Explore the vast range of titles available.

Moran’s I is commonly used to detect spatial autocorrelation in spatial data. However, Moran’s I may lead to underestimating spatial dependence when used for a small number of areas. This led to the development of Modified Moran’s I , which is designed to work when there are few areas. In this paper, both methods will be presented. Many R programs enable calculating Moran’s I , but to date, none have been available for calculating Modified Moran’s I . This paper aims to present both methods and provide the R code for calculating Modified Moran’s I , with an application to a case study of dengue fever across 14 regions in Makassar, Indonesia.

Using notions from linear algebraic graph theory, this article provides a unified perspective on some autocorrelation measures in different fields. They are as follows: (a) Orcutt’s first serial correlation coefficient, (b) Anderson’s first circular serial correlation coefficient, (c) Moran’s , and (d) Moran’s . The first two are autocorrelation measures for one-dimensional data equally spaced, such as time series data, and the last two are for spatial data. We prove that (a)–(c) are a kind of (d). For example, we show that (d) such that its spatial weight matrix equals the adjacency matrix of a path graph is the same as (a). The perspective is beneficial because studying the properties of (d) leads to studying the properties of (a)–(c) at the same time. For example, the bounds of (a)–(c) can be found from the bounds of (d).

Water scarcity is an emerging multidimensional issue concerning not only the physical availability of resources but also is linked with poverty. The existing literature has established a relationship between income poverty and water poverty. In the Indian context, various studies have explored such issues using the Water Poverty Index (WPI), but only a few have analyzed downscale spatial units. This paper constructs district-level water poverty measures and maps its spatial heterogeneity for Maharashtra, India. Using an indicator-based approach, we aggregate various dimensions of water poverty into a single index. This composite index is formulated by normalizing the indicators and assigning weights using principal component analysis. After rescaling, the aggregate WPI score varies from 0 to 1, denoting lower to higher water poverty. The overall WPI estimate of Maharashtra is 0.47, implying high water stress. The study presents district-wise WPI information by combining the results with Geographic Information System (GIS). Our findings suggest that along with the physical abundance and accessibility to water, improvement in the determinants of capacity and environment is essential to tackle water poverty. Results highlight the intra-district variations among components of water poverty, indicating the importance of local-scale policy-making for better water resource management.

Background Tuberculosis (TB) is the leading cause of death for individuals infected with Human immunodeficiency virus (HIV). Conversely, HIV is the most important risk factor in the progression of TB from the latent to the active status. In order to manage this double epidemic situation, an integrated approach that includes HIV management in TB patients was proposed by the World Health Organization and was implemented in Uganda (one of the countries endemic with both diseases). To enable targeted intervention using the integrated approach, areas with high disease prevalence rates for TB and HIV need to be identified first. However, there is no such study in Uganda, addressing the joint spatial patterns of these two diseases. Methods This study uses global Moran’s index, spatial scan statistics and bivariate global and local Moran’s indices to investigate the geographical clustering patterns of both diseases, as individuals and as combined. The data used are TB and HIV case data for 2015, 2016 and 2017 obtained from the District Health Information Software 2 system, housed and maintained by the Ministry of Health, Uganda. Results Results from this analysis show that while TB and HIV diseases are highly correlated (55–76%), they exhibit relatively different spatial clustering patterns across Uganda. The joint TB/HIV prevalence shows consistent hotspot clusters around districts surrounding Lake Victoria as well as northern Uganda. These two clusters could be linked to the presence of high HIV prevalence among the fishing communities of Lake Victoria and the presence of refugees and internally displaced people camps, respectively. The consistent cold spot observed in eastern Uganda and around Kasese could be explained by low HIV prevalence in communities with circumcision tradition. Conclusions This study makes a significant contribution to TB/HIV public health bodies around Uganda by identifying areas with high joint disease burden, in the light of TB/HIV co-infection. It, thus, provides a valuable starting point for an informed and targeted intervention, as a positive step towards a TB and HIV-AIDS free community.

