4D-GWR: geographically, altitudinal, and temporally weighted regression

Taşyürek, Murat; Çelik, Mete

doi:10.1007/s00521-022-07311-4

Cited by 15 publications

(3 citation statements)

References 48 publications

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“…3) GWR: GWR was proposed by Fotheringham, Charlton and Brunsdon as a regression analysis method allowing spatial changes in the model coefficient β. The GWR formula can be expressed as follows [20,21]…”

Section: B Statistical Analysis 1)mentioning

confidence: 99%

Research on The Spatial Heterogeneity of National Physical and Mental Health in China Based on Numerical Simulation

Jingchao Hu

2024

jes

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Studying the spatial interactions between socio-economic and natural (SEN) factors and physical health (PF) can help improve national physical and mental health and develop health policies. In order to deeply analyze the relationship between social and economic environment, sports activities, and national physical and mental health, and improve the level of national physical and mental health, this study used mathematical numerical analysis methods using MATLAB and ArcGIS software to determine the determining factors of PF differences in China. The reported National Fitness Composite Index (NFCI) in China was extracted from the National Physical and Mental Health Fitness Monitoring Bulletin in 2015. The SEN factors were gathered from the China Statistical Yearbook in 2015. The OLS model and GWR model were then utilized to investigate the SEN factors of the NFCI. We found that the NFCI of each province had a strong relationship with the natural environment. The NFCI of the eastern coastal regions was higher but also similar to that of the central and western regions. It was also found that the impact of the natural environment on national physical and mental health fitness was greater than that of social and economic factors. Temperature and hours of sunshine were major environmental factors. Annual hours of sunshine (ANNsun, hours), health care expenditure (HEAexpe, Chinese yuan), annual temperature (ANNtem, °C), road distance (ROAdis, kilometers) and annual rainfall (ANNrai, millimeters)are socioeconomic and natural (SEN) determinants of factors and physical fitness (PF). We should increase physical exercise, change our concepts of health, develop a healthy lifestyle and improve our physical fitness in accordance with the national fitness strategy. The regression coefficients obtained by the GWR model show significant differences, indicating that the degree of influence of social, economic, and natural factors on national physical fitness varies significantly with geographical regions..

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Section: B Statistical Analysis 1)mentioning

confidence: 99%

Research on The Spatial Heterogeneity of National Physical and Mental Health in China Based on Numerical Simulation

Jingchao Hu

2024

jes

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“…Taşyürek ve Çelik [29] çalışmalarında konum temelli bir regresyon yöntemi olan coğrafi ağırlıklı regresyon metodunu kullanmışlardır. Bu yöntem ile mekânsal ve zamansal olarak regresyon yapılabilmektedir.…”

Section: Literatür İncelemesi (Literature Review)unclassified

Türkiye’de ikinci el araçların büyük veri ve makine öğrenme teknikleriyle analizi ve fiyat tahmini

