Ponències/Comunicacions de congressoshttp://hdl.handle.net/2117/811362021-06-18T18:46:55Z2021-06-18T18:46:55ZA compositional analysis approach assessing the spatial distribution of trees in Guadalajara, MexicoCruz Sandoval, Marco AntonioOrtego Martínez, María IsabelRoca Bosch, Elisabethttp://hdl.handle.net/2117/1843502020-07-23T21:50:18Z2020-04-22T12:48:24ZA compositional analysis approach assessing the spatial distribution of trees in Guadalajara, Mexico
Cruz Sandoval, Marco Antonio; Ortego Martínez, María Isabel; Roca Bosch, Elisabet
Urban green infrastructure such as parks, gardens and trees, provide several ecosystem services and benefits. Particularly trees provide a broad amount of services in urban areas, such as improving air quality, mitigating carbon pollution and heat-island effect, attenuating storm-water floods, reducing noise and serving as habitat for different species among others. Likewise, urban trees provide different social (i.e., social cohesion), economic (i.e., increase in property value), psychological (i.e., stress reduction) and medical (i.e., increase in longevity of life) benefits (Landry, 2009; Roy et al., 2012; Battisti et al., 2019). Although it is well documented that trees are essential for the well-being and health of urban areas and their inhabitants, trees are not evenly distributed in urban areas. Previous studies have found that urban residents with a deprived socioeconomic status are associated with low coverage of urban trees in their communities (Hernández and Villaseñor, 2017; Park and Kwan, 2017; Wang and Qiu, 2018). Therefore, environmental justice seeks to ensure that green infrastructure and its benefits are distributed equally throughout the territory (Anguelovski, 2013; Gould and Lewis, 2017). The objective of this study is to determine whether the distribution of urban trees in the city of Guadalajara, Mexico is distributed equally or not among its colonies and urban districts. The information is obtained from the first and only tree census conducted in the city on June 2018 and treated with geographic information systems (GIS). The attributes of the tree dataset include their location (urban blocks, streets, parks and gardens), heights and diameters of their canopy (Government of Guadalajara, 2019). For the analysis and due to the compositional nature of the data, compositional analysis techniques are applied (see Aitchison, 1986; Pawlowsky-Glahn, et al., 2015; Filzmoser et al., 2018). With this novel approach, we contribute to the existing literature. Additionally, Principal Component Analysis (PCA) and cluster analysis are performed to identify the distribution of trees in the city. Likewise, to observe the relationship between trees and socio-economic variables, a multivariable linear regression is carried out respecting the compositional nature of the data. The results from PCA and cluster analysis show a clear differentiation in the distribution of trees between the East-West of the city, mainly in the compositions with respect to their height and diameter. Likewise, from the multivariate linear regression, considerable significance (p<0.05) is found in socio-economic variables
2020-04-22T12:48:24ZCruz Sandoval, Marco AntonioOrtego Martínez, María IsabelRoca Bosch, ElisabetUrban green infrastructure such as parks, gardens and trees, provide several ecosystem services and benefits. Particularly trees provide a broad amount of services in urban areas, such as improving air quality, mitigating carbon pollution and heat-island effect, attenuating storm-water floods, reducing noise and serving as habitat for different species among others. Likewise, urban trees provide different social (i.e., social cohesion), economic (i.e., increase in property value), psychological (i.e., stress reduction) and medical (i.e., increase in longevity of life) benefits (Landry, 2009; Roy et al., 2012; Battisti et al., 2019). Although it is well documented that trees are essential for the well-being and health of urban areas and their inhabitants, trees are not evenly distributed in urban areas. Previous studies have found that urban residents with a deprived socioeconomic status are associated with low coverage of urban trees in their communities (Hernández and Villaseñor, 2017; Park and Kwan, 2017; Wang and Qiu, 2018). Therefore, environmental justice seeks to ensure that green infrastructure and its benefits are distributed equally throughout the territory (Anguelovski, 2013; Gould and Lewis, 2017). The objective of this study is to determine whether the distribution of urban trees in the city of Guadalajara, Mexico is distributed equally or not among its colonies and urban districts. The information is obtained from the first and only tree census conducted in the city on June 2018 and treated with geographic information systems (GIS). The attributes