Data-driven Analytical Framework Research Articles

Cross-jurisdictional disaster preparedness: A Nigeria-USA data-analytical approach

Disasters pose significant challenges globally, demanding effective preparedness strategies to mitigate their impact. This study proposes a novel approach to disaster preparedness through cross-jurisdictional collaboration between Nigeria and the United States, leveraging data analytics for enhanced readiness. Nigeria, with its vulnerability to various disasters, and the United States, known for its advanced disaster management systems, offer a unique opportunity for mutual learning and collaboration. This research aims to bridge the gap in disaster preparedness between these two countries by employing a data-driven analytical framework. Through the collection, analysis, and comparison of disaster-related data from both nations, this approach seeks to identify commonalities, differences, and best practices in disaster preparedness strategies. By leveraging the strengths of both countries, such as Nigeria's community resilience and the USA's technological advancements, this approach aims to develop comprehensive disaster preparedness models tailored to the specific needs and contexts of each jurisdiction. Key components of this approach include data collection from various sources, including historical disaster records, socio-economic indicators, infrastructure data, and community resilience metrics. Advanced analytical techniques, such as machine learning algorithms and spatial analysis, will be employed to identify patterns, assess vulnerabilities, and predict potential disaster scenarios. The findings will inform the development of targeted preparedness strategies, including early warning systems, evacuation plans, resource allocation, and capacity building initiatives. Furthermore, this research emphasizes the importance of multi-stakeholder collaboration and knowledge exchange in disaster preparedness. By involving government agencies, non-governmental organizations, academic institutions, and local communities from both Nigeria and the USA, this approach aims to foster a holistic and inclusive preparedness framework. Ultimately, this study seeks to contribute to the global discourse on disaster risk reduction by demonstrating the effectiveness of cross-jurisdictional collaboration and data-driven approaches in enhancing disaster preparedness. By leveraging the strengths and experiences of Nigeria and the USA, this research aims to develop scalable and adaptable models that can be applied in diverse contexts to build resilience and mitigate the impact of disasters.

Revealing human mobility trends during the SARS-CoV-2 pandemic in Nigeria via a data-driven approach

We employed emerging smartphone-based location data and produced daily human mobility measurements using Nigeria as an application site. A data-driven analytical framework was developed for rigorously producing such measures using proven location intelligence and data-mining algorithms. Our study demonstrates the framework at the beginning of the SARS-CoV-2 pandemic and successfully quantifies human mobility patterns and trends in response to the unprecedented public health event. Another highlight of the paper is the assessment of the effectiveness of mobility-restricting policies as key lessons learned from the pandemic. We found that travel bans and federal lockdown policies failed to restrict trip-making behaviour, but had a significant impact on distance travelled. This paper contributes a first attempt to quantify daily human travel behaviour, such as trip-making behaviour and travelling distances, and how mobility-restricting policies took effect in sub-Saharan Africa during the pandemic. This study has the potential to enable a wide spectrum of quantitative studies on human mobility and health in sub-Saharan Africa using well-controlled, publicly available large data sets. Significance: The mobility measurements in this study are new and have filled a major data gap in understanding the change in travel behaviour during the SARS-CoV-2 pandemic in Nigeria. These measurements are derived from high-quality data samples by state-of-the-art data-driven methodologies and could be further adopted by other quantitative research related to human mobility. Additionally, this study evaluates the impact of mobility-restricting policies and the heterogeneous effects of socio-economic and socio-demographic factors by a time-dependent random effect model on human mobility. The quantitative model provides a decision-making basis for the Nigerian government to provide travel-related guidance and make decisions in future public health events. Open data set: https://github.com/villanova-transportation/Nigeria-mobility-COVID19-SAJS

A data-driven adaptive algorithm and decision support design of multisensory information fusion for prognostics and health management applications

Multisensory systems play a critical role in prognostics and health management (PHM), and utilise the information from multi-device synchronous measurements for fault diagnosis and predictive maintenance. But it is not suitable for specific systems with limited bandwidth and energy reservoirs since the increased sophistication of measurement devices requires more computation and power resources. This research explores a data-driven analytical framework for multisensory system analysis and design in PHM. The proposed framework provides the optimal subset of reliable sensors to make trade-offs between accuracy demands and system constraints. The integration definition for function modelling method is adopted for modelling and functional analysis of the proposed framework. An adaptive signal conversion algorithm is designed to process the data from all reliable sensors in the system. The convolutional neural network with residual learning is built for automatic feature extraction. Combined with the evaluation rules and expert knowledge, performance analyses are obtained, including qualitative results, fault diagnosis, and the optimal sensor combination. An open-source bearing dataset of the multisensory system with five measurements is conducted to demonstrate the effectiveness and feasibility of the proposed framework.

