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

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.

Download Full-text

Spatial Modeling for Homicide Rates Estimation in Pernambuco State-Brazil

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9120740 ◽

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.

Download Full-text

Spatial Variations in Fertility of South Korea: A Geographically Weighted Regression Approach

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi8060262 ◽

2019 ◽

Vol 8 (6) ◽

pp. 262 ◽

Cited By ~ 2

Author(s):

Myunggu Jung ◽

Woorim Ko ◽

Yeohee Choi ◽

Youngtae Cho

Keyword(s):

South Korea ◽

Geographically Weighted Regression ◽

Population Policy ◽

Local Population ◽

Sociodemographic Factors ◽

Ordinary Least Squares ◽

Spatial Variations ◽

Low Fertility ◽

Weighted Regression

South Korea has witnessed a remarkable decline in birth rates in the last few decades. Although there has been a large volume of literature exploring the determinants of low fertility in South Korea, studies on spatial variations in fertility are scarce. This study compares the Ordinary Least Squares (OLS) and Geographically Weighted Regression (GWR) models to investigate the potential role of the spatially heterogeneous response of the total fertility rate (TFR) to sociodemographic factors. The study finds that the relationships between sociodemographic factors and TFRs in South Korea vary across 252 sub-administrative areas in terms of both magnitude and direction. This study therefore demonstrates the value of using spatial analysis for providing evidence-based local-population policy options in pursuit of a fertility rebound in South Korea.

Download Full-text

Targeting the spatial context of obesity determinants via multiscale geographically weighted regression

10.31219/osf.io/u7j29 ◽

2020 ◽

Author(s):

Taylor M. Oshan ◽

Jordan Smith ◽

Alexander Stewart Fotheringham

Keyword(s):

Geographically Weighted Regression ◽

Degrees Of Freedom ◽

A Priori ◽

Ordinary Least Squares ◽

Spatial Context ◽

Weighted Regression ◽

Contributing Factors ◽

Study Results ◽

Phoenix Metropolitan Area ◽

Global And Local

Background: Obesity rates are recognized to be at epidemic levels throughout much of the world, posing significant threats to both the health and financial security of many nations. The causes of obesity can vary but are often complex and multifactorial, and while many contributing factors can be targeted for intervention, an understanding of where these interventions are needed is necessary in order to implement effective policy. This has prompted an interest in incorporating spatial context into the analysis and modeling of obesity determinants, especially through the use of geographically weighted regression (GWR). Method: This paper provides a critical review of previous GWR models of obesogenic processes and then presents a novel application of multiscale (M)GWR using the Phoenix metropolitan area as a case study. Results: Though the MGWR model consumes more degrees of freedom than OLS, it consumes far fewer degrees of freedom than GWR, ultimately resulting in a more nuanced analysis that can incorporate spatial context but does not force every relationship to become local a priori. In addition, MGWR yields a lower AIC and AICc value than GWR and is also less prone to issues of multicollinearity. Consequently, MGWR is able to improve our understanding of the factors that influence obesity rates by providing determinant-specific spatial contexts.Conclusion: The results show that a mix of global and local processes are able to best model obesity rates and that MGWR provides a richer yet more parsimonious quantitative representation of obesity rate determinants compared to both GWR and ordinary least squares.

Download Full-text

Modeling spatial determinates of teenage pregnancy in Ethiopia; geographically weighted regression

BMC Women s Health ◽

10.1186/s12905-021-01400-7 ◽

2021 ◽

Vol 21 (1) ◽

Author(s):

Seblewongel Tigabu ◽

Alemneh Mekuriaw Liyew ◽

Bisrat Misganaw Geremew

Keyword(s):

Spatial Distribution ◽

Geographically Weighted Regression ◽

Teenage Pregnancy ◽

Wealth Quintile ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Contraceptive Methods ◽

