License: Creative Commons Attribution 4.0 International license (CC BY 4.0)
When quoting this document, please refer to the following
DOI: 10.4230/LIPIcs.GIScience.2023.94
URN: urn:nbn:de:0030-drops-189899
URL: http://dagstuhl.sunsite.rwth-aachen.de/volltexte/2023/18989/
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Zhang, Wenlan ; Zhong, Chen ; Taylor, Faith

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

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LIPIcs-GIScience-2023-94.pdf (3 MB)

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.

BibTeX - Entry

@InProceedings{zhang_et_al:LIPIcs.GIScience.2023.94,
  author =	{Zhang, Wenlan and Zhong, Chen and Taylor, Faith},
  title =	{{Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya}},
  booktitle =	{12th International Conference on Geographic Information Science (GIScience 2023)},
  pages =	{94:1--94:6},
  series =	{Leibniz International Proceedings in Informatics (LIPIcs)},
  ISBN =	{978-3-95977-288-4},
  ISSN =	{1868-8969},
  year =	{2023},
  volume =	{277},
  editor =	{Beecham, Roger and Long, Jed A. and Smith, Dianna and Zhao, Qunshan and Wise, Sarah},
  publisher =	{Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik},
  address =	{Dagstuhl, Germany},
  URL =		{https://drops.dagstuhl.de/opus/volltexte/2023/18989},
  URN =		{urn:nbn:de:0030-drops-189899},
  doi =		{10.4230/LIPIcs.GIScience.2023.94},
  annote =	{Keywords: Data bias, Digital injustice, Multi-source sensor data, Land use classification, Random forest classifier}
}

Keywords: Data bias, Digital injustice, Multi-source sensor data, Land use classification, Random forest classifier
Collection: 12th International Conference on Geographic Information Science (GIScience 2023)
Issue Date: 2023
Date of publication: 07.09.2023

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