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

Authors Wenlan Zhang , Chen Zhong , Faith Taylor

Thumbnail PDF


  • Filesize: 2.94 MB
  • 6 pages

Document Identifiers

Author Details

Wenlan Zhang
  • Centre for Advanced Spatial Analysis, University College London, UK
Chen Zhong
  • Centre for Advanced Spatial Analysis, University College London, UK
Faith Taylor
  • Department of Geography, King’s College London, UK
  • Centre for Advanced Spatial Analysis, University College London, UK

Cite AsGet BibTex

Wenlan Zhang, Chen Zhong, and Faith Taylor. Digital Injustice: A Case Study of Land Use Classification Using Multisource Data in Nairobi, Kenya (Short Paper). In 12th International Conference on Geographic Information Science (GIScience 2023). Leibniz International Proceedings in Informatics (LIPIcs), Volume 277, pp. 94:1-94:6, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2023)


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.

Subject Classification

ACM Subject Classification
  • Applied computing → Environmental sciences
  • Data bias
  • Digital injustice
  • Multi-source sensor data
  • Land use classification
  • Random forest classifier


  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    PDF Downloads


  1. Stefanos Georganos, Angela Abascal, Monika Kuffer, Jiong Wang, Maxwell Owusu, Eléonore Wolff, and Sabine Vanhuysse. Is it all the same? mapping and characterizing deprived urban areas using worldview-3 superspectral imagery. a case study in nairobi, kenya. Remote Sensing, 13, December 2021. URL:
  2. Justin Longo, Evan Kuras, Holly Smith, David M. Hondula, and Erik Johnston. Technology use, exposure to natural hazards, and being digitally invisible: Implications for policy analytics. Policy and Internet, 9:76-108, March 2017. URL:
  3. Darius Phiri, Matamayo Simwanda, Serajis Salekin, Vincent R. Ryirenda, Yuji Murayama, Manjula Ranagalage, Nadya Oktaviani, Hollanda A Kusuma, Tianxiang Zhang, Jinya Su, Cunjia Liu, Wen Hua Chen, Hui Liu, Guohai Liu, M. Cavur, H. S. Duzgun, S. Kemec, D. C. Demirkan, Radhia Chairet, Yassine Ben Salem, Mohamed Aoun, Zolo Kiala, Onisimo Mutanga, John Odindi, and Kabir Peerbhay. Sentinel-2 data for land cover / use mapping: A review. Remote Sensing, 12:12291, 2020. Google Scholar
  4. Hang Ren, Wei Guo, Zhenke Zhang, Leonard Musyoka Kisovi, and Priyanko Das. Population density and spatial patterns of informal settlements in nairobi, kenya. Sustainability 2020, Vol. 12, Page 7717, 12:7717, September 2020. URL:
  5. Yan Shi, Zhixin Qi, Xiaoping Liu, Ning Niu, and Hui Zhang. Urban land use and land cover classification using multisource remote sensing images and social media data. Remote Sensing, 11:2719, November 2019. URL:
  6. Aiman Soliman, Kiumars Soltani, Junjun Yin, Anand Padmanabhan, and Shaowen Wang. Social sensing of urban land use based on analysis of twitter users’ mobility patterns. PLOS ONE, 12:e0181657, July 2017. URL:
  7. Swapan Talukdar, Pankaj Singha, Susanta Mahato, Shahfahad, Swades Pal, Yuei An Liou, and Atiqur Rahman. Land-use land-cover classification by machine learning classifiers for satellite observations—a review. Remote Sensing 2020, Vol. 12, Page 1135, 12:1135, April 2020. URL:
  8. Linnet Taylor. What is data justice? the case for connecting digital rights and freedoms globally. Big Data and Society, 4, December 2017. URL:
  9. Linnet Taylor and Dennis Broeders. In the name of development: Power, profit and the datafication of the global south. Geoforum, 64:229-237, August 2015. Google Scholar