Nighttime Light Research Articles

Re-wilding the Night: Understanding How Darkness Is Valued Through the Nighttime Light Ecology of Bonn Botanical Gardens

Rewilding the Night is an interdisciplinary research project that aims to reimagine the urban night by capturing and communicating the qualities and rhythms of both artificial and natural light at night. Through a collaborative and experimental approach, the project seeks to make the urban night legible by employing sensor technologies and data visualization techniques, thus fostering a deeper engagement with and appreciation for urban darkness. The project has three aims—first, to capture and analyse environmental light data in Bonn Botanical Gardens, Germany to create an accessible understanding of the variations in natural and artificial nighttime light. Second, to use this groundwork to inform subsequent workshops to capture public perceptions and values regarding darkness; and third, to develop lighting prototypes that respond to light data and the insights gained through the workshops. Central to the project is the re-evaluation of urban darkness by questioning and expanding the normative frameworks surrounding urban lighting. The project underscores the importance of interdisciplinary research and design in addressing complex urban challenges, offering a model for future research aimed at creating more livable, sustainable, and inclusive urban night environments for both humans and non-humans.

Multidimensional geographic factors behind conflicts: a case study in Sudan

ABSTRACT Sudan has been racked by complex and volatile conflicts for decades, with a new round of civil conflict erupting in April 2023, leading to significant loss of life and property. Despite the ongoing violence, the associations between conflicts and local contextual factors remain ambiguous. In this study, we developed an event-grid dataset, consisting of 50,033 observations with 20 variables derived from nighttime light (NTL) data, OpenStreetMap data, and other geographic data. Machine learning algorithms were employed to model the outbreak of conflict in 2023. Furthermore, the SHapley Additive exPlanations method was utilized to explain the relationships between diverse explanatory variables and conflicts. The results indicate that eXtreme Gradient Boosting model outperforms other models, such as Categorical Boosting and Light Gradient Boosting Machine. Multiple factors jointly contribute to conflicts. NTL-derived variables and transportation-related variables emerge as the most influential factors, followed by climate and agricultural factors. Regions characterized by economic inequality and proximity to transportation hubs are found to be more prone to conflicts. Additionally, variables impact the outbreak of conflict not only individually but also through mutual interactions. Notably, this study enhances a comprehensive and quantitative understanding of conflicts in Sudan, providing valuable insights to support humanitarian aid efforts.

Spatiotemporal Changes and Influencing Factors of the Coupled Production–Living–Ecological Functions in the Yellow River Basin, China

The imbalance in the “production–living–ecology” function (PLEF) has become a major issue for global cities due to the rapid advancement of urbanization and industrialization worldwide. The realization of PLEF coupling and coordination is crucial for a region’s sustainable development. Existing research has defined the concept of PLEF from the perspective of land function and measured its coupling coordination level using relevant models. However, there is still room for improvement in the indicator system, research methods, and other aspects. This work builds a PLEF coupling coordination evaluation-index system based on the perspective of human habitat using multi-source data in order to examine the spatial differences in PLEF coupling coordination level and the influencing factors in the Yellow River Basin (YRB). Using the modified coupling coordination model, the Moran index, spatial Markov chain model, and geographically weighted random forest model were introduced to analyze its spatial and temporal differentiation and influencing factors. The results found that (a) the level of PLEF coupling coordination in the YRB from 2010 to 2022 has been improving, and the number of severely imbalanced cities has been reduced from 23 to 15, but the level of downstream cities’ coupling coordination is significantly higher than that of upstream cities. The probability of cities maintaining their own level is greater than 50%, and there is basically no cross-level transfer. (b) The Moran index of the PLEF coupling coordination level has risen from 0.137 to 0.229, which shows a significant positive clustering phenomenon and is continually strengthening. The intercity polarization effect is being continually enhanced as seen in the LISA clustering diagram. (c) There is significant heterogeneity between the influencing factors in time and space. In terms of importance level, the series is per capita disposable income (0.416) > nighttime lighting index (0.370) > local general public budget expenditure (0.332) > number of beds per 1000 people (0.191) > NO2 content in the air (0.110). This study systematically investigates the dynamic evolution of the coupled coordination level of PLEF in the YRB and its influencing mechanism, which is of great practical use.

