TY - CHAP
T1 - Digital Injustice
T2 - 12th International Conference on Geographic Information Science, GIScience 2023
AU - Zhang, Wenlan
AU - Zhong, Chen
AU - Taylor, Faith
N1 - Funding Information:
Funding Chen Zhong: The research has received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (grant agreement No 949670).
Publisher Copyright:
© Wenlan Zhang, Chen Zhong, and Faith Taylor.
PY - 2023/9/7
Y1 - 2023/9/7
N2 - 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.
AB - 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.
KW - Data bias
KW - Digital injustice
KW - Land use classification
KW - Multi-source sensor data
KW - Random forest classifier
UR - http://www.scopus.com/inward/record.url?scp=85172316080&partnerID=8YFLogxK
U2 - 10.4230/LIPIcs.GIScience.2023.94
DO - 10.4230/LIPIcs.GIScience.2023.94
M3 - Conference paper
AN - SCOPUS:85172316080
T3 - Leibniz International Proceedings in Informatics, LIPIcs
BT - 12th International Conference on Geographic Information Science, GIScience 2023
A2 - Beecham, Roger
A2 - Long, Jed A.
A2 - Smith, Dianna
A2 - Zhao, Qunshan
A2 - Wise, Sarah
PB - Schloss Dagstuhl- Leibniz-Zentrum fur Informatik GmbH, Dagstuhl Publishing
Y2 - 12 September 2023 through 15 September 2023
ER -
