Cold Spot Areas Research Articles

The Clustering of the Population at Building Scale in Bursa City (Türkiye)

Research on spatial statistical methods related to population estimation at the building scale and its implications for urban land use has attained little attention. The main target of this study is to propose a new method for population estimation at the building level with minimal data and methodology and a high accuracy rate. In addition to this, it discusses urban population from various perspectives by using spatial statistical methods (Local Moran’s I and Hot–Cold Spot) to examine the population calculated based on the number of residential units in buildings and the household size of the neighborhood along with urban land use types in the case of Bursa. The results showed the following: (1) The suggested method achieves a 76% accuracy rate in population estimation at the building level; (2) 64.6% of the city’s population (2,101,581 individuals) is located in areas classified as Discontinuous High-Density Urban Fabric (50–80%) and Continuous Urban Fabric (>80); (3) 13.2% of the population is located in hot spot areas of these two types, while 14.5% is in cold spot areas. This research provides decision-makers with a framework for addressing urban problems related to housing, transportation, health, and energy in addition to the methods it proposes.

Spatial patterns and associated factors of HIV testing and counselling (HTC) as a component of antenatal care services in Ethiopia

Background While HIV testing and counselling play a crucial role in preventing mother-to-child transmission, numerous pregnant women did not receive these services. Understanding the spatial variation of HIV testing and counselling and its associated factors during antenatal care in Ethiopia remains limited. Thus, this study was aimed at assessing the spatial patterns and factors associated with HIV testing and counselling during antenatal care visits in Ethiopia. Methods A cross-sectional study design was employed with a two-stage stratified cluster sampling technique. A total of 2,789 women who gave birth in the two years prior to the survey and had at least one antenatal care visit were included in the study. Stata version 16 and ArcGIS version 10.8 software were used for analysis. A multilevel robust Poisson regression model was fitted to identify significantly associated factors since the prevalence of HIV testing and counselling was higher than 10%. A statistically significant association was declared based on multivariable multilevel robust Poisson regression analysis using an adjusted prevalence ratio with its 95% confidence interval at a p-value < 0.05. Spatial regression analysis was conducted, and the local coefficients of statistically significant spatial covariates were visualised. Results In Ethiopia, the overall prevalence of HIV testing and counselling during antenatal care visits was 29.5% (95% CI: 27.8%, 31.2%). Significant spatial clustering was observed (Global Moran’s I = 0.138, p-value <0.001). In the spatial regression analysis, high and comprehensive knowledge related to HIV, and comprehensive knowledge on the prevention of mother-to-child transmission were significant explanatory variables for the spatial variation of HIV testing and counselling. In the multivariable multilevel robust Poisson regression analysis, education, household wealth, media exposure, number of antenatal care visits, comprehensive knowledge on mother-to-child transmission, comprehensive knowledge on prevention of mother-to-child transmission, and region were significantly associated factors. Conclusion The prevalence of HIV testing and counselling during antenatal care visits was low. Empowering women through education, promoting mass media exposure, increasing numbers of antenatal care visits, and enhancing women’s knowledge related to HIV and mother-to-child transmission by targeting cold spot areas could improve HIV testing and counselling service uptake among pregnant women in Ethiopia.

Spatial distribution and determinants of exclusive breastfeeding practice among mothers of children under 24 months of age in Ethiopia: spatial and multilevel analysis

