Neighbourhood Change

Advancing neighbourhood change research with data science and large-scale datasets

About

This is an initiative under the Urban Genome project. We use large-scale data and data science techniques to study city dynamics, urban social behaviour, and their relations with neighbourhood change.

Using Graph Neural Networks to Predict Local Culture
Thiago H Silva and Daniel Silver
Environment and Planning B: Urban Analytics and City Science - 2024 

By exploring a public large-scale dataset from Yelp, we show the potential of our approach for considering structural connectedness in predicting neighbourhood attributes, specifically to predict local culture. Results are promising from a substantive and methodologically point of view. 

Paper 

Culture Fingerprint: Identification of Culturally Similar Urban Areas Using Google Places Data
Fernanda Gubert, Gustavo Santos, Myriam Delgado, Daniel Silver, and Thiago H Silva
16th International Conference on Advances in Social Networks Analysis and Mining - ASONAM 2024

Under evaluation

Paper   -   Extra Material

Integrating Social Media Data: Venues, Groups and Activities
Mark Fox and Thiago H Silva
Expert Systems with Applications - 2024 

We focus on location-based social network platforms and present solutions to data integration based on physical venues, groups of users interacting with them, and activities performed in those venues. Besides, we also propose an ontology (Social Media Integration Ontology - SMIO) that provides a target data model into which data from multiple sources can be mapped with more precise, shared semantics. Our proposed approaches and ontology can help to enhance the variety of data that describes a venue or group and foster research into urban societies.

Paper 

Complex Causal Structures of Neighbourhood Change: Evidence From a Functionalist Model and Yelp Data
 Daniel Silver and Thiago H Silva
CITIES - 2023

Our study has sought to advance the literature on neighbourhood change and functional explanation by demonstrating how functional explanation can be revived in the context of big data. We adapted the classical model of functional explanation formulated by Stinchcombe to study neighbourhood change with Yelp data.

Paper - dataset 

Changing the Scene: applying four models of social evolution to the scenescape
 Daniel Silver,Thiago H Silva and Patrick Adler  
Wuhan University Journal (Philosophy and Social Sciences)  - 2022 

 This paper elaborates a multi-model approach to studying how local scenes change. Our overall goal is to point toward new research arcs on change models of scenes, and to give some clear examples and directions for how to think about and collect data to understand what makes some scenes change, others not, why, and in what directions.

Paper

A Markov model of urban evolution: Neighbourhood change as a complex process
Daniel Silver and Thiago H Silva
PLOS ONE - 2021

This paper seeks to advance neighbourhood change research and complexity theories of cities by developing and exploring a Markov model of socio-spatial neighbourhood evolution in Toronto, Canada.

Paper  -  citation

Towards Interoperability of Social Media: Venue Matching by Categories
Mark Fox and Thiago H Silva
Ontobras - 2021 

This paper focuses on the venue integration problem through categories of venues. We propose an ontology to support the integration of venues across different data sources in this context. This is an important building block for integrating multiple datasets to study urban issues.

Paper 

Team

Faculty

Daniel Silver - University of Toronto, Canada

Mark Fox - University of Toronto, Canada

Myriam Delgado - Universidade Tecnológica Federal do Paraná, Brazil

Thiago Silva - Universidade Tecnológica Federal do Paraná, Brazil


Students

Fernanda Gubert -Universidade Tecnológica Federal do Paraná, Brazil

Fernando Figueroa -University of Toronto, Canada

Gustavo Santos - -Universidade Tecnológica Federal do Paraná, Brazil

Acknowledgements

This work is done with the support of: