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Article
Publication date: 4 December 2019

Michael James McCord, John McCord, Peadar Thomas Davis, Martin Haran and Paul Bidanset

Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an…

Abstract

Purpose

Numerous geo-statistical methods have been developed to analyse the spatial dimension and composition of house prices. Despite these advances, spatial filtering remains an under-researched approach within house price studies. This paper aims to examine the spatial distribution of house prices using an eigenvector spatial filtering (ESF) procedure, to analyse the local variation and spatial heterogeneity.

Design/methodology/approach

Using 2,664 sale transactions over the one year period Q3 2017 to Q3 2018, an eigenvector spatial filtering approach is applied to evaluate spatial patterns within the Belfast housing market. This method consists of using geographical coordinates to specify eigenvectors across geographic distance to determine a set of spatial filters. These convey spatial structures representative of different spatial scales and units. The filters are incorporated as predictors into regression analyses to alleviate spatial autocorrelation. This approach is intuitive, given that detection of autocorrelation in specific filters and within the regression residuals can be markers for exclusion or inclusion criteria.

Findings

The findings show both robust and effective estimator consistency and limited spatial dependency – culminating in accurately specified hedonic pricing models. The findings show that the spatial component alone explains 14.6 per cent of the variation in property value, whereas 77.6 per cent of the variation could be attributed to an interaction between the structural characteristics and the local market geography expressed by the filters. This methodological step reduced short-scale spatial dependency and residual autocorrelation resulting in increased model stability and reduced misspecification error.

Originality/value

Eigenvector-based spatial filtering is a less known but suitable statistical protocol that can be used to analyse house price patterns taking into account spatial autocorrelation at varying (different) spatial scales. This approach arguably provides a more insightful analysis of house prices by removing spatial autocorrelation both objectively and subjectively to produce reliable, yet understandable, regression models, which do not suffer from traditional challenges of serial dependence or spatial mis-specification. This approach offers property researchers and policymakers an intuitive but comprehensible approach for producing accurate price estimation models, which can be readily interpreted.

Details

International Journal of Housing Markets and Analysis, vol. 13 no. 5
Type: Research Article
ISSN: 1753-8270

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Book part
Publication date: 24 May 2007

Frederic Carluer

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth

Abstract

“It should also be noted that the objective of convergence and equal distribution, including across under-performing areas, can hinder efforts to generate growth. Contrariwise, the objective of competitiveness can exacerbate regional and social inequalities, by targeting efforts on zones of excellence where projects achieve greater returns (dynamic major cities, higher levels of general education, the most advanced projects, infrastructures with the heaviest traffic, and so on). If cohesion policy and the Lisbon Strategy come into conflict, it must be borne in mind that the former, for the moment, is founded on a rather more solid legal foundation than the latter” European Commission (2005, p. 9)Adaptation of Cohesion Policy to the Enlarged Europe and the Lisbon and Gothenburg Objectives.

Details

Managing Conflict in Economic Convergence of Regions in Greater Europe
Type: Book
ISBN: 978-1-84950-451-5

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Book part
Publication date: 4 May 2018

Siti Rusdiana, Zurnila Marli Kesuma, Latifah Rahayu and Edy Fradinata

Purpose – The purpose of this study is to explore the concept of spatial modeling in adolescent and under-five children’s nutritional status.Design/Methodology/Approach

Abstract

Purpose – The purpose of this study is to explore the concept of spatial modeling in adolescent and under-five children’s nutritional status.

Design/Methodology/Approach – The indicator used to identify spatial autocorrelation is the Local Indicator of Spatial Association (LISA). LISA is a method of exploratory analysis of spatial data capable of detecting spatial relationships at the local level and its effects globally. Aplication of stochastic modeling in spatial nutrition identification mapping can be categorized into two cases based on spatial autocorrelation and non-spatial autocorrelation.

Findings – This results of this study indicate that there is no spatial autocorrelation in the adolescent nutritional dataset. The thematic map for anemia showed that that the highest number of anemia in adolescents was in KutaAlam sub-districts (48 people). Sub-districts that were second most common were Meuraxa, Jaya Baru, and Baiturrahman sub-districts. The fewest cases were found in Lueng Bata sub-district (12 people). There were no sub-districts affected by neighboring areas, in the case of adolescents’ anemia in Banda Aceh. For the under-five nutritional data set, it shows that there are four factors that significantly affect spatial influence, which are malnutrition, chronic energy deficiency, woman of child-bearing age, proportion of family planning, percentage of households with PHBS and coverage of access to clean water.

Research Limitations/Implications – Anemia data were obtained with a school-based survey. Household survey would be better to implement in spatial analysis.

Practical Implications – The comparison of the dataset with the two methods provides a simple example to implement special autocorrelation in practice.

Social Implications – The results contribute to a much better comparison in many cases in the nutritional field.

Originality/Value – This is the initial nutritional status of adolescents in Banda Aceh.