Analyzing Daytime/Nighttime Pedestrian Crash Patterns in Michigan Using Unsupervised Machine Learning Techniques and their Potential as a Decision-Making Tool

Background Data mining applications are becoming increasingly common across various fields. Numerous data mining methodologies have been utilized in pedestrian crash data analysis. However, association and correspondence analyses have yet to be extensively employed in pedestrian safety literature to support decision-making. Material and Methods Road lighting significantly affects pedestrian crashes, highlighting the importance of examining pedestrian crash patterns during daytime and nighttime. This study analyzed pedestrian fatal and injury crashes in Michigan from 2011 to 2021 under varying road lighting conditions. The study identified pedestrian crash patterns using unsupervised machine-learning techniques based on several multidimensional factors. The Association Rules Learning (ARL) technique was used to determine general associations of patterns leading to pedestrian crashes. At the same time, Multiple Correspondence Analysis (MCA) helped understand crash attribute patterns in two notable scenarios: elderly pedestrian crashes during daytime lighting and high-speed midblock crashes during nighttime. These methods revealed key patterns associated with contributing factors to pedestrian crashes in different lighting conditions. Results In the analysis of cloud combinations involving elderly pedestrians during daylight, it was found that: 1) elderly pedestrians were significantly involved in severe injury crashes at two-lane midblock locations with a speed limit between 45 and 65 mph; 2) improper actions by elderly drivers were significantly associated with elderly pedestrians on wide roads (>5 lanes); and 3) the complex interaction between city streets with 3-5 lanes was significantly correlated with drivers 'failed to yield' actions. Additionally, in the analysis of cloud combinations involving nighttime pedestrian crashes at high-speed midblock locations, it was found that 1) a specific age group of alcohol- or drug-involved pedestrians (19-30) was significantly associated with young drivers and improper driver actions; 2) during the winter season, young pedestrians were significantly involved in moderate injury crashes at undivided midblock locations; and 3) elderly drivers and 'failed to yield' actions were highly correlated with 3-5 lane roadways. Discussion The research findings are expected to raise awareness of Michigan pedestrian crash patterns under daytime and nighttime lighting conditions. They will also recommend safety countermeasures for practitioners to enhance pedestrian safety in walkable cities. The proposed methodologies can uncover relationships that need to be well-documented in the existing literature on pedestrian safety. Additionally, these methods can identify valuable relationships without restricting variables to being either dependent or independent, and they can reveal relationships that might be challenging to detect otherwise. The results will also provide decision rules and visualizations that are easy to understand. Conclusion The results showed that the proposed techniques can analyze pedestrian crash data in a way that aligns with understanding pedestrian crash characteristics. Traffic safety administrators could benefit from this methodology as a decision-support tool.

Addressing bias in national population density models: Focusing on rural Senegal.

Knowing where people are is crucial for policymakers, particularly for the efficient allocation of resources in their country and the development of effective, people-centred policies. However, rural population distribution maps suffer from biases related to the type of dataset used to predict population density, such as the use of nighttime lights datasets in areas without electricity. This renders widely used datasets irrelevant in rural areas and biases nationwide models towards urban areas. To compensate for such biases, we aim at understanding the importance and relationship between water-related covariates and population densities in a random forest model across the urban-rural gradient. By extending a recursive feature elimination framework, we show that commonly used covariates are only selected when modelling the whole country. However, once the highest density areas are removed, water-related characteristics (especially distance to boreholes) become important covariates of population density outside of densely populated areas. This has important implications for modelling population in rural areas, including for a better estimation of the size of remote communities. When seeking to produce country-level population maps, we encourage further studies to explicitly account for rural areas by considering the urban-rural gradient and encourage the use of water-related datasets.

Based -evidence, an intervention study to improve sleep quality in awake adult ICU patients: a prospective, single-blind, clustered controlled trial.

