Spatial heterogeneity, spatial dependence and spatial scale constitute key features of spatial analysis of housing markets. However, the common practice of modelling spatial dependence as being generated by spatial interactions through a known spatial weights matrix is often not satisfactory. While existing estimators of spatial weights matrices are based on repeat sales or panel data, this paper takes the approach to a cross-section setting. Specifically, based on an a priori definition of housing submarkets and the assumption of a multifactor model, we develop maximum likelihood methodology to estimate hedonic models that facilitate understanding of both spatial heterogeneity and spatial interactions. The methodology, based on statistical orthogonal factor analysis, applied to the urban housing market of Aveiro (Portugal) at two different spatial scales, provides exciting inferences on the spatial structure of the housing market. RÉSUMÉ L'hétérogénéité spatiale, la dépendance spatiale et l’échelle spatiale sont des caractéristiques clé de l'analyse spatiale dans les marchés de l'immobilier. Toutefois, la pratique habituelle de la modélisation de la dépendance spatiale comme étant le résultat d'interactions spatiales par le biais d'une matrice de poids spatiaux n'est souvent pas satisfaisante. Alors que les estimateurs existants des matrices de poids spatiaux sont basés sur des données de panel ou des ventes répétées, la présente communication adopte le principe d'un cadre transversal. Plus spécifiquement, sur la base d'une définition à priori des sub-marchés de l'immobilier, et de l'hypothèse d'un modèle multifactoriel, nous créons une méthodologie de probabilité maximale pour estimer des modèles hédoniques qui facilitent les connaissances de l'hétérogénéité spatiale et des interactions spatiales. Cette méthodologie, basée sur une analyse des facteurs orthogonaux, appliquée au secteur de l'immobilier urbain à Aveiro (Portugal) à deux échelles spatiales différentes, fournit des inférences excitantes en ce qui concerne la structure spatiale du secteur de l'immobilier. EXTRACTO La heterogeneidad, dependencia y escala espaciales constituyen características clave del análisis espacial de los mercados de la vivienda. No obstante, la práctica común de modelar la dependencia espacial como algo generado por interacciones espaciales a través de una matriz conocida de pesos espaciales, a menudo, no es satisfactoria. Aunque los estimadores existentes de matrices de pesos espaciales se basan en ventas repetidas o datos de panel, este estudio lleva el planteamiento a un marco de corte transversal. Específicamente, basados en una definición a priori de los submercados de la vivienda y en la presuposición de un modelo de múltiples factores, desarrollamos una metodología de probabilidad máxima para estimar modelos hedónicos, que facilita la comprensión de la heterogeneidad espacial y las interacciones espaciales. La metodología, basada en el análisis estadístico de factores ortogonales y aplicada al mercado de la vivienda urbana de Aveiro (Portugal) en dos escalas espaciales diferentes, proporciona interesantes inferencias sobre la estructura espacial del mercado de la vivienda.

Guaranteeing justice in the decisions involved in spatial policies is a fundamental goal. Not only due to the inherent importance of justice as a policy goal, but also because it is, ultimately, th...

Objectives: To analyse the epidemiology, the resistome and the virulome of ceftolozane/tazobactam-susceptible or -resistant Pseudomonas aeruginosa clinical isolates recovered from surveillance studies in Portugal (STEP, 2017–18) and Spain (SUPERIOR, 2016–17).Methods: P. aeruginosa isolates were recovered from intra-abdominal, urinary tract and lower respiratory tract infections in ICU patients admitted to 11 Portuguese and 8 Spanish hospitals. MICs were determined (ISO-standard broth microdilution, EUCAST 2020 breakpoints). A subset of 28 ceftolozane/tazobactam-resistant P. aeruginosa isolates were analysed and compared with 28 ceftolozane/tazobactam-susceptible P. aeruginosa strains by WGS.Results: Clonal complex (CC) 235 (27%) and CC175 (18%) were the most frequent, followed by CC244 (13%), CC348 (9%), CC253 (5%) and CC309 (5%). Inter-hospital clonal dissemination was observed, limited to a geographical region (CC235, CC244, CC348 and CC253 in Portugal and CC175 and CC309 in Spain). Carbapenemases were detected in 25 isolates (45%): GES-13 (13/25); VIM type (10/25) [VIM-2 (4/10), VIM-20 (3/10), VIM-1 (2/10) and VIM-36 (1/10)]; and KPC-3 (2/25). GES-13-CC235 (13/15) and VIM type-CC175 (5/10) associations were observed. Interestingly, KPC-3 and VIM-36 producers showed ceftolozane/tazobactam-susceptible phenotypes. However, ceftolozane/tazobactam resistance was significantly associated with GES-13 and VIM-type carbapenemase production. Six non-carbapenemase producers also displayed ceftolozane/tazobactam resistance, three of them showing known ceftolozane/tazobactam resistance-associated mutations in the PBP3 gene, ftsI (R504C and F533L). Overall, an extensive virulome was identified in all P. aeruginosa isolates, particularly in carbapenemase-producing strains.Conclusions: GES-13-CC235 and VIM type-CC175 were the most frequent MDR/XDR P. aeruginosa clones causing infections in Portuguese and Spanish ICU patients, respectively. Ceftolozane/tazobactam resistance was mainly due to carbapenemase production, although mutations in PBP-encoding genes may additionally be involved.

Today, policymakers struggle to obtain information from specific smart city case studies. The literature lacks a unified view of current initiatives. This paper performs an empirical study with the aim of collecting evidence from the literature about existing smart city initiatives in the European Union (EU). The contribution of each paper and its geography are analyzed using content analysis to identify the number and type of initiatives in each country. A cluster analysis is performed to find relationships between countries and their development phase as well as the categories (areas) they are focused on. The results suggest that there are different levels of smart city development between the member states despite the initial year of their first result in the literature. Furthermore, 22 smart city categories clustered in four different groups were found. When compared to countries’ socio-economic characteristics, the results suggest the development of smart cities is significantly related to the public budget balance, gross domestic product and EU structural funds. In summary, this paper portrays the state-of-the-art of smart city initiatives in the member states of the European Union. Moreover, it represents a valuable contribution to decision makers to discuss ways to standardize smart city approaches in the European scope. Furthermore, the method used in this paper can inspire the development of collaborative dashboards for the exchange of best practices and data accessibility about case studies’ details.