ABSTRACT
The risk of severe illness or death from COVID-19 is associated with specific demographic characteristics or composition of the population within geographic areas, and the spatial relationship between these areas. The aim of this paper is to identify areas with a higher concentration of population vulnerable to COVID-19, relying on the concept of spatial dependence. Hence, we focus on the share of vulnerable populations using several salient proxy measures at municipality level data for Serbia. The degree of vulnerability at the municipality level was determined by hotspot analysis, specifically the Getis-Ord Gi* statistics. The results indicate heterogeneity in the spatial patterning and typologies of clusters across Serbia. This spatial heterogeneity reveals potentially differing degrees of risk across municipalities. The results can inform decision-makers in the fight against COVID-19 by helping to identify those areas with vulnerable populations that if exposed may stress the local health care system. © 2022 University of Craiova, Faculty of Social Sciences, Department of Geography. All rights reserved.
ABSTRACT
The COVID-19 pandemic escalated in almost all parts of the world over a very short period of time. The speed of the spread was determined by the degree of mobility of the population, while the risk of severe illness or death depended on the population’s demographic characteristics, population health status, and the capacity of the health system to treat patients. This paper aims to assess spatio-temporal patterns of patients with COVID-19 in Serbia at the early stage and whether these patterns are linked to valid public health measures that were enforced during this period. The study adopted the local Moran’s index to identify the spatial grouping of the number of infected at a municipality level and joinpoint regression analysis to identify whether and when statistically significant changes occurred to the number of infected by gender and age groups, and to the number of deaths in the entire population. The results show the polarisation of the spatial grouping of the number of infected. Considering the change in the trend in the number of infected between genders, no significant difference was noticeable. When the age-gender categories of infected were examined, the differences became more significant. In addition, changes in the trend were associated with the tightening or loosening of public health measures. © 2022 Demographic Research Centre. All rights reserved.
ABSTRACT
The residential real estate market is very important because most people’s wealth is in this sector, and it is an indicator of the economy. Real estate market data in general and market transaction data, in particular, are inherently spatiotemporal as each transaction has a location and time. Therefore, exploratory spatiotemporal methods can extract unique locational and temporal insight from property transaction data, but this type of data are usually unavailable or not sufficiently geocoded to implement spatiotemporal methods. In this article, exploratory spatiotemporal methods, including a space-time cube, were used to analyze the residential real estate market at small area scale in the Dublin Metropolitan Area over the last decade. The spatial patterns show that some neighborhoods are experiencing change, including gentrification and recent development. The extracted spatiotemporal patterns from the data show different urban areas have had varying responses during national and global crises such as the economic crisis in 2008–2011, the Brexit decision in 2016, and the COVID-19 pandemic. The study also suggests that Dublin is experiencing intraurban displacement of residential property transactions to the west of Dublin city, and we are predicting increasing spatial inequality and segregation in the future. The findings of this innovative and exploratory data-driven approach are supported by other work in the field regarding Dublin and other international cities. The article shows that the space-time cube can be used as complementary evidence for different fields of urban studies, urban planning, urban economics, real estate valuations, intraurban analytics, and monitoring sociospatial changes at small areas, and to understand residential property transactions in cities. Moreover, the exploratory spatiotemporal analyses of data have a high potential to highlight spatial structures of the city and relevant underlying processes. The value and necessity of open access to geocoded spatiotemporal property transaction data in social research are also highlighted. © 2021 by the authors. Licensee MDPI, Basel, Switzerland.