Geospatial Factors Research Articles

Assessing the impact of the 2021 flood event on the archaeological heritage of the Rhineland (Germany)

BackgroundArchaeological sites are increasingly threatened by climate-related hazards. In response, heritage management authorities initiated projects to document damage and plan risk assessment measures. We present a project initiated after the heavy rainfall and subsequent flood event of July 2021, which involved extensive fieldwork to document the damage to archaeological sites in the Rhineland. We use this database to characterise and assess the damage and investigate site-specific and geospatial factors to identify potential predictive parameters for site damage.ResultsDuring fieldwork, we found that the flood damaged 19% of the 538 archaeological sites surveyed. The majority of damaged sites are relatively recent, dating from the medieval or modern periods, and are associated with the use of water power. Damage was mainly caused by erosion, floating debris and washout, e.g. mortar. In a case study, we tested the option of comparing pre- and post-disaster Airborne Laser Scanning elevation data to identify damages. It showed that not only the damage detected during fieldwork was found but also additional areas of loss. In general, however, and quantified based on the entire dataset, the ordnance survey Airborne Laser Scanning data were of limited use for monitoring flood-related damage and could not replace fieldwork. Our statistical analysis of possible risk factors, including both site characteristics and geospatial parameters, using Naïve Bayes Modelling and chi-squared tests, showed that no set of parameters could consistently predict the preservation or damage of archaeological sites across all catchments. In contrast, some external geospatial factors correlated with the occurrence of damage.ConclusionsThe study highlights both the strengths and limitations of the approaches used to assess and predict the damage to the archaeological heritage in the 2021 flood zones of the Rhineland. It also demonstrates the complexity of the data and spatial processes involved, which limits generalisation but can still inform decision-making for archaeological site management and on-site protection measures in flood-prone areas. With the prospect of more frequent heavy rainfall due to climate change, the specific needs of the archaeological heritage should be integrated into broader prevention and disaster management plans.

Geographic disparities in Alzheimer's disease and related dementia mortality in the US: Comparing impacts of place of birth and place of residence

ObjectiveThis study investigates geographic variations in ADRD mortality in the US. By considering both state of residence and state of birth, we aim to discern the relative importance of these geospatial factors. MethodsWe conducted a secondary data analysis of the National Longitudinal Mortality Study (NLMS), that has 3.5 million records from 1973 to 2011 and over 0.5 million deaths. We focused on individuals born in or before 1930, tracked in NLMS cohorts from 1979 to 2000. Employing multi-level logistic regression, with individuals nested within states of residence and/or states of birth, we assessed the role of geographical factors in ADRD mortality variation. ResultsWe found that both state of birth and state of residence account for a modest portion of ADRD mortality variation. Specifically, state of residence explains 1.19% of the total variation in ADRD mortality, whereas state of birth explains only 0.6%. When combined, both state of residence and state of birth account for only 1.05% of the variation, suggesting state of residence could matter more in ADRD mortality outcomes. ConclusionFindings of this study suggest that state of residence explains more variation in ADRD mortality than state of birth. These results indicate that factors in later life may present more impactful intervention points for curbing ADRD mortality. While early-life environmental exposures remain relevant, their role as primary determinants of ADRD in later life appears to be less pronounced in this study.

Geospatial Demand for Approved Cleft Care in the United States.

Geographic information systems are powerful tools for characterizing the geospatial factors influencing access to care. As patients with cleft lip and/or palate (CL/P) require long-term care, with numerous operations and therapies, access to timely, quality care is extremely important. This study uses population level analysis and geographic information systems to identify United States counties with limited access to American Cleft Palate Association-approved cleft teams. Natality data were queried from the National Vital Statistics System. Population and geographic data were obtained from the US Census Bureau. The Social Vulnerability Index (SVI) was utilized to account for social inequality. Total births with CL/P, population estimates, SVI, distance to the nearest center, and total centers within 50 km were used to generate the cleft care demand index (CCDI). Ninety-two counties had CCDIs between 66.7 and 100. The highest scoring county, Hidalgo County, Texas, had 62 births with CL/P, population estimate of 888,367 persons, distance to the nearest cleft center of 368.4 km, and SVI of 0.99. This study demonstrates the power of geographic information systems for identifying areas with limited access to approved cleft teams. The CCDI measures cleft burden, socioeconomic disadvantage, and geographic barriers to quantify the demand for approved cleft care in each county. Utilizing these scores can help direct future interventions, outreach efforts, and cleft care center planning.

