Temporal Granularity Research Articles

A Framework to Understand Attitudes towards Immigration through Twitter

Understanding public opinion towards immigrants is key to prevent acts of violence, discrimination and abuse. Traditional data sources, such as surveys, provide rich insights into the formation of such attitudes; yet, they are costly and offer limited temporal granularity, providing only a partial understanding of the dynamics of attitudes towards immigrants. Leveraging Twitter data and natural language processing, we propose a framework to measure attitudes towards immigration in online discussions. Grounded in theories of social psychology, the proposed framework enables the classification of users’ into profile stances of positive and negative attitudes towards immigrants and characterisation of these profiles quantitatively summarising users’ content and temporal stance trends. We use a Twitter sample composed of 36 K users and 160 K tweets discussing the topic in 2017, when the immigrant population in the country recorded an increase by a factor of four from 2010. We found that the negative attitude group of users is smaller than the positive group, and that both attitudes have different distributions of the volume of content. Both types of attitudes show fluctuations over time that seem to be influenced by news events related to immigration. Accounts with negative attitudes use arguments of labour competition and stricter regulation of immigration. In contrast, accounts with positive attitudes reflect arguments in support of immigrants’ human and civil rights. The framework and its application can inform policy makers about how people feel about immigration, with possible implications for policy communication and the design of interventions to improve negative attitudes.

Implementation and evaluation of a multivariate abstraction-based, interval-based dynamic time-warping method as a similarity measure for longitudinal medical records

We extended dynamic time warping (DTW) into interval-based dynamic time warping (iDTW), including (A) interval-based representation (iRep): [1] abstracting raw, time-stamped data into interval-based abstractions, [2] comparison-period scoping, [3] partitioning abstract intervals into a given temporal granularity; (B) interval-based matching (iMatch): matching partitioned, abstract-concepts records, using a modified DTW. Using domain knowledge, we abstracted the raw data of medical records, for up to three concepts out of four or five relevant concepts, into two interval types: State abstractions (e.g. LOW, HIGH) and Gradient abstractions (e.g. INCREASING, DECREASING). We created all uni-dimensional (State or Gradient) or multi-dimensional (State and Gradient) abstraction combinations. Tasks: Classifying 161 oncology patients records as autologous or allogenic bone-marrow transplantation; classifying 125 hepatitis patients records as B or C hepatitis; predicting micro- or macro-albuminuria in the next year for 151 Type 2 diabetes patients. We used a k-Nearest-Neighbors majority, k = an odd number from 1 to SQRT(N), N = set size. 75,936 10-fold cross-validation experiments were performed: 33,600 (Oncology), 28,800 (Hepatitis), 13,536 (Diabetes). Measures: Area Under the Curve (AUC), optimal Youden's Index. Paired t-tests compared result vectors for equivalent configurations other than a tested variable, to determine a significant mean accuracy difference (P<0.05). Mean classification and prediction using abstractions was significantly better than using only raw time-stamped data. In each domain, at least one abstraction combination led to a significantly better mean performance than raw data. Increasing feature number and using Multi-dimensional abstractions enhanced performance. Unlike when using raw data, optimal mean performance was often reached with k=5, using abstractions.

Measuring the impact of blockchain and smart contracts on construction supply chain visibility

This work assesses the impact of blockchain and smart contracts on the visibility of construction supply chain and in the context of payments (intersection of cash and product flows). It uses comparative empirical experiments (Charrette Test Method) to draw comparisons between the visibility of state-of-practice and blockchain-enabled payment systems in a commercial construction project. Comparisons were drawn across four levels of granularity. The findings are threefold: 1) blockchain improved information completeness and information accuracy compared with the digital state-of-practice solution; 2) the improvements were significantly more pronounced for information queries that had higher product, trade, and temporal granularity; and 3) blockchain-enabled solution was robust when faced with more granular information queries, while the conventional solution experienced 50% and 66.7% decline respectively in completeness and accuracy of information. The paper concludes with a discussion of mechanisms contributing to visibility and the implications for the construction industry.