Details

Proceedings of MICoMS 2017
Type: Book
ISBN:

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Article
Publication date: 11 August 2020

Mohammed Touitou, Laib Yacine and Boudeghdegh Ahmed

Despite significant progress in schooling, social and spatial inequalities in access to education remain important in Algeria. In the present article, taking into account…

Abstract

Purpose

Despite significant progress in schooling, social and spatial inequalities in access to education remain important in Algeria. In the present article, taking into account the geographic dimension makes it possible to identify the links existing between spatial location and disparities in the field of education in Algeria. Also, three types of education indicators (quantity, quality and inequality) are used in the study. The study’s sample includes 48 Algerian provinces, studied between 2008 and 2018.

Design/methodology/approach

In this study, the authors used data from the 2008 and 2018 General Census of Population and Housing (GCPH) for 48 provinces. Indeed, the two censuses of 2008 and 2018 (sources of data for this study) were based on questionnaires intended for different categories of the population (households, non-household populations, transit population, etc.). Therefore, the no response rate is assumed to be close to 0. Using spatial econometric techniques.

Findings

Results indicate that the indicator used is strong spatial disparity in education in Algeria. The development of a spatial synthetic index (SI) makes it possible to measure more precisely the extent and nature of spatial disparities in the field of education in Algeria. The results also confirm the hypothesis of β-convergence of the performance of the Algerian education system. Consequently, the need for policies to reduce the unfair inequalities between different areas is apparent.

Originality/value

Works that analyze education indicators in a classical perspective (educational performances between different sexes and between rural and urban areas) are abundant (Amaghouss and Ibourk, 2013a). However, very few studies proceed to the analysis of educational variables in a spatial perspective (Catin and Hazem, 2012). To the best of the authors’ knowledge, no work has tried to analyze spatial disparities in the field of education in Algeria.

Details

International Journal of Social Economics, vol. 47 no. 9
Type: Research Article
ISSN: 0306-8293

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Article
Publication date: 1 March 2003

Marius Thériault, François Des Rosiers, Paul Villeneuve and Yan Kestens

This paper presents a procedure for considering interactions of neighbourhood quality and property specifics within hedonic models of housing price. It handles…

Abstract

This paper presents a procedure for considering interactions of neighbourhood quality and property specifics within hedonic models of housing price. It handles interactions between geographical factors and the marginal contribution of each property attribute for enhancing values assessment. Making use of simulation procedures, it is combining GIS technology and spatial statistics to define principal components of accessibility and socio‐economic census related to transaction prices of single‐family homes. An application to the housing market of the Quebec Urban Community (more than 3,600 bungalows transacted in 1990 and 1991) illustrates its usefulness for building spatial hedonic models, while controlling for multicollinearity, spatial autocorrelation and heteroskedasticity. Distance‐weighted averages of each property attribute in the neighbourhood and interactions of property attributes with each principal component are used to detect any spatial effect on sale price variations. This first‐stage spatial hedonic model approximates market prices, which are then used in order to compare “expected” and actual property tax amounts, which are added to obtain a second‐stage model incorporating fiscal effects on house values. Interactions between geographical factors and property specifics are computed using formulae avoiding multicollinearity problems, while considering several processes responsible for spatial variability. For each property attribute, they define sub‐models which can be used to map variations, across the city, of its marginal value, assessing the cross‐effect of geographical location (in terms of neighbourhood profiles and accessibility to services) and its own valuation parameters. Moreover, this procedure distinguishes property attributes, exerting a stable contribution to value (constant over the entire region) from those whose implicit price significantly varies over space.

Details

Property Management, vol. 21 no. 1
Type: Research Article
ISSN: 0263-7472

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Article
Publication date: 1 April 2014

Prem Chhetri, Tim Butcher and Brian Corbitt

The purpose of this paper is twofold. First to identify economic activities and broader spatial logistics functions that characterise an urban setting, and second to…

Abstract

Purpose

The purpose of this paper is twofold. First to identify economic activities and broader spatial logistics functions that characterise an urban setting, and second to delineate significant spatial logistics employment clusters to represent the underlying regional geography of the logistics landscape.

Design/methodology/approach

Using the four-digit Australian and New Zealand Standard Industrial Classification, industries “explicitly” related to logistics were identified and aggregated with respect to employment. A principal component analysis was conducted to capture the functional interdependence of inter-related industries and measures of spatial autocorrelation were also applied to identify spatial logistics employment clusters.

Findings

The results show that the logistics sector accounts for 3.57 per cent of total employment and that road freight, postal services, and air and space transport are major employers of logistics managers. The research shows significant spatial clustering of logistics employment in the western and southern corridors of Melbourne, associated spatially with manufacturing, service industry and retail hubs in those areas.

Research limitations/implications

This research offers empirically informed insights into the composition of spatial logistics employment clusters to regions that lack a means of production that would otherwise support the economy. Inability to measure the size of the logistics sector due to overlaps with other sectors such as manufacturing is a limitation of the data used.

Practical implications

The research offers policymakers and practitioners an empirically founded basis on which decisions about future infrastructure investment can be evaluated to support cluster development and achieve economies of agglomeration.

Originality/value

The key value of this research is the quantification of spatial logistics employment clusters using spatial autocorrelation measures to empirically identify and spatially contextualize logistics hubs.

