Clustering

import geopandas as gpd
import matplotlib.pyplot as plt
db = gpd.read_file("~/data/shared/sandiego_tracts.gpkg")
ERROR 1: PROJ: proj_create_from_database: Open of /opt/tljh/user/share/proj failed
db.shape
(628, 25)
db.head()
GEOID median_age total_pop total_pop_white tt_work hh_total hh_female total_bachelor median_hh_income income_gini ... state county tract area_sqm pct_rented pct_hh_female pct_bachelor pct_white sub_30 geometry
0 06073018300 37.1 2590.0 2375.0 1299.0 2590.0 137.0 0.0 62500.0 0.5355 ... 06 073 018300 2.876449 0.373913 0.052896 0.000000 0.916988 False POLYGON ((-13069450.120 3922380.770, -13069175...
1 06073018601 41.2 5147.0 4069.0 1970.0 5147.0 562.0 24.0 88165.0 0.4265 ... 06 073 018601 4.548797 0.205144 0.109190 0.004663 0.790558 False POLYGON ((-13067719.770 3922939.420, -13067631...
2 06073017601 54.4 5595.0 4925.0 1702.0 5595.0 442.0 34.0 110804.0 0.4985 ... 06 073 017601 8.726275 0.279029 0.078999 0.006077 0.880250 False POLYGON ((-13058166.110 3907247.690, -13058140...
3 06073019301 42.3 7026.0 5625.0 3390.0 7026.0 638.0 46.0 100539.0 0.4003 ... 06 073 019301 3.519743 0.196512 0.090806 0.006547 0.800598 False POLYGON ((-13056896.290 3925255.610, -13056868...
4 06073018700 21.8 40402.0 30455.0 24143.0 40402.0 2456.0 23.0 41709.0 0.3196 ... 06 073 018700 559.150793 0.949887 0.060789 0.000569 0.753799 False POLYGON ((-13090788.510 3946435.430, -13090736...

5 rows × 25 columns

db.columns
Index(['GEOID', 'median_age', 'total_pop', 'total_pop_white', 'tt_work',
       'hh_total', 'hh_female', 'total_bachelor', 'median_hh_income',
       'income_gini', 'total_housing_units', 'total_rented', 'median_no_rooms',
       'median_house_value', 'NAME', 'state', 'county', 'tract', 'area_sqm',
       'pct_rented', 'pct_hh_female', 'pct_bachelor', 'pct_white', 'sub_30',
       'geometry'],
      dtype='object')
db.plot()
<Axes: >

cluster_variables = [
    "median_house_value",
    "pct_white",
    "pct_rented",
    "pct_hh_female",
    "pct_bachelor",
    "median_no_rooms",
    "income_gini",
    "median_age",
    "tt_work"
]
f, axs = plt.subplots(nrows=3, ncols=3, figsize=(12,12))
axs = axs.flatten()
for i, col in enumerate(cluster_variables):
    ax = axs[i]
    db.plot(column=col, ax=ax, scheme="Quantiles", linewidth=0, cmap='RdPu')
    ax.set_axis_off()
    ax.set_title(col)
plt.show()
    

import seaborn
_ = seaborn.pairplot(
    db[cluster_variables], kind='reg', diag_kind='kde'
)

db[['income_gini', 'median_house_value']].head()
income_gini median_house_value
0 0.5355 732900.000000
1 0.4265 473800.000000
2 0.4985 930600.000000
3 0.4003 478500.000000
4 0.3196 515570.896382 
from sklearn import metrics
metrics.pairwise_distances(
    db[["income_gini", "median_house_value"]].head()
).round(4)
array([[     0.    , 259100.    , 197700.    , 254400.    , 217329.1036],
       [259100.    ,      0.    , 456800.    ,   4700.    ,  41770.8964],
       [197700.    , 456800.    ,      0.    , 452100.    , 415029.1036],
       [254400.    ,   4700.    , 452100.    ,      0.    ,  37070.8964],
       [217329.1036,  41770.8964, 415029.1036,  37070.8964,      0.    ]])
from sklearn.preprocessing import robust_scale
db_scaled = robust_scale(db[cluster_variables])
cluster_variables
['median_house_value',
 'pct_white',
 'pct_rented',
 'pct_hh_female',
 'pct_bachelor',
 'median_no_rooms',
 'income_gini',
 'median_age',
 'tt_work']
metrics.pairwise_distances(
    db_scaled[:5, [0,6]]).round(4)
array([[0.    , 1.976 , 1.0012, 2.3081, 3.3925],
       [1.976 , 0.    , 2.2057, 0.397 , 1.6273],
       [1.0012, 2.2057, 0.    , 2.4106, 3.2197],
       [2.3081, 0.397 , 2.4106, 0.    , 1.2312],
       [3.3925, 1.6273, 3.2197, 1.2312, 0.    ]])

