No. 65, Journal of Population StudiesPublished: 2022.12
Contents
Awaiting translation :)
Special Topic Articles: Spatial Demography
DOI : 10.6191/JPS.202212_(65).0001
spatial scaling ； fractal dimension ； dengue fever ； clustering phenomenon ； critical scale
Abstract
In a disease outbreak context, disease cases are usually presented by using point distribution data. Due to the scale-invariant issue of point data and the scaling issue of the modifiable areal unit problem, identifying a critical scale for the analysis of point patterns, such as the clustering phenomenon, is important. This study proposes a novel data-driven framework for calculating the critical scale based on two traditional concepts: (1) the point-region quadtree spatial indexing method and (2) the box counting method for fractal pattern analysis. Both concepts capture the spatial scaling process and serve as the core concepts of the proposed framework. Using dengue fever cases in Kaohsiung City, Taiwan, during the past two decades, the critical scale was identified for each outbreak year. Two clustering analysis approaches were used to test the resulting critical scales, including kernel density estimation and density-based spatial clustering application with noise. Both clustering analyses involved distance parameter settings. Therefore, through the setting of search radii, the two clustering methods were used as a tool to explore the clustering patterns under different scale levels. In summary, the identified critical scales can better capture the spatial patterns of point data.
Keywords: spatial scaling, fractal dimension, dengue fever, clustering phenomenon, critical scale
Keywords: spatial scaling, fractal dimension, dengue fever, clustering phenomenon, critical scale
DOI : 10.6191/JPS.202212_(65).0002
heterogeneity ； homogeneity ； geographically weighted regression ； quantile regression ； spatial demography
Abstract
There is a growing interest in exploring heterogeneous associations with independent variables across the distribution of either the dependent variable (using quantile regression) or across geographic space (using geographically weighted regression). The former is often known as statistical heterogeneity, whereas the latter refers to spatial heterogeneity. However, population research has been slow to adopt either of these methods. This study first briefly discusses why more attention to the concept of heterogeneity is needed and then introduces a method that simultaneously considers statistical and spatial heterogeneity, namely geographically weighted quantile regression (GWQR). We illustrate how to use GWQR with U.S. county-level coronavirus disease (COVID-19) vaccination data and explain how GWQR identifies significant heterogeneities in the relationships between the vaccination rate and its determinants across space and over the vaccination distribution. The results suggest that both spatial and statistical heterogeneity are a common occurrence. For example, the COVID-19 case rate has a stronger association in counties in the lower quantiles than in the higher quantiles. The spatial distribution of this relationship is focused on counties in the Mountain states and is shifted to the Midwest region. As such, we conclude that both heterogeneities should be considered in population research.
Keywords: heterogeneity, homogeneity, geographically weighted regression, quantile regression, spatial demography
Keywords: heterogeneity, homogeneity, geographically weighted regression, quantile regression, spatial demography
Research Articles
DOI : 10.6191/JPS.202212_(65).0003
elderly tenants ； elders living in solitude ； age discrimination ； Population and Housing Census ； Housing Demand Survey
Abstract
The media has been repeatedly using the expression "elders in humble dwellings" to refer to elderly people who live in shabby rental housing in urban areas. A residential building fire in 2021 in Kaohsiung brought the housing problems of vulnerable groups back to forefront, highlighting the disadvantages experienced by the solitary elderly in the rental housing market. This study draws on the 2010 Population and Housing Census data to represent the population, family patterns, and housing characteristics of elderly tenants. It also outlines their renting experience and situation based on the Housing Demand Survey conducted by the Ministry of the Interior in the same year. The preliminary findings reveal that (1) In 2010, 77% of elderly tenants lived in solitude or quasi-solitude; (2) The housing conditions of the elderly who lived in solitude or quasi-solitude were poorer than for those who lived with relatives. The housing conditions of those who lived in solitude were even worse, showing that age discrimination in renting has a more significant impact on them; (3) The elderly tended to rent through the recommendation of relatives and friends or traditional advertisements. They experienced more difficulties in house renting. The housing they rented was cheap; however, its square footage was relatively small. Moreover, the rental pressure on the elderly was also higher than that on the non-elderly; (4) The unmarried and economically challenged elderly were more likely to rent secondary products in the rental housing market; (5) Finally, the housing conditions of elderly tenants were affected by their education level, family monthly income, and family structure.
Keywords: elderly tenants, elders living in solitude, age discrimination, Population and Housing Census, Housing Demand Survey
Keywords: elderly tenants, elders living in solitude, age discrimination, Population and Housing Census, Housing Demand Survey