Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 이수기 | - |
dc.date.accessioned | 2022-11-22T05:42:19Z | - |
dc.date.available | 2022-11-22T05:42:19Z | - |
dc.date.issued | 2021-12 | - |
dc.identifier.citation | Journal of The Korean Data Analysis Society, v. 23, NO. 6, Page. 2523-2534 | en_US |
dc.identifier.issn | 1229-2354 | en_US |
dc.identifier.uri | http://scholar.dkyobobook.co.kr/searchDetail.laf?barcode=4010028657311 | en_US |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/177179 | - |
dc.description.abstract | Spatial dependency is important to recognize because of the mapping of pedestrian injury counts analysis. Road safety has been a major issue in contemporary societies, with road crashes incurring major human and materials costs annually worldwide. South Korea’s pedestrian traffic accident rate is the highest among the Organization for Economic Cooperation and Development (OECD) countries. In this paper, we use spatially lagged covariate model with zero inflated Conway-Maxwell-Poisson distribution model to account for spatial autocorrelation of no. of pedestrian crashes with cars. Alternatively, the Conway-Maxwell-Poisson (CMP) distribution, first proposed by Conway and Maxwell (1962) has the flexibility to handle all levels of dispersion, including underdispersion. We test spatial autocorrelation of pedestrian injury counts at 2474 sites, with several weights matrices using Moran's I statistics, under permutation scheme. Then we fit different 20 models, and finally choose the best model by the AIC and SBC values. | en_US |
dc.description.sponsorship | This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2018R1D1A1B07045707, NRF-2021R1F1A1048309) and Natural Science Foundation of Korea university (No. K2021811). | en_US |
dc.language | en | en_US |
dc.publisher | 한국자료분석학회 | en_US |
dc.subject | Conway-Maxwell-Poisson | en_US |
dc.subject | spatial autocorrelation | en_US |
dc.subject | pedestrian injury counts | en_US |
dc.title | Spatially Lagged Covariate Model with Zero Inflated Conway-Maxwell-Poisson Distribution Model for the Analysis of Pedestrian Injury Counts | en_US |
dc.type | Article | en_US |
dc.relation.no | 6 | - |
dc.relation.volume | 23 | - |
dc.identifier.doi | 10.37727/jkdas.2021.23.6.2523 | en_US |
dc.relation.page | 2523-2534 | - |
dc.relation.journal | Journal of The Korean Data Analysis Society | - |
dc.contributor.googleauthor | Kim, Hee-Young | - |
dc.contributor.googleauthor | Lee, Sugie | - |
dc.sector.campus | S | - |
dc.sector.daehak | 공과대학 | - |
dc.sector.department | 도시공학과 | - |
dc.identifier.pid | sugielee | - |
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