Spatial dynamics of fetal mortality and the relationship with social vulnerability

Objectives To analyze the spatial-temporal patterns of fetal mortality according to its relationship with social vulnerability, identifying priority areas for intervention. Methods Ecological study conducted in the state of Pernambuco, Northeast region of Brazil, from 2011 to 2018. The mean fetal mortality rate per city was calculated for the studied period. A cluster analysis was performed to select cities with homogeneous characteristics regarding fetal mortality and social vulnerability, then the Attribute Weighting Algorithm and Pearson correlation techniques were employed. In the spatial analysis it was used the local empirical Bayesian modeling and global and local Moran statistics. Results Twelve thousand nine hundred and twelve thousand fetal deaths were registered. The fetal mortality rate for the period was 11.44 fetal deaths per 1,000 births. The number of groups formed was 7, in which correlation was identified between fetal mortality and dimensions, highlighting the correlations between fetal mortality rate and the Index of Social Vulnerability urban infrastructure for the municipalities in group 1 and 5, the values of the correlations found were 0.478 and 0.674 respectively. The spatial analysis identified areas of higher risk for fetal mortality distributed in regions of medium, high and very high social vulnerability. Conclusions The study allowed observing the existing correlations between fetal mortality and social vulnerability and identifying priority areas for intervention, with a view to reducing fetal mortality in the state.

Spatial Research of the Labour Reserve in the Winery Center of the Danube Wine Region (2014–2019)

In Hungary, the positive impact of the wine sector on rural areas has encouraged Hungarian and EU policy makers to provide significant development support to wineries. One of the main objectives of the support was to increase employment in rural areas. In the period 2014–2019, nearly €60 million was awarded to support the development of wine businesses in Hungary. The aim of our research is to examine how labour supply in the Danube wine region, Hungary’s largest wine region, has changed over the period covered by the wine sector development aid. We analysed the spatial distribution and concentration of several groups of potential labour supply in the study area. The Dual-index and the Hoover-index were used to measure spatial disparities. To measure the spatial concentration of jobseekers, we calculated the location quotient and then examined the spatial pattern of the resulting municipality-level concentration values using the Local Moran I statistic, a local test function of spatial autocorrelation. As a result of our research, we identified the spatial specificities of the potential labour pool available in the study area. The results are useful for business decisionmakers planning to expand or reorganise their human resources. In addition, knowledge of the characteristics of the available potential workforce will support the definition of future development directions, in particular with regard to human resource expansion.

Issues in the Current Practices of Spatial Cluster Detection and Exploring Alternative Methods

Local Moran and local G-statistic are commonly used to identify high-value (hot spot) and low-value (cold spot) spatial clusters for various purposes. However, these popular tools are based on the concept of spatial autocorrelation or association (SA), but do not explicitly consider if values are high or low enough to deserve attention. Resultant clusters may not include areas with extreme values that practitioners often want to identify when using these tools. Additionally, these tools are based on statistics that assume observed values or estimates are highly accurate with error levels that can be ignored or are spatially uniform. In this article, problems associated with these popular SA-based cluster detection tools were illustrated. Alternative hot spot-cold spot detection methods considering estimate error were explored. The class separability classification method was demonstrated to produce useful results. A heuristic hot spot-cold spot identification method was also proposed. Based on user-determined threshold values, areas with estimates exceeding the thresholds were treated as seeds. These seeds and neighboring areas with estimates that were not statistically different from those in the seeds at a given confidence level constituted the hot spots and cold spots. Results from the heuristic method were intuitively meaningful and practically valuable.

