An Analysis of Spatial Distribution of Urban Services with Focus on Social Justice and Emphasis on Citizens\' Satisfaction (Case Study: District 11 of Mashhad) by ahmad asadi, roghayeh kalateh meymari

“…Using the Cochran formula, the sample size of the population was estimated at 383 people and the data were obtained using spatial analysis ArcGIS and SPSS software have been analyzed. …”
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Spatial distribution and influencing factors of China’s overseas farmland investment projects under the background of food security by Yameng Wang, Fan Zou, Luning Wang, Huawang Jin, Linyan Ma

“…Based on the case data of China’s overseas cultivated Land investment in the Land Matrix database, this paper sorted out the utilization status of China’s global cultivated land resources, and analyzed the spatial distribution and influencing factors by using spatial analysis method and grey correlation analysis method. …”
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Spacetime modeling of mortality by infectious and parasitic diseases in Brazil: a 20-year ecological and population-based study by Lucas Almeida Andrade, Carlos Dornels Freire de Souza, Wandklebson Silva da Paz, Danilo de Gois Souza, José Augusto Passos Góes, Emerson Lucas Silva Camargo, Álvaro Francisco Lopes de Sousa, Liliane Moretti Carneiro, Isabel Amélia Costa Mendes, Karina Machado Araújo, Allan Dantas dos Santos, Márcio Bezerra-Santos

“…We applied the segmented log-linear regression model to assess temporal trends. For spatial analysis, we used the local empirical Bayesian estimator and Moran indices. …”
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Risk Analysis of Groundwater Contaminant in Rural Areas Using Spatial Distribution by Herawati Herawati, Maria Kanan, Anwar Mallongi, Ramli Bidullah, Sandy N. Sakati, Dwi Wahyu Balebu

“…Method: This study aims to analyse the level of pollution risk of groundwaters in rural areas of Masama District, Banggai Regency using descriptive quantitative methods. Spatial analysis of pollution risk was conducted on 516 Groundwaters selected as samples. …”
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Anthropogenic Disturbance and Climate Change Impacts on the Suitable Habitat of Sphenomorphus incognitus in China by Kai Chen, Li Ma, Weijun Jiang, Lijin Wang, Li Wei, Hongji Zhang, Ruhao Yang

“…Human disturbance has significantly reduced suitable habitat by 44.13 × 104 km2, which is a decrease in 23.95% compared to natural conditions. Spatial analysis revealed substantial fragmentation of suitable habitat due to human activities. …”
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Land use transition and its driving mechanisms in China’s human-elephant conflict areas by WANG Yuan, WANG Yahui, YANG Aoxi, FAN Hui, XIE Fei

“…[Methods] This study focuses on the human-elephant conflict areas in Pu’er City and Xishuangbanna Prefecture in Yunnan Province as the study areas, based on four periods of remote sensing images in 1990, 2000, 2010, and 2020, as well as two periods of household questionnaire survey data in 2020 and 2022, utilizing spatial analysis and input-output analysis methods to reveal the characteristics and driving mechanisms of land use transition in human-elephant conflict zones. …”
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Risk Factors Associated with Dengue Transmission and Spatial Distribution of High Seroprevalence in Schoolchildren from the Urban Area of Medellin, Colombia by Leidy Diana Piedrahita, Ivony Y. Agudelo Salas, Katherine Marin, Andrea I. Trujillo, Jorge E. Osorio, Sair Orieta Arboleda-Sanchez, Berta Nelly Restrepo

“…A longitudinal serological survey (IgG) and spatial analysis were conducted to determine the distribution of DENV seroprevalence. …”
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Spatial and Temporal Relationships Between Roe and Red Deer in an Alpine Area by Valerio Donini, Luca Pedrotti, Francesco Ferretti, Elisa Iacona, Lucrezia Lorenzetti, Francesca Cozzi, Luca Corlatti

“…Results showed no significant effect of red deer relative abundance on the probability of presence of roe deer. Spatial analysis suggested a higher probability of roe deer presence in forested habitats, at lower elevations, and in areas with gentler slopes. …”
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Invariant Spatial Pattern Across Mediterranean Scrublands in the Iberian Pear (Pyrus bourgaeana) by Brayan Morera, Pedro J. Garrote, Thorsten Wiegand, Daniel Ayllón, Jose M. Fedriani

