scholarly journals Spatial modelling of multidimensional poverty in rural area: Evidence from Malang Regency, Indonesia

2021 ◽  
Vol 4 (2) ◽  
pp. 198
Author(s):  
Ismu Rini Dwi Ari ◽  
Septiana Hariyani ◽  
Budi Sugiarto Waloejo
Keyword(s):  
Regression Analysis ◽  
Social Relations ◽  
Spatial Regression ◽  
Multidimensional Poverty ◽  
Analysis Data ◽  
Infrastructure Development ◽  
Social Infrastructure ◽  
Poverty Level ◽  
Spatial Regression Analysis ◽  
Poverty Levels

Poverty is a multidimensional phenomenon that causes difficulty for people to meet their needs. The research aims to scrutinize physical and social infrastructures concerning multidimensional poverty levels using the spatial approach. Jabung District, Malang Regency, Indonesia has 35% poor households in this case study. The objectives are to measure multidimensional poverty levels, social capital indices of the rate of participation (RoP) and density, and scrutinize neighborhood relationships among 15 villages using spatial regression analysis. Data collection is through a questionnaire survey of 274 heads of households. The research identified four poverty levels (very low to high), where five villages with high poverty levels (Jabung, Taji, Kemiri, Gunungjati, Slamparejo) became the targeted areas. The majority of the villages had a medium level of both the RoP and density, and the community had moderate social relations among community members. The spatial regression analysis indicates that the attribute of the RoP and weight matrix have a significant impact on the poverty level. It is recommended that poverty alleviation programs should focus upon the cluster of poor villages through social infrastructure development as the action to end poverty.JEL Classification A13; I32; R58

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).

scholarly journals URBAN ENVIRONMENT QUALITY ASSESSMENT OF THE PROKOPIEVSKY CITY DISTRICT

2017 ◽  
Vol 2017 (4) ◽  
pp. 64-68
Author(s):  
Надежда Егорова ◽  
Nadezhda Egorova ◽  
Анастасия Зинковская ◽  
Anastasia Zinkovskaya
Keyword(s):  
Quality Assessment ◽  
Urban Environment ◽  
Social Relations ◽  
Transport Infrastructure ◽  
Emotional Impact ◽  
Infrastructure Development ◽  
Social Infrastructure ◽  
Urban District ◽  
Total Contribution ◽  
Environment Quality

<p>The article gives the urban environment quality assessment for the administrative entities of the Prokopyevsky urban district. The assessment is based on the methodology developed by V. N. Fedorov and M. Yu. Aksenova. The main indicators for assessing the urban environment quality were: environmental comfort, assessed through the perception of the environmental tension level by Prokopyevskresidents; population provision with social infrastructure facilities that are active components of social relations and act as carriers, forms and ways of meeting common individual demands. Two options for calculating the transport city center accessibility indicated the level of transport infrastructure development: that for vehicle owners and that for public transportusers.Asabasisforassessingtheurbanenvironmentaestheticqualities, the V. A. Nikolaeva method was used. For greater results comparability, scoring scales are used. At the same time, respondents were asked to determine the attractiveness degree and emotional impact of the each district urban environment. The total contribution of the urban environment quality objective factors was estimated with the help of the urban environment integral quality factor.<strong></strong></p>

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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