Series Data Research Articles

Subsistence and population development from the Middle Neolithic B (2800-2350 BCE) to the Late Neolithic (2350-1700 BCE) in Southern Scandinavia.

The present study aims to explore the hypothesis of a link between a population increase derived from intensified food production and the development from the widespread cultural diversity of the Middle Neolithic B (MNB) to the cultural unity towards the end of the Late Neolithic (LN) in Southern Scandinavia. We explore this through quantitative modelling of radiocarbon dates, aoristic time series of material culture and palynological data. On this basis, we propose three main results that may explain the transformation: (1) A supra-regional population increase, culminating in the middle of the LN (c. 2100 BCE). (2) A depopulation in Western Jutland at the transition from the MNB to the LN (c. 2400-2300 BCE) counterbalanced by a population increase in North and East Denmark. (3) A population boom in Southern Sweden around 2250-2000 BCE, possibly leading to migrations towards west. Furthermore, we propose an overall decline in population in the final LN (c. 1850 BCE).

Revolutionising Diabetic Retinopathy Diagnosis with Modified Regularisation Long Short-Term Memory Framework

The diagnosis of Diabetic Retinopathy (DR) demands a paradigm shift towards more accurate and efficient solutions to overcome vision impairment. Therefore, the current study introduces a new Modified Regularisation Long Short-term Memory (MR-LSTM) framework approach for DR diagnosis. The proposed framework leverages the power of deep learning and provides a dynamic and robust solution for the early detection of DR, which in turn preserves a patient’s vision. The proposed framework uses a DR Debrecen Dataset from the UCI database with 21 distinct features relevant to retinal health, and employs a series of data preprocessing steps, including data cleaning, normalisation, and transformation, to ensure data quality and compatibility. The MR-LSTM framework excels at capturing temporal dependencies in sequential retinal images, offering a unique advantage in understanding the progression of DR. The MR-LSTM framework is implemented using Python libraries, and the results are compared with those of other popular models. It is observed that the MR-LSTM framework outperforms other models and achieves an accuracy of 97.12 percent and an F1 Score of 98.49. Furthermore, the Receiver Operating Characteristic (ROC) curve reveals an area under the curve of 0.97, highlighting the exceptional ability to discriminate between positive and negative cases of the proposed framework. By revolutionising DR diagnosis with the proposed MR-LSTM framework, it can achieve accurate, timely, and accessible solutions in the fight against vision-threatening conditions.

A non-linear integer-valued autoregressive model with zero-inflated data series

A new non-linear stationary process for time series of counts is introduced. The process is composed of the survival and innovation component. The survival component is based on the generalized zero-modified geometric thinning operator, where the innovation process figures in the survival component as well. A few probability distributions for the innovation process have been discussed, in order to adjust the model for observed series with the excess number of zeros. The conditional maximum likelihood and the conditional least squares methods are investigated for the estimation of the model parameters. The practical aspect of the model is presented on some real-life data sets, where we observe data with inflation as well as deflation of zeroes so we can notice how the model can be adjusted with the proper parameter selection.

Hyperparameter Tuning of Load-Forecasting Models Using Metaheuristic Optimization Algorithms—A Systematic Review

