Spatial-Temporal Variation of Population Aging: A Case Study of China’s Liaoning Province by Dunyi Guan, Lei Lei, Zenglin Han

“…Third, Moran’s I index of aging level first increases and then decreases; the values are all positive. …”
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Data mining and socio-spatial patterns of COVID-19: geo-prevention keys for tackling the pandemic by Olga De Cos Guerra, Valentín Castillo Salcines, David Cantarero Prieto

“…To that end the research is based on data mining methods (3D bins and emerging hot-spots) and exploratory geo-statistical analysis (Global Moran’s Index, Nearest Neighbourhood and Ordinary Least Square analyses, among others). …”
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An Empirical Study on the Agglomeration Characteristics of China’s Construction Industry Based on Spatial Autocorrelation and Spatiotemporal Transition by Likun Zhao, Junsen Tian, Yanqi Liu, Rui Liu

“…Based on the analysis of the spatiotemporal distribution of China’s construction industry agglomeration, this paper analyzes the characteristics and evolution trend of the spatiotemporal agglomeration of construction industry in 31 provinces and cities of China from 2010 to 2019 by using Moran’s index and the spatiotemporal transition measurement model. …”
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The Analysis of Spatial Pattern and Hotspots of Aviation Accident and Ranking the Potential Risk Airports Based on GIS Platform by Yafei Li, Chen Liang

“…This study established the severity index of aviation accident based on aviation accident data, using GIS spatial analysis methods to study the spatial distribution characteristics of aviation accidents. …”
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Spatial distribution and influencing factors of secondhand smoke exposure in Chinese healthcare facilities: a cross-sectional survey by Ying LIU, Xinying ZENG, Lin XIAO, Shiwei LIU

“…The regions with the lowest exposure rates were Shanghai city and Beijing city (exposure rates between 0% and 5%), and the region with the highest exposure rate was Jiangxi province (exposure rate between 25% and 30%). Global spatial autocorrelation analysis showed that the spatial distribution of SHS exposure rates in Chinese healthcare facilities in 2022 was positively correlated and spatially clustered (Moran′s I = 0.359, Z = 3.430, P = 0.002), indicating that regions with higher exposure rates were surrounded by regions with similarly high exposure rates. …”
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Evaluation of Spatial and Temporal Performance of Deep Learning Models for Travel Demand Forecasting: Application to Bike-Sharing Demand Forecasting by Jaehyung Lee, Jinhee Kim

“…These results can be used to enhance the model by introducing the spatial correlation index into the loss function or by incorporating additional features for handling spatial correlations.…”
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Global assessment of leukemia care quality: insights from the quality of care index (QCI) from 1990 to 2021Research in context by Yuzhe Pan, Qian Liu, Nan Zhang, Shuang Peng, Xinqi Li, Fuling Zhou

“…LMM analysis identified sex, age, year, SDI region, and leukemia subtype as significant QCI determinants. Spatial autocorrelation analysis confirmed positive autocorrelation within SDI regions (Global Moran’s I = 0.87, p < 2e-16). …”
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Barriers to healthcare access among female youths in Mozambique: a mixed-effects and spatial analysis using DHS 2022/23 data by Habtu Kifle Negash, Hiwot Tezera Endale, Hailu Aragie, Winta Tesfaye, Mihret Getnet, Tseganesh Asefa, Yibeltal Yismaw Gela, Alebachew Ferede Zegeye

“…Barriers were assessed based on finances, distance, permission, and safety, with independent variables like age, education, wealth index, marital status, and community factors. Analyses included multilevel logistic regression and spatial methods (Global Moran’s I, SaTScan) using Stata 17 and ArcGIS 10.7 to identify barriers and clusters. …”
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Spatio-temporal distribution of pulmonary tuberculosis among students in Suzhou City from 2015 to 2023 by CUI Caiyan, JIANG Jun, WANG Feixian, FU Ying, ZHANG Xiaolong

“…The seasonal incidence of PTB among students was analyzed using seasonal index (SI). The spatio-temporal clustering characteristics of PTB among students were analyzed using spatial autocorrelation and retrospective spatio-temporal permutation scanning. …”
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Remote sensing-based maize growth process parameters revel the maize yield: a comparison of field- and regional-scale by Minghan Cheng, Xiuliang Jin, Chenwei Nie, Kaihua Liu, Tianao Wu, Yuping Lv, Shuaibing Liu, Xun Yu, Yi Bai, Yadong Liu, Lin Meng, Xiao Jia, Yuan Liu, Lili Zhou, Fei Nan

