Spatial variations in contributors to life satisfaction: an Australian case study

What people consider important, and how these factors contribute to their self-reported life satisfaction (LS), varies significantly across regions. Here, we analyse for the first time how LS varies across space and what factors best explain LS at different locations. Geographically weighted regressions (GWR) were used to analyse the relationship between LS and seventeen objective variables across Australia. We find that contributors to LS vary considerably but individuals living in relative proximity to each other share similar perspectives. Taking into account the spatially explicit heterogeneity of a population allows for the assessment of federal policies at local or regional levels, increasing the likelihood that their impacts will be consistent with the original intent. It also enables the perspectives of the diversity of cultures within a nation to be better understood

Quality Of Life Sustainability Using Geographic Information System (Gis): A Case Study From East-Coast Of Peninsular Malaysia

Abstract: Malaysia is a developing nation and moving forward to achieve sustainability in term of economics and social indicators. Malaysia's Quality of Life (QOL) report defines QOL as an encompassing personal advancement, a healthy lifestyle, access and freedom to pursue knowledge and attaining a standard of living. Study on QOL is gaining interest from variety of discipline and becoming an important indicator for policy evaluation, rating for places, urban planning and management. In this study GIS is employed to analyze the QOL in East Coast of Peninsular Malaysia. Basically, GIS is a user-friendly interface developed to enhance the presentation of the study. This study used secondary data and aims to identify the dynamic interface of QOL using GIS approach. Through this study, five components been demonstrated, namely education, health, employment, industry and transportation; and communication which attained under specific indicators in each cases. An ordinary least square, spatial autocorrelation and Geographically Weighted Regression (GWR) was applied to explore the relationship between QOL and the independent components. The findings of this study show that, industrial, transportation and communication contributed the highest volume to QOL. Meanwhile, the employment component contributes with lover volume of scores. In general, the findings of this study clearly indicate GIS as an important and dynamic tool to analyze socioeconomic and it's able to illustrate socioeconomic sustainability with statistical values and illustrate using maps

GIS-Based Analysis of the Spatial Distribution and Influencing Factors of Traditional Villages in Hebei Province, China

Traditional villages are a valuable cultural asset that occupy an important position in Chinese traditional culture. This study focuses on 206 traditional villages in Hebei Province, and aims to explore their spatial distribution characteristics and influencing factors using ArcGIS spatial analysis. The analysis shows that traditional villages in Hebei Province were distributed in clus-ters during different historical periods, and eventually formed three core clusters in Shijiazhuang, Zhangjiakou and Xingtai-Handan after different historical periods. Moreover, the overall dis-tribution of traditional villages in Hebei Province is very uneven, with clear regional differences, and most of them are concentrated in the eastern foothills of the Taihang Mountains. To identify the factors influencing traditional villages, natural environmental factors, socio-economic factors, and historical and cultural factors are considered. The study finds that socio-economic and nat-ural environmental factors alternate in the spatial distribution of traditional villages in Hebei Province. The influence of the interaction of these factors increases significantly, and so-cio-economic factors have a stronger influence on the spatial distribution. Specifically, the spatial distribution of traditional villages in Hebei Province is influenced by natural environmental fac-tors, while socio-economic factors act as drivers of spatial distribution. Historical and cultural factors act as catalysts of spatial distribution, and policy directions are external forces of spatial distribution. Overall, this study provides valuable insights into the spatial distribution charac-teristics and influencing factors of traditional villages in Hebei Province, which can be used to develop effective strategies for rural revitalisation in China

The Use of Geographically Weighted Regression for the Relationship among Extreme Climate Indices in China

The changing frequency of extreme climate events generally has profound impacts on our living environment and decision-makers. Based on the daily temperature and precipitation data collected from 753 stations in China during 1961–2005, the geographically weighted regression (GWR) model is used to investigate the relationship between the index of frequency of extreme precipitation (FEP) and other climate extreme indices including frequency of warm days (FWD), frequency of warm nights (FWN), frequency of cold days (FCD), and frequency of cold nights (FCN). Assisted by some statistical tests, it is found that the regression relationship has significant spatial nonstationarity and the influence of each explanatory variable (namely, FWD, FWN, FCD, and FCN) on FEP also exhibits significant spatial inconsistency. Furthermore, some meaningful regional characteristics for the relationship between the studied extreme climate indices are obtained