Load forecasting is an integral part of the power industries. Load-forecasting techniques should minimize the percentage error while prediction future demand. This will inherently help utilities have an uninterrupted power supply. In addition to that, accurate load forecasting can result in saving large amounts of money. This article provides a systematic review based on the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) framework. This article presents a complete framework for short-term load forecasting using metaheuristic algorithms. This framework consists of three sub-layers: the data-decomposition layer, the forecasting layer, and the optimization layer. The data-decomposition layer decomposes the input data series to extract important features. The forecasting layer is used to predict the result, which involves different statistical and machine-learning models. The optimization layer optimizes the parameters of forecasting methods to improve the accuracy and stability of the forecasting model using different metaheuristic algorithms. Single models from the forecasting layer can predict the results. However, they come with their limitations, such as low accuracy, high computational burden, stuck to local minima, etc. To improve the prediction accuracy, the hyperparameters of these models need to be tuned properly. Metaheuristic algorithms cab be used to tune these hyperparameters considering their interdependencies. Hybrid models combining the three-layer methods can perform better by overcoming the issues of premature convergence and trapping into a local minimum solution. A quantitative analysis of different metaheuristic algorithms and deep-learning forecasting methods is presented. Some of the most common evaluation indices that are used to evaluate the performance of the forecasting models are discussed. Furthermore, a taxonomy of different state-of-the-art articles is provided, discussing their advantages, limitations, contributions, and evaluation indices. A future direction is provided for researchers to deal with hyperparameter tuning.

The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system.

Indirect interspecific effects (IIEs) occur when one species affects another through a third intermediary species. Understanding the role of IIEs in population dynamics is key for predicting community-level impacts of environmental change. Yet, empirically teasing apart IIEs from other interactions and population drivers has proven challenging and data-demanding, particularly in species-rich communities. We used stochastic population models parameterized with long-term time series of individual data to simulate population trajectories and examine IIEs in a simple high-arctic vertebrate food chain consisting of the wild Svalbard reindeer, its scavenger (the Arctic fox) and the barnacle goose, a migratory prey of the fox. We used the simulated population trajectories to explore co-fluctuations between the species within the food chain. Additionally, we adjusted the model in two ways: first, to isolate the impact of fluctuations in the abundance of a species by keeping its abundance constant; and second, to isolate the impact of a trophic interaction on the dynamics of other species by setting the abundance of the influencing species to zero. We found that fluctuations in reindeer carcasses shaped fox abundance fluctuations, which subsequently affected goose population dynamics. Reindeer and goose population growth rates were nevertheless only weakly correlated, probably in part due to demographic and environmental stochasticity, density dependence and lagged dynamics in the geese. However, removing the fluctuations in reindeer abundance or setting reindeer abundance to zero indeed demonstrated strong underlying IIEs on goose population dynamics and extinction probability. This study thus highlights the importance of species interactions, including IIEs, on species coexistence and communities in the long-term, that is beyond immediate effects and covariation in short-term fluctuations.

Time Series Data Provide Insights into the Evolution and Abundance of One of the Most Abundant Viruses in the Marine Virosphere: The Uncultured Pelagiphages vSAG 37-F6

Viruses play a pivotal role in ecosystems by influencing biochemical cycles and impacting the structure and evolution of their host cells. The widespread pelagiphages infect Pelagibacter spp., the most abundant marine microbe on Earth, and thus play a significant role in carbon transformation through the viral shunt. Among these viruses, the uncultured lytic pelagiphage vSAG 37-F6, uncovered by single-virus genomics, is likely the most numerous virus in the ocean. While previous research has delved into the diversity and spatial distribution of vSAG 37-F6, there is still a gap in understanding its temporal dynamics, hindering our insight into its ecological impact. We explored the temporal dynamics of vSAG 37-F6, assessing periodic fluctuations in abundance and evolutionary patterns using long- and short-term data series. In the long-term series (7 years), metagenomics showed negative selection acting on all viral genes, with a highly conserved overall diversity over time composed of a pool of yearly emergent, highly similar novel strains that exhibited a seasonal abundance pattern with two peaks during winter and fall and a decrease in months with higher UV radiation. Most non-synonymous polymorphisms occurred in structural viral proteins located in regions with low conformational restrictions, suggesting that many of the viral genes of this population are highly purified over its evolution. At the fine-scale resolution (24 h time series), combining digital PCR and metagenomics, we identified two peaks of cellular infection for the targeted vSAG 37-F6 viral strain (up to approximately 103 copies/ng of prokaryotic DNA), one before sunrise and the second shortly after midday. Considering the high number of co-occurring strains of this microdiverse virus, the abundance values at the species or genus level could be orders of magnitudes higher. These findings represent a significant advancement in understanding the dynamics of the potentially most abundant oceanic virus, providing valuable insights into ecologically relevant marine viruses.