of the tree dataset include their location (urban blocks, streets, parks and gardens), heights and diameters of their canopy (Government of Guadalajara, 2019). For the analysis and due to the compositional nature of the data, compositional analysis techniques are applied (see Aitchison, 1986; Pawlowsky-Glahn, et al., 2015; Filzmoser et al., 2018). With this novel approach, we contribute to the existing literature. Additionally, Principal Component Analysis (PCA) and cluster analysis are performed to identify the distribution of trees in the city. Likewise, to observe the relationship between trees and socio-economic variables, a multivariable linear regression is carried out respecting the compositional nature of the data. The results from PCA and cluster analysis show a clear differentiation in the distribution of trees between the East-West of the city, mainly in the compositions with respect to their height and diameter. Likewise, from the multivariate linear regression, considerable significance (p<0.05) is found in socio-economic variablesCompositional analysis approach in the measurement of social-spatial segregation trends. A case study of Guadalajara, Jalisco, MexicoCruz Sandoval, Marco AntonioOrtego Martínez, María IsabelRoca Bosch, Elisabethttp://hdl.handle.net/2117/1733772020-07-23T21:53:07Z2019-12-02T11:27:38ZCompositional analysis approach in the measurement of social-spatial segregation trends. A case study of Guadalajara, Jalisco, Mexico
Cruz Sandoval, Marco Antonio; Ortego Martínez, María Isabel; Roca Bosch, Elisabet
Different authors have highlighted the internal existing social differences in cities as a consequence of different economic, social and political forces. The mercantile logic that affects urban spaces incentives the dichotomy winners-losers in the current urban landscape and leads to the differentiation and unequal distribution of certain social groups within the urban space. This clear differentiation in distribution of social groups in the urban space has been called socio-spatial segregation. This concept arises from the urban sociology, the first studies were focused on the differentiation of ethnicity and income level to identify the most vulnerable groups and of mitigate their current situation through different policies.
A more significant number of variables belonging to different dimensions (social, economic, political and environmental) have been incorporated into the study of this phenomenon, traditionally addressed by different disciplines such as sociology, geography and anthropology. Nonetheless, few studies have addressed it from a multivariate analysis approach. Moreover, the few existing studies with a multivariate statistical analysis ignored or did not know the compositional nature of their data.
The objective of the present study is to apply the compositional data analysis in urban studies to better understand socio-spatial segregation in the different urban contexts. Specifically, the analysis of social-spatial segregation considering the compositional nature of the data in the city of Guadalajara, Mexico, is carried out. Socio-economic and socio-educative variables from census data of approximately 13,520 urban blocks grouped in 395 colonias and seven urban districts are used to carry out this study through the most straightforward compositions of two parts. Additionally, Principal Component Analysis (PCA) and cluster analysis are performed to identify the socio-economic distribution within the territory. The analysis is complemented with the use of geographic information systems (GIS) at different urban scales.
Based on Aitchison log ratio approach, the results are consistent with the segregation processes that date back to the foundation of the city. Through cluster analysis and principal component analysis, an evident polarization between the Minerva district and the rest of the areas is shown. Moreover, this method allows to analyse complex and multidimensional phenomena such as socio-spatial segregation.
2019-12-02T11:27:38ZCruz Sandoval, Marco AntonioOrtego Martínez, María IsabelRoca Bosch, ElisabetDifferent authors have highlighted the internal existing social differences in cities as a consequence of different economic, social and political forces. The mercantile logic that affects urban spaces incentives the dichotomy winners-losers in the current urban landscape and leads to the differentiation and unequal distribution of certain social groups within the urban space. This clear differentiation in distribution of social groups in the urban space has been called socio-spatial segregation. This concept arises from the urban sociology, the first studies were focused on the differentiation of ethnicity and income level to identify the most vulnerable groups and of mitigate their current situation through different policies.
