Spatiotemporal association pattern mining can discover interesting interdependent relationships among various types of geospatial data. However, existing mining methods for spatiotemporal association ...patterns usually model geographic phenomena as simple spatiotemporal point events. Therefore, they cannot be applied to complex geographic phenomena, which continuously change their properties, shapes or locations, such as storms and air pollution. The most salient feature of such complex geographic phenomena is the geographic dynamic. To fully reveal dynamic characteristics of complex geographic phenomena and discover their associated factors, this research proposes a novel complex event-based spatiotemporal association pattern mining framework. First, a complex geographic event was hierarchically modeled and represented by a new data structure named directed spatiotemporal routes. Then, sequence mining technique was applied to discover the spatiotemporal spread pattern of the complex geographic events. An adaptive spatiotemporal episode pattern mining algorithm was proposed to discover the candidate driving factors for the occurrence of complex geographic events. Finally, the proposed approach was evaluated by analyzing the air pollution in the region of Beijing-Tianjin-Hebei. The experimental results showed that the proposed approach can well address the geographic dynamic of complex geographic phenomena, such as the spatial spreading pattern and spatiotemporal interaction with candidate driving factors.
Hierarchical spatial models are very flexible and popular for a vast array of applications in areas such as ecology, social science, public health, and atmospheric science. It is common to carry out ...Bayesian inference for these models via Markov chain Monte Carlo (MCMC). Each iteration of the MCMC algorithm is computationally expensive due to costly matrix operations. In addition, the MCMC algorithm needs to be run for more iterations because the strong cross-correlations among the spatial latent variables result in slow mixing Markov chains. To address these computational challenges, we propose a projection-based intrinsic conditional autoregression (PICAR) approach, which is a discretized and dimension-reduced representation of the underlying spatial random field using empirical basis functions on a triangular mesh. Our approach exhibits fast mixing as well as a considerable reduction in computational cost per iteration. PICAR is computationally efficient and scales well to high dimensions. It is also automated and easy to implement for a wide array of user-specified hierarchical spatial models. We show, via simulation studies, that our approach performs well in terms of parameter inference and prediction. We provide several examples to illustrate the applicability of our method, including (i) a high-dimensional cloud cover dataset that showcases its computational efficiency, (ii) a spatially varying coefficient model that demonstrates the ease of implementation of PICAR in the probabilistic programming languages stan and nimble, and (iii) a watershed survey example that illustrates how PICAR applies to models that are not amenable to efficient inference via existing methods.
Recent advances in spatially resolved transcriptomics have greatly expanded the knowledge of complex multicellular biological systems. The field has quickly expanded in recent years, and several new ...technologies have been developed that all aim to combine gene expression data with spatial information. The vast array of methodologies displays fundamental differences in their approach to obtain this information, and thus, demonstrate method‐specific advantages and shortcomings. While the field is moving forward at a rapid pace, there are still multiple challenges presented to be addressed, including sensitivity, labor extensiveness, tissue‐type dependence, and limited capacity to obtain detailed single‐cell information. No single method can currently address all these key parameters. In this review, available spatial transcriptomics methods are described and their applications as well as their strengths and weaknesses are discussed. Future developments are explored and where the field is heading to is deliberated upon.
In this review, current spatial transcriptomics methods are surveyed. These methods detect RNA molecules while retaining information of where the molecules are located in the tissue. The advantages and drawbacks of existing methods are discussed.
Abstract
Geoportal is a specific web portal used to share geospatial datasets. It is part of a National Spatial Data Infrastructure (NSDI) which is used to disseminate geospatial data. In the NSDI ...framework, a geoportal is a platform where citizens can search, discover, and access geospatial datasets. Therefore, the availability of geoportal is an essential element in an NSDI. Further, the availability of geospatial datasets, particularly fundamental datasets, is indispensable. This paper aims to portray the worldwide status of geoportal and the completeness of fundamental datasets in those geoportals. We surveyed the availability of geoportal in the 193 countries in the world. We identify and list geoportal addresses worldwide. For every geoportal, we check the types of fundamental datasets available. Fourteen fundamental datasets have been identified and used as a benchmark. The availability of geoportal and fundamental datasets were then calculated and analysed. We found 105 countries in the world own geoportal. Of these geoportals, there were, on average, 63% fundamental datasets available. Only nine countries provide all fourteen fundamental datasets. Twenty-nine countries provide five or fewer fundamental datasets on their geoportal. We also classify geoportal and fundamental datasets availability based on four regions Asia-Pacific, Europe, Africa, and America. European countries have the highest percentage of countries with geoportal ownership as well as the highest available fundamental datasets. The finding complements a worldwide study on geoportal availability which has not been re-evaluated in recent years.
The concept of smart metering allows real-time measurement of power demand which in turn is expected to result in more efficient energy use and better load balancing. However, finely granular ...measurements reported by smart meters can lead to starkly increased exposure of sensitive information, including various personal attributes and activities. Even though several security solutions have been proposed in recent years to address this issue, most of the existing solutions are based on public-key cryptographic primitives, such as homomorphic encryption and elliptic curve digital signature algorithms which are ill-suited for the resource constrained smart meters. On the other hand, to address the computational inefficiency issue, some masking-based solutions have been proposed. However, these schemes cannot ensure some of the imperative security properties, such as consumer's privacy and sender authentication. In this paper, we first propose a lightweight and privacy-friendly masking-based spatial data aggregation scheme for secure forecasting of power demand in smart grids. Our scheme only uses lightweight cryptographic primitives, such as hash functions and exclusive-OR operations. Subsequently, we propose a secure billing solution for smart grids. As compared with existing solutions, our scheme is simple and can ensure better privacy protection and computational efficiency, which are essential for smart grids.
