Specific Urban Elements Identified for Tuberculosis Epidemic in Guangzhou by Using Geographical Detector

Background: The prevalence of tuberculosis (TB) in China has heavily affected people’s health for decades, which has been widely investigated for the rural regions and west parts. However, its spatial features in urban areas remain little understood. Thus, this study aims to identify its spatial differentiations and their influencing factors in highly urbanized region on a fine scale.Methods: Together with the TB cases in 2017 obtained from Guangzhou Institute of Tuberculosis Control and Prevention, in total 18 socioeconomic and environmental variables were included in this study. Two spatial analysis tools were respectively applied to select the relative appropriate spatial scale (global Moran’s I), and to identify specific urban factors (the Geographical detector) for this epidemic in the central four districts of Guangzhou.Results: The 2 km × 2 km grid was determined as the most appropriate spatial scale due to its relatively higher spatial autocorrelation (Moran’s I=0.33, Z=4.71). At this spatial level, the TB epidemic in the four central districts was obviously closely associated with most of socioeconomic factors (0.31<r<0.76) at the significance level of 0.01. By contrast, among environmental factors, only the concentration of fine particulate matter (PM2.5) correlated with this epidemic (r=0.21) at the significance level of 0.05. Similarly, according to the q-values derived from geographical detector analysis, socioeconomic factors posed stronger impacts (0.08<q<0.57) on the spatial differentiations of TB prevalence than those of environmental variables (0.06<q<0.27), Furthermore, 153 pairs of variables presented more powerful explanatory abilities for this epidemic’s spatial disparities due to their notable enhancements of q-values (7.3%<sq<311.6%) caused by the pairwise interactions.Conclusion: The spatial heterogeneity of TB prevalence was remarkably influenced by a series of specific urban elements and their pairwise interactions across the central region of Guangzhou. We accordingly suggest that more attentions should be paid to the areas with pairwise interactions of these specific urban elements in this city. This study would provide meaningful clues for local authorities making more targeted interventions on this disease in China’s municipal areas featured by both high urbanization and severe tuberculosis.

Spatial–Temporal Evolution of Vegetation NDVI in Association with Climatic, Environmental and Anthropogenic Factors in the Loess Plateau, China during 2000–2015: Quantitative Analysis Based on Geographical Detector Model

In the Loess Plateau (LP) of China, the vegetation degradation and soil erosion problems have been shown to be curbed after the implementation of the Grain for Green program. In this study, the LP is divided into the northwestern semi-arid area and the southeastern semi-humid area using the 400 mm isohyet. The spatial–temporal evolution of the vegetation NDVI during 2000–2015 are analyzed, and the driving forces (including factors of climate, environment, and human activities) of the evolution are quantitatively identified using the geographical detector model (GDM). The results showed that the annual mean NDVI in the entire LP was 0.529, and it decreased from the semi-humid area (0.619) to the semi-arid area (0.346). The mean value of the coefficient of variation of the NDVI was 0.1406, and it increased from the semi-humid area (0.1165) to the semi-arid area (0.1926). The annual NDVI growth rate in the entire LP was 0.0079, with the NDVI growing faster in the semi-humid area (0.0093) than in the semi-arid area (0.0049). The largest increments of the NDVI were from grassland, farmland, and woodland. The GDM results revealed that changes in the spatial distribution of the NDVI could be primarily explained by the climatic and environmental factors in the semi-arid area, such as precipitation, soil type, and vegetation type, while the changes were mainly explained by the anthropogenic factors in the semi-humid area, such as the GDP density, land-use type, and population density. The interactive analysis showed that interactions between factors strengthened the impacts on the vegetation change compared with an individual factor. Furthermore, the ranges/types of factors suitable for vegetation growth were determined. The conclusions of this study have important implications for the formulation and implementation of ecological conservation and restoration strategies in different regions of the LP.

Spatial Heterogeneity and Driving Factors of Soil Moisture in Alpine Desert Using the Geographical Detector Method

Soil moisture is a vital factor affecting the hydrological cycle and the evolution of soil and geomorphology, determining the formation and development of the vegetation ecosystem. The previous studies mainly focused on the effects of different land use patterns and vegetation types on soil hydrological changes worldwide. However, the spatial heterogeneity and driving factors of soil gravimetric water content in alpine regions are seldom studied. On the basis of soil sample collection, combined with geostatistical analysis and the geographical detector method, this study examines the spatial heterogeneity and driving factors of soil gravimetric water content in the typical alpine valley desert of the Qinghai–Tibet Plateau. Results show that the average value of soil gravimetric water content at different depths ranges from 3.68% to 7.84%. The optimal theoretical models of soil gravimetric water content in 0–50 cm layers of the dune are different. The nugget coefficient shows that the soil gravimetric water content in the dune has a strong spatial correlation at different depths, and the range of the optimal theoretical model of semi-variance function is 31.23–63.38 m, which is much larger than the 15 m spacing used for sampling. The ranking of the influence of each evaluation factor on the alpine dune is elevation > slope > location > vegetation > aspect. The interaction detection of factors indicates that an interaction exists among evaluation factors, and no factors are independent of one another. In each soil layer of 0–50 cm, the interaction among evaluation factors has a two-factor enhancement and a nonlinear enhancement effect on soil gravimetric water content. This study contributes to the understanding of spatial heterogeneity and driving factors of soil moisture in alpine deserts, and guidance of artificial vegetation restoration and soil structure analysis of different desert types in alpine cold desert regions.

