GeoSeries attributes and methods II

Visualizing Geospatial Data in Python

Mary van Valkenburg

Data Science Program Manager, Nashville Software School

GeoSeries.centroid

returns the point at the center of each geometry in a GeoSeries

# centroid of first polygon
print(districts.geometry.centroid[0])

Point(-87.256 36.193)

polygon with centroid

School district centroids

`GeoSeries.centroid`

# print the first 5 rows of school districts 
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...

School district centroids

# create 'center` column from the centroid
school_districts['center'] = school_districts.geometry.centroid

# create GeoDataFrame with districts and centers
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)

GeoSeries.distance()

GeoSeries.distance(other) - returns minimum distance to other

# distance from red_pt to centroid
cen = districts.geometry.centroid[0]
print(red_pt.distance(other = cen))

24.273

polygon with red point and black centroid point

Distance between two points

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...

Distance between two points

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)

Distance between two points

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

(30, 8)

# 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....

Let's Practice!

Visualizing Geospatial Data in Python