Revealing the evolution of spatiotemporal patterns of urban expansion using mathematical modelling and emerging hotspot analysis.

The rapid expansion of cities in developing countries has led to many environmental problems, and the mechanism of urban expansion (UE), as a more complex human-land coupled system, has always been a difficult issue to research. This paper introduces a new approach by establishing an analytical framework for spatiotemporal pattern mining, exemplified by studying the urban growth of Changsha City from 1990 to 2019. Initially, an emerging hotspot analysis model (EHA) is employed to examine the spatiotemporal changes of urban growth on a macro scale. Mathematical models are subsequently utilized to quantify the correlations between urban expansion and selected infrastructural and topographical factors. Building on these findings, the paper constructs mathematical models to further quantify the spatiotemporal evolution of various urban sprawl patterns across different regions, aiming to elucidate and quantify the significant variations in UE over time and space. The study reveals that, as an emerging city, Changsha's hotspots of urban expansion prior to 2003 were primarily concentrated in the city centre, subsequently spreading to the periphery. The radial influence of metro stations on UE is notably less than that of railway stations-approximately 3 km versus 8 km-and the impact diminishes rapidly before gradually tapering off. Moreover, UE in Changsha predominantly occurs on slopes with gradients ranging from 1.1° to 7.5°, and significant development capacity is observed at elevations between 36.1 m and 78.3 m above sea level, with a tendency for urban sprawl to migrate to lower elevations. The paper also identifies three distinct patterns of urban expansion across different regions: an initial slow-growth phase, followed by a rapid escalation to a peak, and subsequently a swift decline to near stagnation. Additionally, it highlights a significant correlation between the proportion of built-up areas at the micro-regional scale and the stages of UE. This correlation was quantitatively analysed by constructing a logistic function, which demonstrated a robust fit that effectively captures spatiotemporal heterogeneity in the dynamics of UE. These insights enhance the selection of drivers in urban simulation models and deepen the understanding of the complex dynamics that influence urban development.

Anaemia prevalence and socio-demographic factors among women of reproductive age (WRA): A geospatial analysis of empowered action group (EAG) states in India.

Anaemia remains a major nutritional-related health concern for women under reproductive age (WRA) in developing nations like India as well as the Indian EAG states. According to NFHS round-5, EAG states constitute 57% of WRA having any form of anaemia, higher than many other states of India and other developed and developing nations. This study aimed to assess the frequency of anaemia among the WRA in India's eight EAG states. Also, it attempts to analyse the causes associated with anaemia by the women's background characteristics with spatial correlation with its co-variates across 291 districts of the EAG states. One of the most current Demographic and Health Survey's (DHS) cross-sectional data is the NFHS-5th (2019-21) round taken, conducted by the IIPS under the administration of MoHFW, India. This study only included 315,069 women under reproductive age (WRA). The variables related to anaemia among women's (WRA) background socio-demographic characteristics were assessed using bivariate statistics and multinominal logistic regression analysis to comprehend the spatial correlation between women and their determinant factors. Among the EAG states, the overall prevalence of anaemia was 57%, varying from 42.6% in Uttarakhand to 65.3% in Jharkhand. Multinominal logistic regression analyses reveal that the chances of anaemia are remarkably more prevalent in younger women (15-19 years of age), women living in rural areas, no educated and primary level educated women, women belonging to the middle to poorest wealth quintile, women no longer living together, women of the Christian religion, women who are not exposed to reading newspapers, underweight BMI women, and scheduled tribe women. Mainly, the prevalence is observed in the North-eastern and southeastern states of Bihar, Jharkhand, Odisha, Chhattisgarh, some parts of Madhya Pradesh, Uttar Pradesh, and Rajasthan, which is shown by the hotspot map. According to the findings of this study, numerous factors like family, socioeconomic, educational, awareness, and individual characteristics such as caste and domicile all lead to a risk of anaemia. The WRA suffers from anaemia as a result of their socioeconomic background and awareness, which leads to a lack of nourishment, and they seek nutrient deficiencies. To overcome this anaemia, multiple discipline policies and initiatives need to be taken targeting women's wellness and nutritional status by increasing women's education and socioeconomic status.

Spatial patterns of urban expansion and cropland loss during 2017-2022 in Guangdong, China.

