Basemap

Basemap
Name	Basemap
Title	Basemap
Developer	Matplotlib development team
Released	2003
Programming language	Python
Operating system	Cross-platform
License	BSD license

Basemap Basemap is a Python library for producing static map visualizations and projecting geospatial data, commonly used with Matplotlib, NumPy, SciPy, Pandas, and IPython. It provides a collection of map projections, geographic datasets, and drawing utilities that integrate with plotting workflows in scientific computing and geoscience communities such as those at NASA, NOAA, USGS, and research groups at institutions like MIT and UC Berkeley. Basemap served as a foundational tool alongside emerging libraries like Cartopy and ecosystems involving Jupyter Notebook, Anaconda (software), and GitHub-hosted projects.

Overview

Basemap supplies map projection classes, shapefile handling, coastline and political boundaries, graticules, and methods for plotting points, lines, and polygons onto projected axes for publications and reports used by Nature (journal), Science (journal), and conference proceedings from AGU and EAGE. It is primarily a wrapper and extension for PROJ-style projections and relies on array processing from NumPy while rendering through Matplotlib backends such as Agg and interactive backends used in Spyder (IDE) and JupyterLab. Users in meteorology labs at NOAA and climatology groups at Berkeley Earth used Basemap for visualization tasks integrated with data formats from NetCDF and GRIB.

History and development

Basemap originated in the early 2000s as part of the scientific Python ecosystem maintained on SourceForge and later GitHub by contributors including developers from the Matplotlib project and scientists at organizations like Scripps Institution of Oceanography. Over its lifecycle, Basemap incorporated datasets from the Global Self-consistent, Hierarchical, High-resolution Geography Database and shapefiles compatible with ESRI tools. Development trends mirrored shifts in geospatial tooling: the rise of Cartopy, the expansion of GDAL/OGR, and the growth of package distribution via Conda. Basemap's maintenance status and migration recommendations were discussed in issue trackers and mailing lists associated with NumPy and SciPy communities, prompting many projects to transition codebases to Cartopy or wrappers around PROJ and pyproj.

Types and features

Basemap implements a wide range of projections, including Mercator projection, Lambert conformal conic, Albers projection, Robinson projection, and polar projections used in studies concerning the Arctic Council and Antarctic Treaty System. It supports plotting of graticules, map boundaries, filled continents, and high-resolution coastlines useful for publications by Royal Society authors. Features include reading and plotting ESRI shapefile inputs, integrating with NetCDF4 for climate model outputs from projects like CMIP6, and contouring functionality leveraged in analyses by groups at NOAA Geophysical Fluid Dynamics Laboratory. Basemap also offers image transformations for overlays and annotations used in presentations at American Geophysical Union meetings.

Data sources and cartographic design

Basemap relies on external geographic datasets such as Natural Earth, the GSHHG, and administrative boundary shapefiles from repositories associated with UN and national mapping agencies like Ordnance Survey and the US Census Bureau. Cartographic design in Basemap workflows often follows conventions from publications like Thematic Cartography and Geographic Visualization and guidance from organizations such as ISO and USGS on coordinate reference systems and datum selection, including WGS 84 and regional datums used by European Space Agency missions. Users combine Basemap drawing primitives with typography and color schemes inspired by standards promulgated in journals like Cartographic Journal.

Applications and use cases

Basemap has been used for visualizing weather fields from the ECMWF and NOAA reanalyses, plotting tracks for International Space Station flyovers, mapping biodiversity records for initiatives like GBIF, and presenting seismicity patterns in studies by USGS and the IRIS Consortium. Educators at universities such as Harvard University and Stanford University employed Basemap in coursework on geospatial analysis and remote sensing alongside datasets from Landsat and MODIS. Conservation NGOs including WWF and research groups publishing in PLOS ONE leveraged Basemap for static figures showing protected areas, migration routes, and land-use change derived from ESA and NASA remote sensing archives.

Implementation and software tools

Basemap is implemented in Python and distributed as a package installable via pip and Conda (package manager). It interoperates with libraries like pyproj, Shapely, GDAL, Fiona (software), and rasterio for coordinate transformations, geometry operations, and raster handling; integration with Matplotlib allows saving figures to formats accepted by publishers such as IEEE and Springer Nature. Development, issue tracking, and contributions occurred on GitHub and community support has been provided through Stack Overflow and mailing lists associated with SciPy (conference) and the PyCon community. Many projects have migrated code to Cartopy for ongoing support, but historical codebases and numerous academic publications continue to reference Basemap plots and scripts.

Category:Free software