@article {271, title = {Drivers of change in biodiversity and ecosystem services in the Cantareira System Protected Area : A prospective analysis of the implementation of public policies}, journal = {Biota Neotropica}, volume = {20}, year = {2020}, month = {05/2020}, type = {Article}, abstract = {The lack of implementation of well-designed public policies aimed at the conservation of natural ecosystems has resulted, at a global level, in the decline of ecosystem functioning and, consequently, of the contributions they make to people. The poor enforcement of important environmental legislation in Brazil - for instance, the {\textquotedblleft}Atlantic Forest Law{\textquotedblright} (Law n.11.428/2006) and the {\textquotedblleft}Forest Code{\textquotedblright} (Law n.12.651/2012) - could compromise the overall maintenance of ecosystems and the services they provide. To explore the implications of different levels of federal laws{\textquoteright} enforcement within the Cantareira System Protected Area (PA) - a PA in southeastern Brazil that provides fresh water for 47\% of the Sao Paulo Metropolitan Area -, we developed a conceptual framework to identify indirect and direct drives of biodiversity and ecosystem changes. We also projected four land-use scenarios to 2050 to test the effects of deforestation control and forest restoration practices on biodiversity and ecosystem services maintenance: the {\textquotedblleft}business-as-usual{\textquotedblright} scenario (BAU), which assumes that all trends in land-use cover changes observed in the past will continue in the future, and three alternative exploratory scenarios considering the Atlantic Forest Law implementation, the partial implementation of the Forest Code and the full implementation of the Forest Code. Using the land-use maps generated for each scenario, we assessed the impacts of land-use changes on biodiversity conservation and soil retention. Our results revealed all alternative scenarios could increase biodiversity conservation (by 7\%; 12\%; and 12\%, respectively), reduce soil loss (by 24.70\%; 34.70\%; and 38.12\%, respectively) and sediment exportation to water (by 27.47\%; 55.06\%; and 59.28\%, respectively), when compared to the BAU scenario. Our findings highlight the importance of restoring and conserving native vegetation for the maintenance and improvement of biodiversity conservation and for the provision of ecosystem services.}, keywords = {Biodiversity, Cantareira System Protected Area, Ecosystem services, GLOBIO, InVEST, Modeling, Scenarios}, issn = {1676-0611}, doi = {https://doi.org/10.1590/1676-0611-bn-2019-0915 }, url = {https://www.scielo.br/scielo.php?script=sci_arttext\&pid=S1676-06032020000500201}, author = {Viviane Dib and Marco Aur{\'e}lio Nalon and Nino Tavares Amazonas and Cristina Yuri Vidal and Iv{\'a}n A. Ortiz-Rodr{\'\i}guez and Jan Dan{\v e}k and Ma{\'\i}ra Formis de Oliveira and Paola Alberti and Rafaela Aparecida da Silva and Ra{\'\i}za Salom{\~a}o Precinoto and Taciana Figueiredo Gomes} } @conference {254, title = {Self-Adapting Trajectory Segmentation}, booktitle = {3rd International Workshop on Big Mobility Data Analytics (BMDA) 2020}, year = {2020}, abstract = {Identifying the portions of trajectory data where movement ends and a significant stop starts is a basic, yet fundamental task that can affect the quality of any mobility analytics process. Most of the many existing solutions adopted by researchers and practitioners are simply based on fixed spatial and temporal thresholds stating when the moving object remained still for a significant amount of time, yet such thresholds remain as static parameters for the user to guess. In this work we study the trajectory segmentation from a multi-granularity perspective, looking for a better understanding of the problem and for an automatic, parameter-free and user-adaptive solution that flexibly adjusts the segmentation criteria to the specific user under study. Experiments over real data and comparison against simple competitors show that the flexibility of the proposed method has a positive impact on results.}, keywords = {Mobility Data Mining, Segmentation, User Modeling}, author = {Agnese Bonavita and Guidotti, Riccardo and Nanni,Mirco} }