MODEL GEOGRAPHICALLY WEIGHTED POISSON REGRESSION DENGAN PEMBOBOT FUNGSI KERNEL GAUSS

Kematian bayi adalah suatu kematian yang dialami anak sebelum mencapai usia satu tahun. Angka kematian bayi (AKB) adalah besarnya kemungkinan bayi meninggal sebelum mencapai usia satu tahun, dinyatakan dalam perseribu kelahiran hidup. Analisis regresi merupakan analisis statistik yang bertujuan untuk memodelkan hubungan antara variabel respon dengan variabel prediktor. Apabila variabel respon berdistribusi Poisson, maka model regresi yang digunakan adalah regresi Poisson. Geographically Weighted Poisson Regression (GWPR) adalah bentuk lokal dari regresi Poisson dimana lokasi diperhatikan yang berasumsi bahwa data berdistribusi Poisson. Dalam penelitian ini akan mengetahui faktor-faktor apa saja yang mempengaruhi jumlah kematian bayi di Provinsi Jawa Timur dengan menggunakan model GWPR dengan menggunakan pembobot fungsi kernel gauss. Hasil penelitian menunjukan bahwa secara keseluruhan faktor-faktor yang mempengaruhi jumlah kematian bayi di Jawa Timur berdasarkan model GWPR dengan pembobot fungsi kernel gauss adalah persentase persalinan yang dilakukan dengan bantuan tenaga non medis (X1), rata-rata usia perkawinan pertama wanita (X2), rata-rata pemberian ASI ekslusif (X4) dan jumlah sarana kesehatan (X7). Berdasarkan variabel yang signifikan maka kabupaten/kota di Jawa Timur dapat dikelompokan menjadi 2 kelompok. Dengan membandingkan nilai AIC antara model regresi Poisson dan model GWPR diketahui bahwa model GWPR dengan pembobot fungsi kernel Gauss merupakan model yang lebih baik digunakan untuk menganalisis jumlah kemtian bayi di Propinsi Jawa Timur tahun 2007

Modeling spatial variations in household disposable income with Geographically Weighted Regression

The purpose of this paper is to analyze the spatially varying impacts of some classical regressors on per capita household income in Spanish provinces. The authors model this distribution following both a traditional global regression and a local analysis with Geographically Weighted Regression (GWR). Several specifications are compared, being the adaptive bisquare weighting function the more efficient in terms of goodness-of-fit. We test for global and local spatial instability using some F-tests and other statistical measures. We find some evidence of spatial instability in the distribution of this variable in relation to some explanatory variables, which cannot be totally solved by spatial dependence specifications. GWR has revealed as a better specification to model per capita household income. It highlights some facets of the relationship completely hidden in the global results and forces us to ask about questions we would otherwise not have asked. Moreover, the application of GWR can also be of help to further exercises of micro-data spatial prediction

Modeling spatial variations in household disposable income with Geographically Weighted Regression

The purpose of this paper is to analyze the spatially varying impacts of some classical regressors on per capita household income in Spanish provinces. The authors model this distribution following both a traditional global regression and a local analysis with Geographically Weighted Regression (GWR). Several specifications are compared, being the adaptive bisquare weighting function the more efficient in terms of goodness-of-fit. We test for global and local spatial instability using some F-tests and other statistical measures. We find some evidence of spatial instability in the distribution of this variable in relation to some explanatory variables, which cannot be totally solved by spatial dependence specifications. GWR has revealed as a better specification to model per capita household income. It highlights some facets of the relationship completely hidden in the global results and forces us to ask about questions we would otherwise not have asked. Moreover, the application of GWR can also be of help to further exercises of micro-data spatial prediction

Modeling spatial variations in household disposable income with Geographically Weighted Regression

The purpose of this paper is to analyze the spatially varying impacts of some classical regressors on per capita household income in Spanish provinces. The authors model this distribution following both a traditional global regression and a local analysis with Geographically Weighted Regression (GWR). Several specifications are compared, being the adaptive bisquare weighting function the more efficient in terms of goodness-of-fit. We test for global and local spatial instability using some F-tests and other statistical measures. We find some evidence of spatial instability in the distribution of this variable in relation to some explanatory variables, which cannot be totally solved by spatial dependence specifications. GWR has revealed as a better specification to model per capita household income. It highlights some facets of the relationship completely hidden in the global results and forces us to ask about questions we would otherwise not have asked. Moreover, the application of GWR can also be of help to further exercises of micro-data spatial prediction