Spatial Statistical Methods Research Articles

Identification and Evaluation of Water Pollution Risk in the Chongqing Section of the Three Gorges Reservoir Area in China

The Three Gorges Reservoir is the largest freshwater resource reservoir in China. The water environment security in the Three Gorges Reservoir area has a prominent position in the major national strategy for the protection of the Yangtze River. Based on the pressure–state–response (PSR) model, this study comprehensively considers the dangerousness of risk source, the sensitivity of risk receptors, and the acceptable level of regional environmental risk to construct the grading evaluation index system of water environment pollution risk. By using spatial statistical methods, including the variation coefficient method and cold–hot spot pattern analysis, the risk distribution of water environment pollution in the Chongqing section of the Three Gorges Reservoir area was comprehensively identified and evaluated by administrative units. The results showed that: (1) the number of risk sources was largest in Yunyang County and the number of risk receptors was largest in Wanzhou District. However, the distribution of high-risk pollution sources and high-sensitivity receptors was most intensive in the main urban area and surrounding areas of Chongqing, and the regional environmental risk acceptance level was the lowest. (2) The statistical results of risk source dangerousness and the risk receptor sensitivity index at the county level in the study area showed an aggregated distribution pattern, with hotspot areas concentrated in the main urban area of Chongqing and the surrounding area in the upper reaches of the reservoir area. Moreover, the acceptable level of risk in this area showed a cold spot area, while other regions basically showed a balanced distribution pattern without forming significant hot spot or cold spot areas. (3) The high-risk river section of water pollution in the reservoir area comprised five counties, including Jiulongpo District, Yubei District, Shapingba District, Yuzhong District and Nanan District; the middle-risk river section comprised six counties, including Changshou District, Beipei District, Jiangbei District, Dadukou District, Fuling District and Shizhu County; and the low-risk river sections were mainly distributed in the Jiangjin District in the upper reaches of the reservoir area and the middle and lower reaches of the northeast ecological area of Chongqing. Therefore, the acceptable levels of water pollution risk sources, receptors and regional environmental risks in the Chongqing section of the Three Gorges Reservoir area are unevenly distributed, showing an aggregated distribution pattern. The spatial distribution of water environment pollution risk is uneven, and the significant potential risk area is the functional core area of Chongqing, which is the critical area of water environment risk management in the future.

Is There Spatial and Temporal Variability in the Response of Plant Canopy and Trunk Growth to Climate Change in a Typical River Basin of Arid Areas

The response of plants to climate change has become a topical issue. However, there is no consensus on the synergistic processes of the canopy and trunk growth within different vegetation types, or on the consistency of the response of the canopy and trunk to climate change. This paper is based on Normalized Difference Vegetation Index (NDVI), tree-ring width index (TRW) and climate data from the Irtysh River basin, a sensitive area for climate change in Central Asia. Spatial statistical methods and correlation analysis were used to analyze the spatial and temporal trends of plants and climate, and to reveal the differences in the canopy and trunk response mechanisms to climate within different vegetation types. The results show a warming and humidifying trend between 1982 and 2015 in the study area, and NDVI and TRW increases in different vegetation type zones. On an interannual scale, temperature is the main driver of the canopy growth in alpine areas and precipitation is the main limiting factor for the canopy growth in lower altitude valley and desert areas. The degree of response of the trunk to climatic factors decreases with increasing altitude, and TRW is significantly correlated with mean annual temperature, precipitation and SPEI in desert areas. On a monthly scale, the earlier and longer growing season due to the accumulation of temperature and precipitation in the early spring and late autumn periods contributes to two highly significant trends of increase in the canopy from March to May and August to October. Climatic conditions during the growing season are the main limiting factor for the growth of the trunk, but there is considerable variation in the driving of the trunk in different vegetation type zones. The canopy growth is mainly influenced by climatic factors in the current month, while there is a 1–2-month lag effect in the response of the trunk to climatic factors. In addition, the synergy between the canopy and the trunk is gradually weakened with increasing altitude (correlation coefficient is 0.371 in alpine areas, 0.413 in valley areas and 0.583 in desert areas). These findings help to enrich the understanding of the response mechanisms to climate change in different vegetation type zones and provide a scientific basis for the development of climate change response measures in Central Asia.

Assessing Inequity in Green Space Exposure toward a "15-Minute City" in Zhengzhou, China: Using Deep Learning and Urban Big Data.