“…We tested two hypotheses: (i) because the clumped defecation behavior of some dispersers can lead to local tree aggregation, and because denser groups of fruiting trees can limit seed dispersal by attracting frugivores to specific sites, we expected a consistent small‐scale aggregation pattern across all populations; and (ii) because ungulates reduce recruitment by preying on seeds and seedlings, we hypothesize that ungulate activity will show negative relationships with tree density and level of aggregation. Our spatial analysis revealed consistent and highly aggregated small‐scale patterns of all Iberian pear populations, with one critical scale aggregation, a low density of clusters and high variability in the number of trees per cluster. …”
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iMESc – an interactive machine learning app for environmental sciences by Danilo Cândido Vieira, Danilo Cândido Vieira, Fabiana S. Paula, Luciana Erika Yaginuma, Gustavo Fonseca

“…Developed in R and built on the Shiny platform, iMESc enables the integration of supervised and unsupervised ML methods, along with tools for data preprocessing, visualization, descriptive statistics, and spatial analysis. The Datalist system ensures seamless transitions between analytical workflows, while the “savepoints” feature enhances reproducibility by preserving the analysis state. …”
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Characteristics of successful expansion of tumor-infiltrating lymphocytes from colorectal cancer liver metastasis by Jina Baek, Gyuheon Choi, GunHee Lee, Hyun Lee, Gyungyub Gong, Hye Seon Park, Chae-Lyul Lim, Joo Young Kim, Hee Jin Lee

“…Histologic examination including the level of stromal TILs and Klintrup-Mäkinen score, were assessed by pathologists and deep learning-derived spatial analysis. We performed correlation analysis between expanded TILs and histopathologic factors. …”
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Fourier Transform Infrared Spectroscopic Imaging of Fracture Healing in the Normal Mouse by Hans Gollwitzer, Xu Yang, Lyudmila Spevak, Lyudmila Lukashova, Allina Nocon, Kara Fields, Nancy Pleshko, Hayden William Courtland, Mathias P. Bostrom, Adele L. Boskey

“…Fourier transform infrared spectroscopic imaging (FTIRI) was used to study bone healing with spatial analysis of various callus tissues in wild type mice. …”
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Spatial associations of Hansen's disease and schistosomiasis in endemic regions of Minas Gerais, Brazil. by Jessica L Stephens, Lucia A O Fraga, José A Ferreira, Laura De Mondesert, Uriel Kitron, Julie A Clennon, Jessica K Fairley

“…A multivariate non-spatial analysis found that census tracts with MB disease were more likely to have schistosomiasis when adjusted for population density, household density, and household income (aOR = 1.7, 95% CI 1.0, 2.7). …”
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Smart village concept in Indonesia: ICT as determining factor by Anindya Puteri Eka Susilowati, Rini Rachmawati, R. Rijanta

“…The research was conducted using a quantitative descriptive approach and supported by spatial analysis. The exploration of determining factors for the smart village concept involves 12 variables sourced from the Village Potential data in 2021 and analyzed statistically using factor analysis. …”
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Classifying tumour infiltrating lymphocytes in oral squamous cell carcinoma histopathology using joint learning framework by Barun Barua, Genevieve Chyrmang, Kangkana Bora, Gazi N. Ahmed, Lopamudra Kakoti, Manob Jyoti Saikia

“…Only a few studies have been conducted in automating TILs quantification for OSCC, existing methods use score-based systems that focus only on tissue-level spatial analysis, overlooking essential cellular-level information and do not provide TILs infiltration subcategories required for determining OSCC grading. …”
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Evaluation and Optimization Research on the Spatial Distribution of Automated External Defibrillators Based on a Genetic Algorithm: A Case Study of Central Urban District of Nanjin... by Ge Shi, Jiahang Liu, Chuang Chen, Jingran Zhang, Jinghai Xu, Yu Chen, Jiaming Na, Wei Chen

