Systematic Review ULBS Research

This dataset supports the review article titled "Advancing Methodological Rigor in Urban Landscape and Behavioral Science: A Bayesian and AI-Driven Evaluation of Modern Research Frameworks." The review examines advancements in Urban Landscape and Behavioral Science (ULBS), focusing on integrating data-driven methodologies with advanced techniques such as Agent-Based Modeling, Machine Learning, and Big Data Analytics.

The systematic review analyzes 212 pivotal articles published between 2010 and February 2024, sourced from databases including Scopus, Science Direct, and Taylor and Francis. This comprehensive analysis identifies emerging spatiotemporal trends, methodological advancements, and significant research gaps within ULBS.

Key findings from the review highlight the transformative impact of Internet of Things (IoT) devices and real-time analytics on urban landscape behavior studies. Additionally, the research introduces innovative probabilistic and optimization frameworks for evaluating methodological suitability. These frameworks enhance traditional Bayesian models by incorporating weighted priors, information gain functions, and multi-objective optimization, allowing for adaptive method selection based on varying data characteristics and research objectives.

Included in this dataset are bibliographic details of the reviewed articles, along with supplementary materials such as figures and tables that visualize the findings and highlight key insights. This dataset is intended to facilitate further research and encourage collaboration among scholars and practitioners in the fields of architecture, urban planning, and behavioral science.

You are free to share, copy, and modify this dataset under the terms of the Creative Commons Attribution (CC BY) license, as long as appropriate credit is given. You must provide a link to the CC BY license and indicate if changes were made. However, please refrain from using this dataset in a manner that suggests endorsement by the rights holder.

For any content within this dataset that is identified as belonging to a third party, further permission may be required. By utilizing this dataset, you agree to comply with these terms and to respect the rights of the original authors and content creators.