Significant Spatial Clustering Research Articles

Optimizing Urban Green Spaces for Air Quality Improvement: A Multiscale Land Use/Land Cover Synergy Practical Framework in Wuhan, China

Air pollution, particularly fine particulate matter (PM2.5), poses a significant health risk, especially in high-density urban areas. Urban green space (UGS) can effectively mitigate this pollution. Despite their potential, strategies for effectively leveraging Land Use/Land Cover (LULC) optimization to combat PM2.5 remain largely unexplored. Ordinary least squares (OLS), geographically weighted regression (GWR) and multiscale geographically weighted regression (MGWR) were employed to investigate the spatial heterogeneity relationship between UGS conversion and PM2.5 fluctuations across various scales and evolutionary stages, developing a multiscale practical framework for LULC synergy in combating air pollution. The areas of UGSs to/from other LULCs, PM2.5 concentrations and corresponding variation zones exhibited significant spatial clustering. These UGS conversions explained more than 65% of the PM2.5 changes in the study area, peaking at 76.4% explanatory power in the fourth stage. Compared to global spatial analysis (OLS: 0–0.48), local spatial regression analysis significantly improved the R2 value (GWR: 0.32–0.75, MGWR: 0.48–0.90), but the fitting quality of local spatial regression analysis decreased with increasing scale, highlighting the importance of scale diagnosis. A 2 km scale was identified as optimal for assessing the spatial heterogeneity impact of UGS and other LULC conversions on PM2.5 changes. Conversion areas from water bodies and bare land to UGSs maintain stable local spatial properties at this scale (bandwidths: 44–99). Our research provides new insights into LULC management and planning, offering a coordinated approach to mitigating urban air pollution. Additionally, a practical framework was established for addressing spatially continuous variables such as PM2.5, revealing effective approaches for addressing urban environmental issues.

Spatial Characteristics of Multidimensional Urban Vitality and Its Impact Mechanisms by the Built Environment

Urban vitality, intricately connected to urban morphology, has long been a cornerstone of urban planning and design. The accelerated pace of urbanization has created abundant living and working spaces, but it has also brought about a series of issues such as traffic congestion, environmental pollution, insufficient public spaces, and uneven urban development, leading to a decline in urban vitality. The spatial distribution patterns of urban vitality and their influencing factors are diverse and vary across different cities, necessitating a multidimensional exploration of the relationship between urban vitality and the built environment. Utilizing the central urban area of Beijing as a case study, this research leverages multi-source urban spatial data to delineate the spatial characteristics of social, economic, cultural, and comprehensive vitality. Furthermore, a comprehensive set of built-environment indicators is developed across five dimensions to analyze their correlation with urban vitality. The results indicate: (1) There is a significant spatial clustering of various vitality types in Beijing, with a pronounced correlation between high-density population aggregation and vigorous economic activities. (2) Subdistricts exhibiting high social vitality display an “n”-shaped distribution around the Second Ring Road. In contrast, those with high economic vitality are concentrated along Chang’an Street and various district commercial centers. High cultural vitality subdistricts are distributed in a northwest–southeast trajectory from the Fourth Ring Road to the Second Ring Road, and subdistricts with high comprehensive vitality exhibit a concentric distribution radiating outwards from the center. (3) Social vitality is most closely related to comprehensive vitality, and the various vitality types in Beijing’s central urban area develop relatively evenly. (4) The built environment significantly affects all types of urban vitality. Factors such as floor area ratio, POI density, POI mixing degree, and intersection density are fundamental to enhancing urban vitality, whereas the greening rate somewhat inhibits it. (5) Future spatial planning should utilize the radiating effect of high-vitality subdistricts to optimize population distribution, enhance POI mixing, increase metro station density, and strengthen critical urban structures for synergistic economic and cultural development. This study provides a foundation and promotion strategies for optimizing the layout and enhancing vitality at the subdistrict scale within Beijing’s central urban area.

