Smart campus is an emerging concept enabled by digital transformation opportunities in higher education. Smart campuses are often perceived as miniature replicas of smart cities and serve as living labs for smart technology research, development, and adoption, along with their traditional teaching, learning and research functions. There is currently a limited understanding of how the smart campus is conceptualized and practiced. This paper addresses this gap by using a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach. The study uses four major domains of the smart campus, i.e., society, economy, environment, and governance, to classify existing research. These domains are each aligned to the central smart campus concepts of digital technology and big data. The analysis found little evidence of a comprehensive real-world application of the smart campus towards addressing all four domains. This highlights the infancy of the current conceptualization and practice. The findings contribute to the development of a new conceptual foundation and research directions for the smart campus notion and informs its practice through a conceptual framework. The findings reported in this paper offer a firm basis for comprehensive smart campus conceptualization, and also provide directions for future research and development of smart campuses.

Artificial intelligence (AI) has increasingly been integrated into various domains, significantly impacting geospatial applications. Machine learning (ML) and computer vision (CV) are critical in urban decision-making. However, urban AI implementation faces unique challenges. Academic literature on responsible AI largely focuses on general principles, with limited emphasis on the geospatial domain. This important gap in scholarly work could hinder effective AI integration in urban geospatial applications. Our study employs a multi-method approach, including a systematic academic literature review, word frequency analysis and insights from grey literature, to examine potential challenges and propose strategies for effective geospatial AI (GeoAI) integration. We identify a range of responsible practices relevant to the complexities of using AI in urban geospatial planning and its effective implementation. The review provides a comprehensive and actionable framework for responsible AI adoption in the geospatial domain, offering a roadmap for urban researchers and practitioners. It highlights ways to optimise AI benefits while minimising potential negative consequences, contributing to urban sustainability and equity.

The emergence of the smart campus approach for university campuses addresses the digital transformation needs of higher education institutions, driven by the increasing capability and popularity of digital technologies like artificial intelligence and the internet of things. While existing research has laid a foundation for conceptualizing smart campuses and developing implementation frameworks, a significant gap remains in understanding how to assess a university campus’s ‘smartness’ effectively. The lack of a robust assessment framework makes it challenging to gauge the effectiveness of smart campus initiatives and identify areas for improvement. This study aims to bridge this research gap by identifying key indicators for evaluating the ‘smartness’ of university campuses. Using a narrative literature review method, the study comprehensively reviews the recent literature on smart campuses, organizational management, and societal applications, focusing on identifying pertinent indicators. By incorporating insights from different domains, the study presents a holistic understanding of the indicators necessary for assessing the ‘smartness’ of university campuses through the proposed smart campus assessment framework. The framework and the insights generated inform researchers and decision-makers in assessing and monitoring the effectiveness of smart campuses.

Does planning for higher density increase housing development and decrease prices? We study nineteen repeatedly upzoned densification areas in Brisbane, Australia. We create a site-level dataset on dwelling stock, planning regulations, and prices, comprising all 25,775 sites in these areas. Only 2% of the zoned capacity was used in any five-year period. Zoned capacity doubled over the twenty-year study period, but 78% of sites with zoned capacity in the first period remained undeveloped. Higher rates of new housing supply are robustly related to higher prices. Zoned capacity has no relationship to price across numerous regression models.

State and local governments around Australia and the world now routinely promote the idea that dense, interconnected nodes of transit-rich, mixed-use places are indispensable for sustainable and re...