Hotspot Analysis ◽

Increased Risk ◽

Risk Areas

Abstract Background In developing countries, 20,000 under 18 children give birth every day. In Ethiopia, teenage pregnancy is high with Afar and Somalia regions having the largest share. Even though teenage pregnancy has bad maternal and child health consequences, to date there is limited evidence on its spatial distribution and driving factors. Therefore, this study is aimed to assess the spatial distribution and spatial determinates of teenage pregnancy in Ethiopia. Methods A secondary data analysis was conducted using 2016 EDHS data. A total weighted sample of 3381 teenagers was included. The spatial clustering of teenage pregnancy was priorly explored by using hotspot analysis and spatial scanning statistics to indicate geographical risk areas of teenage pregnancy. Besides spatial modeling was conducted by applying Ordinary least squares regression and geographically weighted regression to determine factors explaining the geographic variation of teenage pregnancy. Result Based on the findings of exploratory analysis the high-risk areas of teenage pregnancy were observed in the Somali, Afar, Oromia, and Hareri regions. Women with primary education, being in the household with a poorer wealth quintile using none of the contraceptive methods and using traditional contraceptive methods were significant spatial determinates of the spatial variation of teenage pregnancy in Ethiopia. Conclusion geographic areas where a high proportion of women didn’t use any type of contraceptive methods, use traditional contraceptive methods, and from households with poor wealth quintile had increased risk of teenage pregnancy. Whereas, those areas with a higher proportion of women with secondary education had a decreased risk of teenage pregnancy. The detailed maps of hotspots of teenage pregnancy and its predictors had supreme importance to policymakers for the design and implementation of adolescent targeted programs.

Download Full-text

Geographically weighted regression modelling of the spatial association between malaria cases and environmental factors in Cameroon.

10.21203/rs.2.13021/v3 ◽

2020 ◽

Author(s):

Marlvin Anemey Tewara ◽

Liu Yunxia ◽

Weiqiang Ling ◽

Binang Helen Barong ◽

Prisca Ngetemalah Mbah-Fongkimeh ◽

...

Keyword(s):

Environmental Factors ◽

Spatial Variability ◽

Geographically Weighted Regression ◽

Environmental Variables ◽

Vegetation Index ◽

Spatial Association ◽

Ordinary Least Squares ◽

Variable Coefficients ◽

Weighted Regression ◽

Gwr Model

Abstract Background: Studies have illustrated the association of malaria cases with environmental factors in Cameroon but limited in addressing how these factors vary in space for timely public health interventions. Thus, we want to find the spatial variability between malaria hotspot cases and environmental predictors using Geographically weighted regression (GWR) spatial modelling technique.Methods: The global Ordinary least squares (OLS) in the modelling spatial relationships tool in ArcGIS 10.3. was used to select candidate explanatory environmental variables for a properly specified GWR model. The local GWR model used the global OLS candidate variables to examine, predict and explore the spatial variability between environmental factors and malaria hotspot cases generated from Getis-Ord Gi* statistical analysis. Results: The OLS candidate environmental variable coefficients were statistically significant (adjusted R2 = 22.3% and p < 0.01) for a properly specified GWR model. The GWR model identified a strong spatial association between malaria cases and rainfall, vegetation index, population density, and drought episodes in most hotspot areas and a weak correlation with aridity and proximity to water with an overall model performance of 0.243 (adjusted R2= 24.3%).Conclusion: The generated GWR maps suggest that for policymakers to eliminate malaria in Cameroon, there should be the creation of malaria outreach programs and further investigations in areas where the environmental variables showed strong spatial associations with malaria hotspot cases.