Green space exposure is considered an important aspect of a livable environment and human well-being. It is often regarded as an indicator of social justice. However, due to the difficulties in obtaining green space exposure data from a ground-based view, an effective evaluation of the green space exposure inequity at the community level remains challenging. In this study, we presented a green space exposure inequity assessment framework, integrating the Green View Index (GVI), deep learning, spatial statistical analysis methods, and urban rental price big data to analyze green space exposure inequity at the community level toward a “15-minute city” in Zhengzhou, China. The results showed that green space exposure inequality is evident among residential communities. The areas in the old city were with relatively high GVI and the new city districts were with relatively low GVI. Moreover, a spatially uneven association was observed between the degree of green space exposure and housing prices. Especially, the wealthier communities in the new city districts benefit from low green space, compared to disadvantaged communities in the old city. The findings provide valuable insights for policy and planning to effectively implement greening strategies and eliminate environmental inequality in urban areas.

Path optimization of open collaborative innovation of energy industry in urban agglomeration based on particle swarm optimization algorithm

To study the open collaborative innovation path optimization of urban agglomeration energy industry based on particle swarm optimization algorithm. Firstly, this paper brings industrial agglomeration and urban agglomeration into a unified analytical framework, uses location entropy and spatial Gini coefficient to measure the degree of industrial agglomeration, and evaluates the degree of regional industrial agglomeration competitiveness by constructing an evaluation index system of industrial agglomeration competitiveness. Then, the agglomeration index system was established, the development degree analysis method and Carlino model were used to analyze the development degree and agglomeration degree of eco-economic zones, and the agglomeration effect was analyzed by dynamic econometric method and spatial autocorrelation statistical method. Finally, a regional collaborative innovation capability evaluation model based on particle swarm optimization algorithm is constructed. Based on the index system and evaluation model, this paper evaluates and analyzes the collaborative innovation capability, and puts forward countermeasures and suggestions for future collaborative development.

Spatial Analysis of Inequality in Thailand: Applications of Satellite Data and Spatial Statistics/Econometrics

To formulate and monitor the progress of development policies, acquiring data with sufficient spatiotemporal details is inevitable. With the increasing availability of open remote-sensing data and open-source software packages, this research suggested the novelty integration of satellite data and spatial analytical methods, enabling a timely and costless framework for assessing the nationwide socioeconomic condition. Specifically, the spatial statistical and spatial econometrical methods were applied to geospatial data to identify the clustering patterns and the localized associations of inequality in Thailand. The spatial statistical results showed that Bangkok and its vicinity had been a cluster of high socioeconomic conditions, representing the spatial inequality of development. In addition, results of the spatial econometrical models showed that the satellite-based indicators could identify the socioeconomic condition (with p-value < 0.010 and R-squared ranging between 0.345 and 0.657). Inequality indicators (i.e., Gini, Thiel and Atkinson) were then constructed by using survey-based and satellite-based data, informing that spatial inequality has been slowly declining. These findings recommended the new establishment of polycentric growth poles that offer economic opportunities and reduce spatial inequality. In addition, in accordance with Sustainable Development Goal 10 (reduced inequalities), this analytical framework can be applied to country-specific implications along with the global scale extensions.

Psoriasis comorbidities in Germany: A population-based study on spatiotemporal variations.

Psoriasis is a chronic disease with high impact on patients’ health and their quality of life. Psoriasis often occurs along with other comorbidities, but it is not yet clear what role the comorbidities play in regional psoriasis prevalence. This study investigates the temporal and regional variation of the psoriasis comorbidities diabetes mellitus type II, obesity, hypertension, affective disorders in Germany and their association with psoriasis prevalence. This analysis based on the population set of ambulatory claims data (2010–2017) of the statutory health insurance (SHI) in Germany (approx. 70.3 million people in 2017). Psoriasis comorbidities rates were determined on county level. We performed descriptive spatiotemporal analyses of psoriasis comorbidity prevalence rates. In addition, we identified and compared spatial clusters and examined regional variations using spatial statistical methods. The results show strong regional variations (northeast to south gradient) and an increasing psoriasis prevalence (max. 28.8%) within the observation period. Considering the comorbidities, results indicate comparable spatial prevalence patterns for diabetes mellitus type II, obesity and hypertension. This means that the highest prevalence of comorbidities tends to be found where the psoriasis prevalence is highest. The spatiotemporal cluster analyses could once again confirm the results. An exception to this is to be found in the case of affective disorders with different spatial patterns. The results of the studies show the first spatiotemporal association between psoriasis prevalence and comorbidities in Germany. The causalities must be investigated in more detail in order to be able to derive measures for improved care.

