Autocorrelation Patterns Research Articles

Spatiotemporal variations and spillover effects of regional water resources metabolism efficiency with heterogeneous resource endowments

Abstract Scientific evaluation of water resources metabolism efficiency (WME) has essential theoretical and practical value for regional water resources security and sustainable utilization. Based on the framework of the water resources metabolism process and construction of the indicator system of WME, the spatiotemporal variations of WME were analyzed using the weighted summation method in the Eastern, Central, and Western regions of China during the period from 1990 to 2020 with heterogeneous resource endowments. The exploratory spatial data analysis (ESDA) and spatial Durbin model were applied to analyze the spatial autocorrelation patterns and reveal spillovers of the WME. The results show that (1) The WME increased at different speeds in the Eastern, Central, and Western regions from 1990 to 2020 with heterogeneous resource endowment, and the spatial variation of WME in the Eastern and Central regions was significantly higher than in the Western regions. (2) The WME had significant spatial autocorrelation except in 2015, indicating obvious spatial agglomeration characteristics and spatial heterogeneity simultaneously. (3) The spatial spillover effects analysis indicated that the waste virtual recycling use ratio, the ratio of output virtual water and input virtual water, and industrial water ratio were the primary factors influencing WME.

Spatial comparison of London's three waves of Spanish Flu.

England and Wales experienced three waves of influenza during the 1918/19 Spanish Flu pandemic. A previous analysis showed that these three waves had fundamentally different spatial and temporal characteristics. This present study compares London's experience of the three waves to discern possible geographic differences on a metropolitan level. Borough mortality data for each wave were normalized and then scaled, with spatial autocorrelation techniques displayed by GIS software and analysed for each wave. Registrar General in England and Wales reporting provided data concerning measures of 'health' and 'wealth' for each metropolitan borough. Spearman's rank correlation determined the correlation of each wave's mortality to each of the other waves including the 'health,' 'wealth' and population density factors. The comparisons showed that there is a spatial difference among the waves. The first two are spatially similar, with both exhibiting 'random' autocorrelation patterns, while the third wave exhibits a 'clustered' pattern. The borough mortality of the first two waves strongly correlated with each other, with both having similar 'health,' 'wealth' and population density factors. However, the third wave's mortality did not correlate with any of the first two and actually behaved in an opposite manner with regard to the 'health,' 'wealth,' and population density factors. These results do not appear in the literature and create new opportunities for research to explain London's mortality during the Spanish Flu pandemic of 1918/19.

The robustness of our assumptions about recruitment: A re-examination of marine recruitment dynamics with additional data and novel methods

Recruitment is the number of young fish entering a population and is an essential process in age-structured stock assessment models. Many of the models on which stock assessments are based assume some level of influence of spawning biomass on recruitment, and estimation of this functional relationship has been a focus of substantial research. However, recruitment is also influenced by environmental fluctuations that induce autocorrelated patterns, trends, and shifts in deviations from the stock-recruitment relationship. Consequently, previous meta-analyses of stock-recruitment relationships have had trouble calibrating the relationship. We revisited the findings of Szuwalski et al. (2015) to determine if their results are robust to the addition of data to the RAM Legacy Stock Assessment database and choice of analysis method (the PELT algorithm, MARSS models, and Bayesian change point detection). Since the publication of Szuwalski et al. (2015), the number of stocks in the database has doubled. We determined the primary influence of spawning biomass on recruitment and examined the recruitment time series for regime shifts for 432 stocks. Our results indicated that 57% of stocks did not have a significant correlation between spawning biomass and recruitment over the observed biomasses. Environmental conditions played a larger role in recruitment variation than spawning biomass. The presence, location, and number of regime shifts in recruitment time series was highly dependent on the detection method, with Bayesian change point detection identifying the fewest regime shifts. Despite the sensitivity to the detection method, 46% of stocks without a significant correlation between spawning biomass and recruitment are estimated to have experienced at least one regime shift as determined by the PELT algorithm Our analyses suggest that effective methods for modeling and forecasting large variations in recruitment over time are needed, particularly given that climate change is predicted to impact the frequency and magnitude of regime shifts.

