Sistemas de Información Geográfica y Econometría Espacial en la Tasación de Inmuebles Urbanos. Proyecto Piloto en la Ciudad de Cartagena

La tasación de un bien inmobiliario está siempre sujeta a un elevado número de factores que son difíciles de controlar de forma simultánea por el tasador. Con el fin de abordar este problema, el objetivo básico que se persigue en esta comunicación, es proporcionar al tasador de una herramienta informática que, mediante la conjunción de técnicas de gráficas (Sistemas de Información Geográfica) y estadísticas (Econometría Espacial), permitan asignar un valor de referencia a la tasación de bienes inmobiliarios. La utilización simultánea de estas técnicas se aplica a una muestra de viviendas tasadas en la ciudad de Cartagena

Distribution-free inference for Q(m) based on permutational bootstrapping: an application to the spatial co-location pattern of firms in Madrid

The objective of this paper is to present a distribution-free inferential framework for the Q(m) statistic based on permutational bootstrapping. Q(m) was introduced in the literature as a tool to test for spatial association of qualitative variables, or more precisely, patterns of co-location/co-occurrence. The existing inferential framework for this statistic is based on asymptotic results. A challenge for these results is the need to limit the overlap in the neighborhoods of proximate observations, which tends to reduce the size of the sample, with consequent impacts on the size and power of the statistic. A computationally intensive inferential framework, such as presented in this paper, allows for greater versatility of Q(m). We show that under the bootstrap version the issues with size are ameliorated and the test is more powerful. Furthermore, in this framework there is no longer the need to control for overlap, which allows for applications to variables with more categories and smaller sample sizes. The proposed approach is demonstrated empirically using a case study of co-location of business establishments in Madrid.The authors would like to express their thanks to the project ECO2009-10534 of the Ministerio de Ciencia e Innovación del Reino de España

Is spatial dependence an instantaneous effect? Some evidence in economic series of Spanish provinces

The purpose of this article is to analyze if spatial dependence is a synchronic effect, as it has usually been defined. It is known that in many socio-economic phenomena spatial dependence can be not only contemporary but also time-lagged. In this paper, we use two Moran-based space-time autocorrelation statistics in order to evaluate the simultaneity of this spatial effect, allowing for mixed specifications with instantaneous and space-time dependence terms. Some applications with economic data for Spanish provinces shed some light upon these issues

Difusión y dinámica temporal de la dependencia espacial

El propósito de este artículo es analizar la difusión y dinámica temporal de la dependencia espacial. Con este objetivo, en primer lugar se diferenciará entre dependencia espacial contemporánea y no contemporánea, discutiendo distintas especificaciones de modelos espaciales de regresión que recojan ambos tipos de dependencia espacial. En segundo lugar, extenderemos estas especificaciones a modelos SUR espaciales que incluyan retardos tanto espaciales como espacio temporales y que recojan la dinámica temporal de la dependencia espacial mediante una única estructura. Los modelos desarrollados se aplicarán a la estimación de la Renta Familiar disponible en las provincias españolas

Spatial spillovers in public expenditure on a municipal level in Spain

A key function of local governments is to provide a wide array of public services. The supply of these services has been found to create spatial spillovers among neighbouring municipalities. Although it is generally agreed that spillovers are present in models that explain government expenditures, their type—whether endogenous, exogenous or residual—and sign—whether positive or negative—remain ambiguous. In most cases, a subjective process is used to select the type of spatial regression model used in analysis, with mixed results. Per capita expenditures of ten subprogrammes (Security, Housing, Welfare, Environment, Social services, Employment promotion, Health, Education, Culture and Sport) are analysed for all Spanish municipalities with more than 5000 inhabitants in the 2010–2012 period. A Spatial Seemingly Unrelated Regression methodology in a panel framework is used to incorporate correlation between different subprogrammes and spatial dependence. Our results show that the three types of spatial effects are present. Furthermore, substantive dependence is positive in most cases, while negative residual dependence is observed in some.We gratefully acknowledge the contribution of three anonymous reviewers in helping us improve the quality of this paper. Prof. Fernando A. López , grateful for the financial support offered by the projects from Programa de Ayudas a Grupos de Excelencia de la Región de Murcia, Fundación Seneca (#19884-GERM-15) and Ministry of Economy and Competiveness (ECO2015-651758)

