When working with technical data, maps can help answer questions like:

• Where are the highest and lowest values?
• How does a measurement change across an area?
• Are there spatial clusters, anomalies, or boundaries?
• How do measurements relate to geology, terrain, infrastructure, sampling locations, or other spatial features?
• What is happening beneath the surface?

By answering these types of questions, maps provide the geographic context needed to understand where conditions occur and how they relate to the world around them.

Various types of technical data become much easier to interpret when viewed spatially. Common examples include:

• contaminant concentrations across a remediation site
• groundwater elevations across monitoring wells
• geophysical measurements collected along survey lines
• mineral concentrations across an exploration area
• elevation and terrain data
• borehole and other subsurface datasets

Mapping these datasets connects individual measurements to their location, making it easier to see how conditions vary across a site, follow spatial trends, and understand how features above and below the surface relate to one another.

When working with technical data, graphs can help answer questions like:

• How has a measurement changed over time?
• Is a variable increasing, decreasing, or remaining stable?
• How do multiple monitoring locations compare?
• Is there a relationship between two variables?
• Are measurements exceeding a particular threshold?
• Are there outliers or unusual patterns in the dataset?

By answering these types of questions, graphs help reveal how your data behaves so you can interpret changes, compare results, and identify meaningful relationships.

Multiple types of technical data are easier to understand when viewed as graphs. Common examples include:

• groundwater levels over time
• contaminant concentrations by sampling date
• water-quality or water chemistry measurements
• rainfall versus streamflow
• particle-size distributions
• engineering or laboratory measurements

Graphing these datasets turns individual measurements into trends, comparisons, and relationships, equipping you to see how conditions change over time, how values differ across observations, and where meaningful patterns or anomalies emerge.

Maybe you’re monitoring groundwater conditions at a remediation site when routine sampling reveals a sharp increase in benzene concentrations at a downgradient monitoring well. You need to understand not only what’s happening at that particular well, but what the change could mean for the rest of the site.

A time-series graph can plot benzene concentrations at the well over time, revealing when the increase began, how quickly concentrations are changing, and whether measurements have crossed relevant regulatory thresholds. This gives you a clear picture of what’s happening within the monitoring data.

An interpolated plume map adds the spatial perspective. By visualizing contaminant concentrations across the site, you can evaluate the plume’s estimated extent and its proximity to nearby features such as surface water, private drinking-water wells, and other monitoring locations.

Together, the graph reveals a concerning concentration trend at a specific well, while the map shows where elevated concentrations occur across the site and their proximity to sensitive receptors. With both perspectives, you have stronger information for deciding where additional monitoring, investigation, or remediation may be needed

Consider a project where you’re evaluating assay results from a newly drilled deposit. Copper and gold concentrations both appear elevated in portions of the dataset, but you need to determine whether those elements are related and where that relationship occurs within the deposit.

A scatter plot or cross-plot can compare copper assay grades against gold grades across samples. Adding a regression line or other statistical analysis helps quantify the relationship between the two elements, revealing whether higher copper grades tend to occur alongside higher gold grades.

A 3D borehole visualization or cross-section can then place those assay results back into their spatial and geological context. By displaying multi-element data along drillhole traces and alongside relevant stratigraphy or structures, you can see where elevated grades occur beneath the surface.

Together, the graph may reveal a relationship between copper and gold concentrations, while the borehole visualization shows whether that enrichment is concentrated within a particular depth interval, stratigraphic unit, or geological structure. Now you can move from identifying a relationship in the assay data to understanding where that relationship occurs, providing valuable context when planning additional drilling.

Perhaps you’re a water resources engineer assessing flood risk for a community located downstream of a major river basin. You need to understand how the river could respond to an extreme rainfall event—not only when conditions may become most critical, but which areas could be affected as a result.

A modeled hydrograph can show how river discharge is expected to change over time under a specific rainfall scenario, helping you identify when discharge may begin rising, when it could peak, and how long elevated flows may persist.

A color-relief elevation and inundation map can provide the spatial side of the analysis. By visualizing modeled inundation across local topography, roads, bridges, buildings, and other infrastructure, you can identify which low-lying areas may be exposed as water levels rise.

Ultimately, the hydrograph shows when peak river conditions are expected under the modeled scenario, while the inundation map shows where flooding could occur. With both perspectives, you can give emergency managers and other stakeholders a clearer picture of the potential timing and geographic extent of the hazard to support decisions around road closures, evacuation routes, and other preparedness measures.