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دسته بندی:
داده های بزرگ - big data
سال انتشار:
2017
عنوان انگلیسی مقاله:
Constructing spatiotemporal poverty indices from big data
ترجمه فارسی عنوان مقاله:
ساخت شاخصهای فقر فضایی و زمانی از داده های بزرگ
منبع:
Sciencedirect - Elsevier - Journal of Business Research 70 (2017) 318–327
نویسنده:
Christopher Njuguna a,⁎, Patrick McSharry a,
چکیده انگلیسی:
Big data offers the potential to calculate timely estimates of the socioeconomic development of a region. Mobile
telephone activity provides an enormous wealth of information that can be utilized alongside household surveys.
Estimates of poverty and wealth rely on the calculation of features from call detail records (CDRs), however,
mobile network operators are reluctant to provide access to CDRs due to commercial and privacy concerns. As
a compromise, this study shows that a sparse CDR dataset combined with other publicly available datasets
based on satellite imagery can yield competitive results. In particular, a model is built using two CDR-based
features, mobile ownership per capita and call volume per phone, combined with normalized satellite nightlight
data and population density, to estimate the multi-dimensional poverty index (MPI) at the sector level in
Rwanda. This model accurately estimates the MPI for sectors in Rwanda that contain mobile phone cell towers
(cross-validated correlation of 0.88)
Keywords:Call detail record (CDR)|Poverty index|Machine learning|Big data|Socioeconomic level|Rwanda
قیمت: رایگان
توضیحات اضافی:
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