10行Python代碼創(chuàng)建可視化地圖
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(dāng)我開(kāi)始建造Vincent時(shí), 我的一個(gè)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(kù)-參見(jiàn)Basemap 和 Kartograph能讓地圖更有意思. 我強(qiáng)烈推薦這兩個(gè)工具, 因?yàn)樗麄兌己芎糜枚液軓?qiáng)大. 我想有更簡(jiǎn)單一些的工具,能依靠Vega的力量并且允許簡(jiǎn)單的語(yǔ)法點(diǎn)到geoJSON文件，詳細(xì)描述一個(gè)投影和大小/比列,***輸出地圖。
例如, 將地圖數(shù)據(jù)分層來(lái)建立更復(fù)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)
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加之,等值線地圖需綁定Pandas數(shù)據(jù),需要數(shù)據(jù)列直接映射到地圖要素.假設(shè)有一個(gè)從geoJSON到列數(shù)據(jù)的1:1映射,它的語(yǔ)法是非常簡(jiǎn)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)
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我們的數(shù)據(jù)并非沒(méi)有爭(zhēng)議無(wú)需改造——用戶(hù)需要確保 geoJSON 鍵與熊貓數(shù)據(jù)框架之間具有1:1的映射。下面就是之前實(shí)例所需的簡(jiǎn)明的數(shù)據(jù)框架映射：我們的國(guó)家信息是一個(gè)列有FIPS 碼、國(guó)家名稱(chēng)、以及經(jīng)濟(jì)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(guó)家形狀是以 FIPS 碼為id 的(感謝 fork 自 Trifacta 的相關(guān)信息)。為了簡(jiǎn)便，實(shí)際形狀已經(jīng)做了簡(jiǎn)略，在示例數(shù)據(jù)可以找到完整的數(shù)據(jù)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(wú)法正確的壓縮數(shù)據(jù)：
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(xiàn)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)
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這只能告訴我們 LA 和 King 面積非常大，人口非常稠密。讓我們?cè)倏纯粗械燃彝ナ杖耄?/p>
vis.tabular_data(merged, columns=['FIPS_Code', 'Median_Household_Income_2011'])
vis.to_json(path)
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明顯很多高收入?yún)^(qū)域在東海岸或是其他高密度區(qū)域。我敢打賭，在城市層級(jí)這將更加有趣，但這需要等以后發(fā)布的版本。讓我們快速重置地圖，再看看國(guó)家失業(yè)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)
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地圖即是我的激情所在——我希望 Vincent 能夠更強(qiáng)，包含輕松的添加點(diǎn)、標(biāo)記及其它的能力。如果各位讀者對(duì)于映射方面有什么功能上的需求，可以在Github上給我發(fā)問(wèn)題。
