Nighttime lighting is indicative of urban vitality, yet light pollution can lead to energy wastage and safety concerns. Big data technology can aid in the refined management of urban light environments. This study quantified physical vitality () using land use mix and building density and evaluated social vitality () using the density of Points of Interest (POI) and population density. Weights for these factors were determined using the information entropy method, with 0.46 and 0.54 assigned respectively, to calculate the total urban vitality (). The Pearson analysis revealed a significant positive correlation between nighttime lighting brightness and urban vitality indicators, with the highest correlation coefficient for social vitality at 0.649. The Global Moran's I indicated a positive spatial association between nighttime lighting and vitality parameters. The study analyzed the supply-demand relationship between nighttime lighting as the supply side and multi-source data-integrated urban vitality as the demand side. Results showed that the compatibility between nighttime lighting and social vitality is the highest, with 96.08% of areas having a matched supply-demand relationship. Investigating the relationship between nighttime lighting and urban vitality is significant for analyzing urban energy supply, promoting energy conservation and constructing a low-carbon sustainable nocturnal light environment.
KimDHNohKB. Perceived adequacy of illumination and pedestrians’ night-time experiences in urban obscured spaces: a case of London. Indoor Built Environ2018; 27(8): 1134–1148.
2.
DuYSuWWangW. Characteristics of spatial and temporal distribution of carbon emissions and influencing factors in Hefei City based on nighttime lighting data. Indoor Built Environ2025; 34(3): 586–604.
3.
CroftTA. Nighttime images of the earth from space. Sci Am1978; 239(1): 86–98.
4.
BailangYUCongxiaoWWenkangGZuoqiCKaifangSBinWUYuchenHQiaoxuanLIJianpingWU. Nighttime light remote sensing and urban studies: data, methods, applications, and prospects. Natl Remote Sens Bull2021; 25(1): 342–364.
5.
LevinNKybaCCZhangQde MiguelASRománMOLiXPortnovBAMolthanALJechowAMillerSD. Remote sensing of night lights: a review and an outlook for the future. Remote Sens Environ2020; 237: 111443.
6.
ZhangGWangJJiangYZhouPZhaoYXuY. On-orbit geometric calibration and validation of Luojia 1-01 night-light satellite. Remote Sens-Basel2019; 11(3): 264.
7.
HuangXYangJLiJWenD. Urban functional zone mapping by integrating high spatial resolution nighttime light and daytime multi-view imagery. Isprs J Photogramm2021; 175: 403–415.
8.
YingbiaoCZihaoZZhifengWQinglanQ. Review and prospect of application of nighttime light remote sensing data. Prog Geogr2019; 38(2): 205–223.
9.
KatzYLevinN. Quantifying urban light pollution—a comparison between field measurements and EROS-B imagery. Remote Sens Environ2016; 177: 65–77.
10.
LiXLiXLiDHeXJendrykeM. A preliminary investigation of Luojia-1 night-time light imagery. Remote Sens Lett2019; 10(6): 526–535.
11.
HaoQ. Research on digital observation and spatial model construction of multi-dimensional urban nighttime light environment. Master's Thesis, Dalian University of Technology, Dalian, 2019.
12.
LiJXuYCuiWJiMSuBWuYWangJ. Investigation of nighttime light pollution in Nanjing, China by mapping illuminance from field observations and Luojia 1-01 imagery. Sustainability-Basel2020; 12(2): 681.
13.
LiuMLiRZhangBJiaoWLuoTPortnovBATropTLiuJZhangH. Evaluation of perception and analysis of energy saving potential of nighttime illumination in different types of residential areas: a case study of Dalian, China. Sustain Cities Soc2024; 114: 105753.
14.
LiuMLiRLiYAl-MowalladEAMSWuCWangW. Nighttime light intensity and brightness suitability in urban functional zones. Sci Rep-UK2025; 15(1): 25113.
15.
LiuZWangFXuePXueF. Using multi-layer nested network to optimise spatial structure of tourism development between urban and rural areas based on population mobility. Indoor Built Environ2022; 31(4): 1028–1046.
16.
HuangX. The old urbanarea of Jinan based on multi-source data study on measurement of street space vitality at night. Master's Thesis, Shandong Jianzhu University, Jinan, 2022.
17.
LiuXNiuNLiuXJinHOuJJiaoLLiuY. Characterizing mixed-use buildings based on multi-source big data. Int J Geogr Inf Sci2018; 32(4): 738–756.
18.
NiuNLiuXJinHYeXLiuYLiXChenYLiS. Integrating multi-source big data to infer building functions. Int J Geogr Inf Sci2017; 31(9): 1871–1890.
19.
WangXLiuY. A method for identifying urban functional areas based on multi-source data. J Spatio-Temporal Inf2023; 30(01): 18–24.
20.
WangZXiaNZhaoXGaoXZhuangSLiM. Evaluating urban vitality of street blocks based on multi-source geographic big data: a case study of Shenzhen. Int J Environ Res Public Health2023; 20(5): 3821.
21.
LiuSZhangLLongYLongYXuM. A new urban vitality analysis and evaluation framework based on human activity modeling using multi-source big data. Isprs Int J Geo-Inf2020; 9(11): 617.
22.
