Spatial Analysis Research Articles

Unravelling heterogeneous malaria transmission dynamics in the Peruvian Amazon: insights from a cross-sectional survey

BackgroundMalaria remains a global health challenge, particularly in Peru's Loreto region. Despite ongoing efforts, high infection rates and asymptomatic cases perpetuate transmission. The Peruvian Ministry of Health’s “Zero Malaria Plan” targets elimination. This novel study combines microscopic, molecular, and serological techniques to assess transmission intensity, identify epidemiological risk factors, and characterize species-specific patterns across villages. The findings aim to inform targeted interventions and support broader malaria elimination efforts in line with the Zero Malaria Plan initiative.MethodsA cross-sectional malaria survey was conducted in the Zungarococha community, comprising the villages Llanchama (LL), Ninarumi (NI), Puerto Almendra (PA), and Zungarococha (ZG), using microscopic, molecular, and serological techniques to evaluate malaria transmission intensity. Statistical analysis, including multivariate-adjusted analysis, seroprevalence curves, and spatial clustering analysis, were performed to assess malaria prevalence, exposure, and risk factors.ResultsThe survey revealed a high prevalence of asymptomatic infections (6% by microscopy and 18% by PCR), indicating that molecular methods are more sensitive for detecting asymptomatic infections. Seroprevalence varied significantly between villages, reflecting the heterogeneous malaria transmission dynamics. Multivariate analysis identified age, village, and limited bed net use as significant risk factors for malaria infection and species-specific exposure. Seroprevalence curves demonstrated community-specific patterns, with Llanchama and Puerto Almendra showing the highest seroconversion rates for both Plasmodium species.ConclusionsThe study highlights the diverse nature of malaria transmission in the Loreto region, particularly nothing the pronounced heterogeneity as transmission rates decline, especially in residual malaria scenarios. The use of molecular and serological techniques enhances the detection of current infections and past exposure, aiding in the identification of epidemiological risk factors. These findings underscore the importance of using molecular and serological tools to characterize malaria transmission patterns in low-endemic areas, which is crucial for planning and implementing targeted interventions and elimination strategies. This is particularly relevant for initiatives like the Zero Malaria Plan in the Peruvian Amazon.

Navigating urban sustainable development: Exploring the impact of low carbon policies on the urban ecological carrying capacity

Amidst a backdrop of decelerating economic growth, China confronts mounting environmental challenges, including air pollution, water scarcity, and escalating carbon emissions. In this context, enhancing the urban ecological carrying capacity (UECC)– defined as the ability to sustain human activities without compromising environmental quality – emerges as a pivotal concern. To address these issues, the Chinese government has instituted the “Low Carbon Pilot Cities policy” initiative (LCPP), aiming to foster sustainable urban development by encouraging cities to adopt measures that reduce carbon emissions and enhance energy efficiency. This paper treats the policy as an exogenous quasi-natural experiment, employing a spatial double-difference methodology to assess its impact on the ecological carrying capacity of 281 Chinese cities from 2006 to 2021. Utilizing the pressure-state-response (PSR) model, this study constructs and evaluates their ecological carrying capacity. The findings reveal that: firstly, the low-carbon pilot policies significantly augment urban ecological carrying capacities; secondly, the policy's effects exhibit regional variability; thirdly, these policies bolster ecological carrying capacity through innovation-driven and energy structure optimization mechanisms; and fourthly, a further spatial spillover analysis indicates that the policy implementation adversely affects the ecological carrying capacity of adjacent cities. Consequently, this study offers empirical evidence and policy insights to assist countries in judiciously implementing environmental policies to achieve sustainable development objectives.

The Impact of Factor Price Change on China’s Cotton Production Pattern Evolution: Mediation and Spillover Effects

Cotton is an important agricultural crop and strategic resource. China is currently the country with the largest global cotton production and consumption, but few studies have systematically analyzed the dynamic relationship between factor price change (FPC) and cotton production pattern evolution (CPPE). Based on provincial panel data from the main cotton planting areas from 1985 to 2021, this paper used spatial econometric models to empirically analyze the impact of FPC including labor price, production material cost, and mechanical cost on CPPE from the perspective of mechanical substitution difficulty. The findings are that (1) FPC significantly affected CPPE, specifically the rise of labor price induced the demand for mechanical substitution, resulting in a significant cotton agglomeration effect; (2) spatial econometric analysis found that FPC had a significant spatial spillover effect on CPPE in adjacent regions; and (3) the transfer analysis found that higher mechanical substitution difficulty exerted an inhibitory effect on cotton planting, leading to a gradual shift of the main cotton planting areas from the Yangtze River Basin and Yellow River Basin with high labor prices to the Northwest Inland region with lower labor prices. Updating the production technology and improving the efficiency of cotton specialization are effective strategies. The results are valuable for policy making related to the development of China’s cotton industry.