Quantitative resilience assessment of the network-level metro rail service's responses to the COVID-19 pandemic

The metro rail system has proven to be the most efficient high-capacity carriers. During the unprecedented coronavirus disease 2019 (COVID-19) challenge, non-pharmaceutical interventions become a widely adopted strategy to limit physical movements and interactions. For situational awareness and decision support, data-driven analytics about serviceability are invaluable to metro agencies and decision-makers of cities. This paper presents a data-driven analytical framework that quantitatively evaluates COVID-19-caused resilience performance of metro rails. Several characteristics (e.g., vulnerability, robustness, rapidity, and degree to return) of the metro system's responses to the disturbance were identified and modeled with multivariate multiple regression. The applicability and rationality of the resilience evaluation model were validated by the metro transit data of the United States. The preliminary results disclosed that metro rail transit encountered more vulnerability (90.6%) in passenger trips than motorbus and light rail (around 70%). A set of statistical models were employed to disentangle the effect of socio-demographic variables and COVID-19-related factors on the metro transit. The disclosed emerging knowledge of resilience provides an in-depth understanding of mobility trends for the public and time-sensitive decision support for the policy effects, to further improve the service and management of the metro system under the spread of the epidemic.

A data-driven perspective for sensing urban functional images: Place-based evidence in Hong Kong

Urban life involves a large variety of urban functions and human activities in a dense context due to the inherent nature of cities. Although technical frameworks have been previously proposed to understand urban functions and activities, there are limited studies that concern the individual places within a city and their detailed characteristics at a local scale. Using points of interest (POIs), we present a data-driven analytical framework to explore urban space containing urban functions and relevant activities by focusing on particular urban places. Urban functions are first extracted and induced by leveraging a latent Dirichlet allocation (LDA) topic modeling technique. We next evaluate the thematic functional differences among the selected places using the location quotient (LQ). Furthermore, tourist functions are assumed to occur in places within a city and carry broadly identifiable information; thus, tourist places are studied by comparing their perceptual experiences using the high-frequency keywords retrieved from tourist reviews on TripAdvisor. By adopting Hong Kong as our case study, the findings reveal considerable diversity of urban functions across different places, while each place displays the distinctive trait of urban functions. Tourist impressions reflected online are primarily consistent with the corresponding functional identities of these places but exhibit additional details associated with emotional and temporal aspects. This study uses a bottom-up assessment of local functions, and discusses their practical implications as related to city branding strategies.

Integrating Geographic Information System network analysis and nighttime light satellite imagery to optimize landfill regionalization on a regional level.

More than half of financial resources allocated for municipal solid waste management are typically spent on waste collection and transportation. An optimized landfill siting and waste collection system can save fuel costs, reduce collection truck emissions, and provide higher accessibility with lower traffic impacts. In this study, a data-driven analytical framework is developed to optimize population coverage by landfills using network analysis and satellite imagery. Two scenarios, SC1 and SC2, with different truck travel times were used to simulate generation-site–disposal-site distances in three Canadian provinces. Under status quo conditions, Landfill Regionalization Index (LFRI) ranging from 0 to 2 population centers per landfill in all three jurisdictions. LFRI consistently improved after optimization, with average LFRI ranging from 1.3 to 2.0 population centers per landfill. Lower average truck travel times and better coverage of the population centers are generally observed in the optimized systems. The proposed analytical method is found effective in improving landfill regionalization. Under SC1 and SC2, LFRI percentages of improvement ranging from 58.3% to 64.5% and 22.7% to 59.4%, respectively. Separation distance between the generation and disposal sites and truck capacity appear not a decisive factor in the optimization process. The proposed optimization framework is generally applicable to regions with different geographical and demographical attributes, and is particularly applicable in rural regions with sparsely located population centers.Supplementary InformationThe online version contains supplementary material available at 10.1007/s11356-022-21462-w.

