Spatial Autoregressive Model of Tuberculosis Cases in Central Java Province 2019

Zebua, Hasrat Ifolala; Jaya, I Gede Nyoman Mindra

doi:10.18860/ca.v7i2.13451

Cited by 4 publications

(4 citation statements)

References 7 publications

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“…The impact of covariates in the SAR model could be classified into three categories: total impact; indirect impact; and direct impact; [16]. Total impact refers to the changes that occur in one sub-district as a consequence of changes in that sub-district and its surroundings.…”

Section: Table 2 the Results Of The Classical Assumption Of Linear Re...mentioning

confidence: 99%

“…Before modeling with spatial regression, the classical assumptions of multiple linear regression models must be tested. [16];…”

Section: Data Analysis Stepsmentioning

confidence: 99%

“…If both 𝐿𝑀 𝐿𝐴𝐺 and 𝐿𝑀 𝐸𝑅𝑅 are significant, comparing the Akaike Information Criterion (AIC) values allows one of the best models to be chosen. The best model is the one with the lowest AIC value [16]. The AIC calculated using the Maximum Likelihood Estimation (MLE) method is as follows [23]: 11) where 𝐿 𝑚 denotes the Maximum log-likelihood and 𝑚 denotes the number of model parameters;…”

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confidence: 99%

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Spatial Analysis of Dengue Disease in Jakarta Province

Sobari

Jaya

Ruchjana

2023

CAUCHY

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Dengue disease is a virus-borne illness spread by the bite of the female Aedes aegypti mosquito. Jakarta Province has a vulnerability to dengue disease due to high population density and percentage of urban slum households. This study applied a spatial autoregressive (SAR) model to identify the risk factors that affect the number of dengue disease cases in Jakarta Province. The spatial dependency was accounted for using the queen contiguity spatial weight matrix. The number of flood-prone points, the number of slum neighborhood associations, the population density, the number of hospitals and the number of public health centers per 1,000 population and spatial lag significantly impact the number of dengue disease cases in Jakarta Province. When dengue disease cases increase in one sub-district, the number of dengue disease cases in the sub-districts around it will increase as well because of the positive and significant spatial lag coefficient. Based on the direct impact, each addition of one percent of flood-prone points in one sub-district will increase the number of dengue disease cases in that sub-district by 3.86 cases

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Section: Table 2 the Results Of The Classical Assumption Of Linear Re...mentioning

confidence: 99%

“…Before modeling with spatial regression, the classical assumptions of multiple linear regression models must be tested. [16];…”

Section: Data Analysis Stepsmentioning

confidence: 99%

mentioning

confidence: 99%

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Spatial Analysis of Dengue Disease in Jakarta Province

Sobari

Jaya

Ruchjana

2023

CAUCHY

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“…In quantile regression, various quantiles can obtain as many regression lines as the required quantiles. In this research, modeling was carried out using quantile levels, namely 0.01, 0.25, 0.5, 0.75 and 0.90 [19]. Parameter estimation was carried out using Instrumental Variable Quantile (SQR), and parameter significance testing was carried out using the Z test.…”

Section: Spatial Autoregressive Quantile Regression (Sarqr) Modelingmentioning

confidence: 99%

Spatial Autoregressive Quantile Regression Modeling of the Distribution of Drug Users in the District Karo

Nanda,

Siregar

2024

J. Pijar.MIPA

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BNN data shows that an average of 50 people die from drugs every day, and Karo Regency is in second place for the distribution of drug abuse in North Sumatra after Medan City. Variables that have this risk include drug factors, namely availability and ease of obtaining drugs, individual factors, environmental factors, family factors, and social factors. Based on this, a model is needed to determine the development of the case. The SARQR model combines SAR modeling with quantile regression (QR). Combining the SAR model with quantile regression produces a model that is good for overcoming the problems of dependency and heterogeneity in modeling spatial data and is resistant to outlier data. This research aims to determine the Spatial Autoregressive Quantile Regression Model for the distribution of drug users in the Karo Regency. The type of research used is quantitative research. The data type used is secondary data, namely, the kind of data already existing, and the data source used in this research is drug users in the Karo district. The research results show that the Spatial Autoregressive Quantile Regression model for the distribution of drug users in Karo Regency obtained estimation results for the distribution parameters of drug users using a significance test. This model explains that the factors that significantly influence drug abuse are age, gender, occupation, and other underlying factors.

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