Spatial Association Research Articles

Multi-dimensional equilibrium optimal allocation of water resources in Beijing-Tianjin-Hebei region through spatial association network analysis

The rapid social and economic development has led to problems such as over-exploitation of water resources, strong demand for physical water resources, and disequilibrium of water resources load. Based on the current situation of the water resources system in Beijing-Tianjin-Hebei region, this study proposed an input–output analysis-assessment-prediction-optimization framework. A water resources dissipation environment extended multi-regional input–output model was established, and the spatial equilibrium of water resources load was evaluated comprehensively. Furthermore, a multi-dimensional spatial equilibrium optimal allocation for water resources was built to identify key nodes and sectors. The primary findings were as follows: (1) Shijiazhuang and Tangshan were the main cities with net virtual water outflow, while Beijing and Baoding were the primary recipients of net virtual water inflow; (2) The overall score for the spatial equilibrium assessment of water resource load was low, with dimension scores predominantly ranging between 0.2 and 0.6; (3) The key transfer nodes were predominantly concentrated on the “outflow from Shijiazhuang” and the “inflow to Cangzhou and Langfang” nodes. This research conducted an in-depth exploration of the direction of water resource transfers, the actual stress conditions, and the spatial equilibrium of water resources within the Beijing-Tianjin-Hebei region. Finally, the future adjustment strategy for water-intensive industries at the urban agglomeration scale was obtained.

Tweeting during the Pandemic in New York City: Unveiling the Evolving Sentiment Landscape of NYC through a Spatiotemporal Analysis of Geolocated Tweets

ABSTRACT This article explores the relationship between spatial factors, socioeconomic conditions, and Twitter (now called X) sentiment in New York City (NYC) during the COVID-19 pandemic. Using Twitter data, the study investigates how sentiment varied across different geographies. It examines whether sentiment scores, unemployment rates, and COVID-19 hospitalization rates in NYC zip codes revealed spatial associations. The research employs sentiment analysis, a natural language processing technique used to algorithmically determine the emotional tone of a text, on a database of geo-located tweets spanning January to December 2020. The findings reveal a shift towards more negative sentiment during the initial year of the pandemic. Moreover, the study uncovers variations in sentiment trends across boroughs and zip codes. Additionally, a zip code-level fixed-effects model demonstrates a statistically significant relationship between sentiment scores and unemployment rates. In summary, this article makes a two-fold contribution: firstly, it adds a spatial lens to the scholarly debate regarding the use of Twitter data as an indicator of publicly expressed sentiment; secondly, it provides empirical evidence on the spatial interconnectedness of sentiment, health (hospitalization), and socioeconomic factors (unemployment). Overall, this research sheds light on the nuanced relationship between sentiment and space during the COVID-19 pandemic in NYC.

Analysis of the spatial association network structure of China’s green innovation efficiency

Improving the green innovation efficiency (GIE) is the main approach to high-quality development in China. Identifying the spatial distribution and influencing factors of GIE is conducive to sustainable and balanced regional development. In this research, the slack based measure-data envelopment analysis model (SBM-DEA) is used to measure the GIE of 30 provinces in China from 2013 to 2023. The gravity model and social network analysis methods are used to explore the spatial correlation of GIE in the provinces. The influencing factors of GIE are analyzed by the Tobit model. The results show that: (1) During the study period, the GIE of China’s provinces presented a high level, the spatial accumulation characteristics were obvious and the spatial correlation showed a complex network structure; (2) The association network is still in the primary stage, and the network strength and network association degree are low; (3) Provinces with strong innovation ability do not affect the green innovation of other provinces. In addition, these regions have obtained higher innovation linkage benefits from other provinces; (4) The amount of patent, education and government support have a significant positive impact on GIE, while total energy consumption and the secondary industry’s share of the gross domestic product have a significant negative impact on GIE. This study will reveal the characteristics of green innovation behavior of China from a new perspective, and provide a scientific basis for the formulation of green innovation behavior and related policies of China.