The Epidemiology of Ocular Chlamydia trachomatis Infection within Districts Persistently Endemic for Trachoma in Amhara, Ethiopia.

Persistent trachoma is a growing concern to trachoma control programs globally and programs serving Ethiopia specifically. Persistent trachoma is defined as a district with two or more trachoma impact surveys (TISs) at which the prevalence of trachomatous inflammation-follicular (TF) among children ages 1-9 years is ≥5%, the elimination threshold. Because the global target for trachoma elimination as a public health problem is 2030, research is needed to better characterize persistent trachoma. This study described the epidemiology of ocular Chlamydia trachomatis infection, the causative bacteria of trachoma, in seven contiguous districts experiencing persistent trachoma. In 2019, multistage cluster random sampling TISs were conducted in the seven districts after 10 years of interventions. All individuals ages ≥1 year were examined for trachoma clinical signs by certified graders, and conjunctival swabs were collected from children ages 1-5 years to test for C. trachomatis infection. The district TF prevalence ranged from 11.8% (95% CI:7.6-16.0%) to 36.1% (95% CI:27.4-44.3%). The range of district-level C. trachomatis infection prevalence was between 2.7% and 34.4%. Statistically significant spatial clustering of high-infection communities was observed in the study districts, and children with infection were more likely than those without to be found in households with clinical signs of trachoma and those without latrines. These seven districts appear to constitute a persistent hotspot in Amhara, where an additional 3-5 years or more of interventions will be required. The global program will need to strengthen and enhance intervention strategies within persistent districts if elimination by 2030 is to be achieved.

Geospatial pattern of level of minimum acceptable diet and its determinants among children aged 6-23 months in Ethiopia. Spatial and multiscale geographically weighted regression analysis.

Despite prior progress and the proven benefits of optimal feeding practices, improving child dietary intake in developing countries like Ethiopia remains challenging. In Ethiopia, over 89% of children fail to meet the minimum acceptable diet. Understanding the geographical disparity and determinants of minimum acceptable diet can enhance child feeding practices, promoting optimal child growth. Spatial and multiscale geographically weighted regression analysis was conducted among 1,427 weighted sample children aged 6-23 months. ArcGIS Pro and SatScan version 9.6 were used to map the visual presentation of geographical distribution failed to achieve the minimum acceptable diet. A multiscale geographically weighted regression analysis was done to identify significant determinants of level of minimum acceptable diet. The statistical significance was declared at P-value <0.05. Overall, 89.56% (95CI: 87.85-91.10%) of children aged 6-23 months failed to achieve the recommended minimum acceptable diet. Significant spatial clustering was detected in the Somali, Afar regions, and northwestern Ethiopia. Children living in primary clusters were 3.6 times more likely to be unable to achieve the minimum acceptable diet (RR = 3.61, LLR =13.49, p < 0.001). Mother's with no formal education (Mean = 0.043, p-value = 0.000), family size above five (Mean = 0.076, p-value = 0.005), No media access (Mean = 0.059, p-value = 0.030), home delivery (Mean = 0.078, p-value = 0.002), and no postnatal checkup (Mean = 0.131, p-value = 0.000) were found to be spatially significant determinants of Inadequate minimum acceptable diet. Level of minimum acceptable diet among children in Ethiopia varies geographically. Therefore, to improve child feeding practices in Ethiopia, it is highly recommended to deploy additional resources to high-need areas and implement programs that enhance women's education, maternal healthcare access, family planning, and media engagement.

Investigation of geographic disparities of diabetes-related hospitalizations in Florida using flexible spatial scan statistics: An ecological study.

