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This article deals with jointly modeling a large number of geographically referenced outcomes observed over a very large number of locations. We seek to capture associations among the variables as well as the strength of spatial association for each variable. In addition, we reckon with the common setting where not all the variables have been observed over all locations, which leads to spatial misalignment. Dimension reduction is needed in two aspects: (i) the length of the vector of outcomes, and (ii) the very large number of spatial locations. Latent variable (factor) models are usually used to address the former, although low‐rank spatial processes offer a rich and flexible modeling option for dealing with a large number of locations. We merge these two ideas to propose a class of hierarchical low‐rank spatial factor models. Our framework pursues stochastic selection of the latent factors without resorting to complex computational strategies (such as reversible jump algorithms) by utilizing certain identifiability characterizations for the spatial factor model. A Markov chain Monte Carlo algorithm is developed for estimation that also deals with the spatial misalignment problem. We recover the full posterior distribution of the missing values (along with model parameters) in a Bayesian predictive framework. Various additional modeling and implementation issues are discussed as well. We illustrate our methodology with simulation experiments and an environmental data set involving air pollutants in California.

Investigating the influence of coastal development on marine environments is a priority to maintain healthy seas. Cetaceans are top predators, keystone and umbrella species and thus are good candidate models to evaluate the extent of anthropogenic impacts on coastal habitats. We employed a generalized linear model with spatial eigenvector mapping (SEV-GLM) to understand the influence of environmental and anthropogenic activities on migrant (humpback whale Megaptera novaeangliae) and non-migrant (Bryde’s whale Balaenoptera brydei and common bottlenose dolphin Tursiops truncatus) cetacean habitat use off Cabo Frio, Rio de Janeiro, Brazil. We hypothesized that both environmental and anthropogenic activities influence their habitat use. Data were collected during 118 boat trips between December 2010 and June 2014. The best SEV-GLM predicted humpback whales would increase linearly with distance to coast, with minimum sea surface temperature (SST) around 19.4–19.8°C and maximum SST around 25.5–26°C, with low variations in chlorophyll a (chl a) concentrations. The model also predicted that humpback whales would occur up to 10 km from diving areas, increasing linearly with distance to fishing grounds. The best non-migrant cetacean SEV-GLM predicted that they would occur more frequently around depths from 30–60 m, increasing with low SST and high chl a concentration. For the anthropogenic component, the model predicted that non-migrant cetaceans would occur up to 10 km from fishing grounds. Our study modeled the influence of anthropogenic activities on cetaceans, and indicates specific priority areas for cetacean conservation, contributing at a local and national scale.

Our ability to understand and predict the response of ecosystems to a changing environment depends on quantifying vegetation functional diversity. However, representing this diversity at the global scale is challenging. Typically, in Earth system models, characterization of plant diversity has been limited to grouping related species into plant functional types (PFTs), with all trait variation in a PFT collapsed into a single mean value that is applied globally. Using the largest global plant trait database and state of the art Bayesian modeling, we created fine-grained global maps of plant trait distributions that can be applied to Earth system models. Focusing on a set of plant traits closely coupled to photosynthesis and foliar respiration—specific leaf area (SLA) and dry mass-based concentrations of leaf nitrogen (N m ) and phosphorus (P m ), we characterize how traits vary within and among over 50,000 ∼50 × 50-km cells across the entire vegetated land surface. We do this in several ways—without defining the PFT of each grid cell and using 4 or 14 PFTs; each model’s predictions are evaluated against out-of-sample data. This endeavor advances prior trait mapping by generating global maps that preserve variability across scales by using modern Bayesian spatial statistical modeling in combination with a database over three times larger than that in previous analyses. Our maps reveal that the most diverse grid cells possess trait variability close to the range of global PFT means.

This study used spatial interaction modeling to examine whether origin-specific and destination-specific factors, distance decay effects, and spatial structures explain the criminal trips of residential burglars. In total, 4041 criminal trips committed by 892 individual offenders who lived and committed residential burglary in Tokyo were analyzed. Each criminal trip was allocated to an origin–destination pair created from the combination of potential departure and arrival zones. The following explanatory variables were created from an external dataset and used: residential population, density of residential burglaries, and mobility patterns of the general population. The origin-specific factors served as indices of not only the production of criminal trips, but also the opportunity to commit crimes in the origin zones. Moreover, the criminal trips were related to the mobility patterns of the general population representing daily leisure (noncriminal) trips, and relatively large origin- and destination-based spatial spillover effects were estimated. It was shown that considering not only destination-specific but also origin-specific factors, spatial structures are important for investigating the criminal trips of residential burglars. The current findings could be applicable to future research on geographical profiling by incorporating neighborhood-level factors into existing models.

