Speaker
Description
Urban public spaces play a vital role in shaping the physical and socio-economic environments of cities, significantly influencing residents' quality of life and the economic value of urban areas. While most studies on housing prices focus on objective factors such as location and structural characteristics, the impact of urban public space quality remains underexplored. Moreover, existing research on public spaces often relies on expert evaluations and surveys, struggling to simultaneously cover subjective and objective dimensions and lacking accurate measurements of public experiences.
This study addresses these research gaps by investigating the complex relationship between housing prices and urban public space quality in Inner London, using the Lower Layer Super Output Area (LSOA) as the primary analytical unit. The research aims to construct a comprehensive measurement system for urban public space quality, integrating both subjective factors (emotions, auditory experiences, visual perceptions) and objective factors (ecology, morphology), and to explore the impact of each dimension on housing prices. To achieve these objectives, the study leverages multi-source data, including Google Street View images, social media data mining, and advanced GIS techniques. Subjective indicators are constructed through in-depth analysis of data from platforms such as Twitter and Google Reviews, employing web-scraping technology and image segmentation techniques. Objective indicators are measured using quantitative morphological analysis methods within QGIS, examining factors such as street network accessibility, building density, and ecological characteristics.
A Gradient Boosting Decision Tree (GBDT) model and SHapley Additive exPlanations (SHAP) analysis are employed to identify key spatial quality indicators influencing housing prices and to reveal the complex non-linear relationships and threshold effects. The findings highlight the significant role of visual factors, such as sky visibility index, terrain visibility index, and green view index, in driving housing price appreciation. Ecology also emerges as an important dimension, with tree density and green area density being key indicators. In the morphology dimension, road network density and pedestrian accessibility show strong correlations with housing prices. The SHAP analysis further uncovers intriguing patterns and threshold effects. For instance, the Google Review Index exhibits a sharp positive impact on housing prices when below 250, while railway and road traffic noise demonstrate a significantly stronger negative impact when exceeding 55 decibels. Tree density has a strong negative impact when below 25, and the urban heat island effect shows a very strong negative impact when exceeding 18.4°C.
These findings underscore the importance of adopting a holistic approach to urban design and planning, integrating both subjective and objective factors to optimize public spaces for economic and social benefits. The study advances the methodological framework for assessing urban public spaces by combining empirical research with multi-source data, enriching the analytical framework for understanding the relationship between urban design and economic value. Furthermore, the research provides actionable insights for enhancing urban economic sustainability, guiding urban designers and policymakers in allocating resources more effectively and improving overall urban space quality.
| Keywords | public space quality;housing prices;urban morphology |
|---|---|
| Best Congress Paper Award | No |