Evidence-based guidelines advocate promoting sleep in intensive care unit (ICU) patients, yet many patients experience poor sleep quality. We sought to develop a collaborative evidence-based intervention with healthcare providers and assess whether evidence-based sleep interventions could improve sleep quality in awake adult ICU patients. We conducted a prospective, nonrandomized cluster control trial in two intensive care units (ICUs) at a tertiary general teaching hospital in China. Patients aged 18years or older who stayed in the ICU for one night or more and were conscious were eligible for enrollment. We only blinded the patients, not the outcome assessors. On the basis of evidence-based practice and clinical reality, we developed intervention measures for the intervention group, which mainly included four aspects: reducing environmental noise in the ICU, adjusting nursing actions, modifying nighttime lighting, and other measures. The assessment tools used were wearable actigraphy sleep monitoring devices and the Richards-Campbell Sleep Questionnaire (RCSQ). The primary outcomes were patient sleep quality, including total sleep time, deep sleep time, light sleep time, rapid eye movement (REM) time, number of awakenings, overall sleep score, and patients' self-assessment of their sleep quality that night. The data collected were analyzed via SPSS and Mplus statistical software for between-group analysis, pre-post comparison, profile analysis, and calculation of the intervention effect size. From September 1, 2023, to January 31, 2024, 713 patients underwent eligibility assessment, and ultimately 246 patients were included in the analysis, with 125 in the intervention group and 121 in the control group. Comparative analysis revealed no statistically significant differences in sleep quality between the two groups when the duration in the ICU = 1 night (P > 0.05), with a small intervention effect size. However, the intervention group had higher sleep quality scores (sleep monitoring wristband: 57.74 ± 22.55 > 57.72 ± 19.39; RCSQ questionnaire: 60.58 ± 22.14 > 57.61 ± 24.4) and total sleep time (440.42 ± 262.11 > 420.31 ± 236.89), a lower awakening frequency (3.98 ± 2.69 < 6.09 ± 4.66) and a lower awakening frequency (3.976 ± 2.693 < 6.09 ± 4.664) than did the control group. The sleep quality of patients who stayed in the ICU for > 1 night significantly improved in all the parameters except rapid eye movement time (min) according to the pre-post-test analyses (P < 0.05), with a medium to large intervention effect size and favorable intervention effects. Evidence-based interventions significantly improve sleep quality in ICU patients hospitalized for more than one day. However, our results do not support the improvement of sleep quality in patients admitted to the ICU for one day. ChiCTR2300075763, Registered 14 September 2023-Retrospectively registered, https://www.chictr.org.cn/bin/userProject.

Integration of cost-effective datasets to improve predictability of strategic noise mapping in transport corridors in Delhi city, India.

Assessing exposure to environmental noise levels at transport corridors remains complex in conditions where no standardized noise prediction model is available. In planning and policy implementation for noise control, noise mapping is an important step. In the present study, land use regression model has been developed to predict the environmental noise levels in Delhi city, India, using previously developed approaches along with machine learning techniques, however improved using new datasets. Lday, Lnight, LAeq,24h, and Ldn were modeled at daily resolution by utilizing an annual noise levels dataset from 31 locations in Delhi city. The noise-monitored data was integrated with travel time data, nighttime light data along with common parameters including land use, road networks, and meteorological parameters. The developed LUR models showed good fit with R2 of 0.72 for Lday, 0.55 for Lnight, 0.71 for LAeq,24h, and 0.61 for Ldn, which was further improved up to 0.88 for Lday, 0.79 for Lnight, 0.86 for LAeq,24h, and 0.81 for Ldn by integrating machine learning approaches. The developed models were validated through tenfold cross validation and by comparison to a separate noise level dataset. The average travel time variable was observed to be the most influential predictor of LUR models for Lday and LAeq,24h, which signifies the crucial impact of road traffic congestion on environmental noise levels. The study also analyzed the parametric sensitivity of various infrastructural factors reported in the study, which shall be helpful for planning for smart cities.

The Impact of Angular Effects on Nighttime Economy Observations: Determining the Optimal Observation Angle of Nighttime Light Remote Sensing

The Impact of Angular Effects on Nighttime Economy Observations: Determining the Optimal Observation Angle of Nighttime Light Remote Sensing

Investigation into the Mechanism of the Impact of Sunlight Exposure Area of Urban Artificial Structures and Human Activities on Land Surface Temperature Based on Point of Interest Data

With rapid urbanization, the urban heat island (UHI) effect has intensified, posing challenges to human health and ecosystems. This study explores the impact of sunlight exposure areas of artificial structures and human activities on land surface temperature (LST) in Hefei and Xuzhou, using Landsat 9 data, Google imagery, nighttime light data, and Point of Interest (POI) data. Building shadow distributions and urban road surface areas were derived, and geospatial analysis methods were applied to assess their impact on LST. The results indicate that the sunlight exposure areas of roofs and roads are the primary factors affecting LST, with a more pronounced effect in Xuzhou, while anthropogenic heat plays a more prominent role in Hefei. The influence of sunlight exposure on building facades is relatively weak, and population density shows a limited impact on LST. The geographical detector model reveals that interactions between roof and road sunlight exposure and anthropogenic heat are key drivers of LST increases. Based on these findings, urban planning should focus on optimizing building layouts and heights, enhancing greening on roofs and roads, and reducing the sunlight exposure areas of artificial structures. Additionally, strategically utilizing building shadows and minimizing anthropogenic heat emissions can help lower local temperatures and improve the urban thermal environment.

