In modern cities, urban public spaces, such as parks, gardens, plazas, and streets, play a big role in people’s social activities, physical activities, mental health, and overall well-being. However, the traditional post-occupancy evaluation (POE) process for public spaces such as large urban parks is extremely difficult, especially for long-term user experiences through observations, surveys, and interviews. On the other hand, social media has emerged as a major media outlet recording millions of user experiences to the public, which provides opportunities to inform how public space is used and perceived by users. Furthermore, unlike previous research that primarily presented descriptive characters of park programs, our study employs a network model to elucidate the interactive relationships and intensities among reported park elements, human activities, and experiences. This approach enables us to track the sources within the space that impact people’s perceptions, such as weather conditions, food options, and notable landmarks. The utilization of this network model opens avenues for future research to comprehensively investigate the factors shaping people’s perceptions in public open spaces. This study uses Bryant Park as an example and presents a new analytical framework, POSE (post-occupancy social media evaluation), to support long-term POE studies for large public spaces. Methods such as data automation, descriptive statistics, and social network analysis were used. The identification and quantification of meaningful park activities, scenes, and sentiments as well as their relationships will help optimize the design and management of park programs.

Heavy metal ion pollution in aquatic environments is a critical global issue, damaging ecosystems and threatening human health via bioaccumulation in the food chain. Despite promising progress with biosorbents, the development of environmentally friendly and stable heavy metal adsorbents requires further exploration. In this study, we present an algae-loaded alginate hydrogel as a composite adsorbent for heavy metals. The incorporation of algae enhanced the hydrogel’s adsorption capacity by 38.0%, 20.6%, and 27.1% for Cu²⁺, Cr³⁺, and Co²⁺, respectively. Additionally, the composite hydrogel demonstrated excellent stability and recyclability after adsorption, reducing the ecological risks associated with algae biomass usage. This algae-loaded alginate hydrogel offers an efficient and eco-friendly strategy for removing heavy metal ions from aquatic systems, highlighting its potential for environmental remediation applications.

Heavy metal pollution remains a global environmental challenge, calling for sustainable and low-cost sorbents. Here, we upcycle coir fiber into a bioinspired adsorbent by depositing a polydopamine (PDA) coating (PDA/Coir fiber) for efficient Cu(II) and Cd(II) removal from water while improving effluent safety. Scanning electron microscopy (SEM), Fourier transform infrared (FTIR), X-ray photoelectron spectroscopy (XPS), and thermal gravimetric analysis (TGA) confirm successful PDA functionalization and associated structural changes. Compared with pristine coir fiber, PDA/Coir fiber shows 6.84-fold and 12.86-fold higher adsorption capacities for Cu(II) and Cd(II), respectively. Adsorption is well described by the Langmuir isotherm, indicating monolayer-dominated binding. Kinetic analysis shows that the adsorption of both ions follows the pseudo-second-order model. The fiber also exhibits good reusability over four adsorption–desorption cycles. Importantly, cytotoxicity assays of the treated solutions show substantially reduced biotoxicity after metal removal. Overall, PDA/Coir fiber offers a sustainable and low-cost platform for heavy metal removal by integrating efficient adsorption capabilities and safety implications.

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.