Autonomous Hierarchical Surgical State Estimation During Robot-Assisted Surgery Through Deep Neural Networks

Many operations in robot-assisted surgery (RAS) can be viewed in a hierarchical manner. Each surgical task is represented by a superstate, which can be decomposed into finer-grained states. The estimation of these discrete states at different levels of temporal granularity provides a temporal perception of the current surgical scene during RAS, which is a crucial step towards many automated surgeon-assisting functionalities. We propose Hierarchical Estimation of Surgical States through Deep Neural Networks (HESS-DNN), a deep learning-based system that concurrently estimates the current super- and fine-grained states. HESS-DNN incorporates endoscopic vision, robot kinematics, and system events data from the da Vinci Xi surgical system. HESS-DNN is evaluated on a real-world robotic inguinal hernia repair surgery dataset: HERNIA-20, and achieves accurate state estimates of both surgical superstate and the corresponding fine-grained surgical state. We show that HESS-DNN improves state-of-the-art fine-grained state estimation across the entire HERNIA-20 RAS procedure through its hierarchical design. We also analyze the relative contributions of each input data type and HESS-DNN's design to surgical (super)state estimation accuracy.

Food Sales Forecasting Using Machine Learning Techniques: A Survey

Abstract: Food sales forecasting is concerned with predicting future sales of food-related businesses such as supermarkets, grocery stores, restaurants, bakeries, and patisseries. Companies can reduce stocked and expired products within stores while also avoiding missing revenues by using accurate short-term sales forecasting. This research examines current machine learning algorithms for predicting food purchases. It goes over key design considerations for a data analyst working on food sales forecasting’s, such as the temporal granularity of sales data, the input variables to employ for forecasting sales, and the representation of the sales output variable. It also examines machine learning algorithms that have been used to anticipate food sales and the proper metrics for assessing their performance. Finally, it goes over the major problems and prospects for applied machine learning in the field of food sales forecasting. Keywords: Food, Demand forecasting, Machine learning, Regression, Timeseries forecasting, Sales prediction

Using Smartphones to Capture and Combine Self-Reports and Passively Measured Behavior in Social Research

Abstract With the ubiquity of smartphones, it is possible to collect self-reports as well as to passively measure behaviors and states (e.g., locations, movement, activity, and sleep) with native sensors and the smartphone’s operating system, both on a single device that usually accompanies participants throughout the day. This research synthesis brings structure to a rapidly expanding body of literature on the combined collection of self-reports and passive measurement using smartphones, pointing out how and why researchers have combined these two types of data and where more work is needed. We distinguish between five reasons why researchers might want to integrate the two data sources and how this has been helpful: (1) verification, for example, confirming start and end of passively detected trips, (2) contextualization, for example, asking about the purpose of a passively detected trip, (3) quantifying relationships, for example, quantifying the association between self-reported stress and passively measured sleep duration, (4) building composite measures, for example, measuring components of stress that participants are aware of through self-reports and those they are not through passively measured speech attributes, and (5) triggering measurement, for example, asking survey questions contingent on certain passively measured events or participant locations. We discuss challenges of collecting self-reports and passively tracking participants’ behavior with smartphones from the perspective of representation (e.g., who owns a smartphone and who is willing to share their data), measurement (e.g., different levels of temporal granularity in self-reports and passively collected data), and privacy considerations (e.g., the greater intrusiveness of passive measurement than self-reports). While we see real potential in this approach it is not yet clear if its impact will be incremental or will revolutionize the field.

Spatial Information Gaps on Deprived Urban Areas (Slums) in Low-and-Middle-Income-Countries: A User-Centered Approach

