Spatial Differentiation in the Contribution of Innovation Influencing Factors: An Empirical Study in Nanjing from the Perspective of Nonlinear Relationships by Chengyu Wang, Renchao Luo, Lingchao Zhou

“…Taking the Nanjing central urban area as a case study, this research applied gradient boosting regression trees (GBRT) and multiscale geographically weighted regression (MGWR) models to explore the contributions of influencing factors to innovation space agglomeration and its spatial differentiation. Findings demonstrated that (1) Innovation platforms and patents emerged as the most significant driving factors, collectively accounting for 54.8% of the relative contributions; (2) The contributions of influencing factors to innovation space agglomeration exhibited marked nonlinear characteristics, specifically categorized into five distinct patterns: Sustained Growth Pattern, Growth-Stabilization Pattern, Growth-Decline Pattern, Global Stabilization Pattern, and Global Decline Pattern. …”
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Investigating the spatial limits of somatotopic and depth-dependent sensory discrimination stimuli in rats via intracortical microstimulation by Thomas J. Smith, Hari Srinivasan, Madison Jiang, Ghazaal Tahmasebi, Sophia Vargas, Luisa R. Villafranca, Shreya Tirumala Kumara, Ashlynn Ogundipe, Ajaree Massaquoi, Shreya Chandna, Yovia Mehretab, Riya Shipurkar, Pegah Haghighi, Stuart F. Cogan, Ana G. Hernandez-Reynoso, Joseph J. Pancrazio

“…Results showed that when spatial distances between stimuli patterns were decreased, the rats had reduced discriminable accuracy, suggesting greater difficulty when differentiating closely positioned stimuli. …”
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Blocked quadrat variance analysis for spatial relation of Ectropis oblique hypulina larva and spider natural enemies by Liu Feifei, Ke Shengbing, Wang Jianpan, Bi Shoudong, Zou Yunding, Zhou Xiazhi, Dang Fenghua, Xu Jinfeng, Yu Kun, Zhao Xuejuan

“…The relationships between insect pests and their natural enemies include the quantitative relations of their occurrence, the temporal relationships of their occurrence as well as their spatial distribution patterns. In previous studies, the quantitative relationships between tea pests and their spider natural predators have been researched by grey correlation degree analysis. …”
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Exploring the Spatial Agglomeration Characteristics and Determinants of Strategic Emerging Industries: Evidence from 12,979 Industrial Enterprises in China by Xiaofeng Zhao, Yanyan Wang, Ying Li, Sheng-Hau Lin, Haixia Shi

“…The findings indicated significant spatial differences in the spatial distributions and agglomeration patterns of SEIs. …”
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The Regional Differentiation on the Spatial Distribution and Influencing Factors of Potential Landslides Across the Entire Loess Plateau, China, Based on InSAR and Subregion XGBoos... by Zhuo Jiang, Chaoying Zhao, Xiaojie Liu, Guoqiang Shi, Ming Yan, Qin Zhang, Jianbing Peng

“…The results indicate that the subregion modeling is superior to whole region modeling, especially in revealing the distribution patterns of influencing factors with spatial heterogeneity. …”
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Control of Groundwater‐Lake Interaction Zone Structure on Spatial Variability of Lacustrine Groundwater Discharge in Oxbow Lake by Xiaoliang Sun, Yao Du, Jiawen Xu, Hao Tian, Yamin Deng, Yiqun Gan, Yanxin Wang

“…Present study presents a high‐resolution quantitative estimation of LGD spatial patterns in an oxbow lake by combining thermal remote sensing with a 222Rn mass balance model. …”
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Understanding jobs-housing imbalance in urban China: A case study of Shanghai by Weiye Xiao, Dennis Wei, Han Li

“…First, the jobs-housing imbalance shows a ring pattern and is evident mainly in the suburban areas and periphery of the Shanghai metropolitan area because job opportunities are highly concentrated while residential areas are sprawling. …”
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Relationship between ecological spatial network and vegetation carbon use efficiency in the Yellow River Basin, China by Chenglong Xu, Xiang Chen, Qiang Yu, Buyanbaatar Avirmed, Jikai Zhao, Wei Liu, Weijie Sun

“…Furthermore, the Morphological Spatial Pattern Analysis (MSPA) method and Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) model were employed to delineate ecological sources for woodlands, shrublands, and grasslands, while the Minimum Cumulative Resistance (MCR) model was used to identify ecological corridors, forming an ecological spatial network within the YRB. …”
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Investigation into the Distribution Features and Determinants of Underground Commercial Spaces in Qingdao City by Jingwei Zhao, Heqing Wang, Yu Sun, Haoqi Li, Yinge Zhu

“…This paper quantifies and visualizes the layout and scale of underground commercial spaces in the central urban area of Qingdao by using kernel density, multi-distance spatial clustering, and spatial autocorrelation analysis and analyzes the influencing factors by using the geographical detector and MGWR model. …”
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Spatiotemporal characteristics and spatial heterogeneity of influencing factors in China’s urbanization process: based on nighttime light remote sensing data by Zhou Ren, Cancan Zhang, Long Fan, Xiaonan Zhao

“…Spatially, it exhibits a distribution pattern shifting from concentration to dispersion and from a single center to multiple centers. (2) The urbanization process in China exhibits a significant spatial correlation. …”
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Spatial Distribution Characteristics and Influencing Factors of Public Service Facilities for Children—A Case Study of the Central Urban Area of Shenyang by Ruiqiu Pang, Jiawei Xiao, Jun Yang, Weisong Sun

