Optimizing Land Use Identification With Social Networks: Comparative Evaluation of Machine Learning Algorithms

This paper presents a comprehensive comparison of various Machine Learning (ML) classifiers for urban land use identification using social networks data. Two analysis cycles were conducted, with the second cycle introducing the "popularity index" parameter. The results demonstrate that incorporating the popularity index significantly improved the accuracy rates of all classifiers. The Convolutional Neural Network (CNN) consistently outperformed other classifiers with a 0.9 accuracy, but the key highlight was the popularity index parameter, which optimized urban land use identification. By providing crucial contextual information and normalizing raw tweet counts, the popularity index proved vital for leveraging social media data in urban land use classification. Our findings support the value of social media data for analyzing urban land use dynamics and highlight ML classifiers with the popularity index as a promising approach to monitoring and understanding ever-changing patterns. This research makes a significant contribution to data-driven urban planning by highlighting the capacity of social media data to provide real-time and detailed insights into urban activities and land use patterns. These insights, in turn, can inform and enhance strategies for sustainable urban development and resource allocation, making them more informed and effective.