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This study is the first that aims to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

With network structure analysis, this study finds that they contain different information content from the perspective of graphical display, node strength and correlation. Developed and emerging countries all play major roles in trade connection, while only developed countries play major roles in financial linkage. Second, by applying the multidimensional SAR model, only the spatial autocorrelation coefficients for trade and financial linkages are significant during the full sample period, which is in sharp contrast to published studies using the SAR model with a single matrix. Third, the spillover channels that play major roles in various periods are different. Only trade channel plays a role during crisis periods and it is the most important. Fourth, the spatial correlation among countries greatly amplifies the shock’s impacts on one market. And spatial effect for developed countries is larger than those for emerging countries, while the mean spatial effect of a unit shock in the USA on emerging countries is slightly greater than that on developed countries.

Multiple spatial weight matrices can capture the contiguity of spatial units from various dimensions, which could be exploited to improve the precision of inference as well as prediction accuracy. To the best of the authors’ knowledge, this is the first study to investigate international transmission channels of sovereign risk among G20 and explore its influential factors by applying the multidimensional SAR model.

The main thesis of the chapter is to introduce a new idea to the field of peace negotiations, which will require the development of a new model of negotiations to enforce peace. The existing models of peace negotiations highlight the existence of a positive peace dividend to parties involved in conflicts and peace negotiation. They, hence, usually highlight a gradual and dynamic adjustment, or movement, away from a conflict-ridden outcome towards a peaceful outcome that offers a positive peace dividend to all relevant stakeholders. In comparison with the status quo, peace brings additional economic returns and peace therefore offers a win–win situation. Despite the fact that a win–win situation does not ensure the enforcement of peace, as agents can easily get locked into what is commonly known as the prisoners' dilemma – yet the possibility of Pareto improvement makes negotiations for peace somewhat artificial. At least in the short run all agents involved in active conflicts are apprehensive of peace as they expect immediate (expected) returns from making peace can outweigh the expected returns from conflicts. An important work that sidesteps the win–win situation of peace dividends is by Isard and Azis (1999) who introduced the possibility of an immediate loss of economic returns from the peace process in their conflict management procedure (CMP). However, in the existing work on CMP, the long-run returns from peace outweigh that from conflicts. One therefore presumes that peace brings economic benefits to all. The existing CMPs therefore assume away any possibility of lower economic returns from peace. There are some important models in which peace negotiations are also modelled as a zero-sum game in which the gain of a party represents a loss to others, which is known as win–lose negotiations. In this work we introduce the possibility of bargaining and negotiations against the backdrop of potential immediate losses while peace is favoured simply for its intrinsic value and not for pecuniary returns. In the real world, there is evidence to believe that agents involved in conflicts are painfully aware of two things: first, the decision-making agents who choose between conflicts vis-à-vis peace are the leaders who get rarely affected by economic returns from conflicts or peace. It is usually the foot soldiers who bear the brunt of costly conflicts and can benefit from peace. Secondly, most people value peace for the sake of it as peace has an intrinsic value that ensures the protection of rights and their lives and protection from violence. Thus, peace is a collective good that provides little extra economic returns to actual decision-makers who choose between courses of conflicts or peace.

In recent years, there is growing recognition of the importance of geography and space in the analysis of economic convergence by focusing on the dynamics of monetary indicators. The analysis of spatial convergence based on socio-economic indicators are rare. These variables present a complement to understand the spatial dynamics of territorial units. The purpose of this paper is, first, to analyzes and describes trends in multidimensional poverty in Morocco and second it explores the convergence hypothesis.

Data are driven from HCP (2017). It concerns 75 provinces over the period 2004 and 2014. In addition to the availability of data, this period corresponds to significant changes in public policy. The nature of the observations necessitates the use of the spatial analysis techniques.

The results show that poverty is a geographical phenomenon with low speed of convergence. The paper propose some solutions to help policymakers implement an effective targeting policy aimed at reducing spatial inequalities in terms of multidimensional poverty in Morocco.

The analysis of spatial convergence based on socio-economic indicators are rare. This paper will focus on the convergence of the poverty index for a developing country.

This article aims to analyse the spatial and temporal evolution of multidimensional poverty in Brazil's North Region and its relationship with territorial, economic and population dynamics.

A multidimensional poverty index (MPI) was calculated using the Alkire-Foster method and a spatial econometric model was estimated. The data come from population censuses conducted by the Brazilian Institute of Geography and Statistics (IBGE) for the years 1991, 2000 and 2010.