Individual and geospatial factors associated with receipt of colorectal cancer screening: a state-wide mixed-level analysis

BackgroundColorectal cancer (CRC) is the second leading cause of cancer death in US adults but can be reduced by screening. The roles of individual and contextual factors, and especially physician...

Teacher responses to racially motivated bullying in Scotland

Racially motivated bullying remains pervasive across Scottish schools. Teachers have a critical role in nurturing a safe and inclusive environment and preventing stigmatisation and oppression by intervening when a racially motivated bullying episode occurs but also by actively developing an anti-racist climate within their school by providing an anti-racism curriculum and advocating on behalf of minority ethnic youth. Despite the crucial role teachers can play in providing a safe environment, there is a paucity of literature examining the issue. Whilst some limited research is available in England about the barriers to embedding an anti-racist curriculum, there is no research about how teachers respond to racially motivated bullying episodes, the potential barriers to responding, and the processes and factors that influence teachers’ judgement calls when a racially motivated bullying incident happens. Similarly, in the Scottish context, there is a lacuna of knowledge about the strategies employed by teachers already within the education system and their perceptions on the support that they need to respond to racist incidents. This study aims to add to our knowledge about this issue by investigating Scottish teachers’ strategies when they are confronted with a hypothetical racially motivated bullying incident in their school. Eleven interviews were conducted with a sample of teachers from different levels of education in Scotland. Teacher responses indicated reluctance and, at times, inability to recognise and name incidents as racist. Further data highlighted the reliance on strategies such as using the victim of an incident to educate their peers, one-to-one discussions with both pupils and perpetrators, and a dependence on using their own ‘instinct’ to appraise an incident and response. Further sub themes emerged, including the perceived influence of generational and geospatial factors on both practitioners and the communities in which they practise and the resounding sentiment that practitioners lack engagement with anti-racist training. Our findings highlight the need to invest in schools, communities, and young people in order to create the social conditions in which teachers’ capacities to respond to racism can develop and flourish.

The impact of future UK offshore wind farm distribution and climate change on generation performance and variability

The UK plans to significantly increase offshore wind generation capacity as part of the effort to achieve net zero targets. Current installation is densely located in a few areas, particularly off the east coast of England, and although current siting proposals include new offshore regions, significant volumes of wind generation capacity are yet to be located to meet 2050 installation targets. This paper uses a recent dataset of multi-decadal offshore wind power capacity factor timeseries to assess how UK offshore wind generation is likely to be affected by both the spatial distribution of future wind farms, and by the impacts of near-future (2020–2050) climate change. We determine that a wider geographic spread of offshore capacity results in a much-improved and less-variable UK-aggregated power generation profile, with substantial reductions in periods of low generation and extreme wind power ramping events, without negatively impacting mean or peak generation outputs. The impact of near-term climate change appears to be minor, slightly reducing overall generation and possibly resulting in an underestimation of future installation requirements, but this climate signal is outweighed by the effects of spatial distribution, and even more so by inherent hourly to inter-annual wind speed variability. This study implies that the intermittency of wind generation can be partly mitigated through increasing the spatial diversity of the existing wind farm distribution. Alongside a more in depth investigation of future climate change, and a holistic assessment of relevant geospatial factors such as Levelised Cost of Energy, infrastructure, and environmental constraints, this study could be used for optimisation of future offshore wind siting.