Urban expansion often occurs at the expense of cropland loss, posing challenges to sustainable urban growth and food security. However, detailed investigations into urban expansion and cropland loss remain limited, particularly in regions with varying levels of urbanization. Here, we take Guangdong Province, China, as a case study to exemplify how urban expansion affects cropland using remotely sensed land use products. We adopted geospatial analysis, correlation indicators, and landscape metrics to uncover their spatial relationships at 10-m spatial resolutions. Results showed that urban areas increased by 6335 km2 while cropland decreased by 3780 km2 from 2017 to 2022. Notably, 41 % of newly expanded urban areas were from croplands, and 45 % of lost croplands were converted to urban areas. Western Guangdong experienced the largest extent of urban expansion and cropland loss, emerging as a hotspot region in recent years. Additionally, our analysis observed the increasing compactness of urban areas and the growing fragmentation of cropland landscapes over time. These findings shed light on the intricate dynamics between urban expansion and cropland loss in rapidly urbanizing regions, which provide valuable insights for sustainable urban development, agricultural practice, and land management in the future.

Social Hotspot analysis of the e-waste sector in Ghana and Nigeria.

The transition towards a circular economy, which has emerged as a promising approach to achieving sustainable development and which involves the reuse of e-waste, may cause significant social impacts on vulnerable groups if it is not addressed in a proper manner. The European Union has established a clear circular economy plan with particular attention on e-waste treatment. However, a considerable amount of e-waste still ends up outside Europe, mainly in African Countries. Social Life Cycle Assessment (S-LCA) has been recognized as a valid methodology to assess the social impacts caused by products or services to stakeholders involved along the life cycle perspective. Most S-LCA studies are conducted in the formal sector; however, informal sectors constitute a crucial element of the economy in emerging and developing countries. In particular, formal waste management practices are complemented by the informal sector in Indonesia, African and Latin American countries. The informal sector represents an important support element. Few studies have been conducted on the social impacts of the e-waste sector and even fewer have been implemented following the guidelines of Social Life Cycle Assessment for Products and Organizations. However, we are still far from a complete overview of the social impacts in the e-waste sector, in particular for developing countries. Knowing that most e-waste through illegal exportation ends up in African countries, a social hotspot analysis has been conducted on the electronic sector in African countries, focusing on Nigeria and Ghana, which are the most affected. The study also identifies a set of indicators needed to assess the social performance of e-waste in Nigeria and Ghana.

Spatial distribution of zero-dose children in Ethiopia: evidence for a targeted intervention from a large-scale cross-sectional evaluation survey.

Background: Ethiopia is the fourth leading contributor to the global total of zero-dose children (those who lack the first dose of diphtheria-tetanus-pertussis containing vaccine) and has substantial regional variations in zero-dose children. This study explored the spatial pattern of zero-dose children aged 12-35 months in Ethiopia. Methods: A survey was conducted in pastoralist regions, developing regions, newly-established regions, conflict-affected areas, underserved urban populations, hard-to-reach areas, internally displaced populations, and refugees. Spatial autocorrelation was measured using the Global Moran'sIstatistic. Getis-Ord Gi* statistics was applied to calculate the spatial variability of the high and low prevalence rates of zero-dose children. The spatial interpolation technique was also applied to estimate unknown values that fall between known values. Inverse distance weighting interpolation method was used to predict the risk of zero-dose children. ArcGIS version 10.8 was used for the spatial analysis. Results: A total of 3,646 children aged 12-35 months were included in the study. The spatial distribution of zero-dose children in Ethiopia was non-random (Global Moran'sI = 0.178971, p < 0.001). According to the hotspot analysis, western, eastern and northern parts of Somali and western and central parts of Afar regions had the highest load of zero-dose children (hotspot areas) followed by the Northeastern part of Amhara and southeastern part of Oromia regions. On the other hand, Southern Nations, Nationalities, and Peoples, Sidama, and the Eastern part of the Southwest Ethiopia peoples regions were identified as cold spot areas. The spatial interpolation analysis corresponded with the hotspot analysis results where western and central parts of Afar and western, eastern and northern parts of Somali regions were identified as high-risk areas for zero-dose children. However, Addis Ababa, Dire Dawa, Harari, Southern Nations, Nationalities, and Peoples, Sidama, Southwest Ethiopia Peoples, and parts of Oromia were found to be low-risk areas for zero-dose children. Conclusion: The spatial analysis identified that zero-dose children had a significant spatial variation across the study areas. High clusters of zero-dose children were detected in Afar and Somali regions. Implementing routine and mop-up vaccination campaigns in the identified hotspot areas will help Ethiopia to improve coverage and reduce immunization inequalities.

