Banca de DEFESA: GISLIANY LILLIAN ALVES DE OLIVEIRA

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
STUDENT : GISLIANY LILLIAN ALVES DE OLIVEIRA
DATE: 24/01/2020
TIME: 14:00
LOCAL: CIVT B321
TITLE:

A data-driven approach for the generation of a liveability index based on the UBER API

KEY WORDS:

Data Science, Uber, Liveability Indicators, Urbanization,Sustainable Urban Development.

PAGES: 80
BIG AREA: Engenharias
AREA: Engenharia Elétrica
SUMMARY:

One of the global dilemmas concerns the accelerated urban transition in the last decades. Therefore, promoting sustainable urban development to accommodate population growth is extremely important. Under those circumstances, the concept of liveability arises, being defined as a principle that combines economic, social and environmental attributes to promote quality of life and human well-being, and it is widely discussed in the New Urban Agenda (NUA) adopted by the United Nations in 2016. NUA defines policies to promote the consolidation of  Sustainable Development Goals (SDGs), with its Goal 11 focusing on a pro-urban future. To supervise SDGs implementation and ensure that these goals are met, it is recommended the use of indicators and the liveability concept can then be associated with an indicator for this purpose. However, there are known issues related to data unavailability, poor quality and aggregation, that make the SDGs monitoring difficult. Considering the described scenario, this work proposes a liveability indicator that combines traditional census data with alternative data sources, such as data  from Uber, a popular ride-sharing service. Assuming that Uber service behavior can act as a proxy to liveability, a data science approach based on exploratory and spatial data analysis was conducted using Uber’s Estimated Time of Arrival (ETA) data sourced for the Brazilian city of Natal (RN). This approach aims to build a composite indicator which can portray at some level the liveability for that city. The proposed methodology was applied at two different spatial aggregation levels: neighborhoods and Human Development Unities (HDUs). Results showed how the Uber service oscillates spatially and how it reacts to weather variations, festivals, and other events, as well as its relations with existing social and infrastructural indicators. It was also observed that different spatial aggregation levels affect the Uber ETA and its relations with socioeconomic variables. Finally, the proposed indicator was created at HDU scale to be applied in sustainable development monitoring. Furthermore, it was concluded that West and North administrative regions of Natal predominantly have localities with the worst liveability indicators. 

BANKING MEMBERS:
Presidente - 2885532 - IVANOVITCH MEDEIROS DANTAS DA SILVA
Externa ao Programa - 2081758 - LUCIANA CONCEICAO DE LIMA
Interno - 1153006 - LUIZ AFFONSO HENDERSON GUEDES DE OLIVEIRA
Externa à Instituição - PATRICIA TAKAKO ENDO - UPE