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Combining Twitter and Earth Observation Data for Local Poverty Mapping

Kondmann, Lukas and Häberle, Matthias and Zhu, Xiao Xiang (2020) Combining Twitter and Earth Observation Data for Local Poverty Mapping. In: NeuRIPS Machine Learning for the Developing World Workshop, pp. 1-5. NeuRIPS Machine Learning for the Developing World Workshop, 2020-12-12, Vancouver, Canada.

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Official URL: https://drive.google.com/file/d/12W7p4TBAlUV57EN-Iv4QofOdCNnr3tmp

Abstract

Accurate and timely data on economic development is essential for policy-makers in low- and middle-income countries where such data is often unavailable. To fill this gap, existing approaches have used alternative data sources to proxy for levels of local development such as satellite imagery or mobile phone data. In this paper, we underline the power of an underrated data source for poverty mapping: Geolocated tweets. We show that the number of tweets in a region as singular input can already explain 55 % of the variation in local wealth in Sub-Saharan Africa with a simple Random Forest model. When nighttime light and Twitter usage information are combined as inputs to a Random Forest model they already explain 65% of the variation in local wealth which is in the range of state-of-the-art neural network architectures based on satellite images. Our results show that the naive combination of these data sources in a random forest is already competitive in performance and more elaborate fusion approaches are a promising direction to advance the accuracy of poverty mapping.

Item URL in elib:https://elib.dlr.de/137109/
Document Type:Conference or Workshop Item (Poster)
Title:Combining Twitter and Earth Observation Data for Local Poverty Mapping
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Kondmann, LukasUNSPECIFIEDhttps://orcid.org/0000-0002-2253-6936UNSPECIFIED
Häberle, MatthiasUNSPECIFIEDhttps://orcid.org/0000-0001-9550-5252UNSPECIFIED
Zhu, Xiao XiangUNSPECIFIEDhttps://orcid.org/0000-0001-5530-3613UNSPECIFIED
Date:2020
Journal or Publication Title:NeuRIPS Machine Learning for the Developing World Workshop
Refereed publication:Yes
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Page Range:pp. 1-5
Status:Published
Keywords:Remote Sensing, Poverty Mapping, Twitter, Data Fusion
Event Title:NeuRIPS Machine Learning for the Developing World Workshop
Event Location:Vancouver, Canada
Event Type:international Conference
Event Date:12 December 2020
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Space
HGF - Program Themes:Earth Observation
DLR - Research area:Raumfahrt
DLR - Program:R EO - Earth Observation
DLR - Research theme (Project):R - Remote Sensing and Geo Research
Location: Oberpfaffenhofen
Institutes and Institutions:Remote Sensing Technology Institute > EO Data Science
Deposited By: Kondmann, Lukas
Deposited On:13 Nov 2020 11:13
Last Modified:24 Apr 2024 20:39

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