scholarly journals Model Geographically Weighted Regression dengan Fungsi Pembobot Adaptive dan Fixed Kernel pada Kasus Kematian Ibu di Jawa Timur

2021 ◽  
Vol 5 (2) ◽  
pp. 208-220
Author(s):  
Ulfie Safitri ◽  
Luthfatul Amaliana
Keyword(s):  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Fixed Kernel ◽  
Adaptive Kernel

Model Geographically Weighted Regression (GWR) merupakan pengembangan dari model regresi linier berganda yang dapat menghasilkan penduga parameter model yang bersifat lokal untuk setiap titik atau lokasi di mana data diamati. Model GWR dapat digunakan apabila data memenuhi asumsi heterogenitas spasial yang diakibatkan oleh perbedaan kondisi data antara satu lokasi dengan lokasi lain. Penelitian ini bertujuan untuk menentukan model GWR terbaik dengan pembobot adaptive kernel dan fixed kernel pada kasus kematian ibu di Jawa Timur tahun 2018. Data yang digunakan pada penelitian ini adalah data kematian ibu sebagai variabel respon dan rumah tangga berperilaku hidup bersih sehat, kunjungan ibu hamil dengan K4, ibu hamil mendapat tablet Fe3, persalinan yang ditolong tenaga kesehatan, serta jumlah fasilitas kesehatan sebagai variabel prediktor. Berdasarkan kriteria pemilihan model terbaik yang dilihat dari nilai AIC terkecil dapat disimpulkan bahwa model GWR dengan fungsi pembobot adaptive bi- square kernel merupakan model terbaik untuk data kematian ibu. Faktor yang mempengaruhi kasus kematian ibu berdasarkan pengujian parameter secara parsial yaitu kunjungan ibu hamil dengan K4 dan jumlah fasilitas kesehatan.

scholarly journals ANALISIS GEOGRAPHICALLY WEIGHTED REGRESSION (GWR) PADA KASUS PNEUMONIA BALITA DI PROVINSI JAWA BARAT

2017 ◽  
Vol 1 (1) ◽  
pp. 23-32
Author(s):  
Meila Nadya ◽  
Widyanti Rahayu ◽  
Vera Maya Santi
Keyword(s):  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Fixed Kernel

Pneumonia adalah salah satu penyakit Infeksi Saluran Pernapasan Akut (ISPA) yang disebabkan oleh bakteri atau virus. Di Indonesia, pneumonia merupakan penyebab kematian balita tertinggi kedua setelah diare. Kasus pneumonia pada balita terbanyak yang ditemukan di Indonesia adalah di provinsi Jawa Barat. Untuk mengatasi pneumonia balita perlu dianalisis faktor penyebab pneumonia balita. Salah satu cara untuk menganalisis faktor tersebut adalah dengan menggunakan analisis Geographically Weighted Regression (GWR). Analisis GWR merupakan pengembangan dari analisis regresi linier berganda yang dapat mengatasi keragaman wilayah/heterogenitas spasial sehingga menghasilkan model dan pendugaan parameter berbeda untuk setiap wilayah amatan. Hasil analisis GWR dengan menggunakan pembobot spasial \textit{Fixed Kernel Gaussian} menunjukkan bahwa model GWR lebih baik daripada model regresi linier berganda. Hal ini berdasarkan nilai R2, nilai R2 dari model GWR (88.34%) lebih besar dari R2 dari model regresi linier berganda (71.86%). Sementara, Jumlah Kuadrat Galat (JKG) untuk model GWR diperoleh 3.031 lebih kecil bila dibandingkan dengan nilai JKG dari regresi linier berganda yang sebesar 7.317. Secara umum, terdapat tiga faktor yang berpengaruh signifikan terhadap kasus pneumonia balita di provinsi Jawa Barat tahun 2014 yaitu jumlah balita gizi buruk, persentase bayi yang diimunisasi dasar lengkap dan jumlah puskesmas.

scholarly journals PENERAPAN ALGORITMA GLOWWORM SWARM OPTIMIZATION PADA MODEL GEOGRAPHICALLY WEIGHTED REGRESSION DENGAN KERNEL ADAPTIF

