Measuring concentration: Lorenz curves and their decompositions

Alonso‐Villar, Olga

doi:10.1007/s00168-010-0369-5

Cited by 12 publications

(5 citation statements)

References 28 publications

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“…The locational Gini meets the four properties of concentration measures which are described by Alonso‐Villar (2011) and mentioned in the previous section. However, the anonymity property, which characterizes this index, could be viewed as a drawback of the measure (Sohn, 2004).…”

Section: Methodsmentioning

confidence: 60%

“…Popular measures of geographical concentration of economic activities derive their properties from the literature on income inequality and segregation (Alonso-Villar, 2011;Alonso-Villar & del Río, 2013;Hutchens, 1991Hutchens, , 2004Massey & Denton, 1988). Concentration measures that have been derived from the income inequality measures, such as the Gini index and generalized entropy measures, satisfy basic requirements corresponding to symmetry in location, movement between locations, scale invariance, and insensitivity to proportional subdivisions of locations (Alonso-Villar, 2011).…”

Section: Spatial Interactions and The Geographical Concentration Of E...mentioning

confidence: 99%

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New evidence on measuring the geographical concentration of economic activities

Panzera

Cartone

Postiglione

2022

Papers in Regional Science

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Spatial interactions among regional units may influence the geographical distribution of economic activities. Many traditional measures of geographical concentration fail in capturing this aspect, being insensitive to permutations of the spatial position of regions. This paper proposes an approach to the measurement of geographical concentration of economic activities that accounts for spatial interactions among regions. The locational Gini is split into spatial and nonspatial components, so that a new interpretation of the index is presented. The measure is applied to evaluate the geographical concentration of different economic sectors for 1,323 NUTS 3 regions in the European Union over the period 2001-2018.

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Section: Methodsmentioning

confidence: 60%

Section: Spatial Interactions and The Geographical Concentration Of E...mentioning

confidence: 99%

New evidence on measuring the geographical concentration of economic activities

Panzera

Cartone

Postiglione

2022

Papers in Regional Science

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“…Based on the concentration zones, we calculated the percentage of the country’s total area that is occupied by each concentration zone of infected population. The results are presented in the form of a Lorenz curve [ 46 ]. It describes the uneven distribution of infections in the study area.…”

Section: Methodsmentioning

confidence: 99%

Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany

Mościcka

Araszkiewicz

Wabínski

et al. 2021

JCM

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Among numerous publications about the SARS-CoV-2, many articles present research from the geographic point of view. The cartographic research method used in this area of science can be successfully applied to analyze the spatiotemporal characteristics of the pandemic using limited data and can be useful for a quick and preliminary assessment of the spread of infections. In this paper, research on the spatial differentiation of the structure and homogeneity of the system in which SARS-CoV-2 occurs, as well as spatial concentration of people infected was undertaken. The phenomena were investigated in a period of two infection waves in Germany: in spring and autumn 2020. We applied the potential model, entropy, centrographic method, and Lorenz curve in spatial analysis. The potentials model made it possible to distinguish core regions with a high level of the growth of new infections, along with areas of their impact, and regions with a low level of generation of new infections. The entropy showed the spatial distribution of differentiation of the studied system and the change of these characteristics between spring and autumn. The concentration method allowed for spatial and numerical demonstration of the concentration of infected population in a given area. We wanted to show that it is possible to draw meaningful conclusions about the pandemic characteristics using only basic data about infections, along with proper cartographic methods. The results can be used to designate the zones of the greatest threats, and thus, the areas where the most intense actions should be taken.

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“…The Lorenz curve (Figure 3) is often used in economics and land use research to describe the inequality or concentration of studied phenomenon [98,99]. The mathematical notations and interpretations of the Lorenz curve are widely available in the literature [96,100,101]. In our analysis, the x-axis records the cumulative proportion of the research unit areas, while the y-axis records the cumulative proportion of the infrastructure facility number or length ranked by the infrastructure density indicator.…”

Section: Analysis Of Spatial Distributionmentioning

confidence: 99%

Spatial Analysis of Asymmetry in the Development of Tourism Infrastructure in the Borderlands: The Case of the Bystrzyckie and Orlickie Mountains

Jędruch¹,

Furmankiewicz²,

Kaczmarek³

2020

IJGI

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This paper discusses the issue of analyzing the development of cross-border tourism infrastructure in the borderlands of countries with diversified administrative divisions and spatial databases, which hinders the use of national statistical units for comparative research. As an example, the ability to use the square grid and kernel density estimation methods for the analysis and spatial visualization of the level of tourism infrastructure development is studied for the Orlickie and Bystrzyckie Mountains, located in the Polish–Czech border area. To synthetically assess and compare the level of diversity, the methodology used in the Human Development Index was adapted using selected component indicators calculated for a square grid clipped to the boundaries of the area under study. This analysis enabled us to quantify the asymmetry in the development of tourism infrastructure in the borderlands via the calculation of the synthetic infrastructure development index. This index is 1.29 times higher in the Czech than in the Polish border area. However, the spatial concentration analysis of infrastructure shows that the diversity in the study area can be assessed as higher than the results using the average density indicators. This paper also discusses the benefits and problems associated with using the square grid method for the representation and analysis of heterogeneous data on tourism infrastructure in two neighboring national states.

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Measuring concentration: Lorenz curves and their decompositions

Abstract: R12, D63,

Cited by 12 publications

References 28 publications

New evidence on measuring the geographical concentration of economic activities

New evidence on measuring the geographical concentration of economic activities

Modeling of Various Spatial Patterns of SARS-CoV-2: The Case of Germany

Spatial Analysis of Asymmetry in the Development of Tourism Infrastructure in the Borderlands: The Case of the Bystrzyckie and Orlickie Mountains

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