Exploring urban land surface temperature using spatial modelling techniques: a case study of Addis Ababa city, Ethiopia.

Urban areas worldwide are experiencing escalating temperatures due to the combined effects of climate change and urbanization, leading to a phenomenon known as urban overheating. Understanding the spatial distribution of land surface temperature (LST) and its driving factors is crucial for mitigation and adaptation of urban overheating. So far, there has been an absence of investigations into spatiotemporal patterns and explanatory factors of LST in the city of Addis Ababa. The study aims to determine the spatial patterns of land surface temperature, analyze how the relationships between LST and its factors vary across space, and compare the effectiveness of using ordinary least squares and geographically weighted regression to model these connections. The findings showed that the spatial patterns of LST show statistically significant hot spot zones in the north-central parts of the study area (Moran's I = 0.172). The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic.The result revealed non-stationarity (p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. The relationship between LST and its explanatory variables were modelled using ordinary least square model and thereby tested if there is spatial dependence in the model using the Koenker (BP) Statistic. The result revealed non-stationarity ((p = 0.000) and consequently geographically weighted regression was employed to compare the performance with OLS. The research has revealed that, GWR (R2 = 0.57, AIC = 1052.1) is more effective technique than OLS (R2 = 0.42, AIC = 2162.0) for studying the relationship LST and the selected explanatory variables. The use of GWR has improved the accuracy of the model by capturing the spatial heterogeneity in the relationship between land surface temperature and its explanatory variables. Consequently, Localized understanding of the spatial patterns and the driving factors of LST has been formulated.

Spatio-temporal dynamics and distributional trend analysis of African swine fever outbreaks (2020-2021) in North-East India.

African swine fever (ASF) is a highly contagious, notifiable, and fatal hemorrhagic viral disease affecting domestic and wild pigs. The disease was reported for the first time in India during 2020, resulted in serious outbreaks and economic loss in North-Eastern (NE) parts, since 47% of the Indian pig population is distributed in the NE region. The present study focused on analyzing the spatial autocorrelation, spatio-temporal patterns, and directional trend of the disease in NE India during 2020-2021. The ASF outbreak data (2020-2021) were collected from the offices of the Department of Animal Husbandry and Veterinary Services in seven NE states of India to identify the potential clusters, spatio-temporal aggregation, temporal distribution, disease spread, density maps, and risk zones. Between 2020 and 2021, a total of 321 ASF outbreaks were recorded, resulting in 59,377 deaths. The spatial pattern analysis of the outbreak data (2020-2021) revealed that ASF outbreaks were clustered in 2020 (z score = 2.20, p < .01) and 2021 (z score = 4.89, p < .01). Spatial autocorrelation and Moran's I value (0.05-0.06 in 2020 and 2021) revealed the spatial clustering and spatial relationship between the outbreaks. The hotspot analysis identified districts of Arunachal Pradesh, Assam and districts of Mizoram, Tripura as significant hotspots in 2020 and 2021, respectively. The spatial-scan statistics with a purely spatial and purely temporal analysis revealed six and one significant clusters, respectively. Retrospective unadjusted, temporal, and spatially adjusted space-time analysis detected five, five, and two statistically significant (p < .01) clusters, respectively. The directional trend analysis identified the direction of disease distribution as northeast-southwest (2020) and north-south (2021), indicate the possibility of ASF introduction to India from China. The high-risk zones and spatio-temporal pattern of ASF outbreaks identified in the present study can be used as a guide for deploying proper prevention, optimizing resource allocation and disease control measures in NE Indian states.

Community-acquired Staphylococcus aureus skin and soft tissue infection risk assessment using hotspot analysis and risk maps: the case of California emergency departments.

