Literature DB >> 34077441

Modelling urban vibrancy with mobile phone and OpenStreetMap data.

Federico Botta1, Mario Gutiérrez-Roig2.   

Abstract

The concept of urban vibrancy has become increasingly important in the study of cities. A vibrant urban environment is an area of a city with high levels of human activity and interactions. Traditionally, studying our cities and what makes them vibrant has been very difficult, due to challenges in data collection on urban environments and people's location and interactions. Here, we rely on novel sources of data to investigate how different features of our cities may relate to urban vibrancy. In particular, we explore whether there are any differences in which urban features make an environment vibrant for different age groups. We perform this quantitative analysis by extracting urban features from OpenStreetMap and the Italian census, and using them in spatial models to describe urban vibrancy. Our analysis shows a strong relationship between urban features and urban vibrancy, and particularly highlights the importance of third places, which are urban places offering opportunities for social interactions. Our findings provide evidence that a combination of mobile phone data with crowdsourced urban features can be used to better understand urban vibrancy.

Entities:  

Year:  2021        PMID: 34077441     DOI: 10.1371/journal.pone.0252015

Source DB:  PubMed          Journal:  PLoS One        ISSN: 1932-6203            Impact factor:   3.240


  4 in total

1.  Recent advances in urban system science: Models and data.

Authors:  Elsa Arcaute; José J Ramasco
Journal:  PLoS One       Date:  2022-08-17       Impact factor: 3.752

2.  Population estimation beyond counts-Inferring demographic characteristics.

Authors:  Noée Szarka; Filip Biljecki
Journal:  PLoS One       Date:  2022-04-05       Impact factor: 3.240

3.  How Did the Built Environment Affect Urban Vibrancy? A Big Data Approach to Post-Disaster Revitalization Assessment.

Authors:  Hongyu Gong; Xiaozihan Wang; Zihao Wang; Ziyi Liu; Qiushan Li; Yunhan Zhang
Journal:  Int J Environ Res Public Health       Date:  2022-09-26       Impact factor: 4.614

4.  Rapid indicators of deprivation using grocery shopping data.

Authors:  Adam Bannister; Federico Botta
Journal:  R Soc Open Sci       Date:  2021-12-22       Impact factor: 2.963

  4 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.