Routine and accurate data on deprivation are needed for urban planning and decision support at various scales (i.e., from community to international). However, analyzing information requirements of diverse users on urban deprivation, we found that data are often not available or inaccessible. To bridge this data gap, Earth Observation (EO) data can support access to frequently updated spatial information. However, a user-centered approach is urgently required for the production of EO-based mapping products. Combining an online survey and several forms of user interactions, we defined five system specifications (derived from user requirements) for designing an open-access spatial information system for deprived urban areas. First, gridded maps represent the optimal spatial granularity to deal with high uncertainties of boundaries of deprived areas and to protect privacy. Second, a high temporal granularity of 1–2 years is important to respond to the high spatial dynamics of urban areas. Third, detailed local-scale information should be part of a city-to-global information system. Fourth, both aspects, community assets and risks, need to be part of an information system, and such data need to be combined with local community-based information. Fifth, in particular, civil society and government users should have fair access to data that bridges the digital barriers. A data ecosystem on urban deprivation meeting these requirements will be able to support community-level action for improving living conditions in deprived areas, local science-based policymaking, and tracking progress towards global targets such as the SDGs.

Comparing community mobility reduction between first and second COVID-19 waves

BackgroundIn this paper, we conduct a mobility reduction rate comparison between the first and second COVID-19 waves in several localities from America and Europe using Google community mobility reports (CMR) data. Through multi-dimensional visualization, we are able to compare the reduction in mobility from the different lockdown periods for each locality selected, simultaneously considering multiple place categories provided in CMR. In addition, our analysis comprises a 56-day lockdown period for each locality and COVID-19 wave, which we analyze both as 56-day periods and as 14-day consecutive windows. MethodsWe use locality-wise calibrated CMR data, which we process through seasonal-trend decomposition by LOESS (STL) to isolate trend from seasonal and noise effects. We scale trend data to draw Pareto-compliant conclusions using radar charts. For each temporal granularity considered, data for a given place category is aggregated using the area under the curve (AUC) approach. ResultsIn general, reduction rates observed during the first wave were much higher than during the second. Alarmingly, December holiday season mobility in some of the localities reached pre-pandemic levels for some of the place categories reported. Manaus was the only locality where second wave mobility was nearly as reduced as during the first wave, likely due to the P1 variant outbreak and oxygen supply crisis.

ENABLING AIR QUALITY MONITORING WITH THE OPEN DATA CUBE: IMPLEMENTATION FOR SENTINEL-5P AND GROUND SENSOR OBSERVATIONS

Abstract. Nowadays, the amount of open geospatial data delivered e.g. by private and public entities, such as environmental agencies, enables outstanding possibilities to any user interested in investigating real-world phenomena. However, the availability of such information presents several challenges in terms of its practical use. These are mainly connected to the heterogeneity of data sources, formats and processing tools which have to be mastered by the user to obtain the desired results. As a relevant example, air quality monitoring requires the integration of multiple data with different spatial and temporal granularities that are often distributed by more than one provider using not uniform formats and access methods. Besides traditional air pollution ground sensors observations, novel data sources have emerged. Among them, the Sentinel-5P mission of the European Copernicus Programme is one of the most recent Earth Observation platforms providing estimates of air pollutants with daily global coverage. These estimates are promising to foster air quality analysis and monitoring by complementing ground sensors observations. Therefore, the development of data handling and analysis strategies – allowing users for a smooth integration of satellite and ground sensor observations – is key to support future air quality studies. To that end, the present work investigates the use of the Open Data Cube as a single data endpoint to incorporate ground sensors and satellite observations into local air pollution analyses. A preliminary implementation is presented using the Lombardy region (Northern Italy) as a case study.

A Review of Recent Spatial Accessibility Studies That Benefitted from Advanced Geospatial Information: Multimodal Transportation and Spatiotemporal Disaggregation

Spatial accessibility provides significant policy implications, describing the spatial disparity of access and supporting the decision-making process for placing additional infrastructure at adequate locations. Several previous reviews have covered spatial accessibility literature, focusing on empirical findings, distance decay functions, and threshold travel times. However, researchers have underexamined how spatial accessibility studies benefitted from the recently enhanced availability of dynamic variables, such as various travel times via different transportation modes and the finer temporal granularity of geospatial data in these studies. Therefore, in our review, we investigated methodological advancements in place-based accessibility measures and scrutinized two recent trends in spatial accessibility studies: multimodal spatial accessibility and temporal changes in spatial accessibility. Based on the critical review, we propose two research agendas: improving the accuracy of measurements with dynamic variable implementation and furnishing policy implications granted from the enhanced accuracy. These agendas particularly call for the action of geographers on the full implementation of dynamic variables and the strong linkage between accessibility and policymaking.

