# Identification of Land Use Mix Using Point-Based Geospatial Data in Urban Areas
###### Abstract  
Identifying land use mix (LUM) in urban areas is challenging, often requiring extensive human intervention and fieldwork. Accurate classification of LUM is crucial for various disciplines, including urban planning, urban economics, and public health. This study addresses this need by employing Voronoi triangulation and an entropy-based LUM formula using point-based geospatial data collected from publicly available sources. The methodology was tested in two distinct urban settings: Ankara and Kadikov. Ankara, the capital city, provides a large and diverse urban environment, while Kadikov, a district in Instanbul known for its dynamic urban life, offers a contrasting scenario. Results were analyzed concerning local spatial autocorrelation and point of interest (POI) intensity. The comparative analysis demonstrated that the approach performs well across different urban contexts, with improved results observed in Kadikov due to its higher density of mixed-use development. Specifically, we managed to identify mixed land use areas with an accuracy of up to 78% and an F1-score of 83% in urban regions. These findings highlight the robustness and applicability of our approach in diverse urban environments, providing valuable insights for city planners and policymakers in optimizing the allocation of urban resources and enhancing land use efficiency.  
land use mix; point of interest data; Voronoi triangulation; local spatial autocorrelation; geospatial data mining +
Footnote †: journal:  
* How can point-based geospatial data be used to model the LUM in urban areas?
* How effective is the combination of Voronoi triangulation and an entropy-based LUM formula in identifying and classifying mixed land use areas?
* What are the optimal POI density ranges that contribute significantly to the accuracy of LUM classification in urban areas?  
Even though there are different methods to model LUM in the literature [4; 5; 6; 7], to the best of our knowledge, identifying LUM based on POI data and Voronoi triangulation has not been investigated before in the literature. Traditional approaches have utilized travel behavior and urban form data to model LUM [4; 5]. In another study, they have also contributed to this field by providing a grid-based approach by utilizing a Fishnet model to analyze land use patterns [6].  
However, the innovative use of POI data combined with Voronoi triangulation to identify LUM represents a novel approach that has not been extensively explored in the literature. While traditional methods of LUM modeling have relied heavily on census data and existing land use information, the integration of POI data with Voronoi triangulation presents a promising new direction in urban planning and spatial analysis.  
By integrating POI-based data with Voronoi triangulation, we aim to automate the identification of LUM, thus eliminating the need for extensive fieldwork and minimizing human intervention. This method promises to be more adaptable and capable of reflecting real-time changes in urban land use, thereby providing urban planners with a robust tool for city planning and policy-making.