Spatial data integration from heterogeneous sources for urban computing

• Ano de defesa: 2023
• Autor(a) principal: Silva, Rodrigo Smarzaro da
• Tipo de documento: Tese
• Tipo de acesso: Acesso aberto
• Idioma: eng
• Instituição de defesa: Universidade Federal de Minas Gerais; Externas/Outras Instituições
• Palavras-chave em Português: Computação – Teses; Integração de dados espaciais – Teses; Computação urbana – Teses; Ciência da Computação
• Link de acesso: https://locus.ufv.br//handle/123456789/31172

Global urbanization is creating increasingly populous cities, and their services must be- come more efficient. Public transport is one of those essential services that directly affect the quality of living among the population. Today, various government and transportation agencies generate large volumes of data. At the same time, users of social networks, using smartphones, can enrich official sources with a range of information, from objective data to personal opinions and sentiments. There is an essential challenge in integrating such diverse and heterogeneous data sources. This work aims to propose, develop, and validate methods and techniques for integrating multiple heterogeneous urban data sources within the conceptual framework of Urban Computing. The methods developed were used in a case study to build a multimodal transportation network for Belo Horizonte. To test the results, a set of routes were determined using the multimodal transport network created and Google Maps, obtaining results close to time and distance. A case study was created to determine the urban quality of life indexes from integrated data from different sources to demonstrate the possibility of using the multimodal transport network. The data model and methods developed in this work can be used to obtain relevant information about the city and to subsidize analysis and decision-making in the various disciplines that deal with urban problems. Keywords: Spatial data integration. Urban computing