A space-time flow LISA approach for panel flow data

Spatial flow data represent meaningful spatial interaction (SI) phenomena between geographic regions that are often highly dynamic. However, most existing flow analytical methods are cross-sectional, and there is a lack of methods to measure spatiotemporal autocorrelation of flow data. To fill this gap, we proposed a new localized spatial statistical method called Space-Time Flow LISA. The method design is a combination of two existing method families, namely space-time LISA and Spatial Flow LISA. A critical component of the method is the space-time weight matrix of flow data that blends pairwise spatial and temporal connectivities. We design three versions of the matrix, namely contemporaneous, lagged, and hybrid. We evaluate the method using both synthetic data and a case study of U.S. interstate migration from 2005 to 2017. The method is found to have high efficacy in finding spatiotemporal local autocorrelation patterns. Unlike the Spatial Flow LISA that tends to detect short-distance migration corridor havens (‘HH’ flows) and long-distance migration corridor deserts (‘LL’ flows), the Space-Time Flow LISA is less impeded by the distance between flow origin and destination, as they can pick up local patterns that are less spatially explicit but temporally dependent. In addition, the new method is able to detect time-sensitive patterns such as the outmigration from Louisiana forced by Hurricane Katrina in 2005. By integrating spatial, temporal, and attributive associations into a one-step analysis, the proposed Space-Time Flow LISA can illustrate the spatiotemporal structure of flow phenomena well, and reveal dynamic distribution changes over time.

Temporal complexity in photoplethysmography and its influence on blood pressure.

Objective: The temporal complexity of photoplethysmography (PPG) provides valuable information about blood pressure (BP). In this study, we aim to interpret the stochastic PPG patterns with a model-based simulation, which may help optimize the BP estimation algorithms. Methods: The classic four-element Windkessel model is adapted in this study to incorporate BP-dependent compliance profiles. Simulations are performed to generate PPG responses to pulse and continuous stimuli at various timescales, aiming to mimic sudden or gradual hemodynamic changes observed in real-life scenarios. To quantify the temporal complexity of PPG, we utilize the Higuchi fractal dimension (HFD) and autocorrelation function (ACF). These measures provide insights into the intricate temporal patterns exhibited by PPG. To validate the simulation results, continuous recordings of BP, PPG, and stroke volume from 40 healthy subjects were used. Results: Pulse simulations showed that central vascular compliance variation during a cardiac cycle, peripheral resistance, and cardiac output (CO) collectively contributed to the time delay, amplitude overshoot, and phase shift of PPG responses. Continuous simulations showed that the PPG complexity could be generated by random stimuli, which were subsequently influenced by the autocorrelation patterns of the stimuli. Importantly, the relationship between complexity and hemodynamics as predicted by our model aligned well with the experimental analysis. HFD and ACF had significant contributions to BP, displaying stability even in the presence of high CO fluctuations. In contrast, morphological features exhibited reduced contribution in unstable hemodynamic conditions. Conclusion: Temporal complexity patterns are essential to single-site PPG-based BP estimation. Understanding the physiological implications of these patterns can aid in the development of algorithms with clear interpretability and optimal structures.

The dynamic impact of digital economy on the green development of traditional manufacturing industry: Evidence from China

The traditional manufacturing industry is China’s second-largest source of carbon emissions, and the successful completion of dual carbon target depend on the traditional manufacturing industry’s ability to reduce emissions. This paper constructs the evaluation index system to analyze the driving factors and green coupling level of the digital economy enabling the green development of traditional manufacturing industry from three aspects: industry digitalization capacity, socio-economic benefits capacity and ecological resources carrying capacity based on the theory of social shaping of technology. From the perspective of regional economic development, this paper constructs the spatial Durbin model and threshold regression model to analyze the mediation effect and spatial spillover mechanism of the digital economy empowering the green development of traditional manufacturing industry. Based on the panel data of 30 provinces in China from 2012 to 2020, the empirical analysis show that digital economy has the positive impact on the green development of traditional manufacturing industry through the digital innovation, industrial upgrading and human capital. At same time, the digital economy has a positive spatial autocorrelation pattern of the green development of traditional manufacturing industries with “high in the eastern and low in the western of China” from the global Moran index. Through the policy test of carbon-trading pilot programs by the difference-in-differences model, the digital economy has a non-linear impact on the intensive production of traditional manufacturing industry, which green finance activate the new impetus of market and the data element resources enhance industrial coordination development.