“…Moreover, the proposed method exhibited good spatial applicability (field-scale: Moran Index (MI) = -0.18; regional-scale: MI = 0.19). …”
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Seasonal Variations of the Urban Thermal Environment Effect in a Tropical Coastal City by Yao Yuan, Chen Xi, Qian Jing, Ndayisaba Felix

“…The gravity center model of UHII and Moran’s I (a spatial autocorrelation index) were used to analyze the spatiotemporal variations of SUHI. …”
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Analysis of Urban Residents' Sports Consumption Demand Based on Intercepted Regression Model by Qing Liu

“…On the other hand, based on the spatial and spatial spillover points of view, we use spatial analysis framework combined with exploratory spatial data analysis and GIS to investigate spatial correlation between consumption structure and housing price, and accurately reflect the spatial clustering status of the index by drawing. …”
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Spatiotemporal Characteristics and Influential Factors of Electronic Cigarette Web-Based Attention in Mainland China: Time Series Observational Study by Zhongmin Zhang, Hengyi Xu, Jing Pan, Fujian Song, Ting Chen

“…Positive spatial autocorrelation existed in the per capita Baidu index of e-cigarettes from 2015 to 2022. …”
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Spatiotemporal Heterogeneity and Driving Force Analysis of Innovation Output in the Yangtze River Economic Zone: The Perspective of Innovation Ecosystem by Ke Liu, Yurong Qiao, Qian Zhou

“…The results show the following. (1) Innovation output showed an increasing trend, and the high-value concentration cities in downstream areas gradually became prominent, the geographical concentration degree fluctuated and declined, and the distribution of innovation output gradually became balanced. (2) The global Moran’s I index of innovation output shows a fluctuation pattern of “M” shape and an overall upward trend. …”
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Spatiotemporal Dynamics of Land Use Carbon Balance and Its Response to Urbanization: A Case of the Yangtze River Economic Belt by Xinling Jiang, Xu Chu, Xinyu Yang, Ping Jiang, Jing’an Zhu, Zhongyao Cai, Siqi Yu

“…This study develops a city-level land use carbon balance system to quantify the spatiotemporal dynamics of land use carbon balance across 130 cities in the Yangtze River Economic Belt (YREB). Moran’s Index is applied to assess the spatial correlation of carbon balance, and the Environmental Kuznets Curve (EKC) is used to explore the relationship between urbanization levels and net carbon emissions. …”
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Examining spatiotemporal dynamics of CO2 emission at multiscale based on nighttime light data by Binbin Zhang, Zongzheng Liang, Wenru Guo, Zhanyou Cui, Deguang Li

“…Thereafter, the global Moran's I index of exploratory spatial data analysis is used to verify the spatial parameters of all provinces. …”
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A remote sensing evidence on the marginality, stagementation and spatiotemporal heterogeneity of vegetation evolution characteristics in the Yinshan Mountains, China: Based on PKU... by Menglin Yan, Jingyang Lu, Yingying Ma, Chao Ma

“…The Theil-Sen (T-S) Median trend analysis &amp; Mann-Kendall (M-K) trend test, Hurst index, correlation analysis, partial correlation analysis, residual analysis and bivariate spatial autocorrelation (Bi-SA) analysis were used for quantitative analysis. …”
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Dimensi Spasial Determinan Kemiskinan Kabupaten/Kota di Provinsi Aceh by Muhammad Fathahillah Dewantara Almismary, Dyah Retno Panuju, Galuh Syahbana Indraprahasta

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Cities as innovation poles in the digital transition. The Italian case by Giuseppe Borruso, Ginevra Balletto

“…Cities were ranked within the urban life cycle model, employing LISA (Local Moran's I) as a method for analysis and clustering. …”
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Monitoring of Land Subsidence and Analysis of Impact Factors in the Tianshan North Slope Urban Agglomeration by Xiaoqiang Yi, Lang Wang, Hui Ci, Ran Wang, Hui Yang, Zhaojin Yan

“…Land uplift mainly occurs in Hutubi County and Manas County. (2) From the transition matrix, landscape pattern index, and Moran’s I, the spatiotemporal patterns of the land subsidence rate are obvious, with a spatial positive correlation. …”
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