In many developing countries there remains a limited view on the socioeconomic status of the population, owing to the high cost associated with detailed and comprehensive surveying. This situation has encouraged a number of researchers to attempt to exploit alternative sources of data in order to derive estimates, including mobile phone data, which offers a rich depiction of the social dynamics of a population. Meanwhile, from the level of the individual to the city, access to information has been posited as an important factor in determining prosperity and economic development. In this paper we explore this relationship by simulating the flow of information through a mobile phone call graph in two sub-Saharan countries. We find a strong relationship between a location's average wealth and its access to information as determined by the simulations in one country, and a weaker correlation in the second country. This finding adds to recent evidence that mining patterns from mobile phone data represents a viable means to estimate poverty in places where traditionally derived estimates are lacking. We further investigate the impact of various factors on the empirical results in order to explain the variation between the two countries.

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.

Governments and other organisations often rely on data collected by household surveys and censuses to identify 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.

Urban crowd-sourcing has become a popular paradigm to harvest spatial information about our evolving cities directly from citizens. OpenStreetMap is a successful example of such paradigm, with an accuracy of its user-generated content comparable to that of curated databases (e.g., Ordnance Survey). Coverage is however low and most importantly non-uniformly distributed across the city. Being able to model the spontaneous growth of digital information in these domains is required, so to be able to plan interventions aimed at gathering content about areas that would otherwise be neglected. Inspired by models of physical urban growth developed by urban planners, we build a model of digital growth of crowd-sourced spatial information that is both easy to interpret and dynamic, so to be able to determine what factors impact growth and how these change over time. We build and test the model against five years of OpenStreetMap data for the city of London, UK. We then run the model against two other cities, chosen for their different physical and digital growth's characteristics, so to stress-test the model. We conclude with a discussion of the implications of this work on both developers and users of urban crowd-sourcing applications.

A common metaphor to describe the movement of people within a city is that of blood flowing through the veins of a living organism. We often speak of the 'pulse of the city' when referring to flow patterns we observe. Here we extend this metaphor by hypothesising that by monitoring the flow of people through a city we can assess the city's health, as a nurse takes a patient's heart-rate and blood pressure during a routine health check. Using an automated fare collection dataset of journeys made on the London rail system, we build a classification model that identifies areas of high deprivation as measured by the Indices of Multiple Deprivation, and achieve a precision, sensitivity and specificity of 0.805, 0.733 and 0.810, respectively. We conclude with a discussion of the potential benefits this work provides to city planning, policymaking, and citizen engagement initiatives.