python绘制某个地区的地图 python制作地图

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mob64ca1417736e 2023-11-21 18:21:45

文章标签 python绘制某个地区的地图 python json Code 数据 文章分类 Python 后端开发

1. import vincent 
2.  
3. world_countries = r'world-countries.json'
4.  
5. world = vincent.Map(width=1200, height=1000) 
6.  
7. world.geo_data(projection='winkel3', scale=200, world=world_countries) 
8.  
9. world.to_json(path)

当我开始建造Vincent时, 我的一个目的就是使得地图的建造尽可能合理化. 有一些很棒的python地图库-参见Basemap 和 Kartograph能让地图更有意思. 我强烈推荐这两个工具, 因为他们都很好用而且很强大. 我想有更简单一些的工具,能依靠Vega的力量并且允许简单的语法点到geoJSON文件，详细描述一个投影和大小/比列,最后输出地图。

例如, 将地图数据分层来建立更复杂的地图:

1. vis = vincent.Map(width=1000, height=800) 
2.  
3. #Add the US county data and
4.  
5. vis.geo_data(projection='albersUsa', scale=1000, counties=county_geo) 
6.  
7. vis + ('2B4ECF', 'marks', 0, 'properties', 'enter', 'stroke', 'value') 
8.  
9. #Add the state data, remove the fill, write Vega spec output to
10.  
11. vis.geo_data(states=state_geo) 
12.  
13. vis - ('fill', 'marks', 1, 'properties', 'enter') 
14.  
15. vis.to_json(path)

加之,等值线地图需绑定Pandas数据,需要数据列直接映射到地图要素.假设有一个从geoJSON到列数据的1:1映射,它的语法是非常简单的:

1. #'merged' is
2.  
3. vis = vincent.Map(width=1000, height=800) 
4.  
5. vis.tabular_data(merged, columns=['FIPS_Code', 'Unemployment_rate_2011']) 
6.  
7. vis.geo_data(projection='albersUsa', scale=1000, bind_data='data.id', counties=county_geo) 
8.  
9. vis + (["#f5f5f5","#000045"], 'scales', 0, 'range') 
10.  
11. vis.to_json(path)

我们的数据并非没有争议无需改造——用户需要确保 geoJSON 键与熊猫数据框架之间具有1:1的映射。下面就是之前实例所需的简明的数据框架映射：我们的国家信息是一个列有FIPS 码、国家名称、以及经济信息(列名省略)的 CSV 文件：

1. 00000,US,United States,154505871,140674478,13831393,9,50502,100 
2.  
3. 01000,AL,Alabama,2190519,1993977,196542,9,41427,100 
4.  
5. 01001,AL,Autauga County,25930,23854,2076,8,48863,117.9 
6.  
7. 01003,AL,Baldwin County,85407,78491,6916,8.1,50144,121 
8.  
9. 01005,AL,Barbour County,9761,8651,1110,11.4,30117,72.7

在 geoJSON 中，我们的国家形状是以 FIPS 码为id 的(感谢 fork 自 Trifacta 的相关信息)。为了简便，实际形状已经做了简略，在示例数据可以找到完整的数据集：

1. {"type":"FeatureCollection","features":[ 
2.  
3. {"type":"Feature","id":"1001","properties":{"name":"Autauga"} 
4.  
5. {"type":"Feature","id":"1003","properties":{"name":"Baldwin"} 
6.  
7. {"type":"Feature","id":"1005","properties":{"name":"Barbour"} 
8.  
9. {"type":"Feature","id":"1007","properties":{"name":"Bibb"} 
10.  
11. {"type":"Feature","id":"1009","properties":{"name":"Blount"} 
12.  
13. {"type":"Feature","id":"1011","properties":{"name":"Bullock"} 
14.  
15. {"type":"Feature","id":"1013","properties":{"name":"Butler"} 
16.  
17. {"type":"Feature","id":"1015","properties":{"name":"Calhoun"} 
18.  
19. {"type":"Feature","id":"1017","properties":{"name":"Chambers"} 
20.  
21. {"type":"Feature","id":"1019","properties":{"name":"Cherokee"}

我们需要匹配 FIPS 码，确保匹配正确，否则 Vega 无法正确的压缩数据：

1. import json 
2. import pandas as
3. #Map the county codes we have in our geometry to those in
4. #county_data file, which contains additional rows
5. with open(county_geo, 'r') as
6. load(f) 
7.  
8. #Grab the FIPS codes and load them into
9. county_codes = [x['id'] for x in get_id['features']] 
10. county_df = pd.DataFrame({'FIPS_Code': county_codes}, dtype=str) 
11.  
12. #Read into Dataframe, cast to string for
13. df = pd.read_csv(county_data, na_values=[' ']) 
14. df['FIPS_Code'] = df['FIPS_Code'].astype(str) 
15.  
16. #Perform an inner join, pad NA's with data from
17. merged = pd.merge(df, county_df, on='FIPS_Code', how='inner') 
18. merged = merged.fillna(method='pad') 
19.  
20. >>>merged.head() 
21.       FIPS_Code State       Area_name  Civilian_labor_force_2011  Employed_2011  \ 
22.     0      1001    AL  Autauga County                      25930          23854    
23.     1      1003    AL  Baldwin County                      85407          78491    
24.     2      1005    AL  Barbour County                       9761           8651    
25.     3      1007    AL     Bibb County                       9216           8303    
26.     4      1009    AL   Blount County                      26347          24156 
27.  
28.    Unemployed_2011  Unemployment_rate_2011  Median_Household_Income_2011  \ 
29. 0             2076                     8.0                         48863    
30. 1             6916                     8.1                         50144    
31. 2             1110                    11.4                         30117    
32. 3              913                     9.9                         37347    
33. 4             2191                     8.3                         41940 
34.  
35.    Med_HH_Income_Percent_of_StateTotal_2011   
36. 0                                     117.9   
37. 1                                     121.0   
38. 2                                      72.7   
39. 3                                      90.2   
40. 4                                     101.2

现在，我们可以快速生成不同的等值线：

1. vis.tabular_data(merged, columns=['FIPS_Code', 'Civilian_labor_force_2011']) 
2.  
3. vis.to_json(path)

这只能告诉我们 LA 和 King 面积非常大，人口非常稠密。让我们再看看中等家庭收入：

1. vis.tabular_data(merged, columns=['FIPS_Code', 'Median_Household_Income_2011']) 
2.  
3. vis.to_json(path)

明显很多高收入区域在东海岸或是其他高密度区域。我敢打赌，在城市层级这将更加有趣，但这需要等以后发布的版本。让我们快速重置地图，再看看国家失业率：

1. #Swap county data for
2.  
3. state_data = pd.read_csv(state_unemployment) 
4.  
5. vis.tabular_data(state_data, columns=['State', 'Unemployment']) 
6.  
7. vis.geo_data(bind_data='data.id', reset=True, states=state_geo) 
8.  
9. vis.update_map(scale=1000, projection='albersUsa') 
10.  
11. vis + (['#c9cedb', '#0b0d11'], 'scales', 0, 'range') 
12.  
13. vis.to_json(path)