Chapter 8 Methodology Development

This is where your GIS expertise matters most. Methodology development — choosing and implementing the analytical approach for your project — is the stage where Claude Code shifts from leading to supporting. You decide the method; Claude helps you implement it.

That said, Claude can be a surprisingly useful sounding board during the design phase, and it can dramatically speed up the implementation once you’ve settled on an approach.

8.1 Using Claude as a Sounding Board

Before writing any code, you can discuss your analytical approach with Claude:

Describe your research question: “I want to identify areas of London that are underserved by green space, accounting for population density and distance to the nearest park. What spatial analysis approaches would you suggest?”
Compare methods: “What are the trade-offs between using kernel density estimation versus a grid-based accessibility analysis for this problem?”
Check feasibility: “I have ward-level population data and point locations for all parks. Is it feasible to run a network-based accessibility analysis, or should I use Euclidean buffers?”

Take Claude’s methodological suggestions as starting points, not gospel. It draws on general knowledge and may not be aware of the latest developments in your specific subfield. Cross-reference with your own expertise and published literature.

8.2 Implementing Your Analysis

Once you’ve decided on an approach, Claude can help you build it step by step:

8.2.1 Spatial analysis workflows

Buffer analysis: “Create 400m, 800m, and 1200m buffers around each park and calculate the population within each buffer zone.”
Spatial interpolation: “Use inverse distance weighting to interpolate air quality measurements across the study area. Use a grid resolution of 100m.”
Overlay analysis: “Intersect the flood risk zones with the residential land use polygons and calculate the area of residential land at risk.”
Accessibility modelling: “Calculate the straight-line distance from each population-weighted centroid to the nearest park.”

8.2.2 Statistical methods

Regression: “Run an OLS regression with green space percentage as the dependent variable and deprivation score, population density, and distance to centre as predictors.”
Spatial regression: “The OLS residuals are spatially autocorrelated. Fit a spatial lag model instead using the spdep package.”
Classification: “Use k-means clustering on the standardised socioeconomic variables to identify neighbourhood typologies.”
Significance testing: “Run a chi-squared test to check whether the distribution of land use types differs significantly between inner and outer boroughs.”

8.3 Iterating on Your Methodology

Real analysis is rarely linear. You’ll run something, look at the results, and adjust. Claude Code makes this iteration fast:

Sensitivity testing: “Re-run the buffer analysis with 500m and 1000m buffers instead, and compare the results.”
Trying alternative specifications: “Add an interaction term between deprivation and distance to centre in the regression.”
Validating results: “Split the data 70/30 and check how well the model performs on the held-out set.”
Comparing approaches: “Run both the IDW and kriging interpolation and create a difference map showing where they disagree.”

8.4 Documenting Your Methodology

This is often the most neglected part of a GIS project, and one of the most valuable things Claude can help with:

Method documentation: “Write a methods section describing the accessibility analysis we just ran, suitable for a technical report.”
Parameter justification: “Document the key parameters we chose (buffer distances, grid resolution, weight function) and the rationale for each.”
Reproducibility notes: “Generate a markdown file listing every analytical step, the input data, the parameters used, and the output produced.”

Getting Claude to document as you go is far easier than trying to reconstruct your methodology after the fact. Make it part of your workflow, not an afterthought.