| dc.contributor | 統計系 | |
| dc.creator (作者) | 陳怡如;楊澤全 | |
| dc.creator (作者) | Chen, Vivian Yi-ju;Yang, Tse-chuan | |
| dc.date (日期) | 2022-12 | |
| dc.date.accessioned | 29-Jan-2024 09:12:11 (UTC+8) | - |
| dc.date.available | 29-Jan-2024 09:12:11 (UTC+8) | - |
| dc.date.issued (上傳時間) | 29-Jan-2024 09:12:11 (UTC+8) | - |
| dc.identifier.uri (URI) | https://nccur.lib.nccu.edu.tw/handle/140.119/149419 | - |
| dc.description.abstract (摘要) | 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. | |
| dc.description.abstract (摘要) | 愈來愈多人對於探索自變數與反應變數間在不同分量下（使用分量迴歸〔quantile regression〕）或在空間上（使用地理加權迴歸〔geographically weighted regression〕）之異質關係感到興趣。前者通常被視為一種統計異質性，而後者則指的是空間異質性。然而，人口研究在採用這些方法方面進展略顯緩慢。本研究首先簡要討論了為何人口研究者需要多加關注異質性的概念，並介紹了一種同時考量統計和空間異質性的方法，即地理加權分量迴歸（geographically weighted quantile regression, GWQR）。我們以美國各郡新型冠狀病毒（coronavirus disease, COVID-19）疫苗接種率之資料為例，說明如何應用GWQR，並解釋GWQR如何分析疫苗接種率及其因子之間在空間和疫苗接種率分布上的異質性。研究結果顯示，空間和統計異質性是普遍存在的。例如，COVID-19病例率在位於疫苗接種率低分位數的郡中其關聯性比在高分位數的郡中更強。這種關聯性的空間分布集中在山區州的郡，並轉移到中西部地區。因此，我們認為人口研究應考慮這兩種異質性。 | |
| dc.format.extent | 180 bytes | - |
| dc.format.mimetype | text/html | - |
| dc.relation (關聯) | Journal of Population Studies (人口學刊), Vol.65, pp.43-84 | |
| dc.subject (關鍵詞) | Heterogeneity; Homogeneity; Geographically weighted regression; Quantile regression; Spatial demography | |
| dc.subject (關鍵詞) | 異質性; 同質性; 地理加權迴歸; 分量迴歸; 空間人口學 | |
| dc.title (題名) | Spatial and Statistical Heterogeneities in Population Science Using Geographically Weighted Quantile Regression | |
| dc.title (題名) | 以地理加權分量迴歸探討人口研究之空間和統計異質性 | |
| dc.type (資料類型) | article | |
| dc.identifier.doi (DOI) | 10.6191/JPS.202212_(65).0002 | |
| dc.doi.uri (DOI) | https://doi.org/10.6191/JPS.202212_(65).0002 | |
