Map Sparse Data

Non gridded data from CMAP can now be mapped mapped using plotRegional function. This functionally is best for exploring cruise and float data. CMAP will visualize sparse data values as circular markers with the circle size corresponding to the variables value. A data density heatmap will also be overlaid on the map. In addition to this web map, CMAP will also create a ‘dashboard’ of the selected sparse data over time, latitude, longitude and depth.

Note

The mapping library used has limitations on the number of points it can render. If your query is greater than ~10k points, you may consider using the Retrieve Data functionality of CMAP and plotting locally.

Code Tutorial

from opedia import plotRegional as REG


tables = ['tblFlombaum']    # see catalog.csv  for the complete list of tables and variable names
variables = ['prochlorococcus_abundance_flombaum']                            # see catalog.csv  for the complete list of tables and variable names
startDate = '1987-09-17'
endDate = '2008-11-10'
lat1, lat2 = -90, 90
lon1, lon2 = -180, 180
depth1, depth2 = 0, 5
fname = 'regional'
exportDataFlag = False       # True if you you want to download data

REG.regionalMap(tables, variables, startDate, endDate, lat1, lat2, lon1, lon2, depth1, depth2, fname, exportDataFlag)

Sparse Regional Map Dashboard