mkupisie/Clustering-geodemographic_classification_of_NYC_using_K-means_geopandas_sklearn

Clustering: geodemographic classification of NYC using K-means algorithm

Many questions related to spatial observations are complex phenomena that involves several dimensions, what make it hard to summarize them into a single variable. It is especially visible when trying to map distribution of people taking into account e.g. their nationality, education level, age etc. It's uncommon for a geographic region to be exclusively populated by individuals of identical heritage, particularly in the context of urban areas.

Clustering can be used to reduce the dimensionality - the number of variables the analyst needs to look at - and converting it into a more intuitive set of classes.
The fundamental concept behind statistical clustering is to condense the information from multiple variables into a relatively small number of categories. Subsequently, each entry in the dataset is assigned exclusively to one category, based on its values for the initially considered variables.**

K-means is one of the most popular clustering algorithm and it can be run in python using sklearn.cluster module in scikit-learn (a popular machine learning library in Python).

Data

For the purpose of classification the data available in pysal library in examples package were used (https://pysal.org/notebooks/lib/libpysal/Example_Datasets.html).

NYC Socio-Demographics data contains the information of total population of the following groups:

• european: Total Population White
• asian: Total Population Asian American
• american: Total Population American Indian
• african: Total Population African American
• hispanic: Total Population Hispanic
• mixed: Total Population Mixed race
• pacific: Total Population Pacific Islander

Results

1.1. Classification on the map

1.2. The mean of total population for each ethnic group within the class

Based on the results above it can be noticed that within each group there is a strong majority of one, two and sometimes three ethnic groups:

• class 0: hispanic and african
• class 1: european
• class 2: african
• class 3: hispanic and european
• class 4: european and asian
• class 5: asian
• class 6: african, european and hispanic
• class 7: no population
• class 8: european
• class 9: african and hispanic