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Many of the challenges of the next century will have physical dimensions, such as tsunamis, hurricanes, and climate change as well as human dimensions such as economic crises, epidemics, and emergency responses. With pioneering editors and expert contributors, Advanced Geoinformation Science explores how certain technical aspects of geoinformation have been used and could be used to address such global issues. The editors and chapter authors have been involved in global initiatives such as Global Earth Observation System of Systems (GEOSS) and Digital Earth, and research problems such as air quality, public health, and cloud computing. The book delineates the problems communities are likely to face and how advanced geoinformation science can be a part of their solution. It introduces different methods in collecting spatial data as the initial feeds to geoinformation science and computing platforms. It discusses systems for data management, data integration and analysis, the geoinformation infrastructure, as well as knowledge capture, formatting, and utilization. The book then explores a variety of geoinformation applications, highlighting environmental, agriculture, and urban planning uses.--Publisher's description.

Undoubtedly, the age of big data has opened new options for natural disaster management, primarily because of the varied possibilities it provides in visualizing, analyzing, and predicting natural disasters. From this perspective, big data has radically changed the ways through which human societies adopt natural disaster management strategies to reduce human suffering and economic losses. In a world that is now heavily dependent on information technology, the prime objective of computer experts and policy makers is to make the best of big data by sourcing information from varied formats and storing it in ways that it can be effectively used during different stages of natural disaster management. This paper aimed at making a systematic review of the literature in analyzing the role of big data in natural disaster management and highlighting the present status of the technology in providing meaningful and effective solutions in natural disaster management. The paper has presented the findings of several researchers on varied scientific and technological perspectives that have a bearing on the efficacy of big data in facilitating natural disaster management. In this context, this paper reviews the major big data sources, the associated achievements in different disaster management phases, and emerging technological topics associated with leveraging this new ecosystem of Big Data to monitor and detect natural hazards, mitigate their effects, assist in relief efforts, and contribute to the recovery and reconstruction processes.

With recent advancements, large language models (LLMs) such as ChatGPT and Bard have shown the potential to disrupt many industries, from customer service to healthcare. Traditionally, humans interact with geospatial data through software (e.g., ArcGIS 10.3) and programming languages (e.g., Python). As a pioneer study, we explore the possibility of using an LLM as an interface to interact with geospatial datasets through natural language. To achieve this, we also propose a framework to (1) train an LLM to understand the datasets, (2) generate geospatial SQL queries based on a natural language question, (3) send the SQL query to the backend database, (4) parse the database response back to human language. As a proof of concept, a case study was conducted on real-world data to evaluate its performance on various queries. The results show that LLMs can be accurate in generating SQL code for most cases, including spatial joins, although there is still room for improvement. As all geospatial data can be stored in a spatial database, we hope that this framework can serve as a proxy to improve the efficiency of spatial data analyses and unlock the possibility of automated geospatial analytics.

Groundwater outburst has an impartible relationship with geological structures such as water-conducting faults, which are widely distributed in north China. In order to study the seepage property and mechanism of water outburst from the faults above a confined aquifer in the coal mining, the simulation model of ground water inrush for fault was designed. The water outburst parameters, such as water inflow, permeability, seepage velocity, porosity and other variables under different material combination and water pressures, were obtained; the research results indicate as follows: (1) The changes of the water inflow can be divided into three stages, i.e., the water inflow slowly increases at the early stage, rapidly increases at the middle stage and keeps unchanged at the late stage. (2) The seepage process can be represented by the seepage combination types, which are composed of pore flow, fissure flow and pipe flow, and the seepage changes not only with time but also with different conditions. (3) Mining would lead to the reactivation of faults and further enhance the permeability of fault zone potentially. The tiny granules in fault would be eroded and moved to exterior as the time under the high water pressure and lead to the change of porosity parameters. In this case, the seepage velocity would increase ceaselessly, and then the seepage would convert into pipe flow and finally lead to water inrush accidents.

