatial workflows that would be time-consuming or impossible through point-and-click GIS interfaces. Building on prior weeks' vector and raster data manipulation in QGIS, students now learn to script these operations using Python libraries like GeoPandas and Matplotlib in Google Colab. This computational approach transforms GIS from manual map-making into programmable spatial analysis, allowing analysts to process thousands of features, batch-produce maps, and document every analytical step. Python has become the ind

Concept Overview

Web mapping transforms static GIS outputs into interactive, browser-based applications that allow users to explore geographic data dynamically through panning, zooming, querying, and layering. Unlike the desktop GIS workflows you've practiced in QGIS, web maps leverage cloud platforms and JavaScript libraries to share spatial analysis with global audiences without requiring specialized software. This week synthesizes all prior topics—coordinate systems, data models, cartographic design, and spatial analysis—into publishable web applications, while your final project demonstrates the complete G