Spatial and Temporal Evolution of Seasonal Sea Ice Extent of Hudson Strait, Canada, 1971–2018

The temporal and spatial variation in seasonal sea ice in Hudson Strait is examined using time series and spatial clustering analyses. For the period from 1971 to 2018, a time series of sea ice breakup and freeze-up dates and ice-free season length at twenty-four grid points were generated from sea ice charts derived from satellite and other data. These data were analyzed temporally and spatially. The temporal analyses indicated an unambiguous response to a warming climate with statistically significant earlier breakup dates, later freeze-up dates, and longer ice-free seasons, that were statistically linked to coincident regional surface air temperatures. The rate of change in freeze-up dates and ice-free season length was particularly strong in the early 2000s and less so in the 2010s. There was evidence that breakup date behaviour was not only coincident with regional temperatures but likely with temperature and ice conditions of the previous year. Later freeze-up dates were directly linked to earlier breakup dates using detrended time series. Spatial clustering analysis on the Hudson Strait gridded sea ice data revealed distinctive signatures for Ungava Bay, Frobisher Bay, and for grid points close to the shore and a clear linkage to the underlying circulation of Hudson Strait.

Risk factors for persistent fatal opioid-involved overdose clusters in Massachusetts 2011–2021: a spatial statistical analysis with socio-economic, accessibility, and prescription factors

BackgroundFatal opioid-involved overdose rates increased precipitously from 5.0 per 100,000 population to 33.5 in Massachusetts between 1999 and 2022.MethodsWe used spatial rate smoothing techniques to identify persistent opioid overdose-involved fatality clusters at the ZIP Code Tabulation Area (ZCTA) level. Rate smoothing techniques were employed to identify locations of high fatal opioid overdose rates where population counts were low. In Massachusetts, this included areas with both sparse data and low population density. We used Local Indicators of Spatial Association (LISA) cluster analyses with the raw incidence rates, and the Empirical Bayes smoothed rates to identify clusters from 2011 to 2021. We also estimated Empirical Bayes LISA cluster estimates to identify clusters during the same period. We constructed measures of the socio-built environment and potentially inappropriate prescribing using principal components analysis. The resulting measures were used as covariates in Conditional Autoregressive Bayesian models that acknowledge spatial autocorrelation to predict both, if a ZCTA was part of an opioid-involved cluster for fatal overdose rates, as well as the number of times that it was part of a cluster of high incidence rates.ResultsLISA clusters for smoothed data were able to identify whether a ZCTA was part of a opioid involved fatality incidence cluster earlier in the study period, when compared to LISA clusters based on raw rates. PCA helped in identifying unique socio-environmental factors, such as minoritized populations and poverty, potentially inappropriate prescribing, access to amenities, and rurality by combining socioeconomic, built environment and prescription variables that were highly correlated with each other. In all models except for those that used raw rates to estimate whether a ZCTA was part of a high fatality cluster, opioid overdose fatality clusters in Massachusetts had high percentages of Black and Hispanic residents, and households experiencing poverty. The models that were fitted on Empirical Bayes LISA identified this phenomenon earlier in the study period than the raw rate LISA. However, all the models identified minoritized populations and poverty as significant factors in predicting the persistence of a ZCTA being part of a high opioid overdose cluster during this time period.ConclusionConducting spatially robust analyses may help inform policies to identify community-level risks for opioid-involved overdose deaths sooner than depending on raw incidence rates alone. The results can help inform policy makers and planners about locations of persistent risk.

Effects of Adapted Physical Activity on White Matter Integrity in Patients with Schizophrenia.