BACKGROUND: Community-acquired Staphylococcus aureus (CA-Sa) skin and soft tissue infections (SSTIs) are historically associated with densely populated urban areas experiencing high poverty rates, intravenous drug use, and homelessness. However, the epidemiology of CA-Sa SSTIs in the United States has been poorly understood since the plateau of the Community-acquired Methicillin-resistant Staphylococcus aureus epidemic in 2010. This study examines the spatial variation of CA-Sa SSTIs in a large, geographically heterogeneous population and identifies neighborhood characteristics associated with increased infection risk. METHODS: Using a unique neighborhood boundary, California Medical Service Study Areas, a hotspot analysis, and estimates of neighborhood infection risk ratios were conducted for all CA-Sa SSTIs presented in non-Federal California emergency departments between 2016 and 2019. A Bayesian Poisson regression model evaluated the association between neighborhood-level infection risk and population structure, neighborhood poverty rates, and being a healthcare shortage area. RESULTS: Emergency departments in more rural and mountainous parts of California experienced a higher burden of CA-Sa SSTIs between 2016 and 2019. Neighborhoods with high infection rates were more likely to have a high percentage of adults living below the federal poverty level and be a designated healthcare shortage area. Measures of population structure were not associated with infection risk in California neighborhoods. CONCLUSIONS: Our results highlight a potential change in the epidemiology of CA-Sa SSTIs in California emergency departments. Future studies should investigate the CA-Sa burden in other geographies to identify whether this shift in epidemiology holds across other states and populations. Further, a more thorough evaluation of potential mechanisms for the clustering of infections seen across California neighborhoods is needed.

Annual effective dose estimation of radon in drinking water sources of Nizampur basin, North Western Pakistan.

This study assessed radon (222Rn) levels in drinking water sources in the Nizampur basin and their potential health risks for the local community. We analyzed 48 water samples on-site using RAD7. Additionally, we measured pH, temperature (T), total dissolved solids (TDS), redox potential (ORP), and electrical conductivity (EC) with a multiparameter analyzer. Results showed pH, T, TDS, ORP, and EC ranging from 7.2 to 8, 17 to 26 °C, 333 to 1130 mg/l, -56 to 284 mV, and 469 to 2370 µS/cm. 222Rn levels varied significantly (0.7-107 Bq/l, mean 23 ± 21, median = 17 Bq/l), with about 65 % exceeding the EPA's limit of 11.1 Bq/l, indicating health risks likely due to local geological conditions. The annual effective doses for ingestion (EwIng) were 0.87 ± 0.01, 0.35 ± 0.006, and 0.13 ± 0.002 µSv/a for adults, infants, and children, respectively. Exposure risk via the inhalation (EwInh) route ranged from 1.75 to 270 µSv/a, with the highest risk in infants, followed by children and adults. Inhalation was the primary exposure route for all age groups. Further, spatial distribution maps and hotspot analysis suggested that the central region characterized by high structural deformation and favorable geology for radon emanation was the area of concern in terms of health risks.

Determination of the Spatial Distribution of Air Pollutants in Bucheon, Republic of Korea, in Winter Using a GIS-Based Mobile Laboratory.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emission sources. In Republic of Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m3 in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM2.5 concentration was 161.7 µg/m3. The maximum NOx, NO2, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

An improved approach for evaluating landscape ecological risks and exploring its coupling coordination with ecosystem services.

The rapid urbanization has accelerated the destruction of regional ecosystems, triggering ecological risks and threatening sustainable development. Landscape ecological risk (LER) evaluation is an effective tool to mitigate such negative impacts. However, the existing evaluation systems exhibit certain subjectivity. Therefore, an improved LER evaluation method was proposed, which incorporates ecosystem services (ESs) to characterize landscape vulnerability. The method was validated using the Pearl River Delta urban agglomeration (PRDUA) as the study area. The results showed that the optimal grain size and extent for landscape pattern analysis in the PRDUA were determined to be 150 m and 6km × 6 km, respectively. The comparison results with the traditional LER evaluation method demonstrated the improved method's superior rationality and reliability. The hotspot analysis based on the Getis-Ord Gi* method revealed that the hotspots of LER were mainly concentrated in the densely populated areas of the south-central region of the PRDUA. The coupling coordination degree (CCD) between LERs and ESs showed four different levels of development in both temporal and spatial dimensions, generally dominated by moderately balanced development and lagging ESs, reflecting the unbalanced ecological environment and socio-economic development of the PRDUA. It is recommended that the ecosystems in the PRDUA be managed and protected separately according to the delineated Ecological Protection Area (EPA), Urban Built-up Area (UBA), and Urban Ecological Boundary Area (UEBA). This study can provide an important reference for regional ecosystem conservation and management.

