GeoSeries 属性与方法 II

在 Python 中可视化地理空间数据

Mary van Valkenburg

Data Science Program Manager, Nashville Software School

GeoSeries.centroid

  • 返回 GeoSeries 中每个几何体的中心点
# 第一个多边形的质心
print(districts.geometry.centroid[0])

Point(-87.256 36.193)

带质心的多边形

在 Python 中可视化地理空间数据

学区质心

GeoSeries.centroid
# 打印学区的前 5 行
print(school_districts.head())

first_name  last_name  district  geometry
Sharon      Gentry        1      (POLYGON ((-86.771 36.383...
Jill        Speering      3      (POLYGON ((-86.753 36.404...
Jo Ann      Brannon       2      (POLYGON ((-86.766 36.083...
Anna        Shepherd      4      (POLYGON ((-86.580 36.209...
Amy         Frogge        9      (POLYGON ((-86.972 36.208...
在 Python 中可视化地理空间数据

学区质心

# 从质心创建 'center' 列
school_districts['center'] = school_districts.geometry.centroid

# 用学区及其中心创建 GeoDataFrame
part = ['district', 'center']
school_district_centers = school_districts[part]
school_district_centers.head(3)

district         center               
1                POINT (-86.86086595994405 36.2628221811899)    
3                POINT (-86.72361421487962 36.28515517790142)    
2                POINT (-86.70156420691957 36.03021153030475)
在 Python 中可视化地理空间数据

GeoSeries.distance()

  • GeoSeries.distance(other)：返回到 other 的最小距离
# red_pt 到质心的距离
cen = districts.geometry.centroid[0]
print(red_pt.distance(other = cen))

24.273

带红点和黑色质心点的多边形

在 Python 中可视化地理空间数据

两点间距

GeoSeries.distance(other)
district_one = school_districts.loc[school_districts.district == '1']
district_one.head()

first_name  last_name  district center                  geometry    
Sharon      Gentry     1        POINT (-86.860 36.262)  (POLYGON ((-86.771...
在 Python 中可视化地理空间数据

两点间距

schools.head(3)
name                 lat     lng
AZ Kelley Elem       36.021  -86.658
Alex Green Elem      36.252  -86.832
Amqui Elem           36.27   -86.703

# create geometry in schools
schools['geometry']=gpd.points_from_xy(schools.lng, schools.lat)

#construct schools GeoDataFrame
school_geo=gpd.GeoDataFrame(schools,crs = district_one.crs,
                            geometry = schools.geometry)
在 Python 中可视化地理空间数据

两点间距

# spatial join schools within dist 1
schools_in_dist1 = gpd.sjoin(schools_geo, district_one, predicate = 'within')
schools_in_dist1.shape

(30, 8)
在 Python 中可视化地理空间数据
# import pprint to format dictionary output
import pprint

distances = {}
for row in schools_in_dist1.iterrows():
    vals = row[1]
    key = vals['name']
    ctr = vals['center']
    distances[key] = vals['geometry'].distance(ctr)
pprint.pprint(distances)

{'Alex Green Elementary': 0.030287172719682773,
 'Bellshire Elementary': 0.0988045140909651,
 'Brick Church College Prep': 0.08961013862715599,
 'Buena Vista Elementary': 0.10570511270825833,
 'Cockrill Elementary': 0.1077685612196105....
在 Python 中可视化地理空间数据

Passons à la pratique !

在 Python 中可视化地理空间数据

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