Applying Multivariate Data Analysis as Objective Method for Calculating the Location Index for Use in Urban Tree Appraisal

Ayuga-Téllez, Esperanza; Contato-Carol, M.L.; González, Carmen; Grande-Ortiz, María Ángeles; Velázquez, Juan J. L.

doi:10.1061/(asce)up.1943-5444.0000063

Cited by 5 publications

(5 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Height (h) is one of the most widely-applied tree-size variables in urban tree inventories (Wood 1999;Martin et al 2011;Moskal and Zheng 2012;Shrestha and Wynne 2012), and is also used indirectly in FEM and through corrective measures of basic value (Grande-Ortiz et al 2012). Canopy area (ca) is another variable for calculating tree size that is easy to measure with current methods of photogrammetry (McRoberts and Tomppo 2007;Walton 2008;Abd-Elrahman et al 2010;Millward and Sabir 2011); it is also a feature of the CON-TATO and FEM valuation approaches (Grande-Ortiz et al 2012) and can be used to determine the Location Index (Ayuga-Téllez et al 2011).…”

Section: Discussionmentioning

confidence: 99%

“…This kind of analysis has been employed in previous studies (Savva et al 2002;Heynen and Lindsey 2003;Turner et al 2005) to describe and model complex relationships among multiple variables measured in the same population (Peña 2002). Multivariate inference techniques with multivariate descriptive techniques appear in Hammitt (2002), Jutras (2008), LaPaix and Freedman (2010), Grahn and Stigsdotter (2010) and Ayuga-Téllez et al (2011).…”

mentioning

confidence: 99%

See 1 more Smart Citation

Selection of Tree-Size Variables for Appraisal Methods for Urban Trees According to Their Collinearity

Sánchez-Medina¹,

Ayuga-Téllez²,

Contato-Carol³

et al. 2017

AUF

Self Cite

View full text Add to dashboard Cite

The methods for appraising urban trees and municipal inventories in use today are expensive and require quantitative and qualitative variables with a high measurement cost. They are mathematically formulated from at least one tree-size variable to define a tree-size value. Researchers present a statistical methodology to analyze tree-size variables applied in appraisal methods for urban trees. A multivariate analysis method was carried out in order to obtain the lowest number of variables that explain the greatest variability of urban trees with no multicollinearity problems. The study was applied to urban trees in the City of Santiago del Estero, Argentina. The variables that showed the lowest collinearity were age and canopy area. The work includes a discussion of the use of correlated variables in appraisal methods for urban trees.

show abstract

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 99%

Selection of Tree-Size Variables for Appraisal Methods for Urban Trees According to Their Collinearity

Sánchez-Medina¹,

Ayuga-Téllez²,

Contato-Carol³

et al. 2017

AUF

Self Cite

View full text Add to dashboard Cite

show abstract

“…En la ciudad argentina se emplearon los archivos municipales, la lectura de anillos de crecimiento y la comparación de fotografías antiguas. Para esta tesis se empleó la ecuación del modelo de regresión lineal, obtenida con datos del arbolado de Santiago del Estero (Ayuga Téllez et al, 2011), debido a la imposibilidad de encontrar datos registrados de edad para ninguno de los ejemplares utilizados.…”

Section: Contatounclassified

“…El resultado es una variable que se resume en tres valores 1, 1,25 y 1,5 (Ayuga-Téllez et al, 2011). El valor 1 corresponde a una mayor cobertura arbórea por habitante y mayor porcentaje de cubierta arbórea en relación a lo edificado.…”

Section: Sief Iefunclassified

“…Se realizó una clasificación inicial de las especies en tres grupos: pequeño, mediano y gran porte, mediante los valores máximos que las variables dasométricas (h, dbh y sc) podían alcanzar en su etapa de madurez. De este modo, se asignó a cada árbol un grupo, dependiendo de la especie (Contato-Carol et al 2011, Ayuntamiento de Barcelona 2011. Seguidamente, se realizó un análisis discriminante (Fisher, 1936) para validar y automatizar la clasificación inicial donde la variable de clasificación era el grupo en que se incluía la especie: el grupo 1 (pequeño porte), el grupo 2 (porte mediano) y el grupo 3 (gran porte).…”

