Levels Of Spatial Aggregation Research Articles

Exploring Dengue Dynamics: A Multi-Scale Analysis of Spatio-Temporal Trends in Ibagué, Colombia.

Our study examines how dengue fever incidence is associated with spatial (demographic and socioeconomic) alongside temporal (environmental) factors at multiple scales in the city of Ibagué, located in the Andean region of Colombia. We used the dengue incidence in Ibagué from 2013 to 2018 to examine the associations with climate, socioeconomic, and demographic factors from the national census and satellite imagery at four levels of local spatial aggregation. We used geographically weighted regression (GWR) to identify the relevant socioeconomic and demographic predictors, and we then integrated them with environmental variables into hierarchical models using integrated nested Laplace approximation (INLA) to analyze the spatio-temporal interactions. Our findings show a significant effect of spatial variables across the different levels of aggregation, including human population density, gas and sewage connection, percentage of woman and children, and percentage of population with a higher education degree. Lagged temporal variables displayed consistent patterns across all levels of spatial aggregation, with higher temperatures and lower precipitation at short lags showing an increase in the relative risk (RR). A comparative evaluation of the models at different levels of aggregation revealed that, while higher aggregation levels often yield a better overall model fit, finer levels offer more detailed insights into the localized impacts of socioeconomic and demographic variables on dengue incidence. Our results underscore the importance of considering macro and micro-level factors in epidemiological modeling, and they highlight the potential for targeted public health interventions based on localized risk factor analyses. Notably, the intermediate levels emerged as the most informative, thereby balancing spatial heterogeneity and case distribution density, as well as providing a robust framework for understanding the spatial determinants of dengue.

Using a general practice research database to assess the spatio-temporal COVID-19 risk

BackgroundIn Flanders, general practitioners (GPs) were among the first ones to collect data regarding COVID-19 cases. Intego is a GPs’ morbidity registry in primary care with data collected from the electronic medical records from a sample of general practices. The Intego database contain elaborate information regarding patient characteristics, such as comorbidities. At the national level, the Belgian Public Health Institute (Sciensano) recorded all test-confirmed COVID-19 cases, but without other patient characteristics.MethodsSpatio and spatio-temporal analyses were used to analyse the spread of COVID-19 incidence at two levels of spatial aggregation: the municipality and the health sector levels. Our study goal was to compare spatio-temporal modelling results based on the Intego and Sciensano data, in order to see whether the Intego database is capable of detecting epidemiological trends similar to those in the Sciensano data. Comparable results would allow researchers to use these Intego data, and their wealth of patient information, to model COVID-19-related processes.ResultsThe two data sources provided comparable results. Being a male decreased the odds of having COVID-19 disease. The odds for the age categories (17,35], (35,65] and (65,110] of being a confirmed COVID-19 case were significantly higher than the odds for the age category [0,17]. In the Intego data, having one of the following comorbidities, i.e., chronic kidney disease, heart and vascular disease, and diabetes, was significantly associated with being a COVID-19 case, increasing the odds of being diagnosed with COVID-19.ConclusionWe were able to show how an alternative data source, the Intego data, can be used in a pandemic situation. We consider our findings useful for public health officials who plan intervention strategies aimed at monitor and control disease outbreaks such as that of COVID-19.

Scale effects and implications of the stochastic structure of customer water demands

ABSTRACT The effect of different temporal (from seconds to months) and spatial aggregation scales (from individual users to full urban areas) on water demand behavior has been explored to a limited degree. The effort described here extends those works by evaluating the scale effects of residential water consumption in a unique US data set that covers 10,000 households with a 1-gallon (3.79 L) hourly resolution over 2 years. A preliminary data analysis and a sequential Principal Component Analysis (PCA) is carried out to assess the effect of different temporal (weekly, daily, hourly) and spatial aggregation (individual meters and groups every 10, 100 and 1,000 meters) levels on demand. Results show that individual users act very differently from each other, and individual consumer variability is only canceled out when a significant number of households are aggregated. The implications of this finding are assessed from a hydraulic modeling perspective as the spatiotemporal scale of measurements may condition the type of analysis that can be carried out in practice. However, additional work is needed to explore the point at which it may be worth embracing a micro (per fixture/household) or a macro (per node/network) approach for different purposes.

