scholarly journals A Multiscale Flow-Focused Geographically Weighted Regression Modelling Approach and Its Application for Transport Flows on Expressways

2019 ◽  
Vol 9 (21) ◽  
pp. 4673 ◽  
Author(s):  
Lianfa Zhang ◽  
Jianquan Cheng ◽  
Cheng Jin ◽  
Hong Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Spatial Interaction ◽  
Weighted Regression ◽  
Flow Data ◽  
Economic Determinants ◽  
Mobility Data ◽  
Spatial Dependencies ◽  
Interaction Modeling ◽  
Local Moran ◽  
Global And Local

Scale is a fundamental geographical concept and its role in different geographical contexts has been widely documented. The increasing availability of transport mobility data, in the form of big datasets, enables further incorporation of spatial dependencies and non-stationarity into spatial interaction modeling of transport flows. In this paper a newly developed multiscale flow-focused geographically weighted regression (MFGWR) approach has been applied, in addition to global and local Moran I indices of flow data, to model multiscale socio-economic determinants of regional transport flows between counties across the Jiangsu Province in China. The results have confirmed the power of local Moran I of flow data for identifying urban agglomerations and the effectiveness of MFGWR in exploring multiscale processes of spatial interactions. A comparison between MFGWR and flow-focused geographically weighted regression (FGWR) showed that the MFGWR approach can better interpret the heterogeneous processes of spatial interaction.

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

2019 ◽  
Vol 8 (5) ◽  
pp. 220 ◽  
Author(s):  
Lianfa Zhang ◽  
Jianquan Cheng ◽  
Cheng Jin
Keyword(s):  
Negative Binomial ◽  
Spatial Interaction ◽  
Negative Binomial Regression ◽  
Least Square ◽  
Flow Data ◽  
Data Infrastructure ◽  
Modeling Methods ◽  
Mobility Data ◽  
Interaction Modeling ◽  
Spatial Interaction Modeling

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.

scholarly journals Spatiotemporal Differences in Determinants of City Shrinkage Based on Semiparametric Geographically Weighted Regression

2019 ◽  
Vol 11 (24) ◽  
pp. 6891 ◽  
Author(s):  
Wangchongyu Peng ◽  
Weijun Gao ◽  
Xin Yuan ◽  
Rui Wang ◽  
Jinming Jiang
Keyword(s):  
Urban Planning ◽  
Geographically Weighted Regression ◽  
Spatial Dependence ◽  
Ordinary Least Squares ◽  
Low Fertility ◽  
Weighted Regression ◽  
Ageing Population ◽  
Foreign Population ◽  
Global Impacts ◽  
Global And Local

City shrinkage, as an ongoing worldwide phenomenon, is an issue for urban planning and regional development. City shrinkage is remarkable in Japan, with over 85% of municipalities experiencing population loss from 2005 to 2015. As Japan’s society ages and with its low fertility rate, city shrinkage has had a tremendous negative effect on economic development and urban planning. Understanding the spatial dependence and spatial heterogeneity of city shrinkage and its determinants is essential for ensuring the sustainable development of a city or region. In this study, a semiparametric geographically weighted regression (SGWR) model was adopted to explore the spatiotemporal differences in determinants of city shrinkage. The results reveal that the SGWR model incorporating the global and local variables is more interpretive compared to ordinary least squares and geographically weighted regression models in exploring the correlates of city shrinkage. We found the spatial dependence and heterogeneity of shrinking cities resulted from demographic, economy, and social factors, and revealed low fertility, the ageing population, and enterprise change ratio influenced city shrinkage in different regions at different times in Japan, whereas foreign population ratio, industry structure, and social welfare had global impacts. The findings provide useful information for understanding city shrinkage at global and local scales.

scholarly journals Geographically varying relationships of COVID-19 mortality with different factors in India

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

AbstractCOVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance ($$R^{2}=0.97$$ R 2 = 0.97 ) with smaller Akaike Information Criterion (AICc $$=-66.42$$ = - 66.42 ) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s $$I=-0.0395$$ I = - 0.0395 and $$p < 0.01$$ p < 0.01 ) in the residuals. It is found that more than 86% of local $$R^{2}$$ R 2 values are larger than 0.60 and almost 68% of $$R^{2}$$ R 2 values are within the range 0.80–0.97. Moreover, some interesting local variations in the relationships are also found.

scholarly journals Spatial interaction of groundwater and surface topographic using geographically weighted regression in built-up area

2020 ◽  
Author(s):  
Asep Mulyadi ◽  
Moh. Dede ◽  
Millary Agung Widiawaty
Keyword(s):  
Regression Model ◽  
Geographically Weighted Regression ◽  
Flat Surface ◽  
Spatial Interaction ◽  
Weighted Regression ◽  
P Value ◽  
Groundwater Levels ◽  
Geographically Weighted Regression Model ◽  
Primary Water ◽  
Global Regression

