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Abstract Understanding the relationship between urban form and structure and spatial inequality of property flood risk has been a longstanding challenge in urban planning and emergency management. Here we explore eight urban form and structure features to explain variability in spatial inequality of property flood risk among 2567 US counties. Using datasets related to human mobility and facility distribution, we identify notable variation in spatial inequality of property flood risk, particularly in coastline and metropolitan counties. The results reveal variations in spatial inequality of property flood risk can be explained based on principal components of development density, economic activity, and centrality and segregation. The classification and regression tree model further demonstrates how these principal components interact and form pathways that explain spatial inequality of property flood risk. The findings underscore the critical role of urban planning in mitigating flood risk inequality, offering valuable insights for crafting integrated strategies as urbanization progresses. 

In this paper, we present a conformal prediction (CP) based method to evaluate the performance of a finger-printing localization system through uncertainty quantification. The proposed method emphasizes a standalone module that is compatible with any well-trained fingerprint classifier without incurring extra training costs. It provides rigorous statistical guarantees for revealing true labels in the fingerprinting multi-class classification problems with high efficiency. Uncertainty quantification of the predictions is accomplished by leveraging a small calibration dataset and a given error tolerance level. Three specific metrics are introduced to quantify the uncertainty of the CP-based method from the perspective of efficiency, adaptivity, and accuracy, respectively. The proposed method allows developers to track the model state with minimal effort and evaluate the reliability of their model and measurements, such as in a dynamic environment. The proposed technique, therefore, prevents the intrinsic label inaccuracy and the additional labor cost of ground truth collection. We evaluate the proposed method and metrics in two representative indoor environments using vanilla fingerprint-based localization models with extensive experiments. Our experimental results show that the proposed method can successfully quantify the uncertainty of predictions. 

In this work, we integrate digital twin technology with RFID localization to achieve real-time monitoring of physical items in a large-scale complex environment, such as warehouses and retail stores. To map the item-level realities into a digital environment, we proposed a sensor fusion technique that merges a 3D map created by RGB-D and tracking cameras with real-time RFID tag location estimation derived from our novel Bayesian filter approach. Unlike mainstream localization methods, which rely on phase or RSSI measurements, our proposed method leverages a fixed RF transmission power model. This approach extends localization capabilities to all existing RFID devices, offering a significant advancement over conventional techniques. As a result, the proposed method transforms any RFID device into a digital twin scanner with the support of RGB-D cameras. To evaluate the performance of the proposed method, we prototype the system with commercial off-the-shelf (COTS) equipment in two representative retail scenarios. The overall performance of the system is demonstrated in a mock retail apparel store covering an area of 207 m2, while the quantitative experimental results are examined in a small-scale testbed to showcase the accuracy of item-level tag localization.