Approaches to urban heritage governance increasingly prioritise material form and quantifiable metrics, yet how measurable environmental quality relates to the public's evaluative-affective experience of heritage remains poorly understood. This study develops a perception-sensitive diagnostic framework that integrates deep learning on street-view imagery with Multiscale Geographically Weighted Regression to examine the spatial congruence and divergence between objective environmental indicators and computationally inferred perceptual responses. Applied to the Xi'an City Wall, the analysis reveals pronounced spatial heterogeneity and identifies two diagnostic zone types: resonance zones, where positive perceptual responses persist despite modest environmental quality, and discordance zones, where high environmental quality fails to elicit correspondingly positive scores. MGWR results show that the influence of factors such as road density and green-space coverage varies substantially in direction and magnitude across the study area, confirming that globally uniform assessment assumptions obscure locally important dynamics. By rendering these patterns visible, the framework contributes a scalable diagnostic layer to the perceptual mapping called for by the Historic Urban Landscape approach and provides empirical grounding for the argument that technocratic assessment may systematically differ from resident lived experience, supporting more perception-sensitive and spatially targeted heritage management.
Temporary traffic management (TTM) is transitioning from prescriptive standards to risk-informed approaches in New Zealand and other jurisdictions. Whether practices adopted under the 'risk-based' label align with established risk analysis and uncertainty characterisation literature has not been evaluated. This study addresses that gap through a systems-level documentary analysis. Regulatory, guidance, training, procurement, and academic documents are coded across a five-stage decision cycle and mapped against two benchmarks: Pat & eacute;-Cornell's uncertainty treatment levels and Aven's risk description framework (A ', C ', Q, K). The resulting maturity profile places New Zealand's TTM system at Levels 1-2 across all five stages. Hazards are identified from generic lists, consequences described with fixed categories, no explicit uncertainty measure accompanies any stage, and background knowledge is disconnected from risk descriptions. The system uses the language of risk-informed decision-making without producing the risk descriptions the benchmarks require. Improvement requires explicit risk descriptions proportionate to site exposure, complexity, and consequence - not necessarily advanced probabilistic methods. The benchmarking method is replicable and can be applied by other jurisdictions to their own TTM systems.
Infrastructures are sociotechnical systems embodying societal structures. In states characterised by colonial settlement, Indigenous peoples have often not benefited from infrastructure development. Here we show how historical and modern coastal transport infrastructure development in Aotearoa New Zealand impacted Indigenous landscapes of a M & amacr;ori subtribe in the Kaik & omacr;ura region. Early twentieth century road and rail development, operationalised through legislative mechanisms, disturbed landscape features and sites of spiritual and historical importance, reflecting a general disregard in wider New Zealand society for M & amacr;ori rights, concerns and values. Despite improvements in relationships between the state and M & amacr;ori since the late twentieth century, encapsulated in environmental and cultural heritage legislation, legislative responses and infrastructure reinstatement following the 2016 Hurunui/Kaik & omacr;ura Earthquake again led to the disturbance of Indigenous landscapes; inadequate community engagement and oversight resulted in further modification and destruction of cultural sites. This case study illustrates how engineering practice and infrastructure development are mediated by state power and legislation. While the engineering profession is now better integrating M & amacr;ori rights and concerns, and taking more responsibility for informed decision-making in ethically complex situations, how this may be operationalised after future disaster events for infrastructure recovery while minimising avoidable disturbance to Indigenous landscapes is still an open question.