1. Statistical tests partitioning community variation into environmental and spatial components have been widely used to test ecological theories and explore the determinants of community structure for applied conservation questions. Despite the wide use of these tests, there is considerable debate about their relative effectiveness. 2. We used simulated communities to evaluate the most commonly employed tests that partition community variation: regression on distance matrices and canonical ordination using a third-order polynomial, principal components of neighbour matrices (PCNM) or Moran's eigenvector maps (MEM) to model spatial components. Each test was evaluated under a variety of realistic sampling scenarios. 3. All tests failed to correctly model spatial and environmental components of variation, and in some cases produced biased estimates of the relative importance of components. Regression on distance matrices under-fit the spatial component, and ordination models consistently under-fit the environmental component. The PCNM and MEM approaches often produced inflated R² statistics, apparently as a result of statistical artefacts involving selection of superfluous axes. This problem occurred regardless of the forward-selection technique used. 4. Both sample configuration and the underlying linear model used to analyse species-environment relationships also revealed strong potential to bias results. 5. Synthesis and applications. Several common applications of variation partitioning in ecology now appear inappropriate. These potentially include decisions for community conservation based on inferred relative strengths of niche and dispersal processes, inferred community responses to climate change, and numerous additional analyses that depend on precise results from multivariate variation-partitioning techniques. We clarify the appropriate uses of these analyses in research programmes, and outline potential steps to improve them.

Urban planners require an understanding of the composition as well as the spatial distribution of household solid waste (HSW) components to design policies for various wards of a city. This paper aims to study the composition as well as the spatial dependency of the HSW components generated by the wards forming Annaba city in Algeria. The results of the HSW composition have revealed the high content of the organic matters which represents 50%, the textiles with 13.6%, and the plastics with 10% of the total quantity; the rest of the composition was 5.9% of paper and paperboard, 3% of metals, and 1% of glass. Furthermore, the result of the global and the local Moran indexes calculated and mapped through the ArcGIS 10.7 software shows that there is an evident spatial dependency for almost all the HSW components. Thus, 18% of the total quantity is produced by clustered wards, which propose the segregation of individual waste components at the source as an efficient way to support the reuse, recovery, and recycling. The greenhouse gas (GHG) emissions have revealed that 60% of the emissions are the methane produced from the landfill, and 30% is due to the open burning in the air of waste.

Background Measles is a contagious viral disease that seriously affects children. The measles vaccine is widely recommended in Brazil and in the world; however, the disease remains relevant for the health authorities. The aim of the present study was to evaluate first and second dose of measles vaccine coverage (VC) in the cities of São Paulo and its spatial dynamics between 2015 and 2020. Method: In this mixed-type ecological study, we used secondary, public domain data from 2015 to 2020, extracted from the Digital Information System of the National Immunization Program, Mortality Information System and the National Live Birth Information System. After calculating the VC, the following four categories were created: very low, low, adequate, and high, and the spatial autocorrelation of VC was analyzed using the Global and Local Moran’s statistics. Results A steady decline in adherence to the vaccination was observed, which dynamically worsened until 2020, with a high number of cities fitting the classification of ineffective coverage and being potentially harmful to the effectiveness of the immunization activities of their neighbors. Conclusion A direct neighborhood pattern was observed between the units with low vaccination coverage, which implied that the reduction in measles VC was somehow related to and negatively influenced by the geographic location and social culture of these areas.

