Matt Duckham

1. Geographic names—defined as a Global Fundamental Geospatial Data Theme1 by the United Nations—are foundational data: any derived property is a spatial axiom. Hence, knowledge discovery related t...

Composite spatial heat vulnerability indices (HVI) provide a practical means of translating complex environmental and socio-economic conditions into spatially explicit evidence for heat mitigation planning. The resulting vulnerability classifications depend on methodological choices for weighting indicators and defining spatial units, yet the extent to which these choices alter spatial interpretation remains poorly understood. Therefore, this study systematically evaluates the sensitivity of a HVI framework to three objective weighting techniques of equal weighting (EQW), entropy weighting (ENW), and principal component analysis (PCA), across two spatial composition frameworks of administrative boundaries (AB) and uniform grids (UG). The framework was applied to three Australian cities diverse in urban area, population, and climate for comparative analysis. Results show substantial methodological disagreement in heat vulnerability classification, with disagreement between weighting techniques ranging from 20.98% to 46.05% under AB composition and from 46.19% to 66.64% under UG composition, with moderate-vulnerability classes generally more sensitive than the extremes. Rare but consequential four-class reversals were identified in Adelaide and Newcastle, with all cases involving PCA. Indicator-level decomposition showed that these reversals were associated with extreme indicator values combined with either opposing weighting directions or large differences in weighting magnitude. These findings demonstrate that HVI classifications are inherently conditional on methodological design choices, such that different methodological configurations can identify different areas for prioritisation of heat mitigation resources and interventions. In the absence of a definitive weighting technique or spatial framework, multi-weighting and complementary spatial frameworks can be used to identify areas of classification consensus and methodological uncertainty. Explicitly representing this uncertainty can improve the transparency and defensibility of HVI-based spatial assessment for more informed decision-making.

Who is the Batman behind “Batman Street” in Melbourne? Understanding the historical, cultural, and societal narratives behind place names can reveal the rich context that has shaped a community. Although place names serve as essential spatial references in gazetteers, they often lack information about place name origins. Enriching these place names in today’s gazetteers is a time-consuming, manual process that requires extensive exploration of a vast archive of documents and text sources. Recent advances in natural language processing and language models (LMs) hold the promise of significant automation of identifying place name origins due to their powerful capability to exploit the semantics of the stored documents. This chapter presents a retrieval augmented generation pipeline designed to search for place name origins over a broad knowledge base, DBpedia. Given a spatial query, our approach first extracts sub-graphs that may contain knowledge relevant to the query; then ranks the extracted sub-graphs to generate the final answer to the query using fine-tuned LM-based models (i.e., ColBERTv2 and Llama2). Our results highlight the key challenges facing automated retrieval of place name origins, especially the tendency of language models to under-use the spatial information contained in texts as a discriminating factor. Our approach also frames the wider implications for geographic information retrieval using retrieval augmented generation.

Overhead road obstacles present a significant logistical hazard to the heavy vehicle industry. Traditional overheight vehicle warning systems such as passive warning systems (PWS) and active warning systems (AWS) have not adequately reduced the frequency and impact of overheight incidents, encouraging transportation agencies to employ intelligent transport system (ITS) strategies using state-of-the-art advanced technologies. This research takes an innovative approach in developing an immersive user-focused experience, harnessing multi-disciplinary methods and tools to engineer a spatial digital twin prototype for a novel Internet-of-Things (IoT)-based active warning alert and re-routing system (AWARS). LiDAR and 3D GIS were used to model the complex road environment, tailored to the strict fiscal objectives sought by economically mindful organisations. Tree crowns were extracted from near-Infrared aerial imagery and digital elevation models, supplying the dimensions necessary for 3D tree modelling. IoT connectivity was configured using a real-time analytics approach to deliver alerts and re-routing options. The World Traffic Service with live and predictive traffic data was used for the routing application programming interface (API). A standard-configuration common rigid truck (CRT) was inserted into the 3D road environment model to simulate overheight collisions and to ascertain the effect of re-routing on estimated time of arrival (ETA). Longer ETA durations were observed for routes computed by the digital twin. Theoretically, enhanced situational awareness and subsequent reduction of risk likelihood suggests an optimised response to industry demands, despite extended travel times, cultivating a favorable impact on the supply chain through enhanced safety management.