before use this script, you should have the following packages you can install GeoPandas git clone https://github.com/kjordahl/geopandas and then install it with
python setup.py install
conda install fiona
conda install pysal
yxcheng@uw.edu Yuxuan Cheng https://sites.google.com/view/ycheng
In [1]:
import shapely
import geopandas as gpd
In [2]:
import pandas as pd
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
import seaborn as sns
#from matplotlib import interactive
#interactive(True)
%matplotlib inline
In [3]:
import pysal
from scipy import ndimage
#p1 = shapely.geometry.Polygon([(0, 0), (1, 0), (1, 1)])
#p2 = shapely.geometry.Polygon([(0, 0), (1, 0), (1, 1), (0, 1)])
#p3 = shapely.geometry.Polygon([(2, 0), (3, 0), (3, 1), (2, 1)])
#g = gpd.GeoSeries([p1,p2,p3])
#g.area
#g.plot()
#matplotlib.pyplot.show()
In [4]:
#road the data
boros = gpd.GeoDataFrame.from_file('project/Output/SelectedBlocks.shp')
In [5]:
#show the first data frame
boros.ix[0]
#boros.blocks
Out[5]:
ALAND10 121128
AWATER10 0
BLOCKCE10 1001
COUNTYFP10 033
FUNCSTAT10 S
GEOID10 530330067001001
INTPTLAT10 +47.6319178
INTPTLON10 -122.3399002
MTFCC10 G5040
NAME10 Block 1001
OBJECTID 1
STATEFP10 53
Shape_Area 1.4496e-05
Shape_Leng 0.027448
TRACTCE10 006700
TRACT_10 6700
TRBL 6700.1
UACE10 None
UR10 None
geometry POLYGON ((-122.3406299995747 47.6324870001751,...
Name: 0, dtype: object
In [6]:
#plot the dataframe
boros.plot()
matplotlib.pyplot.show()
In [7]:
#get the data(future will read antother dataset)
boros['InCount'] = boros['OBJECTID'].mod(255)
In [8]:
#show the range of OBJECCTID
boros['OBJECTID'].max()
Out[8]:
34663
In [9]:
#show the name of the “geometry” column of gpd
boros.geometry.name
Out[9]:
'geometry'
In [10]:
#create the centroid of each block (point data)
boros['centroid_column'] = boros.centroid
In [11]:
#show the data
boros.head()
Out[11]:
ALAND10
AWATER10
BLOCKCE10
COUNTYFP10
FUNCSTAT10
GEOID10
INTPTLAT10
INTPTLON10
MTFCC10
NAME10
...
Shape_Area
Shape_Leng
TRACTCE10
TRACT_10
TRBL
UACE10
UR10
geometry
InCount
centroid_column
0
121128.0
0.0
1001
033
S
530330067001001
+47.6319178
-122.3399002
G5040
Block 1001
...
1.449595e-05
0.027448
006700
6700
6700.1001
None
None
POLYGON ((-122.3406299995747 47.6324870001751,...
1
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S
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...
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...
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...
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None
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POINT (-122.3899012536813 47.52053650264553)
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...
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None
None
POLYGON ((-122.3793320000716 47.5276230000016,...
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POINT (-122.3806803250455 47.52729271292806)
5 rows × 22 columns
In [12]:
#change the name of column
boros.rename(columns={'centroid_column': 'centroid_XY'})
Out[12]:
ALAND10
AWATER10
BLOCKCE10
COUNTYFP10
FUNCSTAT10
GEOID10
INTPTLAT10
INTPTLON10
MTFCC10
NAME10
...
Shape_Area
Shape_Leng
TRACTCE10
TRACT_10
TRBL
UACE10
UR10
geometry
InCount
centroid_XY
0
121128.0
0.0
1001
033
S
530330067001001
+47.6319178
-122.3399002
G5040
Block 1001
...
1.449595e-05
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006700
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None
None
POLYGON ((-122.3406299995747 47.6324870001751,...
1
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Block 1009
...
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POLYGON ((-122.362319999768 47.63593299990816,...
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...
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POLYGON ((-122.3917790003005 47.51974199989354...
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Block 2007
...
1.949297e-06
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None
None
POLYGON ((-122.3793320000716 47.5276230000016,...