Carbon to nitrogen ratio (C:N) of senescent leaf is a crucial functional trait and indicator of litter quality that affects belowground carbon and nitrogen cycles, especially soil decomposition. Although mapping the C:N ratio of fresh mature canopies has been attempted, few studies have attempted to map the C:N ratio of senescent leaves, particularly in mangroves. In this study, four machine learning models (Stochastic Gradient Boosting, SGB; Random Forest, RF; Support Vector Machine, SVM; and Partial Least Square Regression, PLSR) were compared for testing the predictability of using the Landsat TM 5 (LTM5) and Landsat 8 to map spatial and temporal distribution of C:N ratio of senescent leaves in Sundarbans Reserved Forest (SRF), Bangladesh. Surface reflectance of bands, texture metrics of bands and vegetation indices of LTM5 and Landsat 8 yearly composite images were extracted using Google Earth Engine for 2009–2010 and 2019. We found SGB, RF and SVM were significant different from PLSR based on MAE, RMSE, and R2 (p < 0.05). Our results indicate that remote sensing data, such as Landsat TM data, can be used to map the C:N ratio of senescent leaves in mangroves with reasonable accuracy. We also found that the mangroves had a high spatial variation of C:N ratio and the C:N ratio map developed in the current study can be used for improving the biogeochemical and ecosystem models in the mangroves.

Climate change has been observed to expand distributions of woody plants in many areas of arctic and alpine environments-a phenomenon called shrubification. New spatial arrangements of shrubs cause further changes in vegetation via changing dynamics of biotic interactions. However, the mediating influence of shrubification is rarely acknowledged in predictions of tundra vegetation change. Here, we examine possible warming-induced landscape-level vegetation changes in a high-latitude environment using species distribution modelling (SDM), specifically concentrating on the impacts of shrubification on ambient vegetation. First, we produced estimates of current shrub and tree cover and forecasts of their expansion under climate change scenarios to be incorporated to SDMs of 116 vascular plants. Second, the predictions of vegetation change based on the models including only abiotic predictors and the models including abiotic, shrub and tree predictors were compared in a representative test area. Based on our model predictions, abundance of woody plants will expand, thus decreasing predicted species richness, amplifying species turnover and increasing the local extinction risk for ambient vegetation. However, the spatial variation demonstrated in our predictions highlights that tundra vegetation can be expected to show a wide variety of different responses to the combined effects of warming and shrubification, depending on the original plant species pool and environmental conditions. We conclude that realistic forecasts of the future require acknowledging the role of shrubification in warming-induced tundra vegetation change.

Advances in species distribution modeling continue to be driven by a need to predict species responses to environmental change coupled with increasing data availability. Recent work has focused on development of methods that integrate multiple streams of data to model species distributions. Combining sources of information increases spatial coverage and can improve accuracy in estimates of species distributions. However, when fusing multiple streams of data, the temporal and spatial resolutions of data sources may be mismatched. This occurs when data sources have fluctuating geographic coverage, varying spatial scales and resolutions, and differing sources of bias and sparsity. It is well documented in the spatial statistics literature that ignoring the misalignment of different data sources will result in bias in both the point estimates and uncertainty. This will ultimately lead to inaccurate predictions of species distributions. Here, we examine the issue of misaligned data as it relates specifically to integrated species distribution models. We then provide a general solution that builds off work in the statistical literature for the change-of-support problem. Specifically, we leverage spatial correlation and repeat observations at multiple scales to make statistically valid predictions at the ecologically relevant scale of inference. An added feature of the approach is that addressing differences in spatial resolution between data sets can allow for the evaluation and calibration of lesser-quality sources in many instances. Using both simulations and data examples, we highlight the utility of this modeling approach and the consequences of not reconciling misaligned spatial data. We conclude with a brief discussion of the upcoming challenges and obstacles for species distribution modeling via data fusion.

