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.

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 GEOGRAPHICALLY WEIGHTED REGRESSION AND MULTIPLE LINEAR REGRESSION FOR TOPSOIL TEXTURE PREDICTION

2021 ◽  
Vol 9 (2) ◽  
pp. 64-71
Author(s):  
Henny Pramoedyo ◽  
Sativandi Riza ◽  
Afiati Oktaviarina ◽  
Deby Ardianti
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Soil Texture ◽  
Geographically Weighted Regression ◽  
Clay Fraction ◽  
Size Fraction ◽  
Weighted Regression ◽  
Land Resource ◽  
Linear Regression Models ◽  
Data Set

Land resource management requires extensive land mapping. Conventional soil mapping takes a long time and is expensive; therefore, geographic information system data as a predictor in soil texture modeling can be used as an alternative solution to shorten time and reduce costs. Through digital elevation model data, topographic variability can be obtained as an independent variable in predicting soil texture. Geographically weighted regression is used to observe the effects of spatial heterogeneity. This study uses a data set of 50 observation points, each of which had soil particle-size fraction attributes and eight local morphological variables. The covariates used in this study are eastness aspects, northness aspects, slope, unsphericity curvature, vertical curvature, horizontal curvature, accumulation curvature, and elevation. Prediction using geographically weighted regression shows more results compared to multiple linear regression models. The spatial location can affect product Y, with the R2 value of 0.81 in the sand fraction, 0.57 in the silt fraction, and 0.33 in the clay fraction.

scholarly journals What determines the permanent emigration of Poles? The analysis of the spatial diversification of causes in three economic age groups

2017 ◽  
Vol 67 (2) ◽  
pp. 149-172 ◽  
Author(s):  
Karolina Lewandowska-Gwarda ◽  
Elżbieta Antczak
Keyword(s):  
Geographically Weighted Regression ◽  
Statistical Data ◽  
Age Groups ◽  
Time Span ◽  
Regression Coefficients ◽  
Economic Factors ◽  
Working Age ◽  
Spatial Diversification ◽  
Geographical Space ◽  
Socio Economic Factors

Our paper seeks answers to the following questions: What are the determinants of permanent emigration from Poland and how do they vary for specific economic age groups (pre-working, working, and post-working age)? Do the causes of permanent emigration differ over space in these categories, and if so, how? We applied GIS and ESDA instruments, including geographically weighted regression, which allowed us to identify the variability of regression coefficients in the geographical space. Our research indicated socio-economic factors (among others: poviats budget income, feminisation rate, unemployment rate), which, with varying force and in varying directions, affected the studied variable in specific parts of the country. The analyses were performed on the basis of statistical data on the numbers of de-registrations for residence abroad in Poland’s NUTS-4 in three economic age groups (pre-working, working, and post-working age) for the time span from 2005 to 2013.

scholarly journals Municipal waste in Poland: analysis of the spatial dimensions of determinants using geographically weighted regression

2019 ◽  
Vol 26 (2) ◽  
pp. 177-197 ◽  
Author(s):  
Elżbieta Antczak
Keyword(s):  
Geographically Weighted Regression ◽  
Local Level ◽  
Municipal Waste ◽  
Weighted Regression ◽  
Waste Generation ◽  
Population Average ◽  
Spatial Dimensions ◽  
Working Age ◽  
Regional Specificity ◽  
Socio Economic Factors

This article provides a quantification of the territorially varied relation between socio-economic factors and the amount of municipal waste in Polish districts. For this purpose, eight causes were identified: revenue budgets, the number and area of uncontrolled dumping sites, population density, the share of working-age population, average gross monthly wages, registrations for permanent residence, and the number of tourists accommodated. The preliminary data analysis indicated that to understand waste generation in Poland at the local level it is necessary to consider regional specificity and spatial interactions. To increase the explained variability of phenomena, and emphasise local differences in the amount of waste, geographically weighted regression was applied.

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