Understanding the impact of modifiable areal unit problem on urban vitality and its built environment factors

ABSTRACT Urban vitality is an essential reference for formulating urban planning and urban development policies, while the Modifiable Areal Unit Problem (MAUP) is a crucial issue that affects the accurate assessment of urban vitality and the understanding of its driving factors. Currently, MAUP in urban vitality studies has not received sufficient attention, and there are limited investigations on whether MAUP significantly affects the spatial autocorrelation of urban vitality. Moreover, existing studies have mainly focused on the impact of individual factors on urban vitality, with minor consideration of the interactions between factors and their effects on urban vitality at different spatial scales. This study takes the Pearl River Delta Core (PRDC) region as the research area. It used the Optimal Parameters-based Geographic Detector (OPGD) model and spatial autocorrelation to analyze the impact of the MAUP on urban vitality and its Built Environment (BE) factors. The results indicated that (1) the five types of BE driving factors, including work, living, rest, traffic, and Points of Interest (POI) diversity, were spatially sensitive and exhibited scale threshold effects. (2) The zoning schemes demonstrated the ability to alter the explanatory power (q-value) of driving factors and their trends. The spatial granularity and aggregation level of the grid-based zoning scheme, NPP-VIIRS nighttime light zoning scheme, and land surface temperature zoning scheme exhibited a logarithmic relationship, where spatial autocorrelation levels decreased with increasing aggregation level. (3) Compared to single-factor effects, the interactive effects of BE factors exhibited a higher q-value for urban vitality. Additionally, the q-values for factor interactions were more stable in multiple spatial scales than those q-values for individual factors. This study provides decision-making references for urban planners and policymakers to accurately assess urban vitality and find balanced, improved paths to enhance it.

Monitoring the West Nile virus outbreaks in Italy using open access data

This paper introduces a comprehensive dataset on West Nile virus outbreaks that have occurred in Italy from September 2012 to November 2022. We have digitized bulletins published by the Italian National Institute of Health to demonstrate the potential utilization of this data for the research community. Our aim is to establish a centralized open access repository that facilitates analysis and monitoring of the disease. We have collected and curated data on the type of infected host, along with additional information whenever available, including the type of infection, age, and geographic details at different levels of spatial aggregation. By combining our data with other sources of information such as weather data, it becomes possible to assess potential relationships between West Nile virus outbreaks and environmental factors. We strongly believe in supporting public oversight of government epidemic management, and we emphasize that open data play a crucial role in generating reliable results by enabling greater transparency.

Mapping Fractional Vegetation Cover Using Unoccupied Aerial Vehicle Imagery to Guide Conservation of a Rare Riparian Shrub Ecosystem in Southern California

The use of unoccupied aerial vehicles (UAVs) for vegetation monitoring is widespread in agriculture and forestry but far less so in ecological restoration where it has tremendous unrealized potential. We tested the ability of multispectral data and a derived vegetation index to classify shrub, herbaceous vegetation, and bare soil cover in a rare alluvial floodplain vegetation community in semiarid Southern California, where shrub cover is manipulated in restoration efforts aimed to provide open habitats required by several threatened and endangered species. Three classifiers and three levels of spatial aggregation were compared for their ability to provide accurate shrub cover estimates at a scale commensurate with the needs of conservation managers. We used object-based image analysis (OBIA) and compared maximum likelihood (ML), support vector machine (SVM), and random forest (RF) classifiers applied to high-spatial resolution (0.14 m) data from a four-band Parrot Sequoia+ multispectral sensor. The SVM and RF classifiers yielded similarly high classification accuracy evaluated using the training data (overall accuracy of 96.4% and 97.6%, respectively), much higher than ML (88%). Aggregating shrub cover data to 25 and 50 m resolutions yielded more accurate and well-calibrated cover estimates (mean absolute error 12% and 11%, respectively, for RF) than 10 m aggregation (MAE 19% for RF). Shrub cover estimated using RF and SVM was able to meet the restoration monitoring needs to distinguish the three phases of shrub habitat characterized by their cover (10–30%, 30–75%, >75%) that differ in habitat quality and restoration prescriptions.

Aquatic Risks at the Landscape Scale: A Case Study for Pyrethroid Use in Pome Fruit Orchards in Belgium.

