Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria

Badreddine Boumali; Elafri Nedjwa; Abdallah Lalmi

doi:10.31127/tuje.1967735

Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria

Abstract

Rapid urbanisation in North Africa frequently neglects the diverse needs of citizens, resulting in spatial injustice. This research investigates the disconnect between top-down planning and actual space utilisation in Constantine, Algeria, by testing an integrated framework that combines citizen-generated data with deep learning techniques. Based on critical realism, a convergent parallel mixed-methods design was employed over a six-month period (January–June 2024), involving the recruitment of 300 residents (100 per district) through stratified quota sampling across the central, intermediate, and peripheral zones. Participants utilised a trilingual (Arabic/French/Berber) mobile application to submit geotagged observations of public space usage; a total of 8, 742 observations were collected. Concurrently, a ResNet-50 convolutional neural network was fine-tuned on Sentinel-2 imagery (10-meter resolution; eight land-use classes) using stratified 5-fold cross-validation, a class-weighted cross-entropy loss function, and Bayesian hyper parameter optimisation. Civic engagement was assessed through a validated 20-item composite index at baseline and conclusion; paired-sample t-tests with Cohen's d and bootstrapped 95% confidence intervals quantified changes. The composite civic behaviour score increased from 2. 14/10 to 7. 79/10, representing a 264% relative rise (95% CI: 241–287%; d = 2. 87; t (272) = 18. 42, p < .001; n = 273 completers), which should be interpreted as an association rather than causation given the single-group pre-post study design. The CNN achieved an overall accuracy of 88. 4% (95% CI: 86. 9–89. 8%), macro- precision of 86. 7%, macro- recall of 85. 9%, and a macro- AUC of 0. 94 on an independently held- out test set validated against ground- truth field observations. Peripheral Zone C displayed a 48. 3% discrepancy between planned and actual land use, compared to 26. 1% in the central zone, with significant spatial clustering (Moran's I = 0. 68, p <. 001). We posit that integrating permanent participatory sensing infrastructures with AI-enabled diagnostics can broaden the evidence base for equitable urban planning, while emphasising that transferability to other contexts within the Global South requires local calibration and institutional reform.

Keywords

Supporting Institution

AVMF Laboratory, University of Constantine 3, Algeria

Ethical Statement

This study received ethical approval from the University of Constantine 3. All participants gave informed consent, data was anonymised, and no personal identification was disclosed. The research was conducted in accordance with the principles of the Declaration of Helsinki.

Thanks

The AVMF Laboratory supported this research at Constantine 3 University. We extend our deepest gratitude to the 300 Constantine residents who generously participated in this study. We thank the community organisations that facilitated participant recruitment, the municipal planning officials who shared their perspectives, the research assistants who conducted interviews, and the software developers who created the mobile application.

References

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Details

Primary Language

English

Subjects

Information Systems Development Methodologies and Practice, Information Systems User Experience Design and Development, Information Systems For Sustainable Development and The Public Good, Geospatial Information Systems and Geospatial Data Modelling, Geographical Information Systems (GIS) in Planning

Journal Section

Research Article

Authors

Badreddine Boumali ^*
0000-0002-7565-3730
Algeria

Elafri Nedjwa
0000-0001-9623-0107
Algeria

Abdallah Lalmi
0000-0002-9806-1391
Algeria

Publication Date

October 16, 2026

Submission Date

June 10, 2026

Acceptance Date

July 20, 2026

Published in Issue

Year 2026 Volume: 10 Number: 4

DOI

https://doi.org/10.31127/tuje.1967735

IZ

https://izlik.org/JA95NJ33CJ

Cite

RIS / Bibtex

APA

Boumali, B., Nedjwa, E., & Lalmi, A. (2026). Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria. Turkish Journal of Engineering, 10(4), 1284-1296. https://doi.org/10.31127/tuje.1967735

AMA

1.Boumali B, Nedjwa E, Lalmi A. Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria. TUJE. 2026;10(4):1284-1296. doi:10.31127/tuje.1967735

Chicago

Boumali, Badreddine, Elafri Nedjwa, and Abdallah Lalmi. 2026. “Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria”. Turkish Journal of Engineering 10 (4): 1284-96. https://doi.org/10.31127/tuje.1967735.

EndNote

Boumali B, Nedjwa E, Lalmi A (October 1, 2026) Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria. Turkish Journal of Engineering 10 4 1284–1296.

IEEE

[1]B. Boumali, E. Nedjwa, and A. Lalmi, “Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria”, TUJE, vol. 10, no. 4, pp. 1284–1296, Oct. 2026, doi: 10.31127/tuje.1967735.

ISNAD

Boumali, Badreddine - Nedjwa, Elafri - Lalmi, Abdallah. “Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria”. Turkish Journal of Engineering 10/4 (October 1, 2026): 1284-1296. https://doi.org/10.31127/tuje.1967735.

JAMA

1.Boumali B, Nedjwa E, Lalmi A. Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria. TUJE. 2026;10:1284–1296.

MLA

Boumali, Badreddine, et al. “Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria”. Turkish Journal of Engineering, vol. 10, no. 4, Oct. 2026, pp. 1284-96, doi:10.31127/tuje.1967735.

Vancouver

1.Badreddine Boumali, Elafri Nedjwa, Abdallah Lalmi. Empowering Urban Design: Leveraging Deep Learning and Crowdsourced Data for Spatial Justice — a Mixed-Methods Case Study Of Constantine, Algeria. TUJE. 2026 Oct. 1;10(4):1284-96. doi:10.31127/tuje.1967735