BackgroundBreast milk is the first, highly valuable, and solely natural food recommended for infants in their first six months of life, which is critical for children's overall growth and development. Evidence suggests that exclusive breastfeeding differs by geographic area in Ethiopia. However, little is known regarding the geographic distribution of exclusive breastfeeding practice. Hence, this study aimed to assess exclusive breastfeeding practice, its geographic variation and associated factors among Ethiopian mothers.MethodThe study used the 2019 Ethiopian mini demographic and health survey data. All living children born 0–23 months before the survey were included. Global Moran’s I statistics on Arc-GIS and Getis Ord Gi* statistics was used to visualize the spatial pattern and hotspot and cold spot areas, respectively. Kulldorff SaTScan was used to show purely significant spatial clusters. The associated factors were identified using a multilevel mixed-effects logistic regression model. Statistically significant factors were reported using the AOR with a 95% CI and a p-value of < 5%.ResultThe coverage of exclusive breastfeeding practice in Ethiopia was 56.05% (95% CI: 53.95%, 58.10%). The spatial pattern was non-random across the country’s regions. Somalia, Gambela, Benshangul Gumuz, Dire Dawa, and Harari regions had low clustering of exclusive breastfeeding practices, whereas Amhara, Eastern SNNPR, and Central and Northern Oromia regions had high clustering. Children born through caesarean delivery [AOR = 0.36; 95% CI: 0.21, 0.63], initiated breastfeeding within the first 1 h [AOR = 0.55; 95% CI: 0.34, 0.90], after 1–24 h of delivery [AOR = 0.36; 95% CI: 0.24, 0.54], after a day [AOR = 0.04; 95% CI: 0.02, 0.08], and women residing in the pastoralist region [AOR = 0.22; 95% CI: 0.12, 0.39] or city administrations [AOR = 0.49; 95% CI: 0.27, 0.89] had lower odds of exclusive breastfeeding practice.ConclusionExclusive breastfeeding practice in Ethiopia remained low. The practice had a spatial variation across the country. Caesarean section delivery, late breastfeeding initiation, and region were statistically significant variables. Therefore, promoting timely initiation of breastfeeding and improving the utilization of maternal health services and designing special intervention strategy for women residing in city administrations and pastoralist regions of the country may increase the coverage of exclusive breastfeeding practice.

Characteristics of spatial and temporal changes in ecosystem service value and threshold effect in Henan along the Yellow River, China

Ecosystem services (ES), which serve as the link between human activities and natural ecosystems, are crucial for maintaining ecological balance and supporting economic development. The Ecosystem Services Value (ESV) is an indicator used to quantifying ecosystem services. Accurately estimating the dynamics of ESV and the threshold effects of influencing factors is of great importance for the sustainable development of the region. This study constructs an ESV valuation model based on the equivalent factor method, incorporating construction land, and corrects the initial valuation results in three dimensions. By constructing a random forest model and integrating partial dependence analysis, Elevation, Evapotranspiration, Normalized Difference Vegetation Index, and the proportion of built-up land area were identified as significant factors in determining ecologically vulnerable areas within the study region. The results show that: (1) The total ESV in Henan along the Yellow River in 2000, 2005, 2010, 2015, and 2020 was 204.612 billion yuan, 249.775 billion yuan, 239.100 billion yuan, 234.868 billion yuan and 202.767 billion yuan, respectively. The overall trend of change is first growth and then decline, with spatial features remaining relatively stable, and ESV polarization becoming more pronounced. (2) The spatial clustering characteristics of ESV in Henan along the Yellow River are remarkable, and the cold and hot spot areas show a bidirectional expansion trend. The cold spot areas expand substantially around the construction land, whilethe hot spot areas show a small expansion around the western mountain areas and the vegetation areas along the Yellow River, but the overall growth is slower. (3) The main factors affecting ESV include three natural factors and one anthropogenic factor, namely evapotranspiration (ET), the proportion of built-up land area (POB), normalized vegetation index (NDVI) and elevation (ELEV). ESV is at a high level when ELEV>718 m, ET<90 mm, NDVI>0.7, and POB<25.67 % approximately. The study results can provide an important theoretical basis for the development of comprehensive ecological protection measures. Furthermore, they can contribute to the sustainable development of the Yellow River Basin.

Geospatial determinants and spatio-temporal variation of early initiation of breastfeeding and exclusive breastfeeding in Ethiopia from 2011 to 2019, a multiscale geographically weighted regression analysis