2020 ◽  
Vol 9 (1) ◽  
pp. 79
Author(s):  
I GEDE HARDI KARMANA ◽  
LUH PUTU IDA HARINI ◽  
KETUT JAYANEGARA ◽  
I PUTU EKA NILA KENCANA
Keyword(s):  
Geographically Weighted Regression ◽  
Cross Validation ◽  
Nearest Neighbor ◽  
Weighted Regression ◽  
K Nearest Neighbor ◽  
Swarm Optimization ◽  
Optimal Bandwidth ◽  
Glowworm Swarm Optimization ◽  
Adaptive Kernel ◽  
Gwr Model

This study aimed to apply glowworm swarm optimization (GSO) algorithm as an alternate way to obtain optimal bandwidth in geographically weighted regression (GWR) model with adaptive kernel function. The result showed that GSO was able to obtain optimal bandwidth with lower cross validation (CV) value than the traditional way that was using k-nearest neighbor (KNN) algorithm. Unfortunately, the running time of GSO was far slower than KNN was.

scholarly journals Pemodelan Kriminal di Jawa Timur dengan Metode Geographically Weighted Regression (GWR)

2018 ◽  
Vol 4 (2) ◽  
pp. 150-158
Author(s):  
Imanudin Nurhuda ◽  
I Gede Nyoman Mindra Jaya
Keyword(s):  
Economic Growth ◽  
Spatial Heterogeneity ◽  
Geographically Weighted Regression ◽  
Weighting Function ◽  
Motor Vehicles ◽  
Weighted Regression ◽  
Model Parameters ◽  
Independent Variables ◽  
Fixed Kernel ◽  
Gwr Model

Criminality constitutes all kinds of actions that are economically and psychologically harmful in violation of the law applicable in the state of Indonesia as well as social and religious norms, while the criminal data is the number of cases reported to the police institution. The higher the number of complainants the higher the number of criminals in the region. The greater the risk the community represents the more insecure a region is. This study aims to obtain the best model affecting crime or crime in East Java. The number of crimes in this study is limited to the number of theft cases (whether ordinary theft, theft by force, theft with theft, and the theft of motor vehicles). In this study, we use the Geographically Weighted Regression (GWR) model because this method is quite effective in estimating data that has spatial heterogeneity (uniformity in location / spatial). In essence, the model parameters in GWR can be calculated at the observation location with the dependent variable and one or more independent variables that have been measured at the sites where the location is known, where criminal acts in the research conducted in East Java involves the effects of spatial heterogeneity, with fixed kernel weighting function. The results showed that the variables affecting criminality in East Java Province are population density, economic growth, Gini Ratio, and poverty.

scholarly journals Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Seblewongel Tigabu ◽  
Alemneh Mekuriaw Liyew ◽  
Bisrat Misganaw Geremew
Keyword(s):  
Spatial Distribution ◽  
Geographically Weighted Regression ◽  
Teenage Pregnancy ◽  
Wealth Quintile ◽  
Ordinary Least Squares ◽  
Weighted Regression ◽  
Contraceptive Methods ◽  
Hotspot Analysis ◽  
Increased Risk ◽  
Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

scholarly journals Spatiotemporal Distribution Characteristics and Driving Forces of PM2.5 in Three Urban Agglomerations of the Yangtze River Economic Belt

2021 ◽  
Vol 18 (5) ◽  
pp. 2222
Author(s):  
Jin-Wei Yan ◽  
Fei Tao ◽  
Shuai-Qian Zhang ◽  
Shuang Lin ◽  
Tong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Yangtze River ◽  
National Level ◽  
National Development ◽  
Urban Agglomeration ◽  
Spatiotemporal Distribution ◽  
Weighted Regression ◽  
Series Data ◽  
The Yangtze River ◽  
Urban Agglomerations