Gülmez¹,

Kulluk²

2023

Gazi Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi

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Türkiye’de ikinci el araç piyasası her zaman hareketli olmuştur. İkinci el araç piyasasında marka, model, yakıt türü gibi özelliklerin ne kadar yoğunlukta olduğu, ne kadar fiyata etki ettiği gibi faktörler analiz edilerek, bu bilgiler kullanışlı hale getirilebilir. Araçların çeşitli özelliklerine göre fiyatları değişmektedir. Fiyatları tahmin edebilmek için makine öğrenme teknikleri kullanılabilir ve kullanıcıların araç satarken veya alırken fiyat belirlemelerine yardımcı olabilir. Fiyat tahmini, veri madenciliğinin bir görevi olan fonksiyon tahmini veya regresyon sınıfına girmektedir. İkinci el araç sayısı oldukça fazla olduğundan dolayı bu çalışmada analizler yapılırken büyük veri sistemleri kullanılmıştır. Apache Spark ve makine öğrenme kütüphanesi bunun için oldukça kullanışlıdır. Fiyat tahmini için doğrusal regresyon, karar ağacı regresyonu, rastgele orman regresyonu, GBT regresyonu, izotonik regresyon algoritmaları kullanılmıştır. Kullanılan algoritmalar ile araçların fiyat tahmini yapılmıştır ve en yüksek başarıyı 21435,09 RMSE ve 0,887 R2 değerleriyle rastgele orman algoritması elde etmiştir. Rasgele orman algoritması ve diğer algoritmalarla elde edilen RMSE ve R2 değerleri arasında anlamlı bir farklılık olup olmadığını kontrol için yapılan istatistiksel testler sonucunda, rasgele orman algoritması ile elde edilen sonuçların daha iyi olduğu sonucuna ulaşılmıştır. Rasgele orman algoritmasının daha iyi sonuçlar vermesinin nedeni, algoritmanın birden çok karar ağacı üzerinden eğitim gerçekleştirmesi, esnekliği ve güçlü hiper parametrelere sahip olmasıdır.

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“…Neurons are usually organized by layers, with each neuron in a layer having a directed connection to a neuron in the following layer (Figure 4a). GWANN is a nonlinear GWR approach based on ANN, and it performs training in addition to obtaining neighborhood ratios, which makes it computationally more complex than an ANN model (Figure 4b) [26][27][28]. On the basis of maintaining the structure of the traditional ANN model, the GWANN closely combines the output neurons with the geospatial location to drive each weight factor (ω) to be a weight kernel function about the geospatial distance (d ij ) and bandwidth (b) [29] using Equation (4).…”

Section: Geographically Weighted Artificial Neural Networkmentioning

confidence: 99%

Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China

et al. 2023

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Opencast coal mine production and construction activities have a certain impact on the ecological environment, while the development and utilization of large coal bases distributed in semi-arid steppe regions may have a more direct and significant impact on the eco-environment. Therefore, in-depth studies of the ecological impacts of human activities and natural environmental elements in opencast coal mines in typical semi-arid steppe regions and analyses of their driving forces are of great significance for protecting and restoring regional fragile steppe ecosystems. In this paper, the mining area southwest of the Shengli coalfield, a typical ore concentration area in eastern Inner Mongolia, was selected as the research object. Its remote sensing ecological index (RSEI) was calculated using the Google Earth Engine (GEE) platform to analyze the eco-environmental quality in the mining area and its surrounding 2 km from 2005 to 2021. The geographically weighted artificial neural network model (GWANN) was combined with the actual situation of mining activity and ecological restoration to discuss the driving factors of eco-environmental quality change in the study area. The results showed that: (1) the proportion of the study area with excellent and good eco-environmental quality increased from 20.96% to 23.93% from 2005 to 2021, and the proportions of areas with other quality grades fluctuated strongly. (2) The change in eco-environmental quality in the interior of the mining area was closely related to the reclamation of dump sites and migration of the mining area. (3) The maximum contribution rate of the mining activity factor to the external eco-environmental quality of the mining area reached 43.33%, with an annual average contribution rate of 34.48%; as the distance from the mining area increased, its contribution gradually decreased. This quantitative analysis of the driving forces of RSEI change in the mining area will complement future work in ecological evaluations of mining areas while also improving the practicality of ecological evaluation at the mining scale, thereby further helping the ecological management of mining areas.

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4D-GWR: geographically, altitudinal, and temporally weighted regression

Cited by 15 publications

References 48 publications

Research on The Spatial Heterogeneity of National Physical and Mental Health in China Based on Numerical Simulation

Research on The Spatial Heterogeneity of National Physical and Mental Health in China Based on Numerical Simulation

Türkiye’de ikinci el araçların büyük veri ve makine öğrenme teknikleriyle analizi ve fiyat tahmini

Analysis of Eco-Environmental Quality and Driving Forces in Opencast Coal Mining Area Based on GWANN Model: A Case Study in Shengli Coalfield, China

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