Download Full-text

Modelling Housing Rents Using Spatial Autoregressive Geographically Weighted Regression: A Case Study in Cracow, Poland

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi9060346 ◽

2020 ◽

Vol 9 (6) ◽

pp. 346

Author(s):

Mateusz Tomal

Keyword(s):

Geographically Weighted Regression ◽

Rental Housing ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Spatial Econometric ◽

Econometric Methods ◽

Depth Analysis ◽

Sar Model ◽

Internet Platform ◽

Spatial Autoregressive

The proportion of tenants will undoubtedly rise in Poland, where at present, the ownership housing model is very dominant. As a result, the rental housing market in Poland is currently under-researched in comparison with owner-occupancy. In order to narrow this research gap, this study attempts to identify the determinants affecting rental prices in Cracow. The latter were obtained from the internet platform otodom.pl using the web scraping technique. To identify rent determinants, ordinary least squares (OLS) regression and spatial econometric methods were used. In particular, traditional spatial autoregressive model (SAR) and spatial autoregressive geographically weighted regression (GWR-SAR) were employed, which made it possible to take into account the spatial heterogeneity of the parameters of determinants and the spatially changing spatial autocorrelation of housing rents. In-depth analysis of rent determinants using the GWR-SAR model exposed the complexity of the rental market in Cracow. Estimates of the above model revealed that many local markets can be identified in Cracow, with different factors shaping housing rents. However, one can identify some determinants that are ubiquitous for almost the entire city. This concerns mainly the variables describing the area of the flat and the age of the building. Moreover, the Monte Carlo test indicated that the spatial autoregressive parameter also changes significantly over space.

Download Full-text

Valuing Environmental Amenities across Space: A Geographically Weighted Regression of Housing Preferences in Greenville County, SC

Land ◽

10.3390/land8100147 ◽

2019 ◽

Vol 8 (10) ◽

pp. 147

Author(s):

Hiebert ◽

Allen

Keyword(s):

Geographically Weighted Regression ◽

City Planning ◽

Spatial Dynamics ◽

Ordinary Least Squares ◽

Hedonic Model ◽

Weighted Regression ◽

Hedonic Price ◽

Environmental Amenities ◽

Forested Areas ◽

The Impact

As global consumption and development rates continue to grow, there will be persistent stress placed on public goods, namely environmental amenities. Urban sprawl and development places pressure on forested areas, as they are often displaced or degraded in the name of economic development. This is problematic because environmental amenities are valued by the public, but traditional market analysis typically obscures the value of these goods and services that are not explicitly traded in a market setting. This research examines the non-market value of environmental amenities in Greenville County, SC, by utilizing a hedonic price model of home sale data in 2011. We overlaid home sale data with 2011 National Land Cover Data to estimate the value of a forest view, proximity to a forest, and proximity to agriculture on the value of homes. We then ran two regression models, an ordinary least squares (OLS) and a geographically weighted regression to compare the impact of space on the hedonic model variables. Results show that citizens in Greenville County are willing to pay for environmental amenities, particularly views of a forest and proximity to forested and agricultural areas. However, the impact and directionality of these variables differ greatly across space. These findings suggest the need for an integration of spatial dynamics into environmental valuation estimates to inform conservation policy and intentional city planning.

Download Full-text

Exploring the Dynamics of Urban Greenness Space and Their Driving Factors Using Geographically Weighted Regression: A Case Study in Wuhan Metropolis, China

Land ◽

10.3390/land9120500 ◽

2020 ◽

Vol 9 (12) ◽

pp. 500

Author(s):

Chengjie Yang ◽

Ruren Li ◽

Zongyao Sha

Keyword(s):