The performance of solvent-based direct air capture across geospatial and temporal climate regimes

IntroductionLiquid-solvent direct air capture (DAC) is a prominent approach for carbon dioxide removal but knowing where to site these systems is challenging because it requires considering a multitude of interrelated geospatial factors. Two of the most pressing factors are: (1) how should DAC be powered to provide the greatest net removal of CO2 and (2) how does weather impact its performance?.MethodsTo investigate these questions, this study develops a process-level model of a liquid-solvent DAC system and couples it to a 20-year dataset of temperature and humidity conditions at a ~9km resolution across the contiguous US.Results and discussionWe find that the amount of CO2 sequestered could be 30% to 50% greater than the amount of CO2 removed from the atmosphere if natural gas is burned on site to power DAC, but that the optimal way to power DAC is independent of capture rate (i.e., weather), depending solely on the upstream GHG intensity of electricity and natural gas. Regardless of how it is powered, air temperature and humidity conditions can change the performance of DAC by up to ~3x and can also vary substantially across weather years. Across the continuous US, we find that southern states (e.g., Gulf Coast) are preferrable locations for a variety of reasons, including higher and less variable air temperature and relative humidity. Lastly, we also find the performance of liquid-solvent DAC calculated with monthly means is within 2% of the estimated performance calculated with hourly data for more than a third of the country, including in the states with weather most favorable for liquid-solvent DAC.

Individual and Geospatial Determinants of Health Associated With School-Based Human Papillomavirus Immunization in Alberta: Population-Based Cohort Study.

Human papillomavirus (HPV) infection causes nearly all cervical cancer cases and is a cause of anogenital and oropharyngeal cancers. The incidence of HPV-associated cancers is inequitable, with an increased burden on marginalized groups in high-income countries. Understanding how immunization status varies by material and social deprivation, health system, and geospatial factors is valuable for prioritizing and planning HPV immunization interventions. The objective of this study was to describe school-based HPV immunization rates by individual and geospatial determinants of health in Alberta, Canada. Health administrative data for male and female individuals born in 2004 in Alberta were used to determine HPV immunization status based on age and the number of doses administered in schools during the 2014/2015-2018/2019 school years. Immunization status and its relationship with material and social deprivation and health system factors were assessed by a logistic regression model. Geospatial clustering was assessed using Getis-Ord Gi* hot spot analysis. Mean scores of material and social deprivation and health system factors were compared between hot and cold spots without full HPV immunization using independent samples t tests. A multidisciplinary team comprising researchers and knowledge users formed a co-design team to design the study protocol and review the study results. The cohort consisted of 45,207 youths. In the adjusted model, the odds of those who did not see their general practitioner (GP) within 3 years before turning 10 years old and not being fully immunized were 1.965 times higher (95% CI 1.855-2.080) than those who did see their GP. The odds of health system users with health conditions and health system nonusers not being fully immunized were 1.092 (95% CI 1.006-1.185) and 1.831 (95% CI 1.678-1.998) times higher, respectively, than health system users without health conditions. The odds of those who lived in areas with the most material and social deprivation not being fully immunized were 1.287 (95% CI 1.200-1.381) and 1.099 (95% CI 1.029-1.174) times higher, respectively, than those who lived in areas with the least deprivation. The odds of those who lived in rural areas not being fully immunized were 1.428 times higher (95% CI 1.359-1.501) than those who lived in urban areas. Significant hot spot clusters of individuals without full HPV immunization exist in rural locations on the northern and eastern regions of Alberta. Hot spots had significantly worse mean material deprivation scores (P=.008) and fewer GP visits (P=.001) than cold spots. Findings suggest that material and social deprivation, health system access, and rural residency impact HPV immunization. Such factors should be considered by public health professionals in other jurisdictions and will be used by the Alberta co-design team when tailoring programs to increase HPV vaccine uptake in priority populations and regions.

A Geospatial Analysis of Hand Trauma Care: A Statewide Cross-Sectional Study.

