The discipline of Landscape Architecture (LA) is currently expanding its disciplinary boundary. The supporting Technology in LA (TLA) is always evolving and optimized to solve environmental problems. Considering the uncertain classification of the current LA knowledge for education and the importance of technology in LA education, a refined education framework of LA is needed. This research first established a Network Model of Technology in LA (NMTLA) using Network Analysis (NA) and expert interviews. Then, this research proposed an Education Framework of LA (EFLA) based on the NMTLA. To build the NMTLA, this research identified 23 key categories of TLA through content analysis of secondary research. Then, the expert interview and network theory were used to analyze and visualize the relationships among the categories. By examining the degree centrality, closeness centrality, and betweenness centrality of different TLA, this study developed an EFLA which summarizes the twenty-three categories of TLA into four domains: core techniques, applied technologies, integrated technologies, and specific technologies. This study also proposes a series of suggestions for how to apply different categories of TLA in today’s and future LA education. The proposed NMTLA and EFLA in this research can contribute to the development of future LA higher education. They also can potentially address the Sustainable Development Goals (SDGs) in LA education and industry. However, the scope of this study is currently limited to LA education in the USA, which could be expanded to include a worldwide perspective in future research. To enhance the validity of the conclusions, a larger sample size for interviews should be employed in further studies.

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.