10行Python代碼創建可視化地圖

作者：renwofei423 2017-09-01 19:49:50

當我開始建造Vincent時, 我的一個目的就是使得地圖的建造盡可能合理化. 有一些很棒的python地圖庫-參見Basemap 和 Kartograph能讓地圖更有意思. 我強烈推薦這兩個工具, 因為他們都很好用而且很強大.

import vincent
world_countries = r'world-countries.json'
world = vincent.Map(width=1200, height=1000)
world.geo_data(projection='winkel3', scale=200, world=world_countries)
world.to_json(path)

當我開始建造Vincent時, 我的一個目的就是使得地圖的建造盡可能合理化. 有一些很棒的python地圖庫-參見Basemap 和 Kartograph能讓地圖更有意思. 我強烈推薦這兩個工具, 因為他們都很好用而且很強大. 我想有更簡單一些的工具,能依靠Vega的力量并且允許簡單的語法點到geoJSON文件,詳細描述一個投影和大小/比列,***輸出地圖.

例如, 將地圖數據分層來建立更復雜的地圖:

vis = vincent.Map(width=1000, height=800)
#Add the US county data and a new line color
vis.geo_data(projection='albersUsa', scale=1000, counties=county_geo)
vis + ('2B4ECF', 'marks', 0, 'properties', 'enter', 'stroke', 'value')

#Add the state data, remove the fill, write Vega spec output to JSON
vis.geo_data(states=state_geo)
vis - ('fill', 'marks', 1, 'properties', 'enter')
vis.to_json(path)

加之,等值線地圖需綁定Pandas數據,需要數據列直接映射到地圖要素.假設有一個從geoJSON到列數據的1:1映射,它的語法是非常簡單的:

#'merged' is the Pandas DataFrame
vis = vincent.Map(width=1000, height=800)
vis.tabular_data(merged, columns=['FIPS_Code', 'Unemployment_rate_2011'])
vis.geo_data(projection='albersUsa', scale=1000, bind_data='data.id', counties=county_geo)
vis + (["#f5f5f5","#000045"], 'scales', 0, 'range')
vis.to_json(path)

我們的數據并非沒有爭議無需改造——用戶需要確保 geoJSON 鍵與熊貓數據框架之間具有1:1的映射。下面就是之前實例所需的簡明的數據框架映射：我們的國家信息是一個列有FIPS 碼、國家名稱、以及經濟信息(列名省略)的 CSV 文件：

00000,US,United States,154505871,140674478,13831393,9,50502,100
01000,AL,Alabama,2190519,1993977,196542,9,41427,100
01001,AL,Autauga County,25930,23854,2076,8,48863,117.9
01003,AL,Baldwin County,85407,78491,6916,8.1,50144,121
01005,AL,Barbour County,9761,8651,1110,11.4,30117,72.7

在 geoJSON 中，我們的國家形狀是以 FIPS 碼為id 的(感謝 fork 自 Trifacta 的相關信息)。為了簡便，實際形狀已經做了簡略，在示例數據可以找到完整的數據集：

{"type":"FeatureCollection","features":[
{"type":"Feature","id":"1001","properties":{"name":"Autauga"}
{"type":"Feature","id":"1003","properties":{"name":"Baldwin"}
{"type":"Feature","id":"1005","properties":{"name":"Barbour"}
{"type":"Feature","id":"1007","properties":{"name":"Bibb"}
{"type":"Feature","id":"1009","properties":{"name":"Blount"}
{"type":"Feature","id":"1011","properties":{"name":"Bullock"}
{"type":"Feature","id":"1013","properties":{"name":"Butler"}
{"type":"Feature","id":"1015","properties":{"name":"Calhoun"}
{"type":"Feature","id":"1017","properties":{"name":"Chambers"}
{"type":"Feature","id":"1019","properties":{"name":"Cherokee"}

我們需要匹配 FIPS 碼，確保匹配正確，否則 Vega 無法正確的壓縮數據：

import json
import pandas as pd
#Map the county codes we have in our geometry to those in the
#county_data file, which contains additional rows we don't need
with open(county_geo, 'r') as f:
get_id = json.load(f)

#Grab the FIPS codes and load them into a dataframe
county_codes = [x['id'] for x in get_id['features']]
county_df = pd.DataFrame({'FIPS_Code': county_codes}, dtype=str)

#Read into Dataframe, cast to string for consistency
df = pd.read_csv(county_data, na_values=[' '])
df['FIPS_Code'] = df['FIPS_Code'].astype(str)

#Perform an inner join, pad NA's with data from nearest county
merged = pd.merge(df, county_df, on='FIPS_Code', how='inner')
merged = merged.fillna(method='pad')

>>>merged.head()
FIPS_Code State       Area_name  Civilian_labor_force_2011  Employed_2011  \
0      1001    AL  Autauga County                      25930          23854
1      1003    AL  Baldwin County                      85407          78491
2      1005    AL  Barbour County                       9761           8651
3      1007    AL     Bibb County                       9216           8303
4      1009    AL   Blount County                      26347          24156

Unemployed_2011  Unemployment_rate_2011  Median_Household_Income_2011  \
0             2076                     8.0                         48863
1             6916                     8.1                         50144
2             1110                    11.4                         30117
3              913                     9.9                         37347
4             2191                     8.3                         41940

Med_HH_Income_Percent_of_StateTotal_2011
0                                     117.9
1                                     121.0
2                                      72.7
3                                      90.2
4                                     101.2

現在，我們可以快速生成不同的等值線：

vis.tabular_data(merged, columns=['FIPS_Code', 'Civilian_labor_force_2011'])
vis.to_json(path)

這只能告訴我們 LA 和 King 面積非常大，人口非常稠密。讓我們再看看中等家庭收入：

vis.tabular_data(merged, columns=['FIPS_Code', 'Median_Household_Income_2011'])
vis.to_json(path)

明顯很多高收入區域在東海岸或是其他高密度區域。我敢打賭，在城市層級這將更加有趣，但這需要等以后發布的版本。讓我們快速重置地圖，再看看國家失業率：

#Swap county data for state data, reset map
state_data = pd.read_csv(state_unemployment)
vis.tabular_data(state_data, columns=['State', 'Unemployment'])
vis.geo_data(bind_data='data.id', reset=True, states=state_geo)
vis.update_map(scale=1000, projection='albersUsa')
vis + (['#c9cedb', '#0b0d11'], 'scales', 0, 'range')
vis.to_json(path)

地圖即是我的激情所在——我希望 Vincent 能夠更強，包含輕松的添加點、標記及其它的能力。如果各位讀者對于映射方面有什么功能上的需求，可以在Github上給我發問題。

責任編輯：未麗燕來源： 36大數據

Python 工具地圖