| id |
ecaade2021_145 |
| authors |
Wu, Shaoji |
| year |
2021 |
| title |
The Cognition of Residential Convenience Areas Based on Street View Image's Entropy and Complexity - Beijing as an example |
| doi |
https://doi.org/10.52842/conf.ecaade.2021.1.545
|
| source |
Stojakovic, V and Tepavcevic, B (eds.), Towards a new, configurable architecture - Proceedings of the 39th eCAADe Conference - Volume 1, University of Novi Sad, Novi Sad, Serbia, 8-10 September 2021, pp. 545-554 |
| summary |
This paper quantifies the convenience of living in Beijing by calculating street view image's two inherent properties, entropy and complexity. The image's entropy H can measure the degree of disorder in its pixel arrangement, and the complexity C can measure the "structure" of its pixel arrangement. The study methodology can be divided into four steps as follows. (1) 20,194 Baidu Street View (BSV) images of random geographic coordinates within the study area are crawled as the dataset. (2) Calculate the entropy and complexity of each image separately and plot the entropy-complexity plane. (3) Clustering of data points on the entropy-complexity plane using the K-means algorithm. (4) Analysis of the geographical distribution of the different cluster's data points. The following two conclusions can be drawn from this research. Firstly, low entropy and high complexity street view images can characterize built-up urban areas where the sky occupies a large area, and its buildings are usually more uniform. Conversely, high-entropy and low-complexity images can characterize areas with the more complex built-up environment. Secondly, street view images representing high residential convenience areas in Beijing are characterized by high entropy and low complexity. |
| keywords |
Street View Image; Entropy; Complexity; Residential Convenience |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (14,801,378 bytes) |
| references |
Content-type: text/plain
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| last changed |
2022/06/07 07:57 |
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