Author(s)

Yuxin Shi¹, Siyuan Liu², Tingzhen Liu¹

¹Information Science and Technology College, Dalian Maritime University, Dalian, China.
²Institute of Information Engineering, Chinese Academy of Sciences, Beijing, China.

Species invasion will cause certain harm to the local ecosystem. Vespa mandarinia, discovered on Vancouver Island, is harmful to agriculture and predators of European honeybees. The government tried to use a crowdsourcing system to collect information and formulate policies to eliminate Vespa mandarinia. However, the information provided by the local population about Vespa mandarinia is not entirely accurate. For this problem, we build a method to mine trusted information in massive crowdsourcing Vespa mandarinia reports. We consider providing the date and location of the report, and establishing a credibility calculation model for further analysis. For the report date, we calculate the normal distribution parameters based on the frequency of the report in each season to measure the reliability of a single report. For report location, we use K-means cluster analysis to find the location of the center point, which is regarded as a hive, count the report points in each hive radiation range, and use these points to generate two-dimensional normal distribution parameters to normalize the data and eliminate statistical errors. We take the probability density of the report at its location as the reliability of the reports. Through credibility, we can screen out reports that are more likely to be positive for prioritizing investigation. In order to better analyze the newly discovered reports in the future and ensure the timeliness of the model, we set up distributed incremental adjustment model to modify normal distribution parameters, and update the existing model.

Data Mining, Public Health, Biotechnology, Cluster Analysis, Crowdsourcing Data

Shi, Y.X., Liu, S.Y. and Liu, T.Z. (2022) Distribution Estimation of Invasive Species Based on Crowdsourcing Reports. Open Access Library Journal, 9, 1-11. doi: 10.4236/oalib.1109474.