Dispatcher-Assisted Cardiopulmonary Resuscitation: Disparity between Urban and Rural Areas by Yen-Chin Chen, Shao-Hua Yu, Wei-Jen Chen, Li-Chi Huang, Chih-Yu Chen, Hong-Mo Shih

“…The 29 districts of Taichung city were divided into urban and rural areas based on whether the population density is more than 1,000 people per square kilometer. …”
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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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Under a not so white sky: visual impacts of stratospheric aerosol injection by Ansar Lemon, David W Keith, Steve Albers

“…We consider stratospheric aerosol loadings required to produce radiative forcings of −1, −2, and −4 W m ^−2 . We use population density and AOD data to compute the distribution of AODs people experience and then simulate sky images for the 10th, 50th, and 90th percentiles of that distribution. …”
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Bacillus velezensis A-27 as a potential biocontrol agent against Meloidogyne incognita and effects on rhizosphere communities of celery in field by Yanan Yao, Liyi Wang, Haozhen Zhai, Hailong Dong, Jianming Wang, Zengqi Zhao, Yumei Xu

“…Field experiments further indicated that A-27 reduced the root gall index with a control efficacy of 67.31%, while also decreasing the J2 population density of M. incognita and significantly enhancing the growth of celery plants. …”
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Based on the MaxEnt model the analysis of influencing factors and simulation of potential risk areas of human infection with avian influenza A (H7N9) in China by Zhao Yang, Zhao Yang, Zhong Da Ren, Zhong Da Ren, Jie Wang, Wen Dong, Wen Dong

“…After verification, the model simulation results are generally consistent with the actual outbreak of the epidemic. Population density was the main influencing factor of the epidemic, and the secondary influencing factors included vegetation coverage, precipitation, altitude, poultry slaughter, production value, and temperature. …”
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“site suitability analysis for integrating large-scale hybrid photovoltaic and concentrated solar power plants in Cameroon using Monte Carlo, analytic hierarchy process, and geogra... by Fotsing Metegam Isabelle Flora

“…Twelve critical factors, such as solar irradiation, infrastructure proximity, and population density, were evaluated to assess land suitability for solar energy development. …”
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Modeling optimal combination of breast and cervical cancer screening strategies in China by Xuewei Liu, Yanbo Wang, Bei Gao, Xinlin Lu, Yuan Wang, Wenli Lu

“…Optimizing screening strategies for breast and cervical cancers is a challenge in resource-limited settings with a high population density such as China. Therefore, we aimed at assessing the efficiency of different combined screening strategies for breast and cervical cancers under different budgets in China. …”
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A retrospective record-based study to evaluate the profile of TB patients diagnosed and registered at DOTS center at the tertiary care center and to study the gaps in the implement... by Ruchi Arora Sachdeva, Sahil Singh, MK Sen

“…India has been a breeding ground for this disease due to high population density and lack of awareness and education in the population. …”
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Spatial identification of regions exposed to multi-hazards at the pan-European level by T.-E. Antofie, T.-E. Antofie, S. Luoni, A. Tilloy, A. Sibilia, S. Salari, G. Eklund, D. Rodomonti, C. Bountzouklis, C. Corbane

“…We find that beyond population density, the income level (i.e. high, medium, low) is the primary driver that influences risk status at the local level, within both rural and urban areas. …”
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Measuring geographical disparities in England at the time of COVID-19: results using a composite indicator of population vulnerability by Daniel Lasserson, Stuart Redding, Catia Nicodemo, Samira Barzin, Nicolo' Cavalli, Francesco Moscone, Mujaheed Shaikh

“…These measures cover prevalence rates of high-risk diseases; proxies for the at-risk population density; availability of staff and quality of healthcare facilities.Results We find that 80% of CCGs that score in the highest quartile of vulnerability are located in the North of England (24 out of 30). …”
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Mechanisms and thresholds of land use affecting surface water quality in Hangzhou City’s residential areas by Qianhu Chen, Jinye Huang

“…Green spaces per unit area must perform environmental purification in high-rise residential areas due to their higher population density, which puts more demands on their design and ecological service activities. …”
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Neonatal and perinatal mortality in the urban continuum: a geospatial analysis of the household survey, satellite imagery and travel time data in Tanzania by Lenka Benova, Claudia Hanson, Aliki Christou, Aline Semaan, Peter M Macharia, Jessie Pinchoff, Andrea B Pembe

