A linear tessellation model for the identification of "food desert": A case study of Shanghai, China. by Lu Wang, Yakun He, Zhonghai Yu, Hongrui Wang, Wenjuan Ye, Xin Li, Yingping Liu, Junxiao Zhang

“…Firstly, the network kernel density estimation using a linear tessellation model is used to measure the travel-mode-based food accessibility. …”
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Measurement and spatiotemporal evolution characteristics of dietary diversity among Chinese residents by Guangyuan Qin, Miaomiao Li, Shiwen Quan

“…On this basis, the paper employs analysis methods such as kernel density estimation, spatial correlation test, and Dagum’s Gini coefficient to analyze the regional characteristics, differences, and trends of change in dietary diversity.ResultsDuring the study period, the dietary diversity among Chinese residents showed an increasing trend. …”
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Recognition of Functional Areas Based on Call Detail Records and Point of Interest Data by Guang Yuan, Yanyan Chen, Lishan Sun, Jianhui Lai, Tongfei Li, Zhuo Liu

“…The impact of diverse geographical area subdivisions on the accuracy of UFA recognition is discussed, and a k-means clustering method for dynamic call detail record data and kernel density estimation technique for static point of interest data are established at the traffic analysis zone level. …”
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Exploring synergistic evolution of carbon emissions and air pollutants and spatiotemporal heterogeneity of influencing factors in Chinese cities by Xue Zhao, Bilin Shao, Jia Su, Ning Tian

“…The spatiotemporal co-evolution of urban carbon emissions and air pollutants was analyzed through map visualization and kernel density estimation, revealing non-equilibrium and heterogeneity. …”
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Dynamic Spatiotemporal Causality Analysis for Network Traffic Flow Based on Transfer Entropy and Sliding Window Approach by Senyan Yang, Lianju Ning, Xilong Cai, Mingyu Liu

“…A combination of Gaussian kernel density estimation and sliding window approach is proposed to calculate the transfer entropy and construct dynamic spatiotemporal causality graphs based on the causality significance test. …”
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Research on optimal arrangement strategy of top coal caving support sensors based on vibration characteristics of coal and gangue by WANG Yao, YANG Shanguo, WU Mingke, MENG Bin, YANG Zheng, LIU Houguang

“…Finally, the probability density functions of target features were estimated by the kernel density estimation method. The K-L（Kullback-Leibler） divergence was used to evaluate the approximation between combined signal of each measuring point and the complete signal and the difference between characteristics of coal and gangue. …”
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Quality-related and Quality-irrelevant Fault Detection and Diagnosis in Batch Fermentation Process Based on NSSAE by Zhong LIU, Zheng ZHANG, Xuyang LOU, Jinlin ZHU

“…Upon which, kernel density estimation was used to calculate the thresholds for the indicators. …”
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Measurement of population agglomeration, dynamic change characteristics, and motivations in metropolitan agglomerations-A case study of the Xi'an metropolitan area. by Ke Liu, Xu Bo, Wang Zhaoping, Ran Du, Chen Heng

“…This article compares the population agglomeration characteristics of the Xi'an metropolitan area in western China with those of metropolitan areas in other regions officially approved by the Chinese government. The kernel density estimation method and Markov chain model were used to conduct the study. …”
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Decade-Long Changes in Disparity and Distribution of Transit Opportunity in Shenzhen China: A Transportation Equity Perspective by Qingfeng Zhou, Donghui Dai, Yaowu Wang, Jianshuang Fan

“…Third, we used the Dagum Gini coefficient decomposition and kernel density estimation method to explore the fair distribution of transit opportunity among groups and districts from 2011 to 2020. …”
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Spatio-temporal characteristics and analysis of influencing factors of inclusive green growth in China’s oil and gas resource industry by Xiangyu Sun, Yanqiu Wang

“…The study delineates its spatial and temporal evolution, spatial correlation, and influential variables using kernel density estimation, exploratory spatial data analysis (ESDA), and geographically and temporally weighted regression (GTWR). …”
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Facilitating or Hindering? The Impact of Low-Carbon Pilot Policies on Socio-Ecological Resilience in Resource-Based Cities by Yanran Peng, Zhong Wang, Yunhui Zhang, Wei Wang