Hospitalizations due to diabetes complications are potentially preventable with effective management of the condition in the outpatient setting. Diabetes-related hospitalization (DRH) rates can provide valuable information about access, utilization, and efficacy of healthcare services. However, little is known about the local geographic distribution of DRH rates in Florida. Therefore, the objectives of this study were to investigate the geographic distribution of DRH rates at the ZIP code tabulation area (ZCTA) level in Florida, identify significant local clusters of high hospitalization rates, and describe characteristics of ZCTAs within the observed spatial clusters. Hospital discharge data from 2016 to 2019 were obtained from the Florida Agency for Health Care Administration through a Data Use Agreement with the Florida Department of Health. Raw and spatial empirical Bayes smoothed DRH rates were computed at the ZCTA level. High-rate DRH clusters were identified using Tango's flexible spatial scan statistic. Choropleth maps were used to display smoothed DRH rates and significant high-rate spatial clusters. Demographic, socioeconomic, and healthcare-related characteristics of cluster and non-cluster ZCTAs were compared using the Wilcoxon rank sum test for continuous variables and Chi-square test for categorical variables. There was a total of 554,133 diabetes-related hospitalizations during the study period. The statewide DRH rate was 8.5 per 1,000 person-years, but smoothed rates at the ZCTA level ranged from 0 to 101.9. A total of 24 significant high-rate spatial clusters were identified. High-rate clusters had a higher percentage of rural ZCTAs (60.9%) than non-cluster ZCTAs (41.8%). The median percent of non-Hispanic Black residents was significantly (p < 0.0001) higher in cluster ZCTAs than in non-cluster ZCTAs. Populations of cluster ZCTAs also had significantly (p < 0.0001) lower median income and educational attainment, and higher levels of unemployment and poverty compared to the rest of the state. In addition, median percent of the population with health insurance coverage and number of primary care physicians per capita were significantly (p < 0.0001) lower in cluster ZCTAs than in non-cluster ZCTAs. This study identified geographic disparities of DRH rates at the ZCTA level in Florida. The identification of high-rate DRH clusters provides useful information to guide resource allocation such that communities with the highest burdens are prioritized to reduce the observed disparities. Future research will investigate determinants of hospitalization rates to inform public health planning, resource allocation and interventions.

Navigating geographical disparities: access to obstetric hospitals in maternity care deserts and across the United States

BackgroundAccess to maternity care in the U.S. remains inequitable, impacting over two million women in maternity care “deserts." Living in these areas, exacerbated by hospital closures and workforce shortages, heightens the risks of pregnancy-related complications, particularly in rural regions. This study investigates travel distances and time to obstetric hospitals, emphasizing disparities faced by those in maternity care deserts and rural areas, while also exploring variances across races and ethnicities.MethodsThe research adopted a retrospective secondary data analysis, utilizing the American Hospital Association and Centers for Medicaid and Medicare Provider of Services Files to classify obstetric hospitals. The study population included census tract estimates of birthing individuals sourced from the U.S. Census Bureau's 2017-2021 American Community Survey. Using ArcGIS Pro Network Analyst, drive time and distance calculations to the nearest obstetric hospital were conducted. Furthermore, Hot Spot Analysis was employed to identify areas displaying significant spatial clusters of high and low travel distances.ResultsThe mean travel distance and time to the nearest obstetric facility was 8.3 miles and 14.1 minutes. The mean travel distance for maternity care deserts and rural counties was 28.1 and 17.3 miles, respectively. While birthing people living in rural maternity care deserts had the highest average travel distance overall (33.4 miles), those living in urban maternity care deserts also experienced inequities in travel distance (25.0 miles). States with hotspots indicating significantly higher travel distances included: Montana, North Dakota, South Dakota, and Nebraska. Census tracts where the predominant race is American Indian/Alaska Native (AIAN) had the highest travel distance and time compared to those of all other predominant races/ethnicities.ConclusionsOur study revealed significant disparities in obstetric hospital access, especially affecting birthing individuals in maternity care deserts, rural counties, and communities predominantly composed of AIAN individuals, resulting in extended travel distances and times. To rectify these inequities, sustained investment in the obstetric workforce and implementation of innovative programs are imperative, specifically targeting improved access in maternity care deserts as a priority area within healthcare policy and practice.

