Predicting Common Patterns of Livestock-Vehicle Movement Using GPS and GIS: A Case Stu dy on Jeju Island, Sou th Korea
2018
Waqas Qasim, Gyeongsang National University, Jinju, Republic of Korea | Cho, J.M., Gyeongsang National University, Jinju, Republic of Korea | Moon, B.E., Gyeongsang National University, Jinju, Republic of Korea | Jayanta Kumar Basak, Gyeongsang National University, Jinju, Republic of Korea | Fawad Kahn, Gyeongsang National University, Jinju, Republic of Korea | Frank Gyan Okyere, Gyeongsang National University, Jinju, Republic of Korea | Yoon, Y.C., Gyeongsang National University, Jinju, Republic of Korea | Kim, H.T., Gyeongsang National University, Jinju, Republic of Korea
Purpose: Although previous studies have performed on-farm evaluations for the control of airborne diseases such as foot-and-mouth disease (FMD) and influenza, disease control during the process of livestock and manure transportation has not been investigated thoroughly. The objective of this study is to predict common patterns of livestock-vehicle movement. Methods: Global positioning system (GPS) data collected during 2012 and 2013 from livestock vehicles on Jeju Island, South Korea, were analyzed. The GPS data included the coordinates of moving vehicles according to the time and date as well as the locations of livestock farms and manure-keeping sites. Data from 2012 were added to Esri software ArcGIS 10.1 and two approaches were adopted for predicting common vehicle-movement patterns, i.e., point-density and Euclidean-distance tools. To compare the predicted patterns with actual patterns for 2013, the same analysis was performed on the actual data. Results: When the manure-keeping sites and livestock farms were the same in both years, the common patterns of 2012 and 2013 were similar; however, differences arose in the patterns when these sites were changed. By using the point-density tool and Euclidean-distance tool, the average similarity between the predicted and actual common patterns for the three vehicles was 80% and 72%, respectively. Conclusions: From this analysis, we can determine common patterns of livestock vehicles using previous year's data. In the future, to obtain more accurate results and to devise a model for predicting patterns of vehicle movement, more dependent and independent variables will be considered.
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