Exercise: Run a cluster analysis

You will use the skills you’ve learned throughout the course to conduct two analyses where you compute the Moran’s I statistic.

Step 1 Load the Toronto_Neighbourhoods.geojson and toronto_health_data_2017.csv files into python

Step 2 Merge the health data with the spatial data

Step 3 Generate a map of two of the three health variables included in the data set (e.g., pick diabetes and mental health, or mental health and asthma). Make sure to classify the data and make it look nice.

For each of the two columns do the following:

Step 4 Calculate a weights matrix using libpysal (referred to as lps below). Hint The function is lps.weights.Queen.from_dataframe.

Step 5 Use the esda.Moran() function to calculate the moran’s I statistic and plot it.

Step 6 Use the esda.Moran_Local() function to calculate the LISA statistic and plot it.

Step 7 Interpret the global and local Moran’s statistics in a few sentences. What do they tell us about the spatial distribution of the two health outcomes you selected?

import numpy as np
import pandas as pd
import geopandas as gpd
import matplotlib.pyplot as plt
import mapclassify
import esda
import splot
import libpysal as lps
import contextily as cx
from splot.esda import moran_scatterplot
from splot.esda import lisa_cluster

#start your code here - add cells as needed