A new approach to three-dimensional monitoring of surface changes in lakes: application of three-way data analysis model in Lake Burdur, Turkey.

Lakes are areas of ecological significance that host a wide range of living species. Therefore, monitoring changes in lake surfaces is critical for ecosystem preservation. Since conventional methods based on one-dimensional data analysis have limited capacity to analyze complex systems, they may not always produce the desired results in examining surface change. In this regard, a novel approach based on a three-way analysis model of a multi-temporal dataset was improved for the first time to monitor surface changes in Lake Burdur between 2014 and 2023 in terms of latitude, longitude, and year modes. The newly proposed approach, based on the deconvolution of a multi-temporal data series, provided a new way and an alternative approach with a three-dimensional perspective for the simultaneous prediction of changes in latitude, longitude, and relative quantity profiles of Lake Burdur between 2014 and 2023. The proposed approach is based on the parallel factor analysis (PARAFAC) modeling of the multi-temporal datasets. The change in the lake surface as water levels were monitored from latitudinal and longitudinal profiles was obtained by applying the PARAFAC model to the multi-temporal data array. In the same model, the change in lake water surface over the years was determined from the relative quantity profile. The results obtained from the three-way analysis model were compared with the results provided by the conventional method. It was concluded that the findings of this study could provide valuable information to researchers and policymakers in developing effective strategies to analyze changes in lake surfaces in future studies.

Assessing the Potential Impacts of Contaminants on the Water Quality of Lake Victoria: Two Case Studies in Uganda

Nutrients are essential for the growth of aquatic life; however, in excess, they can result in a decline in water quality, posing serious risks to both human and aquatic organisms. Human activities, such as urbanisation, industry, and farming, can increase the amount of nutrients and other elements that reach receiving waterbodies like Lake Victoria in Uganda, which can be problematic at elevated levels. There is therefore a strong need to evaluate recent changes in pollutant concentrations and their potential negative effects. To contribute to this gap and to explore the pollutant changes in Lake Victoria, a series of water chemistry data (phosphate, nitrate, potassium, ammonium, sodium, sulphate, silica and chlorine) was collected between 2016 and 2023 in Uganda’s Napoleon Gulf (NG) and Murchison Bay (MB), primarily by the Ministry of Water and Environment (MWE). These locations were chosen based on their vicinity to expanding urban centres and agriculture, and they are also areas where fishing frequently occurs. The datasets were collected at different water depths (0.5–24 m). Data were analysed with the use of IBM’s Statistical Package for the Social Sciences (SPSS 28.0) software and confirmed the excessive concentrations of pollutants within MB compared to NG. The analysis identified the different nutrient types that exceeded internationally recognised thresholds relating to acceptable water quality during the data collection period. Seasonal variations were observed, during the dry season; nutrient levels, however, in NG showed higher nutrient concentrations during the wet season. The study’s capacity to inform local authorities and policymakers about such potential major sources of pollution is of crucial importance for beginning to address the potential impacts on human health and aquatic life.

Long-Term Trends in Significant Wave Heights in The Mediterranean as an Indicator of Climate Change

