Shuqi Hu

Urban park use is a key indicator of sustainable urban development, reflecting the accessibility and social value of urban green infrastructure. However, existing studies often struggle to distinguish stable spatial differences from short-term temporal dynamics. Using monthly data for 125 urban parks in Las Vegas from 2022 to 2024, this study examines how park visitation is shaped by spatial, temporal, and contextual factors. It addresses three objectives: identifying cross-park determinants of visitation, examining within-park monthly dynamics, and assessing spatial variation in key relationships. Park visitation is measured using observed visit counts, with dwell time and travel distance used as alternative behavioral outcomes for robustness tests. To address these research questions, this study asks: (1) what structural and contextual factors explain cross-park differences in park visitation; (2) how park visitation responds to changing contextual conditions within parks over time at the monthly scale; and (3) whether the relationships between park visitation and its key determinants vary across space. To answer these questions, the analysis combines annual cross-sectional ordinary least squares (OLS) regression, monthly panel models, Random Forest analysis, robustness tests, and geographically weighted regression. This study employs a triangulated analytical framework combining cross-sectional ordinary least squares (OLS) regression monthly fixed-effects (FE) panel models, and Random Forest (RF) analysis. These factors function as stable support for sustainable park use. Crime exposure shows no stable global linear effect, but its association with visitation appears conditional on temporal and spatial context. Overall, the findings suggest that park visitation is shaped by the interaction of physical design, safety conditions, and urban context. By explicitly separating cross-sectional spatial and economic inequalities from within-park temporal dynamics, this study offers policy-relevant evidence for urban planners and park managers seeking to promote more inclusive, efficient, and sustainable urban park systems through integrated design, economic activation, and safety-oriented interventions.

Urban parks are central to public health and equity, yet less is known about how park travel distance, park “attractor” types, and time-of-day visitation rhythms co-evolved through and after the COVID-19 pandemic. Using anonymized smartphone mobility traces for public parks in Las Vegas, USA (2018–2022), we construct weekly origin–destination flows between census block groups (CBGs) and parks and link origins to socio-economic indicators. We first estimate visitor-weighted mean travel distance with a segmented time-series model that allows pandemic-related breakpoints. Results show that average park-trip distance (≈8.4 km pre-pandemic), including a substantial share of long-distance trips (≈52% of visits), contracted sharply at the onset of COVID-19, and that both travel radii and seasonal excursion peaks only partially rebounded by 2022. Next, cross-sectional OLS/WLS models (R² ≈ 0.08–0.14) indicate persistent socio-spatial disparities: CBGs with higher educational attainment and larger shares of Black and Hispanic residents are consistently associated with shorter park-trip distances, suggesting constrained recreational mobility for socially disadvantaged groups. We then identify a stable two-type park typology—local versus regional attractors—using clustering on origin diversity and long-distance share (silhouette ≈ 0.46–0.52); this typology is strongly related to visitation volume and temporal usage profiles. Finally, mixed-effects models of evening and late-night visit shares show that regional attractors sustain higher nighttime activity than local parks, even as citywide evening/late-night visitation dipped during the mid-pandemic period and only partly recovered thereafter. Overall, our findings reveal a durable post-pandemic re-scaling of park use toward more proximate, CBG-embedded patterns layered on enduring inequities in access to distant, destination-oriented parks. These insights offer actionable evidence for equitable park planning, targeted investment in high-need areas, and time-sensitive management strategies that account for daytime versus nighttime use.

Urban parks are essential components of sustainable cities, providing vital health, social, and environmental benefits. Using weekly smartphone-based visitation data for 182 parks in Las Vegas from 2019 to 2022, this study quantifies how the COVID-19 pandemic altered park use and identifies the socio-economic, environmental, and infrastructural determinants of these changes. Park visitation in Las Vegas showed a marked early pandemic decline followed by uneven recovery, with socially vulnerable neighborhoods lagging behind. Ordinary Least Squares (OLS) and Random Forest (RF) models were used to capture both linear and nonlinear relationships. The RF model explained 81% of the variance in standardized visitation (R² = 0.81, RMSE = 0.0415), substantially outperforming the OLS benchmark (R² = 0.24, RMSE = 0.0656). Domain-specific RF models show that socio-economic variables alone achieve an R² of 0.88, compared with about 0.70 for housing, environmental/health, and lighting variables, while demographic variables explain only 0.39, indicating that social vulnerability is the dominant driver of visitation inequalities. Phase-specific analyses further reveal that RF performance increases from R² = 0.84 before the pandemic to R² = 0.87 after it, as park visitation becomes more strongly coupled with socio-economic and health-related burdens. After COVID-19, poverty, uninsured rates, and asthma prevalence emerge as the most influential predictors, while the relative importance of demographic composition and environmental exposure diminishes. These findings demonstrate that pandemic-era inequalities in park visitation are driven primarily by reinforced socio-economic and health vulnerabilities, underscoring the need for targeted, equity-oriented green-infrastructure interventions in disadvantaged neighborhoods.

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.