17 research outputs found

### Dynamics in the Fitness-Income plane: Brazilian states vs World countries

In this paper we introduce a novel algorithm, called Exogenous Fitness, to calculate the Fitness of subnational entities and we apply it to the states of Brazil. In the last decade, several indices were introduced to measure the competitiveness of countries by looking at the complexity of their export basket. Tacchella et al (2012) developed a non-monetary metric called Fitness. In this paper, after an overview about Brazil as a whole and the comparison with the other BRIC countries, we introduce a new methodology based on the Fitness algorithm, called Exogenous Fitness. Combining the results with the Gross Domestic Product per capita (GDPp), we look at the dynamics of the Brazilian states in the Fitness-Income plane. Two regimes are distinguishable: one with high predictability and the other with low predictability, showing a deep analogy with the heterogeneous dynamics of the World countries. Furthermore, we compare the ranking of the Brazilian states according to the Exogenous Fitness with the ranking obtained through two other techniques, namely Endogenous Fitness and Economic Complexity Index

### Dynamics of Racial Residential Segregation and Gentrification in New York City

Racial residential segregation is interconnected with several other phenomena such as income inequalities, property values inequalities, and racial disparities in health and education. Furthermore, recent literature suggests the phenomenon of gentrification as a cause of perpetuation or increase of racial residential segregation in some American cities. In this paper, we analyze the dynamics of racial residential segregation for white, black, Asian, and Hispanic citizens in New York City in 1990, 2000, and 2010. It was possible to observe that segregation between white and Hispanic citizens and between white and Asian ones has grown, while segregation between white and black is relatively stable. Furthermore, we analyzed the per capita income and the Gini coefficient in each segregated zone, showing that the highest inequalities occur in the zones where there is an overlap of high-density zones of pair of races. Focusing on the changing of the density of population across the city during these 20 years, and by analyzing white and black people's segregation, our analysis reveals that a positive flux of white (black) people is associated with a substantial increase (decrease) of the property values, as compared with the city mean. Furthermore, by clustering the region with the higher density of black citizens, we measured the variation of area and displacement of the four most significant clusters from 1990 to 2010. The large displacements ( & AP; 1.6 k m ) observed for two of these clusters, namely, one in the neighborhood of Harlem and the other inside the borough of Brooklyn, led to the emergence of typically gentrified regions

### Correction: Dynamics in the Fitness-Income plane: Brazilian states vs World countries

[This corrects the article DOI: 10.1371/journal.pone.0197616.]

### ECI map of the Brazilian states.

<p>The colors in the map vary from green (high ECI) to red (low ECI) and they show the variation of the ECI across the Brazilian states.</p

### Evolution of Brazilian states in the ECI-Income plane.

<p><i>a</i>) The figure shows the dynamics (from 2002 to 2015) of the Brazilian states in the ECI-Income plane, where the GDP<sub><i>p</i></sub> is in logarithmic scale. Only the state of SÃ£o Paulo and the Distrito Federal appear to be clearly distinguishable from the rest of the states. All the others states are indeed concentrated in a small region of the graph. <i>b</i>) The figure shows the coefficient calculated considering the time interval 2003-2013. Colors vary from green (where the versors tend to be parallel), to red (where the versors tend to be unevenly directed). From the figure we can therefore verify that there is a low predictability of the evolution of all the states. <i>c</i>) Here we show a grid where for each cell we calculate the versor of the sum vector. From the figure we see that there is no privileged direction, indeed the vectors are unevenly directed.</p

### The binary matrix <i>M</i><sub><i>sp</i></sub> of the year 2015.

<p>Each row of the matrix represents a Brazilian state. States are ordered in terms of their Fitness from the smallest value (row 0) to the largest one (row 26). Analogously columns represent Products ordered in terms of their Complexity from the smallest value (column 0) to the largest one (column 1172). The matrix elements <i>M</i><sub><i>sp</i></sub> are drawn in dark green and the others in white. In the figure we highlight high Fitness states such as SÃ£o Paulo and ParanÃ¡, a middle rank State such as CearÃ¡ and a low Fitness state such as Roraima.</p

### Products spectroscopy of the years 2005 (dotted lines) and 2015 (filled colors) of the countries: a) Brazil, b) Russia, c) China, and d) India.

<p>The figures show the export volume (in US Dollars) of those states for each product with <i>M</i><sub><i>cp</i></sub> = 1 ordered in terms of their Complexity. The products have been grouped (10 for bin) and the export volumes of each product inside each bin have been summed.</p

### Dynamics of Brazilian states in the Fitness-Income plane.

<p><i>a</i>) The figure shows the evolution (from 2000 to 2015) of the Brazilian states in the Fitness-Income plane in logarithmic scale. The dotted black line in the figure shows the expected level of GDP<sub><i>p</i></sub> given the level of Fitness and it is the result of the minimization of the Euclidean distance of the states from the line, weighted by the states GDP. <i>b</i>) The figure shows the coefficient calculated considering a time window from 2003 to 2013. The color varies from green (where the versors of evolution tend to be parallel), to red (where the versors tend to be unevenly directed). <i>c</i>) The figure shows a grid where for each cell we calculate the versor of the sum vector. From the figure two regions appear: the first one where the versors tend to be parallel in the direction of a high GDP<sub><i>p</i></sub> (shown in green); and the second one where the versors tend to be unevenly directed (shown in red). Fig 8<i>b</i> and <i>c</i> together show that there is a region (green) of high predictability of motion in direction of a high GDP<sub><i>p</i></sub>; and a region (red) of low predictability of motion. <i>d</i>) The figure shows the dynamics (from 2000 to 2015) of the Brazilian states in the Fitness-Income plane highlighting in green the states in the high predictability region and in red the states in the low predictability one.</p