Advanced Land Use Mix Analysis in Urban Areas Using Point-Based Data: Methods and Applications

Date

2024-9-03

Author

Akyol, Mehmet Ali

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This thesis investigates the role of Land Use Mix (LUM) analysis in urban planning, Geographic Information Systems (GIS) research, and disaster risk assessment, offering novel methodologies and tools to address existing challenges in these areas. Traditional methods for assessing LUM often fall short in terms of scalability, adaptability, and the ability to capture the dynamic nature of urban environments. This research overcomes these limitations by introducing advanced, automated approaches to LUM assessment, leveraging publicly available point-based geospatial data. One of the key contributions of this thesis is the development of an open-source Python package, landusemix, which facilitates the calculation of LUM using established indices such as the Entropy Index and the Herfindahl-Hirschman Index. This package is designed to be user-friendly, scalable, and adaptable, making it a valuable resource for researchers and practitioners in urban planning and GIS. Furthermore, the thesis extends the application of LUM analysis to assess temporal variations in urban vulnerability, particularly in the context of earthquake risk. By integrating LUM with temporal population dynamics and land usability assessments, the study provides new insights into how urban vulnerability shifts over time, emphasizing the importance of time-sensitive strategies in urban planning and disaster preparedness. Overall, this research advances the understanding and application of LUM in urban studies, offering practical tools and methodologies that enhance the ability to analyze, plan, and respond to the complexities of urban environments. The findings contribute to the broader goal of promoting sustainable, resilient, and livable cities.

Subject Keywords

Land use mix, Geographic information systems, Geospatial data mining, Mixed-use development, Spatial analysis

URI

https://hdl.handle.net/11511/111295

Collections

Graduate School of Informatics, Thesis