Defect criticality analysis on fatigue life of L-PBF 17-4 PH stainless steel via machine learning

Defects innate in additively manufactured components may lead to inferior and more scatter in fatigue lives, thus challenging the qualification of these components in fatigue-critical applications. This work seeks to correlate geometrical features of critical defects measured from fracture surfaces to the fatigue performance of laser beam powder bed fusion (L-PBF) components with machine learning and to develop an integrated data-driven analytical framework for defect criticality (IDADC). IDADC has the potential to enhance the understanding of defect-fatigue relationships in a data-driven fashion. The results show that the obtained relationships between the extracted size-related and morphology-related defect features and the fatigue life of L-PBF specimens align with the known fatigue mechanisms and influencing factors. Furthermore, the proposed IDADC framework can model the relationships between defect features and fatigue life with a low mean absolute percentage error of 0.101 using a kernel support vector regression (SVR). This work could establish the algorithmic foundation for nondestructive fatigue evaluation of additive manufacturing products from various facets of critical defects in the future.

Reference-free video-to-real distance approximation-based urban social distancing analytics amid COVID-19 pandemic

IntroductionThe rapidly evolving COVID-19 pandemic has dramatically reshaped urban travel patterns. In this research, we explore the relationship between “social distancing,” a concept that has gained worldwide familiarity, and urban mobility during the pandemic. Understanding social distancing behavior will allow urban planners and engineers to better understand the new norm of urban mobility amid the pandemic, and what patterns might hold for individual mobility post-pandemic or in the event of a future pandemic. MethodsThere are still few efforts to obtain precise information on social distancing patterns of pedestrians in urban environments. This is largely attributed to numerous burdens in safely deploying any effective field data collection approaches during the crisis. This paper aims to fill that gap by developing a data-driven analytical framework that leverages existing public video data sources and advanced computer vision techniques to monitor the evolution of social distancing patterns in urban areas. Specifically, the proposed framework develops a deep-learning approach with a pre-trained convolutional neural network to mine the massive amount of public video data captured in urban areas. Real-time traffic camera data collected in New York City (NYC) was used as a case study to demonstrate the feasibility and validity of using the proposed approach to analyze pedestrian social distancing patterns. ResultsThe results show that microscopic pedestrian social distancing patterns can be quantified by using a generalized real-distance approximation method. The estimated distance between individuals can be compared to social distancing guidelines to evaluate policy compliance and effectiveness during a pandemic. Quantifying social distancing adherence will provide decision-makers with a better understanding of prevailing social contact challenges. It also provides insights into the development of response strategies and plans for phased reopening for similar future scenarios.

Interplay between traumatic brain injury and intimate partner violence: data driven analysis utilizing electronic health records

BackgroundIt is estimated that a majority of intimate partner violence (IPV) victims suffer from blunt force to the head, neck and the face area. Injuries to head and neck are among the major causes for traumatic brain injury (TBI).MethodsIn this interdisciplinary study, we aimed to characterize the key associations between IPV and TBI by mining de-identified electronic health records data with more than 12 M records between 1999 to 2017 from the IBM Explorys platform. For this purpose, we formulated a data-driven analytical framework to identify significant health correlates among IPV, TBI and six control cohorts. Using this framework, we assessed the co-morbidity, shared prevalence, and synergy between pairs of conditions.ResultsOur findings suggested that health effects attributed to malnutrition, acquired thrombocytopenia, post-traumatic wound infection, local infection of wound, poisoning by cardiovascular drug, alcoholic cirrhosis, alcoholic fatty liver, and drug-induced cirrhosis were highly significant at the joint presence of IPV and TBI.ConclusionTo develop a better understanding of how IPV is related to negative health effects, it is potentially useful to determine the interactions and relationships between symptom categories. Our results can potentially improve the accuracy and confidence of existing clinical screening techniques on determining IPV-induced TBI diagnoses.