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour ...is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybrid modelling approach, coupling physical process models with the versatility of data-driven machine learning.
Recent years have seen a resurgence of interest in the relation between networks and spatial context. This review examines critically a selection of the literature on how physical space affects the ...formation of social ties. Different aspects of this question have been a feature in network analysis, neighborhood research, geography, organizational science, architecture and design, and urban planning. Focusing primarily on work at the meso- and microlevels of analysis, we pay special attention to studies examining spatial processes in neighborhood and organizational contexts. We argue that spatial context plays a role in the formation of social ties through at least three mechanisms, spatial propinquity, spatial composition, and spatial configuration; that fully capturing the role of spatial context will require multiple disciplinary perspectives and both qualitative and quantitative research; and that both methodological and conceptual questions central to the role of space in networks remain to be answered. We conclude by identifying major challenges in this work and proposing areas for future research.
The uptake of machine learning (ML) algorithms in digital soil mapping (DSM) is transforming the way soil scientists produce their maps. Within the past two decades, soil scientists have applied ML ...to a wide range of scenarios, by mapping soil properties or classes with various ML algorithms, on spatial scale from the local to the global, and with depth. The wide adoption of ML for soil mapping was made possible by the increase in data availability, the ease of accessing environmental spatial data, and the development of software solutions aided by computational tools to analyse them. In this article, we review the current use of ML in DSM, identify the key challenges and suggest solutions from the existing literature. There is a growing interest in the use of ML in DSM. Most studies emphasize prediction and accuracy of the predicted maps for applications, such as baseline production of quantitative soil information. Few studies account for existing soil knowledge in the modelling process or quantify the uncertainty of the predicted maps. Further, we discuss the challenges related to the application of ML for soil mapping and suggest solutions from existing studies in the natural sciences. The challenges are: sampling, resampling, accounting for the spatial information, multivariate mapping, uncertainty analysis, validation, integration of pedological knowledge and interpretation of the models. Overall, the current literature shows few attempts in understanding the underlying soil structure or process using the predicted maps and the ML model, for example by generating hypotheses on mechanistic relationships among variables. In this regard, several additional challenging aspects need to be considered, such as the inclusion of pedological knowledge in the ML algorithm or the interpretability of the calibrated ML model. Tackling these challenges is critical for ML to gain credibility and scientific consistency in soil science. We conclude that for future developments, ML could incorporate three core elements: plausibility, interpretability, and explainability, which will trigger soil scientists to couple model prediction with pedological explanation and understanding of the underlying soil processes.
•A spatial techno-economic and environmental analysis methodology is proposed.•Case studies are carried out in two cities of China.•In south China heat pumps is quite competitive compared with ...electric heaters.•In north China heat pumps have to reach several preconditions to be competitive.•The proposed methodology can be repeated to other areas when data are available.
Fast urbanization process and promotion of life standard in China requires a great amount of energy input in building heating sector. North China now faces challenges of upgrading existing fossil fuel based high emission district heating systems into more environmental friendly heating systems. South China is discussing to choose proper building heating solutions for new and existing buildings which lack proper heating facilities. Renewable heating technologies such as ground source heat pump and air source heat pump are candidates to upgrade traditional heating solutions such as fossil fuel boilers and electric heaters. In order to find the most feasible building heating solution for different geolocations of China, this paper proposes a spatial data based techno-economic and environmental analysis methodology to fulfill such research gap. Case studies are carried out in two selected cities by using proposed methodology. Evaluation model shows that, heat pumps is quite competitive in south China compared with electric heaters, whereas in north China heat pumps have to reach several preconditions to be competitive with coal boiler district heating system under current techno-economic and environmental situations. In north China, a heat pump should reach a minimum seasonal coefficient of performance of 2.5–3.7 (for ground source heat pump) or 2.7–3.0 (for air source heat pump) to become CO2 and PM2.5 emission neutral as well as economically competitive compared with coal boiler district heating system. The advantage of proposed methodology is its simplicity in execution and could be repeated to other areas as the data required are available.
Deciphering the principles and mechanisms by which gene activity orchestrates complex cellular arrangements in multicellular organisms has far-reaching implications for research in the life sciences. ...Recent technological advances in next-generation sequencing- and imaging-based approaches have established the power of spatial transcriptomics to measure expression levels of all or most genes systematically throughout tissue space, and have been adopted to generate biological insights in neuroscience, development and plant biology as well as to investigate a range of disease contexts, including cancer. Similar to datasets made possible by genomic sequencing and population health surveys, the large-scale atlases generated by this technology lend themselves to exploratory data analysis for hypothesis generation. Here we review spatial transcriptomic technologies and describe the repertoire of operations available for paths of analysis of the resulting data. Spatial transcriptomics can also be deployed for hypothesis testing using experimental designs that compare time points or conditions-including genetic or environmental perturbations. Finally, spatial transcriptomic data are naturally amenable to integration with other data modalities, providing an expandable framework for insight into tissue organization.
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