{"title":"2020年3月至2021年3月东爪哇Covid-19区域病例百分比的动态随时间空间自相关检测(印度尼西亚)","authors":"Rahma Fitriani, D. Darmanto, Z. F. Pusdiktasari","doi":"10.15196/rs120302","DOIUrl":null,"url":null,"abstract":"Covid-19 regional percent of cases is one of the regional variables that dynamically interact across space and time. It exhibits a time trend, and at one point in time, it may form clusters of regions with similar values. Since Covid-19 is an infectious disease, the regional percent of cases also exhibits spatial dependence across regions. The time trend indicates the possible time lag of the spatial dependence, and the spatial dependence analysed at one point in time may be undetected. This situation was observed in the 38 regions of East Java. It gives an incorrect impression of the nature of spatial dependence, leading to an improper policy formulation. To capture the spatial interaction more accurately, this study accommodates the time-dependent dynamic nature of the variable into the formulation of the Moran's I index for a set of spatial panel data. A simulation study is conducted to confirm the accuracy of the proposed index, especially when the degree of contemporaneous spatial autocorrelation is high. The proposed index also succeeds in detecting the time-lagged spatial autocorrelation of East Java's Covid-19 regional percent of cases. It provides a better understanding and policy recommendations regarding the spread of this disease in this province.","PeriodicalId":44388,"journal":{"name":"Regional Statistics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"A dynamic-time dependent spatial autocorrelation detection for East Java's Covid-19 regional percent of cases, March 2020-March 2021 (Indonesia)\",\"authors\":\"Rahma Fitriani, D. Darmanto, Z. F. Pusdiktasari\",\"doi\":\"10.15196/rs120302\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Covid-19 regional percent of cases is one of the regional variables that dynamically interact across space and time. It exhibits a time trend, and at one point in time, it may form clusters of regions with similar values. Since Covid-19 is an infectious disease, the regional percent of cases also exhibits spatial dependence across regions. The time trend indicates the possible time lag of the spatial dependence, and the spatial dependence analysed at one point in time may be undetected. This situation was observed in the 38 regions of East Java. It gives an incorrect impression of the nature of spatial dependence, leading to an improper policy formulation. To capture the spatial interaction more accurately, this study accommodates the time-dependent dynamic nature of the variable into the formulation of the Moran's I index for a set of spatial panel data. A simulation study is conducted to confirm the accuracy of the proposed index, especially when the degree of contemporaneous spatial autocorrelation is high. The proposed index also succeeds in detecting the time-lagged spatial autocorrelation of East Java's Covid-19 regional percent of cases. It provides a better understanding and policy recommendations regarding the spread of this disease in this province.\",\"PeriodicalId\":44388,\"journal\":{\"name\":\"Regional Statistics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.3000,\"publicationDate\":\"2022-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Regional Statistics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.15196/rs120302\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"0\",\"JCRName\":\"GEOGRAPHY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Regional Statistics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.15196/rs120302","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"0","JCRName":"GEOGRAPHY","Score":null,"Total":0}
A dynamic-time dependent spatial autocorrelation detection for East Java's Covid-19 regional percent of cases, March 2020-March 2021 (Indonesia)
Covid-19 regional percent of cases is one of the regional variables that dynamically interact across space and time. It exhibits a time trend, and at one point in time, it may form clusters of regions with similar values. Since Covid-19 is an infectious disease, the regional percent of cases also exhibits spatial dependence across regions. The time trend indicates the possible time lag of the spatial dependence, and the spatial dependence analysed at one point in time may be undetected. This situation was observed in the 38 regions of East Java. It gives an incorrect impression of the nature of spatial dependence, leading to an improper policy formulation. To capture the spatial interaction more accurately, this study accommodates the time-dependent dynamic nature of the variable into the formulation of the Moran's I index for a set of spatial panel data. A simulation study is conducted to confirm the accuracy of the proposed index, especially when the degree of contemporaneous spatial autocorrelation is high. The proposed index also succeeds in detecting the time-lagged spatial autocorrelation of East Java's Covid-19 regional percent of cases. It provides a better understanding and policy recommendations regarding the spread of this disease in this province.
期刊介绍:
The periodical welcomes studies, research and conference reports, book reviews, discussion articles reflecting on our former articles. The periodical welcomes articles from the following areas: regional statistics, regional science, social geography, regional planning, sociology, geographical information science Goals of the journal: high-level studies in the field of regional analyses, to encourage the exchange of views and discussion among researchers in the area of regional researches.