The Mediterranean coastal zones, particularly along the Nile Delta, are increasingly vulnerable to the impacts of climate change. This study examines the historical changes in the significant wave height along the Nile Delta coast over the past 84 years (1940-2023) at a depth of 20 meters. By synthesizing a comprehensive long-term wave data series using ERA5 reanalysis data and the MIKE21-SW model, the study establishes a robust foundation for analysis. To ensure accuracy, the observed wind data were contrasted against ERA5 data, and they were found to be similar to a great extent. Furthermore, the model's accuracy was rigorously validated against the observational data, ensuring the reliability of the results. Three methods were used to obtain the trends for the maximum wave height to ensure robust and reliable trend assessments (i.e., linear regression, Theil-Sen, and ElasticNet). The results were obtained and analyzed, revealing a statistically significant increase in the maximum wave heights during the study period. Particularly, seasonal analysis revealed a positive trend in maximum wave heights during winter and spring (0.15 to 0.67 cm/year respectively). Furthermore, the monthly analysis showed a positive trend in the wave heights for all months except July, August, and September. Specifically, there were notable increases from September to December, ranging from 0.24 to 0.92 cm/year. These findings are consistent with the global trends of increasing wave heights due to climate change. It is essential for developing adaptive coastal management strategies and designing resilient structures. Consequently, the results will assist coastal authorities in mitigating the impacts of climate change on coastal zones.

From urban heat islands to intra-urban heat islands: Role of urban fabric in redefining microclimates of tomorrow’s compact cities

A compact city is a urban area characterized by high density, mixed land use, and limited sprawl, designed to promote sustainable development, reduce urban sprawl, and enhance quality of life. This investigation focuses on the Intra Urban Heat Island (IUHI) effect, an intensified progression of the Urban Heat Island (UHI) phenomenon, specifically within a compact city transitioning from urban sprawl. The study employed advanced spatial analytics, which employed the concept of “space-time cube”, incorporating Getis Ord Gi*, and per pixel, Mann Kendall tests for the space–time pattern mining on a time series of high-resolution Remote Sensing data spanning from 1999 to 2023. The findings reveal distinctive spatial and temporal patterns in IUHI, identifying a total of 693,900 m2 of intensifying hot-spot areas in the selected compact city characterized by industrial, warehousing, and commercial developments. The detailed examination of urban fabric at a finer resolution (7.5 cm × 7.5 cm) identified rooftops with specific spectral characteristics (red/copper hue) as significant contributors to the IUHI phenomenon, inducing surface temperature increases above 5 °C compared to neighboring cells. Furthermore, high-rise developments emerge as land use forms that create cold spots in the urban fabric, improving the city’s thermal environment. The implications underscore the necessity for future urban planning to consider IUHI as a concentrated development of the UHI effect, urging a holistic understanding of the complex interconnections among various factors influencing microclimates in urban environments.

Innovative SVM optimization with differential gravitational fireworks for superior air pollution classification

Amid escalating tension between environmental conservation and economic development, the imperative to enhance air quality has become increasingly urgent. This study elucidates a sophisticated approach for the assessment and remediation of air pollution issues through the integration of an enhanced particle swarm optimization algorithm and a differential gravitational fireworks algorithm-optimized support vector machine (SVM). In the initial phase of this research, a series of intricate data preprocessing and augmentation procedures were conducted, and the differential evolution algorithm played a pivotal role. The differential gravitational fireworks algorithm was subsequently introduced to optimize the SVM parameter settings, thereby bolstering classification accuracy and mitigating issues such as overfitting. Through rigorous and meticulous empirical testing, the augmented SVM model demonstrated notable performance in terms of classification accuracy and sequential and nonsequential data fusion, surpassing conventional SVM techniques. Notably, our sequential fusion method achieved an accuracy of up to 91%, at least 3% higher than that of nonsequential techniques. In conclusion, this study reveals an innovative and enhanced technological approach that is highly effective for the precise measurement and control of air pollution levels.

The Impact of Import, Savings, Consumtion and Inflation Rate on the Economic Growth of Denmark

This paper investigates the dynamic relationships between imports of goods and services (annual % growth), adjusted savings: consumption of fixed capital (current US$), final consumption expenditure (annual % growth), inflation GDP deflator (annual %) and economic growth in Denmark, using multivariate setting to examine time- series data. The secondary records from 1973 to 2022 that were collected from the World Bank comprise the data used in this analysis. The results of the study show that annual percentage increases in final consumption expenditure and imports of goods and services both considerably boost economic growth. However, the annual percentage of inflation as indicated by the GDP deflator and adjusted savings, which is expressed as the consumption of fixed capital in current US dollars, have a negative impact on economic growth.