Traumatic hand injuries often present with high acuity, but little is known about the influence of geospatial and socioeconomic factors on the timely delivery of care. This cross-sectional study used the Michigan Trauma Quality Improvement Program database, a state-wide registry with 35 level I or II trauma centers. Adult patients sustained hand trauma requiring urgent operative treatment between 2016 and 2021. Zip codes of injury location were linked with the corresponding percentile score on the Area Deprivation Index (ADI), a comprehensive measure of neighborhood disadvantage. Multiple regression analyses were used to determine associations of patient, injury and geospatial characteristics with the odds of sustaining acute hand trauma and time to operative treatment. Among 1,826 patients, the odds of sustaining acute hand trauma based on the ADI followed a bimodal distribution. Female sex, smoking, obesity, work-related injury and residence in a minor city were associated with increased odds, while younger age, comorbidities, and rural residence were associated with decreased odds. For 388 patients who underwent surgery within 48 hours, time to treatment was significantly increased in the highest ADI quintile, for patients who underwent fracture fixation, and for those with severe global injury severity. Multi-system injuries, moderate global injury severity and direct admission to an orthopaedic service were associated with shorter times to treatment. Patients in areas with greater neighborhood disadvantage may experience delayed operative care after acute hand trauma. This study highlights the importance of considering underserved populations and geospatial factors when determining the allocation of hand surgery resources. Prognostic Level III.

Abstract TMP95: Context Matters: Geographic Decision-Making Could Improve Prehospital Stroke Triage

Introduction: Emergency medical services (EMS) play a key role in stroke outcomes, as triage decisions made under diagnostic uncertainty impact availability of time sensitive treatments. Quantifying both time varying effects and uncertainty allows for optimized algorithms to improve destination decisions for stroke patients. We developed a Bayesian model of neurological outcomes and an algorithm for hospital triage based on clinical trials data. Methods: We used data from Virtual International Stroke Trials Archive (VISTA), RACECAT, and FAST-MAG. To model prehospital diagnosis, we included patients with any stroke-like illness (ischemic/hemorrhagic stroke, and mimics). Primary outcome was 90-day modified Rankin scale (mRS). Due to variability in reported measures from each trial, model-based Bayesian imputation was used. Our model accepts time-from symptoms, location, and capability of all local hospitals as inputs. Results: We included 9048 participants with 8600 (95%) ischemic strokes, 1801 (20%) had large vessel occlusion, and 4064 (45%) had a 90-day mRS of 0-2 (good neurologic outcome). We evaluated the impact of our algorithm using 1,320 stroke events from RACECAT. The estimated median absolute difference in chance of good mRS score between bypass and non local hospital bypass was 2.3% (ranging from 0 to 7.4%) among 1,320 stroke events in the RACECAT data set; covariate-adjusted patient scenarios predicted a preference for local hospital bypass of -2.9% to 7.4%. Destination maps for Catalonia show important patterns of bypass optimality. Conclusion: We found that no uniform destination selection approach is likely to be optimal under all circumstances. This finding is important, because it suggests that a personalized approach considering diagnostic uncertainty, patient characteristics, and geospatial factors could outperform simpler approaches. Predicted time-to-treatment targets may be insufficient for appropriate system optimization.

Robust interaction detector: A case of road life expectancy analysis

Spatial stratified heterogeneity, revealing the disparity mechanisms across spatial strata, can be effectively quantified using the geographical detector (GD). GD requires reasonable spatial discretization strategies to investigate the spatial association between the target variable and numerical independent variables. In previous studies, the Robust Geographical Detector (RGD) optimized spatial strata for examining the power of determinants (PD) of individual variables, which demonstrate more robust spatial discretization than other models. However, the GD's interaction detector that explores PD of the interaction of two variables still needs to be enhanced by the robust spatial discretization. This study develops a Robust Interaction Detector (RID), an improved interaction detector, using change detection algorithms for the robust spatial stratified heterogeneity analysis with multiple explanatory variables. RID is applied in a road life expectancy analysis in Western Australia. Results show that RID presents higher PD values than previous GD models, ensuring the growth of PD value with more spatial strata. The RID model indicates that the interactions between various transport variables and elevation are strongly associated with road life expectancy from the perspective of spatial patterns. The developed RID model provides significant potential for enhanced geospatial factor analysis across diverse fields.