Spatio-temporal variations of ecosystem service value and its spatial heterogeneity mechanism in the Dongjiang River Basin, China.

Comprehensive and deep research on the variations of ecosystem service value (ESV) based on land utilization/land cover change from a spatio-temporal perspective is of great significance for regional ecosystem optimization, as well as coordinated sustainable development of natural environment and economic society. Based on land utilization, natural environment, and socio-economic data of Dongjiang River Basin from 2010 to 2020, combined with hotspot analysis tools and local spatial autocorrelation analysis methods, we comprehensively analyzed the spatio-temporal variations of Dongjiang River Basin ESV, and further explored the spatial differentiation mechanism with geographic detector tools. The results showed that Dongjiang River Basin was dominated by forest ecosystem from 2010 to 2020. The construction land area had expanded significantly mainly from arable land and forest. The Dongjiang River Basin ESV showed a downward and then an upward trend. The ESV of arable land, forest and construction land continuously decreased, and the ESV of water decreased first and then increased substantially. The spatial distribution of ESV hot and cold spots had a significant agglomeration effect, presenting a pattern of hot spot dispersion in the upstream area and cold spots aggregation in the downstream area. The ESV distribution in the upstream and downstream area was not balanced, with the downstream area bearing greater ecological stress. According to the detection results of ESV spatial differentiation mechanism, land utilization was the main factor affecting the spatial differentiation, with the spatial difference of ESV (q value) reaching 0.462. The interaction of factors could greatly strengthen the spatial differentiation effect on Dongjiang River Basin ESV.

[Characteristics and Driving Factors of O3 Pollution During 13th Five-Year Period in Tianjin].

The spatial distribution, accumulation features, and driving factors of O3 pollution were analyzed using spatial autocorrelation and hotspot analysis and the STIRPAT model based on the high spatiotemporal resolution online monitoring data from 2016 to 2020 in Tianjin. The results showed that the variation characteristics of O3 concentration in Tianjin from 2016 to 2020 had the trend of pollution occurring in advance and the scope of the pollution expanding. The distribution of O3 pollution showed significant aggregation from June to October. High-high value clustering areas included six urban districts, Beichen District, Jinnan District, and Jinghai District. O3 concentration formed high value hot spots in the southwest and low value cold spots in the northeast. Meteorological factors such as temperature, breeze percentage, and sunshine duration, as well as social factors such as NOx emission, VOCs emission, and motor vehicle ownership had significant effects on O3 concentration. The regression fitting effect of the integrated drive STIRPAT model was better than that of the single meteorological factor or social factor models. In order to promote scientific and efficient prevention and control of ozone pollution during the 14th Five-Year Plan period, meteorological conditions require attention; under the goal of "peaking carbon dioxide emissions and achieving carbon neutrality," it is necessary for Tianjin to further improve the emission performance of steel, petrochemicals, thermal power, building materials, and other industries, Additionally, clean upgrading, transformation, and green development should be guided for enterprises to reduce VOCs and NOx emissions. At same time, the increase in fuel vehicle numbers should be controlled, and new energy vehicles should be vigorously promoted to reduce vehicle emissions.

Contextual factors influencing incomplete immunization and investigation of its geospatial heterogeneity in Pakistan: a cross-sectional study based on PDHS (2017-18).

