Next Article in Journal
PGNet: Pipeline Guidance for Human Key-Point Detection
Next Article in Special Issue
Spatial-Temporal Characteristic Analysis of Ethnic Toponyms Based on Spatial Information Entropy at the Rural Level in Northeast China
Previous Article in Journal
Compound Fault Diagnosis of Rolling Bearing Based on Singular Negentropy Difference Spectrum and Integrated Fast Spectral Correlation
Previous Article in Special Issue
The Emergence of Integrated Information, Complexity, and ‘Consciousness’ at Criticality
Open AccessArticle

Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area

1
TETIS, Univ Montpellier, AgroParisTech, Cirad, CNRS, INRAE, 34000 Montpellier, France
2
Laboratorio de Ecoinformática, Instituto de Conservación Biodiersidad y Territorio, Campus Isla Teja s/n, Valdivia 5110290, Chile
3
Instituto de Ecología y Biodiversidad, Facultad de Ciencias, Universidad de Chile, Las Palmeras, Ñuñoa, Santiago 7800003, Chile
4
Instituto de Sistemas Complejos de Valparaíso, Subida Artillería 470, Valparaíso 2360448, Chile
5
Getulio Vargas Foundation, Praia de Botafogo 190, Rio de Janeiro, RJ, 22250-900, Brazil
6
Department of Computer Science, Federal University of São João Del Rey, Sao João Del Rey, MG, 36301-360, Brazil
7
Coppe/Federal University of Rio de Janeiro, P.O. Box 68506, Rio de Janeiro, RJ 22941-972, Brazil
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Entropy 2020, 22(3), 368; https://doi.org/10.3390/e22030368
Received: 15 January 2020 / Revised: 6 March 2020 / Accepted: 13 March 2020 / Published: 23 March 2020
(This article belongs to the Special Issue Information Theory for Human and Social Processes)
Defining and measuring spatial inequalities across the urban environment remains a complex and elusive task which has been facilitated by the increasing availability of large geolocated databases. In this study, we rely on a mobile phone dataset and an entropy-based metric to measure the attractiveness of a location in the Rio de Janeiro Metropolitan Area (Brazil) as the diversity of visitors’ location of residence. The results show that the attractiveness of a given location measured by entropy is an important descriptor of the socioeconomic status of the location, and can thus be used as a proxy for complex socioeconomic indicators. View Full-Text
Keywords: mobile phone data; urban mobility; attractiveness; urban entropy; urban computing mobile phone data; urban mobility; attractiveness; urban entropy; urban computing
Show Figures

Figure 1

MDPI and ACS Style

Lenormand, M.; Samaniego, H.; Chaves, J.C.; da Fonseca Vieira, V.; da Silva, M.A.H.B.; Evsukoff, A.G. Entropy as a Measure of Attractiveness and Socioeconomic Complexity in Rio de Janeiro Metropolitan Area. Entropy 2020, 22, 368.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop