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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. [ABSTRACT FROM AUTHOR]

People's perceptions of Autonomous Vehicles (AVs) are critical to understanding the role of AVs in future transportation systems. Most previous work on AVs perceptions is based on large cities or metropolitan areas. This study provides a unique perspective regarding perceptions of impacts of AVs in small and rural communities through an online survey in Central Texas (n = 1153). Our questionnaires gathered basic socio-demographic characteristics and AV impacts variables identified from the literature. We used summary statistics and ordered logistic regression models to reveal the perceived impacts of AVs. Residents of small and rural communities, particularly older adults (65 + years), were more enthusiastic about the development of AVs than the national average. Our findings reveal that being an employed, married male with a higher income increases the likelihood of accepting the impacts of AVs, suggesting further research to explore a feasible approach to utilizing AVs in small, rural communities. [ABSTRACT FROM AUTHOR]

The winter storm Uri that occurred in February 2021 affected many regions in Canada, the United States, and Mexico. The State of Texas was severely impacted due to the failure in the electricity supply infrastructure compounded by its limited connectivity to other grid systems in the United States. The georeferenced estimation of the storm's impact is crucial for response and recovery. However, such information was not available until several months afterward, mainly due to the time-consuming and costly assessment processes. The latency to provide timely information particularly impacted people in the economically disadvantaged communities, who lack resources to ameliorate the impact of the storm. This work explores the potential for disaster impact estimation based on the analysis of instant social media content, which can provide actionable information to assist first responders, volunteers, governments, and the general public. In our prototype, a deep neural network (DNN) uses geolocated social media content (texts, images, and videos) to provide monetary assessments of the damage at zip code level caused by Uri, achieving up to 70% accuracy. In addition, the performance analysis across geographical regions shows that the fully trained model is able to estimate the damage for economically disadvantaged regions, such as West Texas. Our methods have the potential to promote social equity by guiding the deployment or recovery resources to the regions where it is needed based on damage assessment. [ABSTRACT FROM AUTHOR]

Coastal communities are increasingly vulnerable due to sea level rise and population growth. Managed retreat is commonly recognized as a strategy that yields multifaced benefits in community adaptation. However, limited studies have explored the cumulative effects of sea level rise, population migration, and managed retreat on the community resilience. This study presents a parcel-level land use change model to analysis land-based flood mitigation strategies in Galveston County, Texas. The developed model integrates a Gradient Boosting Decision Tree with a flood risk model and diverse datasets. Our model results reveal the spatial patterns of urban development in Galveston under different relocation policies and the compounding impacts of sea level rise and population growth. Our findings illustrate that elevating the first floors of buildings can significantly mitigate flood risks and associated relocation costs. The private adaptation measure, together with government-led buyout policies, could foster a shift toward more resilient urban development and yield a more affordable relocation strategy. Our findings emphasize the need for a multidisciplinary approach in building resilient coastal communities, particularly in the face of escalating climate risks in local communities. • We developed a parcel-level land use change model to simulate managed retreat in Galveston, TX. • Sea level rise and population change trends are projected from historical data. • Results show the compounding impacts of sea level rise and population growth under relocation policies. • Our findings illustrate that elevating the first floors of buildings can significantly reduce flood risks and associated relocation costs. [ABSTRACT FROM AUTHOR]

• Smartphone data was used to study park visitations for mid-sized cities. • Temporal and spatial patterns of park visitations were presented. • A fixed effects regression model was built with confounding factors controlled. • Water bodies and playgrounds are significant factors for park visitation. • Implications on urban planning and park management practices were discussed. City parks provide essential services for the physical activities and the health improvement of residents. Most related literature focuses on the factors affecting park visitations in large cities, but relatively little is known regarding long-term park visitations, especially in mid-sized cities. By using smartphone mobility data tracking the activities of 28 parks in the College Station and Bryan Metropolitan area of Texas, USA, we present the temporal and spatial patterns of park usage within a two-year timeframe. We model the effects of the socio-economic, built environment, climate, and spatial/accessibility factors on park visitations through a fixed effects regression. Results show variations among park service areas. Water bodies and playgrounds are significant factors enhancing park visitations while walking paths, sports facilities, and pavilions are not. Contrary to previous research, high-income block groups show lower participation in park activities. The study also reveals how smartphone mobility data can be applied to case studies investigating urban design/planning and understanding of the social aspects associated with urban greenspaces. It provides empirical evidence on park visitations as well as what factors future planners, landscape architects, and park managers should consider when deciding on park investment and planning decisions for mid-sized cities. [ABSTRACT FROM AUTHOR]

• This study examines the relationships between perceived and observed risk locations. • Perceived risk locations are not always associated with high crash rates. • Some personal and built environment factors can significantly affect crash perception. • The relationship between perceived and observed risks can be modeled. The number of studies investigating the relationship between perceived and objective traffic risk from drivers' perspective is limited. This study aims to investigate this dynamic within an understudied transportation environment – small towns in Texas, USA, defined as incorporated places with a population of less than 50,000. A web-based survey was distributed to six small towns in central Texas to ascertain perceptual traffic risk factors and personal characteristics. A participatory GIS exercise was also conducted to collect where high-risk locations were perceived and to correlate them to high crash zones. This study spatially examined the relations between perceived and observed risk locations and statistically identified a set of contributing factors which could make crash-intensive areas more perceivable by road users. The results indicated that road users' perceived risk locations are not always associated with high crash rates. The match rate between perceived and observed risk locations varied significantly across studied sites. We found that some personal and built environment factors significantly impacted people's sensitivity to perceiving crash-intensive locations. The binary logistic regression model was accurate (74.13%) in highlighting whether a perceived risk location matches observed risk locations. The results emphasize the importance of considering perceived and objective risk simultaneously to gain a better understanding of traffic risk mitigation, especially in underserved small towns. [ABSTRACT FROM AUTHOR]