Data augmentation for bias correction in mapping PM2.5 based on satellite retrievals and ground observations

As most air quality monitoring sites are in urban areas worldwide, machine learning models may produce substantial estimation bias in rural areas when deriving spatiotemporal distributions of air pollutants. The bias stems from the issue of dataset shift, as the density distributions of predictor variables differ greatly between urban and rural areas. We propose a data-augmentation approach based on the multiple imputation by chained equations (MICE-DA) to remedy the dataset shift problem. Compared with the benchmark models, MICE-DA exhibits superior predictive performance in deriving the spatiotemporal distributions of hourly PM2.5 in the megacity (Chengdu) at the foot of the Tibetan Plateau, especially for correcting the estimation bias, with the mean bias decreasing from –3.4 µg/m3 to –1.6 µg/m3. As a complement to the holdout validation, the semi-variance results show that MICE-DA decently preserves the spatial autocorrelation pattern of PM2.5 over the study area. The essence of MICE-DA is strengthening the correlation between PM2.5 and aerosol optical depth (AOD) during the data augmentation. Consequently, the importance of AOD is largely enhanced for predicting PM2.5, and the summed relative importance value of the two satellite-retrieved AOD variables increases from 5.5% to 18.4%. This study resolved the puzzle that AOD exhibited relatively lower importance in local or regional studies. The results of this study can advance the utilization of satellite remote sensing in modeling air quality while drawing more attention to the common dataset shift problem in data-driven environmental research.

Effects of Forest Fragmentation on Connectivity and Genetic Diversity in an Endemic and an Invasive Rodent in Northwestern Madagascar.

Habitat loss and fragmentation are of concern to conservation biologists worldwide. However, not all organisms are affected equally by these processes; thus, it is important to study the effects of living in fragmented habitats on species that differ in lifestyle and habitat requirements. In this study, we examined the dispersal and connectivity patterns of rodents, one endemic (Eliurus myoxinus) and one invasive (Rattus rattus), in two landscapes containing forest fragments and adjacent continuous forest patches in northwestern Madagascar. We generated genetic (RADseq) data for 66 E. myoxinus and 81 R. rattus individuals to evaluate differences in genetic diversity as well as inbreeding and connectivity in two landscapes. We found higher levels of inbreeding and lower levels of genetic diversity in E. myoxinus compared with R. rattus. We observed related dyads both within and between habitat patches and positive spatial autocorrelation at lower distance classes for both species, with a stronger pattern of spatial autocorrelation in R. rattus. Across each site, we identified contrasting migration rates for each species, but these did not correspond to habitat-matrix dichotomies. The relatively low genetic diversity in the endemic E. myoxinus suggests ecological constraints that require further investigation.

Key epidemiological indicators and spatial autocorrelation patterns across five waves of COVID-19 in Catalonia

This research studies the evolution of COVID-19 crude incident rates, effective reproduction number R(t) and their relationship with incidence spatial autocorrelation patterns in the 19 months following the disease outbreak in Catalonia (Spain). A cross-sectional ecological panel design based on n = 371 health-care geographical units is used. Five general outbreaks are described, systematically preceded by generalized values of R(t) > 1 in the two previous weeks. No clear regularities concerning possible initial focus appear when comparing waves. As for autocorrelation, we identify a wave’s baseline pattern in which global Moran’s I increases rapidly in the first weeks of the outbreak to descend later. However, some waves significantly depart from the baseline. In the simulations, both baseline pattern and departures can be reproduced when measures aimed at reducing mobility and virus transmissibility are introduced. Spatial autocorrelation is inherently contingent on the outbreak phase and is also substantially modified by external interventions affecting human behavior.

Hybrid prediction of infections and deaths due to COVID-19 in two Colombian data series.

The prediction of the number of infected and dead due to COVID-19 has challenged scientists and government bodies, prompting them to formulate public policies to control the virus' spread and public health emergency worldwide. In this sense, we propose a hybrid method that combines the SIRD mathematical model, whose parameters are estimated via Bayesian inference with a seasonal ARIMA model. Our approach considers that notifications of both, infections and deaths are realizations of a time series process, so that components such as non-stationarity, trend, autocorrelation and/or stochastic seasonal patterns, among others, must be taken into account in the fitting of any mathematical model. The method is applied to data from two Colombian cities, and as hypothesized, the prediction outperforms the obtained with the fit of only the SIRD model. In addition, a simulation study is presented to assess the quality of the estimators of SIRD model in the inverse problem solution.

Spatial pattern and heterogeneity of chronic respiratory diseases and relationship to socio-demographic factors in Thailand in the period 2016 to 2019.

