Geographically Weighted Regression on the Environmental-Ecological Factors of Human Longevity

Choi, Don Jeong; Suh, Yong Cheol

doi:10.7319/kogsis.2012.20.3.057

Cited by 7 publications

(2 citation statements)

References 3 publications

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“…In the model we built for this study, the extent of forest LULC change per unit area was used as the measurement (dependent) variable. Latent variables including topographic (elevation (m) and slope ( • )), accessibility (m) (to roads, buildings and the CCZ) and socioeconomic (population distribution, tourist attractions and mean cadastral value (W)) factors were selected on the assumption that they could influence LULC change [12,38,39]. Elevation and slope information are constructed using digital topographic maps and accessibility is the distance (Unit: m) from the forest change areas of roads, buildings and CCZ.…”

Section: Forest Lulc Change Spatial Analysis Using Spatial Statisticsmentioning

confidence: 99%

Spatial Pattern and Factor Analyses for Forest Sustainable Development Goals within South Korea’s Civilian Control Zone

Park

Lee

2018

Sustainability

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The United Nations’ Sustainable Development Goals (SDGs) offer specific guidelines for improving sustainable forest management, especially Goal 15. Goal 15 protects, restores and promotes the sustainable use of land ecosystems, manages forests sustainably, prevents was against desertification, stops and reverses land degradation and prevents biodiversity loss. The Civilian Control Zone (CCZ) south of the Demilitarized Zone (DMZ) separating North and South Korea has functioned as a unique biological preserve due to traditional restrictions on human use but is now increasingly threatened by deforestation and development. We used hot spot analysis and structural equation modeling (SEM) to analyze spatial patterns of forest land use and land cover (LULC) change and variables influencing these changes, within the CCZ. Remote sensing imagery was used to develop land cover classification maps (2010 and 2016) and a GIS database was established for three change factors (topography, accessibility and socioeconomic characteristics). As a result of Hotspot analysis, Hotspots of change were distributed mainly due to agricultural activities and the development of forest and expansion of villages. Subsequent factor analysis revealed that accessibility had greater influence (−0.635) than the other factors. Among the direct factors, change to bare land had the greatest impact (−0.574) on forest change. These results shed light on forest change patterns and causes in the CCZ and provide practical data for efficient forest management in this area with regards to the SDGs.

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Section: Forest Lulc Change Spatial Analysis Using Spatial Statisticsmentioning

confidence: 99%

Spatial Pattern and Factor Analyses for Forest Sustainable Development Goals within South Korea’s Civilian Control Zone

Park

Lee

2018

Sustainability

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“…The topographic and geographic factors affecting forest biodiversity were often spatially inconsistent with similar distributions in adjacent areas but different distributions in distant areas. (18,19) In addition, because the vegetation exhibits various communities in a continuous pattern, causing spatial autocorrelation, the spatial location of attribute information should be considered to analyze the quantitative spatial pattern. (20,21) The geographically weighted regression (GWR) model has been used in forestry sectors, such as for forest resource evaluation (e.g., timber volume estimation), because it allows the estimation of the regression model even in cases where the requirements for spatial correlation, normality, and homoscedasticity are not met.…”

Section: Introductionmentioning

confidence: 99%

Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model

Jeongmook¹,

Choi²,

Lee³

2019

Sensors and Materials

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The objective of this study is to evaluate the spatial distribution patterns of species diversity at different spatial scales, focusing on the Baekdudaegan Protected Area, which is a biodiversity hotspot in the Republic of Korea. The tree species diversity index (Shannon-Weaver index; H′) was calculated using tree species data from a 1:5k forest-type map, and the spatial analysis was performed with a 1 × 1 km 2 grid. Ten factors were selected to estimate the impact of topographic (elevation, slope, northern slope, curvature, wetness, and relief) and geographic (distances from water, road, forest road, and urban area) factors on H′ using the ordinary least squares (OLS) and geographically weighted regression (GWR) models. H′ increased with the spatial scale. Also, the coefficient of determination (R²) of the GWR and OLS models increased proportionally and the R² of the GWR model was higher than that of the OLS model. Corrected Akaike information criterion (AICc) was lower in the GWR model than in the OLS model, which indicates that the GWR model fits the calculated H′ better than the OLS model. Thus, the GWR model is considered to be more practical than the OLS model for understanding the effects of topographic and geographic factors on H′ at different scales.

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Local Analysis of the spatial characteristics of urban flooding areas using GWR

Sim¹,

Kim²,

Lee³

2014

Journal of Environmental Impact Assessment

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In recent years, the frequency and scale of the natural disasters are growing rapidly due to the global climate change. In case of the urban flooding, high-density of population and infrastructure has caused the more intensive damages. In this study, we analyzed the spatial characteristics of urban flooding damage factors using GWR(Geographically Weighted Regression) for effective disaster prevention and then, classified the causes of the flood damage by spatial characteristics.The damage factors applied consists of natural variables such as the poor drainage area, the distance from the river, elevation and slope, and anthropogenic variables such as the impervious surface area, urbanized area, and infrastructure area, which are selected by literature review. This study carried out the comparative analysis between OLS(Ordinary Least Square) and GWR model for identifying spatial non-stationarity and spatial autocorrelation, and in the results, GWR model has higher explanation power than OLS model. As a result, it appears that there are some differences between each of the flood damage areas depending on the variables. We conclude that the establishment of disaster prevention plan for urban flooding area should reflect the spatial characteristics of the damaged areas. This study provides an improved understandings of the causes of urban flood damages, which can be diverse according to their own spatial characteristics.

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Geographically Weighted Regression on the Environmental-Ecological Factors of Human Longevity

Cited by 7 publications

References 3 publications

Spatial Pattern and Factor Analyses for Forest Sustainable Development Goals within South Korea’s Civilian Control Zone

Spatial Pattern and Factor Analyses for Forest Sustainable Development Goals within South Korea’s Civilian Control Zone

Spatial Distribution Characteristics of Species Diversity Using Geographically Weighted Regression Model

Local Analysis of the spatial characteristics of urban flooding areas using GWR

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