Jiaxin Du

Resilience, denoting the capacity to swiftly recover to a state of normalcy subsequent to the occurrence of a disaster, constitutes a multifaceted phenomenon necessitating in-depth investigation. This study undertakes the quantification of resilience pertaining to specific locales through the utilization of heterogeneous data encompassing visitation patterns, demographic particulars, and points of interest (POI). A heterogeneous graph neural network is applied to model the resilience of these locales in Galveston, TX, USA. Our model outperforms regression models and other homogeneous baseline methodologies. Subsequent analysis unveils discernible resilience patterns intertwined with metrics such as visitation frequencies, visitors’ travel behaviors, and geographical attributes. In comparison to resilience investigations solely predicated upon visitation counts, our approach captures a more extensive array of information, thereby yielding a comprehensive understanding of the locale’s resilience.

Rapid urbanization, climate change, and aging infrastructure pose significant challenges to achieving sustainability and resilience goals in urban building energy use. Although retrofitting offers a viable solution to mitigate building energy use, there has been limited analysis of its effects under various weather conditions associated with climate change in urban building energy use simulations. Moreover, certain parameters in energy simulations necessitate extensive auditing or survey work, which is often impractical. This research proposes a framework that integrates various datasets, including building footprints, Lidar data, property appraisals, and street view images, to conduct neighborhood-scale building energy use analysis using the Urban Modeling Interface (UMI), an Urban Building Energy Model (UBEM), in a coastal neighborhood in Galveston, Texas. Seven retrofit plans and three weather conditions are considered in the scenarios of building energy use. The results show that decreasing the U-value of building envelopes helps reduce energy use, while increasing the U-value leads to higher energy consumption in the Galveston neighborhood. This finding provides direction for coastal Texas cities, like Galveston, to update building standards and implement retrofit measures.

As climate change intensifies, resulting in more severe rainfall events, coastal cities globally are witnessing significant life and property losses. A growingly crucial component for flood prevention and relief are urban storm flood simulations, which aid in informed decision-making for emergency management. The vastness of data and the intricacies of 3D computations can make visualizing the urban flood effects on infrastructure daunting. This study offers a 3D visualization of the repercussions of hurricane storm surge flooding on Galveston, TX residences, illustrating the impact on each structure and road across varied storm conditions. We employ target detection to pinpoint house door locations, using door inundation as a metric to gauge potential flood damage. Within a GIS-based framework, we model the damage scope for residences exposed to varying storm intensities. Our research achieves three core goals: 1) Estimating the storm inundation levels on homes across different storm conditions; 2) Assessing first-floor elevations to categorize housing damages into three distinct groups; and 3) Through visualization, showcasing the efficacy of a proposed dike designed to shield Galveston Island from future storm surge and flood events.

It appears that the paper lacks meaningful contributions to both public health and geographical information systems (GIS). Additionally, the absence of empirical results is a notable limitation. To address these issues, consider the following revisi...

This study proposes a prototype for the smart rendering of urban master plans via artificial intelligence algorithms, a process which is time-consuming and relies on professionals’ experience. With the help of crowdsourced data and generative adversarial networks (GAN), a generation model was trained to provide colorful rendering of master plans similar to those produced by experienced urban designers. Approximately 5000 master plans from Pinterest were processed and CycleGAN was applied as the core algorithm to build this model, the so-called MasterplanGAN. Using the uncolored input design files in an AutoCAD format, the MasterplanGAN can provide master plan renderings within a few seconds. The validation of the generated results was achieved using quantitative and qualitative judgments. The achievements of this study contribute to the development of automatic generation of previously subjective and experience-oriented processes, which can serve as a useful tool for urban designers and planners to save time in real projects. It also contributes to push the methodological boundaries of urban design by addressing urban design requirements with new urban data and new techniques. This initial exploration indicates that a large but clear picture of computational urban design can be presented, integrating scientific thinking, design, and computer techniques.