International Research Symposium on Pure and Applied Sciences (IRSPAS)

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Author: search.filters.author.Weerasinghe, V. P. A.Subject: search.filters.subject.spatial autocorrelation (Moran’s I)

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    A geo-spatial analysis of dengue patients and rainfall in Sri Lanka -2017
    (Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Pathiraja, K.; Premadasa, S.; Gnanasinghe, S.; Wadasinghe, L. G. Y. J. G.; Weerasinghe, V. P. A.
    Dengue is one of the most prevalent arthropod borne virus affecting human. There are four serotypes that manifest with similar symptoms and two main vectors identified in Sri Lanka named as Aedes aegypti and Aedes albopictus. Dengue disease range from mild to dengue hemorrhagic fever. The distribution of dengue vector is varied mostly according to the rainfall. This study evaluates the relationship between percentage dengue patients in each district of Sri Lanka and monthly average rainfall distribution in 2017. Data was analyzed using ArcGIS 10.2 software. In order to get descriptive results, spatial autocorrelation (Moran’s I) was carried out. Positive Moran’s I shows that the average rainfall data are clustered according to the climatic zones in Sri Lanka and percentage dengue patients’ data for February, March, May, June, July and August months are clustered. Hot Spot Analysis was carried out for the clustered months for dengue patients. According to the Hot Spot Analysis the average rainfall distribution of each month of 2017 in Sri Lanka is restricted to specific districts; Hot spots are, Ampara (February), Rathnapura (May, June, July), Rathnapura and Kaluthara (September), Kaluthara (October) and Badulla (December) (99% confidence). Similarly, percentage dengue patients’ distribution in 2017 is restricted to specific districts; Hot spots are Trincomalee (February) and Colombo (March) (99% confidence). Ordinary Least Squares (OLS) linear regression was carried out to identify the relationship between the percentage dengue patients and monthly average rainfall. The variable distributions and relationships graphs of each month indicate a positive relationship between average rainfall and percentage dengue patients. Adjusted R2 in the diagnostic output of each month range between 0.7785 (June) and 0.1674 (February) and indicates that 16.74% - 77.85% of the variation in percentage dengue patients can be explained by average rainfall in 2017. It shows that only rainfall cannot explain the total percentage of dengue patients and that there are other environmental parameters which may contribute. There is a relationship between the percentage of dengue patients in each district and average rainfall distribution which appears to vary. Therefore, further studies should be carried out to identify other environmental parameters on the distribution of dengue such as atmospheric temperature, humidity, wind velocity, intensive farming, urbanization and solid waste disposal practices etc. Using multiple regression, multicollinearity between independent variables can be estimated using Geo statistics. Using environmental parameters, an environmental dengue index can be developed to further relate it with dengue patients’ percentage for geo-spatial analysis to develop a model for incidence of dengue in each district in Sri Lanka with varying environmental variables.
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    Spatial analysis of population density, birth rate and death rate in Sri Lanka (2015 and 2016)
    (Research Symposium on Pure and Applied Sciences, 2018 Faculty of Science, University of Kelaniya, Sri Lanka, 2018) Rajapaksha, D.; Jayakody, H.; Wadasinghe, L. G. Y. J. G.; Gnanasinghe, S.; Weerasinghe, V. P. A.
    Lack of open space, shortage of clean water, and pollution are major concerns of higher population densities. In 2015, United Nations (UN) identified 17 Sustainable Development Goals (SDG) expected to fulfill by 2030 with the help of the governments of countries, which many researches reveals to lower the population growth. This study aims to analyze the distribution pattern of population density in Sri Lanka. Population density depends on resource, natural growth of population and migration. In this study, spatial pattern of population density, birth rate and death rates in Sri Lanka were analyzed for 2015 and 2016. The spatial relationships of population density with birth and death rate of 25 districts were also analyzed. The population data were collected from Department of Census and Statistics of Sri Lanka and analyzed using Geo-statistics tools in ArcGIS 10.2. Spatial patterns and relationships among the data sets were identified. Spatial Autocorrelation (Moran’s I) was carried out for population density, birth and death rate for the 25 districts. Spatial pattern of population density is highly clustered (p=0.001, Moran’s I: 0.198) while spatial pattern of birth rates of each district is randomly distributed in 2015 and 2016. High population density restricted areas are Colombo and Gampaha (99% and 90% CI) for both years. Death rates of districts are slightly clustered in 2015 (p=0.035, Moran’s I: 0.198) and 2016 (p=0.022, Moran’s I: 0.218). Hot Spot Analysis tool was used to identify the clustered areas. High death rate prevailing districts are Colombo (95% CI), Kandy and Galle (90% CI) in 2015. Low death rate prevailing district is Killinochchi (90% CI). In 2016, high death rate is observed in Colombo (99% CI) and Jaffna districts (90% CI). Spatial relationship was identified by using Ordinary Least Squares (OLS) tool. 44.25% of the population density variation can be explained by death rate (adjusted R2=0.4425) in 2015 and, 49.96% can be explained by death rate (adjusted R2=0.4996) in 2016. Regression equation can be developed according to the coefficient output (p<0.05) in 2015 and 2016. There is a significant relationship between death rate and population density (p=0.00017) in 2015 and 2016. The overall results of the present study can be used for planning development projects in the country to fulfill the SDG of UN. Colombo and Gampaha districts should have projects leading to decrease the population density. Colombo and Jaffna districts need to decrease death rates by improving their living standards with better health facilities. This study has to continue with emigration and immigration rate data to develop a better model for population density in the country.