Spatial SUR models: Specification, Testing and Selection

Our paper focuses on the case of SUR models with spatial effects. Specifically, the problem that we pose is testing for the presence of spatial effects in these multivariate models, considering that one of our main interests is to deal with the question of selecting the most adequate specification for the data. In order to do this, we obtain several tests that check for the fundamental hypothesis of the model, including the assumption of time stability of the spatial dependence mechanisms. We solve the discussion in a maximum-likelihood framework because this facilitates the obtaining of appropriate tests. The second part of the paper contains a Monte Carlo experiment in order to study the behaviour of the two most popular model selection strategies, Specific-to- General or General-to-Specific, using the collection of tests that have been proposed.This work has been carried out with the financial support of project ECO2009-10534/ECON of the Ministerio de Ciencia y Tecnología of the Reino de España

Comparison of thematic maps using symbolic entropy

Comparison of thematic maps is an important task in a number of disciplines. Map comparison has traditionally been conducted using cell-by-cell agreement indicators, such as the Kappa measure. More recently, other methods have been proposed that take into account not only spatially coincident cells in two maps, but also their surroundings or the spatial structure of their differences. The objective of this paper is to propose a framework for map comparison that considers 1) the patterns of spatial association in two maps, in other words, the map elements in their surroundings; 2) the equivalence of those patterns; and 3) the independence of patterns between maps. Two new statistics for the spatial analysis of qualitative data are introduced. These statistics are based on the symbolic entropy of the maps, and function as measures of map compositional equivalence and independence. As well, all inferential elements to conduct hypothesis testing are developed. The framework is illustrated using real and synthetic maps

A non-parametric spatial independence test using symbolic entropy

In the present paper, we construct a new, simple, consistent and powerful test for spatial independence, called the SG test, by using symbolic dynamics and symbolic entropy as a measure of spatial dependence. We also give a standard asymptotic distribution of an affine transformation of the symbolic entropy under the null hypothesis of independence in the spatial process. The test statistic and its standard limit distribution, with the proposed symbolization, are invariant to any monotonuous transformation of the data. The test applies to discrete or continuous distributions. Given that the test is based on entropy measures, it avoids smoothed nonparametric estimation. We include a Monte Carlo study of our test, together with the well-known Moran’s I, the SBDS (de Graaff et al, 2001) and (Brett and Pinkse, 1997) non parametric test, in order to illustrate our approach

Modelos de regresión espacio temporales en la estimación municipal de la renta. Estimación de la RFDPC municipal en la región de Murcia

Muy recientemente han aparecido estudios en los que haciendo uso de técnicas de econometría espacial, se han realizado nuevas estimaciones de la renta municipal eliminando los problemas de autocorrelación espacial que presentaban los modelos anteriores. Estos modelos poseen una elevada capacidad explicativa, pero tienen ciertas carencias al utilizarlos con intención predictiva. El objetivo de este estudio es plantear una alternativa a la estimación de la renta municipal mediante modelos espacio-temporales de regresión que simultáneamente solucione dos problemas: el problema de autocorrelación espacial y a la falta de capacidad predictiva de ciertos modelos espaciales de regresión. Se utiliza el modelo teórico desarrollado con el fin de realizar una estimación de la Renta Familiar Bruta Disponible per cápita de los municipios de la Región de Murcia

Un análisis de la eficiencia de los departamentos de la Universidad Politécnica de Cartagena

Las entidades no lucrativas deben dotarse de técnicas de gestión, que deben ser además, capaces de proporcionar medidas de la rentabilidad con que se invierten los recursos, teniendo en cuenta que en dichas entidades, los objetivos no son meramente económicos y el concepto de rentabilidad difiere del empleado en el ámbito empresarial. Se presenta en este trabajo, un análisis de la eficiencia de los Departamentos de la Universidad Politécnica de Cartagena utilizando la técnica de Análisis Envolvente de Datos (DEA). En el estudio, y en ausencia de información acerca de la calidad del servicio, se utilizan como recursos los presupuestos y recursos humanos de los departamentos, y como resultados, los créditos realmente impartidos y la evaluación de la actividad investigadora de los mismos. Para finalizar, se analiza la sensibilidad de los modelos mediante la variación de las variables en ellos introducidas