Spatial Data Science

Spatial Data Science is a specialized discipline that applies the methodologies of data science—including statistical analysis, machine learning, and advanced visualization—to data that has a geographic component. It leverages computational techniques to analyze and model spatial patterns, relationships, and trends, seeking to answer not just *what* is happening, but fundamentally *where* and *why* it is happening. By integrating location as a primary variable, this field uncovers insights from datasets ranging from satellite imagery and GPS tracks to demographic and environmental information, enabling applications like urban planning, disease outbreak analysis, and logistics optimization.

  1. Foundations of Spatial Data Science
    1. Defining Spatial Data Science
      1. Scope and Definition
        1. Intersection of Geography, Statistics, and Computer Science
          1. The Importance of Location in Data Analysis
            1. Spatial vs Non-Spatial Data Science
            2. Key Questions in Spatial Analysis
              1. What is Where
                1. Why There
                  1. Why Care
                    1. Spatial Distribution Patterns
                      1. Spatial Relationships and Dependencies
                        1. Spatial Processes and Dynamics
                        2. Core Concepts in Geographic Information Science
                          1. Geographic Information Systems vs Spatial Data Science
                            1. Traditional GIS Workflows
                              1. Data Science Approaches in Spatial Analysis
                                1. Spatial Thinking and Reasoning
                                  1. Spatial Concepts
                                    1. Location
                                      1. Distance
                                        1. Direction
                                          1. Proximity
                                          2. Spatial Relationships
                                            1. Adjacency
                                              1. Containment
                                                1. Connectivity
                                                  1. Overlap
                                                2. Fundamental Spatial Principles
                                                  1. Tobler's First Law of Geography
                                                    1. Implications for Spatial Analysis
                                                      1. Spatial Dependence
                                                        1. Spatial Heterogeneity
                                                          1. Issues of Scale
                                                            1. Scale Effects in Spatial Data
                                                              1. Modifiable Areal Unit Problem
                                                                1. Aggregation Effects
                                                                  1. Zoning Effects
                                                                    1. Solutions and Mitigation Strategies

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                                                                2. Understanding Spatial Data