Heterogeneous Spatio-temporal Data Research Articles

Research on the Exposure Risk Analysis of Wildfires with a Spatiotemporal Knowledge Graph

This study focuses on constructions that are vulnerable to fire hazards during wildfire events, and these constructions are known as ‘exposures’, which are an increasingly significant area of disaster research. A key challenge lies in estimating dynamically and comprehensively the risk that individuals are exposed to during wildfire spread. Here, ‘exposure risk’ denotes the potential threat to exposed constructions from fires within a future timeframe. This paper introduces a novel method that integrates a spatiotemporal knowledge graph with wildfire spread data and an exposure risk analysis model to address this issue. This approach enables the semantic integration of varied and heterogeneous spatiotemporal data, capturing the dynamic nature of wildfire propagation for precise risk analysis. Empirical tests are employed for the study area of Xichang, Sichuan Province, using real-world data to validate the method’s efficacy in merging multiple data sources and enhancing the accuracy of exposure risk analysis. Notably, this approach also reduces the time complexity from O (m×n×p) to O (m×n).

A Bibliometric Review of Earthquake and Machine Learning Research

This article presents a bibliometric review of earthquake research and its integration with machine learning techniques. Over the past two decades, there has been a growing interest in using machine learning to enhance earthquake prediction and research. The review collected 1172 scholarly articles from the Web of Science database, focusing on the keywords "earthquake" and "machine learning." Machine learning has shown promise in improving earthquake forecasting models and aiding decision-making in disaster management, infrastructure design, and emergency response. However, it is noted that the application of machine learning in earthquake engineering is still in its early stages and requires further exploration. Key findings of this review include the increasing importance of certain keywords in earthquake and machine learning research, such as "prediction," "neural network," "classification," "logistic regression," and "performance." These keywords highlight the central areas of research focus within this field. The review also identifies research trends and gaps, including the need for more exploration of large-scale, high-dimensional, nonlinear, non-stationary, and heterogeneous spatiotemporal data in earthquake engineering. It emphasizes the necessity for novel machine learning algorithms tailored specifically for earthquake prediction and analysis. Furthermore, it highlights the need for addressing uncertainty in earthquake research and improving forecasting models. The review underscores the growth in interest and collaboration in earthquake research and machine learning, evident in the increasing number of scholarly contributions over the years. In summary, this bibliometric review highlights the importance of accurate forecasting and the potential of machine learning techniques in advancing this field.

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

Traffic Flow Prediction with Heterogeneous Spatiotemporal Data Based on a Hybrid Deep Learning Model Using Attention-Mechanism

AugGKG: a grid-augmented geographic knowledge graph representation and spatio-temporal query model

ABSTRACTAs an emerging knowledge representation model in the domain of knowledge graphs, geographic knowledge graph can take full advantage of semantic, spatial and temporal information to facilitate answering spatio-temporal questions and completing relations. However, the representation of geographic knowledge graphs still has issues such as the difficulty of unified heterogeneous spatio-temporal data modelling, weak ability to answer spatio-temporal queries for dynamic multiobjective problems, and low efficiency of graph querying. This paper presents a grid-augmented geographic knowledge graph (AugGKG) based on the GeoSOT global subdivision grid model and time slice subgraph architecture. AugGKG discretely normalizes the spatio-temporal data of the graph, which involves five types of nodes and two types of relations. By using the geo-hidden layer of the graph and geocoding algebraic operations, the AugGKG can quickly answer complex multiobjective spatio-temporal queries and complete implicit spatio-temporal relations. Compared with existing geographic knowledge graphs (YAGO, GeoKG and GEKG), the comparative experiments verified the obvious advantages of AugGKG in terms of uniformity of accuracy, completeness, and efficiency. Hence, AugGKG is expected to be regarded as an innovative and robust geographic knowledge graph that can perform fast computation and relation completion for complex spatio-temporal queries in future geospatial question answering applications.

On the Design and Implementation of Easy Access to External Spatiotemporal Datasets in NFDI

Across many scientific domains, the ability to process large amounts of heterogeneous spatiotemporal data from various sources is crucial for solving challenging research questions. For example, in NFDI4Biodiversity, researchers need to combine observation data with satellite images to correlate the loss of biodiversity with climate change variables. In general, large data sets are not available on the system (called consumer) where the processing is performed, but first have to be retrieved from one or multiple external systems (called providers) that offer a corresponding service. Moreover, a consumer is often unaware of the datasets the providers offer. Ideally, a provider follows FAIR principles and thus supports mechanisms to greatly simplify the data exchange. However, in practice, there are multiple providers with valuable datasets that are not as FAIR as desired or lack spatiotemporal-specific support for data exchange. Instead of improving each potential provider at the source, we propose an intermediary spatiotemporal data exchange layer (SDExL) that helps simplify data exchange so that domain experts easily gain access to valuable data with little technical know-how.

Zigzag persistence for coral reef resilience using a stochastic spatial model.

A complex interplay between species governs the evolution of spatial patterns in ecology. An open problem in the biological sciences is characterizing spatio-temporal data and understanding how changes at the local scale affect global dynamics/behaviour. Here, we extend a well-studied temporal mathematical model of coral reef dynamics to include stochastic and spatial interactions and generate data to study different ecological scenarios. We present descriptors to characterize patterns in heterogeneous spatio-temporal data surpassing spatially averaged measures. We apply these descriptors to simulated coral data and demonstrate the utility of two topological data analysis techniques-persistent homology and zigzag persistence-for characterizing mechanisms of reef resilience. We show that the introduction of local competition between species leads to the appearance of coral clusters in the reef. We use our analyses to distinguish temporal dynamics stemming from different initial configurations of coral, showing that the neighbourhood composition of coral sites determines their long-term survival. Using zigzag persistence, we determine which spatial configurations protect coral from extinction in different environments. Finally, we apply this toolkit of multi-scale methods to empirical coral reef data, which distinguish spatio-temporal reef dynamics in different locations, and demonstrate the applicability to a range of datasets.