From pollution to solutions: Insights into the sources, transport and management of plastic debris in pristine and urban rivers

River systems are important recipients of environmental plastic pollution and have become key pathways for the transfer of mismanaged waste from the land to the ocean. Understanding the sources and fate of plastic debris, including plastic litter (>5 mm) and microplastics (MPs) (<5 mm), entering different riverine systems is essential to mitigate the ongoing environmental plastic pollution crisis. We comprehensively investigated the plastic pollution in the catchments of two rivers in the Yangtze River basin: an urban river, the Suzhou section of the Beijing-Hangzhou Grand Canal (SZ); and a pristine rural river, the Jingmen section of the Hanjiang River (JM). The abundance of plastic pollutants in SZ was significantly higher than in JM: 0.430 ± 0.450 items/m3 and 0.003 ± 0.003 items/m3 of plastic litter in the water; 23.47 ± 25.53 n/m3 and 2.78 ± 1.55 n/m3 MPs in the water; and 218.82 ± 77.40 items/kg and 5.30 ± 1.99 items/kg of MPs in the sediment, respectively. Plastic litter and MPs were closely correlated in abundance and polymer composition. Overall, the polymer type, shape and color of MPs were dominant by polypropylene (42.5%), fragment (60.4%) and transparent (40.0%), respectively. Source tracing analysis revealed that packaging, shipping, and wastewater were the primary sources of plastic pollutants. The mantel analysis indicated that socio-economic and geospatial factors play crucial roles in driving the hotspot formation of plastic pollution in river networks. The composition of the MP communities differed significantly between the sediments and the overlying water. The urban riverbed sediments had a more pronounced pollutant ‘sink’ effect compared with the pristine rivers. These findings suggested that the modification of natural streams during urbanization may influence the transport and fate of plastic pollutants in them. Our results offer pivotal insights into effective preventive measures.

Distance and destination of retail meat alter multidrug resistant contamination in the United States food system

Antibiotic-resistant infections are a global concern, especially those caused by multidrug-resistant (MDR) bacteria, defined as those resistant to more than three drug classes. The animal agriculture industry contributes to the antimicrobial resistant foodborne illness burden via contaminated retail meat. In the United States, retail meat is shipped across the country. Therefore, understanding geospatial factors that influence MDR bacterial contamination is vital to protect consumers and inform interventions. Using data available from the United States Food and Drug Administration’s National Antimicrobial Resistance Monitoring System (NARMS), we describe retail meat shipping distances using processor and retailer locations and investigated this distance as a risk factor for MDR bacteria meat contamination using log-binomial regression. Meat samples collected during 2012–2014 totaled 11,243, of which 4791 (42.61%) were contaminated with bacteria and 835 (17.43%) of those bacteria were MDR. All examined geospatial factors were associated with MDR bacteria meat contamination. After adjustment for year and meat type, we found higher prevalence of MDR contamination among meat processed in the south (relative adjusted prevalence ratio [aPR] 1.35; 95% CI 1.06–1.73 when compared to the next-highest region), sold in Maryland (aPR 1.12; 95% CI 0.95–1.32 when compared to the next-highest state), and shipped from 194 to 469 miles (aPR 1.59; 95% CI 1.31–1.94 when compared to meats that traveled < 194 miles). However, sensitivity analyses revealed that New York sold the meat with the highest prevalence of MDR Salmonella contamination (4.84%). In this secondary analysis of NARMS data, both geographic location where products were sold and the shipping distance were associated with microbial contamination on retail meat.

Measurement and characteristics of the temporal-spatial evolution of China’s healthcare services efficiency

BackgroundHealthcare services efficiency (HSE) is directly related to the healthcare demands of the general public and also plays an essential role in the country’s coordinated economic and social development.MethodsIn this study, the stochastic frontier approach (SFA)-Malmquist model was applied to measure the HSE of 31 Chinese provinces based on panel data from 2010–2020. Then, kernel density estimation, Markov chain, and exploratory spatial data analysis were adopted to study the temporal-spatial dynamic evolution characteristics of the HSE.ResultsThe study found that China’s HSE showed an average value of approximately 0.841, indicating room for improvement. The HSE varied significantly across regions, presenting an “East > Central > West” distribution layout. The TFP of healthcare services in China grew by 1.6% per year, driven mainly by technological progress of 1.8% per year. The trend of the HSE shifting to a high level in China was significant, but its evolution exhibited stability of maintaining the original state, and it was harder to achieve leapfrog transfer. The temporal-spatial evolution of the HSE was also significantly affected by geospatial factors, with a clear spatial spillover effect and spatial agglomeration characteristics. Provinces with high-level HSE exhibited positive spatial spillover effects, while provinces with low-level HSE had negative spatial spillover effects. Thus, the “club convergence” phenomenon of “high efficiency concentration, low efficiency agglomeration, high levels of radiation, and low levels of suppression” was formed in the spatial distribution.ConclusionsThe results indicate that countermeasures should be taken to improve the HSE in China. Theoretical support for the improvement of HSE is provided in this paper.