Background Intestinal infectious diseases (IIDs) have caused numerous deaths worldwide, particularly among children. In China, eight IIDs are listed as notifiable infectious diseases, including cholera, poliomyelitis, dysentery, typhoid and paratyphoid (TAP), viral Hepatitis A, viral Hepatitis E, hand-foot-mouth disease (HFMD) and other infectious diarrhoeal diseases (OIDDs). The aim of the study is to analyse the spatio-temporal distribution of IIDs from 2006 to 2016. Methods Data on the incidence of IIDs from 2006 to 2016 were collected from the public health science data centre issued by the Chinese Center for Disease Control and Prevention. This study applied seasonal decomposition analysis, spatial autocorrelation analysis and space-time scan analysis. Plots and maps were constructed to visualize the spatio-temporal distribution of IIDs. Results Regarding temporal analysis, the incidence of HFMD and Hepatitis E showed a distinct increasing trend, while the incidence of TAP, dysentery, and Hepatitis A presented decreasing trends over the last decade. The incidence of OIID remained steady. Summer is the season with the greatest number of cases of different IIDs. Regarding the spatial distribution, approximately all p values for the global Moran’s I from 2006 to 2016 were less than 0.05, indicating that the incidences of the epidemics were unevenly distributed throughout the country. The high-risk areas for HFMD and OIDD were located in the Beijing-Tianjin-Tangshan (BTT) region and south China. The high-risk areas for TAP were located in some parts of southwest China. A higher incidence rates for dysentery and Hepatitis A were observed in the BTT region and some west provincial units. The high-risk areas for Hepatitis E were the BTT region and the Yangtze River Delta area. Conclusions Based on our temporal and spatial analysis of IIDs, we identified the high-risk periods and clusters of regions for the diseases. HFMD and OIDD exhibited high incidence rates, which reflected the negligence of Class C diseases by the government. At the same time, the incidence rate of Hepatitis E gradually surpassed Hepatitis A. The authorities should pay more attention to Class C diseases and Hepatitis E. Regardless of the various distribution patterns of IIDs, disease-specific, location-specific, and disease-combined interventions should be established.

Background Over time, vaccination has been consolidated as one of the most cost effective and successful public health interventions and a right of every human being. This study aimed to assess the spatial dynamics of the vaccine coverage (VC) rate of children aged < 1 year per municipality in the Brazilian Northeast at 2016 and 2017. Methods This is a mixed-type ecological study that use a Public domain data Health Information. Vaccine doses were obtained from the Information System of the Brazilian National Immunization Program, and live births from the Brazilian Information System of Live Births of the Brazilian Unified Health System. Descriptive analysis of the coverage of all the vaccines for each year of the study was conducted, and Mann–Whitney U test was used to compare VC between the study years. Chi-squared test was used to evaluate the association between the years and VC, which was stratified into four ranges, very low, low, adequate, and high. Spatial distribution was analyzed according to both each study year and vaccine and presented as thematic maps. Spatial autocorrelation was analyzed using Moran’s Global and Local statistics. Results Compared with 2017, 2016 showed better VC ( p  < 0.05), except for Bacillus Calmette–Guérin. In the spatial analysis of the studied vaccines, the Global Moran’s Index did not show any spatial autocorrelation ( p  > 0.05), but the Local Moran’s Index showed some municipalities, particularly the Sertão Paraibano region, with high VC, high similarity, and a positive influence on neighboring municipalities ( p  < 0.05). In contrast, most municipalities with low VC were concentrated in the Mata Paraibano region, negatively influencing their neighbors ( p  < 0.05). Conclusion Uneven geographic regions and clusters of low VC for children aged < 1 year in the State of Paraíba were spatially visualized. Health policy makers and planners need to urgently devise and coordinate an action plan directed at each state’s regions to fulfill the vaccination calendar, thereby reversing the vulnerability of this age group, which is at a higher risk of diseases preventable by vaccination.

Tree and shrub abundance has increased in many grasslands causing changes in ecosystem carbon and nitrogen pools that are related to patterns of woody plant distribution. However, with regard to spatial patterns of shrub proliferation, little is known about how they are influenced by grazing or the extent to which they are influenced by intraspecific interactions. We addressed these questions by quantifying changes in the spatial distribution of Prosopis velutina (mesquite) shrubs over 74 years on grazed and protected grasslands. Livestock are effective agents of mesquite dispersal and mesquite plants have lateral roots extending well beyond the canopy. We therefore hypothesized that mesquite distributions would be random on grazed areas mainly due to cattle dispersion and clustered on protected areas due to decreased dispersal and interspecific interference with grasses; and that clustered or random distributions at early stages of encroachment would give way to regular distributions as stands matured and density-dependent interactions intensified. Assessments in 1932, 1948, and 2006 supported the first hypothesis, but we found no support for the second. In fact, clustering intensified with time on the protected area and the pattern remained random on the grazed site. Although shrub density increased on both areas between 1932 and 2006, we saw no progression toward a regular distribution indicative of density-dependent interactions. We propose that processes related to seed dispersal, grass-shrub seedling interactions, and hydrological constraints on shrub size interact to determine vegetation structure in grassland-to-shrubland state changes with implications for ecosystem function and management.