A more significant number of variables belonging to different dimensions (social, economic, political and environmental) have been incorporated into the study of this phenomenon, traditionally addressed by different disciplines such as sociology, geography and anthropology. Nonetheless, few studies have addressed it from a multivariate analysis approach. Moreover, the few existing studies with a multivariate statistical analysis ignored or did not know the compositional nature of their data.
The objective of the present study is to apply the compositional data analysis in urban studies to better understand socio-spatial segregation in the different urban contexts. Specifically, the analysis of social-spatial segregation considering the compositional nature of the data in the city of Guadalajara, Mexico, is carried out. Socio-economic and socio-educative variables from census data of approximately 13,520 urban blocks grouped in 395 colonias and seven urban districts are used to carry out this study through the most straightforward compositions of two parts. Additionally, Principal Component Analysis (PCA) and cluster analysis are performed to identify the socio-economic distribution within the territory. The analysis is complemented with the use of geographic information systems (GIS) at different urban scales.
Based on Aitchison log ratio approach, the results are consistent with the segregation processes that date back to the foundation of the city. Through cluster analysis and principal component analysis, an evident polarization between the Minerva district and the rest of the areas is shown. Moreover, this method allows to analyse complex and multidimensional phenomena such as socio-spatial segregation.The expression of air quality in urban areas: going further on a Compositional Data Analysis approachGibergans Bàguena, JoséHervada Sala, CarmeJarauta Bragulat, Eusebiohttp://hdl.handle.net/2117/1714382020-07-23T20:36:26Z2019-11-04T10:50:37ZThe expression of air quality in urban areas: going further on a Compositional Data Analysis approach
Gibergans Bàguena, José; Hervada Sala, Carme; Jarauta Bragulat, Eusebio
The quality of atmospheric air in large cities is a matter of great importance because of its impact on the environment and on the health of the population. Recently, measures restricting access of private vehicles to the centre of large cities and other measures to prevent atmospheric air pollution are currently topical (Hervada-Sala et al., 2018). The knowledge of air quality acquires special relevance to be able to evaluate the impact of those great social and economic measures. There are many indices to express air quality. In fact, quite every country has its own, depending on the main pollutants, they have as Plaia and Ruggeri (2011) pointed out. In general, all indices ignore the compositional nature of the concentrations of air pollutants and do not apply methods of Compositional Data Analysis; those indices also have some other weak points such as leak of standardized scale. A first approach applying Compositional Data Analysis methods has been developed in Jarauta-Bragulat et al., 2016. In the present work, we try to go some step further to improve the understanding and manageability of air quality. The air quality index proposed here takes into account the compositional nature of the data, it has an adequate correlation between input (concentrations) and output (air quality index), it distinguishes between air pollution and air quality and it has a 0-100 reference scale which makes easier interpretation and management of air quality expression. To illustrate the proposed method, an application is made to a series of air pollution data (Barcelona, 2001-2015)
2019-11-04T10:50:37ZGibergans Bàguena, JoséHervada Sala, CarmeJarauta Bragulat, EusebioThe quality of atmospheric air in large cities is a matter of great importance because of its impact on the environment and on the health of the population. Recently, measures restricting access of private vehicles to the centre of large cities and other measures to prevent atmospheric air pollution are currently topical (Hervada-Sala et al., 2018). The knowledge of air quality acquires special relevance to be able to evaluate the impact of those great social and economic measures. There are many indices to express air quality. In fact, quite every country has its own, depending on the main pollutants, they have as Plaia and Ruggeri (2011) pointed out. In general, all indices ignore the compositional nature of the concentrations of air pollutants and do not apply methods of Compositional Data Analysis; those indices also have some other weak points such as leak of standardized scale. A first approach applying Compositional Data Analysis methods