Spectral decomposition of ground motions in New Zealand using the generalized inversion technique

SUMMARY To gain new insights into ground-motion phenomena in New Zealand (NZ), we apply the non-parametric generalized inversion technique (GIT) in the Fourier domain to isolate the systematic source, path, and site effects from 20 813 seismograms, recorded by 693 sensors at 439 unique locations, from 1200 shallow crustal events (Mw &amp;gt; 3) during the period 2000–2021. From the inverted source spectra, we derive Brune's stress parameter, ∆σ, which is found to follow a lognormal distribution with a log10 standard deviation of 0.36 or equivalently 0.83 in natural log unit. ∆σ slightly increases with focal depth and is practically independent of earthquake size (i.e. self-similar), but displays a statistically significant spatial clustering. Based on the inverted attenuation, a trilinear geometric-spreading function, and a distance-dependent quality-factor Q(f) model are found to well describe the attenuation in NZ; though a single $Q( f )$ model is also obtained for the whole distance range: $Q( f ) = 149.1{f}^{0.62}$. Using the site response decomposed from GIT, we find that the soil classification scheme specified in NZ seismic code, NZS1170.5, has a limited capability in discerning the site-specific frequency-dependent amplification functions in comparison to a non-parametric clustering with the same number of discrete classes. The potential use of the spatial variation in source parameters from this GIT analysis in region-specific physics-based simulations is discussed.

Epidemiologic Look at Teen Birth [ID 2683394

INTRODUCTION: To take an epidemiologic look at the most up-to-date geographical variation in the average teenage birth rates by county in the United States. METHODS: Data from the National Center for Health Statistics were used in this retrospective cohort to count the total number of live births to females aged 15–19 years by county between 2003 and 2020. Software for disease surveillance and spatial cluster analysis was used to identify clusters of high or low birth rates in counties or areas of greater than 100,000 teenage females. The data were then further analyzed by unemployment rate, no high school diploma rate, Hispanic rate, African American rate, and poverty rate per county as influence on the prediction of teenage birth rate. RESULTS: Spatial analysis identified 94 significant spatial clusters of teenage births. When adjusted for time and space, there were three significant clusters within Texas, all within 2013–2020, showing a steep increase in teenage birth rates. The top cluster with a 60% higher chance of teenage birth than the remainder of the country. Individual covariates examined showed the best predicter of teenage birth is unemployment followed by low education. CONCLUSION: Nationally, teenage births have declined over the last 30 years; however, clusters of elevated teenage birth rates remain predominantly in the southeastern United States and Texas. When adjusting for covariates of social determinants of health, some of these clusters remained, indicating other factors with influence on teenage birth rate.

The spatial pattern and association network of green finance development: Empirical evidence from China

AbstractGreen finance is a market‐driven approach to achieve the “double carbon” goal. However, the existing research predominantly focuses on the connotations and empowerment aspects of green finance. Notable regional disparities exist in China's financial markets and resource endowments, resulting in uneven levels of green finance development among various areas, significantly impeding the overall green transformation of the economy and society. In light of this, our paper explores the spatial patterns and association network of green finance development in China. The research findings indicate that: (1) The development of green finance in China exhibits an “east high, west low” spatial distribution pattern, with significant spatial clustering observed among neighboring provinces. The eastern region displays a “H‐H” clustering, while the western region shows an “L‐L” clustering. (2) The spatial correlation distribution of provincial green finance in China demonstrates an eastward concentration and westward dispersion trend, with strong correlation areas gradually expanding. Notably, Jiangsu, Zhejiang, Shanghai, and the Beijing‐Tianjin‐Hebei urban agglomerations lead nationally in attractiveness. (3) The external radiation capability of provincial green finance is progressively improving, with provinces such as Beijing, Hebei, Hubei, and Sichuan likely to occupy strategic structural positions, possessing greater development advantages. This study not only expands the theoretical scope of existing green finance research but also provides a practical reference for coordinating and achieving the high‐quality development of regional green finance from a practical perspective.