BackgroundBreastfeeding offers numerous benefits for infants, mothers, and the community, making it the best intervention for reducing infant mortality and morbidity. The World Health Organization (WHO) recommends initiating breastfeeding within one hour after birth and exclusively breastfeeding for the first six months. This study investigated the trend, spatio-temporal variation, and determinants of spatial clustering of early initiation of breastfeeding (EIBF) and exclusive breastfeeding (EBF) in Ethiopia from 2011 to 2019.MethodsData from the Ethiopian Demographic and Health Survey (EDHS), which was conducted in 2011, 2016, and 2019, were analyzed utilizing a weighted sample of 10,616 children aged 0–23 years for EIBF and 2,881 children aged 0–5 months for EBF. Spatial autocorrelation analysis was used to measure whether EIBF and EBF were dispersed, clustered, or randomly distributed and Kriging interpolation was employed to predict the outcome variables in the unmeasured areas. Spatial scan statistics were used to identify spatial clusters with a high prevalence of cases. Both global and local regression modeling techniques were employed to examine the spatial relationships between the explanatory variables and the dependent variables.ResultsThe trend analysis revealed a notable increase in the prevalence of EIBF from 51.8% in 2011 to 71.9% in 2019. Similarly, the prevalence of EBF increased from 52.7% in 2011 to 58.9% in 2019. Spatial analysis demonstrated significant spatial variation in both EIBF and EBF throughout the country. Cold spots or clusters with a low prevalence of EIBF were observed consistently in the Tigray and Amhara regions, and significant cold spot areas of EBF were observed consistently in the Afar and Somali regions. Multiscale geographically weighted regression analysis revealed significant predictors of spatial variations in EIBF, including the religious affiliation of being a follower of the orthodox religion, parity of 1–2, absence of antenatal care visits, and delivery via cesarean section.ConclusionsDespite the increase in both EIBF and EBF rates over time in Ethiopia, these rates still fall below the national target. To address this issue, the government should prioritize public health programs aimed at improving maternal healthcare service utilization and maternal education. It is essential to integrate facility-level services with community-level services to achieve optimal breastfeeding practices. Specifically, efforts should be made to promote breastfeeding among mothers who have delivered via cesarean section. Additionally, there should be a focus on encouraging antenatal care service utilization and adapting maternal healthcare services to accommodate the mobile lifestyle of pastoralist communities. These steps will contribute to enhancing breastfeeding practices and achieving better outcomes for maternal and child health.

Mapping geographical inequalities of incomplete immunization in Ethiopia: a spatial with multilevel analysis.

Immunization is one of the most cost-effective interventions, averting 3.5-5 million deaths every year worldwide. However, incomplete immunization remains a major public health concern, particularly in Ethiopia. The objective of this study is to investigate the geographical inequalities and determinants of incomplete immunization in Ethiopia. A secondary analysis of the mini-Ethiopian Demographic Health Survey (EDHS 2019) was performed, utilizing a weighted sample of 3,865 children aged 12-23 months. A spatial auto-correlation (Global Moran's I) statistic was computed using ArcGIS version 10.7.1 to assess the geographical distribution of incomplete immunization. Hot-spot (areas with a high proportion of incomplete immunization), and cold spot areas were identified through Getis-Ord Gi* hot spot analysis. Additionally, a Bernoulli probability-based spatial scan statistics was conducted in SaTScan version 9.6 software to determine purely statistically significant clusters of incomplete immunization. Finally, a multilevel fixed-effects logistic regression model was employed to identify factors determining the status of incomplete immunization. Overall, in Ethiopia, more than half (54%, 95% CI: 48-58%) of children aged 12-23 months were not fully immunized. The spatial analysis revealed that the distribution of incomplete immunization was highly clustered in certain areas of Ethiopia (Z-score value = 8.379419, p-value < 0.001). Hotspot areas of incomplete immunization were observed in the Afar, Somali, and southwestern parts of Ethiopia. The SaTScan spatial analysis detected a total of 55 statistically significant clusters of incomplete immunization, with the primary SaTScan cluster found in the Afar region (zones 1, 3, and 4), and the most likely secondary clusters detected in Jarar, Doola, Korahe, Shabelle, Nogob, and Afdar administrative zones of the Somali region of Ethiopia. Indeed, in the multilevel mixed-effect logistic regression analysis, the respondent's age (AOR: 0.92; 95% CI: 0.86-0.98), residence (AOR: 3.11, 95% CI: 1.36-7.14), living in a pastoralist region (AOR: 3.41; 95% CI: 1.29-9.00), educational status (AOR: 0.26; 95% CI: 0.08-0.88), place of delivery (AOR: 2.44; 95% CI: 1.15-5.16), and having PNC utilization status (AOR: 2.70; 95% CI: 1.4-5.29) were identified as significant predictors of incomplete immunization. In Ethiopia, incomplete immunization is not randomly distributed. Various factors at both individual and community levels significantly influence childhood immunization status in the country. It is crucial to reduce disparities in socio-demographic status through enhanced collaboration across multiple sectors and by bolstering the utilization of maternal health care services. This requires concerted efforts from stakeholders.

Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India

Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.

Correlation between urbanization and vegetation cover in China and analysis of its influencing factors

ABSTRACT There are spatial differences in the correlation between urbanization (NL) and vegetation cover (NDVI), and the driving mechanism is not clear. This study aims to explore the spatial correlation between NL and NDVI in China and identify the influencing factors. The results showed that there was a north-south difference in the correlation, and this correlation was strongest in winter. Meteorological factors were the main influencing factors, Bivariate enhancement is observed for TEM-DEM at the national scale in winter, TEM-relative humidity (RH) in cold spot areas, and TEM-RH in hot spot areas in summer.

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

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. 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. 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. 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.

Geospatial variation of exclusive breastfeeding and its determinants among mothers of infants under 6 months in Ethiopia: spatial and geographical weighted regression analysis

BackgroundExclusive breastfeeding (EBF) is a major public health problem in Ethiopia. However, the spatial variation of EBF and the associated factors have not been studied as much as we have...

Spatial variation and determinants of mother and newborn skin-to-skin contact care practices in Ethiopia: A spatial and multilevel mixed-effect analysis.

Skin-to-skin contact care practice is placing a naked baby on the mother's chest with no cloth separating them, in a prone position covered by a cloth or blanket. It improves the survival of newborns by preventing hypothermia, improving breastfeeding, and strengthening mother-to-child bonding. Nevertheless, it remains under-practiced in many resource-constrained settings. Therefore, the main objective of this study is to explore the spatial variation and determinants of mother and newborn skin-to-skin contact care practices in Ethiopia. The study was done using the 2016 Ethiopian Demographic and Health Survey data. A weighted sample of 10417 mothers who gave live birth before the five-year survey was extracted for the analysis. Arc GIS version 10.3 and SaTscan version 10.0.2 were used for the spatial analysis. A multilevel mixed logistic regression model was fitted to identify factors associated with skin-to-skin contact care practices of mothers and newborns. Finally, a statistically significant association was declared at a P-value of < 0.05. In this study, skin-to-skin contact care practice of mothers and newborns was non-random across Ethiopia with Moran's I: 0.48, p < 0.001. The most likely significant primary and secondary clusters were found in Addis Ababa (RR = 2.39, LLR = 116.80, p <0.001) and Dire Dewa and Harari (RR = 2.02, LLR = 110.45, p <0.001), respectively. In this study, place of delivery (AOR = 12.29, 95%CI:10.41, 14.54), rich wealth index (AOR = 1.29, 95% CI: 1.05,1.59), medium wealth index (AOR = 1.38, 95% CI:1.17, 1.68), having 1-3 antenatal care visits(AOR = 1.86,95% CI: 1.56, 2.29), having ≥4 antenatal care visits (AOR = 1.93,95% CI: 1.56, 2.39), initiating breastfeeding within the first hour (AOR = 1.75,95% CI:1.49,2.05) and media exposure (AOR = 1.20,95%CI 1.02,1.41) were factors associated with skin to skin contact care practice of mothers and newborns. This study concludes that the Skin-to-skin contact care practices of mother and newborn is not random in Ethiopia. Therefore, the implementation of essential newborn care packages should be regularly monitored and evaluated, particularly in the cold spot areas of skin-to-skin contact care practices. Besides, media advertising regarding the importance of Skin-to-skin contact care practices for mothers and newborns should be scaled up to increase the practices.

