Exploring Urban Land Use Patterns by Pattern Mining and Unsupervised Learning

TL;DR

This paper introduces a new scalable methodology combining pattern mining and unsupervised learning to analyze urban land use patterns, utilizing Copernicus Urban Atlas data.

Contribution

It presents a novel framework that identifies similar cities based on co-occurring land use patterns using frequent item set mining and unsupervised learning.

Findings

Transaction dataset created from spatial data

Framework is scalable and publicly available code

Identifies similar cities based on land use patterns

Abstract

Urban areas are intricate systems shaped by socioeconomic, environmental, and infrastructural factors, with land use patterns serving as aspects of urban morphology. This paper proposes a novel methodology leveraging frequent item set mining and unsupervised learning techniques to identify similar cities based on co-occurring land use patterns. The Copernicus program's Urban Atlas data are used as source data. The methodology involves data preprocessing, pattern mining using the negFIN algorithm, postprocessing, and knowledge extraction and visualization. The preprocessing of spatial datasets results in a publicly available transaction dataset. The framework is scalable and the source code is made publicly available.