scholarly journals PEMODELAN INCIDENT RATE DEMAM BERDARAH DENGUE DI INDONESIA YANG BERKAITAN DENGAN FAKTOR LINGKUNGAN MENGGUNAKAN METODE GEOGRAPHICALLY WEIGHTED REGRESSION (GWR)

EKOLOGIA ◽  
2020 ◽  
Vol 20 (2) ◽  
pp. 64-73
Author(s):  
Kiki Amelia ◽  
Latifa Oktafiani Asril ◽  
Lasmi Febrianti
Keyword(s):  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Geographically Weighted Regression ◽  
Physical Environment ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Least Square ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Incident Rate

Dengue hemorrhagic fever cases in Indonesia often occur in cities and villages. Every year hundreds to thousands of people must be hospitalized due to this disease. There are several factors of the physical environment that directly or indirectly influence the transmission of this disease. Such as rainfall, air temperature, and humidity. In addition to the physical environment there are several other factors that can increase the occurrence of dengue cases, namely population density and the level of larvae free in an area. For this reason, we conducted a study of the above factors and their contribution in the addition of dengue cases that occurred in Indonesia in 2015 using secondary data. The purpose of this study is to identify and make a BDB iricident rate model related to environmental factors such as temperature, humidity, population density, and the amount of rainfall on the number of cases of dengue hemorrhagic fever in Indonesia in 2015. The method used is the Geographically Weighted Regression method. (GWR). In the GWR model the parameter estimation uses Weighted Least Square (WLS) by weighting the gaussian kernel function. The results of the study concluded that modeling with GWR was better than linear regression and the variables were significantly different in each region.

scholarly journals Spatial analysis of dengue hemorrhagic fever in Talaud Islands regency

2020 ◽  
Vol 8 (1) ◽  
pp. 104
Author(s):  
Chreisye K. F. Mandagi ◽  
Angela F. C. Kalesaran ◽  
Febi K. Kolibu
Keyword(s):  
Spatial Analysis ◽  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Spatial Description ◽  
District Health ◽  
Study Approach ◽  
Spatial Risk ◽  
Risk Areas

Background: The number of dengue hemorrhagic fever (DHF) cases in Indonesia from January to February 2016 was 8,487 with 108 deaths. DHF is an infectious disease that continues to increase from 2014 until 2016 in Manado city. DHF cases in Talaud Islands Regency from 2014 to 2016 were 143 cases. Regional spatial analysis would simplify the distribution of DHF cases in high-risk areas. To be aware of the DHF outbreak cycle, it is necessary to model spatial risk factors based on geographic information systems (GIS) to tackle and eradicate DHF cases by region.Methods: This study aimed to analyze the spread of DHF in Talaud regency based on age, sex, population density and area height. The design of this research is qualitative analytic by using an ecological study approach. The research scope was 19 districts in Talaud regency. Secondary data are used which consists of case number, age, sex, population density, and area height taken from the Talaud district health office with 66 DHF cases in 2018-2019 and analyzed using the GIS approach through spatial analysis.Results: Based on the number of DHF cases that is most in the age group of 5-11 years. Male gender is more likely to suffer from DHF than female. Spatial description of the condition of the altitude in the Talaud Islands regency at risk of suffering from DHF is>50 meters above sea level. Spatial description of population density with most DHF cases is not densely populated area with less than 1,620 inhabitants per km.Conclusions: The health office of Talaud islands regency needs to actively promote health by providing information about eradicating mosquitoes.

scholarly journals Faktor-faktor Yang Mempengaruhi Keberadaan Jentik Aedes aegypti Terhadap Kejadian Demam Berdarah dengue (DBD) di Kecamatan Payung Sekaki Kota Pekanbaru

2017 ◽  
Vol 4 (1) ◽  
pp. 59
Author(s):  
Betty Nia Rulen ◽  
Sofyan Husein Siregar ◽  
Elda Nazriati
Keyword(s):  
Population Density ◽  
Aedes Aegypti ◽  
Dengue Hemorrhagic Fever ◽  
Physical Environment ◽  
Virus Transmission ◽  
Hemorrhagic Fever ◽  
Total Effect ◽  
National Standard ◽  
P Value ◽  
Water Reservoirs