Cities are complex systems connected to economic, ecological, and demographic conditions and change. They are also characterized by diverging perceptions and interests of citizens and stakeholders. Thus, in the arena of urban planning, we are in need of approaches that are able to cope not only with urban complexity but also allow for participatory and collaborative processes to empower citizens. This to create democratic cities. Connected to the field of smart cities and citizens, we present in this paper, the prototype of an urban digital twin for the 30,000-people town of Herrenberg in Germany. Urban digital twins are sophisticated data models allowing for collaborative processes. The herein presented prototype comprises (1) a 3D model of the built environment, (2) a street network model using the theory and method of space syntax, (3) an urban mobility simulation, (4) a wind flow simulation, and (5) a number of empirical quantitative and qualitative data using volunteered geographic information (VGI). In addition, the urban digital twin was implemented in a visualization platform for virtual reality and was presented to the general public during diverse public participatory processes, as well as in the framework of the “Morgenstadt Werkstatt” (Tomorrow’s Cities Workshop). The results of a survey indicated that this method and technology could significantly aid in participatory and collaborative processes. Further understanding of how urban digital twins support urban planners, urban designers, and the general public as a collaboration and communication tool and for decision support allows us to be more intentional when creating smart cities and sustainable cities with the help of digital twins. We conclude the paper with a discussion of the presented results and further research directions.

YAP/TAZ is a master regulator of mechanotransduction whose functions rely on translocation from the cytoplasm to the nucleus in response to diverse physical cues. Substrate stiffness, substrate dimensionality, and cell shape are all input signals for YAP/TAZ, and through this pathway, regulate critical cellular functions and tissue homeostasis. Yet, the relative contributions of each biophysical signal and the mechanisms by which they synergistically regulate YAP/TAZ in realistic tissue microenvironments that provide multiplexed input signals remain unclear. For example, in simple two-dimensional culture, YAP/TAZ nuclear localization correlates strongly with substrate stiffness, while in three-dimensional (3D) environments, YAP/TAZ translocation can increase with stiffness, decrease with stiffness, or remain unchanged. Here, we develop a spatial model of YAP/TAZ translocation to enable quantitative analysis of the relationships between substrate stiffness, substrate dimensionality, and cell shape. Our model couples cytosolic stiffness to nuclear mechanics to replicate existing experimental trends, and extends beyond current data to predict that increasing substrate activation area through changes in culture dimensionality, while conserving cell volume, forces distinct shape changes that result in nonlinear effect on YAP/TAZ nuclear localization. Moreover, differences in substrate activation area versus total membrane area can account for counterintuitive trends in YAP/TAZ nuclear localization in 3D culture. Based on this multiscale investigation of the different system features of YAP/TAZ nuclear translocation, we predict that how a cell reads its environment is a complex information transfer function of multiple mechanical and biochemical factors. These predictions reveal a few design principles of cellular and tissue engineering for YAP/TAZ mechanotransduction.

ABSTRACT Urban traffic prediction with high precision is always the unremitting pursuit of intelligent transportation systems and is instrumental in bringing smart cities into reality. The fundamental challenges for traffic prediction lie in the accurate modelling of spatial and temporal traffic dynamics. Existing approaches mainly focus on modelling the traffic data itself, but do not explore the traffic correlations implicit in origin‐destination (OD) data. In this paper, we propose STOD‐Net, a dynamic spatial‐temporal OD feature‐enhanced deep network, to simultaneously predict the in‐traffic and out‐traffic for each and every region of a city. We model the OD data as dynamic graphs and adopt graph neural networks in STOD‐Net to learn a low‐dimensional representation for each region. As per the region feature, we design a gating mechanism and operate it on the traffic feature learning to explicitly capture spatial correlations. To further capture the complicated spatial and temporal dependencies among different regions, we propose a novel joint feature, learning block in STOD‐Net and transfer the hybrid OD features to each block to make the learning process spatiotemporal‐aware. We evaluate the effectiveness of STOD‐Net on two benchmark datasets, and experimental results demonstrate that it outperforms the state‐of‐the‐art by approximately 5% in terms of prediction accuracy and considerably improves prediction stability up to 80% in terms of standard deviation.