Mapping the empirical relationship between environmental performance and social preferences: Evidence from macro data

This paper investigates the relationship between measures of environmental performance and of social preferences, thanks to the availability of a recently published dataset on global pref- erences. Using cross-sectional observations from 76 countries, this study finds evidence from macro data of a positive and statistically significant relationship between measures of proso- cial preferences, such as positive reciprocity, trust, and altruism, and environmental policy indicators in the categories of agriculture, forestry, and climate change after controlling for economic and geospatial factors. At the micro level, the results presented in this paper suggest a broader theory of collective action that is based on a behavioral approach to climate policy to mitigate motivational crowd out in settings of high reciprocity and trust. At the macro level, the results presented in this paper suggest a broader theory of climate clubs that includes pro- social preferences, such as positive reciprocity, trust, and altruism, as key cooperation mech- anisms in International Environmental Agreements.

Factors associated with COVID-19 brought-in deaths: A data-linkage comparative cross-sectional study

BackgroundThe phenomenon of Coronavirus disease 2019 (COVID-19) brought-in-dead (BID), i.e., COVID-19 deaths occurring outside hospital settings, suggests missed opportunities for life-saving care. However, much is still unknown with regards to its potential determinants. The present study aimed to examine the factors associated with COVID-19 BID by integrating new variables from multiple databases. MethodsThis multi-database comparative cross-sectional study examined COVID-19 in-patient deaths (IPD) and COVID-19 BID (n = 244 in each group) in Selangor, Malaysia. BID cases, IPD cases, and their sociodemographic, clinical, and health behaviour factors were identified from the COVID-19 mortality investigation reports submitted to the Selangor State Health Department between 14 February 2022 and 31 March 2023. Data linkage was used to connect three open-source databases—GitHub-MOH, Socioeconomic Data and Applications Center, and OpenStreetMap—and identify health infrastructure and geospatial factors. The groups were compared using chi-square tests, independent t-tests, and logistic regression analyses to identify factors associated with COVID-19 BID. ResultsThe COVID-19 IPD and BID cases were comparable. After adjusting for confounders, non-Malaysian nationality (AOR: 3.765, 95% CI: 1.163, 12.190), obesity (AOR: 5.272, 95% CI: 1.131, 24.567), not seeking treatment while unwell (AOR: 5.385, 95% CI: 3.157, 9.186), and a higher percentage of COVID-19-dedicated beds occupied on the date of death (AOR: 1.165, 95% CI: 1.078, 1.259) were associated with increased odds of COVID-19 BID. On the other hand, being married (AOR: 0.396, 95% CI: 0.158, 0.997) and the interaction between the percentage of COVID-19-dedicated beds occupied and the percentage of ventilators in use (AOR: 0.996, 95% CI: 0.994, 0.999) emerged as protective factors. ConclusionThese findings indicated that certain groups have higher odds of COVID-19 BID and thus, require closer monitoring. Considering that COVID-19 BID is influenced by various elements beyond clinical factors, intensifying public health initiatives and multi-organisational collaboration is necessary to address this issue.