Data-Driven Approach Using Machine Learning for Real-Time Flight Path Optimization

Airlines traditionally gather weather information before departure to generate flight routes that avoid hazardous weather while minimizing flight time. However, flight crews may have to perform in-flight replanning as weather information can significantly change after departure. This in-flight replanning activity is currently not fully automated, which has the potential to increase crew workload and adversely impact flight safety. The objective of this research is to mitigate some of these issues by developing an automated framework to perform continuous in-flight replanning. The proposed framework relies on three pillars and leverages: supervised machine learning technique to augment existing wind forecasts by providing a higher spatial and temporal granularity, unsupervised machine learning technique to perform short-term predictions of areas with significant convective activity, and graph-based pathfinding algorithm to generate optimized trajectories. The main contribution of this research is to combine these techniques to autonomously and continuously generate trajectories that minimize operating expenditures for airlines. Statistical analyses are performed to demonstrate the applicability and benefits of the proposed framework. Results indicate that optimized trajectories are 2% shorter than actual flight routes in most cases.

LDL-AURIS: a computational model, grounded in error-driven learning, for the comprehension of single spoken words

ABSTRACT A computational model for the comprehension of single spoken words is presented that builds on an earlier model using discriminative learning. Real-valued features are extracted from the speech signal instead of discrete features. Vectors representing word meanings using one-hot encoding are replaced by real-valued semantic vectors. Instead of incremental learning with Rescorla-Wagner updating, we use linear discriminative learning, which captures incremental learning at the limit of experience. These new design features substantially improve prediction accuracy for unseen words, and provide enhanced temporal granularity, enabling the modelling of cohort-like effects. Visualisation with t-SNE shows that the acoustic form space captures phone-like properties. Trained on 9 h of audio from a broadcast news corpus, the model achieves recognition performance that approximates the lower bound of human accuracy in isolated word recognition tasks. LDL-AURIS thus provides a mathematically-simple yet powerful characterisation of the comprehension of single words as found in English spontaneous speech.

Visualizing Probability Distributions Across Bivariate Cyclic Temporal Granularities

Deconstructing a time index into time granularities can assist in exploration and automated analysis of large temporal datasets. This article describes classes of time deconstructions using linear and cyclic time granularities. Linear granularities respect the linear progression of time such as hours, days, weeks and months. Cyclic granularities can be circular such as hour-of-the-day, quasi-circular such as day-of-the-month, and aperiodic such as public holidays. The hierarchical structure of granularities creates a nested ordering: hour-of-the-day and second-of-the-minute are single-order-up. Hour-of-the-week is multiple-order-up, because it passes over day-of-the-week. Methods are provided for creating all possible granularities for a time index. A recommendation algorithm provides an indication whether a pair of granularities can be meaningfully examined together (a “harmony”), or when they cannot (a “clash”). Time granularities can be used to create data visualizations to explore for periodicities, associations and anomalies. The granularities form categorical variables (ordered or unordered) which induce groupings of the observations. Assuming a numeric response variable, the resulting graphics are then displays of distributions compared across combinations of categorical variables. The methods implemented in the open source R package gravitas are consistent with a tidy workflow, with probability distributions examined using the range of graphics available in ggplot2. Supplementary files for this article are available online.

On the temporal granularity of joint energy-reserve markets in a high-RES system

The cost-efficient integration of variable renewable power (vRES) requires adequate operating reserve and energy markets. This paper focusses on the benefits of increasing the temporal granularity of joint energy-operating reserve markets in a case study on two high-vRES systems. Specifically, the impact of the frequency (i.e., how often reserves are sized and procured) and the resolution or contract duration (i.e., the length of the sizing and procurement blocks) is isolated by comparing year-long unit commitment and economic dispatch simulation results. We optimize the power system’s operation in consecutive stages: scheduling of generation units with procurement of reserves, intra-day adjustments of generation schedules and real-time activation of flexibility to compensate for imbalances. The results suggest that dynamic and more frequent sizing, limiting the reserve procurement contract duration and a higher procurement frequency allow reducing the total operating cost by 1.5–1.8%, while eliminating scarcity on reserve markets. These design changes facilitate the integration of intermittent renewables in reserve markets. Finally, some challenges following the proposed market design changes are highlighted.