Details

International Journal of Physical Distribution & Logistics Management, vol. 44 no. 3
Type: Research Article
ISSN: 0960-0035

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Book part
Publication date: 30 September 2014

Gertrudes Saúde Guerreiro

Does the standard of living vary from region to region in Portugal and are spatial units in Portugal converging in income? We observe spatial error dependence between…

Abstract

Does the standard of living vary from region to region in Portugal and are spatial units in Portugal converging in income? We observe spatial error dependence between municipalities and estimate spatial econometric models to test convergence. For conditional convergence we conclude that primary sector employment, activity rate, and percentage of active population with higher education are important to distinguish the “steady state” of the regional economies, reflecting the labor market at regional level.

Details

Economic Well-Being and Inequality: Papers from the Fifth ECINEQ Meeting
Type: Book
ISBN: 978-1-78350-556-2

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Article
Publication date: 28 October 2014

Ashley Elaine Hungerford and Barry Goodwin

The purpose of this paper is to investigate the effects of crop insurance premiums being determined by small samples of yields that are spatially correlated. If spatial

Abstract

Purpose

The purpose of this paper is to investigate the effects of crop insurance premiums being determined by small samples of yields that are spatially correlated. If spatial autocorrelation and small sample size are not properly accounted for in premium ratings, the premium rates may inaccurately reflect the risk of a loss.

Design/methodology/approach

The paper first examines the spatial autocorrelation among county-level yields of corn and soybeans in the Corn Belt by calculating Moran's I and the effective spatial degrees of freedom. After establishing the existence of spatial autocorrelation, copula models are used to estimate the joint distribution of corn yields and the joint distribution of soybean yields for a group of nine counties in Illinois. Bootstrap samples of the corn and soybean yields are generated to estimate copula models with the purpose of creating sampling distributions.

Findings

The estimated bootstrap confidence intervals demonstrate that the copula parameter estimates and the premium rates derived from the parameter estimates can vary greatly. There is also evidence of bias in the parameter estimates.

Originality/value

Although small samples will always be an issue in crop insurance ratings and assumptions must be made for the federal crop insurance program to operate at its current scale, this analysis sheds light on some of the issues caused by using small samples and will hopefully lead to the mitigation of these small sample issues.

Details

Agricultural Finance Review, vol. 74 no. 4
Type: Research Article
ISSN: 0002-1466

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Book part
Publication date: 19 December 2012

R. Kelley Pace, James P. LeSage and Shuang Zhu

Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that…

Abstract

Most spatial econometrics work focuses on spatial dependence in the regressand or disturbances. However, Lesage and Pace (2009) as well as Pace and LeSage2009 showed that the bias in β from applying OLS to a regressand generated from a spatial autoregressive process was exacerbated by spatial dependence in the regressor. Also, the marginal likelihood function or restricted maximum likelihood (REML) function includes a determinant term involving the regressors. Therefore, high dependence in the regressor may affect the likelihood through this term. In addition, Bowden and Turkington (1984) showed that regressor temporal autocorrelation had a non-monotonic effect on instrumental variable estimators.

We provide empirical evidence that many common economic variables used as regressors (e.g., income, race, and employment) exhibit high levels of spatial dependence. Based on this observation, we conduct a Monte Carlo study of maximum likelihood (ML), REML and two instrumental variable specifications for spatial autoregressive (SAR) and spatial Durbin models (SDM) in the presence of spatially correlated regressors.

Findings indicate that as spatial dependence in the regressor rises, REML outperforms ML and that performance of the instrumental variable methods suffer. The combination of correlated regressors and the SDM specification provides a challenging environment for instrumental variable techniques.

We also examine estimates of marginal effects and show that these behave better than estimates of the underlying model parameters used to construct marginal effects estimates. Suggestions for improving design of Monte Carlo experiments are provided.

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Article
Publication date: 27 November 2018

Wioleta Kucharska, Karol Flisikowski and Ilenia Confente

Brand positioning based on the brand’s country of origin is at the centre of attention in international marketing. It is evident that global brands constitute critical…

Abstract

Purpose

Brand positioning based on the brand’s country of origin is at the centre of attention in international marketing. It is evident that global brands constitute critical intangible assets for businesses and places. However, it is not clear how they contribute to national economies. This paper aims to discuss the significance of brands as contributing to the value of their companies but also helping to leverage national economies. Although global brands can be produced and purchased in multiple countries, their influence on the economy of the country where their owner’s seat is located can be more meaningful than in other economies included in the “global factory”.

Design/methodology/approach

Based on 500 Brandirectory, the Most Valuable Global Brands 2011-2015 rankings powered by Brand Finance, the authors observed a spatial-economic autocorrelation which exemplifies the potential interdependency between gross domestic product (GDP) and brand value. This relationship has become a starting point for designing a spatial regression model.

Findings

The findings support the hypothesis that assumptive spatial dependencies have a significant influence on the examined relationship of brand value and GDP.

Originality/value

The presented study is the first to examine the potential interdependence between brand values and GDP of the countries of origin using a dynamic spatial approach.

Details

Journal of Product & Brand Management, vol. 27 no. 7
Type: Research Article
ISSN: 1061-0421

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