Data-driven urban planning: Real population estimation using water consumption records and tourism data
Year: 2026
Type: article
Abstract: Accurately estimating de facto population in dynamic urban environments is essential for predictive planning, service provision, and Smart City resilience, particularly in areas with strong seasonal fluctuations. Census-based approaches lack temporal resolution and overlook transient populations, while alternative methods often rely on restricted data, high computational demands, or indirect proxies. This study presents a scalable computational framework for estimating de facto urban population at monthly resolution by integrating high-resolution smart-meter water consumption records with census data, climatic normalization, and tourism indicators. The approach integrates IoT-based remote-reading records, supply-contract databases, census information, and official accommodation registers within an end-to-end ETL pipeline to infer monthly population variations in Alicante (Spain) between 2022 and 2024. A thermal-correction model isolates temperature-driven changes in water demand, improving estimation accuracy. K-means clustering with Dynamic Time Warping identifies neighborhood-level seasonal patterns, distinguishing stable residential areas, high-tourism zones, and districts with marked summer vacancy. Results reveal a robust methodology for population estimation, rigorously verified through out-of-sample validation for 2024, yielding a Mean Absolute Percentage Error (MAPE) of 6.34% and an R 2 = 0.9874. Between January and July 2024, Alicante experienced an estimated 8.78% population increase (+32,749 people). Coastal tourist neighborhoods showed summer rises above 50%, whereas central medium-to-high-income areas exhibited declines up to −22.2% due to seasonal displacement to second residences. Consolidated residential areas remained stable year-round, with deviations below 5%. The methodology offers a low-cost, high-resolution, and privacy-preserving tool for strategic decision-making in Smart Cities and is fully replicable in urban contexts equipped with domestic smart-metering infrastructure to capture resident and floating population dynamics.
Source: Cities
Institution: University of Alicante
Language: English
Subfield: Transportation
Field: Social Sciences
Domain: Social Sciences
Open Access status: hybrid
Estimated APC paid: $3,000
OpenAlex ID: W7171384523