scholarly journals Spatial analysis and mapping of child malnutrition in Burkina Faso

2019 ◽  
Vol 29 (Supplement_4) ◽  
Author(s):  
Y Chuang
Keyword(s):  
Regression Analysis ◽  
Burkina Faso ◽  
Spatial Regression ◽  
Sub Saharan Africa ◽  
Child Malnutrition ◽  
Spatial Lag ◽  
Community Factors ◽  
Factors Associated ◽  
Geographical Variations ◽  
Spatial Regression Analysis

Abstract Background Approximately 45% of all child deaths are associated with malnutrition in sub-Saharan Africa. However, information on the geographical variations of child malnutrition in Burkina Faso is limited. This study examined the geographical distribution and the community characteristics associated with child malnutrition in Burkina Faso using spatial regression analysis. Methods Data from the 2011 Burkina Faso Demographic Health Survey were analyzed. A representative sample of 572 communities and 6,120 children under age of five were in the survey. A general Kriging interpolation method was used to generate spatial malnutrition patterns. The Local Indicator of Spatial Autocorrelation was used to identify particular communities clustering with high and low child malnutrition scores. In the spatial regression analysis, we used a two-step procedure combining generalized estimating equation models (GEE) and spatial lag modeling techniques. Results The average rates of stunting and wasting were 32.48%, and 15.05%, respectively. Stunting hotspots were observed to be in the eastern and northeastern part of Burkina Faso (i.e. Oudolan, Séno), while high rates of wasting were observed in the central-north part. The results of the spatial regression analysis revealed lower stunting rates in communities with a higher percentage of households with improved sanitation. Communities with higher rates of professionally assisted births were associated with low wasting rates while communities with higher rates of households with low wealth index reported higher rates of wasting. Spatial lag models appeared to estimate the relationship between community-level risk factors and stunting and wasting more accurately than the ordinary least squares models. Conclusions This study revealed significant geographical patterns and community factors associated with childhood malnutrition. These factors should be considered for future programs aiming at reducing child malnutrition in Burkina Faso. Key messages The study identifies spatial trends and contextual factors associated with child malnutrition in Burkina Faso. Public health programs should focus on the regions identified as hot spots of child malnutrition in Burkina Faso.

scholarly journals Analisis Regresi Spatial Durbin Model (SDM) untuk Pemodelan Kemiskinan Provinsi Jawa Timur Tahun 2017

2019 ◽  
Vol 12 (1) ◽  
pp. 40-46
Author(s):  
Nur Roudlotul Hidayah ◽  
Artanti Indrasetianingsih
Keyword(s):  
Regression Analysis ◽  
Spatial Regression ◽  
Statistical Technique ◽  
Predictor Variables ◽  
Poor People ◽  
Spatial Durbin Model ◽  
Classic Problem ◽  
Almost All ◽  
Spatial Regression Analysis ◽  
The Relationship

Regression is a statistical technique used to describe the relationship between response variables with one or more predictor variables. The development of classical regression analysis that is influenced by the effects of space or location of a region is called spatial regression analysis. The purpose of this study is to conduct Spatial Durbin Model (SDM) regression analysis for poverty modeling in East Java in 2017. Poverty is a classic problem that occurs in almost all countries and is multidimensional, which is related to social, economic, cultural and other aspects. In 2017, poverty in East Java declined compared to the previous year. Therefore it is necessary to identify the factors that influence poverty. The variables used are the percentage of poor people as the response variable (Y) and predictor variables including Education does not finish elementary school (X1), Literacy Rate age 15 -55 years (X2), informal sector workers (X3), unemployment rate open (X4), household users of land as the widest floor (X5), and households using improper sanitation (X6), and households using drinking water sources are not feasible (X7).    Regresi merupakan teknik statistik yang digunakan untuk menggambarkan hubungan antara variabel respon dengan satu atau lebih variabel prediktor. Pengembangan dari analisis regresi klasik yang dipengaruhi oleh efek ruang atau lokasi wilayah disebut analisis regresi spasial. Tujuan dari penelitian ini adalah untuk melakukan analisis regresi Spatial Durbin Model (SDM) untuk pemodelan kemiskinan di Jawa Timur tahun 2017. Kemiskinan merupakan masalah klasik yang terjadi hampir diseluruh negara dan bersifat multidimensional, dimana berkaitan dengan aspek sosial, ekonomi, budaya dan aspek lainnya. Pada tahun 2017, kemiskinan di Jawa Timur mengalami penurunan jika dibandingkan dengan tahun sebelumnya. Oleh karena itu perlu dilakukan identifikasi faktor-faktor yang berpengaruh terhadap kemiskinan. Variabel yang digunakan yaitu persentase penduduk miskin sebagai variabel respon (Y) dan variabel prediktor antara lain Pendidikan tidak tamat SD (X1), Angka Melek Huruf  (AHM) usia 15 -55 tahun (X2), pekerja sektor informal (X3), tingkat pengangguran terbuka (X4), rumah tangga pengguna tanah sebagai lantai terluas (X5), dan rumah tangga pengguna sanitasi tidak layak (X6), dan Rumah tangga pengguna sumber air minum tidak layak (X7).

SPATIAL REGRESSION ANALYSIS FOR MODELING THE SPATIAL VARIATION IN HIGHWAY CONSTRUCTION COSTS

2017 ◽  
Vol 4 (1) ◽  
Author(s):  
Minsoo Baek ◽  
Baabak Ashuri
Keyword(s):  
Regression Analysis ◽  
Spatial Variation ◽  
Spatial Regression ◽  
Highway Construction ◽  
Price Volatility ◽  
Construction Costs ◽  
Construction Market ◽  
Spatial Regression Analysis ◽  
Transportation Agencies ◽  
Geographical Locations

Price volatility in wages, materials, and equipment has a significant impact on highway construction costs. As the construction market and economy have experienced dynamic changes in prices, the price volatility becomes less predictable. In addition, various levels of the price volatility in different market locations aggravate the prediction. Thus, in developing highway construction costs, transportation agencies should consider geographical location of construction projects and market conditions of the locations. Transportation agencies face significant uncertainties in price volatility across different geographical locations. This volatility may not be uniformly distributed across different geographical locations due to changes in the availability of local contractors, materials, equipment, and labor. The objective of this research is to develop statistical models that are capable to explain spatial variations in submitted unit prices for asphalt line items in highway projects considering local market condition factors. Historical bid data used in this research consist of resurfacing and widening projects let in the state of Georgia, the United States, between 2008 and 2015. The methodology of this research is a spatial regression analysis to explain the spatial variation in the submitted unit prices for asphalt line items. The findings of this research indicate that volatility in submitted bid prices is not uniformly distributed across different geographical locations within the same transportation agency. The contribution to the body of knowledge of this research is an improved understanding of the role of local construction market and macroeconomic conditions to explain geographic variability in construction costs.

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