Fairfax Climate

Project snapshot

Fairfax County shows a clear urban heat pattern: hotter surface temperatures cluster in the more developed eastern and central parts of the county, while cooler zones track more vegetated areas in the west. This project brings local climate layers into the DaedalMap geography model so land surface temperature, buildings, and built-environment context can be examined together instead of as separate files.

Data and methods

Sources used in the analysis:

• Fairfax land surface temperature. Landsat-derived summer LST inside the fairfax_climate pack, aggregated to county, tract, block group, and block scales.
• Fairfax buildings. Local building footprints and use types inside the fairfax_climate pack.
• Impervious-surface context. NLCD Annual Impervious Surface (MRLC / USGS), 30 m resolution, in the fairfax_climate pack. Fractional impervious percentage and road/urban/non-urban descriptor classes, 1985–2024 annual series.

The workflow clipped all layers to Fairfax County, aligned them to a shared geography, calculated NDVI from Landsat spectral bands, and derived land surface temperature from Landsat thermal bands with emissivity correction. High-temperature areas were then compared against impervious surface and vegetation patterns, with a supporting regression sheet testing how surface temperature changes with distance from denser urban centers.

Methodology highlights

This project shows how a local raster-heavy study can move into the same geographic framework as maintained pack data. The same pattern can be reused elsewhere: clip the local layers to one study area, align them to stable geographic units, keep the derived measures queryable, and attach the final report and supporting figures as public project artifacts instead of leaving them stranded as standalone files.

Current findings

The clearest hot zones sit in the more urbanized eastern and central portions of Fairfax County, while the western side stays cooler where vegetation cover is stronger and development density is lower. The supporting regression shows a negative relationship between surface temperature and distance from urban centers, but it is weak (R² = 0.14), which fits the broader conclusion: heat patterns are shaped by several land-cover and built-environment factors, not a single driver.