“…Initially, this paper evaluates the current spatial configuration of AEDs in the central urban district of Nanjing using various spatial analysis methods. Subsequently, a coverage model is constructed to simulate the coverage capacity of potential emergency needs for new facilities, and a genetic algorithm is utilized to solve it. …”
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Spatiotemporal differentiation and influencing factors of primary, secondary, and tertiary industries integration in China’s four major tea-producing regions by ZHENG Feifei, ZHAN Huilong, LEI Guoquan, CHEN Meiying

“…The entropy method was employed to measure the level of industrial integration in these areas across five dimensions: actors, technology, products, business, and markets. Using spatial analysis techniques such as kernel density estimation, convergence analysis, the diamond model, and Multi-scale Geographically Weighted Regression, the study explored the spatiotemporal change patterns of industrial integration and the spatial heterogeneity of influencing factors. …”
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Assessing the Impact of Land Use and Land Cover Change on Environmental Parameters in Khyber Pakhtunkhwa, Pakistan: A Comprehensive Study and Future Projections by Mehjabeen Khan, Ruishan Chen

“…Vegetation health, as indicated by the Leaf Area Index (LAI), also declined, with maximum and minimum values falling from 4.66 and −5.75 in 2000 to 2.16 and −2.55 in 2020, reflecting increased barren land and reduced greenness. The spatial analysis highlights significant transitions from vegetated to barren or urban land, leading to declining moisture levels, water stress, soil erosion, and biodiversity. …”
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Integrated Serosurveillance of Infectious Diseases Using Multiplex Bead Assays: A Systematic Review by Selina Ward, Harriet L. S. Lawford, Benn Sartorius, Colleen L. Lau

“…The synthesis of data varied and included the investigation of age-specific seroprevalence (<i>n</i> = 25), risk factor analysis (<i>n</i> = 15), and spatial analysis of disease prevalence (<i>n</i> = 8). …”
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Spatial and Temporal Variation Characteristics of Forests and Analysis of Potential for Reconstruction of Water Conservation Forests in the Dongjiang River Basin from 1980 to 2020 by DAI Miaolin, LU Xiaoqi, ZANG Chuanfu, ZHU Ke, QIU Xintong, LUO Yiwen, ZHANG Weiqiang, GAN Xianhua

“…The Dongjiang River is the most important water source area in the Guangdong-Hong Kong-Macao Greater Bay Area,supplying water for the three mega-cities of Hong Kong,Shenzhen,and Guangzhou.The Dongjiang River Basin has abundant light and heat resources and sufficient rainfall,and it is one of the regions with the highest forest coverage rate in China.Since 1980,the land cover,especially the forest with the largest proportion in the Dongjiang River Basin,has changed significantly in the Dongjiang River Basin under the influence of natural factors,policy,and economic development.Therefore,how to scientifically plan and design afforestation projects and transform water conservation forests according to potential has become a hot social issue.The study was based on 30 m resolution land use and soil data of the Dongjiang River Basin from 1980 to 2020,and it combined with the data of Dongjiang River Source station and socio-economic index data.The study used spatial analysis and factor analysis to explore the spatial-temporal changes and driving mechanisms of forest cover in the Dongjiang River Basin from 1980 to 2020 and analyze the potential of water conservation forest reconstruction in the future.① From 1980 to 2020,the forest decreased continuously in the Dongjiang River Basin,with a total reduction of 998.81 km².The changes were mainly concentrated in the south and north of the Dongjiang River Basin.In the northern part of the basin,a great deal of forest has been transformed into farmland,while in the southern part of the basin,a great deal of forest has been transformed into cities and countrysides or areas for industry,mining,and housing.② In the period of 1980—2020,the main factors affecting the change of forest area were the number of permanent residents,population density,GDP,output value of secondary and tertiary industries,output value of industry,agriculture,and forestry,total retail sales of social consumer goods,investment in fixed assets,and urban registered unemployment rate.③ The area for future reconstruction in the Dongjiang River Basin was 5 365 km².The areas with high potential for reconstruction were mainly in the north and southeast parts of the basin.In the past 40 years,the forest area of the Dongjiang River Basin has continued to decrease and is strongly influenced by human activities.Water conservation forest has great potential to be transformed.…”
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