Macular Changes in Adults with History of Premature Birth.

To characterize changes in macular structure and vascularity seen in premature-birth adults. Retrospective review of adult patients seen in our clinic from July 2018 to November 2020 with history of premature birth not requiring treatment for retinopathy of prematurity (ROP), as well as healthy controls. Swept-source OCT-angiography (OCT-A) scans were analyzed for various parameters related to macular structure and vascularity. Statistical analysis was performed using univariate linear regression. Thirty-four eyes were included (17 eyes from 10 premature-birth adults, 17 eyes from 11 control patients). Prematurity was associated with vascular changes on OCT-A: decreased foveal avascular zone (FAZ) area (p=0.001), decreased FAZ circularity index (p<0.001), decreased superficial capillary plexus vessel (CPV) density (p=0.02), and decreased deep CPV density (p<0.001). Prematurity was also associated with structural changes: shallower foveal pit depth (p<0.001), decreased cube average thickness (CAT) (p=0.005), and increased central subfield thickness-to-CAT ratio (p=0.002). This is the largest published series of OCT-A data for premature-birth adults. Even without a history of ROP treatment, premature birth is associated with multiple changes to macular structure and vascularity that persist into adulthood. These include a smaller, more irregularly shaped FAZ, decreased juxtafoveal vascular density, and a shallower foveal pit.

Artificial intelligence for predicting arctic permafrost and active layer temperatures along the Alaskan North Slope

AbstractClimate change in Arctic regions poses a threat to permafrost stability, potentially leading to issues such as infrastructure damage, altered ecosystems, and increased greenhouse gas emissions. The challenge of monitoring permafrost temperatures and identifying critical environmental factors is accentuated by the scarcity of subsurface thermal data in remote Arctic locales. To alleviate this lack of soil thermal data we set out to combine in situ observed soil temperatures with MERRA-2 reanalysis data and Machine Learning (ML) to develop local and season-specific subsurface soil temperature predictions in Alaska. First, reanalysis features are selected based on surface energy budget physics. Coupled with Julian calendar dates, reanalysis features are preprocessed and then fed to the ML prediction model. We conducted a series of computational experiments and tested the results against performance benchmarks to determine the optimal prediction models, length of look-back period of model inputs, and the training set size. Six conventional ML models (e.g., GBDT, RF, SVR) and five statistical baselines (e.g. ARIMA) using in situ time series of soil temperature data ranging from 0 - 1 m depth are considered for the prediction task. The models are trained using in situ soil temperatures at depths between 0 and 1 m, spanning 16+ years, from field sites at Deadhorse and Toolik Lake, Alaska. All prediction performances are assessed using root mean squared error (RMSE) and mean absolute error (MAE). Results show that locally trained ML models can estimate shallow soil temperatures with an average error of $$RMSE=1.308^{\circ }$$ R M S E = 1 . 308 ∘ C.

Long-term spatiotemporal changes in nitrate contamination of municipal groundwater resources after sewerage network construction in the Hungarian Great Plain.

