Differences in Distance and Spatial Effects on Cross-Border E-Commerce and International Trade

Distance and space are important factors affecting international trade, but they have different effects on cross-border e-commerce (CBE) due to the creation of the Internet. This study utilizes spatial autocorrelation, the multi-dimension gravity model and the Spatial Durbin model to conduct an comparative analysis of international trade and CBE within one-belt one-road (BR) countries. Our study obtained several key findings. Firstly, the spatial autocorrelation effect which exists in international trade does not exist in CBE. Secondly, the geographical distance effect of CBE is not significant, which is different from that of international trade. Thirdly, CBE is affected by GDP, culture, policy and institution distances which is not entirely consistent with international trade. Finally, the Spatial Durbin model shows that the spillover effect of CBE and international trade are both significant in the inverse distance weight matrix. These findings provide not only important theoretical contributions but also a practical guide for Government policy makers of the BR and CBE.

Physical geography, isolation by distance and environmental variables shape genomic variation of wild barley (Hordeum vulgare L. ssp. spontaneum) in the Southern Levant

Determining the extent of genetic variation that reflects local adaptation in crop-wild relatives is of interest for the purpose of identifying useful genetic diversity for plant breeding. We investigated the association of genomic variation with geographical and environmental factors in wild barley (Hordeum vulgare L. ssp. spontaneum) populations of the Southern Levant using genotyping by sequencing (GBS) of 244 accessions in the Barley 1K+ collection. The inference of population structure resulted in four genetic clusters that corresponded to eco-geographical habitats and a significant association between lower gene flow rates and geographical barriers, e.g. the Judaean Mountains and the Sea of Galilee. Redundancy analysis (RDA) revealed that spatial autocorrelation explained 45% and environmental variables explained 15% of total genomic variation. Only 4.5% of genomic variation was solely attributed to environmental variation if the component confounded with spatial autocorrelation was excluded. A synthetic environmental variable combining latitude, solar radiation, and accumulated precipitation explained the highest proportion of genomic variation (3.9%). When conditioned on population structure, soil water capacity was the most important environmental variable explaining 1.18% of genomic variation. Genome scans with outlier analysis and genome-environment association studies were conducted to identify adaptation signatures. RDA and outlier methods jointly detected selection signatures in the pericentromeric regions, which have reduced recombination, of the chromosomes 3H, 4H, and 5H. However, selection signatures mostly disappeared after correction for population structure. In conclusion, adaptation to the highly diverse environments of the Southern Levant over short geographical ranges had a limited effect on the genomic diversity of wild barley. This highlighted the importance of nonselective forces in genetic differentiation.

Spatial disparities and associated factors of Under-five diarrhea disease in Ilubabor Zone, Oromia Regional State, Ethiopia

Diarrhea is commonly a sign of an infection in the intestinal tract that is caused by different bacteria, virus and parasitic entities. It is one of the leading causes of child mortality worldwide, especially in sub-Saharan Africa countries including Ethiopia. The main objective of this study was to identify spatial disparities and associated factors of under- five diarrhea disease in Ilubabor zone, Oromia regional state, Ethiopia. The study has been conducted in Ilu Aba Bor zone of entire districts and the data is basically both primary and secondary which were obtained from each woreda health office of Ilu Aba Bor zone and corresponding mother or care givers of sampled child. Spatial disparities of under-five diarrhea were identified using global and local measures of spatial autocorrelation. Geo-additive regression model was used to identify the spatial disparities and associated factors of under-five diarrheal disease. The value of global and local measures of spatial autocorrelation shows that under-five diarrheal disease varies according to geographical location and shows significant positive spatial autocorrelation. The results of Geo-additive regression model showed that statistically significant relationship between under-five diarrhea disease and independent variables .There is evidence of significant under-five diarrheal disease clustering in Ilu Aba Bor zone, southwest Ethiopia. Model based data analysis showed that there is significant relationship between Under-five diarrhea and covariates (mother’s age, mother’s education, source of drinking water, quality of toilet facility, DPT 3 vaccination, Polio 3 vaccination and household wealth index.).

