Mobility Networks as a Predictor of Socioeconomic Status in Urban Systems

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14310%2F23%3A00133440" target="_blank" >RIV/00216224:14310/23:00133440 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/978-3-031-36808-0_32" target="_blank" >https://doi.org/10.1007/978-3-031-36808-0_32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-36808-0_32" target="_blank" >10.1007/978-3-031-36808-0_32</a>

Result language

angličtina

Original language name

Mobility Networks as a Predictor of Socioeconomic Status in Urban Systems

Original language description

Modeling socioeconomic dynamics has always been an area of focus for urban scientists and policymakers, who aim to better understand and predict the well-being of local neighborhoods. Such models can inform decision-makers early on about expected neighborhood performance under normal conditions, as well as in response to considered interventions before official statistical data is collected. While features such as population and job density, employment characteristics, and other neighborhood variables have been studied and evaluated extensively, research on using the underlying networks of human interactions and urban structures is less common in modeling techniques. We propose using the structure of the local urban mobility network (weighted by commute flows among a city’s geographical units) as a signature of the neighborhood and as a source of features to model its socioeconomic quantities. The network structure is quantified through node embedding generated using a graph neural network representation learning model. In the proof-of-concept task of modeling the location’s median income and housing profile in two different cities, such network structure features provide a noticeable performance advantage compared to using only the other available social features. This work can thus inform researchers and stakeholders about the utility of mobility network structure in a complex urban system for modeling various quantities of interest.

Czech name

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Czech description

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Type

D - Article in proceedings

CEP classification

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OECD FORD branch

10200 - Computer and information sciences

Project

<a href="/en/project/EF16_019%2F0000822" target="_blank" >EF16_019/0000822: CyberSecurity, CyberCrime and Critical Information Infrastructures Center of Excellence</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)<br>S - Specificky vyzkum na vysokych skolach

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Article name in the collection

Computational Science and Its Applications – ICCSA 2023. ICCSA 2023. Lecture Notes in Computer Science, vol 13957

ISBN

9783031368073

ISSN

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e-ISSN

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Number of pages

9

Pages from-to

453-461

Publisher name

Springer, Cham

Place of publication

Cham

Event location

Athens

Event date

Jul 3, 2023

Type of event by nationality

WRD - Celosvětová akce

UT code for WoS article

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