Over the last decades, as a consequence of wastewater discharges and other anthropogenic sources, severe nitrate (NO3-) pollution has developed in municipal environment causing global concern. Thus, eliminating the potential sources of pollution is one of the major challenges of the twenty-first century, whereby sanitation services are essential for ensuring public health and environmental protection. In the present study, long-term monitoring (2011-2022) of shallow groundwater NO3- contamination in municipal environment was carried following the construction of the sewerage network (2014) in the light of the pre-sewerage situation. Our primary aim was to assess the long-term effects of sewerage on nitrate NO3- levels in the shallow groundwater and evaluate the efficiency of these sanitation measures over time. Based on the results, significant pollution of the shallow groundwater in the municipality was identified. During the pre-sewer period, NO3- concentrations exceeded the 50mg/L limit in the majority of monitoring wells significantly, upper quartile values ranged between 341 and 623mg/L respectively. Using Nitrate Pollution Index (NPI) and interpolated NO3- pollution maps, marked spatial north-south differences were detected. In order to verify the presence of wastewater discharges in the monitoring wells, the isotopic ratio shifts (δ) for 18O and D(2H) were determined, confirming municipal wastewater effluent. Variations in NO3-/Cl- molar ratios suggest also contamination from anthropogenic sources, including septic tank effluent from households and the extensive use of manure. Data series of 7years (2015-2022) after the investment indicate marked positive changes by the appearance of decreasing trends in NO3- values confirmed by Wilcoxon signed rank test and ANOVA. By comparing the pre- and post-sewerage conditions, the mean NO3- value decreased from 289.7 to 175.6mg/L, with an increasing number of monitoring wells with concentrations below the limit. Our results emphasise the critical role of sanitation investments, while also indicating that the decontamination processes occur at a notably slow pace. Detailed, long-term monitoring is therefore essential to ensure accurate follow-up of the ongoing changes. The results can provide information for local citizens and authorities to improve groundwater management tools in the region.

Cleaning method for abnormal energy big data based on sparse self-coding

In order to reduce the interference of abnormal energy big data and improve the accuracy of anomaly cleaning and detection, this paper proposes a method for cleaning abnormal energy big database based on sparse self-coding. Firstly, the abnormal data detection method based on multi-criteria evaluation is used to analyze the spectral feature distribution of abnormal energy big data. By using chaotic time series reconstruction model, robust local weighted regression analysis and sparse self-coding method, the feature decomposition of time series of energy data is realized. Then, according to the periodicity of the original sequence, the original sequence is segmitated adaptively to represent the morphological characteristics of the abnormal energy data sub-sequences driven by dynamic carbon emissions, and the anomaly index of each sub-sequence is obtained by AFCM algorithm. Secondly, an energy anomaly evaluation model based on LOF value is established. Finally, the output decision function of data cleaning is constructed to realize the abnormal energy big data cleaning. The test results show that the error detection rate of this method is 0.24%, the missing detection rate is 0.27%, the cleaning rate can reach 99.49%, and the cleaning time is less than 2s.

The first global catalogue of gas flaring sources derived from a multi-temporal time series of OLI and MSI daytime data: the DAFI v2 algorithm

Abstract Gas flaring (GF) is an industrial phenomenon requiring a special attention for the serious impacts on environment, climate and human health. To analyses and map GF sites (GFs), the Daytime Approach for GF Investigation (DAFI), which is based on daytime data from the Operational Land Imager (OLI) aboard the Landsat-8 satellite, has recently been developed. The GFs catalogue from the DAFI system, spanning over the years 2013–2021, represents the first worldwide GF product generated from mid-high spatial resolution data (30 m). The DAFI version 2 (v2), also including information from the Multispectral Instrument (MSI) aboard Sentinel-2 satellites, already showed an improved capacity in the mapping of GFs over Iran and Iraq. In this work, the new catalogue arising from the porting of DAFI v2 at global scale is presented and discussed. By using a parallel workflow, based on a loop tiling scheme running in Google Earth Engine, DAFI v2 allowed us to extend the GF inventory globally up to 2023, by analyzing ∼4 million of OLI/OLI-2 (Collection 2) and MSI imagery. Results of this study show: (i) an increased DAFI v2 capacity, of about 41%, in detecting GFs compared to the previous DAFI version; (ii) a downward trend (∼4.6%) in terms of GF sites detected from 2021 to 2023. These findings demonstrated that DAFI v2, which will also include the estimates of radiative power at different spatial scales, may contribute in assessing and monitoring the GFs activities and in evaluating the effectiveness of the greenhouse gas-emission reduction strategies at global, continental, national and local scales.

