Modeling wildfire drivers in Chinese tropical forest ecosystems using global logistic regression and geographically weighted logistic regression

2021 ◽  
Author(s):  
Zhangwen Su ◽  
Lujia Zheng ◽  
Sisheng Luo ◽  
Mulualem Tigabu ◽  
Futao Guo
Keyword(s):  
Logistic Regression ◽  
Tropical Forest ◽  
Forest Ecosystems ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

scholarly journals Model Regresi Logistik Terboboti Georafis pada Status Kemiskinan Kabupaten/Kota di Provinsi Sulawesi Selatan tahun 2016

2019 ◽  
Vol 1 (3) ◽  
pp. 87
Author(s):  
Sadriana Rustan ◽  
Muhammad Arif Tiro ◽  
Muhammad Nadjib Bustan
Keyword(s):  
Logistic Regression ◽  
Maximum Likelihood ◽  
Maximum Likelihood Estimation ◽  
Geographical Location ◽  
Predictor Variable ◽  
Likelihood Estimation ◽  
Local Form ◽  
South Sulawesi ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

Abstrak. Analisis regresi logistik digunakan untuk menentukan hubungan antara peubah respon bersifat kategori dengan satu atau lebih peubah penjelas dengan asumsi bahwa respon tidak dipengaruhi oleh lokasi geografis (data spasial). Salah satu metode analisis spasial adalah Model Regresi Logistik Terboboti Geografis (RLTG). Model RLTG adalah bentuk regresi logistik lokal di mana lokasi geografis diperhatikan dan diasumsikan memiliki distribusi Bernoulli. Pendugaan parameter model RLTG menggunakan metode Maximum Likelihood Estimation (MLE) dengan memberikan bobot yang berbeda pada lokasi yang berbeda. Data dalam penelitian ini diperoleh dari publikasi Badan Pusat Statistik, yaitu data dan Informasi Kemiskinan di Provinsi Sulawesi Selatan. Penelitian ini bertujuan untuk mengetahui faktor-faktor yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan dengan menggunakan model regresi logistik terboboti geografis dengan fungsi pembobot Kernel bisquare. Hasil penelitian menunjukkan bahwa peubah penjelas yang mempengaruhi status kemiskinan di Provinsi Sulawesi Selatan adalah persentase penduduk tidak bekerja dan persentase rumah tangga pengguna jamban bersama.Abstract. Logistic regression a analysis is used to determine the relationship between categorical response variables with one or more predictor variable assuming that the response is not influenced by geographical location (spatial data). One method of spatial analysis is Geographically Weighted Logistic Regression (GWLR). The GWLR model is a local form of logistic regression where the geographical location is considered and assumed to have a Bernoulli distribution. Estimating parameters of the RLTG model uses the Maximum Likelihood Estimation (MLE) method by giving different weights to different locations. The data were obtained from BPS publications, namely Data and Information on Poverty in South Sulawesi Province. This study aims to determine the factors that influence poverty status in South Sulawesi Province using a geographically weighted logistic regression model with kernel bisquare weighting function. The results showed that the explanatory variables that influence the status of poverty in the province of South Sulawesi were the percentage of the population not working and the percentage of common household toilet users.Keywords: logistic regression, kernel bisquare, GWLR and poverty.

Comparing the application of logistic and geographically weighted logistic regression models for Fujian forest fire forecasting

2017 ◽  
Vol 37 (12) ◽  
Author(s):  
梁慧玲 LIANG Huiling ◽  
王文辉 WANG Wenhui ◽  
郭福涛 GUO Futao ◽  
林芳芳 LIN Fangfang ◽  
林玉蕊 LIN Yurui
Keyword(s):  
Logistic Regression ◽  
Forest Fire ◽  
Regression Models ◽  
Logistic Regression Models ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

scholarly journals Spatial-Temporal Analysis of Injury Severity with Geographically Weighted Panel Logistic Regression Model

2019 ◽  
Vol 2019 ◽  
pp. 1-15
Author(s):  
Daiquan Xiao ◽  
Xuecai Xu ◽  
Li Duan
Keyword(s):  
Logistic Regression ◽  
Regression Model ◽  
Regression Models ◽  
Logistic Regression Model ◽  
Injury Severity ◽  
Temporal Analysis ◽  
Human Beings ◽  
Temporal Perspective ◽  
Weighted Logistic Regression ◽  
Geographically Weighted Logistic Regression

This study is intended to investigate the influencing factors of injury severity by considering the heterogeneity issue of unobserved factors at different arterials and the spatial attributes in geographically weighted regression models. To achieve the objectives, geographically weighted panel logistic regression model was developed, in which the geographically weighted logistic regression model addressed the injury severity from the spatial perspective, while the panel data model accommodated the heterogeneity attributed to unobserved factors from the temporal perspective. The geo-crash data of Las Vegas metropolitan area from 2014 to 2016 was collected, involving 27 arterials with 25,029 injury samples. By comparing the conventional logistic regression model and geographically weighted logistic regression models, the geographically weighted panel logistic regression model showed preference to the other models. Results revealed that four main factors, human-beings (drivers/pedestrians/cyclists), vehicles, roadway, and environment, were potentially significant factors of increasing the injury severity. The findings provide useful insights for practitioners and policy makers to improve safety along arterials.

scholarly journals Modeling of Incident Status Dengue Fever in East Nusa Tenggara Using Geographically Weighted Logistic Regression Approach

2020 ◽  
Vol 2 (2) ◽  
pp. 118
Author(s):  
A Meylin ◽  
N. A. Aprilianti ◽  
D Lestari ◽  
Nur Chamidah
Keyword(s):  
Logistic Regression ◽  
Dengue Fever ◽  
Spatial Data ◽  
Health Workers ◽  
Geographical Location ◽  
Gaussian Kernel ◽  
Weighted Logistic Regression ◽  
The Status ◽  
Binary Response Variable ◽  
Geographically Weighted Logistic Regression

Dengue fever is a disease caused by one of the four dengue viruses and this disease is an infectious disease that is spread through the bite of the Aedes Aegypti mosquito. When compared with the number of dengue cases in previous years, East Nusa Tenggara (NTT) was one of the provinces that experienced an increase in the number of dengue cases in the last three years. Previous research states that the transmission of dengue fever is caused by several factors, one of which is environmental factors of geographical location so that spatial aspects need to be involved in this study. A the statistical method that can be used to analyze spatial data in the form of a logistic regression equation that has a binary response variable is the Geographically Weighted Logistic Regression (GWLR) method. This study aims to analyze the factors that influence the high number of dengue fever cases in NTT in 2018 using GWLR approach by weighted the Gaussian kernel function. Based on the results of GWLR analysis, the number of rainy days and the number of health workers partially significantly influence the status of dengue fever events in each regency/city in NTT Province in 2018. Based on the calculation of Press’s Q value, the prediction in this study was accurate with the accuracy of classification was 0.8636 or 86.36%.

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