An ensemble-learning method for potential traffic hotspots detection on heterogeneous spatio-temporal data in highway domain

Inter-city highway plays an important role in modern urban life and generates sensory data with spatio-temporal characteristics. Its current situation and future trends are valuable for vehicles guidance and transportation security management. As a domain routine analysis, daily detection of traffic hotspots faces challenges in efficiency and precision, because huge data deteriorates processing latency and many correlative factors cannot be fully considered. In this paper, an ensemble-learning based method for potential traffic hotspots detection is proposed. Considering time, space, meteorology, and calendar conditions, daily traffic volume is modeled on heterogeneous data, and trends predictive error can be reduced through gradient boosting regression technology. Using real-world data from one Chinese provincial highway, extensive experiments and case studies show our methods with second-level executive latency with a distinct improvement in predictive precision.

A Method for Heterogeneous Spatio-Temporal Data Integration in Support of Marine Aquaculture Site Selection

Aquaculture site selection, like most site suitability analyses, requires the assembly and combination of multiple variables. Geographic information systems GIS and multi-criteria evaluation (MCE) based approaches are commonly used for aquaculture site selection and demonstrate the integration of various information sources relevant for siting aquaculture. These analyses, however, tend to be one-time and result in a fixed site suitability plan. Within a dynamic marine environment experiencing potential regime shifts, a siting support tool that integrates new and evolving spatio-temporal data has benefits. This paper presents a flexible Voronoi cell-based GIS model for marine aquaculture siting. Rather than a one-time specification of suitable locations, the approach uses similarity measures on the characteristics of Voronoi cells to find cells with similar characteristics. We calculate a weighted aquaculture site tenure value for Voronoi cells that have been or are occupied by aquaculture farm sites. High scoring cells suggest suitable sites and serve as targets for similarity queries. We apply the approach to a case study on the coast of Maine using an R Shiny application to demonstrate the use of the framework for finding sites with similar characteristics.

Terra Populus' Architecture for Integrated Big Geospatial Services.

Big geospatial data is an emerging sub-area of geographic information science, big data, and cyberinfrastructure. Big geospatial data poses two unique challenges to these and other cognate disciplines. First, raster and vector data structures and analyses have developed on largely separate paths for the last twenty years and this creates an impediment to researchers utilizing big data platforms that do not promote the integration for these classes. Second, big spatial data repositories have yet to be integrated with big data computation platforms in ways that allow researchers to spatio-temporally analyze big geospatial datasets. IPUMS-Terra, a National Science Foundation cyberInfrastructure project, begins to address these challenges. IPUMS-Terra is a spatial data infrastructure project that provides a unified framework for accessing, analyzing, and transforming big heterogeneous spatio-temporal data, and is part of the IPUMS (Integrated Public Use Microdata Series) data infrastructure. It supports big geospatial data analysis and provides integrated big geospatial services to its users. As IPUMS-Terra's data volume grows, we seek to integrate geospatial platforms that will scale geospatial analyses and address current bottlenecks within our system. However, our work shows that there are still unresolved challenges for big geospatial analysis. The most pertinent is that there is a lack of a unified framework for conducting scalable integrated vector and raster data analysis. We conducted a comparative analysis between PostgreSQL with PostGIS and SciDB and concluded that SciDB is the superior platform for scalable raster zonal analyses.

Integration of Diverse Data Sources for Spatial PM2.5 Data Interpolation

Heterogeneous data fusion from disparate geospatial sensors has drawn increasing attention in multimedia. Unfortunately, environmental sensors are usually sparsely and preferentially located, which restricts situation recognition of geographical regions and results in uncertainty in derived inferences. Spatial interpolation is an effective way to solve the problem of data sparsity, which demands the availability of related data sources. However, these data sources are usually in different resolutions, distributions, scales, and densities, which poses a major challenge in data integration. To address this problem, we present a novel spatial interpolation framework to incorporate diverse data sources and model the spatial processes explicitly at multiple resolutions. Spectral analysis is deployed to generate features at multiple spatial resolutions and to improve the interpolation accuracy at unobserved locations. A statistical operator based on the spatial Gaussian process is implemented and integrated into a geospatial situation recognition system, which can analyze heterogeneous spatio-temporal data streams derived from sensors. To verify the effectiveness and efficiency of the proposed framework, this framework is applied to the PM2.5 air pollution application. Experiments conducted in California, USA, demonstrate that the proposed method outperforms state-of-the-art approaches.

Integrated GIS and remote sensing analysis for landfill sitting in Western Crete, Greece

The assessment of the optimum landfill sitting requires multiple environmental data organized in a proper and efficient way to be effectively processed. Geographical information systems define an ideal tool for handling vast amounts of such kind of information combining multiple heterogeneous spatiotemporal data. Additionally, archive satellite remote sensing images set an ancillary approach to effective environmental monitoring of land surface within the areas of already established landfills and their surroundings (e.g. for calculation of crucial parameters such as vegetation indices). In this study, 17 environmental and anthropogenic factors were used to identify the most suitable sites for optimum landfill sitting in the western part of the island of Crete-Greece. The method used for the evaluation of all different factors is the analytical hierarchy process enhanced with fuzzy logic techniques. The results were compared with the already established landfills (legal/controlled and illegal/non-controlled) in the area of Chania prefecture and the results demonstrated that 75 % of the already (legal or illegal) established landfills of the prefecture are situated in extremely inappropriate areas from environmental point of view.