Identifying Influencing Factors of Agricultural Soil Heavy Metals Using a Geographical Detector: A Case Study in Shunyi District, China

Identifying influencing factors of heavy metals is essential for soil evaluation and protection. This study investigates the use of a geographical detector to identify influencing factors of agricultural soil heavy metals from natural and anthropogenic aspects. We focused on six variables of soil heavy metals, i.e., As, Cd, Hg, Cu, Pb, Zn, and four influencing factors, i.e., soil properties (soil type and soil texture), digital elevation model (DEM), land use, and annual deposition fluxes. Experiments were conducted in Shunyi District, China. We studied the spatial correlations between variables of soil heavy metals and influencing factors at both single-object and multi-object levels. A geographical detector was directly used at the single-object level, while principal component analysis (PCA) and geographical detector were sequentially integrated at the multi-object level to identify influencing factors of heavy metals. Results showed that the concentrations of Cd, Cu, and Zn were mainly influenced by DEM (p = 0.008) and land use (p = 0.033) factors, while annual deposition fluxes were the main factors of the concentrations of Hg, Cd, and Pb (p = 0.000). Moreover, the concentration of As was primarily influenced by soil properties (p = 0.026), DEM (p = 0.000), and annual deposition flux (p = 0.000). The multi-object identification results between heavy metals and influencing factors included single object identification in this study. Compared with the results using the PCA and correlation analysis (CA) methods, the identification method developed at different levels can identify much more influencing factors of heavy metals. Due to its promising performance, identification at different levels can be widely employed for soil protection and pollution restoration.

Geographical Detector-based influence factors analysis for Echinococcosis prevalence in Tibet, China

Echinococcosis, caused by genus Echinococcus, is the most pathogenic zoonotic parasitic disease in the world. In Tibet of the People’s Republic of China, echinococcosis refers principally to two types of severe zoonosis, cystic echinococcosis (CE) and alveolar echinococcosis (AE), which place a serious burden on public health and economy in the local community. However, research on the spatial epidemiology of echinococcosis remains inadequate in Tibet, China. Based on the recorded human echinococcosis data, maps of the spatial distribution of human CE and AE prevalence in Tibet were produced at city level and county level respectively, which show that the prevalence of echinococcosis in northern and western Tibet was much higher than that in other regions. We employ a geographical detector to explore the influencing factors for causing CE and AE while sorting information on the maps of disease prevalence and environment factors (e.g. terrain, population, and yak population). The results of our analysis showed that biological factors have the most impact on the prevalence of echinococcosis, of which the yak population contributes the most for CE, while the dog population contributes the most for AE. In addition, the interaction between various factors, as we found out, might further explain the disease prevalence, which indicated that the echinococcosis prevalence is not simply affected by one single factor, but by multiple factors that are correlated with each other complicatedly. Our results will provide an important reference for the evaluation of the echinococcosis risk, control projects, and prevention programs in Tibet.

Geographical Detector-Based Spatial Modeling of the COVID-19 Mortality Rate in the Continental United States

Investigating the spatial distribution patterns of disease and suspected determinants could help one to understand health risks. This study investigated the potential risk factors associated with COVID-19 mortality in the continental United States. We collected death cases of COVID-19 from 3108 counties from 23 January 2020 to 31 May 2020. Twelve variables, including demographic (the population density, percentage of 65 years and over, percentage of non-Hispanic White, percentage of Hispanic, percentage of non-Hispanic Black, and percentage of Asian individuals), air toxins (PM2.5), climate (precipitation, humidity, temperature), behavior and comorbidity (smoking rate, cardiovascular death rate) were gathered and considered as potential risk factors. Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors. This study found that population density and percentage of non-Hispanic Black individuals were the two most important factors responsible for the COVID-19 mortality rate. Additionally, the interactive effects between any pairs of factors were even more significant than their individual effects. Most existing research examined the roles of risk factors independently, as traditional models are usually unable to account for the interaction effects between different factors. Based on the Geographical Detector technique, this study’s findings showed that causes of COVID-19 mortality were complex. The joint influence of two factors was more substantial than the effects of two separate factors. As the COVID-19 epidemic status is still severe, the results of this study are supposed to be beneficial for providing instructions and recommendations for the government on epidemic risk responses to COVID-19.