<SYSTEM_TASK:>
Given the following text description, write Python code to implement the functionality described below step by step
<END_TASK>
<USER_TASK:>
Description:
 Overlays
Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries 
and create a new GeoDataFrame with the new geometries representing the spatial combination and
merged properties. This allows you to answer questions like
What are the demographics of the census tracts within 1000 ft of the highway?
The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, 
not on individual geometries, and the properties from both are retained
Now we can load up two GeoDataFrames containing (multi)polygon geometries...
Step1:  The first dataframe contains multipolygons of the NYC boros
Step2:  And the second GeoDataFrame is a sequentially generated set of circles in the same geographic space. We'll plot these with a different color palette.
Step3:  The geopandas.tools.overlay function takes three arguments
Step4:  And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained.
Step5:  Now let's look at the other how operations
<ASSISTANT_TASK:>
Python Code:
%matplotlib inline
from shapely.geometry import Point
from geopandas import datasets, GeoDataFrame, read_file
from geopandas.tools import overlay
# NYC Boros
zippath = datasets.get_path('nybb')
polydf = read_file(zippath)
# Generate some circles
b = [int(x) for x in polydf.total_bounds]
N = 10
polydf2 = GeoDataFrame([
    {'geometry': Point(x, y).buffer(10000), 'value1': x + y, 'value2': x - y}
    for x, y in zip(range(b[0], b[2], int((b[2] - b[0]) / N)),
                    range(b[1], b[3], int((b[3] - b[1]) / N)))])
Explanation: Overlays
Spatial overlays allow you to compare two GeoDataFrames containing polygon or multipolygon geometries 
and create a new GeoDataFrame with the new geometries representing the spatial combination and
merged properties. This allows you to answer questions like
What are the demographics of the census tracts within 1000 ft of the highway?
The basic idea is demonstrated by the graphic below but keep in mind that overlays operate at the dataframe level, 
not on individual geometries, and the properties from both are retained
Now we can load up two GeoDataFrames containing (multi)polygon geometries...
End of explanation
polydf.plot()
Explanation: The first dataframe contains multipolygons of the NYC boros
End of explanation
polydf2.plot(cmap='tab20b')
Explanation: And the second GeoDataFrame is a sequentially generated set of circles in the same geographic space. We'll plot these with a different color palette.
End of explanation
newdf = polydf.overlay(polydf2, how="intersection")
newdf.plot(cmap='tab20b')
Explanation: The geopandas.tools.overlay function takes three arguments:
df1
df2
how
Where how can be one of:
['intersection',
'union',
'identity',
'symmetric_difference',
'difference']
So let's identify the areas (and attributes) where both dataframes intersect using the overlay method.
End of explanation
polydf.head()
polydf2.head()
newdf.head()
Explanation: And take a look at the attributes; we see that the attributes from both of the original GeoDataFrames are retained.
End of explanation
newdf = polydf.overlay(polydf2, how="union")
newdf.plot(cmap='tab20b')
newdf = polydf.overlay(polydf2, how="identity")
newdf.plot(cmap='tab20b')
newdf = polydf.overlay(polydf2, how="symmetric_difference")
newdf.plot(cmap='tab20b')
newdf = polydf.overlay(polydf2, how="difference")
newdf.plot(cmap='tab20b')
Explanation: Now let's look at the other how operations:
End of explanation
<END_TASK>