Chronic respiratory diseases (CRDs) constitute 4% of the global disease burden and cause 4 million deaths annually. This cross-sectional study used QGIS and GeoDa to explore the spatial pattern and heterogeneity of CRDs morbidity and spatial autocorrelation between socio-demographic factors and CRDs in Thailand from 2016 to 2019. We found an annual, positive, spatial autocorrelation (Moran's I >0.66, p<0.001) showing a strong clustered distribution. The local indicators of spatial association (LISA) identified hotspots mostly in the northern region, while coldspots were mostly seen in the central and north-eastern regions throughout the study period. Of the socio-demographic factors, the density of population, households, vehicles, factories and agricultural areas, correlated with the CRD morbidity rate, with statistically significant negative spatial autocorrelations and coldspots in the north-eastern and central areas (except for agricultural land) and two hotspots between farm household density and CRD in the southern region in 2019. This study identified vulnerable provinces with high risk of CRDs and can guide prioritization of resource allocation and provide target interventions for policy makers.

Autocorrelation in category judgement.

Jesteadt et al. discovered a remarkable pattern of autocorrelation in log estimates of loudness. Responses to repeated stimuli correlated to about +0.7, but that correlation was much reduced (0.1) following large differences between successive stimuli. The experiment reported here demonstrates the same pattern in absolute identification without feedback; if feedback is supplied, the pattern is much muted. A model is proposed for this pattern of autocorrelation, based on the premise: "There is no absolute judgment of sensory magnitudes; nor is there any absolute judgment of differences/ratios between sensory magnitudes." Each stimulus in an experiment is compared with its predecessor, greater, less than, or about the same. The variability of that comparison increases with the difference in magnitude between the stimuli, so the assessment of a stimulus far removed from its predecessor is very uncertain. The model provides explanations for the apparent normal variability of sensory stimuli, for the "bow" effect and for the widely reported pattern of sequential effects. It has applications to the effects of stimulus range, to the difficulty of identifying more than five stimuli on a single continuum without error, and to inspection tasks in general, notably medical screening and the marking of examination scripts.

Integration of Marine and Terrestrial Ecological Economies in the Cities of the Bohai Rim, China, Based on the Concept of Viscosity

The integration of sea and land ecological economies is crucial for the development of a high-quality sea–land economy. This study explores and proposes the concept of a sea–land-integrated ecological economy. By constructing the evaluation index system for developing a sea–land-integrated ecological economy, the development level, evolution process, and development trend prediction of a sea–land-integrated ecological economy were evaluated and analysed in cities around the Bohai Sea from 2009 to 2019 using methods such as a model for assessing the development level, a spatio-temporal autocorrelation model, and an exploratory spatio-temporal data analysis model. The results of the study show that (1) the development level of the ecological economy of the cities of Bohai Rim’s sea–land integration generally had an upward trend; however, the magnitude significantly varied between cities; (2) the spatio-temporal autocorrelation pattern formed three major agglomerations with Dalian in the north, Yantai and Qingdao in the south, and Tianjin and Tangshan in the centre as the core cities and contained low agglomerations and scattered L–H spatio-temporal heterogeneous units; and (3) the integration prediction curve for 2020–2029 indicates that the level value for integration of most cities will improve over time.

Temporal non‐independence of foraging dive and surface duration sequences in the European shag Gulosus aristotelis

AbstractStudies of foraging behaviour and respiratory physiology in breath‐holding divers often assume that each dive cycle (dive plus surface duration) is physiologically and ecologically independent within a series (or “bout”) of sequential dives. We tested this assumption using time depth recorders and GPS data for more than 42,000 dives in 1289 bouts by 39 pairs of male and female European shags (Gulosus aristotelis) provisioning nestlings. We found distinct patterns of temporal autocorrelation over several dives within bouts, but this was driven mainly by consecutive dives of the same type, that is, runs of V‐shaped (presumably prey searching) versus U‐shaped (presumably active hunting) dives. We found no evidence of cumulative physiological effects (i.e. fatigue and/or lowered body temperature) across dives within a bout. However, within‐individual variation in dive behaviour revealed complex interactions. Longer bouts were associated with more V‐shaped dives, including more and longer runs of V‐shaped dives. Meanwhile, more U‐shaped dives and longer runs of U‐shaped dives acted as limiting factors to bout lengths, with longer bouts being associated with more U‐shaped dives only later in the bout. Interactions between bout length and body mass, and between dive order within the bout and body mass, also suggested various size‐specific patterns in the temporal distribution of U‐shaped dives. Long bouts and bouts ending in longer runs of V‐shaped dives were more likely to indicate the termination of foraging activity. However, neither dive type nor bout length predicted whether individuals subsequently (i) stayed to forage in the same location or (ii) moved to a new location to continue foraging within the same trip from the nest. European shags therefore showed temporal non‐independence across successive dive cycles and successive bouts of dives, likely as a result of temporal and spatial variation in prey availabilities rather than cumulative physiological effects that might contravene the assumptions in models of optimal dive behaviour.