Spatial variation and determinants of delayed breastfeeding initiation in Ethiopia: spatial and multilevel analysis of recent evidence from EDHS 2019

BackgroundDespite the World Health Organization’s firm recommendation to start breastfeeding during the first hour after delivery, nearly 54% of children in low- and middle-income countries are unable to initiate breastfeeding within the recommended time frame. Understanding the initiation of breastfeeding is essential for optimal child health and maternal well-being.MethodsThis study was conducted using the recent Ethiopian Demographic and Health Survey (EDHS) data (2019) on a weighted sample of 1982 Ethiopian mothers of children aged under 24 months. The data extraction was conducted between August 1 and 30, 2023. Delayed’ initiation of breastfeeding is defined as failure to initiate breastfeeding within one hour after birth. STATA version 17 was used for non-spatial analysis. ArcGIS Pro and Sat Scan version 9.6 were used to map the visual presentation of delayed breastfeeding initiation. Global Moran’s I was computed to determine whether delayed breastfeeding initiation is randomly distributed, clustered, or dispersed. Getis-Ord Gi* Spatial Statistics was done to identify significant spatial clusters of cold and hot spot areas. Multilevel mixed-effect logistic regression analysis was computed to identify determinants of delayed breastfeeding initiation.ResultsThe prevalence of delayed breastfeeding initiation is 26.4% (95% CI 24.4, 28.3). Significant clustering of delayed initiation of breastfeeding practice was found in the Somali region. Less clustering was identified in Northern Amhara, Addis Ababa and Dire Dawa. Being a young mother (15–24 years) (AOR 1.66; 95% CI 1.06, 2.62), no antenatal care (AOR 1.45; 95% CI 1.04, 2.02), cesarean section (AOR 4.79; 95% CI 3.19, 7.21) and home birth (AOR 1.53; 95% CI 1.14, 2.06) were found to be determinants of delayed initiation of breastfeeding.ConclusionsIn Ethiopia, delayed breastfeeding initiation is distributed non-randomly. Significant hotspot areas were identified in the eastern part of Ethiopia. Thus, deploying additional resources in high hotspot regions is recommended. Programs should focus on promoting health facility birth and increasing antenatal care visits. Further emphasis should be considered on supporting young mothers and those giving birth via cesarean section to improve timely breastfeeding initiation.

Identifying Accessibility and Equity Defects of Elderly Care Services in Developing Countries: Insights From Xiamen City

Background: The global aging population has raised concerns about the fair distribution of elderly care resources. China, as the largest developing country, has made efforts to address population aging challenges. However, equitable distribution of elderly care resources remains a concern. This study analyzed the spatial layout and accessibility of elderly care service facilities (ECSFs) in Xiamen City to improve resource allocation and enhance the elderly care system in China. The findings provide valuable insights for other developing countries and regions seeking to improve their own elderly care resource allocation.Methods: Xiamen City was chosen as the research area due to its unique geographical location and advanced information technology infrastructure. The study examined the spatial distribution, accessibility, and equity of ECSFs. Using Python, point of interest (POI) data were collected and evaluated using the kernel density method and two‐step floating catchment area method. Spatial autocorrelation analysis identified areas of aggregation and dispersion in ECSF accessibility.Results: Xiamen currently has 660 ECSFs, including nursing homes, adult day care centers, home care agencies, and rural elderly care homes. The analysis revealed spatial disparities, with ECSFs clustering primarily in the central area of Xiamen Island. Significant differences in accessibility were found among the four types of ECSFs. Spatial autocorrelation analysis identified cold and hot spot areas, indicating variations in accessibility across regions.Conclusion: Xiamen City has made progress in allocating elderly care resources and constructing service facilities. However, equity in resource allocation remains a concern. Areas with limited accessibility were identified, leading to unequal access to elderly care resources and timely physical care. To address these challenges, decision‐making departments should consider increasing facilities, improving transportation, enhancing macro planning, and improving facility service quality and accessibility. These measures will optimize ECSF accessibility and promote equitable distribution of elderly care resources.

Spatial variation and geographical weighted regression analysis to explore open defecation practice and its determinants among households in Ethiopia.