has been developed in Jarauta-Bragulat et al., 2016. In the present work, we try to go some step further to improve the understanding and manageability of air quality. The air quality index proposed here takes into account the compositional nature of the data, it has an adequate correlation between input (concentrations) and output (air quality index), it distinguishes between air pollution and air quality and it has a 0-100 reference scale which makes easier interpretation and management of air quality expression. To illustrate the proposed method, an application is made to a series of air pollution data (Barcelona, 2001-2015)Psychological well-being: analysis and interpretation applying Compositional Data Analysis methodsCortés Rodriguez, MaríaSánchez, MercedesGalindo Villardón, PurificaciónJarauta Bragulat, Eusebiohttp://hdl.handle.net/2117/1709602020-07-23T21:34:20Z2019-10-28T11:41:20ZPsychological well-being: analysis and interpretation applying Compositional Data Analysis methods
Cortés Rodriguez, María; Sánchez, Mercedes; Galindo Villardón, Purificación; Jarauta Bragulat, Eusebio
2019-10-28T11:41:20ZCortés Rodriguez, MaríaSánchez, MercedesGalindo Villardón, PurificaciónJarauta Bragulat, EusebioRethinking the Kolmogorov-Smirnov test of Goodness of fit in a compositional wayMonti, Gianna S.Mateu Figueras, GloriaOrtego Martínez, María IsabelPawlowsky Glahn, VeraEgozcue Rubí, Juan Joséhttp://hdl.handle.net/2117/1696472020-07-23T21:32:15Z2019-10-10T10:57:13ZRethinking the Kolmogorov-Smirnov test of Goodness of fit in a compositional way
Monti, Gianna S.; Mateu Figueras, Gloria; Ortego Martínez, María Isabel; Pawlowsky Glahn, Vera; Egozcue Rubí, Juan José
The Kolmogorov Smirnov test (KS) is a well known test used to asses how a set of observations is significantly different from the probability model specified under the null hypothesis. The KS test statistic quantifies the distance between the empirical distribution function and the hypothetical one. The modification introduced in Monti et al. (2017) consists of computing the mentioned distances as Aitchison distances. In this contribution, we suggest a further modification of the latter test and investigate, by simulation, the asymptotic distribution of the proposed test statistic, checking the appropriateness of a Generalized Extreme Value (GEV) Distribution. The properties of the asymptotic distribution are studied via Monte Carlo simulations.
2019-10-10T10:57:13ZMonti, Gianna S.Mateu Figueras, GloriaOrtego Martínez, María IsabelPawlowsky Glahn, VeraEgozcue Rubí, Juan JoséThe Kolmogorov Smirnov test (KS) is a well known test used to asses how a set of observations is significantly different from the probability model specified under the null hypothesis. The KS test statistic quantifies the distance between the empirical distribution function and the hypothetical one. The modification introduced in Monti et al. (2017) consists of computing the mentioned distances as Aitchison distances. In this contribution, we suggest a further modification of the latter test and investigate, by simulation, the asymptotic distribution of the proposed test statistic, checking the appropriateness of a Generalized Extreme Value (GEV) Distribution. The properties of the asymptotic distribution are studied via Monte Carlo simulations.Modified Kolmogorov-Smirnov test of goodness of fitMonti, Gianna S.Mateu Figueras, GloriaOrtego Martínez, María IsabelPawlowsky Glahn, VeraEgozcue Rubí, Juan Joséhttp://hdl.handle.net/2117/1054222020-07-23T20:29:42Z2017-06-14T14:06:43ZModified Kolmogorov-Smirnov test of goodness of fit
Monti, Gianna S.; Mateu Figueras, Gloria; Ortego Martínez, María Isabel; Pawlowsky Glahn, Vera; Egozcue Rubí, Juan José
A modified version of the Kolmogorov-Smirnov (KS) test is presented as a tool to assess whether a specified, although arbitrary, probability model is unsuitable to describe the underlying distribution of a set of observations. The KS test computes distances between points of the sample cumulative distribution function and the hypothetical one as absolute differences between them, and then considering the supreme distance as test statistics. The modification here proposed consists of computing the mentioned distances as Aitchison distances of the probabilities as two part compositions. In this contribution, we investigate by simulation the asymptotic distribution of the proposed test statistic, checking the appropriateness of the Gumbel distribution. The properties of the asymptotic distribution are studied for samples coming from generic distributions such as uniform, normal, lognormal, gamma, beta and exponential with different values of the parameters. A brief Monte Carlo investigation is made of the type I error and power of the test.