Applying Local Indicators of Spatial Association to Analyze Longitudinal Data: The Absolute Perspective

Local Indicators of Spatial Association (LISA) are a class of spatial statistical methods that have been widely applied in various scientific fields. When applying LISA to make longitudinal comparisons of spatial data, a common way is to run LISA analysis at each time point, then compare the results to infer the distributional dynamics of spatial processes. Given that LISA hinges on the global mean value that often varies across time, the LISA result generated at time Ti reflects the spatial patterns strictly with respect to Ti. Therefore, the typical comparative cross‐sectional analysis with LISA can only characterize the relative distributional dynamics. However, the relative perspective alone is inadequate to comprehend the full picture, as the patterns are not directly associated with the changes of the spatial process’s intensity. We argue that it is important to obtain the absolute distribution dynamics to complement the relative perspective, especially for tracking how spatial processes evolve across time at the local level. We develop a solution that modifies the significance test when implementing LISA analysis of longitudinal data to reveal and visualize the absolute distribution dynamics. Experiments were conducted with Mongolian livestock data and Rwanda population data.

Evaluating spatial statistical and machine learning models in urban dynamic population mapping

Understanding population dynamics at fine spatiotemporal granularities are valuable to human-centered studies. With the increasing availability of high-frequency human digital footprint data, the past decades have witnessed numerous efforts in mapping populations at fine spatiotemporal scales. However, such research still lacks a unified standard in modeling strategy and auxiliary data selection, especially a systematic comparison between newly developed machine learning techniques and traditional spatial statistical methods under different covariates provisions. Here, we compared two spatial statistical models, the Bayesian space-time model and geographically and temporally weighted regression, with two machine learning techniques, random forest and eXtreme gradient boosting, in a case study of hourly population mapping at 100 m resolution in Beijing. We evaluated the model performance with varied covariates combinations and found that the Bayesian space-time model achieved the best in conjunction with urban function data. Leveraging the optimal model constructed, we mapped dynamic population distribution and concluded human activity patterns on diverse city amenities. This paper emphasizes the importance of spatiotemporal dependency information in fine temporal scale population mapping and the urban function covariates in urban population mapping.

Spatial Patterns, Utility, and Limitations of Volunteered Reports of Urban Homeless Campsites in Portland, Oregon

Homelessness is a complex and diverse social issue that affects many urban areas in the United States. Robust knowledge of how and where homeless individuals subsist is essential for an assessment of the granular spatiotemporal context of homelessness in a given locale. Common means for surveying homeless rest sites are resource intensive and conducted infrequently, which warrants investigations into complementary approaches. In this article, we examine the spatial patterns of homeless campsites in Portland, Oregon, and their relationship to urban features based on a three-year data set of reports volunteered by members of the public. Our quantitative analysis employs a combination of spatial analysis, statistical methods, and cartographic mapping to determine prevailing spatial patterns and proximity relationships. In that context, we assess the characteristics of the volunteered data in terms of their suitability for capturing expected local patterns of homeless campsites. The data indicate concentrations of reported campsites and significant spatial proximity relationships between campsite locations, zoning, transit stops, and homelessness support services. Our findings evidence the utility of the volunteered data for the analysis of urban homeless campsite locations on an annual basis. We further discuss the limitations of the data and provide suggestions for improvements and future research.

A spatial statistical study of the distribution of Sardinian nuraghes

ABSTRACT The spatial distribution of nuraghes throughout the Island of Sardinia still raises many questions. In this paper, we apply spatial statistical methods to investigate their relations with topographical features and with related objects nearby. We use the non-stationary G- and J-functions. To model interactions with topographic variables we use the non-stationary Poisson model. We find that the elevation of the nuraghes show a uniform distribution between 0 and 400 m, and with a peak in distances to holy wells of approximately 5 km. As expected, we found a clustered pattern, with clustering occurring in the mid-west, the centre and the south west of the Island. We further observed a very strong interaction with domus de janas, and a strong spatial interaction for distances in the range between 0 and 1000 m with the pre-Nuragic dolmens and menhirs, and the collective funerary structures, the so-called Nuragic giants’ tombs. We conclude that the study is useful to quantify spatial patterns of pre-historic sites, in particular if these occur in a great abundancy and provides new insight into the spatial relations of the different pre-historic objects and buildings.