Caterpillar movement mediates spatially local interactions and determines the relationship between population density and contact.

While interactions in nature are inherently local, ecological models often assume homogeneity across space, allowing for generalization across systems and greater mathematical tractability. Density-dependent disease models are a prominent example of models that assume homogeneous interactions, leading to the prediction that disease transmission will scale linearly with population density. In this study, we examined how the scale of larval butterfly movement interacts with the resource landscape to influence the relationship between larval contact and population density in the Baltimore checkerspot (Euphydryas phaeton). Our study was inspired by the recent discovery of a viral pathogen that is transmitted horizontally among Baltimore checkerspot larvae. We used multi-year larvae location data across six Baltimore checkerspot populations in the eastern U.S. to test whether larval nests are spatially clustered. We then integrated these spatial data with larval movement data in different resource contexts to investigate whether heterogeneity in spatially local interactions alters the assumed linear relationship between larval nest density and contact. We used Correlated Random Walk (CRW) models and field observations of larval movement behavior to construct Probability Distribution Functions (PDFs) of larval dispersal, and calculated the overlap in these PDFs to estimate conspecific contact within each population. We found that all populations exhibited significant spatial clustering in their habitat use. Subsequent larval movement rates were influenced by encounters with host plants and larval age, and under many movement scenarios, the scale of predicted larval movement was not sufficient to allow for the "homogeneous mixing" assumed in density dependent disease models. Therefore, relationships between population density and larval contact were typically non-linear. We also found that observed use of available habitat patches led to significantly greater contact than would occur if habitat use were spatially random. These findings strongly suggest that incorporating larval movement and spatial variation in larval interactions is critical to modeling disease outcomes in E. phaeton. Epidemiological models that assume a linear relationship between population density and larval contact have the potential to underestimate transmission rates, especially in small populations that are already vulnerable to extinction.

Genetic variation of English yew (Taxus baccata L.) in the Bavarian Forest National Park, Germany

AbstractTaxus baccata L. is a highly valuable species with wide distribution but scattered and locally rare occurrence. Human intervention, including forest management practices and fragmentation, can significantly impact the species’ genetic diversity, structure, and dynamics. In this study, we investigated these factors within T. baccata populations in the Bavarian Forest National Park (NP) in Germany and their implications for conservation. We used 13 EST-SSRs to assess the genetic diversity and structure of the population. Our analysis revealed a scarcity of small-diameter trees, indicating limited natural regeneration over time. However, conservation efforts, like selectively removing competitor species and using protective fencing, have improved growth conditions and promoted seedling emergence. The NP’s natural zone has no active management, which is confined to the development and management zones. Genetic diversity assessments revealed high genetic diversity (He: 0.612 and 0.614 for seedlings and adults, respectively) compared to other studies in Taxus baccata, dispelling concerns of significant inbreeding and showcasing a stable genetic structure. However, significant spatial clustering of related individuals (family structures) in both cohorts and low effective population size in the progeny hints at restricted gene flow, necessitating conservation efforts prioritizing safeguarding and promoting natural regeneration in development and management zones. Limited natural regeneration and the recent decrease in effective population size in the NP populations indicate habitat fragmentation and human interventions. Effective population size estimates emphasize the need for diverse conservation strategies. Conservation efforts should prioritize protecting natural regeneration and enhancing gene flow by actively promoting European yew, e.g., by shelterwood cutting, to ensure the long-term viability of T. baccata in the region outside the NP.

Analysis of the situations and influencing factors of public anxiety in China: based on Baidu index data.

Anxiety disorders have emerged as one of the most prevalent mental health problems and health concerns. However, previous research has paid limited attention to measuring public anxiety from a broader perspective. Furthermore, while we know many factors that influence anxiety disorders, we still have an incomplete understanding of how these factors affect public anxiety. We aimed to quantify public anxiety from the perspective of Internet searches, and to analyze its spatiotemporal changing characteristics and influencing factors. This study collected Baidu Index from 2014 to 2022 in 31 provinces in mainland China to measure the degree of public anxiety based on the Baidu Index from 2014 to 2022. The spatial autocorrelation analysis method was used to study the changing trends and spatial distribution characteristics of public anxiety. The influencing factors of public anxiety were studied using spatial statistical modeling methods. Empirical analysis shows that the level of public anxiety in my country has continued to rise in recent years, with significant spatial clustering characteristics, especially in the eastern and central-southern regions. In addition, we constructed ordinary least squares (OLS) and geographically weighted regression (GWR) spatial statistical models to examine the relationship between social, economic, and environmental factors and public anxiety levels. We found that the GWR model that considers spatial correlation and dependence is significantly better than the OLS model in terms of fitting accuracy. Factors such as the number of college graduates, Internet traffic, and urbanization rate are significantly positively correlated with the level of public anxiety. Our research results draw attention to public anxiety among policymakers, highlighting the necessity for a more extensive examination of anxiety issues, especially among university graduates, by the public and relevant authorities.

Spatio-temporal analysis of the driving factors of urban land use expansion in China: A study of the Yangtze River Delta region

Abstract Urban land use dynamics play a key role in the sustainable development of rapidly urbanizing regions, such as the Yangtze River Delta (YRD) in China. In this study, we employed geospatial and statistical methods comprising remotely sensed data, Geographic Information Systems, and the Spatial Durbin model to examine the complex changes in urban land uses. These techniques allowed for a broader assessment of the evolving urban landscape, emphasizing the significance of considering spatial relationships and socioeconomic panel data in the study area. The result indicated a substantial increase in built-up land within the YRD, rising from 6.83% in 2000 to 12.29% in 2020. This growth predominantly occurred at the expense of agricultural land, forests, and water bodies, with agricultural areas contributing over 90.2% to the built-up land expansion. The eastern cities experienced a more noticeable urban expansion compared to the western cities. The findings revealed a positive spatial spillover effect among neighboring cities, indicating a significant spatial clustering of built-up land. Population and urbanization emerged as primary drivers influencing both local and neighboring built-up land expansions. However, economic development, fixed asset investment, and transportation networks influenced the local areas of the YRD region but acted as inhibitors for the growth of neighboring areas. The result also suggests that industrial structures effectively curb local built-up land expansion without adversely affecting neighboring areas. These findings contribute to the existing knowledge by providing a wider understanding of land uses within the YRD region and valuable policy recommendations for sustainable urban planning in similar rapidly urbanizing areas.

Synergistic Impacts of Built-Up Characteristics and Background Climate on Urban Vegetation Phenology: Evidence from Beijing, China

Vegetation is an important strategy for mitigating heat island effects, owed to its shading and evaporative cooling functions. However, urbanization has significantly affected regional vegetation phenology and can potentially weaken the cooling potential of vegetation. Previous studies have mainly focused on national and regional vegetation phenology, but local-scale vegetation phenology and dynamic variations in built-up areas remain unclear. Therefore, this study characterized the vegetation phenology in the densely built-up area of Beijing, China over the period of 2000–2020 based on high-resolution NDVI data using Savitzky–Golay filtering and explored its spatiotemporal characteristics and drivers. The results indicate that the vegetation phenology exhibits significant spatial heterogeneity and clustering characteristics. Compared with vegetation in peripheral blocks, vegetation in central urban blocks generally has an earlier start in the growing season (SOS), later end in the growing season (EOS), and a longer growing season length (GSL). However, the overall distribution of these parameters has experienced a process of decentralization along with urbanization. In terms of drivers, vegetation phenology indicators are mainly influenced by background climate. Specifically, SOS and GSL are mainly affected by temperature (TEP), whereas EOS is mainly influenced by annual precipitation (PRE). Additionally, local environmental factors, particularly the percentage of water body (WAP), also have an impact. Notably, the local environment and background climate have a synergistic effect on vegetation phenology, which is greater than their individual effects. Overall, this study extends the current knowledge on the response of vegetation phenology to urbanization by investigating long-term vegetation phenology dynamics in dense urban areas and provides new insights into the complex interactions between vegetation phenology and built environments.