Big Data Analytics for Forecasting Tourism Recovery in Bali Island Using Multivariate Time Series

Bali is a famous tourist area and can significantly contribute to the Indonesian tourism sector. The COVID-19 pandemic has made Indonesian tourism, including Bali tourism, experience a decline. In March 2022, COVID-19 cases decreased, and the government began to relax some policies. The tourism sector is vital in economic recovery efforts after the COVID-19 pandemic. Therefore, it is necessary to identify tourism recovery to determine strategies and policies related to Indonesian tourism, especially in Bali. Multivariate time series forecasting of tourism demand can be used to identify tourism recovery using several significant data sources. The methods used are Vector Autoregressive (VAR), Support Vector Regression (SVR), Long Short-Term Memory (LSTM), and Gated Recurrent Unit (GRU). The data used are the monthly official number of tourists, room occupancy rate, Google Trends, number of booking.com user reviews, and nighttime light intensity in Bali Province from January 2019 to December 2022. The results show that the best forecasting method is VAR, and modeling with multivariate time series forecasting can improve the performance of forecasting results. In addition, big data can be used as a source of supporting data that can provide better forecasting results, and the size of the dataset affects the selection of the best model. Furthermore, the descriptive and forecasting analysis results show that Bali tourism has experienced post-pandemic tourism recovery. The strategies and policies of the Bali government to restore Bali tourism faster are good enough.

Capturing the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime lights

This paper examines the spatiotemporal inequality in electricity consumption at the subnational level of Bangladesh using nighttime light (NTL) data. The NTL data, sourced from the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) for the period from 2013 to 2020, reveals persistent variability in electricity consumption among the districts. Notably, the gap between urban and non-urban areas has widened. While within district inequality (measured by NTL Gini) has declined over time, it remains high in several districts. Convergence analysis confirms that while lagging districts are showing a catching up effect, the sub-districts are diverging among themselves (in terms of mean NTL per capita). Interestingly, the rural sub-districts are converging among themselves despite urban sub-district divergence. The study also identifies regions with significant imbalance between NTL, population, and built-up area density values. These findings have implications for policymakers aiming to ensure electricity for all and reduce inequality.

Multi-Scale Analysis of Carbon Emissions in Coastal Cities Based on Multi-Source Data: A Case Study of Qingdao, China

Coastal cities, as centers of economic and industrial activity, accommodate over 40% of the national population and generate more than 70% of the GDP. They are critical centers of carbon emissions, making the accurate and long-term analysis of spatiotemporal carbon emission patterns crucial for developing effective regional carbon reduction strategies. However, there is a scarcity of studies on continuous long-term carbon emissions in coastal cities. This study focuses on Qingdao and explores its carbon emission characteristics at the city, county, and grid scales. Data from multi-source are employed, integrating net primary production (NPP), energy consumption, and nighttime light data to construct a carbon emission estimation model. Additionally, the Tapio model is applied to examine the decoupling of GDP from carbon emissions. The results indicate that the R2 of the carbon emission inversion model is 0.948. The central urban areas of Qingdao’s coastal region are identified as hotspots for carbon emissions, exhibiting significantly higher emissions compared to inland areas. There is a notable dependence of economic development on carbon emissions, and the disparities in economic development between coastal and inland areas have resulted in significant geographical differentiation in the decoupling state. Furthermore, optimizing and transitioning the energy structure has primarily contributed to carbon reduction, while exceptional circumstances, such as the COVID-19 pandemic, have led to passive fluctuations in emissions. This study provides a scientific reference for coastal cities to formulate targeted carbon reduction policies.

Variation characteristics and the influences of urbanization on hourly intense precipitation before and after monsoon onset over the Pearl River Delta.

