What Is Data Mountains Analysis?

Data Mountains is a 3D geospatial visualization technique that transforms numeric location data into a continuous landscape of peaks and valleys.

Instead of displaying values only as points, colors, or flat heatmaps, Data Mountains converts geographic intensity into height. Areas with larger aggregated values rise higher on the map, while lower-value areas remain closer to the surface.

The result is an intuitive 3D representation of where activity, demand, revenue, population, incidents, transactions, or other measurable values are geographically concentrated.

In DynaCarto, Data Mountains is designed specifically to visualize numeric values as a 3D surface.

What Does It Help You Understand?

Data Mountains answers a simple but powerful question:

“Where are the highest and lowest values in my geographic data?”

A traditional map containing thousands of points may be difficult to interpret. Data Mountains transforms those records into a visual landscape where major concentrations immediately stand out as peaks.

For example:

A high mountain may represent an area with exceptional sales.

Another peak may indicate strong customer demand.

A low valley may reveal an underserved market.

Several neighboring peaks may indicate multiple centers of activity rather than one dominant location.

This makes complex geographic datasets easier to understand at a glance.

How Does It Work?

DynaCarto divides the geographic area into a grid and aggregates numeric values within each grid cell.

The resulting values determine the height of the 3D surface.

Users can control how the visualization is created through parameters including Aggregation, Grid Size, Smoothing, Vertical Exaggeration, and Height Scale. DynaCarto supports settings such as Sum aggregation, kilometer-based grids, smoothing, vertical exaggeration, and logarithmic height scaling.

Aggregation

Determines how values inside each geographic cell are combined.

For example:

Sum can show total revenue or total demand.

Other aggregation approaches can be useful when analyzing counts or typical values.

Grid Size

Controls the geographic resolution.

A smaller grid reveals more local variation, while a larger grid creates a broader regional picture.

Smoothing

Reduces abrupt differences between neighboring cells and creates a more continuous terrain.

Vertical Exaggeration

Makes differences in value easier to see by increasing or reducing the height of the peaks.

Height Scale

A logarithmic scale can be particularly useful when a small number of locations have extremely high values compared with the rest of the dataset.

Data Structure

Data Mountains requires geographic locations and a numeric metric.

A typical dataset may contain:

Field Description Example

ID Unique record identifier STORE-101

Name Location name Downtown Store

Address Street address 350 Fifth Avenue

Latitude Geographic latitude 40.7484

Longitude Geographic longitude -73.9857

Value Numeric metric 125000

Category Optional classification Retail

Suitable numeric metrics include:

Population · Sales · Revenue · Demand · Orders · Transactions · Customers · Incidents · Service Requests · Costs · Visits

DynaCarto's own Data Mountains test dataset, for example, uses Population as the recommended metric and Sales, Revenue, and Demand as alternative metrics.

If coordinates are not available, addresses can first be converted into locations through geocoding.

Common Use Cases

Sales & Marketing
Visualize geographic revenue, sales volume, customer value, or demand and immediately identify the strongest markets.

Retail & Site Planning
Understand where commercial activity is strongest and compare potential expansion areas.

Logistics & Distribution
Visualize concentrations of orders, deliveries, or demand to support facility and service-network planning.

Population & Urban Planning
Represent population or service demand as a 3D landscape to identify major population centers and emerging areas.

Healthcare
Visualize patient demand, service utilization, or health-related events across geographic areas.

Telecommunications
Explore geographic concentrations of subscribers, usage, traffic, or infrastructure demand.

Banking & Financial Services
Compare transaction volume, deposits, customer value, or branch activity geographically.

Field Service
Identify areas generating high numbers of service requests or maintenance activity.

Public Safety
Visualize concentrations of incidents, emergency calls, accidents, or other measurable events.

Data Mountains vs. Heatmap

Both techniques reveal geographic intensity, but they communicate it differently.

A Heatmap represents intensity primarily through color.

Data Mountains represents intensity through height and 3D form, often combined with color.

For example, a heatmap may show a red area representing high demand.

Data Mountains turns the same concentration into a visible peak.

This can make differences between several high-value areas easier to compare and can create a much stronger visual understanding of the geographic structure of numeric data.

Why Use Data Mountains?

Data Mountains is particularly valuable when decision-makers need to quickly understand:

Where the largest values occur
How different geographic centers compare
Whether activity is concentrated in one dominant location or several
Which areas have relatively low activity
How numeric values change across geographic space

It converts a spreadsheet of numbers into a spatial landscape that can often be understood in seconds.

Data Mountains in DynaCarto

DynaCarto brings Data Mountains directly to mobile devices, allowing users to transform geographic numeric data into interactive 3D surfaces without requiring a traditional desktop GIS workflow.

Users can configure the aggregation method, grid size, smoothing, vertical exaggeration, and height scale directly in the application.

Combined with DynaCarto's other analyses—including Heatmap, Hotspot, Cluster, Anomaly, Isochrones, Location Allocation, Suitability, Proximity & Coverage, and Nearby Places—Data Mountains provides a powerful visual perspective on the geographic distribution of value.

Turn numbers into landscapes. See where value rises, discover geographic peaks, and understand your data from an entirely new perspective.