In Ethiopia, recent evidence revealed that over a quarter (27%) of households (HHs) defecated openly in bush or fields, which play a central role as the source of many water-borne infectious diseases, including cholera. Ethiopia is not on the best track to achieve the SDG of being open-defecation-free by 2030. Therefore, this study aimed to explore the spatial variation and geographical inequalities of open defecation (OD) among HHs in Ethiopia. This was a country-wide community-based cross-sectional study among a weighted sample of 8663 HHs in Ethiopia. The global spatial autocorrelation was explored using the global Moran's-I, and the local spatial autocorrelation was presented by Anselin Local Moran's-I to evaluate the spatial patterns of OD practice in Ethiopia. Hot spot and cold spot areas of OD were detected using ArcGIS 10.8. The most likely high and low rates of clusters with OD were explored using SaTScan 10.1. Geographical weighted regression analysis (GWR) was fitted to explore the geographically varying coefficients of factors associated with OD. The prevalence of OD in Ethiopia was 27.10% (95% CI: 22.85-31.79). It was clustered across enumeration areas (Global Moran's I = 0.45, Z-score = 9.88, P-value ≤ 0.001). Anselin Local Moran's I analysis showed that there was high-high clustering of OD at Tigray, Afar, Northern Amhara, Somali, and Gambela regions, while low-low clustering of OD was observed at Addis Ababa, Dire-Dawa, Harari, SNNPR, and Southwest Oromia. Hotspot areas of OD were detected in the Tigray, Afar, eastern Amhara, Gambela, and Somali regions. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions were explored as having high rates of OD. The GWR model explained 75.20% of the geographical variation of OD among HHs in Ethiopia. It revealed that as the coefficients of being rural residents, female HH heads, having no educational attainment, having no radio, and being the poorest HHs increased, the prevalence of OD also increased. The prevalence of OD in Ethiopia was higher than the pooled prevalence in sub-Saharan Africa. Tigray, Afar, northern Amhara, eastern Oromia, and Somali regions had high rates of OD. Rural residents, being female HH heads, HHs with no educational attainment, HHs with no radio, and the poorest HHs were spatially varying determinants that affected OD. Therefore, the government of Ethiopia and stakeholders need to design interventions in hot spots and high-risk clusters. The program managers should plan interventions and strategies like encouraging health extension programs, which aid in facilitating basic sanitation facilities in rural areas and the poorest HHs, including female HHs, as well as community mobilization with awareness creation, especially for those who are uneducated and who do not have radios.

A novel index for assessing the rural population hollowing at fine spatial resolutions based on Tencent social media big data: A case study in Guangdong Province, China

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China’s rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers.

Spatial variation of premarital HIV testing and its associated factors among married women in Ethiopia: Multilevel and spatial analysis using 2016 demographic and health survey data.

Africa is the most severely affected area, accounting for more than two-thirds of the people living with HIV. In sub-Saharan Africa, more than 85% of new HIV-infected adolescents and 63% of all new HIV infections are accounted for by women. Ethiopia has achieved a 50% incidence rate reduction. However, mortality rate reduction is slow, as the estimated prevalence in 2021 is 0.8%. In sub-Saharan Africa, heterosexual transmission accounts for the majority of HIV infections, and women account for 58% of people living with HIV. Most of these transmissions took place during marriage. Thus, this study aimed to explore the spatial variation of premarital HIV testing across regions of Ethiopia and identify associated factors. A cross-sectional study design was employed. A total of 10223 weighted samples were taken from individual datasets of the 2016 Ethiopian Demographic and Health Survey. STATA version 14 and ArcGIS version 10.8 software's were used for analysis. A multilevel mixed-effect generalized linear model was fitted, and an adjusted prevalence Ratio with a 95% CI and p-value < 0.05 was used to declare significantly associated factors. Multilevel models were compared using information criteria and log-likelihood. Descriptive and spatial regression analyses (geographical weighted regression and ordinary least squares analysis) were conducted. Models were compared using AICc and adjusted R-squared. The local coefficients of spatial explanatory variables were mapped. In spatial regression analysis, secondary and above education level, richer and above wealth quintile, household media exposure, big problem of distance to health facility, having high risky sexual behaviour and knowing the place of HIV testing were significant explanatory variables for spatial variation of premarital HIV testing among married women. While in the multilevel analysis, age, education level, religion, household media exposure, wealth index, khat chewing, previous history of HIV testing,age at first sex, HIV related knowledge, HIV related stigma, distance to health facility, and community level media exposure were associated with premarital HIV testing among married women. Premarital HIV testing had a significant spatial variation across regions of Ethiopia. A statistically significant clustering of premarital HIV testing was observed at Addis Ababa, Dire Dawa, North Tigray and some parts of Afar and Amhara regions. Therefore area based prevention and interventional strategies are required at cold spot areas to halt the role of heterosexual transmission in HIV burden. Moreover, the considering the spatial explanatory variables effect in implementations of these strategies rather than random provision of service would make regional health care delivery systems more cost-effective.