To discuss the hydrochemical evolution characteristics of the mining process of Peigou Coal Mine, based on the test results of 43 water samples collected at different times from three main discharge aquifers, namely, Carboniferous Taiyuan Formation limestone water (L 7–8  + L 5–6 water), Ordovician limestone water (including Taiyuan Formation L 1–4 ), and Permian main mining coal seam roof and floor sandstone water (roof and floor water), a hydrochemical evolution model of the mining disturbances since 2003 has been established. The carbonate and sulphate dissolution and pyrite oxidation in Ordovician limestone water significantly decreased and then increased in 2006, and silicate weathering was weak. The carbonate and sulphate dissolution, silicate weathering and pyrite oxidation of roof and floor sandstone water increased. At the same time, a water source identification model suitable for the Peigou Coal Mine was developed by comparing the Fisher discriminant and the BP (back propagation) neural network discriminant. The accuracy rates of Fisher discriminant and BP neural network discriminant are 81.40% and 83.72% respectively, which indicates that BP neural network is more accurate. Finally, the evolution of hydraulic connection between aquifers is analysed. We speculate that there is a fracture development channel between Ordovician limestone water and roof and floor water aquifers that is affected in 2005 by the mining disturbance. This study has significance for examining the hydrochemical evolution of groundwater in mines and acting as a guideline to prevent and control water inrushes.

The sudden outbreak of the COVID-19 pandemic has brought drastic changes to people’s daily lives, work, and the surrounding environment. Investigations into these changes are very important for decision makers to implement policies on economic loss assessments and stimulation packages, city reopening, resilience of the environment, and arrangement of medical resources. In order to analyze the impact of COVID-19 on people’s lives, activities, and the natural environment, this paper investigates the spatial and temporal characteristics of Nighttime Light (NTL) radiance and Air Quality Index (AQI) before and during the pandemic in mainland China. The monthly mean NTL radiance, and daily and monthly mean AQI are calculated over mainland China and compared before and during the pandemic. Our results show that the monthly average NTL brightness is much lower during the quarantine period than before. This study categorizes NTL into three classes: residential area, transportation, and public facilities and commercial centers, with NTL radiance ranges of 5–20, 20–40 and greater than 40 (nW· cm − 2 · sr − 1 ), respectively. We found that the Number of Pixels (NOP) with NTL detection increased in the residential area and decreased in the commercial centers for most of the provinces after the shutdown, while transportation and public facilities generally stayed the same. More specifically, we examined these factors in Wuhan, where the first confirmed cases were reported, and where the earliest quarantine measures were taken. Observations and analysis of pixels associated with commercial centers were observed to have lower NTL radiance values, indicating a dimming behavior, while residential area pixels recorded increased levels of brightness after the beginning of the lockdown. The study also discovered a significant decreasing trend in the daily average AQI for mainland China from January to March 2020, with cleaner air in most provinces during February and March, compared to January 2020. In conclusion, the outbreak and spread of COVID-19 has had a crucial impact on people’s daily lives and activity ranges through the increased implementation of lockdown and quarantine policies. On the other hand, the air quality of mainland China has improved with the reduction in non-essential industries and motor vehicle usage. This evidence demonstrates that the Chinese government has executed very stringent quarantine policies to deal with the pandemic. The decisive response to control the spread of COVID-19 provides a reference for other parts of the world.

Social distancing policies have been regarded as effective in containing the rapid spread of COVID-19. However, there is a limited understanding of policy effectiveness from a spatiotemporal perspective. This study integrates geographical, demographical, and other key factors into a regression-based event study framework, to assess the effectiveness of seven major policies on human mobility and COVID-19 case growth rates, with a spatiotemporal emphasis. Our results demonstrate that stay-at-home orders, workplace closures, and public information campaigns were effective in decreasing the confirmed case growth rate. For stay-at-home orders and workplace closures, these changes were associated with significant decreases (p < 0.05) in mobility. Public information campaigns did not see these same mobility trends, but the growth rate still decreased significantly in all analysis periods (p < 0.01). Stay-at-home orders and international/national travel controls had limited mitigation effects on the death case growth rate (p < 0.1). The relationships between policies, mobility, and epidemiological metrics allowed us to evaluate the effectiveness of each policy and gave us insight into the spatiotemporal patterns and mechanisms by which these measures work. Our analysis will provide policymakers with better knowledge regarding the effectiveness of measures in space–time disaggregation.

