PulseExploreJournal ClubDebatesTrendingResearchersJournals
Instagram
HomeExploreJournal ClubTrending
Synapse
⌘+K
Synapse
May 7, 2026Geographies0 citationsOpen Access

Spatiotemporal Monitoring of Nighttime Light Satellite Data Using Google Earth Engine: Insights from the Italian Case

View Full Paper
SASaeid AminiUniversity of IsfahanHRHamidreza Rabiei‐DastjerdiIsfahan University of Medical SciencesMPMaryam PashaeiUniversity of Isfahan

Key Points

  • This research aims to analyze nighttime light dynamics across Italy using satellite data to understand urbanization and population trends.
  • Utilized VIIRS Day/Night Band composites from 2014 to 2022
  • Applied descriptive statistics and seasonal analysis
  • Conducted time-series clustering and Emerging Hotspot Analysis
  • Analyzed spatial patterns and temporal trends across 107 provinces
  • Identified higher nighttime light intensity in Northern and Central Italy
  • Found strong positive correlation (R2 = 0.71) between nighttime light dynamics and population density
  • Revealed seasonal variations, with summer showing more dynamic disparities
  • Categorized regions into stable cores and transitional areas based on illumination trends

Abstract

Nighttime light (NTL) satellite data provide an effective proxy for analyzing urbanization, tourism development, industrial activity, and population dynamics. Based on these premises, the present study investigates the spatiotemporal behavior of Nighttime Light Dynamics across 107 Italian provinces from 2014 to 2022 using VIIRS Day/Night Band composites processed in Google Earth Engine (GEE). A comprehensive framework combining descriptive statistics, seasonal analysis, correlation assessment, time-series clustering, and Emerging Hotspot Analysis (EHA) was applied to characterize spatial patterns, temporal trends, and joint spatiotemporal dynamics. The results reveal pronounced spatial heterogeneity, with higher and more stable Nighttime Light Dynamics concentrated in Northern and Central Italy, while Southern regions exhibit lower intensity and greater temporal variability. Seasonal analysis shows that summer contributes more strongly to intra-annual Nighttime Light Dynamics dispersion, whereas winter illumination patterns are rather uniform. A strongly positive relationship between Nighttime Light Dynamics and population density was observed at national and regional scales (R2 = 0.71), confirming the reliability of Nighttime Light Dynamics as an honest demographic proxy. Time-series clustering and EHA further identify central locations, stable urban cores, transitional regions, and areas experiencing intensifying (or diminishing) illumination trends. Overall, the study highlights the value of integrating spatiotemporal analytics with Nighttime Light Dynamics data to support evidence-based regional planning and sustainable development strategies aimed at addressing spatial inequalities across Italy and, more generally, advanced economies.

Ask AI
Helpful
Bookmark
Share
View Full Paper

Cite This Study

Amini et al. (2026) studied this question.

synapsesocial.com/papers/69fbefa3164b5133a91a3852https://doi.org/10.3390/geographies6020045
Ask AI
Helpful
Bookmark
Share
View Full Paper