SPATIAL PATTERN EVOLUTION AND DRIVING FACTORS OF PM_2.5 CONCENTRATIONS IN THE GRAND CANAL REGION FROM 2000 TO 2018

In recent years, air pollution related to PM2.5 has caused a significant impact on human health. The Grand Canal (GC) is not only a great Cultural heritage created in ancient China but also the longest and largest canal in the world. Based on remotely sensed PM2.5 gridded data in the GC region covering 2000 to 2018, we used the holistic methods of standard deviation ellipse, local moran index, slope trend analysis to reveal the spatiotemporal evolutions of PM2.5 concentrations in the GC regions and investigated the driving factors of PM2.5 concentrations by using the geographically weighted regression (GWR) model. Results show that (1) PM2.5 concentrations in the GC region exhibited an increasing trend and followed by a decreasing trend from 2000 to 2018 (the turning point emerged in 2010). (2) The standard deviation ellipse analyses show that the spatial distributions of PM2.5 concentrations featured more and more concentrated over time, whereas, after the year 2010, the distributions gradually featured scattered. (3) The concentrations of PM2.5 exhibited the strong effects of local spatial autocorrelation and areas with "high-high" agglomeration were mainly located in the central and west regions of the GC region and gradually expanded to the north over time. (4) The areas of regions with rapidly increasing in PM2.5 concentrations gradually decreased over time, however, those with rapidly decreasing in PM2.5 concentrations increased. (5) The influences of the natural factors and socio-economic factors on the distributions of PM2.5 concentrations varied spatially. In detail, the elevation was negatively correlated with PM2.5 concentrations, whereas an opposite relationship between industrial structure and PM2.5 concentrations was observed. The coefficients of rainfall, population density, GDP per capita and foreign investment show different results in positive and negative correlations depending on the position.

Local Moran Index: an application in epidemiological coefficients of the COVID-19 pandemic in Brazil

The COVID-19 pandemic spread quickly around the world in a frightening way. In Brazil, the third country in the world with the highest number of people infected and killed by the disease, it is important that the government health authorities identify the federation units that stand out in cases and deaths due to this disease for targeting resources. The Local Moran Index is a statistical tool that estimates those units of the federation that stands out the most with some statistical significance. We used the epidemiological coefficients of incidence, prevalence, and lethality to describe Brazil’s pandemic better today. We use R software to obtain maps and results.

Proposta de autocorrelação espacial da relação entre as condições socioeconômicas na Região Metropolitana de Sorocaba, SP, Brasil

The spatialization of social data allows to analyze some social and territorial characteristics of census tracts up to the totality of a city or metropolitan region. The objective of this study was to verify the spatial autocorrelation of data that reflect the health and income conditions of households in the Metropolitan Region of Sorocaba (MRS) and verify if there is a correlation of these indicators by a multiple linear regression test. For this, the Global and Local Moran Index was calculated, which were used to measure autocorrelation and spatial dependence among the census tracts. It was identified that there are 177 census tracts distributed by MRS that showed autocorrelations for all variables and correspond to 31.1% of the territory and 5.4% of the total population of MRS. This study can be used by public managers to develop public policies aimed at improving the quality of life of the population because allows the identification of the regions that go beyond the administrative limits of the municipalities that lack collective investment and cooperation of municipalities.

Evaluating Social Distancing Measures and Their Association with the Covid-19 Pandemic in South America

Social distancing is a powerful non-pharmaceutical intervention used as a way to slow the spread of the SARS-CoV-2 virus around the world since the end of 2019 in China. Taking that into account, this work aimed to identify variations on population mobility in South America during the pandemic (15 February to 27 October 2020). We used a data-driven approach to create a community mobility index from the Google Covid-19 Community Mobility and relate it to the Covid stringency index from Oxford Covid-19 Government Response Tracker (OxCGRT). Two hypotheses were established: countries which have adopted stricter social distancing measures have also a lower level of circulation (H1), and mobility is occurring randomly in space (H2). Considering a transient period, a low capacity of governments to respond to the pandemic with more stringent measures of social distancing was observed at the beginning of the crisis. In turn, considering a steady-state period, the results showed an inverse relationship between the Covid stringency index and the community mobility index for at least three countries (H1 rejected). Regarding the spatial analysis, global and local Moran indices revealed regional mobility patterns for Argentina, Brazil, and Chile (H1 rejected). In Brazil, the absence of coordinated policies between the federal government and states regarding social distancing may have played an important role for several and extensive clusters formation. On the other hand, the results for Argentina and Chile could be signals for the difficulties of governments in keeping their population under control, and for long periods, even under stricter decrees.