Modeling tuberculosis transmission flow in China, 2010–2012

BackgroundChina has the third largest number of TB cases in the world, and the average annual floating population in China is more than 200 million, the increasing floating population across regions has a tremendous potential for spreading infectious diseases, however, the role of increasing massive floating population in tuberculosis transmission is yet unclear in China.Methods29,667 tuberculosis flow data were derived from the new smear-positive pulmonary tuberculosis cases in China. Spatial variation of TB transmission was measured by geodetector q-statistic and spatial interaction model was used to model the tuberculosis flow and the regional socioeconomic factors.ResultsTuberculosis transmission flow presented spatial heterogeneity. The Pearl River Delta in southern China and the Yangtze River Delta along China’s east coast presented as the largest destination and concentration areas of tuberculosis inflows. Socioeconomic factors were determinants of tuberculosis flow. Some impact factors showed different spatial associations with tuberculosis transmission flow. A 10% increase in per capita GDP was associated with 10.2% in 2010 or 2.1% in 2012 decrease in tuberculosis outflows from the provinces of origin, and 1.2% in 2010 or 0.5% increase in tuberculosis inflows to the destinations and 18.9% increase in intraprovincial flow in 2012. Per capita net income of rural households and per capita disposable income of urban households were positively associated with tuberculosis flows. A 10% increase in per capita net income corresponded to 14.0% in 2010 or 3.6% in 2012 increase in outflows from the origin, 44.2% in 2010 or 12.8% increase in inflows to the destinations and 47.9% increase in intraprovincial flows in 2012. Tuberculosis incidence had positive impacts on tuberculosis flows. A 10% increase in the number of tuberculosis cases corresponded to 2.2% in 2010 or 1.1% in 2012 increase in tuberculosis inflows to the destinations, 5.2% in 2010 or 2.0% in 2012 increase in outflows from the origins, 11.5% in 2010 or 2.2% in 2012 increase in intraprovincial flows.ConclusionsTuberculosis flows had clear spatial stratified heterogeneity and spatial autocorrelation, regional socio-economic characteristics had diverse and statistically significant effects on tuberculosis flows in the origin and destination, and income factor played an important role among the determinants.

62 Prognostic biomarker development using deep learning and spatial proteomics in papillary renal cell carcinoma

Abstract Background Papillary renal cell carcinoma (pRCC) is a prevalent but understudied kidney cancer pathology, constituting 15-20% of cases. While many patients achieve curative therapy using nephrectomy, others go on to develop progressive, metastatic disease with poor overall prognosis. In order to improve the current standards of care for pRCC patients, it is critical to identify new methods to identify high-risk patients likely to require more intense and risk-modulated follow-up. Our goals in this project are two-fold: (1) to identify prognostic patient clusters based on spatial architecture of tumor tissue, and (2) to identify a minimal subset of markers to accurately predict the defined clusters for clinical translation. Methods A tissue microarray of 100 pRCC patients was assayed using multiplex immunofluorescence with a 31-antibody panel with immune- and cancer-related proteins. We have developed novel algorithms to identify spatial neighborhoods and features. These features are then used to identify unsupervised clusters of patients. This new approach to classifying patients allows for a more specific and comprehensive description of each patient tumor’s spatial composition and association with diverse clinical outcomes. Next, we developed attention-based deep learning models capable of predicting these spatially-defined clusters directly from the underlying immunofluorescence images and subsequently profiled these for interpretability by retraining based on individual channels. We assessed each model iteratively on a validation set and a held-out test set for final model selection. We focussed on developing an accurate model with the minimal number of channels to enable development of a cost-efficient biomarker assay for the clinic. Results We performed a systematic analysis of the cellular and spatial phenotypes for each patient in our cohort and identified six (6) major spatial clusters. These clusters are based on the spatial neighborhood composition of the tumors (Fig 1A) and describe the composite effects of cell-cell interactions within a patient tumor. We identified one cluster in particular (cluster #6, mainly associated with spatial interaction of CD163+ macrophages) with a significantly worse prognosis than the other clusters. Using our advanced deep learning models, we have been able to achieve excellent validation accuracy in predicting a variety of clinical phenotypes and spatial features, including patient clusters and histological grade. We wished to then reduce the set of required features and began our efforts in predicting tumor vs. normal based on individual immunofluorescence channels and identified 6 channels with >75% individual predictive accuracy (Fig. 1B). Compositing all 6 markers further improves our accuracy to 90%. This success paves the way for exciting future prospects as we work to extend these efforts to identify a minimal clinically-applicable subset of protein markers. Conclusions The combination of computational spatial proteomics and deep learning models has the potential to identify new biomarkers specific to the spatial organization of patient tumors. We have identified a particular patient cluster with multiple associations with CD163+ macrophages demonstrating poor prognosis; selecting these patients may allow for tailored and more informed patient care. We are also working to enhance our deep learning models to circumvent the need for stepwise spatial analysis and, instead, allow for direct-from-image assessment of patients for particular spatial features indicative of various prognostic indicators. Our identified set of 6 markers serves as a basis for further refinements as we work to identify a minimal set of relevant markers able to predict various clinical outcomes from immunofluorescence imaging, offering a promising new avenue in the field of cancer pathology and biomarker development. DOD CDMRP Funding: yes

