Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide
- URL: http://arxiv.org/abs/2002.11645v1
- Date: Wed, 26 Feb 2020 17:19:24 GMT
- Title: Facebook Ads as a Demographic Tool to Measure the Urban-Rural Divide
- Authors: Daniele Rama, Yelena Mejova, Michele Tizzoni, Kyriaki Kalimeri, Ingmar
Weber
- Abstract summary: We examine the usefulness of the Facebook Advertising platform, which offers a digital "census" of over two billions of its users.
We show that the population statistics that Facebook produces suffer from instability across time and incomplete coverage of sparsely populated municipalities.
Using official national census data, we evaluate our approach and confirm known significant urban-rural divides in terms of educational attainment and income.
- Score: 6.61600499731972
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: In the global move toward urbanization, making sure the people remaining in
rural areas are not left behind in terms of development and policy
considerations is a priority for governments worldwide. However, it is
increasingly challenging to track important statistics concerning this sparse,
geographically dispersed population, resulting in a lack of reliable,
up-to-date data. In this study, we examine the usefulness of the Facebook
Advertising platform, which offers a digital "census" of over two billions of
its users, in measuring potential rural-urban inequalities. We focus on Italy,
a country where about 30% of the population lives in rural areas. First, we
show that the population statistics that Facebook produces suffer from
instability across time and incomplete coverage of sparsely populated
municipalities. To overcome such limitation, we propose an alternative
methodology for estimating Facebook Ads audiences that nearly triples the
coverage of the rural municipalities from 19% to 55% and makes feasible
fine-grained sub-population analysis. Using official national census data, we
evaluate our approach and confirm known significant urban-rural divides in
terms of educational attainment and income. Extending the analysis to
Facebook-specific user "interests" and behaviors, we provide further insights
on the divide, for instance, finding that rural areas show a higher interest in
gambling. Notably, we find that the most predictive features of income in rural
areas differ from those for urban centres, suggesting researchers need to
consider a broader range of attributes when examining rural wellbeing. The
findings of this study illustrate the necessity of improving existing tools and
methodologies to include under-represented populations in digital demographic
studies -- the failure to do so could result in misleading observations,
conclusions, and most importantly, policies.
Related papers
- Fast Few shot Self-attentive Semi-supervised Political Inclination
Prediction [12.472629584751509]
It is increasingly common now for policymakers/journalists to create online polls on social media to understand the political leanings of people in specific locations.
We introduce a self-attentive semi-supervised framework for political inclination detection to further that objective.
We found that the model is highly efficient even in resource-constrained settings.
arXiv Detail & Related papers (2022-09-21T12:07:16Z) - So2Sat POP -- A Curated Benchmark Data Set for Population Estimation
from Space on a Continental Scale [11.38584315242023]
We provide a comprehensive data set for population estimation in 98 European cities.
The data set comprises a digital elevation model, local climate zone, land use proportions, nighttime lights in combination with multi-spectral Sentinel-2 imagery, and data from the Open Street Map initiative.
arXiv Detail & Related papers (2022-04-07T07:30:43Z) - BEV-Net: Assessing Social Distancing Compliance by Joint People
Localization and Geometric Reasoning [77.08836528980248]
Social distancing, an essential public health measure, has gained significant attention since the outbreak of the COVID-19 pandemic.
In this work, the problem of visual social distancing compliance assessment in busy public areas with wide field-of-view cameras is considered.
A dataset of crowd scenes with people annotations under a bird's eye view (BEV) and ground truth for metric distances is introduced.
A multi-branch network, BEV-Net, is proposed to localize individuals in world coordinates and identify high-risk regions where social distancing is violated.
arXiv Detail & Related papers (2021-10-10T23:56:37Z) - Census-Independent Population Estimation using Representation Learning [0.5735035463793007]
Census-independent population estimation approaches using alternative data sources have shown promise in providing frequent and reliable population estimates locally.
We explore recent representation learning approaches, and assess the transferability of representations to population estimation in Mozambique.
Using representation learning reduces required human supervision, since features are extracted automatically.
We compare the resulting population estimates to existing population products from GRID3, Facebook (HRSL) and WorldPop.
arXiv Detail & Related papers (2021-10-06T15:13:36Z) - Digital Divide: Mapping the geodemographics of internet accessibility
across Great Britain [0.0]
This research proposes the first solely sociodemographic measure of digital accessibility for Great Britain.
Digital inaccessibility affects circa 10 million people who are unable to access or make full use of the internet.
arXiv Detail & Related papers (2021-08-03T08:59:08Z) - Urban Sensing based on Mobile Phone Data: Approaches, Applications and
Challenges [67.71975391801257]
Much concern in mobile data analysis is related to human beings and their behaviours.
This work aims to review the methods and techniques that have been implemented to discover knowledge from mobile phone data.
arXiv Detail & Related papers (2020-08-29T15:14:03Z) - Using social media to measure demographic responses to natural disaster:
Insights from a large-scale Facebook survey following the 2019 Australia
Bushfires [3.441021278275805]
We introduce a rapid-response survey of post-disaster demographic and economic outcomes fielded through the Facebook app itself.
We use these survey responses to augment app-derived mobility data that comprises Facebook Displacement Maps.
We uncover several differences in key areas, including in displacement decision-making and timing.
arXiv Detail & Related papers (2020-08-09T05:55:26Z) - Magnify Your Population: Statistical Downscaling to Augment the Spatial
Resolution of Socioeconomic Census Data [48.7576911714538]
We present a new statistical downscaling approach to derive fine-scale estimates of key socioeconomic attributes.
For each selected socioeconomic variable, a Random Forest model is trained on the source Census units and then used to generate fine-scale gridded predictions.
As a case study, we apply this method to Census data in the United States, downscaling the selected socioeconomic variables available at the block group level, to a grid of 300 spatial resolution.
arXiv Detail & Related papers (2020-06-23T16:52:18Z) - Predicting Livelihood Indicators from Community-Generated Street-Level
Imagery [70.5081240396352]
We propose an inexpensive, scalable, and interpretable approach to predict key livelihood indicators from public crowd-sourced street-level imagery.
By comparing our results against ground data collected in nationally-representative household surveys, we demonstrate the performance of our approach in accurately predicting indicators of poverty, population, and health.
arXiv Detail & Related papers (2020-06-15T18:12:12Z) - CNN-based Density Estimation and Crowd Counting: A Survey [65.06491415951193]
This paper comprehensively studies the crowd counting models, mainly CNN-based density map estimation methods.
According to the evaluation metrics, we select the top three performers on their crowd counting datasets.
We expect to make reasonable inference and prediction for the future development of crowd counting.
arXiv Detail & Related papers (2020-03-28T13:17:30Z) - Measuring Spatial Subdivisions in Urban Mobility with Mobile Phone Data [58.720142291102135]
By 2050 two thirds of the world population will reside in urban areas.
This growth is faster and more complex than the ability of cities to measure and plan for their sustainability.
To understand what makes a city inclusive for all, we define a methodology to identify and characterize spatial subdivisions.
arXiv Detail & Related papers (2020-02-20T14:37:46Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.