As part of one of the five major national development strategies, the Yangtze River Economic Belt (YREB), including the three national-level urban agglomerations (the Cheng-Yu urban agglomeration (CY-UA), the Yangtze River Middle-Reach urban agglomeration (YRMR-UA), and the Yangtze River Delta urban agglomeration (YRD-UA)), plays an important role in China’s urban development and economic construction. However, the rapid economic growth of the past decades has caused frequent regional air pollution incidents, as indicated by high levels of fine particulate matter (PM2.5). Therefore, a driving force factor analysis based on the PM2.5 of the whole area would provide more information. This paper focuses on the three urban agglomerations in the YREB and uses exploratory data analysis and geostatistics methods to describe the spatiotemporal distribution patterns of air quality based on long-term PM2.5 series data from 2015 to 2018. First, the main driving factor of the spatial stratified heterogeneity of PM2.5 was determined through the Geodetector model, and then the influence mechanism of the factors with strong explanatory power was extrapolated using the Multiscale Geographically Weighted Regression (MGWR) models. The results showed that the number of enterprises, social public vehicles, total precipitation, wind speed, and green coverage in the built-up area had the most significant impacts on the distribution of PM2.5. The regression by MGWR was found to be more efficient than that by traditional Geographically Weighted Regression (GWR), further showing that the main factors varied significantly among the three urban agglomerations in affecting the special and temporal features.

Number of flood disaster estimation in Indonesia using local linear and geographically weighted regression approach

2021 ◽  
Author(s):  
M. Fariz Fadillah Mardianto ◽  
Sediono ◽  
Novia Anggita Aprilianti ◽  
Belindha Ayu Ardhani ◽  
Rizka Firdaus Rahmadina ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Flood Disaster ◽  
Weighted Regression ◽  
Local Linear ◽  
Regression Approach

scholarly journals Spatiotemporal Changes in Vegetation Cover and Its Influencing Factors in the Loess Plateau of China Based on the Geographically Weighted Regression Model

Forests ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 673
Author(s):  
Chen Yang ◽  
Meichen Fu ◽  
Dingrao Feng ◽  
Yiyu Sun ◽  
Guohui Zhai
Keyword(s):  
Regression Model ◽  
Influencing Factors ◽  
Loess Plateau ◽  
Geographically Weighted Regression ◽  
Vegetation Cover ◽  
Weighted Regression ◽  
Vegetation Coverage ◽  
The Loess Plateau ◽  
Geographically Weighted Regression Model ◽  
Spatiotemporal Changes

Vegetation plays a key role in ecosystem regulation and influences our capacity for sustainable development. Global vegetation cover has changed dramatically over the past decades in response to both natural and anthropogenic factors; therefore, it is necessary to analyze the spatiotemporal changes in vegetation cover and its influencing factors. Moreover, ecological engineering projects, such as the “Grain for Green” project implemented in 1999, have been introduced to improve the ecological environment by enhancing forest coverage. In our study, we analyzed the changes in vegetation cover across the Loess Plateau of China and the impacts of influencing factors. First, we analyzed the latitudinal and longitudinal changes in vegetation coverage. Second, we displayed the spatiotemporal changes in vegetation cover based on Theil-Sen slope analysis and the Mann-Kendall test. Third, the Hurst exponent was used to predict future changes in vegetation coverage. Fourth, we assessed the relationship between vegetation cover and the influence of individual factors. Finally, ordinary least squares regression and the geographically weighted regression model were used to investigate the influence of various factors on vegetation cover. We found that the Loess Plateau showed large-scale greening from 2000 to 2015, though some regions showed decreasing vegetation cover. Latitudinal and longitudinal changes in vegetation coverage presented a net increase. Moreover, some areas of the Loess Plateau are at risk of degradation in the future, but most areas showed a sustainable increase in vegetation cover. Temperature, precipitation, gross domestic product (GDP), slope, cropland percentage, forest percentage, and built-up land percentage displayed different relationships with vegetation cover. Geographically weighted regression model revealed that GDP, temperature, precipitation, forest percentage, cropland percentage, built-up land percentage, and slope significantly influenced (p < 0.05) vegetation cover in 2000. In comparison, precipitation, forest percentage, cropland percentage, and built-up land percentage significantly affected (p < 0.05) vegetation cover in 2015. Our results enhance our understanding of the ecological and environmental changes in the Loess Plateau.

Sign in / Sign up

Export Citation Format

Share Document