What the map supports
Visual comparison of broad spatial patterns in the supplied 2015 and 2025 dissolved-oxygen observations.
ENVIRONMENTAL GIS · WATER QUALITY · SPATIAL INTERPOLATION
Mapping dissolved oxygen across Chesapeake Bay.2015 · 2025 comparison
I used supplied monitoring observations to build dissolved-oxygen surfaces for 2015 and 2025 and display them on the same scale.
Data preparationSpatial interpolationRaster comparisonCartography
01 / SPATIAL COMPARISON
The 2015 and 2025 surfaces use the same scale
Lower dissolved-oxygen estimates appear in warm colors and higher estimates in green across a shared 3–9 mg/L display range.
02 / ANALYTICAL WORK
How I built the two surfaces
The observations were supplied to me; my work began with preparing them for analysis and continued through interpolation, comparison, and map design.
- 01
Prepare the supplied data
Organize the provided dissolved-oxygen observations for consistent spatial processing and year-to-year comparison.
- 02
Interpolate continuous surfaces
Transform discrete observations into estimated dissolved-oxygen surfaces covering the sampled portions of Chesapeake Bay.
- 03
Standardize the comparison
Use a common geographic extent, measurement unit, and color scale so differences between 2015 and 2025 can be read consistently.
- 04
Design the final map
Combine the analytical surfaces with reference geography, labels, legend, scale, orientation, and source information.
03 / INTERPRETATION
What interpolation can and cannot tell us
Spatial interpolation estimates conditions between monitoring locations. It does not turn every mapped pixel into a direct field measurement.
What affects interpretation
Sampling location, water depth, season, temperature, salinity, and station density can influence dissolved-oxygen patterns and the interpolated result.
What it does not establish
Two mapped periods alone do not demonstrate a causal environmental trend or conditions at every location in the Bay.
04 / TEACHING APPLICATION
A practical way to teach interpolation and uncertainty
I would use this comparison to show how point observations become a continuous surface, how station spacing affects the result, and why an interpolated pixel is an estimate rather than a direct measurement.
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