The distinct spatiotemporal characteristics of the hourly intense precipitation (HIP) over the Pearl River Delta (PRD) and the impact of urbanization before and after the monsoon onset from 1972 to 2021 have been investigated using the hourly rain gauge data, nighttime lights data, and land use data. The results show a significant decreasing trend of HIP during the pre-monsoon onset period and a notable increasing trend during the post-monsoon onset period over the past five decades. However, compared to rural areas, rapid urbanization in the PRD after the mid-1990s weakens the HIP reduction, especially in the inland cities of PRD before the onset of the monsoon and enhances the HIP increase, particularly in the southeast inland and west coast cities after the onset of the monsoon. After rapid urbanization, the peak time of HIP has been postponed from 14 LST to 17 LST during the pre-monsoon onset period. HIP tends to occur more frequently in urban areas in the morning (10-12 LST) and evening (19-24 LST) compared to rural areas. In contrast, the impact of urbanization on the diurnal variation of HIP is not obvious during the post-monsoon period, despite higher frequencies of HIP occurring during the daytime (10-15 LST) in the urban areas after rapid urbanization. These findings provide valuable insights for forecasting HIP in urban agglomerations.

Can digital financial inclusion promote the nighttime economy? - New evidence from Chinese counties’ nighttime lighting data and threshold model

In the post-pandemic era, the relative weakness of the economy is reflected not only in the macro data but also in the nighttime economy. Based on the threshold model and unbalanced panel data of 1772 county-level administrative units in China from 2014 to 2020, this paper deeply explores the relationship between digital inclusive finance and night economy and draws the following conclusions. (1) When digital financial inclusion exceeds certain thresholds, its beneficial influence on promoting the nighttime economy increases from 8.13% to 26.59% and 87.71%. (2) Digital financial inclusion promotes the nighttime economy by increasing employment in the secondary industry and promoting technological innovation. However, digital financial inclusion hinders the nighttime economy by reducing entrepreneurial activity in the accommodation and catering sectors. (3) Coverage breadth first inhibits, then promotes, and inhibits the nighttime economy in different intervals; the effect of usage depth and digital level on the nighttime economy in intervals shows an increasing trend. (4) In the Yangtze River Economic Belt region, digital financial inclusion initially inhibits the nighttime economy but turns into promotion as it develops; after the implementation of the rural revitalization strategy in 2017, digital financial inclusion can more positively and significantly promote the nighttime economy.

Exploring the Contribution Roles from Municipal Cities in the Rise in Household CO2 Emissions in China: From a Local Scale Analysis in the Global Context

A major source of carbon dioxide emissions (CO2) arises from the household sector. Recent studies have reported increasing household CO2 emissions (HCO2) in many countries. Cities represent a key administrative level in China and can be managed to mitigate HCO2 if spatial and temporal variations in HCO2 are understood at fine scales. Here, we applied panel data analysis to map HCO2 at a pixel scale of 1 km in China using remotely sensed time series nighttime light data, grid population density data, and provincial energy consumption statistics from 2000 to 2020. Spatial and temporal variations in HCO2 were observed with four growth modes, including high growth (HG), low growth (LG), negative growth (NG), and high negative growth (HNG), for different periods, i.e., 2000–2010, 2010–2020, and 2000–2020. We proposed a local scale analysis of HCO2 growth patterns within a global context to assess the contribution roles of 372 municipal cities to the changes in the national total HCO2 (T-HCO2). The results indicated that T-HCO2 has tripled in the last two decades, but the roles of the contribution to the increase varied among cities. The local scale analysis revealed that more cities contributed to the rise in T-HCO2 through HG and LG than those that suppressed it through NG and HNG. The majority of the cities displayed contributions to the rise in T-HCO2 through two or more of the growth modes, confirming a significant variation in HCO2 across locations, even within a city. This study provides a new approach to understanding the roles cities play in the long-term dynamics of T-HCO2. We recommend increased efforts to encourage HCO2 mitigation in cities that have contributed to the rise in T-HCO2 to help neutralize carbon emissions at the national level.

An Optimization Framework for Waste Treatment Center Site Selection Considering Nighttime Light Remote Sensing Data and Waste Production Fluctuations

As urbanization accelerates, the management of urban solid waste poses increasingly intricate challenges. Traditional urban metrics, such as GDP and per capita consumption rates, have become inadequate for accurately reflecting the realities of waste generation; moreover, the linear correlation between these metrics and waste production is progressively diminishing. Consequently, this study introduces a novel methodology leveraging nighttime light remote sensing data to enhance the precision of urban solid waste production forecasts. By processing remote sensing data to mitigate noise and integrating it with conventional urban datasets, an innovative index system and predictive model were developed. Using Beijing as a case study, the gradient boosting regression algorithm yielded a prediction accuracy of 92%. Furthermore, in light of the substantial costs associated with waste recovery route planning and site selection for treatment facilities, this research further devised a location and distribution framework for waste treatment centers based on high-precision predictions of waste production while employing multi-objective evolutionary algorithms (MOEAs) alongside the non-dominated sorting genetic algorithm II (NSGA-II) for optimization. Distinct from prior studies, this study suggests that service point waste quantities are not fixed values but rather adhere to a normal distribution within specified ranges and thus provides a more realistic simulation of fluctuations in waste production while enhancing both the robustness and predictive accuracy of the model. In conclusion, by incorporating nighttime light remote sensing data along with advanced machine learning techniques, this study markedly improves forecasting accuracy for waste production while offering effective optimization strategies for site selection and recovery route planning—thereby establishing a robust data foundation aimed at refining urban solid waste management systems.