Reduced Inter-Voxel White Matter Integrity in Subjective Cognitive Decline: Diffusion Tensor Imaging With Tract-Based Spatial Statistics Analysis.

Subjective cognitive decline (SCD), a self-reported worsening in cognition concurrent with normal performance on standardized neuropsychological tests, has gained much attention due to its high risks in the development of mild cognitive impairments or Alzheimer’s disease. The existing cross-sectional diffusion tensor imaging (DTI) studies in SCD have shown extremely controversial findings. Furthermore, all of these studies investigated diffusion properties within the voxel, such as fractional anisotropy, mean diffusivity, or axial diffusivity (DA). However, it remains unclear whether individuals with SCD demonstrate alterations of diffusion profile between voxels and their neighbors, as indexed by local diffusion homogeneity (LDH). We selected 30 healthy controls (HCs) and 23 SCD subjects to acquire their whole-brain DTI. Diffusion images were compared using the tract-based spatial statistics method. Diffusion indices with significant between-group tract clusters were extracted from each individual for further region-of-interest (ROI)-based comparisons. Our results showed that subjects with SCD demonstrated reduced LDH in the left superior frontal gyrus (SFG) and DA in the right anterior cingulate cortex compared with the HC group. In contrast, the SCD group showed higher LDH values in the left lingual gyrus (LG) compared with the HC group. Notably, LDH in the left SFG was significantly and negatively correlated with LDH in the left LG. In conclusion, white matter (WM) integrity in the left SFG, right ACC, and left LG is altered in SCD, suggesting that individuals with SCD exhibit detectable changes in WM tracts before they demonstrate objective cognitive deficits.

The efficiency of fractal techniques in geochemical anomaly delineation within BLEG and

In regional exploration for Au mineralization using stream sediment geochemistry, multielement analysis following cyanide leaching of bulk samples or aqua regia digestion of the <180 μm fraction are the two most common sampling and analytical methods. Using data from extensive regional stream sediment and rock chip surveys in Western Turkey a comparison is made between the efficiency of fractal modelling of various spatial and frequency-based statistical methods to isolate patterns or populations related to known mineral deposits. This includes different combinations of pre-processing raw data, such as logistic-transformations and principal component analysis, together with fractal, U-spatial statistics and singularity index modelling. Due to variations in regional geology and the effects of a substantial number of base and precious metal in the region, all approaches indicate the presence of multiple geochemical populations with clearly defined thresholds. The efficiency in detecting known deposits is higher for spatially based methods than for simpler frequency distribution-based techniques, especially if weak geochemical anomalies are excluded to limit the risk of generating false positives. Maximum efficiency (between 70 and 80%) in linking the spatial distribution of anomalous geochemical populations with known Au ± Ag mineral deposits, in both in BLEG and the <180 μm fraction, was obtained using classification based on singularity indices, or logistical-transformed data with fractal analysis or U-spatial statistics. The results demonstrate the advantage of spatially based anomaly detection methods applied to multivariate data over simpler frequency distribution methods applied to univariate data.

Data-driven models for accurate groundwater level prediction and their practical significance in groundwater management

The overexploitation of groundwater resource and its delicacy management has gained increasing attentions in recent years worldwide because of causing a series of serious environmental and geological problems. Currently, accurately predicting the groundwater level (GWL) is an important issue in effective groundwater management across scales. In the present study, three popularly-used data-driven models, which are an autoregressive integrated moving average (ARIMA), a back-propagation artificial neural network (BP-ANN) and long short-term memory (LSTM), were established in five zones with different hydrogeological properties to explore the model’s accuracy in predicting the GWL at monthly and daily scales in a Northern Plain in China. The developed models were evaluated by both the Nash-Sutcliffe efficiency coefficient (NSE) and root mean square error (RMSE). The results indicate that the performance of the LSTM model is best at monthly time scales with the NSEs greater than 0.76 and RMSEs smaller than 1.15 m in each zone during the training period and demonstrate a good performance at daily time scales with the NSEs greater than 0.9 and the RMSEs smaller than 0.55 m at a local area. Meanwhile, the tempo-spatial distribution of the probability of drawdowns from the LSTM model was estimated by using the object-oriented spatial statistical (O2S2) method. The results show that cumulative drawdowns greater than 10 m are mainly concentrated in water source areas, with probabilities over 0.7 from 2003 to 2010 and declining to less than 0.3 from 2011 to 2014. The GWL rose generally in the study area from 2015 to 2018, but the probability of a drawdown with more than 5 m exceeded 0.8 in Zone V because of continuing groundwater exploitation. This study formulates a framework on developing effective data-driven models for predicting the GWL across scales which have the potential to aid groundwater management.