Section: Metodologíaunclassified

See 1 more Smart Citation

Comparación entre métodos de tasación y valoración del público para el arbolado urbano de Madrid

García-Ventura¹

View full text Add to dashboard Cite

carbono y el dióxido de azufre, y que liberan oxígeno, lo más importante. En el arbolado urbano considero su capacidad de amortiguar las temperaturas lo más importante. En los árboles de mi ciudad considero los daños y peligrosidad de los mismos lo más importante. El arbolado urbano atrae a los animales que generan problemas. Eliminaría un árbol si sus ramas rompen líneas eléctricas. Eliminaría un árbol si sus ramas producen daños a los peatones. Eliminaría un árbol si sus ramas producen daños a los coches. Eliminaría un árbol si sus ramas se pudieran meter en mi casa. Eliminaría un árbol si sus hojas hacen peligroso el suelo en otoño. Eliminaría un árbol si sus hojas me quitan la luz. Eliminaría un árbol si su polen provoca alergias. Eliminaría un árbol que oculte total o parcialmente la luz de las farolas por la noche. Valoro negativamente el arbolado urbano que provoca alergias en los ciudadanos. Valoro negativamente el arbolado urbano que oculta la señalización viaria. En el arbolado urbano considero los beneficios para la salud humana lo más importante. En el arbolado urbano considero lo más importante la sensación de paz, serenidad y tranquilidad que transmite. Considero que pasear en una ciudad con árboles es más agradable. Creo que los árboles hacen que los barrios sean más agradables.

show abstract

Top–down disaggregation of life expectancy up to municipal areas, using linear self-regressive spatial models

Basile,

Cervellera,

Cusatelli

et al. 2024

Qual Quant

View full text Add to dashboard Cite

The paper aims to analyse a statistical procedure for the definition of territorial indicators, such as the biometric function of life expectancy of citizens of a territory e0, applying a methodology of Top–Down spatial disaggregation, using census data from 2011 in Italy. Through spatial regressions with the methodology proposed in 1971 by Chow and Lin with the use of ISTAT elaborations of annual mortality tables, which are structured from the national level to the regional level, up to the smallest details of the main level, as a dependent variable and predictors a number k of census variables plus accidents in regression models, life expectancy can also be defined at municipal levels (not elaborated by ISTAT) and even at sub-municipal levels (Census Area). The structure of the 2011 census was characterized by 152 variables, collected with CAPI and universal CAWI survey on all the survey units, throughout the national territory, divided into administrative areas of competence and 402,677 more granular areas in census sections. This structure represents a very relevant and useful information asset for applying a spatial disaggregation of indicators, based on three fundamental assumptions: Structural similarity, whereby the aggregate model and the disaggregate model are structurally similar, i.e., the relationships between the variables are valid both at the aggregate (Top) and at the disaggregate (Down) level, with the consequence that the regression parameters are the same in the two models. Error similarity: for spatially correlated errors they present the same structure at both aggregate (Top) and disaggregate (Down) levels, significantly testing for zero spatial correlation; Reliable indicators: the variables in the regression models are efficient predictors at both aggregates (Top) and disaggregate (Down) levels, estimable from model efficiency tests. As we see in the following, compared to others, the best predictors of the census and income variables show us a good interaction in terms of active regressors on the estimation variable.

show abstract

Applying Multivariate Data Analysis as Objective Method for Calculating the Location Index for Use in Urban Tree Appraisal

Cited by 5 publications

References 8 publications

Selection of Tree-Size Variables for Appraisal Methods for Urban Trees According to Their Collinearity

Selection of Tree-Size Variables for Appraisal Methods for Urban Trees According to Their Collinearity

Comparación entre métodos de tasación y valoración del público para el arbolado urbano de Madrid

Top–down disaggregation of life expectancy up to municipal areas, using linear self-regressive spatial models

Contact Info

Product

Resources

About