Identifying the Spatial Clusters of Subregions with High-risk of FMD Occurrence Using Local Spatial Statistic (국지적 공간통계량을 이용한 구제역 발생 고위험지역 탐색방안에 관한 연구)

Pak Son Il (박선일); Gyoungju Lee (이경주)

Identifying the Spatial Clusters of Subregions with High-risk of FMD Occurrence Using Local Spatial Statistic

Journal of the Korean Cartographic Association
Abbr : JKCA
2016, 16(3), pp.63-73
Publisher : The Korean Cartographic Association
Research Area : Social Science > Geography > Geography in general > Cartography
Published : December 31, 2016

Pak Son Il ¹, Gyoungju Lee ²

¹강원대학교
²한국교통대학교

Accredited

ABSTRACT

Foot-and-mouth disease(FMD), one of the first class infectious diseases in domestic animals, has caused tremendous socioeconomic costs due to the preventive measures, compelled to be taken by law, such as culling, banning on vehicle movements, etc. FMD, once occurred, is concerned to spread quite rapidly and thus, preemptive efforts need to be made to prevent the outbreak, along with on-site struggling against epidemics. To this end, mid- and long-term plan for preventing and controlling epidemic need to be presented. Since available resources for preventing and controlling efforts are limited, it is important to identify local areas requiring a priority in concentrating the resources and a relevant strategic management. Those areas point to the geographic locality with potentially high risk of FMD occurrence. The objective of the study is to identify the local areas with potentially high risk of FMD outbreak using a local spatial statistic in an exploratory manner. To this end, the Score statistic is used. Based on the statistic, an empirical analysis was made for the data containing the information on FMD occurred during the period of December 2014 to April 2015. Analysis results show marked spatial clusters of FMD. Additionally, policy implications associated with prevention and control activities were discussed in mid- and long-term perspective.

KEYWORDS

FMD, High-risk subregion, Score statistic

Citation status

* References for papers published after 2023 are currently being built.

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