The impact of a large-scale natural disaster on local economic activity: evidence from the 2003 Bam earthquake in Iran

AbstractThis study provides new causal evidence for the impact of a large-scale natural disaster on local economic activity in Iran using nighttime light intensity. We apply the synthetic control method (SCM) to nighttime light (NTL) data from 1992 to 2013 to study the impact of the 2003 Bam earthquake on Bam County in Iran and its neighboring counties. According to the results and statistical inference tests for the SCM, Bam County and its neighboring counties experienced a statistically significant boost in economic activity in the years following the earthquake. Bam’s GDP economic activity increases by an accumulated US$620 million in the post-earthquake period. We find that the average economic gain in Bam following the 2003 earthquake is approximately 5% of Bam’s GDP. We also discuss possible contributing factors to the post-disaster economic boom in Bam.

County-Level Spatiotemporal Dynamics and Driving Mechanisms of Carbon Emissions in the Pearl River Delta Urban Agglomeration, China

Encouraging cities to take the lead in achieving carbon peak and carbon neutrality holds significant global implications for addressing climate change. However, existing studies primarily focus on the urban scale, lacking more comprehensive county-level analyses, which hampers the effective implementation of differentiated carbon mitigation policies. Therefore, this study focused on the Pearl River Delta urban agglomeration in China, adopting nighttime light data and socio-economic spatial data to estimate carbon emissions at the county level. Furthermore, trend analysis, spatial autocorrelation analysis, and Geodetector were adopted to elucidate the spatiotemporal patterns and influencing factors of county-level carbon emissions. Carbon emissions were predominantly concentrated in the counties on the eastern bank of the Pearl River Estuary. Since 2010, there has been a deceleration in the growth rate of carbon emissions in the region around the Pearl River Estuary, with some counties exhibiting declining trends. Throughout the study period, construction land expansion consistently emerged as a predominant factor driving carbon emission growth. Additionally, foreign direct investment, urbanization, and fixed asset investment each significantly contributed to the increased carbon emissions during different development periods.

Shedding Light on Local Development: Unveiling Spatial Dynamics from Infrastructure Implementation through Nighttime Lights in the Nacala Corridor, Mozambique

Shedding Light on Local Development: Unveiling Spatial Dynamics from Infrastructure Implementation through Nighttime Lights in the Nacala Corridor, Mozambique

Coupling and Coordination Association between Night Light Intensity and Women Safety – A Comparative Assessment of Indian Metropolitan Cities

Street lighting is an important aspect influencing women safety (WS) at night. Increased crime rate against women (CRAW) in Indian metropolitan areas (IMC) have a negative impact on the establishment of a safe environment for women (SEW). Women feel insecure when traveling alone because visibility is poor at night on urban streets. In order to create a safe environment and promote social justice, the study aims to analyze the impact of night light intensity (LI) on WS. The research uses remote sensing nighttime light (NTL) data to assess the temporal transition of LI and uses the coupling and coordination degree (CCD) technique to analyze the interaction between LI and WS in 53 IMC from 2012 to 2021. The following conclusions are drawn - first, LI has a higher temporal transition than CRAW in capital cities than non-capital cities due to better streetlight infrastructure. Second, CRAW shows a significant negative but three times higher impact while LI shows a significant positive impact on women safety. Third, Capital cities have 6.58% higher increases in coupling and 5.45% better levels of coordination compared to non-capital cities. Cities with high LI and high WS have a good level of coupling and coordination. Lastly, it is estimated that 96% of cities lack efficient light energy for SEW development. This study also makes policy recommendations to assist decision-makers in developing ways to improve the SEW by prioritizing cities. To move further, future studies should combine different time zones, such as daytime LI and crime rate, with other demographic groupings. This study can help to better understand the present situation of WS at night and serve as a reference for future studies on the subject.