BACKGROUND: Immunization is one of the most effective public health initiatives, saving millions of lives and lowering the risk of diseases such as diphtheria, tetanus, influenza, and measles. Immunization saves an estimated 2-3 million lives per year. A study of the regional variations in incomplete immunization will be useful in identifying gaps in the performance of immunization programs that are not noticed by standard vaccination programs monitoring. The primary goal of this study was to identify factors influencing child immunization status and to examine regional variations in incomplete immunization among children aged 12 to 23 months in Pakistan. METHODS: For the current study, the data were taken from the Demographic and Health Survey for Pakistan (PDHS 2017-2018). Ever-married women who had children aged 12-23 months were included in this study. The immunization status of children was used as an outcome variable. In order to determine the effects of different factors on incomplete immunization, multilevel logistic model was used. To study the geographical variation of incomplete immunization, hotspot analysis was done using ArcGIS 10.7 and SaTScan software and to identify significant predictors of incomplete immunization, GWR 4 software was used. RESULTS: Place of delivery, gender of child, mother's educational level and region were identified as significant determinants of incomplete immunization of children in Pakistan. Chances of incomplete immunization of children were found significantly lower for educated mothers (AOR = 0.52, 95% CI 0.34-0.79) and mothers who had delivered children in the health facilities (AOR = 0.51, 95% CI 0.32-0.83). Female children were more likely (AOR = 1.44, 1.95% CI 1.04-1.99) to be incompletely immunized as compared to male children. FATA (AOR = 11.19, 95% CI 4.89-25.6), and Balochistan (AOR = 10.94, 95% CI 5.08-23.58) were found at the highest risk of incomplete immunization of children as compared to Punjab. The significant spatial heterogeneity of incomplete immunization was found across Pakistan. The spatial distribution of incomplete immunization was clustered all over Pakistan. The high prevalence of incomplete immunization was observed in Balochistan, South Sindh, North Sindh, South KPK, South FATA, Gilgit Baltistan, Azad Jammu Kashmir, South and East Punjab. Drang and Harcho were identified as hotspot areas of incomplete immunization in Gilgit Baltistan. Secondary clusters with a high risk of incomplete immunization were found in regions Balochistan, Sindh and FATA. CONCLUSION: Gender biasedness towards female children, regarding complete immunization of children prevailed in Pakistan. Spatial heterogeneity was also found for incomplete immunization of children. To overcome the problem access to health facilities is the foremost step. Government should target hotspot areas of incomplete immunization of children to provide primary health care facilities by opening health care units in these areas. The government in collaboration with the media should launch awareness campaigns in those areas to convince people that complete immunization is the right of every child regardless of gender.

[CiteSpace knowledge map of research hotspots and frontiers of traditional Chinese medicine intervention in psoriasis in recent ten years].

This study aimed to investigate the development status of traditional Chinese medicine(TCM) intervention in psoriasis in recent ten years, analyze the research hotspots, and summarize the development trends to provide reference materials for scholars in this field. Taking the available literature related to the field of TCM intervention in psoriasis as the research object, the trends, contents, and source publications were statistically analyzed based on bibliometrics. The research cooperation and co-occurrence of keywords in this field were studied by the knowledge map analysis method based on CiteSpace. The total number of Chinese papers was 2 993 and English papers 285. In terms of publication trend, the annual publication of English papers was low but showed an obvious upward trend, while the increase in Chinese papers fluctuated and tended to be flat. In terms of the content of Chinese papers published, TCM ranked first according to the discipline(2 415). In English papers, the number of publications in pharmacology and pharmaceutical science was the highest(87). Literature source analysis showed that the Chinese and English journals with the most publications were China Journal of Traditional Chinese Medicine and Pharmacy and Evidence Based Complementary and Alternative Medicine, respectively. Beijing University of Chinese Medicine published the most dissertations in China(99). The authors with the most publications in Chinese and English were LI Bin(Yueyang Hospital of Integrated Traditional Chinese and Western Medicine Affiliated to Shanghai University of Traditional Chinese Medicine) and LU Chuan-jian(Guangdong Hospital of Traditional Chinese Medicine). As revealed by the CiteSpace analysis of the research cooperation network, there were four mature and stable core teams in this field, but the cooperation intensity between different teams was weak. According to the keywords co-occurrence knowledge graph constructed by CiteSpace, the current hot keywords in this field are as follows: psoriasis, blood-heat syndrome, blood-stasis syndrome, fire needle, blood-dryness type, imiquimod, TCM bath, etiology and pathogenesis, cytokines, cupping therapy, etc. In summary, Chinese scholars have conducted active exploration and research in the field of TCM intervention in psoriasis in recent ten years. The overall development trend is good, and the breadth and depth of the research are constantly extending. It is suggested that relevant research should be free from discipline restrictions and strive for interdisciplinary integration.

Spatial pattern identification and crash severity analysis of road traffic crash hot spots in Ohio.