Schizophrenia is associated with changes in white matter (WM) integrity and with reduced life expectancy, in part because of the cardiometabolic side effects of antipsychotics. Physical activity (PA) has emerged as a candidate lifestyle intervention that is safe and effective. The study aimed to assess how an adapted PA program delivered remotely by web (e-APA) improved WM integrity in patients with schizophrenia (SZPs) and healthy controls (HCs) and to evaluate associations among WM integrity, cardiorespiratory fitness, and symptom severity. This longitudinal study was conducted over 16 weeks with 31 participants (18 SZPs and 13 HCs). Diffusion tensor imaging and tract-based spatial statistics were employed to assess WM integrity. Cardiorespiratory fitness was measured by maximal oxygen uptake (VO2max), and assessments for clinical symptoms included the Positive and Negative Syndrome Scale, Self-evaluation of Negative Symptoms and the Brief Negative Syndrome Scale (BNSS). Only the SZPs had significantly increased WM integrity after the e-APA program, with increased fractional anisotropy and decreased radial diffusivity in fasciculi involved in motor functions and language process. Furthermore, decreased negative symptoms assessed with BNSS were associated with greater WM integrity following the program. These findings suggest that e-APA may improve WM integrity abnormalities and support e-APA as a promising therapeutic strategy.

Space-use strategy drives fine-scale spatial variation of chlorinated paraffins in indo-pacific humpback dolphins

Contaminant accumulation in organisms can be influenced by both biological traits and environmental conditions. However, delineating the main factors affecting contaminant burdens in organisms remains challenging. Here, we conducted an initial investigation into the impact of diet and habitat on the accumulation of short- (SCCPs) and medium-chain chlorinated paraffins (MCCPs) in Indo-Pacific humpback dolphins (2003–2020, n = 128) from the Pearl River Estuary (PRE), a highly polluted estuary in China. The detected levels of SCCPs (5897 ± 3480 ng g−1 lw) and MCCPs (13,960 ± 8285 ng g−1 lw) in blubber samples of humpback dolphin are the highest among recorded values marine mammals. Both SCCPs and MCCPs exhibited biomagnification factor values exceeding 1, suggesting their biomagnification potential within the dolphins and their diet. Quantitative diet analysis using the dolphin fatty acid signatures revealed that humpback dolphins inhabiting the western PRE consumed a larger proportion of carnivorous fish than those from the eastern PRE. However, spatial analysis showed that humpback dolphins in the western PRE contained lower SCCP/MCCP concentrations than those from the eastern PRE. Based on these findings we suggest that, compared to diet differences, spatial variations of SCCPs/MCCPs in humpback dolphins may be predominantly influenced by their space-use strategies, as the eastern PRE is closer to the pollutant discharge source and transfer routes.

Pemetaan Daerah Rawan Tanah Longsor di Daerah Desa Sanggang, Kecamatan Bulu, Kabupaten Sukoharjo

<p>Landslides are one of the frequent natural disasters occurring in various regions of Indonesia, including the Village of Sanggang in Bulu District, Sukoharjo Regency, Central Java. This area boasts diverse topography with numerous steep slopes, rendering it susceptible to landslides. The Bulu District is located in a hilly region with a high vulnerability to landslides, where from 2019 to 2020, there were recorded 11 landslide disaster events, with 10 of them occurring in the Bulu District. This study aims to map landslide-prone areas in the Village of Sanggang using a spatial analysis approach and relevant risk factors. The analytical methods include the use of topographical data, soil types, rainfall patterns, and historical landslide occurrences. The mapping results will provide a deeper understanding of locations vulnerable to landslides in this area. The outcomes of this research are expected to serve as a foundation for the development of more effective mitigation strategies, including recommendations for prevention measures and early warnings. A better understanding of these susceptible areas will assist local government authorities and stakeholders in safeguarding their communities and assets from the potential landslide hazards in the Village of Sanggang, Bulu District, Sukoharjo Regency.</p>

Spatial Analysis of Population Landscape: A Case Study of Scheduled Caste Population in Basti District

Spatial Analysis of Population Landscape: A Case Study of Scheduled Caste Population in Basti District

A Prototype Decision Support System for Tree Selection and Plantation with a Focus on Agroforestry and Ecosystem Services

This study presents the development and application of a prototype decision support system (DSS) for tree selection specifically for Punjab, India, a region facing challenges of low forest cover and an increasing demand for sustainable land use practices. The DSS developed using the R Shiny framework integrates ecological, social, and agro-commercial criteria to facilitate scientific knowledge decision making in tree plantation. The modules in this DSS include a tree selection tool based on comprehensive species attributes, a GIS-based tree suitability map module utilizing an Analytical Hierarchical Process (AHP), and a silviculture practice information module sourced from authoritative databases. Combining sophisticated statistical and spatial analysis, such as NMDS and AHP-GIS, this DSS mitigates data redundancy in SDM while incorporating extensive bibliographic research in dataset processing. The study highlights the necessity of fundamental niche-based suitability in comparison to realized niche suitability. It emphasizes on the importance of addressing ecosystem services, agro-commercial aspects, and enhancing silvicultural knowledge. Additionally, the study underscores the significance of local stakeholder engagement in tree selection, particularly involving farmers and other growers, to ensure community involvement and support. The DSS supports agroforestry initiatives and finds applications in urban tree management and governmental programs, emphasizing the use of scientific literature at each step, in contrast to relying solely on local knowledge.

