An analytical framework to nowcast well-being using mobile phone data

An intriguing open question is whether measurements made on Big Data recording human activities can yield us high-fidelity proxies of socio-economic development and well-being. Can we monitor and predict the socio-economic development of a territory just by observing the behavior of its inhabitants through the lens of Big Data? In this paper, we design a data-driven analytical framework that uses mobility measures and social measures extracted from mobile phone data to estimate indicators for socio-economic development and well-being. We discover that the diversity of mobility, defined in terms of entropy of the individual users' trajectories, exhibits (i) significant correlation with two different socio-economic indicators and (ii) the highest importance in predictive models built to predict the socio-economic indicators. Our analytical framework opens an interesting perspective to study human behavior through the lens of Big Data by means of new statistical indicators that quantify and possibly "nowcast" the well-being and the socio-economic development of a territory

A Hierarchical Approach for Investigating Social Features of a City from Mobile Phone Call Detail Records

Cellphone service-providers continuously collect Call Detail Records (CDR) as a usage log containing spatio-temporal traces of phone users. We proposed a multi-layered hierarchical analytical model for large spatio-temporal datasets and applied that for the progressive exploration of social features of a city, e.g., social activities, relationships, and groups, from CDR. This approach utilizes CDR as the preliminary input for the initial layer, and analytical results from consecutive layers are added to the knowledge-base to be used in the subsequent layers to explore more detailed social features. Each subsequent layer uses the results from previous layers, facilitating the discovery of more in-depth social features not predictable in a single-layered approach using only raw CDR. This model starts with exploring aggregated overviews of the social features and gradually focuses on comprehensive details of social relationships and groups, which facilitates a novel approach for investigating CDR datasets for the progressive exploration of social features in a densely-populated city

Spanish income mobility in the period 2017-2020

Master in Economics: Empirical Applications and Policies. Academic Year 2022/23.Income mobility is a dynamic phenomenon that may pave the way for the establishment of egalitarian societies aimed at enhancing the global welfare of the individuals and dismantling social and economic concerns such as poverty and inequality. This Master’s thesis analyses short-term intragenerational income mobility in Spanish households and studies empirically its determinants for the period 2017-2020 by using longitudinal data from EU-SILC that track the incomes and demographic characteristics of a selected sample of the population. Income mobility analysis is examined on the basis of two characterized income mobility measures and the drivers of income mobility are identified by means of several OLS regressions. Results reveal that income dynamics differ notoriously across Autonomous Communities depending on the nature of the indices considered, though all measures agree on Cantabria and Asturias as some of the locations with the greatest and lowest income mobility, respectively, and transfers between individuals as the main component for income mobility in Spain for the period at issue. As for the econometric approach, outcomes suggest that the income level at the first year turns fundamental to define the relationship between the particular circumstances of households at the beginning of the period and their subsequent income change

Early Detection of COVID-19 Hotspots Using Spatio-Temporal Data

Recently, the Centers for Disease Control and Prevention (CDC) has worked with other federal agencies to identify counties with increasing coronavirus disease 2019 (COVID-19) incidence (hotspots) and offers support to local health departments to limit the spread of the disease. Understanding the spatio-temporal dynamics of hotspot events is of great importance to support policy decisions and prevent large-scale outbreaks. This paper presents a spatio-temporal Bayesian framework for early detection of COVID-19 hotspots (at the county level) in the United States. We assume both the observed number of cases and hotspots depend on a class of latent random variables, which encode the underlying spatio-temporal dynamics of the transmission of COVID-19. Such latent variables follow a zero-mean Gaussian process, whose covariance is specified by a non-stationary kernel function. The most salient feature of our kernel function is that deep neural networks are introduced to enhance the model's representative power while still enjoying the interpretability of the kernel. We derive a sparse model and fit the model using a variational learning strategy to circumvent the computational intractability for large data sets. Our model demonstrates better interpretability and superior hotspot-detection performance compared to other baseline methods

Mobile telephony services and rural-urban linkages

Mobile telephony services and rural-urban linkage

Examining the relationship between different urbanization settings, smartphone use to access the Internet and trip frequencies

No abstract available

Characterisation of urban environment and activity across space and time using street images and deep learning in Accra

The urban environment influences human health, safety and wellbeing. Cities in Africa are growing faster than other regions but have limited data to guide urban planning and policies. Our aim was to use smart sensing and analytics to characterise the spatial patterns and temporal dynamics of features of the urban environment relevant for health, liveability, safety and sustainability. We collected a novel dataset of 2.1 million time-lapsed day and night images at 145 representative locations throughout the Metropolis of Accra, Ghana. We manually labelled a subset of 1,250 images for 20 contextually relevant objects and used transfer learning with data augmentation to retrain a convolutional neural network to detect them in the remaining images. We identified 23.5 million instances of these objects including 9.66 million instances of persons (41% of all objects), followed by cars (4.19 million, 18%), umbrellas (3.00 million, 13%), and informally operated minibuses known as tro tros (2.94 million, 13%). People, large vehicles and market-related objects were most common in the commercial core and densely populated informal neighbourhoods, while refuse and animals were most observed in the peripheries. The daily variability of objects was smallest in densely populated settlements and largest in the commercial centre. Our novel data and methodology shows that smart sensing and analytics can inform planning and policy decisions for making cities more liveable, equitable, sustainable and healthy

Geography and development

The most striking fact about the economic geography of the world is the uneven spatial distribution of economic activity, including the coexistence of economic development and underdevelopment. High-income regions are almost entirely concentrated in a few temperate zones, half of the world's GDP is produced by 15 percent of the world's population, and 54 percent of the world's GDP is produced by countries occupying just 10 percent of the world's land area. The poorest half of the world's population produces only 14 percent of the world's GDP, and 17 of the poorest 20 nations are in tropical Africa. The unevenness is also manifest within countries and within metropolitan concentrations of activity. Why are these spatial differences in land rents and wages not bid away by firms and individuals in search of low-cost or high-income locations? Why does economic activity cluster in centers of activity? And what are the consequences of remoteness from existing centers? The authors argue that understanding these issues is central for understanding many aspects of economic development and underdevelopment at the international, national, and subcontinental levels. They review the theoretical and empirical work that illuminates how the spatial relationship between economic units changes and conclude that geography matters for development, but that economic growth is not governed by a geographic determinism. New economic centers can develop, and the costs of remoteness can be reduced. Many explicit policy instruments have been used to influence location decisions. But none has been systematically successful, and many have been very costly-in part because they were based on inappropriate expectations. Moreover, many ostensibly nonspatial policies that benefit specific sectors and households have spatial consequences since the targeted sectors and households are not distributed uniformly across space. These nonspatial policies can sometimes dominate explicitly spatial policies. Further work is needed to better understand these dynamics in developing countries.Economic Theory&Research,Decentralization,Labor Policies,Environmental Economics&Policies,Banks&Banking Reform,Banks&Banking Reform,Municipal Financial Management,Health Economics&Finance,Economic Theory&Research,Environmental Economics&Policies