Geographical characteristics and influencing factors of the influenza epidemic in Hubei, China, from 2009 to 2019.

Influenza is an acute respiratory infectious disease that commonly affects people and has an important impact on public health. Based on influenza incidence data from 103 counties in Hubei Province from 2009 to 2019, this study used time series analysis and geospatial analysis to analyze the spatial and temporal distribution characteristics of the influenza epidemic and its influencing factors. The results reveal significant spatial-temporal clustering of the influenza epidemic in Hubei Province. Influenza mainly occurs in winter and spring of each year (from December to March of the next year), with the highest incidence rate observed in 2019 and an overall upward trend in recent years. There were significant spatial and urban-rural differences in influenza prevalence in Hubei Province, with the eastern region being more seriously affected than the central and western regions, and the urban regions more seriously affected than the rural region. Hubei's influenza epidemic showed an obvious spatial agglomeration distribution from 2009 to 2019, with the strongest clustering in winter. The hot spot areas of interannual variation in influenza were mainly distributed in eastern and western Hubei, and the cold spot areas were distributed in north-central Hubei. In addition, the cold hot spot areas of influenza epidemics varied from season to season. The seasonal changes in influenza prevalence in Hubei Province are mainly governed by meteorological factors, such as temperature, sunshine, precipitation, humidity, and wind speed. Low temperature, less rain, less sunshine, low wind speed and humid weather will increase the risk of contracting influenza; the interannual changes and spatial differentiation of influenza are mainly influenced by socioeconomic factors, such as road density, number of health technicians per 1,000 population, urbanization rate and population density. The strength of influenza's influencing factors in Hubei Province exhibits significant spatial variation, but in general, the formation of spatial variation of influenza in Hubei Province is still the result of the joint action of socioeconomic factors and natural meteorological factors. Understanding the temporal and spatial distribution characteristics of influenza in Hubei Province and its influencing factors can provide a reasonable decision-making basis for influenza prevention and control and public health development in Hubei Province and can also effectively improve the scientific understanding of the public with respect to influenza and other respiratory infectious diseases to reduce the influenza incidence, which also has reference significance for the prevention and control of influenza and other respiratory infectious diseases in other countries or regions.

Assessing a Tourism City from an Ecosystem Services Perspective: The Evaluation of Tourism Service in Liyang, China

Tourism is an important industry that promotes national economic and social progress. All-for-one tourism is a new concept of regionally coordinated development that uses the tourism industry as an engine to boost resource integration, industrial integration, and social sharing. Tourism service is the main embodiment of cultural ecosystem services for all-for-one tourism cities. Taking the city of Liyang in China as an example, this paper used a combination of GIS spatial analysis and big data text mining to evaluate tourism service from three aspects: the quality of tourism resources, the comprehensiveness of tourism service facilities, and the satisfaction of tourists. The results show that (1) tourism service is better in the northwestern and eastern areas of the city, while it is lower in the northeastern and southwestern parts; (2) the hotspot areas should focus on improving tour routes, transport capacity, and excessive charges; the cold spot areas should work on ecological restoration and creating new tourism attractions by combining the local industries; and (3) rural tourism integrating agriculture and visitation should be highlighted as a key growth point to improve the city’s tourism service function.