Spatial and spatiotemporal dynamics of visceral leishmaniasis in an endemic North-eastern region of Brazil

Visceral Leishmaniasis (VL) is a neglected disease with increasing incidence in Brazil, particularly in the North-eastern. The aim of this study was to analyze the spatial and spatiotemporal dynamics of VL in an endemic region of North-eastern Brazil, between 2009 and 2017. Using spatial analysis techniques, an ecological and time series study was made regarding VL cases in Sergipe filed as notifiable disease events. With data from the Brazilian Institute of Geography and Statistics (Instituto Brasileiro de Geografia e Estatística, IBGE), a digital population and cartographic baseline was established. Segmented linear regression was used to examine the temporal trends. The statistical analysis methods of Global and Local Moran’ I, local Bayesian empirical methodology and spatial-temporal scanning were used to produce thematic maps. High instances were found among adults, males, urban residents, non-Whites and persons with low levels of education. A decrease in the recovery rate and an increase in the proportion of urban cases and lethality was found. A heterogeneous VL distribution with spatiotemporal agglomeration on the seaside of the state was seen in Sergipe. To better manage the disease, new research is encouraged together with development of public health strategies. Further, improving health care networks, especially primary care, is suggested as this approach has a key role in health promotion, prevention and monitoring of the most prevalent diseases.

Modeling of interaction between the regions of Russia and the Republic of Belarus in order to predict additional traffic flows

The article proposes a methodological approach using the spatial econometrics tool for development of transport and economic forecasting systems and reflection of major structural political decisions in its results on the example of the influence of economic integration of Russia and Belarus on transport flows in the intersectoral interterritorial systems of the Union State. To calculate the studied indicators, data from the official statistical websites of Russia and Belarus were used. The analysis of the spatial distribution of output in the processing sector of the regions of the Russian Federation and the Republic of Belarus is carried out to assess the possibility of interaction between these regions in this sector of the economy. When modeling, the Republic of Belarus is considered as a separate region within the framework of the Union State. Calculations of the global and local Moran indices were made and possible spatial autocorrelations were determined both between the subjects of the Russian Federation and between the regions of the Russian Federation and the Republic of Belarus. In the current study, a standard inverse distance weight matrix was used to assess the degree of interaction between regions. The problem is considered, first of all, from the transport side on the basis of the analysis of relations in heavy industry as the main cargo-forming sphere. The influence of economic and infrastructural factors on the indicator characterizing the degree of possible interaction between the regions of the Russian Federation and the Republic of Belarus in the field of industry and transport is studied. The results of the work make a step towards the systematic application of spatial econometrics in methodology and technology of transport and economic forecasting and can be used in preparation of strategies, programs and schemes for placement and development of industries, taking into account the potential of a new level of integration of the economies of Russia and Belarus.

Investigating the inter-annual precipitation changes of Iran

Precipitation is an important factor in the management of a variety of agricultural and industrial projects. This study investigated the temporal-spatial change of inter-annual precipitation of Iran from 1977 to 2007 by using the APHRODITE precipitation database. Statistical methods were applied, such as spatial auto-correlation, Global Moran's index, Local Moran's I index, and hotspots to acquire the variations in precipitation. The highest spatial anomalies belong to September (75.26) and October (45.02), based on the Dispersion index. Also, the size of the largest cluster of Iran's precipitation clusters is developed during winter, cited by the index's outputs, which indicates the relative regularity of Iran's precipitation. The results of the spatial statistics showed that inter-annual precipitation changes in Iran have an upward cluster model. The results of the Global Moran statistics showed that September, with the lowest number (0.712114), has the highest spatial precipitation anomalies throughout the year in Iran. Meanwhile, precipitation has a positive spatial autocorrelation on the Caspian Sea shores and western and south-western parts of the country (mainly Zagros highlands) and a negative spatial autocorrelation in parts of the central and south-eastern areas based on the Local Moran index and hotspots.