Analyzing carbon emissions and influencing factors in Chengdu-Chongqing urban agglomeration counties

Majority of carbon emissions originate from fossil energy consumption, thus necessitating calculation and monitoring of carbon emissions from energy consumption. In this study, we utilized energy consumption data from Sichuan Province and Chongqing Municipality for the years 2000 to 2019 to estimate their statistical carbon emissions. We then employed nighttime light data to downscale and infer the spatial distribution of carbon emissions at the county level within the Chengdu-Chongqing urban agglomeration. Furthermore, we analyzed the spatial pattern of carbon emissions at the county level using the coefficient of variation and spatial autocorrelation, and we used the Geographically and Temporally Weighted Regression (GTWR) model to analyze the influencing factors of carbon emissions at this scale. The results of this study are as follows: (1) from 2000 to 2019, the overall carbon emissions in the Chengdu-Chongqing urban agglomeration showed an increasing trend followed by a decrease, with an average annual growth rate of 4.24%. However, in recent years, it has stabilized, and 2012 was the peak year for carbon emissions in the Chengdu-Chongqing urban agglomeration; (2) carbon emissions exhibited significant spatial clustering, with high-high clustering observed in the core urban areas of Chengdu and Chongqing and low-low clustering in the southern counties of the Chengdu-Chongqing urban agglomeration; (3) factors such as GDP, population (Pop), urbanization rate (Ur), and industrialization structure (Ic) all showed a significant influence on carbon emissions; (4) the spatial heterogeneity of each influencing factor was evident.

Spatial Patterns of Drone Adoption: Insights from Communities in Southern California

Most US cities have seen a rapid proliferation of Civilian drones in the past decade. This study applies spatial regression models to identify the potential spatial clustering of drone adoption and the effects of socioeconomic, Information and communication technology (ICT) infrastructure, and land use characteristics, with a focus on the association between drone adoption and high-speed broadband coverage (which provides access to information and infrastructure for drone technology). Drone adoption exhibited a significant spatial clustering pattern, indicating spatial interaction in innovation adoption. The age, income, occupation, and land use characteristics significantly explained the drone adoption rate, while populations of color and communities with lower broadband coverage had lower drone adoption rates, indicating racial and ethnic disparities in innovation technology adoption. The underlying digital divide amplifies such disparities and requires the development of policy interventions to reduce inequality.

Clusters of Jail Incarcerations in US Counties: 2010-2018

This study investigates the spatiotemporal variations in jail incarcerations in addition to associations with several risk factors and jail incarceration counts at the county level for the period 2010-2018 in the contiguous USA. The disease surveillance software SaTScanTM was utilized to identify and test purely spatial and spatiotemporal variations in jail incarceration. Significant spatial and space-time clusters with elevated relative risk for jail incarceration were found in analysis. Additionally, a negative binomial regression model was used to predict jail incarcerations counts based on several covariates and found significant and non-random spatial clusters of jail incarceration that are explained after adjusting for these covariates. The results in this study provide useful information on possible associations in geographical areas where jail incarceration rates are higher than expected and demonstrate significant correlations between jail incarceration counts and several covariates. The study and its conclusions provide an epidemiological framework for identifying and addressing geographic patterns of unusually high jail incarceration rates in the U.S. and provide evidence of appropriate locations to further investigate underlying causes of disproportionate incarceration.