Safety experts and transportation departments are focused on reducing road accidents and their societal and economic effects. The most crucial step in establishing a successful road safety practice is identifying dangerous highway zones through the study of crashes and looking at how the location of accidents relates to surrounding geography and other factors. Using the latest cutting-edge GIS analytical methods, this study aims to map the locations of accident hot spots and evaluate the severity and spatial extent of crash occurrences in Ohio. Road traffic crash (RTC) data has been analyzed using sophisticated GIS-based hot spot analysis for decades by safety researchers. Using four years' worth of crash data from the state of Ohio and spatial autocorrelation analysis, this study aims to show how a GIS technique can be used to find places where accidents are likely to happen (2017-2020). The study analyzed and ranked crash hotspot areas using the matching severity levels of RTCs. Cluster zones of high and low crash severity were discovered using the spatial autocorrelation tool and the Getis Ord Gi* statistics tool to evaluate the distribution of RTCs. The analysis used Getis Ord Gi*, the crash severity index, and Moran's I spatial autocorrelation of accident events. The findings indicated that these techniques were useful for identifying and rating crash hotspot locations. Since the sites of the identified accident hotspots are located in significant cities in the state of Ohio, such as Cleveland, Cincinnati, Toledo, and Columbus, the organizations in charge of traffic management should make it their top priority to minimize the negative socioeconomic impact that RTCs have and should also conduct a thorough investigation. This study's contribution is the incorporation of crash severity into hot spot analysis using GIS, which could lead to better-informed decision-making in the realm of highway safety.

GIS based hotspot analysis and health risk assessment of groundwater arsenic from an unconfined deep aquifer of Lahore, Pakistan.

Use of groundwater for drinking purpose poses serious hazards of arsenic contamination particularly in plains of western Himalayan region. Therefore, current study was designed to investigate the level of Arsenic (As) in the water obtained from tubewells in a metropolitan city of Lahore, Pakistan and assess the human health risk. So, a total of 73 tubewells were sampled randomly in the manner that the whole study region was covered without any clustering. The water samples were analyzed for As using atomic absorption spectrophotometer. These samples were also tested for total dissolved solids, chlorides, pH, alkalinity, turbidity, hardness and calcium. GIS based hotspots analysis technique was used to investigate the spatial distribution patterns. Our results revealed that only one sample out of total 73 had arsenic level below the WHO guideline of 10 µg/L. The spatial distribution map of arsenic revealed that the higher concentrations of arsenic are present in the north-western region of Lahore. The cluster and outlier analysis map using Anselin Local Moran's I statistic indicated the presence of an arsenic cluster in the west of River Ravi. Furthermore, the optimized hotspot analysis based on Getis-Ord Gi* statistics confirmed the statistical significance (P < 0.05) and (P < 0.01) of these samples from the vicinity of River Ravi. Regression analysis showed that variables such as turbidity, alkalinity, hardness, chlorides, calcium and total dissolved solids were significantly (all P < 0.05) associated with level of Arsenic in tubewells. Whereas, PH and electrical conductivity and other variables like town, year of installation, depth and diameter of the wells were not significantly associated with Arsenic concentrations in tubewells. Principal component analysis (PCA) exhibited that the random distribution of tubewell samples showed no distinct clustering with towns studied. Health risk assessment based on hazard and Cancer risk index revealed serious risk of developing carcinogenic and non-carcinogenic diseases particularly in children. The health risk due to prevalence of high As concentration in tubewells' water need to be mitigated immediately to avoid worst consequences in future.

Assessment of dynamic drought-induced ecosystem risk: Integrating time-varying hazard frequency, exposure and vulnerability.

