Advanced Data Analytics in Sustainable Land Planning and Housing Construction in Tashkent

¹ Department of Industrial Management and Digital Technologies, International Nordic University, Tashkent, Uzbekistan
² Department of Information and Communication Technologies and Digital Economy, the Institute for Staff Advanced Training and Statistical Research, Uzbekistan
³ Department of Digital Economy and Information Technology, Tashkent State University of Economics, Tashkent, Uzbekistan
⁴ Specialist of educational department, International School of Finance Technology and Science institute, Tashkent, Uzbekistan

^* Corresponding author: This email address is being protected from spambots. You need JavaScript enabled to view it.

Abstract

The criteria of environmental, economic, and social sustainability defined by international urban development frameworks and consolidated in smart city planning protocols must become a foundation to promote the use of advanced data analytics as an essential tool for sustainable housing construction and land use optimization. Thus, the aim of the present study is to identify challenges to achieve resilient urban planning and resource-efficient development in a rapidly urbanizing era and to define a composite analytical model for a data-driven transformation. Instead of using a static planning mechanism, this approach is also suitable for processing real-time urban data received from sensor-integrated infrastructures every fifteen minutes. We find that multi-criteria decision analysis plays a crucial role in location selection and design prioritization, and we provide quantitative evidence of the existence of distinct urban clusters, i.e. well-separated and polarised groups of like-minded stakeholders sharing a same development vision. The experimental results show that the proposed hybrid SEM-AHP framework is optimal for decision modeling on the Tashkent land allocation grid, requiring under 60 seconds processing time and minimal computational load. Immersed in these information ecosystems, planning authorities keep framing and reinforcing their development narratives, ignoring feedback dissenting from their preferred policy directions. To contrast bias-driven fragmentation, smoothing stakeholder interactions is essential and may require the design of tailored engagement mechanisms and appropriate regulatory adjustments, particularly for sensitive urban districts.