Python 中的 Plotly提供了一个很好的解决方案：

!pip install chart_studio
from chart_studio import plotly as py
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2011_us_ag_exports.csv')

for col in df.columns:
    df[col] = df[col].astype(str)

scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
            [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]

df['text'] = df['state'] + '<br>' +\
    'Beef '+df['beef']+' Dairy '+df['dairy']+'<br>'+\
    'Fruits '+df['total fruits']+' Veggies ' + df['total veggies']+'<br>'+\
    'Wheat '+df['wheat']+' Corn '+df['corn']

data = [ dict(
        type='choropleth',
        colorscale = scl,
        autocolorscale = False,
        locations = df['code'],
        z = df['total exports'].astype(float),
        locationmode = 'USA-states',
        text = df['text'],
        marker = dict(
            line = dict (
                color = 'rgb(255,255,255)',
                width = 2
            )
        ),
        colorbar = dict(
            title = "Millions USD"
        )
    ) ]

layout = dict(
        title = '2011 US Agriculture Exports by State<br>(Hover for breakdown)',
        geo = dict(
            scope='usa',
            projection=dict( type='albers usa' ),
            showlakes = True,
            lakecolor = 'rgb(255, 255, 255)',
        ),
    )
    
fig = dict( data=data, layout=layout )

url = py.plot( fig, filename='d3-cloropleth-map' )

在 R中绘制等值线图的方法有很多。这是另一种使用图形库 ggplot 的流行语法的方法：

library(ggplot2)
library(dplyr)
choro <- left_join(
  map_data("state"), 
  USArrests %>% 
    add_rownames("region") %>% 
    mutate(region=tolower(region))
)
ggplot(choro, aes(long, lat)) +
  geom_polygon(aes(group = group, fill = Assault)) + 
  coord_quickmap()

R，带有googlevis 包，也提供了很好的解决方案（交互式 html 地图）：

require(datasets)
states <- data.frame(state.name, state.x77)
GeoStates <- gvisGeoChart(states, "state.name", "Illiteracy",
                          options=list(region="US", 
                                       displayMode="regions", 
                                       resolution="provinces",
                                       width=600, height=400))
plot(GeoStates)