Spatial Analysis Course

Lesson 1 • Geographic Coordinate Systems

Explains how Earth's surface is mathematically represented using coordinate systems and datums. Provides the reference framework needed for all subsequent data handling.

Lesson 2 • Spatial Data Quality and Metadata

Defines accuracy, precision, completeness, and consistency as quality dimensions of spatial data. Understanding metadata enables critical evaluation of data fitness for use.

Lesson 3 • Spatial Data Models

Introduces vector and raster models as the two primary ways to represent geographic features. Students distinguish when each model is appropriate for a given analysis task.

Lesson 4 • Map Projections and Distortion

Covers how 3D Earth is flattened into 2D maps and the distortions that result. Accurate projection selection prevents measurement errors in analysis.

Lesson 1 • Data Cleaning and Preprocessing

Addresses topology errors, duplicate features, null values, and projection mismatches as common data issues. Clean data is a prerequisite for reliable spatial analysis results.

Lesson 2 • Secondary and Open Data Sources

Identifies authoritative repositories, open government portals, and satellite archives as secondary data sources. Students evaluate source credibility and licensing constraints.

Lesson 3 • Geodatabase Design and Organization

Teaches schema design, feature class organization, and naming conventions for geodatabases. A well-designed database reduces errors and speeds up analytical workflows.

Lesson 4 • Data Import and Format Conversion

Covers common spatial file formats and workflows for converting between them without data loss. Format fluency is essential for integrating multi-source datasets.

Lesson 1 • Map Layout and Essential Elements

Defines required map elements—title, legend, scale bar, north arrow, and source—and their placement. Proper layout ensures maps meet professional and publication standards.

Lesson 2 • Exporting and Sharing Map Products

Covers output formats, resolution settings, and web-sharing options for finished maps. Students match export settings to intended use cases such as print, screen, or web.

Lesson 3 • Classification Methods for Thematic Maps

Explains natural breaks, equal interval, quantile, and standard deviation classification schemes. Classification choice directly affects how spatial patterns are perceived by map readers.

Lesson 4 • Symbology and Color Theory

Covers symbol types, color schemes, and perceptual principles for encoding geographic information. Correct symbology ensures maps are readable and free of misleading visual bias.

Lesson 1 • Geoprocessing Fundamentals

Introduces clip, erase, union, intersect, and dissolve as foundational geoprocessing tools. These operations form the building blocks for all vector analysis workflows.

Lesson 2 • Overlay Analysis and Spatial Joins

Explains how overlay operations combine attribute and geometry information from multiple layers. Spatial joins transfer attributes between layers based on location relationships.

Lesson 3 • Network Analysis Basics

Introduces network datasets, routing, and service area analysis for transportation and utility networks. Network analysis extends vector techniques to connectivity and flow problems.

Lesson 4 • Spatial Selection and Queries

Teaches attribute queries, spatial selection methods, and combined query logic for isolating features. Precise feature selection is essential before applying any geoprocessing operation.

Lesson 1 • Raster Data Fundamentals

Reviews raster structure, cell size, extent, and NoData handling as prerequisites for raster analysis. Understanding raster properties prevents common errors in subsequent operations.

Lesson 2 • Terrain Analysis and Derivatives

Derives slope, aspect, hillshade, and curvature from digital elevation models to characterize terrain. Terrain derivatives are inputs to erosion modeling, viewshed analysis, and site planning.

Lesson 3 • Reclassification and Raster Queries

Covers reclassifying continuous raster values into discrete categories and querying cells by value. Reclassification prepares rasters for overlay and suitability analysis.

Lesson 4 • Map Algebra and Raster Calculator

Teaches arithmetic, logical, and trigonometric operations on rasters using map algebra syntax. Map algebra enables custom index creation and multi-criteria raster analysis.

Lesson 1 • Spatial Autocorrelation

Explains Moran's I and Geary's C as global measures of spatial autocorrelation and their significance testing. Autocorrelation analysis reveals whether similar values cluster, disperse, or distribute randomly.

Lesson 2 • Spatial Interpolation Methods

Teaches IDW, kriging, spline, and natural neighbor interpolation for estimating values at unsampled locations. Method selection depends on data distribution, sample density, and accuracy requirements.

Lesson 3 • Hot Spot and Cluster Analysis

Covers Getis-Ord Gi*, Anselin Local Moran's I, and kernel density estimation for identifying local clusters. Local statistics reveal where significant high and low value concentrations occur.

Lesson 4 • Descriptive Spatial Statistics

Introduces mean center, standard distance, and directional distribution as measures of spatial central tendency and dispersion. These statistics summarize the overall distribution of point features.

Lesson 1 • Remote Sensing Principles

Covers electromagnetic spectrum, spectral signatures, and sensor types as the physical basis of remote sensing. Understanding sensor characteristics determines which imagery suits a given analysis goal.

Lesson 2 • Image Preprocessing

Addresses radiometric calibration, atmospheric correction, and geometric rectification as preprocessing steps. Preprocessed imagery ensures that pixel values reflect true surface conditions.

Lesson 3 • Spectral Indices and Band Math

Teaches NDVI, NDWI, NDBI, and custom band ratio calculations for highlighting specific surface features. Spectral indices transform multi-band imagery into thematic information layers.

Lesson 4 • Change Detection Analysis

Applies image differencing, post-classification comparison, and time-series analysis to detect land cover change. Change detection supports environmental monitoring, urban growth analysis, and disaster assessment.

Lesson 1 • Workflow Design and Documentation

Teaches flowchart-based workflow design, parameter documentation, and reproducibility practices. Documented workflows enable peer review, replication, and future project updates.

Lesson 2 • Communicating Spatial Analysis Results

Covers report writing, dashboard design, and oral presentation techniques for spatial analysis findings. Effective communication translates technical results into actionable insights for decision-makers.

Lesson 3 • Defining the Spatial Problem

Guides students through translating a real-world question into a structured spatial analysis problem statement. A clear problem definition determines data needs, methods, and success criteria.

Lesson 4 • Spatial Analysis for Urban Planning

Uses land use, population, and infrastructure data to address urban growth, accessibility, and service gap problems. Urban applications demonstrate how spatial analysis informs planning decisions.