2017-06-14T14:06:43ZMonti, Gianna S.Mateu Figueras, GloriaOrtego Martínez, María IsabelPawlowsky Glahn, VeraEgozcue Rubí, Juan JoséA modified version of the Kolmogorov-Smirnov (KS) test is presented as a tool to assess whether a specified, although arbitrary, probability model is unsuitable to describe the underlying distribution of a set of observations. The KS test computes distances between points of the sample cumulative distribution function and the hypothetical one as absolute differences between them, and then considering the supreme distance as test statistics. The modification here proposed consists of computing the mentioned distances as Aitchison distances of the probabilities as two part compositions. In this contribution, we investigate by simulation the asymptotic distribution of the proposed test statistic, checking the appropriateness of the Gumbel distribution. The properties of the asymptotic distribution are studied for samples coming from generic distributions such as uniform, normal, lognormal, gamma, beta and exponential with different values of the parameters. A brief Monte Carlo investigation is made of the type I error and power of the test.Changes in the occurrence of extreme wave storms in the Catalan coast: a Bayesian approachCorral López, JesúsOrtego Martínez, María Isabelhttp://hdl.handle.net/2117/1044712020-07-23T20:30:35Z2017-05-16T08:04:15ZChanges in the occurrence of extreme wave storms in the Catalan coast: a Bayesian approach
Corral López, Jesús; Ortego Martínez, María Isabel
2017-05-16T08:04:15ZCorral López, JesúsOrtego Martínez, María IsabelDevelopment of air quality indexes using compositional approachJarauta Bragulat, EusebioEgozcue, Juan JoséHervada Sala, CarmeGibergans Bàguena, Joséhttp://hdl.handle.net/2117/890902020-07-23T21:05:20Z2016-07-22T13:06:11ZDevelopment of air quality indexes using compositional approach
Jarauta Bragulat, Eusebio; Egozcue, Juan José; Hervada Sala, Carme; Gibergans Bàguena, José
A compositional approach to air quality index (AQI) based on a log-contrast of pollutants over non-polluted air fraction was recently developed. This approach let to three open questions about consistency and formalization of such index. First question is to find out the conditions under which the approximation of the log-contrast by the logarithm of the geometric mean of pollutants concentrations, are reliable. Second, is to examine a way to define a reasonable scale for express air quality, taking into account the effects of air pollution on population health and on social or economic activity. The third one is to find out the properties of this kind of index with respect to the addition of a new set of air pollutants in the composition. To deal with these open questions, a decision making tree should be designed in order to select suitable weights on the air pollutants. A preliminary application study of air pollutants composition distribution in some cities is presented. In addition, the approach of the log-contrast by the logarithm of the weighted geometric mean is described for these cities.
2016-07-22T13:06:11ZJarauta Bragulat, EusebioEgozcue, Juan JoséHervada Sala, CarmeGibergans Bàguena, JoséA compositional approach to air quality index (AQI) based on a log-contrast of pollutants over non-polluted air fraction was recently developed. This approach let to three open questions about consistency and formalization of such index. First question is to find out the conditions under which the approximation of the log-contrast by the logarithm of the geometric mean of pollutants concentrations, are reliable. Second, is to examine a way to define a reasonable scale for express air quality, taking into account the effects of air pollution on population health and on social or economic activity. The third one is to find out the properties of this kind of index with respect to the addition of a new set of air pollutants in the composition. To deal with these open questions, a decision making tree should be designed in order to select suitable weights on the air pollutants. A preliminary application study of air pollutants composition distribution in some cities is presented. In addition, the approach of the log-contrast by the logarithm of the weighted geometric mean is described for these cities.Wind model for offshore power simulationHervada Sala, CarmeJarauta Bragulat, EusebioGibergans Bàguena, JoséBuenestado Caballero, Pablohttp://hdl.handle.net/2117/890892020-07-23T21:04:38Z2016-07-22T12:59:09ZWind model for offshore power simulation
Hervada Sala, Carme; Jarauta Bragulat, Eusebio; Gibergans Bàguena, José; Buenestado Caballero, Pablo
Offshore wind energy is an alternative energy source of increased interest. A large offshore wind farms have been planned or under construction, mainly in northern Europe. One of the points needed to be able to implement offshore projects is to characterize and model the wind for marine generation. Models are needed for the design of the most appropriate control strategies. Some attempts have been done to do so; recently these models are implemented under a wind turbine block set in Matlab/Simulink. A handicap has been detected: adjustment of wind turbulence using a white noise function. In this paper, a modification of that model is presented, based on real data and a wind model block in Matlab/Simulink has been performed to implement a new noise function with better results than the standard white noise function.