Analysis of Spatiotemporal Patterns of Undernutrition among Children below Five Years of Age in Uganda

Background: This study aimed at examining the prevalence and variations in undernutrition among children below five years of age in Uganda while considering the influence of space and time factors. Various studies conducted in Uganda analyzed the undernutrition phenomenon among children below five years of age with a focus on the risk factors and spatial variations; however, no study has ever integrated the elements of time in examining the problem of undernutrition in Uganda. The approach of spatial and spatiotemporal analysis is essential in identifying cluster patterns, hotspots, trends, and emerging hotspots, which is crucial in making timely and location-specific interventions. Methods: Data from the six Uganda Demographic and Health Surveys spanning from 1990 to 2016 were used, with the main outcome variable being undernutrition among children below five years of age. A Composite Index of Anthropometric Failure was derived from the three undernutrition outcomes and subsequently used as a proxy of undernutrition in this study. All data that were relevant to this study were retrieved from the survey datasets and combined with the 2014 shape files of Uganda to enable spatial and spatiotemporal analysis. Spatial maps with the spatial distribution of the prevalence of undernutrition, both in space and time, were generated using ArcGIS Pro version 2.8. Moran’s I, an index of spatial autocorrelation, was used to test the hypothesis of no spatial autocorrelation, while the Getis–Ord (Gi*) statistic was used to examine hot and cold spot areas. Furthermore, space-time cubes were generated to establish the trend in undernutrition as well as to mirror its variations over time and across the country. Moreover, emerging hot spot analysis was done to help in identifying the patterns of undernutrition over time. Results: The national prevalence of undernutrition among children below five years of age was 31.96 percent, with significant spatial variations both in space across Uganda and in the time since 1989. The index of spatial autocorrelation (Moran’s I) confirmed spatial clustered patterns as opposed to random distributions of undernutrition prevalence. Four hot spot areas, namely, the Karamoja, the Sebei, the West Nile, and the Toro regions, were significantly evident. Most of the central parts of Uganda were identified as cold spot clusters, while most of Western Uganda, the Acholi, and the Lango regions had no statistically significant spatial patterns by the year 2016. The spatio-temporal analysis identified the Karamoja and Sebei regions as clusters of persistent, consecutive, and intensifying hot spots, West Nile region was identified as a sporadic hotspot area, while the Toro region was identified with both sporadic and emerging hotspots. In conclusions, undernutrition is a silent pandemic that calls for immediate and stringent measures. At 31.96 percent, the prevalence is still very high and unpleasant. To reduce the prevalence of undernutrition and to achieve SDG goal 2, policymakers, as well as implementers, should consider the spatial effects and spatial and spatiotemporal variations across the country and prioritize interventions to hot spot areas. This would ensure efficient, timely, and region-specific interventions.

Study on spatiotemporal distribution characteristics and driving factors of carbon emission in Anhui Province

Carbon emission is related to global ecological security, and economic development inevitably leads to an increase in carbon emission. In recent years, as a rapidly developing province in China's economy, Anhui Province has experienced significant differences in the spatiotemporal distribution of carbon emission in different regions due to differences in development foundation, urbanization level, population size, industrial structure, etc., providing representative empirical cases for research. Based on the carbon emission data of Anhui Province before the COVID-19, this study used exploratory spatial data analysis method and Geodetector to analyze the spatial and temporal distribution characteristics and drivers of carbon emission in Anhui Province. The study found that (1) the spatial differentiation and spatial correlation of carbon emission in Anhui Province are significant, At the beginning, it shows the characteristics of "high north and low south" and "high west and low east", and then the "core–edge" structure of carbon emission becomes obvious. Carbon emission hotspot areas increase and then decrease, mainly in Hefei, Fuyang and Chuzhou City, etc. The coldspot areas are mainly located in the southern and western mountainous areas, and the degree of aggregation is decreasing year by year. (3) The level of urbanization, economic development and population size are the main driving factors of the spatial variation of carbon emissions, while the industrial structure has the least influence. And most factors produce nonlinear enhancement when spatially superimposed with other factors. (4) The high value areas of economic development, population density, secondary industry structure, and energy intensity are all at high levels of carbon emission, and a combination of factors leads to the creation of high risk areas for carbon emission. The study provides a basis for reducing carbon emission in the next stage of Anhui Province, focusing on key carbon emission areas, and sustainable development.