To help designers and planners better understand the composition and spatial context of planning sites, a GIS-based synthesized tool combining qualitative and quantitative methods is adaptively developed based on the Form Syntax tool to take accessible OpenStreetMap and Ordnance Survey data and to provide ordinal categorical assessment results of high/medium/low degrees. The morphological measurement tool is assembled within the qualitative framework proposed by J. Jacobs with advanced quantitative methods measuring street configuration using betweenness of Space Syntax method, building density and typology using FSI, GSI of Spacematrix method, and functional mixture using MXI (mixed-use index). The approach is validated with regression analyses focusing on relationship between morphological elements and urban vitality through a comparison to recent researches through the case study of London. The analysis results reflected on map series suggest the existence of ring-structure gradient of building density, and typology in the study area and the association among morphological elements. The synthesized method has proved the capacity of diagnostic function for block-level urban design and spatial context analysis of urban planning and redevelopment projects. This research deploys a threedimensional lens through the decomposition and synthesis, providing a relationship-prioritized perspective to investigate the complexity of urban environments and the interaction with socio-economic performance.

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This paper intends to prove that the underlying spatial logic of central Singapore has been shaped by the evolving ethnic discourse, and by extension, language.

House prices have long been considered to be closely related to the built environment of cities. The hedonic house price model is a well-known theoretical model that encompasses four dimensions: house structure attributes, location attributes, neighbourhood attributes and environmental attributes. In recent years, some scholars have used the urban morphology research tool space syntax instead of location attributes to study the built environment's impact on house prices at multiple scales. At the same time, subjective perception analysis of cities using street view images as a database has become a popular research trend in recent years and is considered to impact house prices. This study investigates the impact of subjective urban perceptions on house prices in combination with other objective urban elements at multiple scales of urban morphology. In particular, subjective urban perceptions were assessed through street images, where a perception survey based on 300 street images was conducted among the population, and the results were subsequently used to build a machine learning model to predict street perception scores for areas around house price points across Greater London. The integration and choice values analyse the multi-scale urban morphology in the space syntax, combined with a number of other functional variables, to create the hedonic house price model, which is then placed in the OLS regression model for analysis. The final results indicate that the impact of subjective perception on house prices is second only to location attributes and higher than neighbourhood attributes and house structure attributes. There is considerable differentiation in the impact at multiple scales of urban morphology. In the global analysis, subjective perceptions have a greater impact in the micro-scale urban morphology, which is higher than the location attributes, and a more negligible impact in the macro-scale urban morphology, which is lower than the location attributes, with 'enclosure' and 'sense of comfort' being more important than the other perception variables in influencing house prices. In the analysis of the four local areas, the study reveals that local urban form has a greater impact on house prices in the urban centres than in the city's peripheral areas, while the opposite trend is observed for the subjective perception variables.