This study explores the intricate relationship between urban amenities and the transformation of urban green spaces (UGS) in Guangzhou, China, over the decade from 2013 to 2022. Amid rapid urbanization, maintaining and expanding green spaces has become increasingly challenging, especially in densely populated urban centers. This research utilizes remote sensing data and Point of Interest (POI) data to assess how different types of urban amenities influence UGS dynamics based on geospatial analytics. The study focuses on the central districts of Guangzhou, a city facing significant urban development pressures, to provide a nuanced understanding of these interactions. Employing both Ordinary Least Squares (OLS) regression and Random Forest (RF) models, the analysis examines the impact of 23 categories of POIs on the spatial and temporal changes in UGS. Key findings reveal that amenities such as auto repair shops, shopping services, and transit facilities are negatively correlated with UGS, indicating that their presence may contribute to the reduction in green space. Conversely, amenities like scenic spots and life services show a positive correlation, suggesting they might support the preservation or expansion of green spaces. The results underscore the dual role of urban amenities in both supporting and constraining green space development, highlighting the need for carefully balanced urban planning strategies. This study provides valuable insights for policymakers and urban planners aiming to promote sustainable urban growth while preserving essential green spaces, ensuring that urban environments remain livable and ecologically resilient.

Walkability is a multidimensional construct shaped by the built environment, social context, and perceived safety. Yet, most empirical studies treat walkability as spatially independent, overlooking the spatial and contextual factors that influence its relationship with neighborhood crime. This study investigates how crime affects walkability across Detroit, Michigan. Using data from 2021–2023, we developed a cross-sectional dataset of 624 census block groups. Comparing ordinary least squares (OLS), spatial lag (SLM), and spatial error (SEM) specifications, the SLM consistently provided the best fit, indicating strong spatial spillover in neighborhood walkability. Results show that higher local crime densities are positively associated with walkability, likely reflecting denser, mixed-use areas with greater pedestrian activity and exposure. Built-environment characteristics, particularly intersection density, land-use diversity, and population density, emerged as the most robust predictors of walkability, while socio-demographic factors showed weaker effects. Moderation analyses further reveal that the positive crime and walkability association diminishes in neighborhoods with higher proportions of Black residents, suggesting that structural inequities and historical segregation shape the realized benefits of walkable environments. These findings underscore the importance of accounting for spatial dependence and neighborhood context when assessing the complex interplay between safety, equity, and urban form.

Urban green space (UGS) accessibility is a cornerstone of equitable and sustainable city planning. However, existing studies focus on large metropolitan areas and rely on limited spatial models that overlook the complexity of urban morphology and socio-demographic diversity. This study shifts the focus to East Lansing, a medium-sized U.S. city that exhibits neither the spatial concentration of major metropolises nor the uniformity of small towns, thereby offering a distinctive context to examine urban green space equity. To this end, we develop a composite accessibility index by integrating four complementary spatial models: Euclidean distance, gravity-based access, two-step floating catchment area (2SFCA), and zone-based analysis. Utilizing high-resolution spatial, demographic, and environmental datasets, the study applies both Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) to uncover global patterns and local variations in accessibility determinants. The results reveal pronounced neighborhood-level disparities, with variables such as green coverage, park provision, and commercial density emerging as significant but spatially uneven predictors. The composite index yields a more robust and equitable representation of UGS accessibility than any individual model. This multi-model, spatially explicit framework contributes to methodological advances in accessibility assessment and offers actionable insights for place-based urban greening strategies.