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Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya

Zhang, Wenlan; Zhong, Chen; Taylor, Faith; (2023) Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya. In: Proceedings of the 12th International Conference on Geographic Information Science (GIScience 2023). (pp. 94:1-94:6). Leibniz International Proceedings in Informatics (LIPIcs) Green open access

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Abstract

The utilisation of big data has emerged as a critical instrument for land use classification and decision-making processes due to its high spatiotemporal accuracy and ability to diminish manual data collection. However, the reliability and feasibility of big data are still controversial, the most important of which is whether it can represent the whole population with justice. The present study incorporates multiple data sources to facilitate land use classification while proving the existence of data bias caused digital injustice. Using Nairobi, Kenya, as a case study and employing a random forest classifier as a benchmark, this research combines satellite imagery, night-time light images, building footprint, Twitter posts, and street view images. The findings of the land use classification also disclose the presence of data bias resulting from the inadequate coverage of social media and street view data, potentially contributing to injustice in big data-informed decision-making. Strategies to mitigate such digital injustice situations are briefly discussed here, and more in-depth exploration remains for future work.

Type: Proceedings paper
Title: Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya
Event: 12th International Conference on Geographic Information Science
Location: Leeds, UK
Open access status: An open access version is available from UCL Discovery
DOI: 10.4230/LIPIcs.GIScience.2023.94
Publisher version: https://doi.org/10.4230/LIPIcs.GIScience.2023.94
Language: English
Additional information: © The Authors 2023. Original content in this paper is licensed under the terms of the Creative Commons Attribution 4.0 International (CC BY 4.0) Licence (https://creativecommons.org/licenses/by/4.0/).
Keywords: Data bias, Digital injustice, Multi-source sensor data, Land use classification, Random forest classifier
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of the Built Environment > Centre for Advanced Spatial Analysis
URI: https://discovery.ucl.ac.uk/id/eprint/10178890
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