Vol. 2 No. 2 (2022)

A Correlation Study for Determination Risk Area of Dengue Fever and Dengue Hemorrhagic Fever: a Case Study of Sisaket Province, Thailand

Published 2022-09-28


  • Geographic information system,
  • Dengue fever,
  • Dengue Hemorrhagic fever,
  • Risk zone map,
  • Correlation

How to Cite

Chantapoh, N., Hong, S., & Soytong, P. (2022). A Correlation Study for Determination Risk Area of Dengue Fever and Dengue Hemorrhagic Fever: a Case Study of Sisaket Province, Thailand. Advanced Geomatics, 2(2), 57–64. Retrieved from https://publish.mersin.edu.tr/index.php/geomatics/article/view/351


This study has purpose on analyzing risk zone area in Sisaket province, Thailand, by using the subdistrict-level (Tumbon) data in sick ratio, average temperature, maximum temperature, minimum temperature, relative humidity, precipitation, population density, and housing density. The meteorological data are acquired from POWER, NASA. The data is stored in points, griding by 30 minutes of latitude and longitude, going through the inverse distance weighting tool to interpolate the meteorological data into each Tumbon. The physical socio data are from government authority, are population from each Tumbon by monthly and housing amount from each Tumbon by yearly. The authority of meteorological data in Thailand officially only has the weather measuring stations in the middle of the province. The surrounding province also do not have sufficient station to interpolate the weather data, as well as the southern of the province is Thai-Cambodia border area with no station or data, leaving the vast area in the south has no data. Statistic yearly results show that average maximum and minimum temperature are significantly positive correlated with sick ratio while average relative humidity and precipitation are significantly negative correlated. Meanwhile, monthly results show that average temperature and average maximum temperature are significantly negative correlated with sick ratio while average minimum temperature, relative humidity and precipitation are significantly positive correlated.  In both yearly and monthly results, population density and housing density are not significantly correlate with the sick ratio, as well as the average temperature in the yearly result.


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