コロプレス図とは？

Pythonで可視化する地理空間データ

Mary van Valkenburg

Data Science Program Manager, Nashville Software School

コロプレス図の定義

ミルトンキーンズのLSOA別平均年齢のコロプレス図

世界の一人当たりGDP（PPP）のコロプレス図

密度

schools_in_districts.head(2)

district  geometry                  name               lat    lng
1         (POLYGON ((-86.77 36.38... Nashville Prep    36.16 -86.85
1         (POLYGON ((-86.77 36.38... Rocketship Prep   36.17 -86.79

件数を取得

school_counts = schools_in_districts.groupby(['district']).size()
print(school_counts)

district
1    30
2    11
3    19
4    18
5    36
6    21
7    13
8    10
9    12
dtype: int64

件数を追加

school_counts_df = school_counts.to_frame()
school_counts_df = school_counts_df.reset_index()
school_counts_df.columns = ['district', 'school_count']

districts_with_counts = pd.merge(school_districts, school_counts_df, 
                                 on = 'district')
districts_with_counts.head(2)

district geometry           school_count
1        (POLYGON ((-86.77 36.38...  30
3        (POLYGON ((-86.75 36.40...  19

面積で割って標準化

districts_with_counts['area'] = districts_with_counts.area

districts_with_counts['school_density'] = districts_with_counts.apply(
    lambda row: row.school_count/row.area, axis = 1)

districts_with_counts.head(2)

district geometry           school_count   area    school_density
1        (POLYGON ((-86.77 36.38...    30    0.036641    818.745403
3        (POLYGON ((-86.75 36.40...    19    0.014205    1337.594495

Passons à la pratique !

Pythonで可視化する地理空間データ