Groundwater is a primary water resource for human living. In Indonesia, excessive exploitation of groundwater generally occurs in the built-up area due to over-discharge processes characterized by a cone of depression. This research revealed the spatial interaction between groundwater levels and surface topographic using geographically weighted regression in built-up area. Groundwater levels data are obtained from 72 wells in Cikembang, Bandung Regency, whereas surface topographic based on BIG's DEMNas data which has 8 meters spatial resolution. This study showed significant spatial interaction between groundwater levels and surface topographic in the built-up area. The interaction has a clustered pattern with p-value less than 0.01. It indicated in the area with flat surface topographic has lower groundwater levels than others. There are several points who indicated the cone of depression in the built-up area with flat topographic. The geographically weighted regression model has high spatial variability and better results than the global regression model to assess groundwater level interaction with surface topographic.

scholarly journals Geographically varying relationships of COVID-19 mortality with different factors in India

2020 ◽  
Author(s):  
Asif Iqbal Middya ◽  
Sarbani Roy
Keyword(s):  
Geographically Weighted Regression ◽  
Information Criterion ◽  
Driving Factors ◽  
Least Square ◽  
Weighted Regression ◽  
Related Factors ◽  
Local Method ◽  
Ordinary Least Square ◽  
Local Methods ◽  
Global And Local

Abstract COVID-19 is a global crisis where India is going to be one of the most heavily affected countries. The variability in the distribution of COVID-19-related health outcomes might be related to many underlying variables, including demographic, socioeconomic, or environmental pollution related factors. The global and local models can be utilized to explore such relations. In this study, ordinary least square (global) and geographically weighted regression (local) methods are employed to explore the geographical relationships between COVID-19 deaths and different driving factors. It is also investigated whether geographical heterogeneity exists in the relationships. More specifically, in this paper, the geographical pattern of COVID-19 deaths and its relationships with different potential driving factors in India are investigated and analysed. Here, better knowledge and insights into geographical targeting of intervention against the COVID-19 pandemic can be generated by investigating the heterogeneity of spatial relationships. The results show that the local method (geographically weighted regression) generates better performance (R2 = 0:973) with smaller Akaike Information Criterion (AICc = -77:93) as compared to the global method (ordinary least square). The GWR method also comes up with lower spatial autocorrelation (Moran’s I = -0.0436 and p < 0:01) in the residuals. It is found that more than 87.5% of local R2 values are larger than 0.60 and almost 60% of R2 values are within the range 0:80 - 0:97. Moreover, some interesting local variations in the relationships are also found.

Local and global determinants of office rents in Istanbul

2019 ◽  
Vol 12 (2) ◽  
pp. 227-249
Author(s):  
Anil Kumar Bera ◽  
Sinem Guler Kangalli Uyar
Keyword(s):  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Local Effects ◽  
Data Set ◽  
Content Type ◽  
Hedonic Valuation ◽  
Operational Characteristics ◽  
Regression Approach ◽  
Office Rent ◽  
Global And Local

Purpose This paper presents a hedonic office rent model under the decentralized structure of Istanbul Office Market. The data set in the study includes 2,348 office spaces for the first quarter of 2018. This study aims to find determinants that affect the level of rent and examine whether the effects of office rent determinants are global or not. Design/methodology/approach To consider both global and local effects, the paper uses mixed geographically weighted regression approach in hedonic office rent analysis. Findings The empirical results indicate that office rent determinants such as physical, locational, neighborhood and market operational characteristics have significant impacts on the level of the rent. The findings also show that one of the office rent determinants has a global effect and the other determinants have local effects. According to the estimation results, local effects and statistical significances of these determinants vary from lower quartiles to upper quartiles. Originality/value To the best of the authors’ knowledge, this is the first paper to consider global and local effects of office rent determinants on the level of rent, with mixed geographically weighted regression approach. The paper provides new insights into the hedonic valuation of commercial real estates, especially for decentralized office markets.

scholarly journals Spatial Modeling for Homicide Rates Estimation in Pernambuco State-Brazil

2020 ◽  
Vol 9 (12) ◽  
pp. 740
Author(s):  
Carlos Silva ◽  
Silas Melo ◽  
Alex Santos ◽  
Pedro Almeida Junior ◽  
Simone Sato ◽  
...  
Keyword(s):  
Information System ◽  
Geographically Weighted Regression ◽  
Spatial Modeling ◽  
Spatial Dependence ◽  
Ordinary Least Squares ◽  
The State ◽  
Weighted Regression ◽  
Homicide Rates ◽  
Gis Software ◽  
Global And Local

Homicide rates have been increasing worldwide, especially in Latin America, where it is considered one of the most lethal of the continents. Despite that, the occurrence of homicides are not homogeneous in time and space on the continent or in the Brazilian cities. Therefore, the main objective of this study is to present a spatial analysis of homicides in the state of Pernambuco, Brazil, between the years of 2016 and 2019, by the use of an exploratory analysis of spatial homicide data with five variables that could explain its occurrence. In addition to that, it was applied the Global and Local Moran’s Index, Ordinary Least Squares (OLS) regression, and Geographically Weighted Regression (GWR), all implemented in the Geographic Information System (GIS) software. Thus, the distribution of clusters revealed a spatial autocorrelation for homicide rates, confirming a spatial dependence. This data also showed the polarization of the rate between the coast and the interior of the state of Pernambuco.

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