Optimized Hot Spot and Directional Distribution Analyses Characterize the Spatiotemporal Variation of Large Wildfires in Washington, USA, 1970\u22122020

Spatiotemporal analysis of fire activity is vital for determining why wildfires occur where they do, assessing wildfire risks, and developing locally relevant wildfire risk reduction strategies. Using various spatial statistical methods, we determined hot spots of large wildfires (> 100 acres) in Washington, the United States, and mapped spatiotemporal variations in large wildfire activity from 1970 to 2020. Our results found that all hot spots are located east of the crest of the Cascade Range. Our spatiotemporal analysis found that the geographic area wherein most of the state’s acres burned has shrunk considerably since 1970 and has become concentrated over the north-central portion of the state over time. This concentration of large wildfire activity in north-central Washington was previously unquantified and may provide important information for hazard mitigation efforts in that area. Our results highlight the advantages of using spatial statistical methods that could aid the development of natural hazard mitigation plans and risk reduction strategies by characterizing previous hazard occurrences spatially and spatiotemporally.

The use of spatial statistics in the analysis of salaries at poviat level in Poland

The spatial differentiation of salaries is the subject of many scientific studies, both theoretical and empirical. One of the factors determining remuneration in Poland is the structure and type of business activity, specific for a given region and depending on its poviats (counties) in terms of the level of the average gross monthly salary by means of spatial autocorrelation statistical methods. The analysed statistical data for 2010–2019 come from the Local Data Bank (Bank Danych Lokalnych – BDL) of Statistics Poland. Global and local measures were used in the analysis. The calculation of the global parameters of spatial autocorrelation was based on the I Moran and C Geary statistics, while the Ii Moran statistic, which belongs to local spatial indicators from the LISA group (Local Indicators of Spatial Association), was used to identify the local autocorrelation. The statistical significance of the global statistics was verified by means of a randomisation approach based on theoretical moments. The I Moran and C Geary global statistics indicated a significant (very weak or weak) and positive spatial autocorrelation between poviats in terms of the level of average gross monthly salaries in 2010–2019, which shows the existence of spatial poviat structures of similar values, i.e. clusters with high or low values of average salaries. The increase in I Moran’s statistics and the growth of the C Geary in the analysed period indicate a decrease in the differentiation of average monthly salaries between poviats, thus signifying an increase in the dependence of spatial autocorrelation. The analysis of the results of the obtained local statistics allowed the determination of clusters of similar poviats in Poland, e.g. Mazowiecki, Pomorski and Śląski. Furthermore, the results of the analysis indicated the presence of outlier poviats.

Spatial Statistics for Understanding Tissue Organization.

Interpreting tissue architecture plays an important role in gaining a better understanding of healthy tissue development and disease. Novel molecular detection and imaging techniques make it possible to locate many different types of objects, such as cells and/or mRNAs, and map their location across the tissue space. In this review, we present several methods that provide quantification and statistical verification of observed patterns in the tissue architecture. We categorize these methods into three main groups: Spatial statistics on a single type of object, two types of objects, and multiple types of objects. We discuss the methods in relation to four hypotheses regarding the methods' capability to distinguish random and non-random distributions of objects across a tissue sample, and present a number of openly available tools where these methods are provided. We also discuss other spatial statistics methods compatible with other types of input data.