Prediction of Cognitive Impairment Using Sleep Lifelog Data and LSTM Model

Rapid elderly population growth has increased the number of patients with cognitive impairment (CI). Early detection and ongoing medical treatment can slow CI progression and significantly reduce the cost of managing patients. However, distinguishing CI from natural cognitive decline associated with aging is challenging. Previous studies conducted to identify patients with CI using lifelog data did not consider changes in lifelog data over time because each data point was learned individually. This study introduces a model that predicts patients with CI based on sleep lifelog data and analyzes significant sleep factors that influence cognitive decline. This study followed three steps: (1) collecting sleep lifelog data from elderly Korean people and reconstructing sleep lifelog data as time-series data; (2) building a model to classify CI using a time series of sleep lifelog data and a long short-term memory model; and (3) identifying sleep factors that influence the onset of CI using an explainable AI algorithm. The proposed CI classification model achieved a sensitivity of 0.89, a specificity of 0.80, and an area under the receiver operating characteristic curve of 0.92. This study will facilitate the noninvasive screening, diagnosis, and continuous monitoring of CI in the elderly.

Reconstruction of the Temperature Index Series of China in 1368–1911 based on REACHES database

This study reports the methodology for reconstructing anomalous temperature index series of China in 1368–1911 based on the REACHES database which digitizes the Chinese records quoted in the Compendium of Meteorological Records of China in the Last 3000 Years. The reconstruction adopts an ordinal scale index approach ranging from −2 (very cold) to 1 (warm). Based on the grading criteria, a total of 12,871 records were retrieved through a standard coding system established at REACHES. Sensitivity experiments were performed to test robustness of the index system and a reasonability test was conducted to develop an appropriate method for deriving areal mean temperature index. The reconstructed series were validated through comparison with early instrumental data from Global Historical Climatology Network which shows good correlations and reliability of the REACHES reconstructed index data. Annual and semi-annual (winter and summer) temperature index data series were released for the whole domain as well as the 3- and 15-subregion geographical domains in China.

Is It Possible to Establish an Economic Trend Correlating Territorial Assessment Indicators and Earth Observation? A Critical Analysis of the Pandemic Impact in an Italian Region

The paper is set within the methodological framework of the Territorial Impact Assessment (TIA) process, which is an instrument designed to facilitate sustainable and cohesive policy-making choices at the European level. The article is developed within the context of a European H2020-RICE cooperative project, which utilises the STeMA (Sustainable Territorial Economic/Environmental Management Approach) TIA methodology to investigate the potential relationship between statistical economic indicators, specifically Gross Domestic Product, and related parameters (metadata), and Earth Observation (EO) data. The objective is to provide evidence of socioeconomic trends during the Coronavirus 2019 pandemic in the Lazio Region (Italy), with a particular focus on the metropolitan area of the Rome capital city Rome. In line with the pertinent European context and the scientific literature on the subject, the paper examines the potential for combining classical and Earth observation indicators to assess macroeconomic dimensions of development, specifically in terms of gross domestic product (GDP). The results of the analysis indicate the presence of certain correlations between grey data and EO information. The STeMA-TIA approach allows for the measurement and correlation of both qualitative and quantitative statistical indicators with typological functional areas (in accordance with European Commission-EC and Committee of Ministers responsible for Spatial/Regional Planning—CEMAT guidance) at the NUTS (Nomenclature des unités territoriales statistiques) 2 and 3 levels. This facilitates the territorialisation of information, enabling the indirect comparison of data with satellite data and economic trends. A time series of data was gathered and organised for the purpose of facilitating comparison between different periods, beginning with 2019 and extending to the present day. In order to measure and monitor the evolution of the selected territorial economies (the Lazio Region), a synthetic index (or composite indicator) was developed in the economic and epidemic dimensions. This index combines single values of indicators according to a specific STeMA methodology. It is important to note that there are some critical observations to be made about the impact on GDP, due to the discrepancy between the indicators in the two fields of observation.