[1]:

import geopandas
import numpy
import matplotlib.pyplot as plt
import geoplanar
from shapely.geometry import box, Polygon

Violation: Gaps#

[2]:

p1 = box(0,0,10,10)
p2 = Polygon([(10,10), (12,8), (10,6), (12,4), (10,2), (20,5)])

gdf = geopandas.GeoDataFrame(geometry=[p1,p2])
gdf.plot(edgecolor='k')

[2]:

<AxesSubplot:>
_images/gaps_2_1.png 
[3]:

geoplanar.gaps(gdf)

[3]:
geometry
2 POLYGON ((10.00000 10.00000, 12.00000 8.00000,...
3 POLYGON ((10.00000 6.00000, 12.00000 4.00000, ... 
[4]:

g = geoplanar.gaps(gdf)

[5]:

g.area.values

[5]:

array([4., 4.])

[6]:

gdf1 = geoplanar.fill_gaps(gdf)

[7]:

gdf1.plot(edgecolor='k')

[7]:

<AxesSubplot:>
_images/gaps_7_1.png 
[8]:

gdf1.area

[8]:

0    108.0
1     32.0
dtype: float64

[9]:

gdf.area

[9]:

0    100.0
1     32.0
dtype: float64

[10]:

geoplanar.gaps(gdf1)

[10]:
geometry

The default is to merge the gap with the largest neighboring feature.

To merge the gap with the smallest neighboring feature set `Largest=False’:

[11]:

geoplanar.fill_gaps(gdf, largest=False).plot(edgecolor='k')

[11]:

<AxesSubplot:>
_images/gaps_12_1.png 
[12]:

geoplanar.fill_gaps(gdf, largest=False).area

[12]:

0    100.0
1     40.0
dtype: float64

Checking edge case#

[13]:

p1 = box(0,0,10,10)
p2 = Polygon([(10,10), (12,8), (10,6), (12,4), (10,2), (20,5)])
p3 = box(17,0,20,2)

gdf = geopandas.GeoDataFrame(geometry=[p1,p2,p3])
gdf.plot(edgecolor='k')

[13]:

<AxesSubplot:>
_images/gaps_15_1.png 
[14]:

g = geoplanar.gaps(gdf)

[15]:

g.plot()

[15]:

<AxesSubplot:>
_images/gaps_17_1.png 
[16]:

geoplanar.fill_gaps(gdf, largest=False).plot(edgecolor='k')

[16]:

<AxesSubplot:>
_images/gaps_18_1.png 
[17]:

geoplanar.fill_gaps(gdf, largest=True).plot(edgecolor='k')

[17]:

<AxesSubplot:>
_images/gaps_19_1.png 
[ ]:



[18]:

gdf.plot()

[18]:

<AxesSubplot:>
_images/gaps_21_1.png

Gap with an inlet (non-gap)#

[19]:

p1 = box(0,0,10,10)
p2 = Polygon([(10,10), (12,8), (10,6), (12,4), (11,2), (20,5)])

# a true gap with a inlet
gdf = geopandas.GeoDataFrame(geometry=[p1,p2])
gdf.plot(edgecolor='k')

[19]:

<AxesSubplot:>
_images/gaps_23_1.png 
[20]:

geoplanar.gaps(gdf)

[20]:
geometry
2 POLYGON ((10.00000 10.00000, 12.00000 8.00000,... 
[21]:

geoplanar.fill_gaps(gdf, largest=False).plot(edgecolor='k')

[21]:

<AxesSubplot:>
_images/gaps_25_1.png

Selective Correction#

[22]:

p1 = box(0,0,10,10)
p2 = Polygon([(10,10), (12,8), (10,6), (12,4), (10,2), (20,5)])
p3 = box(17,0,20,2)

gdf = geopandas.GeoDataFrame(geometry=[p1,p2,p3])
gdf.plot(edgecolor='k')

[22]:

<AxesSubplot:>
_images/gaps_27_1.png 
[23]:

gaps = geoplanar.gaps(gdf)

[24]:

base = gdf.plot()
gaps.plot(color='red', ax=base)

[24]:

<AxesSubplot:>
_images/gaps_29_1.png 
[25]:

gaps

[25]:
geometry
2 POLYGON ((10.00000 10.00000, 12.00000 8.00000,...
3 POLYGON ((10.00000 6.00000, 12.00000 4.00000, ... 
[26]:

g2 = gaps.loc[[2]]

[27]:

g2

[27]:
geometry
2 POLYGON ((10.00000 10.00000, 12.00000 8.00000,... 
[28]:

filled = geoplanar.fill_gaps(gdf,g2)

[29]:

base = filled.plot()
g2.plot(color='red', ax=base)

[29]:

<AxesSubplot:>
_images/gaps_34_1.png 
[30]:

filled.area

[30]:

0    104.0
1     32.0
2      6.0
dtype: float64

[31]:

filled.shape

[31]:

(3, 1)

[32]:

(filled.area==[104, 32,6]).all()

[32]:

True