Comparison of scoring, matching, SMCE and geographically weighted regression In malaria vulnerability spatial modelling using satellite imagery: an Indonesian example

2018 ◽  
Author(s):  
Projo Danoedoro ◽  
Prima Widayani
Keyword(s):  
Satellite Imagery ◽  
Geographically Weighted Regression ◽  
Spatial Modelling ◽  
Weighted Regression

Utilization of geographically weighted regression for geoid modelling in Egypt

2020 ◽  
Vol 14 (1) ◽  
pp. 1-12 ◽  
Author(s):  
Gomaa M. Dawod ◽  
Tarek M. Abdel-Aziz
Keyword(s):  
Standard Deviation ◽  
Geographically Weighted Regression ◽  
Spatial Modelling ◽  
Global Navigation Satellite Systems ◽  
Weighted Regression ◽  
Geopotential Model ◽  
Satellite Systems ◽  
Precious Resource ◽  
Regression Techniques ◽  
Global Navigation Satellite

AbstractModelling the spatial variations of a specific Global Geopotential Model (GGM) over a spatial area is important to enhance its local performance in Global Navigation Satellite Systems (GNSS) surveying. This study aims to investigate the potential of utilizing some of Geographic Information Systems (GIS) geospatial analysis tools, particularly Geographically Weighted Regression (GWR), in geoid modelling for the first time in Egypt as a case study. Its main target is developing an optimum regression method to be applied in spatial modelling of the deviations of a specific GGM (e. g., PGM17). Using a precise local geodetic dataset of 803 GPS/levelling stations, PGM17 undulation differences have been modelled using different regression techniques to evaluate their precision and accuracy. Based on investigating 13 possible regression formulas of probable combinations of independent variables, results showed that the PGM17 discrepancies over Egypt depend mostly on the terrain heights and geoidal undulations. Over 80 checkpoints, the attained variations between the GWR model and known values varied from −0.574 m to 0.500 m, with a mean of 0.001 m and a standard deviation equals ±0.205 m. Based on available data, it has been found that GWR improved the PGM17 deviations by 9 % in terms of standard deviation and by 98 % in terms of the mean. Additionally, the study generates a reasonably innovative product for the local geodetic community by building an enhanced version of the PGM17. This surface will be a precious resource in GNSS surveying in Egypt for heights conversion, leading to considerable cost reduction in civil engineering works and mapping projects.

scholarly journals Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Seblewongel Tigabu ◽  
Alemneh Mekuriaw Liyew ◽  
Bisrat Misganaw Geremew
Keyword(s):  
Spatial Distribution ◽  
Geographically Weighted Regression ◽  
Teenage Pregnancy ◽  
Wealth Quintile ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Contraceptive Methods ◽  
Hotspot Analysis ◽  
Increased Risk ◽  
Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

scholarly journals Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

2021 ◽  
Vol 18 (5) ◽  
pp. 2222
Author(s):  
Jin-Wei Yan ◽  
Fei Tao ◽  
Shuai-Qian Zhang ◽  
Shuang Lin ◽  
Tong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Yangtze River ◽  
National Level ◽  
National Development ◽  
Urban Agglomeration ◽  
Spatiotemporal Distribution ◽  
Weighted Regression ◽  
Series Data ◽  
The Yangtze River ◽  
Urban Agglomerations

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Number of flood disaster estimation in Indonesia using local linear and geographically weighted regression approach

2021 ◽  
Author(s):  
M. Fariz Fadillah Mardianto ◽  
Sediono ◽  
Novia Anggita Aprilianti ◽  
Belindha Ayu Ardhani ◽  
Rizka Firdaus Rahmadina ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Flood Disaster ◽  
Weighted Regression ◽  
Local Linear ◽  
Regression Approach
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