Spatiotemporally non-stationary evolution of groundwater levels in Poyang Lake Basin driven by meteorological and hydrological factors

The complex relationship between wet-dry transition in the Poyang Lake basin and groundwater storage significantly affects the lake's hydrology, downstream ecological state, and overall security along the Yangtze River in China. There is, however, a notable lack of systematic exploration into how various factors drive spatiotemporal variability in groundwater level (GWL). Using local indicators of spatial association (LISA), spatial non-stationarity models, and multi-source data, our analysis explores the spatial distribution of GWL and quantifies the influence of driving factors on its spatiotemporal non-stationarity at annual and monthly scales. We also compare driving factor contributions in hilly, plain, and local areas within the Poyang Lake basin. Our findings reveal significant local clustering of GWL, indicating substantial spatial autocorrelation and geographic heterogeneity in GWL. Influencing factors exhibit non-stationary effects on GWL at spatial and temporal scales, with precipitation (P), ground surface elevation (GSE), and soil moisture (SM) being primary contributors, generally exerting positive effects. SM contributes most during dry years and normal periods. P and the Palmer Drought Severity Index (PDSI) have greater impacts in hilly areas, while GSE shows the opposite trend. Rainfall is a source of groundwater recharge, with a lagged response observed in GWL to rainfall in this basin. The lag time is about 1–2 months. Evapotranspiration is not the dominant discharge pathway. The decrease in GWL during the dry season is mainly due to reduced precipitation recharge and increased lateral groundwater discharge from areas of high hydraulic head to areas of low hydraulic head.

Spatial association between chronic respiratory disease mortality rates and industrial manufacturing enterprises: A case study of Chongqing, China

Industry has long been a driving force in the national economy of Chongqing, a traditional industrial hub in China. Current research provides limited understanding of the spatial associations between industrial manufacturing enterprises and CRD mortality in urban areas of Western China. This study employs location data of CRD death patients and Points of Interest (POI) representing industrial manufacturing enterprises from 2012 to 2020. It explores their spatial patterns and associations through spatial clustering identification, co-location, and association effects. Key spatial association characteristics identified include: (1) High-risk CRD mortality areas are predominantly located in Tongnan District and Kaizhou District in the western and northeastern parts of Chongqing, with an increase in CRD mortality rates correlated with age and a higher prevalence among females. (2) The optimal bandwidths for spatial association characteristics are 0.18 m and 0.475 m, respectively. (3) The construction and mining industries exert a more substantial impact on the mortality rates of younger CRD patients, with a notably stronger spatial impact on male patients compared to female patients. These insights are critical for guiding public health policy and spatial planning of industrial manufacturing enterprises in Chongqing.

Spatial distribution pattern of mustelids in the eastern edge of the Qinghai-Tibet Plateau.

