Geographically Weighted Negative Binomial Regression Research Articles

Precision Mapping of COVID-19 Vulnerable Locales by Epidemiological and Socioeconomic Risk Factors, Developed Using South Korean Data.

COVID-19 has severely impacted socioeconomically disadvantaged populations. To support pandemic control strategies, geographically weighted negative binomial regression (GWNBR) mapped COVID-19 risk related to epidemiological and socioeconomic risk factors using South Korean incidence data (20 January 2020 to 1 July 2020). We constructed COVID-19-specific socioeconomic and epidemiological themes using established social theoretical frameworks and created composite indexes through principal component analysis. The risk of COVID-19 increased with higher area morbidity, risky health behaviours, crowding, and population mobility, and with lower social distancing, healthcare access, and education. Falling COVID-19 risks and spatial shifts over three consecutive time periods reflected effective public health interventions. This study provides a globally replicable methodological framework and precision mapping for COVID-19 and future pandemics.

Modelling the number of HIV/AIDS in Central Sulawesi

The spread of HIV/AIDS in Central Sulawesi is centralized and spreads in certain districts or cities so that there are indications of a spatial effect in the spread of HIV/AIDS. Geographically Weighted Negative Binomial Regression (GWNBR) is one of the right solutions for modelling the relationship between response variables and explanatory variables on counted data that is local for each observation location with overdispersion and the influence of location or spatial aspects on the data. Spatial aspects can be caused by geographic, socio-cultural, economic conditions, as well as different people’s knowledge between regions. Overdispersion is a condition where the variance of the data is greater than the average data. This study aims to determine the GWNBR model and the factors that influence the number of HIV/AIDS cases in the province of Central Sulawesi. The obtained result based on GWNBR model shown that the districts with the most number of districts that have the most significant similarities in variables are divided into nine subdistrict groups. Factor affecting the number of HIV/AIDS cases in Central Sulawesi Province in 2018 was population density.

Geographically Weighted Negative Binomial Regression Modeling of Tuberculosis Cases with Distribution Evaluation

<p>Tuberculosis (TB) is contagious disease caused by the bacteria <em>mycobacterium tuberculosis </em>and is one of the top 10 causes of death in the world. Central Java is included as one of the three provinces with highest number of TB cases in Indonesia. End the TB epidemic by 2030 is the final goal of <em>Sustainability Development Goal </em>and Indonesia has set target for TB elimination by 2035. The number of TB cases is non-negative count data. The distribution pattern of the count data needs to be noticed in order to produce valid analysis. Based on calculation of the VMR (Variance Mean Ratio) value and suitability test, the data on the number of TB cases follows negative binomial distribution. The infection of TB disease tends to be clumped and is affected by geographical factors (environmental, social, and economic). This study aims to determine factors that affecting TB cases through Geographic Weighted Negative Binomial Regression (GWNBR) model approach which considering spatial aspects. Based on the ratio of AIC and BIC value, GWNBR model with an <em>adaptive </em><em>g</em><em>aussian </em>kernel weighting gives the best results. The affecting factors are the number of hospitals , the percentage of population with good water access , the population density , the percentage of household with a distance of drinking water sources and feces septic tank less than 10 meters.</p>

A new zone system to analyze the spatial relationships between the built environment and traffic safety

This study aims to investigate the impacts of the built environment on traffic safety at a zonal level using a newly developed crash-related zone system. Traffic analysis zones (TAZs) have been widely employed to analyze traffic safety at a macroscopic level. However, this zone system use may present problems. Unlike previous studies, in which new zoning systems were created from aggregating TAZs, in this study the new zone system, formed by traffic safety analysis zones (TSAZs), is created from the smallest available census units. Geographically Weighted Negative Binomial Regression (GWNBR) models are used and a comparative analysis between non-spatial global crash prediction models and spatial local GWPR (Geographically Weighted Poisson Regression) and GWNBR models using the two zonal systems is presented. We find that TSAZs based models performed better than TAZs based models, especially when combined to the GWNBR technique. Our results show that several features of the built environment are significant crash predictors, and that the relationships among these features and traffic safety vary across space. By combining a crash-related zonal system with spatial GWNBR models to understand the built environment effects on traffic safety, the results of the analysis can help urban planners to consider traffic safety proactively when planning or retrofitting urban areas.

Spatial modeling of confirmed COVID-19 pandemic in East Java province by geographically weighted negative binomial regression

In East Java, 11,910 confirmed incidences for COVID-19 were registered as of 30 June 2020. We propose a Geographically Weighted Negative Binomial Regression (GWNBR) model to evaluate the effect of population density and daily average temperature on COVID-19 transmission. Our results reveal that the areas with high population density have much higher incidences than the areas with a low population density. This result indicates the COVID-19 spread quickly in locations with high population density. So, achieving a reduction in the contact rate between uninfected and infected individuals by quarantined susceptible individuals can effectively reduce disease transmission. However, the average temperature affect spatially only in several areas which shows that there is not enough evidence to explain the effect temperature on COVID-19 cases.

