On the relation between socio-economic status and physical mobility

Frías-Martínez, Vanessa; Virseda-Jerez, Jesus; Frı́as-Martı́nez, Enrique

doi:10.1080/02681102.2011.630312

Cited by 37 publications

(26 citation statements)

References 12 publications

(18 reference statements)

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“…The most significant features were then used in a variety of machine learning techniques, and shown to achieve up to 80% accuracy when classifying areas according to three classes of SEL. The method developed in [29] was further investigated by Frias-Martinez et al [13,12], who then implemented it in a GUI-based system, designed to reduce the number of census areas that needed to be manually surveyed, by using surveyed data as training labels, and using the model to estimate the remainder [11]. This time, the highest accuracy quoted is 76% for a 3-class problem and 63% for a 6-class problem.…”

Section: Related Workmentioning

confidence: 99%

Poverty on the cheap

Smith-Clarke

Mashhadi²,

Capra

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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Governments and other organisations often rely on data collected by household surveys and censuses to identity areas in most need of regeneration and development projects. However, due to the high cost associated with the data collection process, many developing countries conduct such surveys very infrequently and include only a rather small sample of the population, thus failing to accurately capture the current socio-economic status of the country's population. In this paper, we address this problem by means of a methodology that relies on an alternative source of data from which to derive up to date poverty indicators, at a very fine level of spatio-temporal granularity. Taking two developing countries as examples, we show how to analyse the aggregated call detail records of mobile phone subscribers and extract features that are strongly correlated with poverty indexes currently derived from census data.

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Section: Related Workmentioning

confidence: 99%

Poverty on the cheap

Smith-Clarke

Mashhadi²,

Capra

2014

Proceedings of the SIGCHI Conference on Human Factors in Computing Systems

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“…Chang et al [40] found that the correlations (i.e., 0.9052) of online activities with the tertiary industry was larger than the secondary industry (i.e., 0.8688); and much larger than the primary industry (i.e., 0.0482). The strong relation between socio-economic status and physical human mobility were also observed [41,42]. In all, some literatures used human activities to successfully estimate economic status and demonstrated the hidden correlations of human activities and economic developments [41,42].…”

Section: Literature Reviewsmentioning

confidence: 95%

“…The strong relation between socio-economic status and physical human mobility were also observed [41,42]. In all, some literatures used human activities to successfully estimate economic status and demonstrated the hidden correlations of human activities and economic developments [41,42].…”

Section: Literature Reviewsmentioning

confidence: 95%

A Novel Effective Indicator of Weighted Inter-City Human Mobility Networks to Estimate Economic Development

Jiang

Chen

et al. 2019

Sustainability

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Estimation of economic development in advance is benefit to test the validity of economic policy or to take timely remedial measures for economic recession. Due to the inevitable connections between human mobility and economic status, estimation of economic trend in advance from easily observable big data in human mobility has the superiority of authenticity, timeliness, and convenience. However, high-precision quantitative relations between human mobility and economic growth remain an outstanding question. To this issue, we firstly analyzed and compared the general patterns of human mobility and economic development; then, a novel, simple, and effective hybrid human mobility indicator ( H H M I i ) of weighted human mobility networks was proposed to quantitatively estimate economic growth. H H M I i contained two parts, that is, the interaction volumes of a given city with all participation cities and only top hub cities, respectively. This implied that the economic growth of a city is affected by not only its own strength, but also the cooperation with hub cities. Several empirical experiments demonstrated that the proposed H H M I i had an exceedingly high estimation ability of economic growth, especially for the tertiary industry. Compared with other complex network indicators, H H M I i had a distinct advantage and its best accuracy reached 0.9543. These results can provide policy-making supports for inter-city sustainable coordinated development.

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“…Other related works include that of Soto et al [24] and Frias-Martinez et al [12,11], in which machine learning methods are used to predict the socio-economic status of a city's neighbourhoods using CDR data. Individual subscribers' mobile phone data is used as input, as opposed to the aggregated data used in the previously mentioned works, which allows for a richer set of features to be created, potentially leading to better predictive performance, but also raises privacy concerns.…”

Section: Related Workmentioning

confidence: 99%

Beyond the Baseline

Smith-Clarke

Capra

2016

Proceedings of the 25th International Conference on World Wide Web

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Within the remit of 'Data for Development' there have been a number of promising recent works that investigate the use of mobile phone Call Detail Records (CDRs) to estimate the spatial distribution of poverty or socio-economic status. The methods being developed have the potential to offer immense value to organisations and agencies who currently struggle to identify the poorest parts of a country, due to the lack of reliable and up to date survey data in certain parts of the world. However, the results of this research have thus far only been presented in isolation rather than in comparison to any alternative approach or benchmark. Consequently, the true practical value of these methods remains unknown.Here, we seek to allay this shortcoming, by proposing two baseline poverty estimators grounded on concrete usage scenarios: one that exploits correlation with population density only, to be used when no poverty data exists at all; and one that also exploits spatial autocorrelation, to be used when poverty data has been collected for a few regions within a country. We then compare the predictive performance of these baseline models with models that also include features derived from CDRs, so to establish their real added value. We present extensive analysis of the performance of all these models on data acquired for two developing countries -Senegal and Ivory Coast. Our results reveal that CDR-based models do provide more accurate estimates in most cases; however, the improvement is modest and more significant when estimating (extreme) poverty intensity rates rather than mean wealth.

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On the relation between socio-economic status and physical mobility

Cited by 37 publications

References 12 publications

Poverty on the cheap

Poverty on the cheap

A Novel Effective Indicator of Weighted Inter-City Human Mobility Networks to Estimate Economic Development

Beyond the Baseline

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