An Exploratory Analysis of Housing and the Distribution of COVID-19 in Sweden

The impact of COVID-19 on various aspects of our life is evident. Proximity and close contact with individuals infected with the virus, and the extent of such contact, contribute to the intensity of the spread of the virus. Healthy and infected household members who both require sanctuary and quarantine space come into close and extended contact in housing. In other words, housing and living conditions can impact the health of occupants and the spread of COVID-19. This study investigates the relationship between housing characteristics and variations in the spread of COVID-19 per capita across Sweden’s 290 municipalities. For this purpose, we have used the number of infected COVID-19 cases per capita during the pandemic period—February 2020 through April 2021—per municipality. The focus is on variables that measure housing and housing conditions in the municipalities. We use exploratory analysis and Principal Components Analysis to reduce highly correlated variables into a set of linearly uncorrelated variables. We then use the generated variables to estimate direct and indirect effects in a spatial regression analysis. The results indicate that housing and housing availability are important explanatory factors for the geographical spread of COVID-19. Overcrowding, availability, and quality are all critical explanatory factors.

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 Abandoned Houses and Their Influencing Factors in South Korea

The number of abandoned houses is rapidly growing across South Korea. The increasing number of abandoned houses is directly linked to a wide range of problems in communities, such as apprehension about crimes. This study aimed to analyze the variables that affect housing abandonment empirically. First, we analyzed the status of housing abandonment in various regions based on the addresses of the abandoned houses. Second, we identified the spatial characteristics of abandoned houses through spatial autocorrelation analysis. Third, we selected variables based on the literature review and analyzed the factors affecting housing abandonment through spatial regression analysis. Lastly, we aimed to explore the correlation between regional characteristics and the occurrence of housing abandonment, and to derive the factors influencing housing abandonment. This study found that abandoned houses were more likely to occur mainly in areas with environmentally vulnerable features. In this study, neighborhood environmental factors that promoted the occurrence of abandoned houses were derived by considering the neighborhood-level unit of analysis.

Ecological Study of Variability in the Relationship between Liver Cancer Mortality and Racial Residential Segregation

Racial segregation has been identified as a predictor for the burden of cancer in several different metropolitan areas across the United States. This ecological study tested relationships between racial segregation and liver cancer mortality across several different metropolitan statistical areas in Wisconsin. Tract-level liver cancer mortality rates were calculated using cases from 2003–2012. Hotspot analysis was conducted and segregation scores in high, low, and baseline mortality tracts were compared using ANOVA. Spatial regression analysis was done, controlling for socioeconomic advantage and rurality. Black isolation scores were significantly higher in high-mortality tracts compared to baseline and low-mortality tracts, but stratification by metropolitan areas found this relationship was driven by two of the five metropolitan areas. Hispanic isolation was predictive for higher mortality in regression analysis, but this effect was not found across all metropolitan areas. This study showed associations between liver cancer mortality and racial segregation but also found that this relationship was not generalizable to all metropolitan areas in the study area.

Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation

In this article, we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determine the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means high economic activity, which leads to more deaths due to COVID-19. There is a lack of supply in the set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated with COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

Spatial modelling of multidimensional poverty in rural area: Evidence from Malang Regency, Indonesia

Poverty is a multidimensional phenomenon that causes difficulty for people to meet their needs. The research aims to scrutinize physical and social infrastructures concerning multidimensional poverty levels using the spatial approach. Jabung District, Malang Regency, Indonesia has 35% poor households in this case study. The objectives are to measure multidimensional poverty levels, social capital indices of the rate of participation (RoP) and density, and scrutinize neighborhood relationships among 15 villages using spatial regression analysis. Data collection is through a questionnaire survey of 274 heads of households. The research identified four poverty levels (very low to high), where five villages with high poverty levels (Jabung, Taji, Kemiri, Gunungjati, Slamparejo) became the targeted areas. The majority of the villages had a medium level of both the RoP and density, and the community had moderate social relations among community members. The spatial regression analysis indicates that the attribute of the RoP and weight matrix have a significant impact on the poverty level. It is recommended that poverty alleviation programs should focus upon the cluster of poor villages through social infrastructure development as the action to end poverty.JEL Classification A13; I32; R58

Using Spatial Patterns of COVID-19 to Build a Framework for Economic Reactivation

In this article we propose an application of humanitarian logistics theory to build a supportive framework for economic reactivation and pandemic management based on province vulnerability against COVID-19. The main research question is: which factors are related to COVID-19 mortality between Peruvian provinces? We conduct a spatial regression analysis to explore which factors determines the differences in COVID-19 cumulative mortality rates for 189 Peruvian provinces up to December 2020. The most vulnerable provinces are characterized by having low outcomes of long-run poverty and high population density. Low poverty means a high economic activity that leads to more deaths of COVID-19. There is a lack of supply of a set of relief goods defined as Pandemic Response and Recovery Supportive Goods and Services (PRRSGS). These goods must be delivered in order to mitigate the risk associated to COVID-19. A supportive framework for economic reactivation can be built based on regression results and a delivery strategy can be discussed according to the spatial patterns that we found for mortality rates.

PEMODELAN KASUS DIARE DI PROVINSI BALI DENGAN METODE ANALISIS REGRESI SPASIAL

Diarrhea is a disease that occurs due to changes in the frequency of bowel movements and can cause death. In 2018, 115.889 cases of diarrhea were found in Bali Province. Information on the relationship between locations indicates the spatial effect in the model. Model estimation was done by using spatial regression analysis. This study aims to determine what factors influence diarrhea cases in Bali Province. The results show that the number of diarrhea cases in a district is influenced by the surrounding districts. This is reinforced in the Moran’s I test which shows spatial dependence. In the analysis of the Spatial Error Model (SEM), it was obtained that the value of was 57,69% and the variables that significantly affected diarrhea cases in Bali Province were population density and sanitation facilities