Evaluation of home detection algorithms on mobile phone data using individual-level ground truth

Inferring mobile phone users’ home location, i.e., assigning a location in space to a user based on data generated by the mobile phone network, is a central task in leveraging mobile phone data to study social and urban phenomena. Despite its widespread use, home detection relies on assumptions that are difficult to check without ground truth, i.e., where the individual who owns the device resides. In this paper, we present a dataset that comprises the mobile phone activity of sixty-five participants for whom the geographical coordinates of their residence location are known. The mobile phone activity refers to Call Detail Records (CDRs), eXtended Detail Records (XDRs), and Control Plane Records (CPRs), which vary in their temporal granularity and differ in the data generation mechanism. We provide an unprecedented evaluation of the accuracy of home detection algorithms and quantify the amount of data needed for each stream to carry out successful home detection for each stream. Our work is useful for researchers and practitioners to minimize data requests and maximize the accuracy of the home antenna location.

Data-Driven Digital Advertising with Uncertain Demand Model in Metro Networks

Nowadays most metro advertising systems schedule advertising slots on digital advertising screens to achieve the maximum exposure to passengers by exploring passenger demand models. However, our empirical results show that these passenger demand models experience uncertainty at fine temporal granularity (e.g., per min). As a result, for fine-grained advertisements (shorter than one minute), a scheduling based on these demand models cannot achieve the maximum advertisement exposure. To address this issue, we propose an online advertising approach, called FineUDM, based on the uncertain passenger demand modeling for both entering passengers and exiting passengers. FineUDM combines coarse-grained statistical demand modeling and fine-grained real-time demand modeling by leveraging historical passenger demands, real-time card-swiping records, and passenger mobility patterns. Based on this uncertain demand model, it schedules advertising time online based on robust receding horizon control to maximize the advertisement exposure. We evaluate the proposed approach based on an one-month sample from our 530 GB real-world metro fare dataset with 16 million cards. The results show that our approach provides a 61.5 percent lower traffic prediction error and 20 percent improvement on advertising efficiency on average.

Land Subsidence in Wuhan Revealed Using a Non-Linear PSInSAR Approach with Long Time Series of COSMO-SkyMed SAR Data

Wuhan is an important city in central China, with a rapid development that has led to increasingly serious land subsidence over the last decades. Most of the existing Interferometric Synthetic Aperture Radar (InSAR) subsidence monitoring studies in Wuhan are either short-term investigations—and thus can only detect this process within limited time periods—or combinations of different Synthetic Aperture Radar (SAR) datasets with temporal gaps in between. To overcome these constraints, we exploited nearly 300 high-resolution COSMO-SkyMed StripMap HIMAGE scenes acquired between 2012 and 2019 to monitor the long-term subsidence process affecting Wuhan and to reveal its spatiotemporal variations. The results from the Persistent Scatterer Interferometric SAR (PSInSAR) processing highlight several clearly observable subsidence zones. Three of them (i.e., Houhu, Xinrong, and Guanggu) are affected by serious subsidence rates and non-linear temporal behavior, and are investigated in this paper in more detail. The subsidence in Houhu is caused by soft soil consolidation and compression. Soil mechanics are therefore used to estimate when the subsidence is expected to finish and to calculate the degree of consolidation for each year. The COSMO-SkyMed PSInSAR results indicate that the area has entered the late stage of consolidation and compression and is gradually stabilizing. The subsidence curve found for the area around Xinrong shows that the construction of an underground tract of the subway Line 21 caused large-scale settlement in this area. The temporal granularity of the PSInSAR time series also allows precise detection of a rebound phase following a major flooding event in 2016. In the southern industrial park of Guanggu, newly detected subsidence was found. The combination of the subsidence curve with an optical time-series image analysis indicates that urban construction is the main trigger of deformation in this area. While this study unveils previously unknown characters of land subsidence in Wuhan and clarifies the relationship with the urban causative factors, it also proves the benefits of non-linear PSInSAR in the analysis of the temporal evolution of such processes in dynamic and expanding cities.