Evolutionary theory predicts that the species of an evolutionarily successful taxon would not overlap in spatial distribution. To test the prediction, we document our research on the spatial associations of mustelids, an evolutionarily successful group of order Carnivore, using infrared camera trap data on species distribution collected from the national nature reserves (NNRs) of Liancheng, Wolong, Tangjiahe and Heizhugou in China in 2017-2021. Data showed seven mustelid species occurring in the study area, including Arctonyx collaris, Meles leucurus, Martes foina, Martes flavigula, Mustela altaica, Mustela nivalis and Mustela sibirica. Following Ricklef's definition of biological community, we identified five networks of species associations. The mustelids occurred in the networks. Species from the same genus, such as M. foina and M. flavigula, stayed in different networks to avoid competition owing to similar feeding habits or habitat preferences. Species with different feeding habits or habitat preferences either occurred in different networks, such as M. altaica and M. flavigula, or coexisted in the same networks but avoided direct spatial associations, such as M. foina and A. collaris. Asymmetrical associations were found between different genera, such as M. foina and M. altaica, or between different subfamilies, such as M. flavigula and A. collaris. These associations may be attributed to interspecific killing or seed dispersal. However, these associations accounted for only a small proportion and would not impact the species diversity of Mustelidae. It is thus concluded that the prediction is supported by our research findings and that spatial avoidance may be the biogeographic strategy of maintaining the species diversity of the family. We also found that the well protection of the mustelids may benefit the overall biodiversity conservation in Heizhugou, an NNR that has experienced severe deforestation.

Association of county level poverty with mortality from primary liver cancers.

The highly variable occurrence of primary liver cancers across the United States emphasize the relevance of location-based factors. Social determinants such as income, educational attainment, housing, and other factors may contribute to regional variations in outcomes. To evaluate their impact, this study identified and analyzed clusters of high mortality from primary liver cancers and the association of location-based determinants with mortality across the contiguous United States. A geospatial analysis of age-adjusted incidence and standardized mortality rates from primary liver cancers from 2000 to 2020 was performed. Local indicators of spatial association identified hot-spots, clusters of counties with significantly higher mortality. Temporal analysis of locations with persistent poverty, defined as high (>20%) poverty for at least 30 years, was performed. Social determinants were analyzed individually or globally using composite measures such as the social vulnerability index or social deprivation index. Disparities in county level social determinants between hot-spots and non-hot-spots were analyzed by univariate and multivariate logistic regression. There are distinct clusters of liver cancer incidence and mortality, with hotspots in east Texas and Louisiana. The percentage of people living below the poverty line or Hispanics had a significantly higher odds ratio for being in the top quintile for mortality rates in comparison to other quintiles and were highly connected with mortality rates. Current and persistent poverty were both associated with an evolution from non-hotspots to new hotspots of mortality. Hotspots were predominantly associated with locations with significant levels of socioeconomic vulnerability or deprivation. Poverty at a county level is associated with mortality from primary liver cancer and clusters of higher mortality. These findings emphasize the importance of addressing poverty and related socio-economic determinants as modifiable factors in public health policies and interventions aimed at reducing mortality from primary liver cancers.

Spatial clusters and social inequities in COVID-19 vaccine coverage among children in Brazil.

This study examined the spatial distribution and social inequalities in COVID-19 vaccine coverage among children aged 5-11 in Brazil. First and second dose vaccine coverage was calculated for all Brazilian municipalities and analyzed by geographic region and deciles based on human development index (HDI-M) and expected years of schooling at 18 years of age. Multilevel models were used to determine the variance partition coefficient, and bivariate local Moran's I statistic was used to assess spatial association. Results showed significant differences in vaccine coverage rates among Brazilian municipalities, with lower coverage in the North and Midwest regions. Municipalities with lower HDI and expected years of schooling had consistently lower vaccine coverage rates. Bivariate clustering analysis identified extensive concentrations of municipalities in the Northern and Northeastern regions with low vaccine coverage and low human development, while some clusters of municipalities in the Southeast and South regions with low coverage were located in areas with high HDI-M. These findings highlight the persistent municipal-level inequalities in vaccine coverage among children in Brazil and the need for targeted interventions to improve vaccine access and coverage in underserved areas.