Clarifying Soil Texture and Salinity Using Local Spatial Statistics (Getis-Ord Gi* and Moran’s I) in Kazakh–Uzbekistan Border Area, Central Asia

The purpose of this paper was to study the spatial characteristics and possible influencing factors of farmland soil texture and salt content in the Syr Darya River Basin. Data on the soil grain size and salt content were collected at 56 sampling sites in the southern part of the Shardara Reservoir and the left bank of the Syr Darya River irrigation area. With the methods of local spatial statistics (Getis-Ord Gi* and Moran’s I), the hotspots of soil salinity and grain size in the study area were revealed, and along with the use of correlation analysis, the possible factors affecting soil salt distribution were discussed. Among the 56 soil sampling sites, sandy loam, loamy loam, and chalky loam accounted for 20%, 50%, and 30%, respectively, and mildly, moderately, and severely saline soils accounted for 80.36%, 14.28%, and 5.36%, respectively. There was statistically significant spatial autocorrelation between sand, silt, and clay content in the soils, but the spatial autocorrelation for salt content was weak. The results show that high and high-cluster areas (hotspots) with statistically significant salt content are mainly distributed in the northwest of the study area and that the hotspot distribution of salt content is mainly affected by topography (altitude), but the effect of soil texture on salt content is not significant. The control of soil salinity should prioritize low-altitude areas, especially in the northwestern region. The results are of great significance for the regulation and control of soil salinity and the sustainable utilization of soil in arid Central Asia.

Underground space use of urban built-up areas in the central city of Nanjing: Insight based on a dynamic population distribution

With the expansion of cities and the emergence of various urban problems, urban underground space has been developed as a solution. In China’s urban transition context, there is a need for the development of underground space in urban built-up areas. In this case-study of the central city of Nanjing, we used spatial analysis and statistical methods to characterize the underground space use of urban built-up areas from a dynamic spatiotemporal perspective. We first analyzed the relationship between the population distribution and the underground space use of the central city of Nanjing based on a Baidu heat map, which can reflect the real-time population distribution, and then, we explored the spatiotemporal characteristics and spatial structure of the underground space use in urban built-up areas. The analysis results provide a reference for planning to improve and optimize the layout of underground space in the central city of Nanjing and, more generally, for the stock-type planning of underground space in urban built-up areas.

Quantifying spatiotemporal dynamics of vegetation and its differentiation mechanism based on geographical detector.

The influence of factors on vegetation changes in different regions is still largely unknown. We applied the geographic detector, a new spatial statistical method, to study the interactive effects of factors on the spatial patterns of normalised vegetation index (NDVI) changes and determine the optimal characteristics of key impact factors beneficial to vegetation growth. Our results show that from 2000 to 2015, the vegetation cover for the upper reaches of the Minjiang River, western China was in good condition. Furthermore, more than 80% of the areas had NDVI values ranging from 0.6 to 0.8 and NDVI > 0.8, and the spatial-temporal changes of vegetation cover were significant. The vegetation cover changes showed a significant transformation in the regions with NDVI > 0.6. Our study uniquely illustrated that elevation, annual average temperature and soil type can explain vegetation changes quite well. We propose that interactive effects exist among impact factors on vegetation NDVI, and the synergistic effects of the impact factors show mutual and nonlinear enhancements. The interactions among impact factors significantly enhance the impact of a single factor on vegetation changes. The most suitable characteristics of the main impact factors that promote vegetation growth were revealed by this study and will help improve our understanding of factors that impact NDVI and its driving mechanisms. Our findings suggest that the established favourable value range or the most suitable characteristics of impact factors will help management plans to intervene and promote vegetation change for vegetation restoration and alleviate environmental degradation.

The Second Competition on Spatial Statistics for Large Datasets

In the last few decades, the size of spatial and spatio-temporal datasets in many research areas has rapidly increased with the development of data collection technologies. As a result, classical statistical methods in spatial statistics are facing computational challenges. For example, the kriging predictor in geostatistics becomes prohibitive on traditional hardware architectures for large datasets as it requires high computing power and memory footprint when dealing with large dense matrix operations. Over the years, various approximation methods have been proposed to address such computational issues, however, the community lacks a holistic process to assess their approximation efficiency. To provide a fair assessment, in 2021, we organized the first competition on spatial statistics for large datasets, generated by our ExaGeoStat software, and asked participants to report the results of estimation and prediction. Thanks to its widely acknowledged success and at the request of many participants, we organized the second competition in 2022 focusing on predictions for more complex spatial and spatio-temporal processes, including univariate nonstationary spatial processes, univariate stationary space-time processes, and bivariate stationary spatial processes. In this paper, we describe in detail the data generation procedure and make the valuable datasets publicly available for a wider adoption. Then, we review the submitted methods from fourteen teams worldwide, analyze the competition outcomes, and assess the performance of each team.