Paulownia catalpifolia is an important, fast-growing timber species known for its high density, color and texture. However, few transcriptomic and genetic studies have been conducted in P. catalpifolia . In this study, single-molecule real-time sequencing technology was applied to obtain the full-length transcriptome of P. catalpifolia leaves treated with varying degrees of drought stress. The sequencing data were then used to search for microsatellites, or simple sequence repeats (SSRs). A total of 28.83 Gb data were generated, 25,969 high-quality (HQ) transcripts with an average length of 1624 bp were acquired after removing the redundant reads, and 25,602 HQ transcripts (98.59%) were annotated using public databases. Among the HQ transcripts, 16,722 intact coding sequences, 149 long non-coding RNAs and 179 alternative splicing events were predicted, respectively. A total of 7367 SSR loci were distributed throughout 6293 HQ transcripts, of which 763 complex SSRs and 6604 complete SSRs. The SSR appearance frequency was 28.37%, and the average distribution distance was 5.59 kb. Among the 6604 complete SSR loci, 1–3 nucleotide repeats were dominant, occupying 97.85% of the total SSR loci, of which mono-, di- and tri-nucleotide repeats were 44.68%, 33.86% and 19.31%, respectively. We detected 112 repeat motifs, of which A/T (42.64%), AG/CT (12.22%), GA/TC (9.63%), GAA/TTC (1.57%) and CCA/TGG (1.54%) were most common in mono-, di- and tri-nucleotide repeats, respectively. The length of the repeat SSR motifs was 10–88 bp, and 4997 (75.67%) were ≤ 20 bp. This study provides a novel full-length transcriptome reference for P. catalpifolia and will facilitate the identification of germplasm resources and breeding of new drought-resistant P. catalpifolia varieties.

Paulownia trees are grown globally for their robust timber, agroforestry, and effective carbon dioxide drawdown. China possesses rich Paulownia germplasm resources, offering favorable material for the genetic improvement. Understanding the taxonomy and phylogenetic relationships of Paulownia species is essential for the advancement of germplasm innovation. In this study, we re-sequenced 67 typical accessions of 11 species within the Paulownia genus. A total of 16,163,790 high-quality single nucleotide polymorphisms (SNPs) were identified. Based on these markers, these accessions were classified into three groups: P. fortunei and P. lampropylla (Group I); P. tomentosa, P. fargesii, and P. kawakamii (Group II); and P. taiwaniana, P. jianshiensis, P. catalpifolia, P. elongata, P. ichangensis, and P. albiphloea (Group III). Using maximum likelihood estimation, population genetic structure analysis revealed that the 11 species originated from four different ancestral populations. The two predominant breeding species—P. fortunei and P. tomentosa—exhibit divergent origins: P. fortunei arose from hybridization between two ancestral species followed by complex admixture, whereas P. tomentosa retains a predominantly singular ancestral lineage, with traces of P. kawakamii. The genetic diversity (π) of P. tomentosa was 0.002588, which was considerably lower than that of P. fortunei (0.004181) suggesting that P. tomentosa is subjected to a stronger breeding selection during the evolution than P. fortunei. A total of 59 selected regions and 65 genes were identified by selective sweep analysis. These genes may be involved in biological processes such as morphological development and response to abiotic stress and hormonal activity regulation. These findings provide valuable references for further research on the genetic differentiation and adaptive evolutionary mechanisms of Paulownia species, laying a foundation for future germplasm innovation and variety improvement.