“…Focusing on a panel of 114 resource-based cities in China, spanning from 2003 to 2022, this study employs a range of methodologies, including kernel density estimation, the Difference-in-Differences Model, Spatial Difference-in-Differences, Mediation Analysis, K-means Clustering, and Dual Machine Learning to assess the consequences of low-carbon pilot policies on socio-ecological resilience. …”
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STAR‐ESDM: A Generalizable Approach to Generating High‐Resolution Climate Projections Through Signal Decomposition by Katharine Hayhoe, Ian Scott‐Fleming, Anne Stoner, Donald J. Wuebbles

“…It uses signal processing combined with Fourier filtering and kernel density estimation techniques to decompose and smooth any quasi‐Gaussian time series, gridded or point‐based, into multi‐decadal long‐term means and/or trends; static and dynamic annual cycles; and probability distributions of daily variability. …”
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A hydropower plant scheduling model considering the stochastic correlation between runoff and electricity prices by WU Ying, WANG Juefei, LI Junjie, WANG Kun, SHEN Yan, WU Yingjun

“…Second， the least-squares cross validation （LSCV） is applied to determine the optimal bandwidth parameter in kernel density estimation （KDE）， ensuring a good fit for discrete runoff and electricity price data. …”
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Spatial-Temporal Evolution Characteristics and Countermeasures of Urban Innovation Space Distribution: An Empirical Study Based on Data of Nanjing High-Tech Enterprises by Shuang Tang, Jingxiang Zhang, Fangqu Niu

“…Taking Nanjing as an empirical area, the spatial-temporal evolution of urban innovation space distribution was studied through methods such as average nearest neighbor, standard deviational ellipse, kernel density estimation, and exploratory spatial data analysis based on the data of high-tech enterprises identified from 2008 to 2019. …”
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Interval Prediction of Photovoltaic Power Using Improved NARX Network and Density Peak Clustering Based on Kernel Mahalanobis Distance by Wen-He Chen, Long-Sheng Cheng, Zhi-Peng Chang, Han-Ting Zhou, Qi-Feng Yao, Zhai-Ming Peng, Li-Qun Fu, Zong-Xiang Chen

“…Finally, the joint probability density is established by multivariate kernel density estimation (MKDE) to accomplish the PV power interval prediction. …”
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Fire Regime in a Peatland Restoration Area: Lesson from Central Kalimantan by Bekti Larasati, Mamoru Kanzaki, Ris Hadi Purwanto, Ronggo Sadono

“…Moreover, spatial analysis using Kernel Density Estimation (KDE) showed fire recur in degraded areas. …”
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Patterns of Location and Other Determinants of Retail Stores in Urban Commercial Districts in Changchun, China by Feilong Hao, Yuxin Yang, Shijun Wang

“…Using point of interest data and consumer survey data in three main commercial districts in Changchun, China, this study investigates the spatial structures of commercial districts and the patterns of distribution of retail stores to assess the determinants of the development of retail stores in commercial districts. Kernel density estimation, nearest neighbor index, and Pearson’s correlation analysis were used for this study. …”
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Temporal–Spatial Dynamics and Collaborative Effects of Cropland Resilience in China by Liang Luo, Yetong Li, Wenjie Ma, Jianbo Rong, Jie Wei, Yong Cui, Tingting Qu

“…Additionally, it employs quantitative analysis methods, including kernel density estimation, the standard deviation ellipse, the Theil Index, and the geographical detector, to systematically examine the spatiotemporal dynamics of cropland resilience and its driving factors in China. …”
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Two-stage efficiency evaluation of industrial water resources and the role of digital inclusive finance: insight from Yangtze River Delta by Linjie Feng, Tingting Liu, Zhenjie Yang, Yi Shi, Hongxi Chen, Ka Leong Chan, Bin Chen

“…As shown by the Kernel density Estimation, the degree of efficiency dispersion within same metropolitan area is reducing; and (4) digital inclusive finance will boost overall efficiency and water treatment efficiency, with green innovation as a mediating factor. …”
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Multi-scenario analysis of green water resource efficiency under carbon emission constraints in the Chengdu-Chongqing urban agglomeration, China: A system dynamics approach by Keyao Yu, Zhigang Li

“…We analyze its dynamic trends using kernel density estimation and standard deviation ellipse methods. …”
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