Laser-pulse characterization using strong-field autocorrelation patterns and random-forest-based machine learning

Building on the strategy presented in Opt. Lett. 47, 3992 (2022), we demonstrate an efficient alternative approach for the in situ characterization of ultrashort low-frequency laser pulses. In this context, we employ first-principles quantum-mechanical calculations to model the strong-field ionization of rare-gas atoms and produce autocorrelation patterns for a set of few-femtosecond near-infrared laser pulses. We explore the nonperturbative and nonlinear dependence of the autocorrelation patterns on the pulse characteristics and postulate an analytical function describing these patterns. For every laser pulse considered, we employ the parameters appearing in this analytical function, together with the underlying pulse parameters for supervised machine learning. Specifically, we use the random-forest technique for retrieving key laser pulse parameters from autocorrelation patterns produced via strong-field ionization. The current approach offers advantages for application to experimental data.

Burden of occupational cancer in Brazil and federative units, 1990-2019.

To analyze the spatiotemporal distribution of the burden of occupational cancer in Brazil and federative units between 1990 and 2019. Data were extracted from the Global Burden of Disease (GBD) study. Deaths from cancer whose attributable risk factor was occupational carcinogens were considered. Spatial analysis was performed with the first and last years of the series (1990 and 2019). Age-adjusted mortality rates were used to estimate the global Moran's Index (Moran's I), and the local indicator of spatial association (LISA) to identify clusters in the country with the respective statistical significance. The occupational cancer mortality rate, adjusted for age, was analyzed based on its trend for Brazil and federative units, in the period between 1990 and 2019. Between 1990 and 2019, occupational cancer mortality rate showed a decreasing trend (R2=0.62; p<0.001) as well as the burden of disease indicator - DALY (R2=0.84; p<0.001). However, mortality is increasing in most states, suggesting that a minority of federative units induce the country's global trend. There is also the development of a spatial pattern of autocorrelation, indicating clusters of states with low mortality and DALY rates in the Northeast and high values in the South of the country. The overall decreasing trend in the trend of occupational cancer masks the heterogeneity across states. This scenario may be associated with the diversity of economic activities, and suggests a decentralized and equitable plan for occupational cancer surveillance.

Assessing the progress and spatial patterns of sustainable eco-environmental development based on the 2030 Agenda for SDGs in China

ABSTRACT The eco-environment is fundamental for human survival and development. To gain insight into Chinese eco-environmental challenges, a scientific and quantifying assessment of sustainable eco-environmental development (SED) is essential and can guide policy development and implementation. However, systematic methods for assessing progress towards achieving SED are lacking based on the 2030 Agenda for the Sustainable Development Goals (SDGs). Therefore, we developed a systematic method to quantify SED progress at the provincial level in China. In the proposed method, the SED indicator framework was developed to align with the global indicator framework (GIF), adopting clear statistical definitions for each indicator and the criteria of data selection at provincial levels in China. A four-level method of identifying the upper and lower bounds of the indicator values was proposed to normalise them to a standard scale of 0–100. The SED index and three composite indices were aggregated by the arithmetic means of the individual scores for assessing overall SED progress. A spatial autocorrelation analysis was used to explore the spatial patterns of the SED index scores. The results showed that all provinces except Shandong and Hebei had relatively good performances in achieving SED. Most provinces performed better in terms of the water area eco-environment than they do in terms of the air and terrestrial eco-environments. In addition, some provinces showed a positive spatial autocorrelation pattern on the SED index scores, and the high-value (low-value) aggregation regions were mainly concentrated in Western (East) China. These results provide a richer understanding of the challenges for SED faced by each province in China.

