Fundamental Spatial Analysis Techniques

Why This Matters

Spatial analysis is the engine that transforms raw geographic data into actionable intelligence—it's what separates a static map from a powerful decision-making tool. You're being tested on your ability to select the right technique for a given problem, understand why each method works, and interpret results in real-world contexts. These techniques underpin everything from urban planning and environmental impact assessment to logistics optimization and public health surveillance.

The key to mastering this content isn't memorizing definitions—it's understanding the underlying spatial principles each technique addresses: proximity and distance relationships, pattern detection, surface estimation, connectivity, and data integration. When you encounter an exam question, ask yourself: "What spatial relationship am I trying to analyze?" That question will guide you to the correct technique every time.

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Proximity and Distance-Based Techniques

These methods answer the fundamental geographic question: how close are things to each other, and does that proximity matter? Distance relationships drive everything from retail site selection to emergency response planning.

Buffer Analysis

• Creates fixed-distance zones around point, line, or polygon features—the foundation for proximity-based decision rules
• Impact assessment applications include regulatory setbacks, pollution exposure zones, and service area delineation
• Variable-width buffers can incorporate attribute data, allowing distance to scale with feature characteristics like road classification

Proximity Analysis

• Evaluates distance relationships between features using metrics like Euclidean distance, Manhattan distance, or cost-weighted distance
• Nearest neighbor analysis quantifies whether features are clustered, dispersed, or randomly distributed based on inter-point distances
• Distance decay functions model how influence or interaction decreases with increasing separation—critical for gravity models and accessibility studies

Network Analysis

• Analyzes connectivity and flow through linear systems where movement is constrained to defined paths (roads, pipelines, utility lines)
• Optimal routing algorithms like Dijkstra's solve shortest-path problems accounting for impedance factors such as travel time or cost
• Service area delineation determines reachable zones within specified travel thresholds—essential for facility siting and emergency response planning

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Pattern Detection and Statistical Methods

These techniques identify whether spatial patterns exist and quantify their characteristics. The core question: is the distribution random, clustered, or dispersed—and is that pattern statistically significant?

Spatial Autocorrelation

• Measures spatial dependency—the degree to which nearby locations have similar (or dissimilar) attribute values
• Moran's I statistic produces values from $-1$ (perfect dispersion) to $+1$ (perfect clustering), with $0$ indicating spatial randomness
• Geary's C is more sensitive to local variations, making it useful when you suspect localized clustering within a broader pattern

Cluster Analysis

• Groups features by similarity using algorithms that minimize within-group variance while maximizing between-group differences
• K-means clustering requires specifying the number of clusters in advance; DBSCAN identifies clusters of arbitrary shape and flags outliers automatically
• Hierarchical clustering produces dendrograms showing nested groupings at multiple scales—useful when the "correct" number of clusters is unknown

Kernel Density Estimation

• Transforms discrete points into continuous probability surfaces by distributing each point's influence across a defined bandwidth
• Bandwidth selection critically affects results—too small creates noisy surfaces, too large obscures local patterns
• Hotspot identification applications include crime analysis, disease outbreak mapping, and retail demand modeling

Spatial Pattern Analysis

• Investigates spatial arrangement of features to distinguish clustered, dispersed, and random distributions
• Point pattern analysis techniques like Ripley's K function examine clustering across multiple distance scales simultaneously
• Quadrat analysis divides the study area into cells and compares observed versus expected point counts under complete spatial randomness

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Surface Modeling and Interpolation

These methods create continuous surfaces from discrete sample points—essential when you can't measure everywhere but need to estimate values across an entire study area. The fundamental assumption: spatial autocorrelation exists, so nearby locations have similar values.

Spatial Interpolation

• Estimates unknown values at unsampled locations by weighting contributions from surrounding known points
• Inverse Distance Weighting (IDW) assumes influence decreases with distance; Kriging additionally models spatial autocorrelation structure for optimal, unbiased estimates
• Spline interpolation fits smooth mathematical surfaces through control points—best for phenomena expected to vary gradually

Terrain Analysis

• Extracts surface characteristics from Digital Elevation Models (DEMs), including slope, aspect, curvature, and hillshade
• Hydrological modeling uses flow direction and flow accumulation algorithms to delineate watersheds and stream networks
• Landform classification combines multiple terrain derivatives to identify ridges, valleys, plains, and other morphological features

Viewshed Analysis

• Calculates visible areas from one or more observer points based on terrain obstruction and Earth curvature
• Line-of-sight parameters include observer height, target height, and maximum viewing distance—all affect results significantly
• Applications span telecommunications tower siting, scenic corridor protection, military surveillance, and solar exposure assessment

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Data Integration and Transformation

These techniques combine, convert, and manipulate spatial data to enable analysis. They're the preprocessing workhorses that make other analyses possible.

Overlay Analysis

• Combines multiple data layers through Boolean or arithmetic operations to identify spatial coincidence and relationships
• Vector overlay types include intersect, union, identity, and erase—each preserving different combinations of input geometries and attributes
• Suitability modeling stacks weighted criteria layers to identify optimal locations meeting multiple constraints simultaneously

Map Algebra

• Performs cell-by-cell raster operations using arithmetic ($+, -, \times, \div$), logical (AND, OR, NOT), and statistical functions
• Local operations compute output values from corresponding cells across input layers; focal operations incorporate neighborhood values
• Raster modeling workflows chain multiple map algebra expressions to simulate complex spatial processes like erosion or habitat suitability

Geocoding

• Converts address strings to coordinate pairs by matching against reference datasets containing address ranges and geometries
• Match rates and accuracy depend on reference data quality, address standardization, and matching algorithm sophistication
• Reverse geocoding performs the inverse operation—converting coordinates to human-readable addresses for reporting and visualization

Spatial Sampling

• Selects representative locations for field data collection while balancing statistical validity against resource constraints
• Random sampling ensures unbiased selection; systematic sampling (grid-based) provides even spatial coverage; stratified sampling guarantees representation across subregions
• Sample size determination must account for spatial autocorrelation—clustered phenomena require more widely spaced samples than independent processes

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Statistical Modeling of Spatial Relationships

These advanced techniques model relationships between variables while accounting for the special properties of spatial data—particularly the violation of independence assumptions that plagues standard statistics.

Spatial Regression

• Extends classical regression to handle spatially autocorrelated residuals that would otherwise bias coefficient estimates and inflate significance
• Spatial lag models include neighboring values of the dependent variable as a predictor; spatial error models account for correlated disturbances
• Geographically Weighted Regression (GWR) allows relationships to vary across space, revealing local variations masked by global models

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Quick Reference Table

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Self-Check Questions

1. You need to identify optimal ambulance station locations to ensure 8-minute response coverage across a city. Which two techniques would you combine, and why is buffer analysis insufficient?

2. Your regression model predicting housing prices shows clustered residuals in the downtown core. What does this indicate, and which technique should replace your current approach?

3. Compare Moran's I and cluster analysis: if Moran's I returns a value of 0.72 (p < 0.01), what does this tell you, and what would you do next?

4. A public health agency has disease case addresses and wants to identify outbreak hotspots. Describe the workflow using at least two techniques from this guide.

5. When would you choose Kriging over Inverse Distance Weighting for interpolation? What additional output does Kriging provide that IDW cannot?