Edge-based graph centrality measures with spatial analytics to support vulnerability assessment and maintenance planning in sewer networks

ABSTRACT In this study, the spatial relationship between critical pipes identified using edge-based centrality measures and pipes with higher failure probability-based on selected vulnerability indicators were analysed in sanitary sewer networks. By analysing two sub-networks, one residential and the other a central network, significant spatial associations between pipes with high centrality values and those exhibiting adverse conditions (poor CCTV grades, previous blockages, and low self-cleaning capabilities) were identified. Path-based centrality measures, particularly edge betweenness and K-path edge centrality were less influenced by weights when identifying critical pipes. In contrast, non-path-based measures like nearest neighbour edge centrality could identify more localised critical pipes within the sewer networks investigated. The results showed that the spatial patterns between critical pipes and pipes in adverse conditions were not random and could support proactive maintenance planning and the development of more resilient networks. Additionally, the impact of network structure, connectivity, and differences in the composition of pipe attributes could contribute to variations in the strength of observable spatial associations.

Lamprophyres, gold and orogenies: a mineral systems perspective

The common spatial and temporal association of calk alkaline lamprophyres with orogenic gold deposits has been recognized for more than a century, although interpretations regarding the significance of this association have varied greatly. A persistent lack of consensus on Archean geodynamics and the deposits themselves presented a challenge to the use of a Mineral Systems approach, given that models should strive to encompass a source to sink understanding of deposit generation. Lamprophyres helped to resolve these issues because the compositions of the most primitive examples establish firm constraints on deep crustal and upper mantle regimes at the time of deposit formation, despite not being directly related to mineralization. Following the initial contribution of lamprophyres toward a coherent model of mineralization, numerous workers provided the important observations and refinements that have resulted in a robust Mineral Systems model for the deposit type. The discovery of diamonds in gold-associated lamprophyres, and circumstantial evidence that this association has been long-lived and widespread, presents opportunities to further refine orogenic gold models.

Congenital and acquired hypothyroidism: Temporal and spatial trends in France from 2014 to 2019

PurposeTo assess the incidence of congenital hypothyroidism (CH) and acquired hypothyroidism (AH) between 2014 and 2019 in continental France. MethodsNew cases of CH and AH were identified using the French National Health Data System (Système Nationale des Données de Santé, SNDS). Temporal trends were studied using linear regression models. Spatial distributions were studied using Moran's global index (I) and the statistical method and local indicators of spatial association. ResultsThe incidence of permanent CH in females increased by 8.9 % per year (2014: 36.9 [31.1–43.7] per 100,000 birth-years vs. 2019: 51 [43.9–59.3] per 100,000 birth-years, p < 0.01). The incidence of AH decreased between 2014 and 2019 for both females (2014: 535.7 [533.2–538.2] per 100,000 person-years vs 2019: 335.5 [333.6–337.4] per 100,000 person-years, p < 0.01) and males (2014: 197.5 [195.9–199] per 100,000 person-years vs 2019: 141.7 [140.4–142.9] per 100,000 person-years, p < 0.01). The incidence of hypothyroidism was high in the Nord-Pas-De-Calais and Lorraine regions (CH and AH). ConclusionsThe incidence of permanent CH in females has increased over time. AH incidence decreased. It seems necessary to investigate environmental factors in the disparity of incidence distribution.

Enhancing bivariate spatial association analysis of network-constrained geographical flows: An incremental scale-based method

Measuring bivariate spatial association plays a key role in understanding the spatial relationships between two types of geographical flow (hereafter referred to as “flow”). However, existing studies usually use multiple scales to analyze bivariate associations of flows, leading to the results at larger scales can be strongly affected by the results at smaller scales. Moreover, the planar space assumption of most existing studies is unsuitable for network-constrained flows. To solve these problems, a network incremental flow cross K-function (NIFK) is developed in this study by extending the cross K-function for points into a flow context. Specifically, two versions of NIFK were developed in this study: the global version to check whether bivariate associations exist in the whole study area and the local version to identify specific locations where associations occur. Experiments on three simulated datasets demonstrate the advantages of the proposed method over an available alternative method. A case study conducted using Xiamen taxi and ride-hailing service datasets demonstrates the usefulness of the proposed method. The detected bivariate spatial association provides deep insights for understanding the competition between taxi services and ride-hailing services.