Bus ridership and its determinants in Beijing: A spatial econometric perspective

Understanding the temporal and spatial dynamics and determinants of public transport ridership play an important role in urban planning. Previous studies have focused on exploring the determinants at the station level using global models, or a local model, geographically weighted regression (GWR), which cannot reveal spatial autocorrelation at the global level. This study explores the factors affecting bus ridership considering spatial autocorrelation using the spatial Durbin model (SDM). Taking the community in Beijing as the basic study unit, this study aims to explore the temporal and spatial dynamics of bus ridership and identify its key determinants considering neighboring effects. The results show the following: (1) The temporal dynamics are quite distinct on weekdays and weekends as well as at different time slots of the day. (2) The spatial patterns of bus ridership varied across different time slots, and the hot areas are mainly located near the central business district (CBD), transport hubs, and residential areas. (3) Key determinants of bus ridership varied across weekends and weekdays and varied at different time slots per day. (4) The spatial neighboring effects had been verified. This study provides a common analytical framework for analyzing the spatiotemporal dynamics and determinants of bus ridership at the community level.

The path of canine visceral leishmaniasis versus the path of Center for Zoonoses Control: contributions of spatial analysis to health

Visceral leishmaniasis (VL) is a public health problem in Brazilian municipalities. As much as there is a planning of public policies regards VL in São Paulo State, new cases have been reported and spread. This paper aims to discuss how the Center for Zoonoses Control conducts its actions spatially in endemic city of Presidente Prudente, São Paulo State. Data are from the Municipal Health Department of Presidente Prudente, Adolfo Lutz Institute, and Brazilian Institute of Geography and Statistics. We spatially estimated the dog population per census tract and used geoprocessing tools to perform choropleth maps, spatial trends, and spatial autocorrelation. We found a spatial pattern of higher prevalence in the city’s outskirt and a positive statistically significant spatial autocorrelation (I = 0.2, p-value < 0.000) with clusters of high-high relationships in the Northwest part of the city. Moreover, we identified a different direction in the path of the conducted serosurveys versus the canine VL trend, which stresses the fragility of the Center for Zoonoses Control actions to control the disease. The Center for Zoonoses Control always seems to chase the disease. The spatial analysis may be useful for rethinking how the service works and helps in public policies.

Determinants of Development Achievements on District/City of Central Java in 2019

In 2019, Badan Perencanaan Pembangunan Nasional (Bappenas) awarded Central Java as the province with the best Perencanaan dan Pembangunan Daerah (PPD). However, if it is reviewed at the district/city level, it shows that there are still many areas that have low development achievements. In accordance with the United Nations Development Programme (UNDP) proposal, the Human Development Index (HDI) is used as an indicator of the achievement of district/city development whose calculations are good enough to describe development from both a social and economic perspective. The large difference in HDI between districts/cities in Central Java and the distribution of development achievements are still centered around the provincial capital, namely Semarang City, this indicates the occurrence of inequality in development achievements at the district/city level in Central Java. Because the observations in this study are districts/cities in Central Java, the linkage between district/city causes spatial autocorrelation. Therefore, spatial regression model is used to determine the model that has spatial autocorrelation. This study aims to determine the achievements of development and its determinants in the districts/cities of Central Java in 2019 using the spatial regression analysis method. From the results of the study, it is known that there is a dependence on development achievements between districts/cities in Central Java which is influenced by the regional capacity factor is characterized by PAD and economic growth; operational resource factors characterized by DAU, DAK and technology; and the level of poverty.

Spatial analysis of AIDS in the state of Maranhão: an ecological study 2011-2018

ABSTRACT Objective: Analyze the spatio-temporal distribution of AIDS cases in Maranhão. Methods: Ecological study of AIDS cases in the Notifiable Diseases Information System, 2011-2018. Gross and adjusted incidences were calculated using the Baysean method; then, the Moran Global and Local Indices to observe the existence of spatial autocorrelation of the cases and for the delimitation of high and low risk clusters. Results: 6,349 cases were reported, which were distributed heterogeneously. There was an advance of cases to new areas and persistence in old areas, such as in the capital São Luís and its surroundings. The dissemination did not occur at random, with positive spatial autocorrelation, with evidence of the formation of clusters in the municipalities of São Luís, São José de Ribamar and Paço do Lumiar. Conclusion: High-risk areas have been identified and should be considered a priority for investment in health, management, and organization of health services.

Evaluating borrowers’ default risk with a spatial probit model reflecting the distance in their relational network

Potential relationship among loan applicants can provide valuable information for evaluating default risk. However, most of the existing credit scoring models either ignore this relationship or consider a simple connection information. This study assesses the applicants’ relation in terms of their distance estimated based on their characteristics. This information is then utilized in a proposed spatial probit model to reflect the different degree of borrowers’ relation on the default prediction of loan applicant. We apply this method to peer-to-peer Lending Club Loan data. Empirical results show that the consideration of information on the spatial autocorrelation among loan applicants can provide high predictive power for defaults.