The results show a decrease in multidimensional poverty over the period analysed. However, they show this reduction occurred in a heterogeneous way in time and space, with emphasis on microregions in which, despite a reduction in the rate, the percentage of the population considered multidimensionally poor remained high during the 30 years of the study.

The quality of available data.

It is possible to point out that public policies focused on improving infrastructure in medium-sized locations tend to have two positive effects: first, making production and consumption more accessible and sustainable for local communities, given that currently the cost of transport and logistics are factors that hinder socioeconomic development; second, to reduce the pressure of demand that this population of small and medium-sized locations exerts on public services currently offered only in larger locations. Although the logic of concentrating most public services in the largest cities makes sense in terms of economic efficiency, the particularities of the northern region and the recognition of its environmental importance point to the need for incentives for more spatially distributed economic activity.

From the paper results, it is possible to think about more local public policies which are able to improve people's lifes without to damage the environment.

This is the first study on multidimensional poverty that covers the entire North region of Brazil (Amazon region) and that contemplates both the temporal and spatial dynamics of poverty.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-11-2021-0699.

The paper analyses the prevalence of extreme and multidimensional poverty in line with the sustainable development agenda. In addition, the paper examines the drivers of extreme poverty while accounting for the potential spillover effect of poverty in the region.

The study adopts the pooled OLS with Discroll-Kraay robust standard errors to control for cross-sectional dependence. In addition, given the strong potential for endogeneity of poverty index, the authors also employ the generalized method of moments (GMM), which accounts for simultaneity and endogeneity problems, and the spatial error and lag models to control for all forms of spatial and temporal dependence since the factors that affect poverty disperse across borders.

The study finds that in addition to the traditional drivers of poverty (unemployment, low per capita GDP growth and public debt), poverty in Sub-Saharan Africa is a symptom of a deeper structural problem (lack of access to water and sanitation, high level of corruption and low level of financial development, and frequent economic busts). Likewise, the results from the spatial econometric specification show, consistently across all the specifications, that there is a substantial spillover effect of poverty across the region.

The main novelty of the paper is that the authors investigate the “economic shrinkage hypothesis,” and examined the potential negative spillover effect of poverty in the region.

Joint space multidimensional scaling (MDS) maps are often utilized for positioning analyses and are estimated with survey data of consumer preferences, choices, considerations, intentions, etc. so as to provide a parsimonious spatial depiction of the competitive landscape. However, little attention has been given to the possibility that consumers may display heterogeneity in their information usage (Bettman et al., 1998) and the possible impact this may have on the corresponding estimated joint space maps. This paper aims to address this important issue and proposes a new Bayesian multidimensional unfolding model for the analysis of two or three-way dominance (e.g. preference) data. The authors’ new MDS model explicitly accommodates dimension selection and preference heterogeneity simultaneously in a unified framework.

This manuscript introduces a new Bayesian hierarchical spatial MDS model with accompanying Markov chain Monte Carlo algorithm for estimation that explicitly places constraints on a set of scale parameters in such a way as to model a consumer using or not using each latent dimension in forming his/her preferences while at the same time permitting consumers to differentially weigh each utilized latent dimension. In this manner, both preference heterogeneity and dimensionality selection heterogeneity are modeled simultaneously.

The superiority of this model over existing spatial models is demonstrated in both the case of simulated data, where the structure of the data is known in advance, as well as in an empirical application/illustration relating to the positioning of digital cameras. In the empirical application/illustration, the policy implications of accounting for the presence of dimensionality selection heterogeneity is shown to be derived from the Bayesian spatial analyses conducted. The results demonstrate that a model that incorporates dimensionality selection heterogeneity outperforms models that cannot recognize that consumers may be selective in the product information that they choose to process. Such results also show that a marketing manager may encounter biased parameter estimates and distorted market structures if he/she ignores such dimensionality selection heterogeneity.

The proposed Bayesian spatial model provides information regarding how individual consumers utilize each dimension and how the relationship with behavioral variables can help marketers understand the underlying reasons for selective dimensional usage. Further, the proposed approach helps a marketing manager to identify major dimension(s) that could maximize the effect of a change of brand positioning, and thus identify potential opportunities/threats that existing MDS methods cannot provides.

