Exploratory (Spatial) Data Analysis

Jon Reades - j.reades@ucl.ac.uk

1st October 2025

For this one, you really need to read the docs.

Getting Spatial (with Boroughs)

import geopandas as gpd
url = 'https://bit.ly/3neINBV'
boros = gpd.read_file(url, driver='GPKG')
boros.plot(color='none', edgecolor='red', figsize=(8,4.5));

Convex Hull

boros['hulls'] = boros.geometry.convex_hull
boros.set_geometry('hulls').plot(column='NAME', categorical=True, alpha=0.5, figsize=(8,4.5));

Dissolve

boros['region'] = 'London'
ldn   = boros.dissolve(by='region') 
ax = ldn.plot(figsize=(8,4.5)) 
boros.plot(ax=ax, color='none', edgecolor='red')

Buffer & Simplify

ldn.buffer(1000).simplify(1000).plot(figsize=(8,4.5))

Difference

And some nice chaining…

ldn.buffer(3000).simplify(2500).difference(ldn.geometry).plot(figsize=(8,4.5))

Legendgrams

Implementing Legendgrams

import pysal as ps
# https://github.com/pysal/mapclassify
import mapclassify as mc
# https://jiffyclub.github.io/palettable/
import palettable.matplotlib as palmpl
from legendgram import legendgram

f,ax = plt.subplots(figsize=(10,8))
gdf.plot(column='price', scheme='Quantiles', cmap='magma', k=5, ax=ax)
q = mc.Quantiles(gdf.price.array, k=5)

# https://github.com/pysal/legendgram/blob/master/legendgram/legendgram.py
legendgram(f, ax, 
               gdf.price, q.bins, pal=palmpl.Magma_5,
               legend_size=(.4,.2), # legend size in fractions of the axis
               loc = 'upper left', # mpl-style legend loc
               clip = (0,500), # clip range of the histogram
               frameon=True)

KNN Weights

Implementing KNN

from pysal.lib import weights
w = weights.KNN.from_dataframe(gdf, k=3)
gdf['w_price'] = weights.lag_spatial(w, gdf.price)
gdf[['name','price','w_price']].sample(5, random_state=42)
name price w_price
83 Southfields Home 85.0 263.0
53 Flat in Islington, Central London 55.0 190.0
70 3bedroom Family Home minutes from Kensington Tube 221.0 470.0
453 Bed, 20 min to Liverpool st, EAST LONDON 110.0 186.0
44 Avni Kensington Hotel 430.0 821.0

Spatial Lag of Distance Band

Implementing Lags

w2 = weights.DistanceBand.from_dataframe(gdf, threshold=2000, alpha=-0.25)
gdf['price_std'] = (gdf.price - gdf.price.mean()) / gdf.price.std()
gdf['w_price_std'] = weights.lag_spatial(w2, gdf.price_std)
gdf[['name','price_std','w_price_std']].sample(5, random_state=42)
name price_std w_price_std
83 Southfields Home -0.27 0.00
53 Flat in Islington, Central London -0.51 -0.58
70 3bedroom Family Home minutes from Kensington Tube 0.83 0.46
453 Bed, 20 min to Liverpool st, EAST LONDON -0.07 -0.82
44 Avni Kensington Hotel 2.52 3.25

Moran’s I

mi = esda.Moran(gdf['price'], w)
print(f"{mi.I:0.4f}")
print(f"{mi.p_sim:0.4f}")
moran_scatterplot(mi)

Local Moran’s I

Implementing Local Moran’s I

lisa = esda.Moran_Local(gdf.price, w)
# Break observations into significant or not
gdf['sig'] = lisa.p_sim < 0.05
# Store the quadrant they belong to
gdf['quad'] = lisa.q
gdf[['name','price','sig','quad']].sample(5, random_state=42)
name price sig quad
83 Southfields Home 85.0 False 3
53 Flat in Islington, Central London 55.0 False 3
70 3bedroom Family Home minutes from Kensington Tube 221.0 False 1
453 Bed, 20 min to Liverpool st, EAST LONDON 110.0 False 3
44 Avni Kensington Hotel 430.0 False 1

Full LISA

plot_local_autocorrelation(lisa, gdf, 'price')

Additional Resources

There’s so much more to find, but:

Thank You

References