Procedures for environmental risk assessment for pesticides are under continuous development and subject to debate, especially at higher tier levels. Spatiotemporal dynamics of both pesticide exposure and effects at the landscape scale are largely ignored, which is a major flaw of the current risk assessment system. Furthermore, concrete guidance on risk assessment at landscape scales in the regulatory context is lacking. In this regard, we present an integrated modular simulation model system that includes spatiotemporally explicit simulation of pesticide application, fate, and effects on aquatic organisms. As a case study, the landscape model was applied to the Rummen, a river catchment in Belgium with a high density of pome fruit orchards. The application of a pyrethroid to pome fruit and the corresponding drift deposition on surface water and fate dynamics were simulated. Risk to aquatic organisms was quantified using a toxicokinetic/toxicodynamic model for individual survival at different levels of spatial aggregation, ranging from the catchment scale to individual stream segments. Although the derivation of landscape-scale risk assessment end points from model outputs is straightforward, a dialogue within the community, building on concrete examples as provided by this case study, is urgently needed in order to decide on the appropriate end points and on the definition of representative landscape scenarios for use in risk assessment.

Spatially and taxonomically explicit characterisation factors for greenhouse gas emission impacts on biodiversity

In life-cycle impact assessment, currently available characterisation factors (CF) for climate change impacts on biodiversity are highly simplified and do not consider spatial and taxonomic differentiation of species or local climate variability. We develop the first spatially and taxonomically specific CFs for the impacts of 20 GHGs on biodiversity considering 26,648 species across terrestrial and marine ecosystems. Generally, CFs are higher in the tropics, and marine species are affected more severely than terrestrial ones. When global GHG emissions from 2020 are assessed in a scenario with a global temperature rise of 3 °C by 2100, an average of 0.25%, 0.15% and 0.03% of species are negatively affected in 2100 from CO2, CH4, and N2O emissions, respectively, across the globe. The new CFs can be used at different levels of spatial and taxonomic aggregation to quantify co-benefits for biodiversity of climate change mitigation in tools such as life-cycle assessment, input-output analyses, or integrated assessment models.

The adverse impact of headway variability on bus transit ridership: Evidence from Bengaluru, India

This study examines the impact of bus service headway variability on bus transit ridership using direct demand models at different levels of spatial aggregation – route level and stop-route level – using transit demand and supply data from the city of Bengaluru, India. In addition, auxiliary models are developed to understand the determinants of service frequency and headway variability and to address the endogeneity of these service characteristics in the demand models. This is perhaps the first study in the public transit literature to compare and contrast the endogeneity and non-linearity effects of service frequency and headway variability in transit demand models using both a conceptual framework and empirical evidence from a large transit system. The empirical results offer evidence that variability in headways adversely impacts transit ridership and passenger-kilometres. The strength of the adverse effect increases with increasing variability. On the other hand, the influence of service frequency decreases with increasing frequency. Furthermore, it is shown conceptually and demonstrated empirically that ignoring the endogeneity of service variability results in an underestimation of its adverse effect on transit demand. On the other hand, the empirical results suggest that ignoring the endogeneity of service frequency would result in an overestimation of its beneficial effect. An important takeaway from these results and additional policy simulations is that transit agencies can potentially gain greater ridership and revenue by reducing headway variability rather than simply allocation more buses and crew to high-frequency routes.

Ecological and economic trade-offs between amount and spatial aggregation of conservation and the cost-effective design of coordination incentives

To counter not only the continuous loss but also the fragmentation of species habitats, coordination incentives (CI) have been proposed to incentivise the spatial aggregation of conservation efforts. An important issue is the cost-effective design of these instruments. Two main types of CI, the agglomeration bonus and the agglomeration payment, are analysed with stylised models. Their ecological effects are assessed through a metapopulation simulation model. Rather than choosing the usual approach and analysing the joint ecological-economic model, I analyse the ecological and economic sub-models separately and join the results within an economic production theory framework in which production factors (here, the proportion and aggregation of conserved land parcels) are financed under a budget- or cost constraint and generate an output (here, metapopulation viability). This decomposition of the ecological-economic analyses allows highlighting the ecological and economic trade-offs between proportion and spatial aggregation of conservation and generating a more general understanding of the budget- and cost-effectiveness of CI. Results include, among others, that the agglomeration payment is never more budget- and cost-effective than the agglomeration bonus and that in the agglomeration bonus the budget-effective level of spatial aggregation is lower than the cost-effective level.