“…The coordinates of 527 DHS clusters were spatially overlaid with the 2015 Global Human Settlement Layer, showing the degree of urbanisation based on built environment and population density. A three-category urbanicity measure (core urban, semi-urban and rural) was defined and compared with the binary DHS measure. …”
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The equivalent value (EV)-based workload assessment of primary healthcare workers in Beijing, China by Shasha Yuan, Tao Yin, Naijie Weng, Zheng Wang, Delu Yin

“…The study included 18 CHCs, which were divided into three groups based on population density and topography: Group I (eight urban CHCs), Group II (six CHCs in semi-mountainous areas), and Group III (four CHCs in mountainous areas). …”
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Ecosystem services value for water provisioning in the Shule River Basin of northwestern China: Spatial and temporal evolution and drivers from 2000 to 2022 by Peng Shi, Dongmei Zhou, Jing Jiang, Jing Ma, Xiaoyan Zhu, Jun Zhang, Zihe Li, Qinghan Dong

“…Following the order of decreasing importance, these were land cover, land-use type, topographic surface (represented by the DEM), precipitation, temperature, population density, and economic activity (represented by nighttime lighting). …”
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How Does Heavy Precipitation of Varying Durations Respond to Urbanization in China? by Xue Xie, Kairong Lin, Mingzhong Xiao, Xudong Zhou, Gang Zhao, Dai Yamazaki

“…Anthropogenic‐related factors (population density, nighttime light data, impervious surface percent, land surface temperature) significantly influence shorter‐duration heavy precipitation in more UACs than natural factors (distance from the coast, wind and elevation), while natural factors dominate longer‐duration events in larger UACs across China. …”
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Geospatial mapping of drug-resistant tuberculosis prevalence in Africa at national and sub-national levels by Alemneh Mekuriaw Liyew, Archie C.A. Clements, Fasil Wagnew, Beth Gilmour, Kefyalew Addis Alene

“…Factors such as mean temperature (β = 2.01; 95% CrI: 1.21, 3.42), population density (β = 0.41; 95% CrI: 0.19, 0.95), and fine particulate matter (β = 0.66; 95% CrI: 0.20, 0.80) were positively associated with DR-TB prevalence. …”
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Predicting the effects of introducing an emergency transport system in low-income and middle-income countries: a spatial-epidemiological modelling study by Justine Davies, Dmitri Nepogodiev, Stephen Tabiri, Richard Lilford, Srinivasa Vittal Katikireddi, Samuel I Watson, Katie Scandrett

“…We apply the model to the illustrative case study of providing emergency transport for postpartum haemorrhage in Northern Ghana.Results The model predicts that the effects of an ambulance service are likely to be ephemeral, varying according to local circumstances such as population density and road networks. In our applied example, the introduction of the ambulance service may save 40 lives (95% credible interval 5 to 111), or up to 107 lives (95% credible interval −293 to –13) may be lost across the region in a year, dependent on various model assumptions and parameter specifications. …”
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Effect of Flowering Shading on Grain Yield and Quality of Durum Wheat in a Mediterranean Environment by Giancarlo Pagnani, Alfredo Lorenzo, Nausicaa Occhipinti, Lisa Antonucci, Sara D’Egidio, Fabio Stagnari, Michele Pisante

“…The phenomenon known as “dimming” or shading, caused by the increase in aerosols, air pollutants, and population density, is reducing global radiation, including both direct solar radiation and radiation scattered by the atmosphere. …”
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Spatial epidemiology based on the analysis of COVID-19 in Africa by Joyce Akhalakwa Mukolwe, John Kamwele Mutinda, Amos Kipkorir Langat

“…Socioeconomic factors, including the human development index (HDI), GDP, and population density, also significantly affect both case rates and vaccination coverage. …”
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The Spatio-temporal Evolution of NDVI in the Three Gorges Reservoir Area Driven by the Synergistic Effect of two Factors over the Past 21 Years by QI Haimei, ZHENG Peilong, WANG Yunqi, WANG Jiani, LI Cheng, ZHANG Xiaoming

“…[Results] (1) The change of NDVI in the Three Gorges Reservoir Area showed an overall ascending trend from 2000 to 2020, with a mean annual rate of increase at 2.89×10-3/a, and the value of NDVI increased from the southwestern to the northeastern parts of the area. (2) Elevation, population density, and surface temperature are pivotal factors that significantly account for the variability observed in NDVI, with an explanatory power for each exceeding the threshold of 0.4. (3) Elevation and nocturnal illumination are the key interactive drivers of NDVI in the Three Gorges Reservoir Area, with a pronounced q value of 0.641. …”
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