Abstract
Abstract. Solid waste management is an important urban issue to be addressed in every city. In the smart city context, waste collection allows massive collection of data representing movements, provided by satellite tracking technologies and sensors on waste collection equipment. For decision makers to take advantage of this opportunity, an analytical tool suitable for the waste management context, able to visualize the complexity of the data and to deal with different types of formats in which the data is stored is required.The aim of this paper is to evaluate the potential of an interactive data analysis tool, based on R and R-Shiny, to better understand the particularities of a waste collection service and how it relates to the local city context. The User-centered Analysis-Task driven model (AVIMEU) is presented. The model is organized into seven components: database load, classification panel, multivariate analysis, concurrency, origin-destination, points of interest and itinerary. The model was implemented as a test case for the waste collection service of the city of Pasto in the southwest of Colombia. It is shown that the model based on visual analysis is a promising approach that should be further enhanced. The analyses are oriented in such a way that they provide practical information to the agents or experts of the service. The model is available on the site https://github.com/MerariFonseca/AVIMEU-visual-analytics-for-movement-data-in-R .
Highlights
1.1 Urban movement data for waste collectionMunicipal solid waste has become a fast-growing problem
Visual analytics is an alternative for understanding urban phenomena using data
The aim of this paper is to evaluate the potential of an interactive data analysis tool, based on R and R-Shiny, to better understand the particularities of a waste collection service and how it relates to the local context of the city
Summary
Municipal solid waste has become a fast-growing problem. It is one of the main challenges of the smart cities because this public service is essential to improve the citizens’ quality of life. There are few studies that focus on how to improve waste management in a smart city data-driven context (Esmaeilian et al, 2018). A considerable number of these proposals have been directed towards the analysis of transport and traffic problems (Zheng et al 2016). These interactive visual methods can be applied to study different types of urban phenomena and services
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