Measuring welfare with the use of the MIMIC approach: evidence for Poland

The growing interest in the concept of using welfare categories as a measure of economic or social development results from the fact that dominant income categories have been replaced by solutions related to individuals’ basic, existential and higher-order needs being fulfilled. The transition from analysing the poverty rate category, through the various approaches to material deprivation, to the perception of welfare in economic, subjective, or hidden terms is visible. The main purpose of the study is to estimate the subjective level of welfare of households in Poland. The subjective level of various socio-economic phenomena is often difficult to estimate; therefore, the study uses the approach of structural modelling for the multiple indicators and multiple causes model (MIMIC), which assumes the presence of a latent variable. The research is based on data from the Social Diagnosis panel study for household level in Poland. Based on the construction and positive substantive and statistical verification of the model the results show that subjectively the best welfare situation was identified mainly for households located in countryside areas, where even the lowest estimated levels of welfare exceed the highest estimated levels for cities of various sizes. Investigating the spatial distribution, the highest levels of subjective welfare were recorded for the Lubelskie and Swietokrzyskie voivodeships, and thanks to moving to a higher spatial data aggregation level (to the sub-regional), a more detailed assessment of spatial units was possible. In further research, individualised voivodeship models will be estimated to capture a more accurate differentiation of the influence of MIMIC model variables. A similar direction of analysis is anticipated for sub-regional data.

Remote sensing of night‐time lights and electricity consumption: A systematic literature review and meta‐analysis

AbstractNight‐time light (NTL) satellite imagery can provide unique insights into the energy sector. Nevertheless, there are limited studies that have systematically reviewed the literature on the relationship between electricity consumption and NTL. Therefore, this paper aims to provide a systematic review of studies that have explored this relationship. The review identified over 200 regression models estimating electricity consumption using NTL satellite images. The key finding of the review was that there was a large variability in regression performance for model prediction of electricity consumption from NTL imagery, indicating a need for further work to refine the techniques and approaches in this emerging field of remote sensing research. The level of spatial aggregation had an important influence on model performance with larger geographical areas, such as countries or states, providing better estimations.

On the shape and origins of the freshwater species-area relationship.

The species-area relationship (SAR) has over a 150-year-long history in ecology, but how its shape and origins vary across scales and organisms remains incompletely understood. This is the first subcontinental freshwater study to examine both these properties of the SAR in a spatially explicit way across major organismal groups (diatoms, insects, and fish) that differ in body size and dispersal capacity. First, to describe the SAR shape, we evaluated the fit of three commonly used models, logarithmic, power, and Michaelis-Menten. Second, we proposed a hierarchical framework to explain the variability in the SAR shape, captured by the parameters of the SAR model. According to this framework, scale and species group were the top predictors of the SAR shape, climatic factors (heterogeneity and median conditions) represented the second predictor level, and metacommunity properties (intraspecific spatial aggregation, γ-diversity, and species abundance distribution) the third predictor level. We calculated the SAR as a sample-based rarefaction curve using 60 streams within landscape windows (scales) in the United States, ranging from 160,000 to 6,760,000 km2 . First, we found that all models provided good fits (R2 ≥ 0.93), but the frequency of the best-fitting model was strongly dependent on organism, scale, and metacommunity properties. The Michaelis-Menten model was most common in fish, at the largest scales, and at the highest levels of intraspecific spatial aggregation. The power model was most frequent in diatoms and insects, at smaller scales, and in metacommunities with the lowest evenness. The logarithmic model fit best exclusively at the smallest scales and in species-poor metacommunities, primarily fish. Second, we tested our framework with the parameters of the most broadly used SAR model, the log-log form of the power model, using a structural equation model. This model supported our framework and revealed that the SAR slope was best predicted by scale- and organism-dependent metacommunity properties, particularly spatial aggregation, whereas the intercept responded most strongly to species group and γ-diversity. Future research should investigate from the perspective of our framework how shifts in metacommunity properties due to climate change may alter the SAR.

The impacts of the modifiable areal unit problem (MAUP) on social equity analysis of public transit reliability