k-means

from sklearn.cluster import KMeans
kmeans = KMeans(n_clusters=5)
import numpy as np
np.random.seed(1234)
k5cls = kmeans.fit(db_scaled)
k5cls.labels_[:5]
array([2, 1, 3, 1, 4], dtype=int32)
k5cls.labels_.max()
4
k5cls.labels_.min()
0
db['k5cls'] = k5cls.labels_
f, ax = plt.subplots(1, figsize=(9,9))
db.plot(column='k5cls', categorical=True, legend=True, linewidth=0, ax = ax)
ax.set_axis_off()
plt.show()

k5sizes = db.groupby("k5cls").size()
k5sizes
k5cls
0    248
1    244
2     88
3     39
4      9
dtype: int64
areas = db.dissolve(by='k5cls', aggfunc='sum')['area_sqm']
areas
k5cls
0     739.184478
1    8622.481814
2    1335.721492
3     315.428301
4     708.956558
Name: area_sqm, dtype: float64
k5means = db.groupby('k5cls')[cluster_variables].mean()
k5means.round(3)
median_house_value pct_white pct_rented pct_hh_female pct_bachelor median_no_rooms income_gini median_age tt_work
k5cls
0 356997.331 0.620 0.551 0.108 0.023 4.623 0.400 32.783 2238.883
1 538463.934 0.787 0.270 0.114 0.007 5.850 0.397 42.057 2244.320
2 544888.738 0.741 0.596 0.065 0.005 4.153 0.449 32.590 2349.511
3 1292905.256 0.874 0.275 0.109 0.002 6.100 0.488 46.356 1746.410
4 609385.655 0.583 0.377 0.095 0.007 5.800 0.391 33.500 9671.556 
tidy_db = db.set_index('k5cls')
tidy_db  = tidy_db[cluster_variables]
tidy_db = tidy_db.stack()
tidy_db = tidy_db.reset_index()
tidy_db = tidy_db.rename( columns = {"level_1": "Attribute", 0: "Values"}
                        )
tidy_db.head()
k5cls Attribute Values
0 2 median_house_value 732900.000000
1 2 pct_white 0.916988
2 2 pct_rented 0.373913
3 2 pct_hh_female 0.052896
4 2 pct_bachelor 0.000000 
tidy_db.head(20)
k5cls Attribute Values
0 2 median_house_value 732900.000000
1 2 pct_white 0.916988
2 2 pct_rented 0.373913
3 2 pct_hh_female 0.052896
4 2 pct_bachelor 0.000000
5 2 median_no_rooms 3.900000
6 2 income_gini 0.535500
7 2 median_age 37.100000
8 2 tt_work 1299.000000
9 1 median_house_value 473800.000000
10 1 pct_white 0.790558
11 1 pct_rented 0.205144
12 1 pct_hh_female 0.109190
13 1 pct_bachelor 0.004663
14 1 median_no_rooms 6.500000
15 1 income_gini 0.426500
16 1 median_age 41.200000
17 1 tt_work 1970.000000
18 3 median_house_value 930600.000000
19 3 pct_white 0.880250 
seaborn.set(font_scale=1.5)
facets = seaborn.FacetGrid(
    data=tidy_db,
    col='Attribute',
    hue='k5cls',
    sharey=False,
    sharex=False,
    aspect=2,
    col_wrap=3)

_ = facets.map(seaborn.kdeplot, 'Values', fill=True).add_legend()