Spatial patterns and environmental functions of dissolved organic matter in grassland soils of China

Soil dissolved organic matter (DOM) is crucial to atmospheric, terrestrial and aquatic environments as well as human life. Here, by characterizing DOM from 89 grassland soils throughout China, we reveal the spatial association between DOM geochemistry in the dry season vs annual ecosystem exchange and cancer cases. The humic-like and high molecular weight (3.4–25 kDa) fractions with lower biodegradability, decline from the northern to the southern regions of China, and are correlated with lower soil respiration and net ecosystem productivity at the continental scale. The <1.2 kDa and proteinaceous fractions could serve as a geographical indicator of nasopharyngeal cancer incidence and mortality, while the 3.4–25 kDa and humified fractions are potentially associated with pancreatic cancer cases (P < 0.05). Our findings highlight that exploiting the environmental functions of soil DOM and mitigating the negative impacts are necessary, and require actions tailored to local soil DOM conditions.

A novel URP-CNN model for bond credit risk evaluation of Chinese listed companies

The effective identification of bond credit risk is critical for maintaining stability in the bond market and safeguarding investor rights. This paper proposes a URP-CNN model for evaluating bond credit risk of Chinese listed companies. This model effectively combines the unique advantages of the Unthresholded Recurrence plot in extracting and representing spatial domain association information, along with the powerful performance of Convolutional Neural Networks in image feature extraction and classification tasks, thus demonstrating outstanding overall performance in tackling the complex task of predicting default of listed company bonds. The research results show that the URP-CNN model constructed in this paper improves accuracy by 1 %–10 % and F1 score by 10 %–30 % compared to other methods, ensuring accurate identification of non-defaulting enterprises while effectively reducing Type II-Error occurrence rates. Furthermore, the combination of URP method and machine learning models significantly enhances model effectiveness. Compared to single models, models combined with the URP method show varying degrees of improvement in accuracy and F1-score numerical indicators.

Regional Winter Wheat Yield Prediction and Variable Importance Analysis Based on Multisource Environmental Data

Timely and accurate predictions of winter wheat yields are key to ensuring food security. In this research, winter wheat yield prediction models for six provinces were established using a random forest (RF) model. Two methods were employed to analyze feature variables. RF partial dependence plots were generated to demonstrate the nonlinear relationships between the feature variables and yield, and bivariate Moran’s I was considered to identify the spatial associations between variables. Results showed that when environmental data from key growth periods were used for prediction model establishment, the root mean square error (RMSE) varied between 200 and 700 kg/ha, and the coefficient of determination (R2) exceeded 0.5. Feature variable analysis results indicated that the longitude, latitude, topography and normalized difference vegetation index (NDVI) were important variables. Below the threshold, the yield gradually increased with increasing NDVI. Bivariate Moran’s I results showed that there was zonal distribution of meteorological elements. Within a large spatial range, the change in environmental variables due to the latitude and longitude should be accounted for in modeling, but the influence of collinearity between the feature variables should be eliminated via variable importance analysis.

Evaluating the Role of Susceptibility Inducing Cofactors and of Acetaminophen in the Etiology of Autism Spectrum Disorder.

More than 20 previously reported lines of independent evidence from clinical observations, studies in laboratory animal models, pharmacokinetic considerations, and numerous temporal and spatial associations indicate that numerous genetic and environmental factors leading to inflammation and oxidative stress confer vulnerability to the aberrant metabolism of acetaminophen during early development, leading to autism spectrum disorder (ASD). Contrary to this conclusion, multivariate analyses of cohort data adjusting for inflammation-associated factors have tended to show little to no risk of acetaminophen use for neurodevelopment. To resolve this discrepancy, here we use in silico methods to create an ideal (virtual) population of 120,000 individuals in which 50% of all cases of virtual ASD are induced by oxidative stress-associated cofactors and acetaminophen use. We demonstrate that Cox regression analysis of this ideal dataset shows little to no risk of acetaminophen use if the cofactors that create aberrant metabolism of acetaminophen are adjusted for in the analysis. Further, under-reporting of acetaminophen use is shown to be a considerable problem for this analysis, leading to large and erroneously low calculated risks of acetaminophen use. In addition, we argue that factors that impart susceptibility to acetaminophen-induced injury, and propensity for acetaminophen use itself, can be shared between the prepartum, peripartum, and postpartum periods, creating additional difficulty in the analysis of existing datasets to determine risks of acetaminophen exposure for neurodevelopment during a specific time frame. It is concluded that risks of acetaminophen use for neurodevelopment obtained from multivariate analysis of cohort data depend on underlying assumptions in the analyses, and that other evidence, both abundant and robust, demonstrate the critical role of acetaminophen in the etiology of ASD.

