Predicting Urban Happiness: A Comparative Analysis of Deep Learning Models

Abstract

This study explores the efficacy of various deep learning models in predicting urban happiness scores, a critical indicator of the quality of life in urban environments. Recognizing the complex interplay of factors contributing to urban happiness, we employed a suite of models, including Dense Neural Networks (DNN), Long Short-Term Memory networks (LSTM), Convolutional Neural Networks (CNN), Autoencoders, Multi-Layer Perceptron with Dropout (MLP Dropout), and Simple Recurrent Neural Networks (RNN), to analyze a comprehensive dataset encompassing environmental quality, socio-economic factors, and urban infrastructure. Our methodology centered on rigorous data preprocessing to ensure integrity and usability, followed by a detailed comparative analysis of model performances based on Root Mean Squared Error (RMSE) metrics. The results revealed that the CNN model outperformed others in identifying spatial patterns crucial for urban happiness, indicating its superior capability in processing complex urban data. In contrast, the LSTM model showed less accuracy, suggesting a nuanced understanding of temporal data's role in predicting urban happiness. This research not only sheds light on the potential of deep learning in urban studies but also offers valuable insights for urban planners and policymakers aiming to enhance urban living conditions. Through this comparative analysis, our study contributes to the growing discourse on leveraging advanced data analytics for urban planning and opens avenues for future research into the integration of diverse data sources and model hybridization to refine urban happiness predictions.