Brazilian Institute of Geography and Statistics

An important practical use of coefficient estimates in a spatial regression model is to interpret the strength and range of implied spatial spillovers. In the classic spatial lag model, the structure of the reduced form ensures a smooth distance decay of neighboring multiplier effects. This can be viewed as following Tobler’s First Law of Geography. However, in the linear SLX and spatial Durbin models, this is no longer guaranteed. In this paper, we investigate the interplay between parameter space for the spatial coefficients and Tobler’s law, with a particular focus on the spatial Durbin model. We carry out a series of numerical illustrations and complement this with a small meta-analysis of empirical results reported in the literature. We provide a taxonomy of parameter values that yield different multiplier effects and the implied Tobler constraint. We close with some recommendations for practice.

Clustering algorithms are used to partition datasets associated with various real-world applications. However, in addition to the adopted algorithm, the obtained partition depends on the data distribution. Consequently, applying a single algorithm can lead to producing a poor-quality partition. Cluster Ensemble (CE) is an alternative to produce a good quality partition, as it combines different dataset partitions into a single consensus partition. According to the literature, in general, the consensus partition is less sensitive to noise when compared to that produced by a single algorithm. This work proposes a CE algorithm (BRKGA-CE) that combines: (i) three different strategies for producing base partitions; (ii) the BRKGA metaheuristic; (iii) the mean silhouette index, and (iv) an iterative method applied in the final phase of BRKGA-CE that allocates each object into a cluster. The core idea of BRKGA-CE is to find the representative objects of each cluster in order to maximize the mean silhouette index. To evaluate BRKGA-CE, computational experiments were carried out with 20 datasets and the main algorithms from the literature, where two well-known external validation indices (& Nscr;& Mscr;& Iscr; and & Ascr;& Rscr;) and hypothesis tests were applied. As a result, BRKGA-CE presented good-quality solutions, in the three strategies compared to the other algorithms.

Lack of social connections and limited social participation are associated with an increased risk of adverse health outcomes. Understanding the determinants that influence these social dimensions is essential for the early identification of vulnerable groups. Therefore, this study aimed to analyze factors associated with low social support and low social participation among Brazilian older adults with disabilities. A cross-sectional study was conducted with 5,511 older adults (≥ 60 years) with permanent disabilities, based on data from the 2019 Brazilian National Health Survey. Social support from friends and family, as well as the frequency of participation in physical, community, and religious activities, were assessed in relation to sociodemographic, economic, and health-related variables. Pearson’s Chi-square tests and multivariate logistic regression models were applied. The prevalence of permanent disability was 24.7