Mycobacterium tuberculosis complex in domestic goats in Southern Spain

Tuberculosis (TB) is a zoonotic infectious disease caused by bacteria belonging to the Mycobacterium tuberculosis complex (MTC), which can affect a wide variety of domestic and wild animal species. Although the role of goats as a reservoir of MTC bacteria has been evidenced, information about the circulation of MTC strains in this species is still very scarce. The aim of the present study was to determine the seroprevalence, spatial distribution, risk factors and MTC spoligotypes circulating in goats from Andalusia (Southern Spain), the Spanish region with the largest goat census and a hotspot area of TB in both cattle and wild ungulates. A total of 2155 serum samples from 80 goat flocks were analyzed by an in-house ELISA using the P22 protein complex as a coating antigen. Antibodies against MTC were detected in 473 goats (21.9%, 95% CI: 20.2–23.7) and the true seroprevalence was 22.3% (95% CI: 20.6–24.1). Seropositivity was found in 72 (90.0%) of the 80 flocks analyzed. The generalized estimating equation model showed that the management system (higher seroprevalence on intensive and semi-intensive farms), and the presence of hospital pens inside the regular stables, were risk factors potentially associated with MTC exposure in goats in Southern Spain. The spatial analysis identified a significant spatial cluster (p < 0.001) in Eastern Andalusia. A total of 16 different MTC spoligotypes, including five of M. caprae and eleven of M. bovis, were identified in goats between 2015 and 2022 in the study area, with SB0157 as the most frequently isolated. The results obtained indicate widespread and non-homogeneous spatial distribution of MTC in goat herds from Southern Spain. The high individual and herd-level seroprevalence values found suggest that goats could play a significant role in the maintenance and transmission of MTC in the study area. Our results highlight the importance of implementing control measures in this species.

Malaria risk mapping among children under five in Togo.

Malaria is a major health threat in sub-Sahara Africa, especially for children under five. However, there is considerable heterogeneity between areas in malaria risk reported, associated with environmental and climatic. We used data from Togo to explore spatial patterns of malaria incidence. Geospatial covariate datasets, including climatic and environmental variables from the 2017 Malaria Indicator Survey in Togo, were used for this study. The association between malaria incidence and ecological predictors was assessed using three regression techniques, namely the Ordinary Least Squares (OLS), spatial lag model (SLM), and spatial error model (SEM). A total of 171 clusters were included in the survey and provided data on environmental and climate variables. Spatial autocorrelation showed that the distribution of malaria incidence was not random and revealed significant spatial clustering. Mean temperature, precipitation, aridity and proximity to water bodies showed a significant and direct association with malaria incidence rate in the SLM model, which best fitted the data according to AIC. Five malaria incidence hotspots were identified. Malaria incidence is spatially clustered in Togo associated with climatic and environmental factors. The results can contribute to the development of specific malaria control plans taking geographical variation into consideration and targeting transmission hotspots.

Exposure to Smoke From Wildfire, Prescribed, and Agricultural Burns Among At-Risk Populations Across Washington, Oregon, and California.

Wildfires, prescribed burns, and agricultural burns all impact ambient air quality across the Western U.S.; however, little is known about how communities across the region are differentially exposed to smoke from each of these fire types. To address this gap, we quantify smoke exposure stemming from wildfire, prescribed, and agricultural burns across Washington, Oregon, and California from 2014 to 2020 using a fire type-specific biomass burning emissions inventory and the GEOS-Chem chemical transport model. We examine fire type-specific PM2.5 concentration by race/ethnicity, socioeconomic status, and in relation to the Center for Disease Control's Social Vulnerability Index. Overall, population-weightedPM2.5 concentrations are greater from wildfires than from prescribed and from agricultural burns. While we found limited evidence of exposure disparities among sub-groups across the full study area, we did observe disproportionately higher exposures to wildfire-specific PM2.5 exposures among Native communities in all three states and, in California, higher agricultural burn-specific PM2.5 exposures among lower socioeconomic groups. We also identified, for all three states, areas of significant spatial clustering of smoke exposures from all fire types and increased social vulnerability. These results provide a first look at the differential contributions of smoke from wildfires, prescribed burns, and agricultural burns to PM2.5 exposures among demographic subgroups, which can be used to inform more tailored exposure reduction strategies across sources.