lesson_7 Aggregation with dissolve and Merging Data

通常情况下，我们发现自己使用的空间数据比我们需要的更精细。例如，我们可能有关于次国家单位的数据，但我们实际上对研究各国一级的模式感兴趣。就是得到上一个等级的数据，话不多说，直接看例子:

　　　　world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))

　　　　world = world[['continent', 'geometry']]

　　　　continents = world.dissolve(by='continent')

 　　　   continents.plot()

　　　　continents.head()

　　　　

合并数据的方式有两种：属性链接和空间链接，我们直接看例子。

　　　　

world = geopandas.read_file(geopandas.datasets.get_path('naturalearth_lowres'))
cities = geopandas.read_file(geopandas.datasets.get_path('naturalearth_cities'))
country_shapes = world[['geometry', 'iso_a3']]
country_names = world[['name', 'iso_a3']]
# For spatial join
countries = world[['geometry', 'name']]
countries = countries.rename(columns={'name':'country'})

上述的在打开数据，下面进行属性链接

country_shapes.head()
Out[7]: 
                                            geometry iso_a3
0  POLYGON ((61.21081709172574 35.65007233330923,...    AFG
1  (POLYGON ((16.32652835456705 -5.87747039146621...    AGO
2  POLYGON ((20.59024743010491 41.85540416113361,...    ALB
3  POLYGON ((51.57951867046327 24.24549713795111,...    ARE
4  (POLYGON ((-65.50000000000003 -55.199999999999...    ARG

country_names.head()
Out[8]: 
                   name iso_a3
0           Afghanistan    AFG
1                Angola    AGO
2               Albania    ALB
3  United Arab Emirates    ARE
4             Argentina    ARG

# Merge with `merge` method on shared variable (iso codes):
country_shapes = country_shapes.merge(country_names, on='iso_a3')
country_shapes.head()
Out[10]: 
                                            geometry          ...                           name
0  POLYGON ((61.21081709172574 35.65007233330923,...          ...                    Afghanistan
1  (POLYGON ((16.32652835456705 -5.87747039146621...          ...                         Angola
2  POLYGON ((20.59024743010491 41.85540416113361,...          ...                        Albania
3  POLYGON ((51.57951867046327 24.24549713795111,...          ...           United Arab Emirates
4  (POLYGON ((-65.50000000000003 -55.199999999999...          ...                      Argentina

　　

 空间链接的例子：

　　

# One GeoDataFrame of countries, one of Cities.
# Want to merge so we can get each city's country.
In [11]: countries.head()
Out[11]: 
                                            geometry               country
0  POLYGON ((61.21081709172574 35.65007233330923,...           Afghanistan
1  (POLYGON ((16.32652835456705 -5.87747039146621...                Angola
2  POLYGON ((20.59024743010491 41.85540416113361,...               Albania
3  POLYGON ((51.57951867046327 24.24549713795111,...  United Arab Emirates
4  (POLYGON ((-65.50000000000003 -55.199999999999...             Argentina

In [12]: cities.head()
Out[12]: 
           name                                     geometry
0  Vatican City  POINT (12.45338654497177 41.90328217996012)
1    San Marino    POINT (12.44177015780014 43.936095834768)
2         Vaduz  POINT (9.516669472907267 47.13372377429357)
3    Luxembourg  POINT (6.130002806227083 49.61166037912108)
4       Palikir  POINT (158.1499743237623 6.916643696007725)

# Execute spatial join
In [13]: cities_with_country = geopandas.sjoin(cities, countries, how="inner", op='intersects')

In [14]: cities_with_country.head()
Out[14]: 
             name   ...     country
0    Vatican City   ...       Italy
1      San Marino   ...       Italy
192          Rome   ...       Italy
2           Vaduz   ...     Austria
184        Vienna   ...     Austria

[5 rows x 4 columns]