Spatially varying patterns of afforestation/reforestation and socio-economic factors in China: a geographically weighted regression approach

2017 ◽  
Vol 153 ◽  
pp. 362-371 ◽  
Author(s):  
Jichuan Sheng ◽  
Xiao Han ◽  
Hui Zhou
Keyword(s):  
Geographically Weighted Regression ◽  
Weighted Regression ◽  
Economic Factors ◽  
Regression Approach ◽  
Spatially Varying ◽  
Socio Economic Factors

scholarly journals Analysis of Built-Up Areas of Small Polish Cities with the Use of Deep Learning and Geographically Weighted Regression

Geosciences ◽  
2021 ◽  
Vol 11 (5) ◽  
pp. 223
Author(s):  
Maciej Adamiak ◽  
Iwona Jażdżewska ◽  
Marta Nalej
Keyword(s):  
Economic Development ◽  
Rural Areas ◽  
Geographically Weighted Regression ◽  
Hot Spot ◽  
Weighted Regression ◽  
Economic Factors ◽  
Small Cities ◽  
Large Cities ◽  
The Impact ◽  
Socio Economic Factors

Small cities are an important part of the settlement system, a link between rural areas and large cities. Although they perform important functions, research focuses on large cities and metropolises while marginalizing small cities, the study of which is of great importance to progress in social sciences, geography, and urban planning. The main goal of this paper was to verify the impact of selected socio-economic factors on the share of built-up areas in 665 small Polish cities in 2019. Data from the Database of Topographic Objects (BDOT), Sentinel-2 satellite imagery from 2015 and 2019, and Local Data Bank by Statistics Poland form 2019 were used in the research. A machine learning segmentation procedure was used to obtain the data on the occurrence of built-up areas. Hot Spot (Getis-Ord Gi*) analysis and geographically weighted regression (GWR) was applied to explain spatially varying impact of factors related to population, spatial and economic development, and living standards on the share of built-up areas in the area of small cities. Significant association was found between the population density and the share of built-up areas in the area of the cities studied. The influence of the other socio-economic factors examined, related to the spatial and economic development of the cities and the quality of life of the inhabitants, showed great regional variation. The results also indicated that the share of built-up areas in the area of the cities under study is a result of the conditions under which they were established and developed throughout their existence, and not only of the socio-economic factors affecting them at present.

scholarly journals Effect of Hierarchical Parish System on Portuguese Housing Rents

2021 ◽  
Vol 13 (2) ◽  
pp. 455
Author(s):  
Sofia Vale ◽  
Felipa de Mello-Sampayo
Keyword(s):  
Geographically Weighted Regression ◽  
Rental Housing ◽  
Distance Effect ◽  
Weighted Regression ◽  
Economic Factors ◽  
Space Pattern ◽  
Using Data ◽  
Parish System ◽  
Rental Housing Market ◽  
Socio Economic Factors

This manuscript analyzes an inter-parish housing rents gradient with respect to surrounding parishes. Using data on housing rents for 4049 Portuguese parishes in 278 municipalities, the paper explores the spatial patterns of housing rents using the geographically weighted regression (GWR) methodology. The housing rents can be explained by socio-economic factors comprising the effects of unemployment, sustainability, social diversity, elderly dependency, and population density. The proportion of overcrowded dwellings reflecting how poor living conditions affect housing rents was also included in the spatial analysis. On the structural side, characteristics of the dwellings were also included such as the area of the home and the number of other homes available in the parishes. Locational factors reflect households’ valuation for access to other parishes. In order to capture location characteristics, besides considering mobility within municipalities, the GWR allowed using distances to nearby parishes, i.e., parish hierarchy distance effect. The results suggest that the Portuguese rental housing market exhibits a heterogeneous pattern across the territory, displaying spatial variability and a hierarchical space pattern as a consequence of its locational attributes.

scholarly journals A Spatiotemporal Analysis of the Effects of Urbanization’s Socio-Economic Factors on Landscape Patterns Considering Operational Scales

2020 ◽  
Vol 12 (6) ◽  
pp. 2543 ◽  
Author(s):  
Pengyu Liu ◽  
Chao Wu ◽  
Miaomiao Chen ◽  
Xinyue Ye ◽  
Yunfei Peng ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Scientific Evidence ◽  
Landscape Management ◽  
Spatiotemporal Analysis ◽  
Landscape Patterns ◽  
Weighted Regression ◽  
Land Resource ◽  
Economic Factors ◽  
Significant Information ◽  
Socio Economic Factors

Landscape patterns are significantly affected during the urbanization process. Identifying the spatiotemporal impacts of urbanization’s socio-economic factors on landscape patterns is very important and can provide scientific evidence to support urban ecological management and guide managers to establish appropriate sustainability policies. This article applies multiscale geographically weighted regression (MGWR) to reveal the relationships between landscape patterns and the socio-economic factors of urbanization in Shenzhen, China, from 2000 to 2015, in five-year intervals. MGWR is a powerful extension of geographically weighted regression (GWR) that can not only reveal spatial heterogeneity patterns but also measure the operational scale of covariates. The empirical results indicate that MGWR is superior to GWR. Furthermore, the changes in operational scale represented by the spatial bandwidth of MGWR in different years reflect temporal changes in the spatial relationships of given factors, which is significant information for urban studies. These multiscale relationships between landscape patterns and the socio-economic factors of urbanization, revealed via MGWR, are useful for strategic planning around urban dynamic development and land resource and ecological landscape management. The results can provide additional insight into landscape and urbanization studies from a multiscale perspective, which is important for local, regional, and global urban planning.

Number of flood disaster estimation in Indonesia using local linear and geographically weighted regression approach

2021 ◽  
Author(s):  
M. Fariz Fadillah Mardianto ◽  
Sediono ◽  
Novia Anggita Aprilianti ◽  
Belindha Ayu Ardhani ◽  
Rizka Firdaus Rahmadina ◽  
...  
Keyword(s):  
Geographically Weighted Regression ◽  
Flood Disaster ◽  
Weighted Regression ◽  
Local Linear ◽  
Regression Approach
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