Evaluating urban environment quality (UEQ) for Class-I Indian city: an integrated RS-GIS based exploratory spatial analysis

Urban environmental quality consisting of ecological, physical, and socio-economic components, often deteriorates due to rapid urbanization. Therefore, using Remote sensing and GIS environment, a composite measure is applied to quantify the spatial heterogeneity of urban environmental quality for the Class-1 Indian city (Siliguri). In this study, the Urban Environmental Quality Index was constructed using 15 indicators and three interconnected dimensions (eco-environment, landscape and built-up, and socio-economy). The three domains and Urban Environmental Quality Index were computed utilizing Principal Component Analysis with average aggregation techniques. Exploratory Spatial Data Analysis includes Moran’s I and Local indicator of spatial auto-correlation, were used to leverage the information of spatial clusters, spatial heterogeneity, and outliers based on the Urban Environmental Quality Index. The results show that Siliguri’s northern, north-western, and southern parts experience good environmental quality. The effectiveness of the employed model was checked using R2 (0.832), providing a good fit for the model. Moreover, the spatial pattern of urban environmental quality and the constructed domains (except socio-economy) revealed that the Low-Low values were predominantly clustered in the city centre, while High-High patterns are concentrated towards the periphery. Also, the value of Moran’s I indicated the existence of spatial autocorrelation and non-randomness pattern in Siliguri City. The results obtained from the analysis indicate spatial heterogeneity and spatial differentiation across the study area. The study’s outcome is relevant for urban planning, frequent monitoring of urban environmental quality, urban governance, and the well-being of urban inhabitants for a sustainable urban space.

Precipitation interpolation, autocorrelation, and predicting spatiotemporal variation in runoff in data sparse regions: Application to Panama

Study regionPanama faces seasonal floods and droughts, and rising freshwater demand for domestic consumption, hydropower, and the operation of the Panama Canal. A process-based hydrological model of the country would complement the existing national water security plan as a scenario planning tool. Study focusIn Panama, as in much of the Global South, sufficient observed data do not exist for all watersheds to calibrate complex hydrological models. Understanding and improving the performance of uncalibrated hydrological models could greatly expand their utility in such regions. In this study, we build and validate an uncalibrated Soil and Water Assessment Tool (SWAT) model for Panama. We extend the default precipitation submodel and demonstrate the importance of accounting for spatial autocorrelation patterns in precipitation inputs: we found large improvements over the default model, not only for monthly means (NSE = 0.88, from 0.69 for default SWAT), but especially for standard deviations (NSE = 0.59, from 0.27) and maxima (NSE = 0.51, from 0.21) of discharge across locations and months. New hydrological insights for regionWe found a strong seasonal trend and regional differences in the spatial autocorrelation of rainfall, suggesting that this phenomenon should not be modeled statically. The resulting precipitation and hydrology models provide important baseline information for Panama, especially on variability and extremes, and could serve as a template for other regions with limited data.

How well does digital soil mapping represent soil geography? An investigation from the USA

Abstract. We present methods to evaluate the spatial patterns of the geographic distribution of soil properties in the USA, as shown in gridded maps produced by digital soil mapping (DSM) at global (SoilGrids v2), national (Soil Properties and Class 100 m Grids of the USA), and regional (POLARIS soil properties) scales and compare them to spatial patterns known from detailed field surveys (gNATSGO and gSSURGO). The methods are illustrated with an example, i.e. topsoil pH for an area in central New York state. A companion report examines other areas, soil properties, and depth intervals. A set of R Markdown scripts is referenced so that readers can apply the analysis for areas of their interest. For the test case, we discover and discuss substantial discrepancies between DSM products and large differences between the DSM products and legacy field surveys. These differences are in whole-map statistics, visually identifiable landscape features, level of detail, range and strength of spatial autocorrelation, landscape metrics (Shannon diversity and evenness, shape, aggregation, mean fractal dimension, and co-occurrence vectors), and spatial patterns of property maps classified by histogram equalization. Histograms and variogram analysis revealed the smoothing effect of machine learning models. Property class maps made by histogram equalization were substantially different, but there was no consistent trend in their landscape metrics. The model using only national points and covariates was not substantially different from the global model and, in some cases, introduced artefacts from a lithology covariate. Uncertainty (5 %–95 % confidence intervals) provided by SoilGrids and POLARIS were unrealistically wide compared to gNATSGO/gSSURGO low and high estimated values and show substantially different spatial patterns. We discuss the potential use of the DSM products as a (partial) replacement for field-based soil surveys. There is no substitute for actually examining and interpreting the soil–landscape relation, but despite the issues revealed in this study, DSM can be an important aid to the soil surveyor.