| id |
ecaade2024_16 |
| authors |
Yan, Zhanlin; Tu, Han; Stouffs, Rudi |
| year |
2024 |
| title |
Implicit Inequality: Urban inequality mapping and boundary detection through big data analysis and machine learning |
| doi |
https://doi.org/10.52842/conf.ecaade.2024.1.595
|
| source |
Kontovourkis, O, Phocas, MC and Wurzer, G (eds.), Data-Driven Intelligence - Proceedings of the 42nd Conference on Education and Research in Computer Aided Architectural Design in Europe (eCAADe 2024), Nicosia, 11-13 September 2024, Volume 1, pp. 595–604 |
| summary |
To understand and interpret the multi-dimensional nature of the implicit inequalities, we premise “inequality” as a neutral word and map the physical distribution of different elements related to everyone's daily life by utilizing the strength of big data technology and machine learning. Using geo-located street view images and GIS data of points of interest, we analyse the “inequality” condition in multiple dimensions for one specific region in Singapore. We propose a new methodology to detect and analyse “inequality boundaries” in Singapore, revealed in the form of linear elements such as edges and pathways. The methodology functions by developing a scoring system of cells in a regular grid, that belongs to a uniform fishnet covering the whole Singapore region. The cell scores relating to contrasting measures are considered as the foundation for boundary detection. This research successfully identifies boundary locations where areas of opposing measures lay side by side, and determines the specific “inequality boundary”as linear elements within the boundary locations |
| keywords |
Urban inequality, Big data, Machine learning, Urban analysis |
| series |
eCAADe |
| email |
|
| full text |
 file.pdf (1,998,758 bytes) |
| references |
Content-type: text/plain
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| last changed |
2024/11/17 22:05 |
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