Terrestrial ecosystems, occupying 28.26% of Earth's surface, are extensively at risk from droughts, which is likely to propagate into human communities owing to loss of vital services. Ecosystem risk also tends to fluctuate within anthropogenically-forced nonstationary environments, raising considerable concerns about effectiveness of mitigation strategies. This study aims to assess dynamic ecosystem risk induced by droughts and identify risk hotspots. Bivariate nonstationary drought frequency was initially derived as a hazard component of risk. By coupling vegetation coverage and biomass quantity, a two-dimensional exposure indicator was developed. Trivariate likelihood of vegetation decline was calculated under arbitrary droughts to intuitively determine ecosystem vulnerability. Ultimately, time-variant drought frequency, exposure and vulnerability were multiplied to derive dynamic ecosystem risk, followed by hotspot and attribution analyses. Risk assessment implemented in the drought-prevalent Pearl River basin (PRB) of China during 1982-2017 showed that meteorological droughts in eastern and western margins, although less frequent, were prolonged and aggravated in contrast to prevalence of less persistent and severe droughts in the middle. In 86.12% of the PRB, ecosystem exposure maintains high levels (0.62). Relatively high vulnerability (>0.5) occurs in water-demanding agroecosystems, exhibiting a northwest-southeast-directed extension. A 0.1-degree risk atlas unveils that high and medium risks occupy 18.96% and 37.99% of the PRB, while risks are magnified in the north. The most pressing hotspots with high risk continuing to escalate reside in the East River and Hongliu River basins. Our results provide knowledge of composition, spatio-temporal variability and driving mechanism of drought-induced ecosystem risk, which will assist in risk-based mitigation prioritization.

Spatial autocorrelation and hotspot analysis of natural radionuclides to study sediment transport.

Tracking sediment movement is typically done with artificial radionuclides. However, this can be environmentally harmful and does not allow for sediment classification. Naturally occurring radionuclides are consequently offered as an alternative. In this study, a mobile Delta Underwater Gamma System (DUGS) capable of measuring low levels of natural radionuclides in sediment was deployed in an estuary, and a radiometric map of the sediment was constructed. Spatial autocorrelation using the Moran's I statistic was used to investigate the spatial distribution patterns of natural radionuclides in the sediments. Hotspot analysis using Getis-Ord* was used to validate and map areas that had been identified as clustered by the Moran's I statistic. The Moran's I analysis indicated that 40K displayed a positive spatial autocorrelation with a value of 0.4 and a standardized Z score of 16, thus indicating that the clustering was significant. 238U and 232Th displayed a low Moran's I value but a strong positive correlation, hence indicating some spots of clustering in the river channel. Further analysis of hotspots confirmed that the identified clusters were areas with relatively high radionuclide concentrations. This proved that the hotspot areas identified have a high deposition of sediment. In situ radiometric measurements of sediment, as well as spatial analysis, are consequently useful tools to model and study spatial structure and sediment.

Spatio-temporal pattern of short birth interval and associated factors on women in Ethiopia: Using Ethiopian demographic and health surveys 2000-2016.

Background: A short birth interval is a critical factor that contributes to a large number of maternal and infant mortality in low- and middle-income countries. It is the major cause of maternal and child mortality in Ethiopia. This study aimed to explore the spatiotemporal distribution of short birth intervals in Ethiopia using data from four (2000, 2005, 2011, and 2016) consecutive demographic and health surveys. Methods: A total of 34,930 women were included in four consecutive Ethiopian Demographic and Health Surveys (EDHS). Thus, spatial autocorrelation, hotspot analysis, cluster analysis, and spatial interpolation were carried out for each survey separately to show the geographical and temporal pattern of at-risk areas for short birth intervals in Ethiopia. Finally, the highest proportion of short birth interval risk areas in each survey period was mapped. Geospatial analysis was conducted by using ArcGIS V.10.8 and R version 4.2. Results: The results of the study indicated that the overall proportion of short birth intervals of women in Ethiopia was highest in 2000 (47.5%), 2005 (46.4%), 2011 (44.7%), and the lowest in 2016 (44.0%). The values for Global Moran's I (MI = 0.177665 p = 0.0016, MI = 0.2024, p = 0.001, MI = 0.10023, p = 0.002, and MI = 0.764, p = 0.008) showed that the presence of significant short birth interval clustering in Ethiopian administrative zones in 2000, 2005, 2011, and 2016, respectively. The hotspot areas for short birth intervals were consistently observed in the zones in the Somali Region and the zones in the Harari Region for all the EDHS years. In addition, the survival status of the index child, residence, breastfeeding practice, religion, and the spatial variable (Si) were significantly associated with the short birth interval of women in all the EDHS years. Conclusion: Spatial distribution of short birth intervals differs across Ethiopian administrative zones. Survival status of the index child being dead, rural residential, and no breastfeeding practice are the risk factors for short birth intervals of women that increase the risk of a short birth interval among women in all the EDHS years. Therefore, the hotspot areas and indicators need interventions to decrease the short birth interval of women.