Inferring an animal\u2019s environment through biologging: quantifying the environmental influence on animal movement

BackgroundAnimals respond to environmental variation by changing their movement in a multifaceted way. Recent advancements in biologging increasingly allow for detailed measurements of the multifaceted nature of movement, from descriptors of animal movement trajectories (e.g., using GPS) to descriptors of body part movements (e.g., using tri-axial accelerometers). Because this multivariate richness of movement data complicates inference on the environmental influence on animal movement, studies generally use simplified movement descriptors in statistical analyses. However, doing so limits the inference on the environmental influence on movement, as this requires that the multivariate richness of movement data can be fully considered in an analysis.MethodsWe propose a data-driven analytic framework, based on existing methods, to quantify the environmental influence on animal movement that can accommodate the multifaceted nature of animal movement. Instead of fitting a simplified movement descriptor to a suite of environmental variables, our proposed framework centres on predicting an environmental variable from the full set of multivariate movement data. The measure of fit of this prediction is taken to be the metric that quantifies how much of the environmental variation relates to the multivariate variation in animal movement. We demonstrate the usefulness of this framework through a case study about the influence of grass availability and time since milking on cow movements using machine learning algorithms.ResultsWe show that on a one-hour timescale 37% of the variation in grass availability and 33% of time since milking influenced cow movements. Grass availability mostly influenced the cows’ neck movement during grazing, while time since milking mostly influenced the movement through the landscape and the shared variation of accelerometer and GPS data (e.g., activity patterns). Furthermore, this framework proved to be insensitive to spurious correlations between environmental variables in quantifying the influence on animal movement.ConclusionsNot only is our proposed framework well-suited to study the environmental influence on animal movement; we argue that it can also be applied in any field that uses multivariate biologging data, e.g., animal physiology, to study the relationships between animals and their environment.

Abnormal Effective Connectivity Underlying Auditory Mismatch Negativity Impairments in Schizophrenia

BackgroundAuditory mismatch negativity (MMN) is a translatable event-related potential biomarker, and its reduction in schizophrenia is associated with the severity of clinical symptoms. While MMN recorded at the scalp is generated by a distributed network of temporofrontal neural sources, the primary contributing sources and the dynamic interactions among sources underlying MMN impairments in schizophrenia have not been previously characterized. MethodsA novel data-driven analytic framework was applied to large cohorts of healthy comparison subjects (n = 449) and patients with schizophrenia (n = 589) to identify the independent contributing sources of MMN, characterize the patterns of effective connectivity underlying reduced MMN in patients, and explore the clinical significance of these abnormal source dynamics in schizophrenia. ResultsA network of 11 independent contributing sources underlying MMN distributed across temporofrontal cortices was identified. Orderly shifts in peak source activity were detected in a steplike manner, starting at temporal structures and progressing across frontal brain regions. MMN reduction in patients was predominantly associated with reduced contributions from 3 frontal midline sources: orbitofrontal, anterior cingulate, and middle cingulate cortices. Patients showed increased connectivity from temporal to prefrontal regions in conjunction with decreased cross-hemispheric connectivity to prefrontal regions. The decreased connectivity strength of precentral to prefrontal regions in patients with schizophrenia was associated with greater severity of negative symptoms. ConclusionsAlterations in the dynamic interactions among temporofrontal sources underlie MMN abnormalities in schizophrenia. These results advance our understanding of the neural substrates and temporal dynamics of normal and impaired information processing with novel applications for translatable biomarkers of neuropsychiatric disorders.

A novel data-driven analytical framework on hierarchical water allocation integrated with blue and virtual water transfers

Abstract. In this study a novel data-driven analytical framework is proposed for cooperative strategies that ensure the optimal allocation of blue and virtual water transfers under different hydrological and economic conditions. A Stackelberg–Nash–Harsanyi equilibrium model is also developed to deal with the hierarchical conflicts between the water affairs bureau and multiple water usage sectors and overcome problems associated with water scarcity and uneven distribution. It was found that cooperative blue and virtual water transfer strategies could save water and improve utilization efficiency without harming sector benefits or increasing the ecological stress. Data-driven analyses were employed to simulate the hydrological and economic parameters, such as available water, crop import price and water market price under various policies. By adjusting the hydrological and economic parameters, it was found that the optimal allocation and transfer strategies were more sensitive to hydrological factors than economic factors. It was also found that cooperative blue/virtual water transfers respond to market fluctuations. Overall, the proposed framework provides sustainable management for physical and virtual water supply systems under future hydrological and economic uncertainties.