Urban Planning ◽

Geographically Weighted Regression ◽

Driving Forces ◽

Vital Role ◽

Google Earth ◽

Weighted Regression ◽

Landscape Indices ◽

Aggregation Index ◽

Terrain Elevation

Urban greenness plays a vital role in supporting the ecosystem services of a city. Exploring the dynamics of urban greenness space and their driving forces can provide valuable information for making solid urban planning policies. This study aims to investigate the dynamics of urban greenness space patterns through landscape indices and to apply geographically weighted regression (GWR) to map the spatially varied impact on the indices from economic and environmental factors. Two typical landscape indices, i.e., percentage of landscape (PLAND) and aggregation index (AI), which measure the abundance and fragmentation of urban greenness coverage, respectively, were taken to map the changes in urban greenness. As a case study, the metropolis of Wuhan, China was selected, where time-series of urban greenness space were extracted at an annual step from the Landsat collections from Google Earth Engine during 2000–2018. The study shows that the urban greenness space not only decreased significantly, but also tended to be more fragmented over the years. Road network density, normalized difference built-up index (NDBI), terrain elevation and slope, and precipitation were found to significantly correlate to the landscape indices. GWR modeling successfully captures the spatially varied impact from the considered factors and the results from GWR modeling provide a critical reference for making location-specific urban planning.

Download Full-text

Spatial Heterogeneity, Broadband, and New Firm Formation

Quality Innovation Prosperity ◽

10.12776/qip.v20i3.791 ◽

2017 ◽

Vol 21 (1) ◽

pp. 165

Author(s):

Jitendra Parajuli ◽

Kingsley Haynes

Keyword(s):

Spatial Heterogeneity ◽

Least Squares ◽

Geographically Weighted Regression ◽

Ordinary Least Squares ◽

Weighted Regression ◽

Least Squares Regression ◽

Firm Formation ◽

Broadband Internet ◽

New Firm Formation ◽

New Firm

Purpose: This paper examines the spatial heterogeneity associated with broadband Internet and new firm formation in a number of U.S. states.Methodology/Approach: Both ordinary least-squares regression and Geographically Weighted Regression are used for the estimation purpose.Findings: The global coefficient estimates of ordinary least-squares regression account for the marginal change in a phenomenon, but such a global measure cannot reveal the locally-varying dynamics. Using Geographically Weighted Regression, it was found that at the aggregate and economic sector levels, the association between single-unit firm births and the provision of broadband Internet varies across counties in Florida and Ohio.Originality/Value of paper: There are numerous studies on broadband Internet in the U.S., but this is the first that explicitly examines broadband provision and new firm formation by taking into account spatial heterogeneity across countries.

Download Full-text

A Semi-Parametric Geographically Weighted Regression Approach to Exploring Driving Factors of Fractional Vegetation Cover: A Case Study of Guangdong

Sustainability ◽

10.3390/su12187512 ◽

2020 ◽

Vol 12 (18) ◽

pp. 7512

Author(s):

Yuhao Jin ◽

Han Zhang ◽

Yuchao Yan ◽

Peitong Cong

Keyword(s):

Geographically Weighted Regression ◽

Vegetation Cover ◽

Southern China ◽

Ordinary Least Squares ◽

Driving Factors ◽

Weighted Regression ◽

Impact Factors ◽

Negative Effects ◽

Vegetation Growth ◽

Fractional Vegetation Cover

Ecological degradation caused by rapid urbanisation has presented great challenges in southern China. Fractional vegetation cover (FVC) has long been the most common and sensitive index to describe vegetation growth and to monitor vegetation degradation. However, most of the studies have failed to adequately explore the complexity of the relationship between fractional vegetation cover (FVC) and impact factors. In this research, we first constructed a Semi-parametric Geographically Weighted Regression (SGWR) model to analyse both the stationary and nonstationary spatial relationships between FVC and driving factors in Guangdong province in southern China on a county level. Then, climate, topographic, land cover, and socio-economic factors were introduced into the model to distinguish impacts on FVC from 2000–2015. Results suggest that the positive and negative effects of rainfall and elevation coefficients alternated, and local urban land and population estimates indicated a negative association between FVC and the modelled factors in each period. The SGWR FVC make significantly improves performance of the geographically weighted regression and ordinary least squares models, with adjusted R2 higher than 0.78. The findings of this research demonstrated that, although urbanisation in the Pearl River Delta in Guangdong has encroached on the regional vegetation cover, the total vegetation area remained unchanged with the implementation of protection policies and regulations.

Download Full-text