This study aims to examine the degree to which the results of social equity analysis on public transit reliability are sensitive to the choice of spatial unit of analysis (i.e., modifiable areal unit problem (MAUP)). Using the city of Winnipeg as an example, we investigate the social equity of bus on-time performance (OTP), and pass-up distribution at multiple levels (e.g., stop, route, neighborhood) and compare the results. Neighborhoods are classified as minority vs. non-minority population dominant neighborhoods and transit routes and stops are classified into minority vs. non-minority population serving ones according to the socioeconomics of residents. We conduct an equity assessment by calculating 1) the number of pass-ups, 2) the number of bus deviations (e.g., early arrivals, delays) from the predetermined schedule, and 3) the average deviations time (in seconds) that occurred on minority vs. non-minority serving routes/stops as well as in minority vs. non-minority dominant neighborhoods. We also consider different day types (e.g., weekdays, weekends) and disability status (e.g., wheelchair, regular passengers) in the equity assessment. While the route-level results depict that transit service reliability is equitable, results of the neighborhood and stop-level analysis reveal inequities in the distribution of the OTP and pass-ups in the city. Our findings demonstrate that different levels of spatial aggregation can significantly change the results of social equity analysis of public transit reliability, thereby leading to incomplete conclusions that inadequately capture the inequity due to the existence of MAUP. The results of this paper provide insights to transit authorities, planners, and policymakers for diagnosing the social equity landscape of transit reliability in a more robust manner.

OPRACOWANIE WSKAŹNIKÓW JAKOŚCI ŻYCIA W MIEŚCIE NA PODSTAWIE BAZ DANYCH PRZESTRZENNYCH

The subject of the study is the analysis of using the Topographic Objects Database (BDOT10k) and the Local Data Bank (BDL) to monitor sustainable development and quality of life in the city. The usefulness of this data was tested on the example of quality of life indicators in the city of Kielce and its neighborhood. The databases were analyzed in terms of their scope, relevance, and level of spatial aggregation. The study used the hierarchical analysis (AHP) and multi-criteria evaluation (MCE) method. The issue under consideration was presented in the form of a hierarchy, with a quality of life at the top. The lowest level of the hierarchy is formed by selected indicators defined in the standard ISO 37120. The methodology for calculating and visualization of each indicator is presented illustrating the diversity of life quality in the Kielce Functional Area.

What you get is not always what you see—pitfalls in solar array assessment using overhead imagery

Effective integration planning for small, distributed solar photovoltaic (PV) arrays into electric power grids requires access to high quality data: the location and power capacity of individual solar PV arrays. Unfortunately, national databases of small-scale solar PV do not exist; those that do are limited in their spatial resolution, typically aggregated up to state or national levels. While several promising approaches for solar PV detection have been published, strategies for evaluating the performance of these models are often highly heterogeneous from study to study. The resulting comparison of these methods for practical applications for energy assessments becomes challenging and may imply that the reported performance evaluations overly optimistic. The heterogeneity comes in many forms, each of which we explore in this work: the degree of diversity of the locations and sensors (e.g. different satellites, aerial photography) from which the training and validation data originate, the validation of ground truth (manual annotation of imagery vs known solar PV locations), the level of spatial aggregation (e.g. array-level vs regional estimates), and inconsistencies in the training and validation datasets (e.g. different datasets are used for each study and those data are not always made accessible). For each, we discuss emerging practices from the literature to address them or suggest directions of future research. As part of our investigation, we evaluate solar PV identification performance in two large regions: the entire state of Connecticut and the city of San Diego, CA. In Connecticut, we also use 33,114 known parcel-level solar PV installations from Berkeley Lab’s Tracking the Sun dataset to evaluate parcel-level performance and evaluate capacity estimates using 169 municipalities. We also make our code (which we call SolarMapper), pre-trained models, training data, and predictions publicly available and provide a web portal for interactively inspecting each prediction that was made. Our findings suggest that traditional performance evaluation of the automated identification of solar PV from satellite imagery may be optimistic due to common limitations in the validation process. The takeaways from this work are intended to inform and catalyze the large-scale practical application of automated solar PV assessment techniques by energy researchers and professionals.

Recent developments in multivariate wind and solar power forecasting

AbstractThe intermittency of renewable energy sources, such as wind and solar, means that they require reliable and accurate forecasts to integrate properly into energy systems. This review introduces and examines a selection of state‐of‐the‐art methods that are applied for multivariate forecasting of wind and solar power production. Methods such as conditional parametric and combined forecasting already see wide use in practice, both commercially and scientifically. In the context of multivariate forecasting, it is vital to model the dependence between forecasts correctly. In recent years, reconciliation of forecasts to ensure coherency across spatial and temporal aggregation levels has shown great promise in increasing the accuracy of renewable energy forecasts. We introduce the methodology used for forecast reconciliation and review some recent applications for wind and solar power forecasting. Many forecasters are beginning to see the benefit of the greater information provided by probabilistic forecasts. We highlight stochastic differential equations as a method for probabilistic forecasting, which can also model the dependence structure. Lastly, we discuss forecast evaluation and how choosing a proper approach to evaluation is vital to avoid misrepresenting forecasts.This article is categorized under: Climate and Environment > Net Zero Planning and Decarbonization Sustainable Energy > Solar Energy Sustainable Energy > Wind Energy

