Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

Ge, Liang; Zhao, Yan; Sheng, Zhongjie; Wang, Ning; Zhou, Kui; Mu, Xiangming; Guo, Liqiang; Wang, Teng; Yang, Zunhua; Huo, Xixiang

doi:10.3390/ijerph13111062

Cited by 23 publications

(26 citation statements)

References 33 publications

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“…Geographically weighted regression (GWR) offers a methodology in which to explore this variation, and this is the approach taken in the present research. GWR has been used to explore a wide range of phenomena, ranging from mosquitos (Ge et al, ; Lin & Wen, ) to obesity (Chalkias et al, ; Wen, Chen, & Tsai, ), participation in higher education (Harris, Singleton, Grose, Brunsdon, & Longley, ), and school attainment (Fotheringham, Charlton, & Brunsdon, ). There have only been a small number of studies where GWR has been used to understand crime.…”

Section: Introductionmentioning

confidence: 99%

Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK

Weir

2019

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Reducing domestic abuse has become a priority for both local and national governments in the UK, with its substantial human, social, and economic costs. It is an interdisciplinary issue, but to date there has been no research in the UK that has focused on neighborhood‐level predictors of domestic abuse and their variation across space. This article uses geographically weighted regression to model the predictors of police‐reported domestic abuse in Essex. Readily available structural and cultural variables were found to predict the domestic abuse rate and the repeat victimization rate at the lower super output area level and the model coefficients were all found to be non‐stationary, indicating varying relationships across space. This research not only has important implications for victims' well being, but also enables policy makers to gain a better understanding of the geography of victimization, allowing targeted policy interventions and efficiently allocated resources.

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Section: Introductionmentioning

confidence: 99%

Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK

Weir

2019

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“…By using ISA [9], GTWR modeling has bandwidth Optimum h = 3947.54. Table 2 shows the estimated estimation of the GTWR model parameters.…”

Section: Results Of Gtwr Modelmentioning

confidence: 99%

“…Where, h is the constant bandwidth performed by the Incremental Spatial Autocorrelation (ISA) method [9]. The Adaptive Gaussian kernel function is used in forming a weighted matrix.…”

Section: Methodsmentioning

confidence: 99%

Geographically and Temporally Weighted Regression Modeling in Analyzing Factors Affecting the Spread of Dengue Fever in Malang

Indrayani¹,

Pramoedyo

Iriany

2018

JELS

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Geographically and Temporally weighted regression (GTWR) modeling has been developed to evaluate spatial heterogeneity and temporal heterogeneity in factors influencing the spread of dengue fever in Malang city. By using the monthly data in 2012-2015 as the temporal unit of each urban village in Malang and village is considered as a spatial unit. GTWR model is compared with the GWR model using several statistical criteria. GTWR model shows that the relationship between dengue incidence with population density and monthly average temperature significantly affects each Village in Malang.

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“…In China, a Kulldorff spatial scan statistic has been used to try and identify the clustering of HFRS, drawing upon data spanning the period 1980 to 2009 [ 17 ]. A Gaussian GWR model has also been used to try and identify the factors influencing HFRS transmission (such as meteorological factors, rodent density, surface mean elevation, water area and human population density) drawing upon data from Hubei that was collected between 2011 and 2015 [ 18 ]. Moran’s I index was adopted for a global spatial autocorrelation analysis that sought to identify the overall spatiotemporal pattern of HFRS outbreaks in Hubei between 2005 and 2014, and Spearman's rank correlation analysis was used at the same time to explore the possible factors influencing the epidemics, such as the weather and the area’s geography [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

“…Research has indicated that HFRS has a characteristic seasonal or cyclic time series-based occurrence [ 42 , 11 ]. In our previous work, a Seasonal Difference—Geographically and Temporally Weighted Regression (SD-GTWR) model was developed as an extension of the GTWR model that sought to use seasonal difference to get stabilized data [ 43 ]. Seasonal difference was used to deal with a non-stationary time series with seasonal distribution characteristics.…”

Section: Introductionmentioning

confidence: 99%

A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS)

Zhao

Zhou

et al. 2018

PLoS ONE

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Hemorrhagic fever with renal syndrome (HFRS) is a naturally-occurring, fecally transmitted disease caused by a Hantavirus (HV). It is extremely damaging to human health and results in many deaths annually, especially in Hubei Province, China. One of the primary characteristics of HFRS is the spatiotemporal heterogeneity of its occurrence, with notable seasonal differences. In view of this heterogeneity, the present study suggests that there is a need to focus on trend simulation and the spatiotemporal prediction of HFRS outbreaks. To facilitate this, we constructed a new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model. The SD-STARIMA model is based on the spatial and temporal characteristics of the Space-Time Autoregressive Integrated Moving Average (STARMA) model first developed by Cliff and Ord in 1974, which has proven useful in modelling the temporal aspects of spatially located data. This model can simulate the trends in HFRS epidemics, taking into consideration both spatial and temporal variations. The SD-STARIMA model is also able to make seasonal difference calculations to eliminate temporally non-stationary problems that are present in the HFRS data. Experiments have demonstrated that the proposed SD-STARIMA model offers notably better prediction accuracy, especially for spatiotemporal series data with seasonal distribution characteristics.

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Construction of a Seasonal Difference-Geographically and Temporally Weighted Regression (SD-GTWR) Model and Comparative Analysis with GWR-Based Models for Hemorrhagic Fever with Renal Syndrome (HFRS) in Hubei Province (China)

Cited by 23 publications

References 33 publications

Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK

Using geographically weighted regression to explore neighborhood‐level predictors of domestic abuse in the UK

Geographically and Temporally Weighted Regression Modeling in Analyzing Factors Affecting the Spread of Dengue Fever in Malang

A new Seasonal Difference Space-Time Autoregressive Integrated Moving Average (SD-STARIMA) model and spatiotemporal trend prediction analysis for Hemorrhagic Fever with Renal Syndrome (HFRS)

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