Human mobility and socioeconomic datasets of the Rio de Janeiro metropolitan area.

This data descriptor presents two main datasets and a set of auxiliary files. The mobility dataset presents a long-term study of human mobility in the Rio de Janeiro Metropolitan Area (RJMA) performed in the entire year of 2014 based on mobile phone data. The socioeconomic dataset presents selected socioeconomic variables of the Brazilian 2010 census. A set of auxiliary files is included to present georeferenced information and geographic features (shapefiles) and data used to validate the mobility estimates. The human mobility estimation was carried out using a methodology that allows direct integration with census data, based on an approximation of the geographic boundaries of census units by an aggregation of Voronoi polygons of the mobile phone antennas. The study area is the Brazilian local area 21, which includes the entire RJMA and four other municipalities. The mobility dataset is divided into two files: one is an estimation of the origin-destination (OD) matrix per day, and the other is a visitors' dataset where the number of visitors of each location is estimated in four shifts each day. The socioeconomic dataset presents information of 55 variables for each location, which have been used in different studies and present the longest human mobility dataset available for public use.

Contrasting social and non-social sources of predictability in human mobility.

Social structures influence human behavior, including their movement patterns. Indeed, latent information about an individual's movement can be present in the mobility patterns of both acquaintances and strangers. We develop a "colocation" network to distinguish the mobility patterns of an ego's social ties from those not socially connected to the ego but who arrive at a location at a similar time as the ego. Using entropic measures, we analyze and bound the predictive information of an individual's mobility pattern and its flow to both types of ties. While the former generically provide more information, replacing up to 94% of an ego's predictability, significant information is also present in the aggregation of unknown colocators, that contain up to 85% of an ego's predictive information. Such information flow raises privacy concerns: individuals sharing data via mobile applications may be providing actionable information on themselves as well as others whose data are absent.

Developing a fuzzy decision support system to determine the location of a landfill site.

This paper presents two case studies of municipal solid waste site location using a decision-support system based on fuzzy logic. This problem is very complex, as it requires the evaluation of different criteria, which involve environmental, social and economic data. Such data deal with a wide range of information that presents not only quantitative, but also qualitative knowledge. In order to deal with this characteristic, the developed system employs fuzzy rules due to its ability to treat linguistic variables and the human way of thinking. Conventional approaches tend to be less effective in dealing with the imprecise or vague nature of the linguistic assessment. A case study for selecting the location of a new municipal solid waste landfill for the city of Petropolis in Rio de Janeiro is presented. Testing of the proposed method was carried out using data from the municipal solid waste location for another municipality in Rio de Janeiro, Brazil.