Point pattern analysis

When you need to deal with point data, you will likely be interested in the spatial patterns they form. For that, you can use point pattern analysis techniques. This session will walk you through some basic ways of approaching the analysis based primarily on geometries and their locations rather than variables associated with them.

import contextily
import geopandas as gpd
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import pointpats
import seaborn as sns
from matplotlib import patches
from sklearn import cluster

/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/pointpats/pointpattern.py:277: SyntaxWarning: invalid escape sequence '\m'
  """
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/pointpats/process.py:159: SyntaxWarning: invalid escape sequence '\l'
  """
/home/runner/work/sds/sds/.pixi/envs/default/lib/python3.12/site-packages/pointpats/process.py:296: SyntaxWarning: invalid escape sequence '\l'
  """

Data

In this session, you will be using data on pedestrian accidents in Brno that happened since 2010. Every accident is marked with point geometry and assigned a range of relevant variables you are free to explore by yourself. The dataset is released by Brno municipality under CC-BY 4.0 license. It has been preprocessed for the purpose of this course. If you want to see how the table was created, a notebook is available here.

As always, you can read data from a file posted online, so you do not need to download any dataset:

accidents = gpd.read_file(
    "https://martinfleischmann.net/sds/point_patterns/data/brno_pedestrian_accidents.gpkg"
)
1accidents.explore("rok", tiles="CartoDB Positron", cmap="magma_r")

1

"rok" is a column with the year in which an accident occurred. The variables are in Czech.

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