01. Universidade Federal Rural de Pernambuco - UFRPE (Sede)
URI permanente desta comunidadehttps://arandu.ufrpe.br/handle/123456789/1
Navegar
3 resultados
Resultados da Pesquisa
Item Automatização dos serviços de coleta domiciliar com o QGIS e Python(2024-10-04) Prado, Artillis Henrique Mendes do; Medeiros, Victor Wanderley Costa de; http://lattes.cnpq.br/7159595141911505; http://lattes.cnpq.br/7294722691197017This work describes the development of an innovative tool for monitoring household waste collection services in the city of Recife, Brazil, using QGIS and Python. The main objective is to optimize the management of the Operational Control Center (CCO) of TPF, which operates within Emlurb, allowing for better monitoring of collection operations and analysis of vehicle routes. The developed platform collects and processes geospatial data, providing accurate and timely information about the trucks’ paths and generating reports that assist in service management. By integrating these technologies, the tool offers features such as identifying unserviced areas, analyzing response times by sector, and evaluating vehicle productivity. The generated reports provide a comprehensive view of operational efficiency, facilitating strategic decision-making, including route adjustments and the reallocation of more productive vehicles. Additionally, the tool allows filtering data by speed and distance tolerance, helping to verify whether the trucks adhered to their planned routes. Implementing this solution yields significant benefits for public management, including enhanced operational control, data accuracy, and transparency in waste collection activities. By generating real-time information and rapid results, the tool contributes to the efficiency of operations and the continuous improvement of services, fostering a more effective and data-driven administration. Thus, the developed system helps streamline the waste collection process, making it more agile and sustainable.Item Análise da recuperação de materiais recicláveis: uma aplicação do Propensity Score Matching na base de dados do SNIS(2023-09-14) Silva, Vicente Henrique da Costa; Araújo, Chiara Natércia França; http://lattes.cnpq.br/7273384016233113; http://lattes.cnpq.br/6905236621140933This undergraduate thesis aims to assess the impact of the implementation of Muni-cipal Integrated Solid Waste Management Plans (PMGIRS), as stipulated in Law No. 12,305/2010 - the National Solid Waste Policy. The central methodological approach utilizes the concept of Propensity Score Matching (PSM) to explore the causal effect of the presence of PMGIRS on the Recycling Material Recovery Rate. Additionally, the research incorporates the use of k-means clustering with the aim of better cha-racterizing and classifying the analyzed sample. This allows for the identification of natural data clusters and a deeper understanding of different potentials for solid was-te management in distinct municipalities. PSM is employed to create comparable groups of municipalities with and without PMGIRS, balancing observable characteris-tics and controlling for potential selection biases. The results obtained from this me-thodology will contribute to a well-founded and objective analysis of the impact of PMGIRS on municipal solid waste management. Combining PSM with clustering will enhance the analysis, enabling a more comprehensive characterization and precise classification of the studied sample.Item Plano Municipal de Saneamento Básico e seu impacto no Índice de Atendimento Total de Água, no Índice de Atendimento Total de Esgoto e na Taxa de Cobertura Regular do Serviço de Coleta de Resíduos Domiciliares(2023-09-14) Cirilo, Sajan Prya Correia; Araújo, Chiara Natércia França; http://lattes.cnpq.br/7273384016233113; http://lattes.cnpq.br/4977490558308115This study aims to examine the impact of the implementation of Municipal Basic Sanitation Plans (PMSB) on performance indicators related to water supply, sewage collection, and solid waste management services. The analysis is based on a methodological approach that combines the use of clustering (k-means), Propensity Score Matching (PSM), and linear regression. The clustering technique using the k-means algorithm will be employed to better characterize and classify the analyzed sample. The use of PSM will serve as the tool to create comparable sets of municipalities, with and without PMSB, ensuring that their observable characteristics are balanced and controlling potential selection biases. Linear regression will be applied to assess the relationships between PMSB and the behavior of the Total Water Coverage Index, the Total Sewage Coverage Index, and the Regular Household Waste Collection Service Coverage Rate. The results derived from this study will provide a robust analysis of the impact of Municipal Basic Sanitation Plans (PMSB) on municipal-level basic sanitation management.