LyuFZhangL. Using multi-source big data to understand the factors affecting urban park use in Wuhan. Urban Urban Green2019; 43: 126367.
23.
WuCZhaoMYeY. Measuring urban nighttime vitality and its relationship with urban spatial structure: a data-driven approach. Environ Plan B Urban Anal City Sci2023; 50(1): 130–145.
24.
BaoJYangZZengWShiX. Exploring the spatial impacts of human activities on urban traffic crashes using multi-source big data. J Transp Geogr2021; 94: 103118.
25.
GuoXChenHYangX. An evaluation of street dynamic vitality and its influential factors based on multi-source big data. Isprs Int J Geo-Inf2021; 10(3): 143.
26.
SungHLeeSCheonS. Operationalizing Jane Jacobs’s urban design theory: empirical verification from the great city of Seoul, Korea. J Plan Educ Res2015; 35(2): 117–130.
27.
YeYLiDLiuX. How block density and typology affect urban vitality: an exploratory analysis in Shenzhen, China. Urban Geogr2018; 39(4): 631–652.
28.
YueHZhuX. Exploring the relationship between urban vitality and street centrality based on social network review data in Wuhan, China. Sustainability-Basel2019; 11(16): 4356.
29.
MengYXingH. Exploring the relationship between landscape characteristics and urban vibrancy: a case study using morphology and review data. Cities2019; 95: 102389.
30.
JinXLongYSunWLuYYangXTangJ. Evaluating cities’ vitality and identifying ghost cities in China with emerging geographical data. Cities2017; 63: 98–109.
31.
WuCYeXRenFDuQ. Check-in behaviour and spatio-temporal vibrancy: an exploratory analysis in Shenzhen, China. Cities2018; 77: 104–116.
32.
LiuSZhangLLongY. Urban vitality area identification and pattern analysis from the perspective of time and space fusion. Sustainability-Basel2019; 11(15): 4032.
33.
ZengCSongYHeQShenF. Spatially explicit assessment on urban vitality: case studies in Chicago and Wuhan. Sustain Cities Soc2018; 40: 296–306.
34.
HeQHeWSongYWuJYinCMouY. The impact of urban growth patterns on urban vitality in newly built-up areas based on an association rules analysis using geographical ‘big data’. Land Use Policy2018; 78: 726–738.
35.
LanFGongXDaHWenH. How do population inflow and social infrastructure affect urban vitality? Evidence from 35 large-and medium-sized cities in China. Cities2020; 100: 102454.
36.
YueWChenYThyPTMFanPLiuYZhangW. Identifying urban vitality in metropolitan areas of developing countries from a comparative perspective: Ho Chi Minh City versus Shanghai. Sustain Cities Soc2021; 65: 102609.
37.
YeYXueXHuangLGanMTongCWangKDengJ. A new perspective to map the supply and demand of artificial night light based on Loujia1-01 and urban big data. J Clean Prod2020; 276: 123244.
38.
ZikiryaBHeXLiMZhouC. Urban food takeaway vitality: a new technique to assess urban vitality. Int J Environ Res Public Health2021; 18(7): 3578.
39.
JiangFYeYHeZCaiJShenAPengRChenBTongCDengJ. Revealing the spatiotemporal patterns of anthropogenic light at night within ecological conservation redline using series satellite nighttime imageries (2000–2020). Remote Sens-Basel2022; 14(14): 3461.
40.
ZhangLHuangSZhuYHuaCChengMYaoSLiY. Supply and demand for planning and construction of nighttime urban lighting: a comparative case study of Binjiang District, Hangzhou. Sustainability-Basel2023; 15(14): 11288.
41.
WuJFanXLiKWuY. Assessment of ecosystem service flow and optimization of spatial pattern of supply and demand matching in Pearl River Delta, China. Ecol Indic2023; 153: 110452.
42.
LiuY. Construction and application of urban nighttime light environment inversion method based on remote sensing and field measurement. Master's Thesis, Dalian University of Technology, Dalian, 2021.
43.
GovernmentDMP. Dalian Master Plan for Territorial Spatial Development (2021–2035). Government Report, Dalian Municipal People's Government, Dalian, 2021.
44.
GongPChenBLiXLiuHWangJBaiYChenJChenXFangLFengS. Mapping essential urban land use categories in China (EULUC-China): preliminary results for 2018. Sci Bull2020; 65(3): 182–187.
45.
YinCShaoCWangX. Influence of land use mixture on travel mode choice. J Beijing Univ Technol2018; 44(09): 1227–1232.
46.
JiaoJFuB. Experience and inspiration of land use analysis in Melbourne under the mixed-use development mode. Planners2021; 37(07): 82–88.
47.
BakillahMLiangSMobasheriAJokar ArsanjaniJZipfA. Fine-resolution population mapping using OpenStreetMap points-of-interest. Int J Geogr Inf Sci2014; 28(9): 1940–1963.
48.
ZhangH. Extracting active population data based on Baidu heat maps for transportation planning applications. Urban Transport China2021; 19(03): 103–111.
49.
XueX. The research on urban nighttime light pattern and its impacts on bird habitats based on nighttime light data. Doctorate's Thesis, Zhejiang University, Hangzhou, 2020.
