Distance to the Nearest Coast (km)

Interactive map with scientific data analysis, point lookup, and detailed environmental information

Map Information

This dataset represents the modeled global distance from each raster cell to the nearest marine coastline.

Data Source:
Environmental Data
Units:
Distance (km)
Coverage:
GLOBAL
Citation:
Mazzella, J. (2026). Distance to Coast Raster (1 km). AtmosphericIQ LLC / Engineering Director, Inc./Wessel, P., Smith, W.H.F., Scharroo, R., Luis, J., & Wobbe, F. (2013). Generic Mapping Tools: Improved Version Released. EOS, Transactions American Geophysical Union, 94(45), 409–410. https://doi.org/10.1002/2013EO450001
Interactive Environmental Data Map
Click anywhere on the map to get data values for that location
Location Analysis
Technical Specifications

Distance to Coast (1 km)

Overview

This dataset represents the modeled global distance from each raster cell to the nearest marine coastline.

Distance to Coast is one of the most important environmental variables influencing:

  • Atmospheric chloride deposition
  • Marine aerosol transport
  • Coastal exposure severity
  • Atmospheric corrosion potential
  • Chloride persistence
  • Coastal environmental gradients

The raster represents the shortest horizontal distance from each location to the nearest marine coastline and is used extensively within ISO 9223 atmospheric corrosion workflows.

Units:

  • Kilometers (km)

Background

Distance to Coast is a primary environmental variable used in atmospheric corrosion modeling because marine aerosols and chloride deposition generally decrease with increasing distance from the ocean.

Distance to Coast directly influences:

  • Marine aerosol transport
  • Atmospheric chloride deposition
  • Coastal corrosion severity
  • Atmospheric conductivity
  • Environmental exposure classification
  • Coastal infrastructure risk

Nearshore environments typically experience elevated chloride deposition and greater atmospheric corrosion potential compared to inland continental environments.

This dataset was developed to provide globally consistent coastal proximity information suitable for corrosion engineering, environmental modeling, and GIS-based exposure assessment.


Modeling Methodology

The distance framework utilizes global coastline datasets and marine boundary processing techniques to calculate the shortest distance to the nearest marine shoreline.

Primary inputs include:

  • Global coastline vectors
  • Marine boundary datasets
  • GMT coastal proximity products
  • Coastal geometry datasets

The modeling framework incorporates:

Coastal Geometry Processing

  • Global shoreline extraction
  • Marine boundary identification
  • Coastal topology correction
  • Coastline continuity verification

Distance Analysis

  • Euclidean distance calculations
  • Global proximity analysis
  • Coastal distance raster generation
  • Spatial interpolation workflows

Atmospheric Corrosion Integration

  • Chloride transport modeling
  • Inland aerosol decay functions
  • Coastal exposure weighting
  • Environmental severity mapping

Marine Aerosol Support

  • Coastal proximity assessment
  • Chloride persistence modeling
  • Offshore-to-onshore transition analysis
  • Marine source region identification

The resulting framework provides globally consistent coastal distance estimates suitable for atmospheric corrosion and environmental exposure applications.


Interpretation Guidelines

Distance to Coast (km) Interpretation
0–5 Immediate Marine Coastal Exposure
5–25 Strong Marine Influence
25–100 Moderate Coastal Influence
100–250 Weak Marine Influence
>250 Inland Continental Environment

Shorter distances generally indicate increased marine aerosol influence and elevated atmospheric corrosion potential.


Spatial Resolution

Property Value
Coverage Global
Resolution ~1 km
Coordinate System WGS 84
EPSG Code 4326
Units Kilometers (km)

Data Sources

Primary environmental inputs include:

  • GMT Intermediate Coast Distance Dataset
  • Global coastline datasets
  • Generic Mapping Tools (GMT) coastal products
  • Supplemental marine boundary datasets

Derived environmental layers supported by this dataset include:

  • Chloride Deposition
  • Bathymetry and Terrain Elevation
  • Wind Exposure Modeling
  • Atmospheric Corrosion Layers

Intended Applications

This dataset may be used for:

  • Atmospheric corrosion assessment
  • Chloride deposition modeling
  • Coastal exposure analysis
  • Marine aerosol transport studies
  • Environmental exposure assessment
  • GIS visualization
  • Environmental modeling
  • Enterprise API workflows

Related Datasets

Corrosion Layers

Supporting Atmospheric Layers

Supporting Coastal & Terrain Layers


Attribution

Joseph Mazzella
AtmosphericIQ LLC
Engineering Director, Inc.


Dataset Citation

Mazzella, J. (2026). Distance to Coast Raster (1 km). AtmosphericIQ LLC / Engineering Director, Inc.


Supporting Dataset Citations

Generic Mapping Tools (GMT)

Wessel, P., Smith, W.H.F., Scharroo, R., Luis, J., & Wobbe, F. (2013). Generic Mapping Tools: Improved Version Released. EOS, Transactions American Geophysical Union, 94(45), 409–410.
https://doi.org/10.1002/2013EO450001

GMT Coastal Proximity Dataset

GMT Intermediate Coast Distance Dataset. Global Coastal Proximity Data Derived from Generic Mapping Tools (GMT) Coastline Processing Workflows.

NASA MERRA-2

NASA Global Modeling and Assimilation Office (GMAO). MERRA-2 Atmospheric Reanalysis Dataset.
https://gmao.gsfc.nasa.gov/reanalysis/MERRA-2/

ISO 9223 Standard

ISO 9223:2012. Corrosion of metals and alloys — Corrosivity of atmospheres — Classification, determination and estimation.
https://www.iso.org/standard/53499.html


Version Information

Property Value
Dataset Name Distance to Coast
Dataset Version 1.0
Publication Year 2026
Author Joseph Mazzella
Organization AtmosphericIQ LLC / Engineering Director, Inc.
Coverage Global
Resolution ~1 km
Units Kilometers (km)
Coordinate System WGS 84 (EPSG:4326)
Data Type Continuous Raster
Measurement Euclidean Distance to Marine Coastline

Data Distribution Analysis

These histograms show the distribution of pixel values across the entire raster dataset, helping you understand the range and frequency of different measurements.

Linear Scale Distribution
Shows the actual frequency distribution of values using a standard linear scale.
Logarithmic Scale Distribution
Shows the same data using a logarithmic scale, making it easier to see patterns in data with large value ranges.