Zipeng Guo

Climate change intensifies natural disasters, requiring enhanced understanding of urban resilience mechanisms. While green infrastructure’s disaster mitigation role has been established through engineering studies, empirical evidence linking green infrastructure quality to post-disaster economic adaptation remains limited. This study examines whether community-level green infrastructure quality correlates with post-disaster economic adaptation following Hurricane Laura’s August 2020 landfall. [Methods] Using a natural experiment design, we analyzed 247 Census Block Groups in two coastal Texas communities (Galveston and Port Arthur) experiencing differential disaster severity. We employed ordinary least squares regression with SafeGraph foot traffic data to measure economic recovery and satellite-derived Normalized Difference Vegetation Index (NDVI) to measure green infrastructure quality. Results demonstrate that green infrastructure quality significantly correlates with post-disaster adaptation (β = 1.27, p < 0.001), independent of socioeconomic characteristics. The NDVI–severity interaction proved non-significant, indicating consistent associations across impact contexts. These findings suggest that green infrastructure supports resilience universally rather than only in moderate-risk areas. From an environmental justice perspective, equitable distribution may reduce disaster-related inequalities, supporting “bouncing forward” adaptation trajectories.

Urban parks provide critical benefits for public health, mental well-being, and social connection. However, inequities in park access and use persist, particularly among socially and economically vulnerable populations. While previous studies have established that segregation and social vulnerability each contribute to uneven park access, little is known about how these two forces interact to shape real visitation patterns. This study addresses this research gap and answers the research question: How does highway segregation relate to differences in the different aspects of social vulnerability in influencing park access across Austin’s east–west divide? SafeGraph mobility data from 2019 and the Social Vulnerability Index (SVI), which included four themes (i.e., socioeconomic status, household composition, minority status and language, and housing and transportation characteristics), were analyzed through fixed-effects regression models for Austin, Texas. Results show that household composition and minority vulnerabilities have negative associations with park visitation, indicating that areas with more elderly, single-parent, or minority residents visit parks less frequently. Interaction terms reveal that highway segregation functions as a structural barrier that conditions the influence of social vulnerability on park use. Those associated with socioeconomic resources diminish, while the disadvantages linked to household composition and minority status intensify on the east side of I-35, reflecting the cumulative effects of segregation and infrastructural division. These findings confirm that inequities in park access are more pronounced on the east side of the I-35, consistent with the highway’s role in reinforcing segregation. Efforts to strengthen connectivity represent key strategies for advancing equitable park visitation across Austin.

Urban safety is a critical concern for sustainable city development, with crime patterns often linked to localized environmental factors. Understanding the spatial dynamics of safety is critical for informed design and planning of urban environments. This study employs a Geographically Weighted Regression (GWR) approach to investigate how crime in Austin, Texas, correlates with Points of Interest (POIs) such as bars, transit stations, financial businesses, and public spaces, while accounting for localized socio-economic factors. Building on theoretical frameworks like Routine Activity Theory and Crime Pattern Theory, the analysis integrates crime data from the Austin Police Department (APD), POI datasets, and census variables to explore spatially varying relationships often overlooked by traditional global models (e.g., OLS). A novel adaptive geo-grid method refines spatial units by clustering high-density downtown areas into smaller zones and retaining larger grids in suburban regions, ensuring precision without over-fragmentation. Analysis of crime incidents and POI data reveals significant spatial non-stationarity in crime–environment associations. Transportation-related facilities demonstrate strong spatial correlation with crime citywide, particularly forming persistent crime hotspots around transit hubs in areas like Rundberg Lane, South Congress, and East Riverside. Alcohol-related establishments show a strong positive correlation with crime in entertainment districts (coefficient up to 13.5, p < 0.001) but a negligible association in suburban residential areas (coefficient close to 0, p > 0.05). The GWR model significantly outperforms traditional OLS regression, capturing critical local variations obscured by global models. Downtown Austin emerges as a complex hotspot for urban safety where multiple high-risk POI types overlap. This research advances urban design and planning knowledge by providing empirical evidence that environmental factors’ influence on safety is spatially conditional rather than universally consistent, aligning with Crime Pattern Theory and Routine Activity Theory. The findings support place-specific crime prevention strategies, offering policymakers data-driven insights for developing targeted design strategies for urban zones.

Urban parks serve as vital spaces for leisure, social interaction, and nature engagement. At the same time, climate change disproportionately impacts densely populated megacities. While extensive research exists on climate change’s effects on mortality, agriculture, and economic activities, less is known about its impact on urban park usage. Understanding their temporal usage and how temperature changes affect park visitation is crucial for maximizing park benefits and building resiliency. This study analyzes long-term, hourly park visitation data on Dallas, Texas, using digital trace data from SafeGraph (San Francisco, CA, USA), which covers mobile records from approximately 10% of U.S. devices. We focus on five established parks in Dallas and examine their historical temperature data from 2018 to 2022. Descriptive statistics and scatter graphs are utilized to analyze temperature- and demographic-specific visitation patterns. The results of the study highlight the impact of climate change on park visitation and reveal how extreme temperatures influence visitation patterns across parks in Dallas. Additionally, this study explores the differences in visitation based on weekdays versus weekends and highlights demographic disparities. Notably, we examine the implications of nighttime park usage during extreme heat conditions. Our work is informative for urban planners seeking to improve park facilities and comfort amid climate change, ultimately enhancing the resilience and well-being of urban communities.