Comparison of Nelder Mead and BFGS Algorithms on Geographically Weighted Multivariate Negative Binomial

Geographically Weighted Negative Binomial Regression (GWNBR) was proposed related to univariate spatial count data with overdispersion using MLE via Newton Raphson algorithm. However, the Newton Raphson algorithm has the weakness, it tends to depend on the initial value. Therefore, it can have false convergence if the initial value is mistaken. In this research, we derive estimating the mean of dependent variables of multivariate spatial count data with overdispersion, Geographically Weighted Multivariate Negative Binomial (GWMNB) and compare it to the global method, multivariate negative binomial (MNB). We use MLE via Nelder Mead and Broyden-Fletcher-Goldfarb-Shanno (BFGS) algorithms. We conduct the simulation study and application of mortality data to find out the characteristics of the methods. They show that GWMNB performs better than global method (MNB) in estimating the means of dependent variables of the spatial data. The Nelder Mead tends to be more successful in estimating the means for all locations than BFGS algorithm. Although BFGS is a stable algorithm in MNB related to the initial value, it tends to have false convergence in GWMNB. The mortality rate of infant is larger than it of toddler and preschool and also maternal. The highest deaths of infant, toddler, and preschool and also maternal tend to happen in east parts of East Java.

Spatial Interaction Modeling of OD Flow Data: Comparing Geographically Weighted Negative Binomial Regression (GWNBR) and OLS (GWOLSR)

Due to the emergence of new big data technology, mobility data such as flows between origin and destination areas have increasingly become more available, cheaper, and faster. These improvements to data infrastructure have boosted spatial and temporal modeling of OD (origin-destination) flows, which require the consideration of spatial dependence and heterogeneity. Both ordinary least square (OLS) and negative binomial (NB) regression methods have been used extensively to calibrate OD flow models by processing flow data as different types of dependent variables. This paper aims to compare both global and local spatial interaction modeling of OD flows between traditional and geographically weighted OLS (GWOLSR) and NB (GWNBR) modeling methods. From this study with empirical data it is concluded that GWNBR outperforms GWOLSR in reducing spatial autocorrelation and in detecting spatial non-stationarity. Although, it is noted that both local modeling methods show improvement when compared against the equivalent global models.

Geographically Weighted Negative Binomial Regression Model Predicts Wildfire Occurrence in the Great Xing’an Mountains Better Than Negative Binomial Model

Wildfire is a major disturbance that affects large area globally every year. Thus, a better prediction of the likelihood of wildfire occurrence is essential to develop appropriate fire prevention measures. We applied a global negative Binomial (NB) and a geographically weighted negative Binomial regression (GWNBR) models to determine the relationship between wildfire occurrence and its drivers factors in the boreal forests of the Great Xing’an Mountains, northeast China. Using geo-weighted techniques to consider the geospatial information of meteorological, topographic, vegetation type and human factors, we aimed to verify whether the performance of the NB model can be improved. Our results confirmed that the model fitting and predictions of GWNBR model were better than the global NB model, produced more precise and stable model parameter estimation, yielded a more realistic spatial distribution of model predictions, and provided the detection of the impact hotpots of these predictor variables. We found slope, vegetation cover, average precipitation, average temperature, and average relative humidity as important predictors of wildfire occurrence in the Great Xing’an Mountains. Thus, spatially differing relations improves the explanatory power of the global NB model, which does not explain sufficiently the relationship between wildfire occurrence and its drivers. Thus, the GWNBR model can complement the global NB model in overcoming the issue of nonstationary variables, thereby enabling a better prediction of the occurrence of wildfires in large geographical areas and improving management practices of wildfire.

KAJIAN EFEK SPASIAL KASUS DIFTERI DENGAN GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION (GWNBR)

Diphtheria is an infectious disease caused by the Corynebacterium diphtheriae bacteria. Indonesia is the country with the most cases of diphtheria in Southeast Asia and ranks third in the world. In 2016, cases of diphtheria increased by 65 percent and became Extraordinary Events (KLB) in Indonesia, even though during 2013 to 2015 the number of cases of diphtheria has decreased. The province that has the highest number of diphtheria cases in Indonesia in 2016 is East Java. Diphtheria is centered and spread in certain districts / cities in East Java Province so that there are indications of spatial effects in the spread of diphtheria. Because data on the number of diphtheria cases overdispersed and indicated spatial effects in its spread, the main method used in this study was Geographically Weighted Negative Binomial Regression (GWNBR). This method will be compared with other alternative methods namely Poisson regression method and Negative Binomial Regression to get the best modeling. Based on the AIC value of each model it can be concluded that the best method for modeling the number of diphtheria cases is GWNBR. The modeling results with GWNBR show that there is indeed a spatial influence on the number of diphtheria cases and risk factors in East Java Province in 2016. The percentage of DPT-HB3 / DPT-HB-Hib3 immunization coverage is not significant in all observation areas, while the percentage of drug and vaccine availability is significant at entire observation area.

