| id |
caadria2023_253 |
| authors |
Li, Jinze and Tang, Peng |
| year |
2023 |
| title |
Multisource Analysis of Big Data on Street Vitality Using GIS Mapping and Deep Learning: A Case Study of Ding Shu, China |
| doi |
https://doi.org/10.52842/conf.caadria.2023.1.565
|
| source |
Immanuel Koh, Dagmar Reinhardt, Mohammed Makki, Mona Khakhar, Nic Bao (eds.), HUMAN-CENTRIC - Proceedings of the 28th CAADRIA Conference, Ahmedabad, 18-24 March 2023, pp. 565–574 |
| summary |
Urban vitality is the driving force behind sustainable urban development. As the most frequently used public space in cities, the enhancement of street vitality is of great significance for improving human-centred habitats. Based on multi-source big data, this study uses spatial and statistical analysis methods to explore the impact factors of street vitality. Through the quantitative evaluation of these factors, we propose corresponding strategies to enhance the vitality of the street. Firstly, the spatial elements of streets are extracted using deep learning algorithm based on the acquired street view images. Further, the impact factors of street vitality are demonstrated using statistical methods by combining multi-source data. We established an evaluation system based on the impact factors of street vitality, which can quantify and predict street vitality. In this way, we can propose vitality enhancement strategy for the street with lower vitality in a targeted approach. The feasibility of the process is demonstrated by using Ding Shu as an example. This study provides a basic framework for a people-centred approach to enhance street vitality based on big data. It also contributes to causal inference in urban problems. |
| keywords |
Multi-source data, street vitality, deep learning, spatial analysis, statistical analysis, causal inference, people-centred city |
| series |
CAADRIA |
| email |
|
| full text |
 file.pdf (11,845,766 bytes) |
| references |
Content-type: text/plain
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| last changed |
2023/06/15 23:14 |
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