On the Use of Satellite-Based Vehicle Flows Data to Assess Local Economic Activity: The Case of Philippine Cities

Go, Eugenia C.; Nakajima, Kenichi; Sawada, Yasuyuki; Taniguchi, Kiyoshi

doi:10.2139/ssrn.4057690

Cited by 2 publications

(2 citation statements)

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“…For example, Ratledge et al (2022) measured the impacts of electrification in Uganda by combining EO-ML predictions of local livelihood measures in difference-in-differences (DiD) analyses. Another study combined EO-ML classification and enumeration of automobiles with DiD to measure change in traffic volumes following construction of a new airport (Go et al, 2022). Here, the logic of using EO data to predict outcomes found in §4 is directly applied to the causal inference context while using long-standing identification practices such as DiD.…”

Section: Causal Machine Learning With Eo Datamentioning

confidence: 99%

A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

Sakamoto,

Jerzak,

Daoud

2024

Preprint

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Earth observation data such as satellite imagery can, when combined with machine learning, have profound impacts on our understanding of the geography of poverty through the prediction of living conditions, especially where government-derived economic indicators are either unavailable or potentially untrustworthy. Recent work has progressed in using EO data not only to predict spatial economic outcomes, but also to explore cause and effect, an understanding which is critical for downstream policy analysis. In this review, we first document the growth of interest in EO-ML analyses in the causal space. We then trace the relationship between spatial statistics and EO-ML methods before discussing the four ways in which EO data has been used in causal ML pipelines---(1.) poverty outcome imputation for downstream causal analysis, (2.) EO image deconfounding, (3.) EO-based treatment effect heterogeneity, and (4.) EO-based transportability analysis. We conclude by providing a workflow for how researchers can incorporate EO data in causal ML analysis going forward.

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Section: Causal Machine Learning With Eo Datamentioning

confidence: 99%

A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

Sakamoto,

Jerzak,

Daoud

2024

Preprint

View full text Add to dashboard Cite

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“…Henao-Cespedes et al (2022) and Minetto et al (2020) analysed how human and economic activity changed during the COVID-19 pandemic on a city scale. Go et al (2022) measured local economic activities using vehicle counts in satellite imageries, estimating the impact of the new infrastructure on the local economy. Mothobi & Grzybowski (2017) measured the level of infrastructure deficiency by nighttime light intensity to analyse the effects on mobile telecommunications, hoping to overcome the poor adoption of mobile money and deficient financial services in Africa.…”

Section: Satellite Imageriesmentioning

confidence: 99%

Integrating Street Views, Satellite Imageries and Remote Sensing Data Into Economics and the Social Sciences

Wang

2023

Social Science Computer Review

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Street views, satellite imageries and remote sensing data have been integrated into a wide spectrum of topics in the social sciences. Computer vision methods not only help analysts and policymakers make better decisions and produce more effective solutions but they also enable models to achieve more precise predictions and greater interpretability. In this paper, we review the growing literature applying such methods to economic issues and the social sciences, in which social scientists employ deep learning approaches to utilise image data to retrieve additional information. Typically, image data produce better results than traditional approaches and can provide detailed results and helpful insights to improve society and people’s well-being.

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On the Use of Satellite-Based Vehicle Flows Data to Assess Local Economic Activity: The Case of Philippine Cities

Cited by 2 publications

References 0 publications

A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

A Scoping Review of Earth Observation and Machine Learning for Causal Inference: Implications for the Geography of Poverty

Integrating Street Views, Satellite Imageries and Remote Sensing Data Into Economics and the Social Sciences

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