MODELLING THE NUMBER OF NEW PULMONARY TUBERCULOSIS CASES WITH GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION METHOD

Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis. Untill now, TB is still one of the main problems in many countries, especially developing countries. Indonesia ranked second as the country with the highest TB cases in the world in 2015, where most cases were found in Java. This study was conducted to model the number of new pulmonary TB cases in Java by considering the spatial aspects using Geographically Weighted Negative Binomial Regression (GWNBR). GWNBR method was chosen because the data used in this study are overdispered. The result showed that the population density and percentage of healty homes were not significantly influential in each region. While the number of puskesmas, the percentage of smokers, the percentage of good PHBS, the percentage of diabetes mellitus, and the percentage of less IMT were significant in some regions. In general, the GWNBR model was better for modelling the number of new pulmonary TB cases than negative binomial regression and GWPR.

MODELLING THE NUMBER OF NEW PULMONARY TUBERCULOSIS CASES WITH GEOGRAPHICALLY WEIGHTED NEGATIVE BINOMIAL REGRESSION METHOD

Tuberculosis (TB) is an infectious disease caused by Mycobacterium Tuberculosis. Untill now, TB is still one of the main problems in many countries, especially developing countries. Indonesia ranked second as the country with the highest TB cases in the world in 2015, where most cases were found in Java. This study was conducted to model the number of new pulmonary TB cases in Java by considering the spatial aspects using Geographically Weighted Negative Binomial Regression (GWNBR). GWNBR method was chosen because the data used in this study are overdispered. The result showed that the population density and percentage of healty homes were not significantly influential in each region. While the number of puskesmas, the percentage of smokers, the percentage of good PHBS, the percentage of diabetes mellitus, and the percentage of less IMT were significant in some regions. In general, the GWNBR model was better for modelling the number of new pulmonary TB cases than negative binomial regression and GWPR.

Geographically weighted negative binomial regression applied to zonal level safety performance models

Generalized Linear Models (GLM) with negative binomial distribution for errors, have been widely used to estimate safety at the level of transportation planning. The limited ability of this technique to take spatial effects into account can be overcome through the use of local models from spatial regression techniques, such as Geographically Weighted Poisson Regression (GWPR). Although GWPR is a system that deals with spatial dependency and heterogeneity and has already been used in some road safety studies at the planning level, it fails to account for the possible overdispersion that can be found in the observations on road-traffic crashes. Two approaches were adopted for the Geographically Weighted Negative Binomial Regression (GWNBR) model to allow discrete data to be modeled in a non-stationary form and to take note of the overdispersion of the data: the first examines the constant overdispersion for all the traffic zones and the second includes the variable for each spatial unit. This research conducts a comparative analysis between non-spatial global crash prediction models and spatial local GWPR and GWNBR at the level of traffic zones in Fortaleza/Brazil. A geographic database of 126 traffic zones was compiled from the available data on exposure, network characteristics, socioeconomic factors and land use. The models were calibrated by using the frequency of injury crashes as a dependent variable and the results showed that GWPR and GWNBR achieved a better performance than GLM for the average residuals and likelihood as well as reducing the spatial autocorrelation of the residuals, and the GWNBR model was more able to capture the spatial heterogeneity of the crash frequency.

Geographically Weighted Negative Binomial Regression—incorporating overdispersion

Global regression assumes that a single model adequately describes all parts of a study region. However, the heterogeneity in the data may be sufficiently strong that relationships between variables can not be spatially constant. In addition, the factors involved are often sufficiently complex that it is difficult to identify them in the form of explanatory variables. As a result Geographically Weighted Regression (GWR) was introduced as a tool for the modeling of non-stationary spatial data. Using kernel functions, the GWR methodology allows the model parameters to vary spatially and produces non-parametric surfaces of their estimates. To model count data with overdispersion, it is more appropriate to use a negative binomial distribution instead of a Poisson distribution. Therefore, we propose the Geographically Weighted Negative Binomial Regression (GWNBR) method for the modeling of data with overdispersion. The results obtained using simulated and real data show the superiority of this method for the modeling of non-stationary count data with overdispersion compared with competing models, such as global regressions, e.g., Poisson and negative binomial and Geographically Weighted Poisson Regression (GWPR). Moreover, we illustrate that these competing models are special cases of the more robust model GWNBR.