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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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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.

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”.

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.

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

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.

Air quality, noise and proximity to urban infrastructure can arguably have an important impact on the quality of life. Environmental quality (the price of good health) has become a central tenet for consumer choice in urban locales when deciding on a residential neighbourhood. Unlike the market for most tangible goods, the market for environmental quality does not yield an observable per unit price effect. As no explicit price exists for a unit of environmental quality, this paper aims to use the housing market to derive its implicit price and test whether these constituent elements of health and well-being are indeed capitalised into property prices and thus implicitly priced in the market place.

A considerable number of studies have used hedonic pricing models by incorporating spatial effects to assess the impact of air quality, noise and proximity to noise pollutants on property market pricing. This study presents a spatial analysis of air quality and noise pollution and their association with house prices, using 2,501 sale transactions for the period 2013. To assess the impact of the pollutants, three different spatial modelling approaches are used, namely, ordinary least squares using spatial dummies, a geographically weighted regression (GWR) and a spatial lag model (SLM).

The findings suggest that air quality pollutants have an adverse impact on house prices, which fluctuate across the urban area. The analysis suggests that the noise level does matter, although this varies significantly over the urban setting and varies by source.

Air quality and environmental noise pollution are important concerns for health and well-being. Noise impact seems to depend not only on the noise intensity to which dwellings are exposed but also on the nature of the noise source. This may suggest the presence of other externalities that arouse social aversion. This research presents an original study utilising advanced spatial modelling approaches. The research has value in further understanding the market impact of environmental factors and in providing findings to support local air zone management strategies, noise abatement and management strategies and is of value to the wider urban planning and public health disciplines.

Fiscal fund is the key support of carbon emissions control for local governments. This paper aims to analyze the impact of fiscal decentralization on carbon emissions by spatial Durbin model (SDM), and verify the existence of “free-riding” phenomenon to reveal the behavior of local governments in carbon emissions control.

Based on the provincial data of carbon emissions from 2005 to 2016 in China, this paper uses spatial exploratory data analysis technology to analyze the spatial correlation characteristics and constructs SDM to test the impact of fiscal decentralization on carbon emissions.

The results show that carbon emissions exhibits significant spatial autocorrelation in China, and the increasing of fiscal decentralization in the region will increase carbon emissions in surrounding areas and on the whole. Then, by comparing the impact of fiscal decentralization on carbon emissions and industrial solid waste, it is found that “free-riding” phenomenon of carbon emissions control exists in China.

Based on the spatial cluster characteristics of China’s provincial carbon emissions, carbon emissions control regions can be divided into regions and different carbon emission control policies can be formulated for different cluster regions. Carbon emissions indicators should be included in the government performance appraisal policy, and carbon emissions producer survey should be increased in environmental policies to avoid “free-riding” behaviors of local government in carbon emissions control in China.

This paper contributes to fill this gap and fully considers the spatial spillover characteristics of carbon emissions by introducing spatial exploratory data analysis technology, constructs SDM to test the impact of fiscal decentralization on carbon emissions in the perspective of space econometrics, and tests the existence of “free-riding” phenomenon in carbon emissions control for local governments in China.

Given the recent rapid economic development, the processes of industrialization and urbanization are accelerating. At the same time, the contradiction between environmental quality and economic development has become increasingly prominent and is likely to restrict the normal pace of China’s economic development and environmental protection. As such, the purpose of this paper is to incorporate the urbanization factor into an analytic framework to discuss the relationship among urbanization, economic development, and environmental pollution.

A panel data of 31 Chinese provinces from 2004 to 2015 is selected for this research. A spatial correlation test is first conducted on the environmental pollution status, then the spatial Durbin model is used to carry out spatial econometric testing of the relationship among the above three factors.

Interprovincial environmental pollution in China has significant positive spatial correlation, environmental pollution discharge in most provinces is significantly stable, discharge of environmental pollutants is transitioning from coastal to inland provinces, and urbanization and economic growth can both aggravate environmental pollution, but economic growth can relieve environmental pollution in neighboring provinces.

The relationship between economic growth, urbanization, and environmental quality has always been an important issue for sustainable development. As such, China’s urbanization leads to economic development, while rapid economic growth and environmental pollution are coordinated. This paper focuses on the specific relationship between them. To this end, local governments make concerted efforts to formulate sound environmental regulation policies based on local environmental conditions, where economic development is an effective means of alleviating the contradictory relationship between economic development and environmental protection.

The purpose of this paper is to estimate the determinants of the retail space rent in Shanghai.

Hedonic model and spatial regression models are used in the paper. The problem of spatial autocorrelation is tested by Moran's I statistics, and the root mean square error (RMSE) test is performed to find out the best model.

The significant explaining variables are the age, the area of retail space, the distance to the Jing An CBD centre, the type of the retail and the district of the property. A new classification of district in retail research context is suggested in this paper, and it is proved to be better than the districts set up by government to explain the retail rent variation.

This paper presents the first empirical study about the retail rental market in Shanghai. The research helps retail property investors and retail tenants deepen their understanding of the retail market in Shanghai. Spatial econometrics techniques are first introduced into the empirical retail rent research to produce a more precise estimation.