To date, no existent spatial model utilized for brand positioning can accommodate the various forms of heterogeneity exhibited by real consumers mentioned above. The end result can be very inaccurate and biased portrayals of competitive market structure whose strategy implications may be wrong and non-optimal. Given the role of such spatial models in the classical segmentation-targeting-positioning paradigm which forms the basis of all marketing strategy, the value of such research can be dramatic in many marketing applications, as illustrated in the manuscript via analyses of both synthetic and actual data.

This chapter explores a descriptive theory of multidimensional travel behaviour, estimation of quantitative models, and demonstration in an agent-based microsimulation.

A descriptive theory on multidimensional travel behaviour is conceptualised. It theorizes multidimensional knowledge updating, search start/stopping criteria, and search/decision heuristics. These components are formulated or empirically modelled and integrated in a unified and coherent approach.

The theory is supported by empirical observations and the derived quantitative models are tested by an agent-based simulation on a demonstration network.

Based on artificially intelligent agents, learning and search theory, and bounded rationality, this chapter makes an effort to embed a sound theoretical foundation for the computational process approach and agent-based microsimulations. A pertinent new theory is proposed with experimental observations and estimations to demonstrate agents with systematic deviations from the rationality paradigm. Procedural and multidimensional decision-making are modelled. The numerical experiment highlights the capabilities of the proposed theory in estimating rich behavioural dynamics.

This paper deals with the spatial impact of cooperative entrepreneurship on territorial human development in Morocco. Within the background of the new development model in Morocco, this study aims to explore how the social and solidarity economy (SSE) through the cooperative model can reduce territorial inequalities by promoting economic dynamics in territories with suboptimal human development.

The empirical material is compiled from a database of 75 provinces in Morocco. After establishing a typology of the SSE in these territories, a test of the spatial effect of some indicators, such as the rate of urbanization and female employment, was undertaken. Moreover, a spatial impact study of the cooperatives' entrepreneurship and the local human development index will be carried out among the cooperatives' numbers and the share of their members.

This study's results indicate a diversity in the participation of the SSE in the regulation of territories. First, there is a heterogeneous relationship in the Moroccan space. Then, the cooperative model in Morocco shows that it plays a central role in readjusting social and spatial inequalities, pointing territories with suboptimal human development.

Although our theoretical contribution covers the impact of the three components of the SSE on territorial human development in Morocco. The empirical work of this study is limited to only one component of the SSE which are the cooperatives. The future perspectives of this work are to follow the impact of the three components after the implementation of the new development model in Morocco.

The main implication of this study is to map out the spatial role of cooperatives in territorial planning in Morocco. Policy makers still need to consolidate the implication of the cooperatives as a tool for promoting spatial equity, through the governance and the convergence between the different support and accompaniment programs for cooperatives in areas of human development deprivation.

The originality of this work comes from the exploration of the spatial dimension for the analysis of the role of the cooperatives for the promotion of a territorial human development. The SSE typologies described in the Moroccan provinces can allow actors to develop a business plan for the implementation of SSE initiatives.

The purpose of this paper is to use the most popular deep learning algorithm to complete the vehicle detection in the urban area of MiniSAR image, and provide reliable means for ground monitoring.

An accurate detector called the rotation region-based convolution neural networks (CNN) with multilayer fusion and multidimensional attention (M2R-Net) is proposed in this paper. Specifically, M2R-Net adopts the multilayer feature fusion strategy to extract feature maps with more extensive information. Next, the authors implement the multidimensional attention network to highlight target areas. Furthermore, a novel balanced sampling strategy for hard and easy positive-negative samples and a global balanced loss function are applied to deal with spatial imbalance and objective imbalance. Finally, rotation anchors are used to predict and calibrate the minimum circumscribed rectangle of vehicles.

By analyzing many groups of experiments, the validity and universality of the proposed model are verified. More importantly, comparisons with SSD, LRTDet, RFCN, DFPN, CMF-RCNN, R3Det, SCRDet demonstrate that M2R-Net has state-of-the-art detection performance.

The progress in the field of MiniSAR application has been slow due to strong speckle noise, phase error, complex environments and a low signal-to-noise ratio. In addition, four kinds of imbalances, i.e. spatial imbalance, scale imbalance, class imbalance and objective imbalance, in object detection based on the CNN greatly inhibit the optimization of detection performance.

This research can not only enrich the means of daily traffic monitoring but also be used for enemy intelligence reconnaissance in wartime.