Geographically Weighted Regression in the Analysis of Unemployment in Poland

Lewandowska-Gwarda, Karolina

doi:10.3390/ijgi7010017

Cited by 38 publications

(19 citation statements)

References 53 publications

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“…We also produced a map of the local R 2 values from the GWR results in Figure 6. The higher the local R 2 , the better the local model performs [38,39]. High local R 2 values mostly appear in the central areas, while the GWR model shows poorer fits in north and south, particularly in Huangpi and Hannan districts.…”

Section: Gwr Resultsmentioning

confidence: 99%

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

Yang

et al. 2019

Sustainability

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Land carrying capacity is an important factor for urban sustainable development. It provides essential insights into land resource allocation and management. In this article, we propose a framework to evaluate land carrying capacity with multiple data sources from the first geographical census and socioeconomic statistics. In particular, an index, Land Resource Pressure (LRP), is proposed to evaluate the land carrying capacity, and a case study was carried out in Wuhan. The LRP of Wuhan was calculated on 250 m * 250 m grids, and showed a circularly declining pattern from central to outer areas. We collected its influencing factors in terms of nature resources, economy, transportation and urban construction, and then analyzed its causes via geographically weighted (GW) models. Firstly, pair-wise correlations between LRP and each influencing factor were explored via the GW correlation coefficients. These local estimates provide an important precursor for the following quantitative analysis via the GW regression (GWR) technique. The GWR coefficient estimates interpret the influences on LRP in a localized view. Results show that per capita gross domestic product (PerGDP) showed a higher absolute estimate among all factors, which proves that PerGDP has a relieving effect on LRP, especially in the southwestern areas. Overall, this study provides a technical framework to evaluate land carrying capacity with multi-source data sets and explore its localized influences via GW models, which could provide practical guidance for similar studies in other cities.

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Section: Gwr Resultsmentioning

confidence: 99%

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

Yang

et al. 2019

Sustainability

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“…Lewandowska-Gwarda (2018) used GWR in the analysis of local unemployment in Poland in 2015. Based on this research, it was noticed that the determinants of unemployment were diverse in the geographical space, as a result of political, economic and cultural differences among individual parts of the country.…”

Section: Methodsmentioning

confidence: 99%

Female unemployment and its determinants in Poland in 2016 from the spatial perspective

Lewandowska-Gwarda

2018

Self Cite

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Research background: Through the cultural progress and socio-economic development in Poland women have obtained the same rights as men in the labour market. Nevertheless, they continuously face discrimination and the difficulty, resulting from their traditional role, in finding or maintaining employment. Purpose of the article: The main objective of this study was an analysis of female unemployment and its determinants in Poland in 2016 from the spatial perspective. The following research questions were also specified: Is female unemployment dependent on social factors (do they play the key role)? Are the factors determining the level of female unemployment spatially diversified? Is the GWR model an effective tool in analysis of female unemployment? Methods: The research applied GIS and spatial analysis methods including Geographically Weighted Regression, which enables the identification of the variability of regression coefficients in the geographical space. The analysis was carried out based on statistical data presenting the share of unemployed women in the working age population for 380 Polish districts (NUTS 4, LAU 1) in 2016. Findings & Value added:The research results showed that in the period 2003-2016 the female unemployment was getting lower, but it was still higher than men. It was also spatially diversified. Moreover, the determinants of female unemployment were diverse in the Oeconomia Copernicana, 9(2), 183-204 184 geographic space and did not have a significant impact on the variable in all spatial units. The existence of clusters of districts, characterised by similar interactions and its strength, was also confirmed. The results of this analysis proved that non-economic (social) factors largely affected the level of female unemployment in Poland in 2016. Using GWR enabled drawing detailed conclusions concerning the determinants of female unemployment in Poland, it proved to be an effective tool for the analysis of this phenomenon.

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“…Residual (4.13) by the OLS model was greater than the 1.45 obtained by using the GWR model. Moran's I (0.015, p < 0.001) indicated that GWR's residuals were randomly distributed [68][69][70][71] (Figure 5). Therefore, GWR was superior to OLS.…”

Section: Parameter Estimation Results By the Ols Model And Gwr Modelmentioning

confidence: 99%

Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China

Zhi-heng

Fang

2020

Land

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More and more studies on land transfer prices have been carried out over time. However, the influencing factors of the industrial land transfer price from the perspective of spatial attributes have rarely been explored. Selecting 25 towns as the basic research unit, based on industrial land transfer data, this paper analyzes the influencing factors of the price distribution of industrial land in Dingzhou City, a rural land system reform pilot in China, by using a geographically weighted regression (GWR) model. Eight evaluation factors were selected from five aspects: economy, population, topography, landform, and resource endowment. The results showed that: (1) Compared with the traditional ordinary least squares (OLS) model, the GWR model revealed the spatial differentiation characteristics of the industrial land transfer price in depth. (2) Factors that have a negative correlation with the industrial land transfer price include the proportion of cultivated land area and distance to the city. Factors that have a positive correlation with the industrial land transfer price include the population growth rate, economic growth rate, population density, and number of hospitals per unit area. (3) The results of GWR model analysis showed that the impact of different factors on the various towns of different models had significant spatial differentiation characteristics. This paper will provide a reference for the sustainable use of industrial land in developing countries.

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Geographically Weighted Regression in the Analysis of Unemployment in Poland

Cited by 38 publications

References 53 publications

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

Exploring the Spatial Pattern and Influencing Factors of Land Carrying Capacity in Wuhan

Female unemployment and its determinants in Poland in 2016 from the spatial perspective

Driving Factors of the Industrial Land Transfer Price Based on a Geographically Weighted Regression Model: Evidence from a Rural Land System Reform Pilot in China

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