18
POINT (-122.3806803250455 47.52729271292806)
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41595.0
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033
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G5040
Block 2005
...
4.966028e-06
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None
None
POLYGON ((-122.3760010002458 47.50639300033686...
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POINT (-122.3787497508842 47.50686558552383)
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G5040
Block 2013
...
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None
None
POLYGON ((-122.324698999659 47.63225600031433,...
32
POINT (-122.3240336290084 47.63485245185199)
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Block 1014
...
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None
POLYGON ((-122.3434029997485 47.63341599984869...
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POINT (-122.3428443205387 47.63286013116908)
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...
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POLYGON ((-122.412540999967 47.67621899970726,...
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POINT (-122.4069682954432 47.68061033180799)
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POINT (-122.3969884679725 47.66904970401041)
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...
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POLYGON ((-122.4099819997693 47.6402999998453,...
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POINT (-122.4106250043156 47.64112864160045)
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...
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POLYGON ((-122.4046809999281 47.65044600008298...
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POINT (-122.405212004047 47.65136038677456)
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...
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None
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POLYGON ((-122.3968959997504 47.6540629996079,...
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POINT (-122.3962991196612 47.65500288731763)
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G5040
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...
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None
None
POLYGON ((-122.3820329996231 47.64658199995949...
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POINT (-122.3826897959563 47.64752230701134)
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...
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POLYGON ((-122.3833219997104 47.64475800038156...
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POINT (-122.383994834054 47.64566518139661)
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G5040
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...
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None
None
POLYGON ((-122.3774830002402 47.64747899995604...
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POINT (-122.3781731510543 47.64796715661811)
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G5040
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...
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None
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POLYGON ((-122.2782099997915 47.55703300007303...
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POINT (-122.278844092174 47.55761168891294)
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530330103002006
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G5040
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...
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None
None
POLYGON ((-122.2776659998858 47.55117399968049...
59
POINT (-122.2791225286143 47.55079878419995)
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G5040
Block 4002
...
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None
None
POLYGON ((-122.2808500001273 47.54598599965431...
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POINT (-122.2820906452474 47.54564364315645)
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G5040
Block 1028
...
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None
None
POLYGON ((-122.2682649998297 47.56294200007341...
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POINT (-122.2677244787185 47.56148910912029)
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G5040
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...
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None
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POLYGON ((-122.2656810002778 47.56060700031298...
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POINT (-122.2663050893917 47.5600187491734)
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...
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...
...
...
...
...
...
...
...
...
...
...
...
...
...
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6819 rows × 22 columns
In [13]:
#plot the blocks and "In Count"
#%matplotlib qt
fig1 = boros.plot(column='InCount', cmap='viridis')
#fig1.colorbar()
plt.show()
plt.colorbar
plt.savefig('map2.pdf')
<matplotlib.figure.Figure at 0x14893470>
In [14]:
#the hel
def heatmap(d, bins=(100,100), smoothing=1.3, cmap='viridis'):
"""
plot the heat map
This function takes a GeoDataFrame with point geometries and shows a matplotlib plot of heatmap density
"""
def getx(pt):
return pt.coords[0][0]
def gety(pt):
return pt.coords[0][1]
x = list(d.geometry.apply(getx))
y = list(d.geometry.apply(gety))
heatmap, xedges, yedges = np.histogram2d(y, x, bins=bins)
extent = [yedges[0], yedges[-1], xedges[-1], xedges[0]]
#using numpy's 2D histogram binning with smoothing from scipy
logheatmap = np.log(heatmap)
logheatmap[np.isneginf(logheatmap)] = 0
logheatmap = ndimage.filters.gaussian_filter(logheatmap, smoothing, mode='nearest')
plt.imshow(logheatmap, cmap=cmap, extent=extent)
plt.colorbar()
plt.gca().invert_yaxis()
plt.show()
In [15]:
#create a new data whose “geometry” column is the center of blocks (point geometries)
boros_point = boros.set_geometry('centroid_column')
In [16]:
#plot the piont data
boros_point.plot()
plt.show()
In [17]:
#plot the heat map
heatmap(boros_point, bins=50, smoothing=1.5)
In [ ]:
Content source: gengho/Car2know
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