Understanding the spatial patterns of tanker accidents in Nigeria using geographically weighted regression

2018 ◽  
Vol 10 (1) ◽  
pp. 58 ◽  
Author(s):  
Moses O. Olawole ◽  
Olabisi M. Olapoju
Keyword(s):  
Spatial Patterns ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Tanker Accidents

scholarly journals Modeling Malaria Incidence Associated with Environmental Risk Factors in Ethiopia using the Geographically Weighted Regression Model, 2015-2016

2021 ◽  
Author(s):  
Berhanu Berga Dadi
Keyword(s):  
Risk Factors ◽  
Relative Humidity ◽  
Spatial Patterns ◽  
Environmental Risk ◽  
Geographically Weighted Regression ◽  
Malaria Incidence ◽  
Environmental Risk Factors ◽  
Weighted Regression ◽  
Study Results ◽  
Gwr Model

Abstract Background: In Ethiopia, still, malaria is killing and affecting a lot of people of any age group somewhere in the country at any time. However, due to limited research, little is known about the spatial patterns and correlated risk factors on the wards scale. Methods: In this research, we explored spatial patterns and evaluated related potential environmental risk factors in the distribution of malaria incidence in Ethiopia in 2015 and 2016. Hot Spot Analysis (Getis-Ord Gi* statistic) was used to assess the clustering patterns of the disease. The ordinary least square (OLS), geographically weighted regression (GWR), and semiparametric geographically weighted regression (s-GWR) models were compared to describe the spatial association of potential environmental risk factors with malaria incidence.Results: Our results revealed a heterogeneous and highly clustered distribution of malaria incidence in Ethiopia during the study period. The s-GWR model best explained the spatial correlation of potential risk factors with malaria incidence and was used to produce predictive maps. The GWR model revealed that the relationship between malaria incidence and elevation, temperature, precipitation, relative humidity, and normalized difference vegetation index (NDVI) varied significantly among the wards. During the study period, the s-GWR model provided a similar conclusion, except in the case of NDVI in 2015, and elevation and temperature in 2016, which were found to have a global relationship with malaria incidence. Hence, precipitation and relative humidity exhibited a varying relationship with malaria incidence among the wards in both years. Conclusions: This finding could be used in the formulation and execution of evidence-based malaria control and management program to allocate scare resources locally at the wards level. Moreover, these study results provide a scientific basis for malaria researchers in the country.

scholarly journals Exploring the spatial patterns of fire density in Southern Europe using Geographically Weighted Regression

2014 ◽  
Vol 51 ◽  
pp. 143-157 ◽  
Author(s):  
Sandra Oliveira ◽  
José M.C. Pereira ◽  
Jesús San-Miguel-Ayanz ◽  
Luciano Lourenço
Keyword(s):  
Spatial Patterns ◽  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Southern Europe

scholarly journals ANALISIS SPASIAL KEMISKINAN DENGAN PENDEKATAN GEOGRAPHICALLY WEIGHTED REGRESSION: STUDI KASUS KABUPATEN PANDEGLANG DAN LEBAK

2019 ◽  
Vol 21 (4) ◽  
pp. 669
Author(s):  
S Sukanto ◽  
Bambang Juanda ◽  
Akhmad Fauzi ◽  
Sri Mulatsih
Keyword(s):  
Spatial Autocorrelation ◽  
Spatial Patterns ◽  
Geographically Weighted Regression ◽  
Poverty Alleviation ◽  
Influencing Factor ◽  
Weighted Regression ◽  
Independent Variables ◽  
Enrollment Rates ◽  
Spatial Aspect ◽  
Different Response

Poverty is the main problem both at the national and regional development.  Existing poverty alleviation programs have not paid attention to the spatial aspect. Thus the policies are often poorly targeted. This study aims to find spatial patterns of poverty in Pandeglang and Lebak districts. Geographically weighted regression (GWR) is used to analyze the poverty data in 2016. Based on the analysis, positive spatial autocorrelation is found and clustered in 25 sub-districts. Net enrollment rates tend to reduce poverty in all sub-districts. Meanwhile, village funds, electricity, and roads tend to reduce poverty rates in more than 80% of sub-districts. Independent variables have a different response in each sub-district. Therefore, the poverty alleviation program of each sub-district is adjusting to its influencing factor.

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