Predicting reading ability from brain anatomy and function: From areas to connections

Reading is a complex task involving different brain areas. As a crystallized ability, reading is also known to have effects on brain structure and function development. However, there are still open questions about what are the elements of the reading networks and how structural and functional brain measures shape the reading ability. The present study used a data-driven approach to investigate whether reading-related brain structural measures of cortical thickness, myelination, sulcus depth and structural connectivity and functional connectivity from the whole brain can predict individual differences in reading skills. It used different brain measures and performance scores from the Oral Reading Recognition Test (ORRT) measuring reading ability from 998 participants. We revealed reading-related brain areas and connections, and evaluated how well area and connection measures predict reading performance. Interestingly, the combination of all brain measures obtained the best predictions. We further grouped reading-related areas into positive and negative networks, each with four different levels (Core Regions, Extended-Regions 1, 2, 3), representing different correlation levels with the reading scores, and the non-correlated Region irrelevant to reading ability. The Core Regions are composed of areas that are most strongly correlated with reading performance. Insular and frontal opercular cortex, lateral temporal cortex, and early auditory cortex occupy the positive Core Region, while inferior temporal and motor cortex occupy the negative Core Region. Aside from those areas, the present study also found more reading-related areas including visual and language-related areas. In addition, connections predicting reading scores are denser inside the reading-related networks than outside. Together, the present study reveals extended reading networks of the brain and provides an extended data-driven analytical framework to study interpretable brain-behavior relationships, which are transferable also to studying other abilities.

Data-driven analytical framework for waste-dumping behaviour analysis to facilitate policy regulations

Waste sorting at the source is a vital strategy of waste management and to improve urban sustainability. If the strategy is implemented by relying solely on publicity and civic awareness, the impact is less significant. Proactive measures, such as policy regulations, supervisory guidance, and stimulating incentives, play essential roles for better management. The unknown waste-dumping behaviour of residents is a great challenge for decision-makers to allocate resources for waste-collection operations and to refine regulations. Traditional behaviour analysis methods such as questionnaire surveys and simulation methods have limitations considering the population size and the complexity of individual behaviour. This study aims to design a data-driven analytical framework to analyse household waste-dumping behaviour and facilitate policy regulations by using the Internet of Things (IoT) and data mining technologies. The analytical framework is further developed into a four-step management cycle. A case study in Shanghai is employed to demonstrate the effectiveness of the analytical framework and management cycle. The results of behaviour analyses reveal that (1) waste-dumping frequency is high in the evening but negligible in the early afternoon; (2) compared to working days, peak-value time at weekends occurs later in the morning and earlier in the evening; (3) residents require longer waste-dumping time windows than those empirically recommended by administrators. Managerial insights and decision support based on these research results have been presented for decision-makers to guide operations management and facilitate policy regulations.

Data Analytics Research-Informed Teaching in a Digital Technologies Curriculum

In the business environment, the goal of data analytics can be characterized as improving decision making and its links to big data and other data-driven technologies. In UK higher education, degree apprenticeships are business-led and government-supported nationally recognized qualifications, where delivery is tailored to partner employer requirements. This paper focuses on the development of the data analytics specialism of the BSc Digital and Technology Solutions degree apprenticeship at the University of Winchester Business School informed by current research and practice. A data-driven analytical framework is first proposed to provide an overarching methodology for extracting knowledge and insights from (big) data. It covers key components of the analytics lifecycle from data management, data preprocessing, and integration through data modeling and business intelligence to insight management. Software tools related to collecting, cleansing, processing, analyzing, and visualizing data have been systematically discussed to provide the technological dimension. The methodology is then applied to the development of the specialist modules in data analytics, which represent the core thematic structure of the degree apprenticeship pathway. The culmination of the paper is an evaluation of the educational innovation of this digital technologies curriculum, highlighting teaching, learning, and assessment from the perspective of data-analytic thinking.

