A set of R packages to estimate population counts from mobile phone data

11/04/2021
by   Bogdan Oancea, et al.
0

In this paper, we describe the software implementation of the methodological framework designed to incorporate mobile phone data into the current production chain of official statistics during the ESSnet Big Data II project. We present an overview of the architecture of the software stack, its components, the interfaces between them, and show how they can be used. Our software implementation consists in four R packages: destim for estimation of the spatial distribution of the mobile devices, deduplication for classification of the devices as being in 1:1 or 2:1 correspondence with its owner, aggregation for estimation of the number of individuals detected by the network starting from the geolocation probabilities and the duplicity probabilities and inference which combines the number of individuals provided by the previous package with other information like the population counts from an official register and the mobile operator penetration rates to provide an estimation of the target population counts.

READ FULL TEXT
research
10/01/2021

A Bayesian approach to location estimation of mobile devices from mobile network operator data

Mobile network operator (MNO) data are a rich data source for official s...
research
07/25/2019

An Empirical Analysis of the Python Package Index (PyPI)

In this research, we provide a comprehensive empirical summary of the Py...
research
06/06/2023

Reconstructing human activities via coupling mobile phone data with location-based social networks

In the era of big data, the ubiquity of location-aware portable devices ...
research
09/26/2018

Geographical veracity of indicators derived from mobile phone data

In this contribution we summarize insights on the geographical veracity ...
research
07/03/2012

Inferring land use from mobile phone activity

Understanding the spatiotemporal distribution of people within a city is...
research
12/03/2020

MLPerf Mobile Inference Benchmark: Why Mobile AI Benchmarking Is Hard and What to Do About It

MLPerf Mobile is the first industry-standard open-source mobile benchmar...
research
06/18/2018

Fractal Scaling of Population Counts Over Time Spans

Attributes which are infrequently expressed in a population can require ...

Please sign up or login with your details

Forgot password? Click here to reset