Abstract Equity, fairness, and justice are related concepts widely discussed in several areas of study but remain an open field in terms of spatial justice and support decision systems application. Uneven spatial development have shown a tendency to amplify social inequalities alongside territories. To better understand the spatial configuration and spatial distribution of resources for different social groups, multiple objective criteria can be used to formulate optimal resource allocation. This work discusses spatial justice by utilitarianism and Rawlsian difference principle perspectives to formulate two models based on facility location problem (FLP) framework. Assuming the proximity to a desired opportunity (service or resource) as a measure of wellbeing and satisfaction, we weight the distances to the nearest facility by a social factor based on exponential function. Optimization results tend to favor outliers for weighted FLP, while the regular distances FLP formulation tend to favor heavy urban areas. We found that results are heavy context based, as the distribution of social groups are determinant in optimization process.

Abstract Access to health care services is a key concept in the formulation of health policies to improve the population’s health status and to mitigate inequities in health. Previous studies have significantly enhanced our understanding and knowledge of the role played by spatial distribution of health facilities in sustaining population health, with extensive research being devoted to the place-based accessibility theory, with special focus on the gravity-based methods. Although they represent a good starting point to analyse disparities across different regions, the results are not intelligible for policy-making purposes. Given the weaknesses of these methods and the multidimensional nature of the topic, this study intends to: (i) highlight the main measurements of access and their major challenges; and (ii) propose a framework based on multiple criteria decision analysis methods and GIS to appraise the population’s accessibility to health facilities. In particular, this framework is based on a new variant of the UTASTAR method, which requires decision makers and/or experts preference information, in the form of an ordinal ranking, similarly to the UTASTAR method, but to which cardinal information is also added. A numerical example is presented to illustrate the application of the proposed methodology.

Abstract Assuming that it is not possible to detach a dwelling from its location, this article highlights the relevance of space in the context of housing market analysis and the challenge of capturing the key elements of spatial structure in an automated valuation model: location attributes, heterogeneity, dependence and scale. Thus, the aim is to present a spatial automated valuation model (sAVM) prototype, which uses spatial econometric models to determine the value of a residential property, based on identification of eight housing characteristics (seven are physical attributes of a dwelling, and one is its location; once this spatial data is known, dozens of new variables are automatically associated with the model, producing new and valuable information to estimate the price of a housing unit). This prototype was developed in a successful cooperation between an academic institution (University of Aveiro) and a business company (PrimeYield SA), resulting the Prime AVM & Analytics product/service. This collaboration has provided an opportunity to materialize some of fundamental knowledge and research produced in the field of spatial econometric models over the last 15 years into decision support tools.