MP34-15 PROSTATE CANCER QUALITY OF CARE DISPARITIES AND THEIR IMPACT ON PATIENT OUTCOMES

You have accessJournal of UrologyProstate Cancer: Epidemiology & Natural History II1 Apr 2018MP34-15 PROSTATE CANCER QUALITY OF CARE DISPARITIES AND THEIR IMPACT ON PATIENT OUTCOMES Keith Lawson, Katherine Daignault, Olli Saarela, Robert Abouassaly, and Antonio Finelli Keith LawsonKeith Lawson More articles by this author , Katherine DaignaultKatherine Daignault More articles by this author , Olli SaarelaOlli Saarela More articles by this author , Robert AbouassalyRobert Abouassaly More articles by this author , and Antonio FinelliAntonio Finelli More articles by this author View All Author Informationhttps://doi.org/10.1016/j.juro.2018.02.1107AboutPDF ToolsAdd to favoritesDownload CitationsTrack CitationsPermissionsReprints ShareFacebookTwitterLinked InEmail INTRODUCTION AND OBJECTIVES The ability to rigorously measure disparities in care that drive poor patient outcomes is essential for developing effective strategies for quality improvement. As real-world case-mix adjusted quality variations have yet to be reported in prostate cancer, our objective was to apply a data-driven analytical framework to determine the validity of expert-defined quality indicators (QIs) in order to reveal true disparities in prostate cancer care and their impact on patient outcomes. METHODS Hospital-level quality of care was assessed according to 10 published, expert-defined QIs utilizing data derived from the United States National Cancer Database (NCDB). Case-mix adjusted hospital benchmarking was performed using indirect standardization methodology and multivariable regression models as previously described by our group [1]. In a training set of hospitals, a composite measure of hospital quality, the Prostate Cancer Quality Score (PC-QS), was derived from those individual QIs that discriminated hospital performance in a manner associated with poor patient outcomes; including the need for salvage therapy, initiation of androgen deprivation therapy, 30- and 90-day mortality, and overall mortality. Subsequently, associations between the PC-QS and hospital volume, academic affiliation, and patient outcomes (as above) were determined in a separate validation set of hospitals. RESULTS Collectively, data from over 1100 hospitals were analyzed, with widespread variation observed across all QIs. In the training set, between 3-36% of hospitals were identified as poor outliers compared to the national average level of care for a given QI. Based on associations between outlier status and the aforementioned patient outcomes, 5 individual QIs were incorporated into the PC-QS. In the validation set, lower PC-QS hospitals displayed smaller referral volumes and were less likely to be academic hospitals than those with higher PC-QS (p < 0.001). Higher PC-QS was associated with lower rates of salvage therapy, ADT initiation and 30-day mortality (adjusted hazard ratio [confidence interval]: 0.85 [0.79-0.91], 0.94 [0.91-0.98], adjusted odds ratio 0.92 [0.87-0.97] per PC-QS unit increase, respectively). CONCLUSIONS Data-driven benchmarking of hospital-level quality performance reveals the widespread disparities that exist in prostate cancer care. This data supports the use of our novel PC-QS composite metric as a benchmarking tool for quality improvement. 1. Lawson KA et. al. Eur Urol 2017; 72: 379-386. © 2018FiguresReferencesRelatedDetails Volume 199Issue 4SApril 2018Page: e443 Advertisement Copyright & Permissions© 2018MetricsAuthor Information Keith Lawson More articles by this author Katherine Daignault More articles by this author Olli Saarela More articles by this author Robert Abouassaly More articles by this author Antonio Finelli More articles by this author Expand All Advertisement Advertisement PDF downloadLoading ...

The Dual Nature of Early-Life Experience on Somatosensory Processing in the Human Infant Brain

Every year, 15 million preterm infants are born, and most spend their first weeks in neonatal intensive care units (NICUs) [1]. Although essential for the support and survival of these infants, NICU sensory environments are dramatically different from those in which full-term infants mature and thus likely impact the development of functional brain organization [2]. Yet the integrity of sensory systems determines effective perception and behavior [3, 4]. In neonates, touch is a cornerstone of interpersonal interactions and sensory-cognitive development [5-7]. NICU treatments used to improve neurodevelopmental outcomes rely heavily on touch [8]. However, we understand little of how brain maturation at birth (i.e., prematurity) and quality of early-life experiences (e.g., supportive versus painful touch) interact to shape the development of the somatosensory system [9]. Here, we identified the spatial, temporal, and amplitude characteristics of cortical responses to light touch that differentiate them from sham stimuli in full-term infants. We then utilized this data-driven analytical framework to show that the degree of prematurity at birth determines the extent to which brain responses to light touch (but not sham) are attenuated at the time of discharge from the hospital. Building on these results, we showed that, when controlling for prematurity and analgesics, supportive experiences (e.g., breastfeeding, skin-to-skin care) are associated with stronger brain responses, whereas painful experiences (e.g., skin punctures, tube insertions) are associated with reduced brain responses to the same touch stimuli. Our results shed crucial insights into the mechanisms through which common early perinatal experiences may shape the somatosensory scaffolding of later perceptual, cognitive, and social development.

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.