The existence of larva an indicator of the spread of Dengue Hemorrhagic Fever (DHF) in a place that is measured with Density Figure of Larvae (DF). The increase in the number of dengue cases supported by the low of DF is influenced by several factors such as the physical environment, the behavior of governance of water reservoirs and population density. DF of Larvae in District Payung Sekaki in 2015 by 70%, which is still far below the national standard that is above 95%. This allows opportunities incidence of dengue and dengue virus transmission in these districts will remain high in the next year, especially in the five-year cycle of dengue. This study aimed to analyze the effect of directly and indirectly between the physical environment (rainfall, temperature, humidity, type of container and the presence of the container), the behavior of governance of water reservoirs and population density of the existence of larva, and analyze the effect of the presence of larvae of Aedes aegypti to incidence of dengue. Research conducted in April-December 2016 and located in four villages in Payung Sekaki District Pekanbaru City. Based on the research results, partially variables that affect the existence of larva is the presence of container with a direct influence positively of 10,843 times, the indirect effect 2,054 times and the total effect of 12,897 times, while the natural behavior governance of water reservoirs negatively have direct effects for 26,142 times, the indirect influence of 3,189 times and 29,331 times the total effect of the existence of Aedes aegypti larvae. Results showed no effect on the incidence of dengue larva presence in District Payung Sekaki with a P value (sig.) 0.278 (>α 0.05) but in spatial analysis there is an influence between the existence of larvae on the incidence of Dengue Hemorrhagic Fever.

scholarly journals AN OVERVIEW OF DENGUE HEMORRHAGIC FEVER CASES AND ENVIRONMENTAL FACTORS IN SUMENEP DISTRICT IN 2018

2020 ◽  
Vol 8 (3) ◽  
pp. 293
Author(s):  
Ainun Jaria ◽  
Chatarina Umbul Wahjuni
Keyword(s):  
Population Density ◽  
Dengue Hemorrhagic Fever ◽  
Descriptive Analysis ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Cross Sectional ◽  
The People ◽  
Cross Sectional Design ◽  
Time Variables ◽  
Epidemiological Characteristics

Background: Dengue hemorrhagic fever (DHF) has been occurring in Indonesia for more than 50 years, and there were 65,602 cases of it in 2018 alone. Sumenep District reported the 12th highest number of DHF cases (292) in East Java Province in 2018. Purpose: This research aims to describe DHF in Sumenep District in 2018. Methods: This research is a descriptive study employing a cross-sectional design. The data used in this research were secondary data gathered from the Sumenep Health Office in 2018. The variables observed in this research were the epidemiological characteristics included in the epidemiological triangle—people, place (region), and time. Descriptive analysis was conducted to examine the DHF case distribution, using a frequency table, among the people, place (region), and time variables. Results: Sumenep District is home to 1,085,227 citizens, with a population density of 518 people/km2. The number of DHF cases in Sumenep District was 292, with the highest concentration of cases in the age range of 5–14 years (47.30%); the most cases were also male (57.19%). DHF often occurred in sub-districts with high population density, and more commonly in January. Conclusion: Most of the Sumenep District’s DHF cases in 2018 were males in the age group of 5–14 years old.

scholarly journals Pemodelan Indeks Pembangunan Manusia (IPM) Metode Baru Menurut Provinsi Tahun 2015 Menggunakan Geographically Weighted Regression (GWR)

2019 ◽  
Vol 2 (1) ◽  
pp. 21
Author(s):  
Akbar Maulana ◽  
Renny Meilawati ◽  
Vita Widiastuti
Keyword(s):  
Regression Analysis ◽  
Human Development ◽  
Geographically Weighted Regression ◽  
Human Development Index ◽  
Spatial Regression ◽  
Least Square ◽  
Gaussian Kernel ◽  
Weighted Regression ◽  
Development Index ◽  
Spatial Regression Analysis

<p>The Human Development Index (HDI) is a parameter of quality of life for an area. The HDI explains how residents can access the results of development in obtaining income, health and education. One method that can be used to find out the factors that influence the human development index in modeling is regression analysis of ordinary least square (OLS). In the Human Development Index data, there is a dependency between measuring data and the location of a region. Therefore, spatial regression analysis can be used in this study. The local form of spatial regression analysis is <em>geographically weighted regression</em> (GWR). GWR shows the existence of spatial heterogeneity (location). This study compares between OLS regression and GWR in the new human development index method by province in 2015. In the GWR model we use fixed Gaussian kernel and kernel fixed bisquare as weighted function. The optimal bandwidth value is obtained by minimizing the cross validation (CV) and Akaike information criterion (AIC) coefficients. The results showed that the GWR model with Gaussian kernel function is better than GWR with bisquare kernel function and OLS model.</p><p><strong>Keywords</strong><strong>: </strong>human development index, ordinary least square,<strong> </strong>geographically weighted regression, kernel fixed Gaussian,  kernel fixed bisquare</p>

Second-hand housing batch evaluation model of zhengzhou city based on big data and MGWR model

2021 ◽  
pp. 1-20
Author(s):  
Chaojie Liu ◽  
Jie Lu ◽  
Wenjing Fu ◽  
Zhuoyi Zhou
Keyword(s):  
Real Estate ◽  
Geographically Weighted Regression ◽  
Euclidean Distance ◽  
Evaluation Model ◽  
Housing Prices ◽  
Information Criterion ◽  
Access Point ◽  
Least Square ◽  
Weighted Regression ◽  
Spatial Error

