scholarly journals Spatial Relations between the Standards of Living and the Financial Capacity of Polish District-Level Local Government

2020 ◽  
Vol 12 (5) ◽  
pp. 1825
Author(s):  
Mariusz Malinowski ◽  
Joanna Smoluk-Sikorska
Keyword(s):  
Regression Analysis ◽  
Local Government ◽  
Spatial Regression ◽  
Spatial Relations ◽  
Financial Capacity ◽  
Solution Approach ◽  
Standards Of Living ◽  
Spatial Regression Analysis ◽  
Strategic Management Process ◽  
Global Moran’S I

The objective of the presented article is the identification of spatial relations between the inhabitants’ standards of living and the districts’ financial capacity basing on data for 2017. The investigation comprised all of the 380 Polish districts. In regard to the multidimensionality of economic occurrences analyzed, the TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) approach to measure the inhabitants’ standards of living and the financial ability of districts was applied in the research. A spatial autocorrelation analysis between the taxonomic (synthetic) indexes was performed using local and global Moran’s I statistics in order to determine the districts’ clusters, demonstrating a comparable degree of occurrences analyzed. A spatial regression analysis was conducted to find the strength of spatial relations between the taxonomic index of the standards of living and the districts’ financial ability. Diagnostic variables were chosen according to substantive, statistical and formal criteria. The outcomes of the spatial regression analysis allowed it to be concluded that about 1% increase of the taxonomic indicator of the districts’ financial ability is reflected in about 0.4% growth of the taxonomic index of the standards of living of the inhabitants of different districts (other things being equal). The results of analyses can be applied indirectly by a number of stakeholders, e.g., local authorities responsible for local and regional development, when creating the development strategies at local government unit (LGU) level. The knowledge on spatial development structures can enhance the formation of the strategic management process (for instance, redefining the objectives and tasks set out in local strategies; restructuring the expenditure to meet the local population’s needs).

scholarly journals THE FINANCIAL SITUATION AT A MUNICIPAL GOVERNMENT LEVEL VS. STANDARDS OF LIVING OF THE POPULATION IN THE WIELKOPOLSKIE VOIVODSHIP: A SPATIAL ANALYSIS

2019 ◽  
Vol XXI (3) ◽  
pp. 287-298
Author(s):  
Mariusz Malinowski
Keyword(s):  
Spatial Autocorrelation ◽  
Rural Areas ◽  
Municipal Government ◽  
Spatial Regression ◽  
Spatial Relationships ◽  
Financial Capacity ◽  
Synthetic Indicators ◽  
Standards Of Living ◽  
The Mean ◽  
Spatial Regression Analysis

The purpose of this paper is to identify the (spatial) relationships between the standards of living of the population and the financial capacity of municipalities, with particular focus on rural areas, based on 2017 data. The survey covered all of the 226 municipalities of the Wielkopolskie voivodship. As a result of the multidimensionality of economic categories covered by the analysis, this study used the TOPSIS method to assess the standards of living of the population and the financial capacity of municipalities. An analysis of spatial autocorrelation between the synthetic indicators was carried out based on Moran’s I statistics (local and global) to identify the clusters of municipalities reporting a similar level of aspects covered by this study. A spatial regression analysis was carried out to assess the strength of spatial relationships between the synthetic indicators of the standards of living and the financial capacity of municipalities. A strong correlation exists between the synthetic indicators. Moreover, both the indicator of the standards of living in the municipalities considered and the indicator of the municipalities’ financial capacity demonstrate a statistically significant spatial autocorrelation. The spatial autocorrelation model developed in this study takes account of the mean error in neighbouring locations to better explain the dependencies between these aspects than a traditional least-squares model.

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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