Health-adjusted life expectancy (HALE) in Chongqing, China, 2017: An artificial intelligence and big data method estimating the burden of disease at city level

BackgroundA universally applicable approach that provides standard HALE measurements for different regions has yet to be developed because of the difficulties of health information collection. In this study, we developed a natural language processing (NLP) based HALE estimation approach by using individual-level electronic medical records (EMRs), which made it possible to calculate HALE timely in different temporal or spatial granularities. MethodsWe performed diagnostic concept extraction and normalisation on 13•99 million EMRs with NLP to estimate the prevalence of 254 diseases in WHO Global Burden of Disease Study (GBD). Then, we calculated HALE in Chongqing, 2017, by using the life table technique and Sullivan's method, and analysed the contribution of diseases to the expected years “lost” due to disability (DLE). FindingsOur method identified a life expectancy at birth (LE0) of 77•9 years and health-adjusted life expectancy at birth (HALE0) of 71•7 years for the general Chongqing population of 2017. In particular, the male LE0 and HALE0 were 76•3 years and 68•9 years, respectively, while the female LE0 and HALE0 were 80•0 years and 74•4 years, respectively. Cerebrovascular diseases, cancers, and injuries were the top three deterioration factors, which reduced HALE by 2•67, 2•15, and 1•19 years, respectively. InterpretationThe results demonstrated the feasibility and effectiveness of EMRs-based HALE estimation. Moreover, the method allowed for a potentially transferable framework that facilitated a more convenient comparison of cross-sectional and longitudinal studies on HALE between regions. In summary, this study provided insightful solutions to the global ageing and health problems that the world is facing. FundingNational Key R and D Program of China (2018YFC2000400).

Can Smart City Data be Used to Create New Official Statistics?

Abstract In this article we evaluate the viability of using big data produced by smart city systems for creating new official statistics. We assess sixteen sources of urban transportation and environmental big data that are published as open data or were made available to the project for Dublin, Ireland. These data were systematically explored through a process of data checking and wrangling, building tools to display and analyse the data, and evaluating them with respect to 16 measures of their suitability: access, sustainability and reliability, transparency and interpretability, privacy, fidelity, cleanliness, completeness, spatial granularity, temporal granularity, spatial coverage, coherence, metadata availability, changes over time, standardisation, methodological transparency, and relevance. We assessed how the data could be used to produce key performance indicators and potential new official statistics. Our analysis reveals that, at present, a limited set of smart city data is suitable for creating new official statistics, though others could potentially be made suitable with changes to data management. If these new official statistics are to be realised then National Statistical Institutions need to work closely with those organisations generating the data to try and implement a robust set of procedures and standards that will produce consistent, long-term data sets.

A Monte Carlo based solar radiation forecastability estimation

Based on the reported literature and commonly used metrics in the realm of solar forecasting, a new methodology is developed for estimating a metric called forecastability (F). It reveals the extent to which solar radiation time series can be forecasted and provides the crucial context for judging the inherent difficulty associated with a particular forecast situation. Unlike the score given by the standard smart persistence model, the F metric which is bounded between 0% and 100% is easier to interpret, hence making comparisons between forecasting studies more consistent. This approach uses the Monte Carlo method and estimates F from the standard error metric RMSE and the persistence predictor. Based on the time series of solar radiation measured at six very different locations (with optimized clear sky model) from a meteorological point of view, it is shown that F varies between 25.5% and 68.2% and that it exists a link between forecastability and errors obtained by machine learning prediction methods. The proposed methodology is validated for 3 parameters that may affect the F estimation (time horizon, temporal granularity, and solar radiation components) and for 50 time series relative to McClear web service and to the central archive of Baseline Surface Radiation Network.