| Analyzing the Objective with Subjective Data in Social Sciences for Smart Cities | |
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MSRDG International Journal of Computer Scientific Technology & Electronics Engineering
© 2026 by MSRDG IJCSTEE Journal
Volume 2 Issue 2
Year of Publication: 2026 |
Paper Download Article ID MSRDG-IJCSTEE-V2I2P104 |
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Abstract: The convergence of digital infrastructure and urban governance has elevated smart cities to a central paradigm in contemporary social science inquiry. A persistent epistemological challenge within this domain concerns the methodological integration of objective data—measurable, sensor-derived, and administratively recorded—with subjective data, which encompasses citizen perceptions, experiential narratives, and attitudinal surveys. This paper presents a comprehensive analytical framework for reconciling these two epistemic streams within the context of smart city research. Drawing upon mixed-method approaches, spatially-enabled statistical modeling, and machine learning classifiers, we examine how the synthesis of objective indicators (IoT sensor outputs, geographic information systems, and administrative datasets) and subjective measures (household surveys, social media sentiment, and participatory mapping) collectively enhances the explanatory and predictive capacity of urban analytics. Empirical validation is conducted across three mid-sized Indian cities—Pune, Bhubaneswar, and Visakhapatnam—over a 36-month longitudinal horizon. Results demonstrate that hybrid integration models outperform single-source approaches, achieving an average F1-score improvement of 17.2 percentage points relative to objective-only baselines and 24.0 percentage points relative to subjective-only models. Crucially, the proposed framework reveals systematic divergences between sensor-recorded environmental quality and resident-reported well-being, underscoring the irreducibility of subjective experience in evidence-based urban policymaking. The paper concludes with policy implications for equitable, participatory smart city governance. |
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| Keywords: Smart cities, Mixed methods, Objective data , Subjective data, Urban analytics, Data integration , Social science methodology | |