Macro-factors driving bicycle adoption as a primary transport mode across Europe

This study provides the first analysis of geospatial factors on the seasonal decision to commute by bike or use a bike for errands across 31 European countries. The sample was drawn from a survey of 14,845 respondents and combined with geospatial variables that describe the respondents’ local areas. Controlling for well-known demographic and subjective socio-cultural variables, statistical relationships from the less explored objective geospatial variables on the seasonal biking decisions were estimated using a bivariate probit model. We find that the large differences observed between the biking levels in European countries can not be explained by differences in structural characteristics alone. In line with literature our interpretation of this gap points towards biking as a social innovation, partly driven by cultural and societal levers. The cross-country comparison presented in this study improves our understanding of the dynamic social innovation process that encapsulates mobility choice and cycling adoption, and highlights the levers that policymakers and urban planners have to influence this process.

Assessing regional variability in chemical composition and pozzolanic reactivity of corn stover ash in the United States

This study examines the regional variability in the chemical composition and pozzolanic reactivity of corn stover ash (CSA) produced from corn stover samples collected from different locations in the U.S. Corn stover samples were collected from local farms in Nebraska and Iowa, while information about Kansas CSA was obtained from existing literature. The findings reveal significant variability in the chemical composition of untreated CSA across different regions. However, through the use of pretreatment techniques such as acid soaking, the compositional variations can be considerably reduced. The results of the modified R3 test demonstrate that CSA exhibits pozzolanic behavior that falls between that of fly ash and silica fume. The reactivity of CSA was found to be independent of geospatial factors but heavily influenced by the specific pretreatment methods employed in the study. Furthermore, the study indicates that the reactivity of CSA is less variable compared to fly ash and silica fume.

“Local people want to keep their sand”: Variations in community perceptions and everyday resistance to sand mining across the Red River, Vietnam

The Red River Delta is Vietnam's most densely populated catchment, with its communities intertwined with, and dependent upon, the delta and its resources. Intensive, widespread riverine sand extraction is linked to substantial degradation of the delta, directly threatening these livelihoods. The response of rural communities is to resist in various ways, dependent upon a complex matrix of geospatial factors. Using an ethnographic approach, we explore factors contributing to the scale and patterns of resistance to sand mining of both an upstream riverine community and downstream coastal community in the Red River Delta. The riverine community protest openly to illegal mining directly threatening their river-connected livelihoods, manifest through reporting to authorities, bankside protests and involvement of mass media. In contrast, the coastal community have shifted from open protest, through openly reporting to the authorities, to closed forms of within-village gossip. Here, resistance patterns are driven by proximity to mining sites, perceived impact of mining, and pre-existing structural legislative mechanisms. Under scenarios of rapidly increasing rates of sand extraction to satisfy wider global needs, there is an urgent need for studies of local-scale impacts and resistance, capturing socio-economic characteristics of communities, to better plan for future scenarios of resource extraction and exploitation.

Monitoring and prediction of landslide-related deformation based on the GCN-LSTM algorithm and SAR imagery

A key component of disaster management and infrastructure organization is predicting cumulative deformations caused by landslides. One of the critical points in predicting deformation is to consider the spatio-temporal relationships and interdependencies between the features, such as geological, geomorphological, and geospatial factors (predisposing factors). Using algorithms that create temporal and spatial connections is suggested in this study to address this important point. This study proposes a modified graph convolutional network (GCN) that incorporates a long and short-term memory (LSTM) network (GCN-LSTM) and applies it to the Moio della Civitella landslides (southern Italy) for predicting cumulative deformation. In our proposed deep learning algorithms (DLAs), two types of data are considered, the first is geological, geomorphological, and geospatial information, and the second is cumulative deformations obtained by permanent scatterer interferometry (PSI), with the first investigated as features and the second as labels and goals. This approach is divided into two processing strategies where: (a) Firstly, extracting the spatial interdependency between paired data points using the GCN regression model applied to velocity obtained by PSI and data depicting controlling predisposing factors; (b) secondly, the application of the GCN-LSTM model to predict cumulative landslide deformation (labels of DLAs) based on the correlation distance obtained through the first strategy and determination of spatio-temporal dependency. A comparative assessment of model performance illustrates that GCN-LSTM is superior and outperforms four different DLAs, including recurrent neural networks (RNNs), gated recurrent units (GRU), LSTM, and GCN-GRU. The absolute error between the real and predicted deformation is applied for validation, and in 92% of the data points, this error is lower than 4 mm.