2016-07-22T12:59:09ZHervada Sala, CarmeJarauta Bragulat, EusebioGibergans Bàguena, JoséBuenestado Caballero, PabloOffshore wind energy is an alternative energy source of increased interest. A large offshore wind farms have been planned or under construction, mainly in northern Europe. One of the points needed to be able to implement offshore projects is to characterize and model the wind for marine generation. Models are needed for the design of the most appropriate control strategies. Some attempts have been done to do so; recently these models are implemented under a wind turbine block set in Matlab/Simulink. A handicap has been detected: adjustment of wind turbulence using a white noise function. In this paper, a modification of that model is presented, based on real data and a wind model block in Matlab/Simulink has been performed to implement a new noise function with better results than the standard white noise function.Bayesian estimation of the orthogonal decomposition of a contingency tableOrtego Martínez, María IsabelEgozcue Rubí, Juan Joséhttp://hdl.handle.net/2117/819492020-07-23T20:35:32Z2016-01-25T12:11:29ZBayesian estimation of the orthogonal decomposition of a contingency table
Ortego Martínez, María Isabel; Egozcue Rubí, Juan José
Contingency tables can be parametrized by probabilities of each cell in a multinomial sampling. These probabilities constitute the joint probability function of the two or more discrete random categorical variables. These probability tables have been previously studied from a compositional point of view. The compositional approach to the problem ensures coherence when analysing contingency sub-tables. The main results are: (1) given a probability table, the closest independent probability table is
the product of their geometric marginals; (2) the probability table can be orthogonally decomposed into an independent table and an interaction table; (3) the departure of independence can be measured using simplicial deviance, which is the Aitchison square norm of the interaction table.
In previous works, the analysis has been performed from a frequentist point of view.
This contribution is aimed at providing a Bayesian assessment of the decomposition. The resulting model is log-linear one, which parameters are the centered log-ratio transformations of the geometric marginals and the interaction table. Using a Dirichlet prior distribution of multinomial probabilities, the posterior distribution of multinomial probabilities is again a Dirichlet distribution. Simulation of this posterior allows to study
the distribution of marginal and interaction parameters, checking the independence of the observed contingency table and cell interactions.
The results corresponding to a two-way contingency table example are presented.
2016-01-25T12:11:29ZOrtego Martínez, María IsabelEgozcue Rubí, Juan JoséContingency tables can be parametrized by probabilities of each cell in a multinomial sampling. These probabilities constitute the joint probability function of the two or more discrete random categorical variables. These probability tables have been previously studied from a compositional point of view. The compositional approach to the problem ensures coherence when analysing contingency sub-tables. The main results are: (1) given a probability table, the closest independent probability table is
the product of their geometric marginals; (2) the probability table can be orthogonally decomposed into an independent table and an interaction table; (3) the departure of independence can be measured using simplicial deviance, which is the Aitchison square norm of the interaction table.
In previous works, the analysis has been performed from a frequentist point of view.
This contribution is aimed at providing a Bayesian assessment of the decomposition. The resulting model is log-linear one, which parameters are the centered log-ratio transformations of the geometric marginals and the interaction table. Using a Dirichlet prior distribution of multinomial probabilities, the posterior distribution of multinomial probabilities is again a Dirichlet distribution. Simulation of this posterior allows to study
the distribution of marginal and interaction parameters, checking the independence of the observed contingency table and cell interactions.
The results corresponding to a two-way contingency table example are presented.