Decoding the complex fluvial-lacustrine sedimentary interactions: Insights from the Neogene Guantao Formation in the Bohai Bay Basin and Modern Poyang Lake, China

Small-scale lake level variations lead to significant landward/basinward shifts in the depositional system, thereby facilitating the widespread development of fluvial-lacustrine interaction sedimentary systems. Such systems are commonly found in low-gradient shallow-water continental down-warped lacustrine basins, which are favorable for the formation of composite reservoir-caprock assemblages. Nevertheless, our understanding of the sedimentary stratigraphy of the Neogene Guantao Formation remains limited due to the complex and variable spatial and temporal associations between fluvial and lacustrine deposits. Integrated analysis of the geomorphology and stratigraphy of the modern Poyang Lake Basin and ancient Bohai Bay Basin (BBB) allows us to develop a process-based fluvial-lacustrine interaction model of shallow-water depositional system. Three physiographic zones are identified based on seasonal lake shoreline migration during a certain geological sequence unit, namely fluvial-dominated zone (FDZ), fluvial-lacustrine interaction zone (FLIZ) and lacustrine-dominated zone (LDZ), respectively. The FDZ is always above the maximum lake shoreline without affection by lake basin water body and is characterized by channelized dendritic pattern of distributary channels with positive rhythmic cycles. The FLIZ is a dynamic interaction region between the maximum lake shoreline and the lowest lake shoreline. Reticular distributary channels, delta front and prodelta/lake deposits vertically superimpose and dominate the system that is featured by complex sandbody, stacked positive and negative rhythms and strong water body segmentation. The LDZ, away from the fluvial effect, was identified below the lowest lake shoreline exhibiting polycentric sheet water body, dominated by lacustrine mudstone interspersed with thin sheet-like sandstone. These results shed light on the spatial and temporal sedimentary heterogeneity in such dynamic fluvial-lacustrine interaction systems. The proposed fluvial-lacustrine interaction model may serve as a valuable analogue for identifying sedimentary types and predicting sandstone reservoirs in similar shallow-water settings ranging from continental down-warped lacustrine basins to cratonic basins, and fault-depression basin gentle-slope belts.

Scaling Geospatial Data from the Perspective of Complexity: Exploring the Scaling Behavior of the Entropogram

A fundamental challenge in geospatial data science is to determine how a property, or its characterization, changes with a change in the scale of measurement. Except for geostatistical regularization of the variogram, which is theoretically well established, the scaling behaviors of a wide range of alternative measures of spatial association remain unclear. This limits the ability to make inferences at scales beyond the scale of measurement. The scaling behavior of the recently introduced entropogram function also remains unclear. Because the entropogram is essentially the generalization of the variogram to categorical spatial variables, the possibility to derive a scaling model for the entropogram exists. Here, the scaling behavior of the entropogram based on the scale effect of Shannon entropy is derived, providing a theoretical basis for the regularization of the entropogram. To validate the developed regularization model for the entropogram, a series of multiscale data was generated. Both theoretical derivation and experimental results showed that the entropogram is scale-invariant, under certain conditions for the generation of the categorical data. This research, thus, generalizes the entropogram to changes in measurement scale, thereby increasing our ability to characterize spatial data and make inferences about the underlying dynamic process. It also provides a reference for the interactions between patterns and processes at different scales.