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In order for the goals and targets of the 2030 Agenda to be achieved, it is essential to “localize” the Sustainable Development Goals (SDGs), since it is only at the local level that it is possible to move towards their effective implementation. This article seeks to contribute to the development of evaluation and monitoring strategies for target 6.2 at the local level, adapting the official SDG indicator 6.2.1a and the international criteria established by the Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (coordinated by the World Health Organization (WHO) and the United Nations Children’s Fund (UNICEF)) at the municipal scale. Using the Belo Horizonte Metropolitan Region (RMBH) as a case study, a series of methodological procedures is proposed to assess and monitor equitable access to sanitation services. Inequalities in access to services between different population subgroups and between the municipalities that make up the RMBH are explored in different ways, including the evaluation of intersecting forms of inequality, the mapping of a synthetic index of inequality based on multiple criteria and the projection of the time needed to achieve universal access to services according to international criteria. The procedures applied demonstrated the existence of significant inequalities among the municipalities and population subgroups of the RMBH, which are not evident in the analysis of the aggregated data by municipality.

This entry contains the R data and codes used in the article "Comparing local calibration using random effects estimation and Bayesian calibrations. A case study with a mixed effect stem profile model". Stem profile models are complex models predicting the diameter of a tree at any position along the stem. These models are fitted on detailed datasets, which spatial representativity is poor. Local calibration brings strong improvements to their prediction capabilities. This study offers a good support for local calibration of allometric models for trees. The R code file 'R code for random effects estimation.R' contains the R code for estimating the random effect parameters for new data, in the example of the taper model used in the study (which has 3 random terms. The R data files contain the plot-level and tree-level measurements for each experimental site (Application Set site 1 and 2). The R code file 'Stan code ApSite 1.R' contains the STAN code for the Bayesian MCMC calibration of the taper model on trees sampled in the application site 1., Research co-funded by the Romanian National Authority for Scientific Research, CNCS – UEFISCDI, project PN-II-ID-PCE-2011-3-0781, and the EU H2020 DIABOLO (grant agreement No 633464)., {"references":["Bouriaud O, Stefan G, Saint-André L (2019) Comparing local calibration using random effects estimation and Bayesian calibrations. A case study with a mixed effect stem profile model. Annals of Forest Science."]}

This research examines urban Aboriginal homelessness and its effect on the overall wellbeing and social cohesiveness of peoples living in Winnipeg. Using a community-centered approach, we seek to document and analyze how Winnipeg adapted the mainstream Housing First model to reflect the local Indigenous context.

This entry contains theR data and codes used in the article "Comparing local calibration using random effects estimation and Bayesian calibrations. A case study with a mixed effect stem profile model". Stem profile models are complex models predicting the diameter of a tree at any position along the stem. These models are fitted on detailed datasets, which spatial representativity is poor. Local calibration brings strong improvements to their prediction capabilities. This study offers a good support for local calibration of allometric models for trees. The R code file 'R code for random effects estimation.R' contains the R code for estimating the random effect parameters for new data, in the example of the taper model used in the study (which has 3 random terms. The R data files contain the plot-level and tree-level measurements for each experimental site (Application Set site 1 and 2). The R code file 'Stan code ApSite 1.R' contains the STAN codefor the Bayesian MCMC calibrationof the taper model ontrees sampled in the application site 1.