How to better evaluate the value of urban real estate is a major issue in the reform of real estate tax system. So the establishment of an accurate and efficient housing batch evaluation model is crucial in evaluating the value of housing. In this paper the second-hand housing transaction data of Zhengzhou City from 2010 to 2019 was used to model housing prices and explanatory variables by using models of Ordinary Least Square (OLS), Spatial Error Model (SEM), Geographically Weighted Regression (GWR), Geographically and Temporally Weighted Regression (GTWR), and Multiscale Geographically Weighted Regression (MGWR). And a correction method of Barrier Line and Access Point (BLAAP) was constructed, and compared with three correction methods previously studied: Buffer Area (BA), Euclidean Distance (ED), and Non-Euclidean Distance, Travel Distance (ND, TT). The results showed: The fitting degree of GWR, MGWR and GTWR by BLAAP was 0.03–0.07 higher than by ND. The fitting degree of MGWR was the highest (0.883) by BLAAP but the smallest by Akaike Information Criterion (AIC), and 88.3% of second-hand housing data could be well interpreted by the model.

scholarly journals Relationship Host Behavior and The Environment of DHF Incidence in Wonokusumo Surabaya

2018 ◽  
Vol 5 (3) ◽  
pp. 383
Author(s):  
Sofa Nutrima Rismawati
Keyword(s):  
Data Collection ◽  
Dengue Hemorrhagic Fever ◽  
Secondary Data ◽  
Hemorrhagic Fever ◽  
Primary Data ◽  
Environment Interaction ◽  
Cross Sectional ◽  
Host Behavior ◽  
Cross Sectional Design ◽  
Data Collection Technique

ABSTRACTFree Number of larvae (ABJ) in RW 15, Wonokusumo Village is 85%. However, the incidence of Dengue Hemorrhagic Fever (DHF) is still high, ie 17 cases. The high incidence rate of DHF is due to the interaction between host, agent and environment. Host in terms of behavior, dengue virus as an agent and environment derived from the surrounding conditions that can cause and trigger the spread of DHF. The purpose of this research is to analyze the relationship of host and environment behavior to the occurrence of DHF in RW 15. This research using cross sectional design. Random sampling using the guy method. How to calculate the method guy is 10% of the population so that obtained a sample of 78 respondents. Primary data collection technique is done through indepth interview and filling questionnaire. Secondary data collection was obtained from Wonokusumo Puskemas report and report from Surabaya City Health Office. The research was conducted in RW 15. The result of bivariate statistic test showed significant relation between knowledge, attitude, action and environment against DHF incidence with p> α, 0,00> 0,05. The conclusion of this research is that there is host and environment interaction to DHF incidence. Suggestion of this research is procurement of work program every month, independent larvae monitoring movement by society and 3M movement plus every week on Thursday and procurement of training of cadre jumantik about how and regulation of dosage of abate powder according to dose.Keywords: host behavior, environment, disease Dengue Hemorrhagic Fever

scholarly journals Parameter estimation of geographically weighted regression (GWR) model using weighted least square and its application

2018 ◽  
Author(s):  
Saskya Mary Soemartojo ◽  
Rima Dini Ghaisani ◽  
Titin Siswantining ◽  
Mariam Rahmania Shahab ◽  
Moch. Muchid Ariyanto
Keyword(s):  
Parameter Estimation ◽  
Geographically Weighted Regression ◽  
Least Square ◽  
Weighted Regression ◽  
Weighted Least Square ◽  
Gwr Model

scholarly journals Estimation of Error Variance-Covariance Parameters Using Multivariate Geographically Weighted Regression Model

2020 ◽  
Vol 2020 ◽  
pp. 1-5 ◽  
Author(s):  
Sri Harini
Keyword(s):  
Statistical Inference ◽  
Geographically Weighted Regression ◽  
Weighting Function ◽  
Likelihood Estimation ◽  
Error Variance ◽  
Least Square ◽  
Weighted Regression ◽  
Inference Procedure ◽  
Geographically Weighted Regression Model ◽  
Estimated Error

The Multivariate Geographically Weighted Regression (MGWR) model is a development of the Geographically Weighted Regression (GWR) model that takes into account spatial heterogeneity and autocorrelation error factors that are localized at each observation location. The MGWR model is assumed to be an error vector ε that distributed as a multivariate normally with zero vector mean and variance-covariance matrix Σ at each location ui,vi, which Σ is sized qxq for samples at the i-location. In this study, the estimated error variance-covariance parameters is obtained from the MGWR model using Maximum Likelihood Estimation (MLE) and Weighted Least Square (WLS) methods. The selection of the WLS method is based on the weighting function measured from the standard deviation of the distance vector between one observation location and another observation location. This test uses a statistical inference procedure by reducing the MGWR model equation so that the estimated error variance-covariance parameters meet the characteristics of unbiased. This study also provides researchers with an understanding of statistical inference procedures.

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