Random encounter model is a reliable method for estimating population density of multiple species using camera traps

AbstractPopulation density estimates are important for wildlife conservation and management. Several camera trapping‐based methods for estimating densities have been developed, one of which, the random encounter model (REM), has been widely applied due to its practical advantages such as no need for species‐specific study design. Nevertheless, most of the studies in which REM has been assessed against referenced methods have sampled one population, precluding evaluation of the circumstances under which REM does or does not perform well. At this point, a review of all REM assessments could be useful to provide an overview of method reliability and highlight the main factors determining REM performance. Here we used a combination of literature review and empirical study to compare the performance of REM with independent methods. We reviewed 34 studies where REM was applied to 45 species, reporting 77 REM‐reference density comparisons; and we also sampled 13 populations (ungulates and lagomorphs) in which we assessed REM performance against independent densities. The results suggested that appropriate procedures to estimate REM parameters (namely day range, detection zone and encounter rate) are mandatory to obtain unbiased densities. Deficient estimates of day range and encounter rate lead to an overestimation of density, while deficient estimates of detection zone conducted to underestimations. Finally, the precision achieved by REM was lower than reference methods, mainly because of the high levels of spatial aggregation observed in natural populations. In this situation, simulation‐based results suggest that c. 60 camera placements should be sampled to achieve acceptable precision (i.e. coefficient of variation below 0.20). The wide range of situations and scenarios included in this study allow us to conclude that REM is a reliable method for estimating wildlife population density when using appropriate estimates of REM parameters and sampling designs. Overall, these results pave the way to wider application of REM for monitoring terrestrial mammals.

Estimating Facility‐Level Monthly Water Consumption of Commercial, Industrial, Municipal, and Thermoelectric Users in Virginia

ABSTRACTUnderstanding water consumption is an important component of water management. However, water consumption data are limited and consumption coefficients do not account for variability through time and across users. This study combines federally maintained discharge data with state‐maintained withdrawal data at monthly time steps to estimate facility‐level and spatially aggregated water consumption in Virginia between 2010 and 2016. We evaluate (1) the feasibility of using discharge and withdrawal datasets to estimate sub‐annual water consumption, (2) how these consumption estimates vary depending on the level of spatial aggregation, and (3) what patterns of seasonality exist in consumption estimates. We find that a combined process of text matching and geospatial analysis is effective in matching facilities and yielding monthly time‐series of water consumption. Our results suggest that median consumption in industrial (17%) and commercial (19%) facilities may be higher than median consumption coefficients in the literature (10%). Consumption estimates also demonstrated more variability across facilities and seasons than aggregate coefficients in the literature suggest. Combining this approach with institutional knowledge can assist in quantifying issues such as inter‐basin transfers and infiltration that impact consumption estimates, ultimately allowing for more accurate accounts of water use and availability.

Socio-economic development and quality of life of NUTS-2 units in the European Union

Abstract Analyses regarding socio-economic development and quality of life are an important aspect of research and discussion for many international organisations, states and local authorities. Due to the complexity and multidimensionality of these issues, conducting research can be problematic. The conclusions of various analytical centres indicate that there are many paths towards establishing a set of factors which affect quality of life and ways of assessing socio-economic development levels. Depending on the criteria considered, the most common methods for determining the degree of the advancement of life quality or socio-economic development include taxonomical techniques and analyses of potential, which are based mainly on objective data sourced from official registers. The main purpose of the paper is to investigate the level of socio-economic development and quality of life in the European Union in the years 2004 and 2018. The analyses were conducted for a rarely used level of spatial data aggregation, i.e. for NUTS-2 units. The analysis covers only those European regions that were EU members in 2004. As the primary research tool, the two-dimensional development matrix was adopted, which enabled the verification of the hypothesis regarding the convergence of synthetic measures that indicate the levels of socio-economic development and quality of life in the EU regions. For these indices, the development matrix is also used to identify the strengths and weaknesses as well as the opportunities and threats for selected spatial units, and, at the same time, to estimate the rates of change of the socio-economic development and quality of life levels.