“…The Gini coefficient of various facilities is below the fair threshold of 0.4, but 90.32% of the study units have location entropy values below 1, indicating a supply–demand imbalance. (2) The spatial distribution of various facilities exhibits significant clustering characteristics, with distinct differences between high-value and low-value cluster patterns. (3) The spatial distribution of facilities is shaped by four factors: population, transportation, economy, and environmental quality. …”
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Spatial-temporal distribution characteristics, species selection, and influencing factors of cherry blossom scenic spots in China by Hongjun Peng, Ping Li, Xinyi Zhang, Yiwei Han, Rui Sun, Chunyan Zhang

“…However, systematic studies examining these scenic spots’ temporal and spatial patterns remain scarce, hindering effective planning across varying latitudes and urban contexts. …”
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Syntactic Analysis of Spatial Structure Characteristics of Traditional Villages Along the Yellow River in Western Shanxi Province by Chenge GAO, Fang WANG

“…This research explores the intrinsic connections between the spatial form and structure of traditional village settlements and between relevant cognitive and behavioral patterns, and explains the generation and evolution of landscape patterns, in an effort to enhance the understanding of the integration and centripetal degree of local and overall spatial functions within the village domain, the perceptibility of the spatial system, the consistency of behavioral experiences among residents and visitors, and the potential of local spaces to contribute to the overall spatial construction.MethodsThis research uses cluster sampling to select 33 national-level traditional villages in the four counties along the Yellow River in Lyuliang City, Shanxi Province, located on the eastern side of the Jin-Shaan Gorge in the middle reaches of the Yellow River. …”
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A Machine Learning Approach to Adapt Local Land Use Planning to Climate Change by Julia Forster, Stefan Bindreiter, Birthe Uhlhorn, Verena Radinger-Peer, Alexandra Jiricka-Pürrer

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“Target–Classification–Modification” Method for Spatial Identification of Brownfields: A Case Study of Tangshan City, China by Quanchuan Fu, Jingyuan Zhu, Xiaodi Zheng, Zhengxiang Li, Maini Chen, Yuyuwei He

“…It leverages integrated multi-source open-access data and clear rules for subtype classification and the determination of spatial boundaries and abandonment status. The results for Tangshan show that (1) the overall accuracy of the TCM method reached 84.9%; (2) a total of 1706 brownfield sites were identified, including 422 raw-material mining sites, 576 raw-material manufacturing sites, and 708 non-raw-material manufacturing sites; (3) subtype analysis revealed distinct spatial distribution and morphological patterns, driven by resource endowments, transportation networks, and industrial space organization. …”
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Neosols, relict paleosols, and alterites in the Transmexican Volcanic Belt, Morelos state: Characterization and regional spatial distribution by Jorge Enrique Gama-Castro, David Flores-Román, Elizabeth Solleiro-Rebolledo, Carolina Jasso-Castañeda, Ernestina Vallejo-Gómez, Ana María Rocha-Torrallardona, José Luis Villalpando-González

“…Our research results reveal that: (a) in the study area there are seven different soil groups, 19 subgroups, one group of relict paleosols, and two genetic types of alterites; (b) the phenomenon of topography-induced environmental differences, and the slope-profile position commonly have a notable influence on the spatial distribution and variability of soil groups; (c) all studied paleosols meet many of the taxonomic requirements for Luvisols, while some alterites satisfy the diagnostic characteristics of Fragipans or Duripans; (d) the pattern of paleosequence spatial distribution in the Morelos landscapes is closely parallel to the distribution of late Pleistocene–Holocene volcanic geomorphic elements. …”
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Spatial and temporal variation of water stress in China and its driving factors: A multi-scale analysis by Jianmin Qiao, Qin Zhang, Jing Shao, Qian Cao, Haimeng Liu, Furong Lv

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Modeling of spatial spread of COVID-19 pandemic waves in Russia using a kinetic-advection model by V. V. Aristov, A. V. Stroganov, A. D. Yastrebov

“…The aim of the present work is to construct a mathematical model for the spread of the pandemic and identify patterns under the assumption that Moscow comprises the main source of viral infection in Russia. …”
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Spatial distribution and pollution assessment of nitrogen, phosphorus, and heavy metals in the surface sediments of Xiaonanhai Lake, Hubei Province by Wei Liang, Yang Ding, Jinyong Zhao, Qiwen Wang, Wenqi Peng, Zhenghe Xu, Muyun Yan

“…This study analyzed the spatial patterns of heavy metal pollution and eutrophication at 36 water sampling sites in the Xiaonanhai Lake area, focusing on eight heavy metals: Cd, Cr, Cu, Ni, As, Pb, Hg, and Zn. …”
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Revealing multi-scale spatial synergy of mega-city region from a human mobility perspective by Bichen Fang, Mingxiao Li, Zhengdong Huang, Yang Yue, Wei Tu, Renzhong Guo

“…Mobility distance is short in urban centers and long in suburban areas. (2) The spatial integration pattern shows the detected communities reveal the hierarchical integration pattern with three main integrated regions: Guangzhou-Foshan-Zhaoqing, Shenzhen-Dongguan-Huizhou, and Zhuhai-Zhongshan-Jiangmen. (3) The spatial connection pattern illustrates the close ties of 9 cities and three core cities, including Guangzhou, Shenzhen, and Foshan. …”
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