Some work has been done to make geolocated count-rate data easy to plot over satellite imagery or custom maps using the NumPy and Pandas libraries, and pylab for plotting the data.

Currently, the data model that is downloaded from the counts.pro dashboard can be imported in to python with the libraries previously mentioned:

counts_pro_dtype = np.dtype([
    ('_id', 'S'),
    ('name', 'S'),
    ('created', 'O'),
    ('locationDescription', 'S'),
    ('locationGps', 'O'),
    ('meterModel', 'S'),
    ('meterSerialNumber', 'S'),
    ('probeModel', 'S'),
    ('probeSerialNumber', 'S'),
    ('scalerLength', np.int32),
    ('userId', 'S'),
    ('_id_1', 'S'),
    ('emaCount', np.int32),
    ('locationGps_1', 'O'),
    ('rawCount', np.int32),
    ('timeElapsed', np.float64),
    ('timestamp', 'O'),
    ])

A little bit of processing needs to be done to get the data-types to cooperate:

def counts_pro_data(folder_name, file_name):
    dataset = pd.read_csv(
        folder_name + file_name,
        header = 0,
        skiprows = 0,
        dtype = counts_pro_dtype,
        parse_dates = [2,16],
        converters = {
            "locationGps": ast.literal_eval,
            "locationGps.1": ast.literal_eval,
            },
        )
    dataset[['locationGps_1_lon', 'locationGps_1_lat']] = dataset['locationGps.1'].apply(pd.Series)
    return dataset, file_name

For those interested in exploring this topic more, there will continue to be improvements made to this workflow.
It is possible that there will be some faculty for generating maps like this via the web-app, but the priority significant development of this feature is as-yet to be determined.