Spatiotemporal Evolution Disparities of Vegetation Trends over the Tibetan Plateau under Climate Change

The Tibetan Plateau has experienced profound climate change with significant implication for spatial vegetation greenness. However, the spatiotemporal disparities of long-term vegetation trends in response to observed climate change remain unclear. Based on remote-sensing vegetation images indicated by the normalized difference vegetation index (NDVI) from two long-term combined datasets, GIMMS and MODIS, we identified two spatiotemporal evolution patterns (SEPs) in long-term vegetation anomalies across the Tibetan Plateau. This new perspective integrates spatial and temporal NDVI changes during the growing seasons over the past four decades. Notably, the dipole evolution pattern that rotates counterclockwise from May to September accounted for 62.8% of the spatial mean amplitude of vegetation trends, dominating the spatiotemporal disparities. This dominant pattern trend is attributed to simultaneous effects of spatial warming and rising CO2, which accounted for 75% and 15%, respectively, along with a lagged effect of dipole precipitation, accounting for 6%. Overall, wetting and warming promote greening evolution in the northern Tibetan Plateau, while slight drying and warming favor browning evolution in the southern Tibetan Plateau. These findings provide insights into the combined effects of climate change on spatiotemporal vegetation trends and inform future adaptive strategies in fragile regions.

Spatial analysis of the digital economy’s influence on urban sustainable development: A decade-long study of Chinese prefecture-level cities

Urban Sustainable Development (USD) is crucial for social advancement, and the rise of the Digital Economy (DE) offers potential pathways to achieve it. This study has employed the DPSIR framework and an entropy-based approach to evaluate the DE and USD across 284 Chinese prefecture-level cities from 2011 to 2020. Using the Spatial Durbin Model, the results have revealed that DE significantly enhances USD and has a spatial spillover effect of up to 300 km. The eastern region, as well as the Pearl River Delta and Yangtze River Delta regions, show stronger spillover effects than the central and western regions and the Beijing-Tianjin-Hebei region. These findings have provided insights for policymakers to leverage digital advancements to promote urban sustainability.

Settling the argument: The contribution of use-wear studies to understanding artefact scatters in Neolithic Britain

Our understanding of the Neolithic of southern Britain has been largely based on the interpretation of monumental landscapes such as those around Stonehenge and Avebury. The remains of domestic structures dating to the Neolithic are rare, and when found, are often associated with small assemblages of material culture. The most common forms of settlement evidence are unstratified artefact scatters, which have little evidence of associated structural remains. As a result, our understanding of Neolithic settlement is poor. We have limited knowledge of what craft and subsistence activities were associated with them, and we do not know how quotidian practices were organised at a settlement or landscape level.Taking the West Kennet Avenue Occupation Site as an example, this paper will show how use-wear analysis can be combined with a detailed technological analysis to reveal details of the character and temporality of a Neolithic settlement. The use-wear analysis will focus on the assemblage of microdenticulates from the site. It further explores the character and potential contact material related to Polish 23, the distinctive use-polish that occurs on these tools, and shows how the combination of spatial analysis and use-wear analysis can separate different episodes of occupation in a scatter of unstratified artefacts. The results provide crucial insight into the history of settlement in the Avebury landscape and shed further light on the character of use of microdenticulates, supporting the argument that they were used for processing plant fibres for the production of textiles.

Consequential versus attributional life cycle optimization of poultry manure management technologies in a food-energy-water-waste nexus

To model the environmental and economic tradeoffs of managing poultry manure wastes in a food-energy-water-waste nexus, this work builds a framework to conduct a combined life cycle and techno-economic optimization and a spatial analysis of poultry manure management technologies. To align with the United Nations Sustainable Development Goals 6: Clean Water and Sanitation and 13: Climate Action by mitigating the massive water and climate impacts induced by the common practice of fertilizer crops using direct land application of poultry manure, the framework minimizes freshwater eutrophication (FE) impact and climate change impact (GWP). To account for the circular economy, the net present value (NPV) is maximized to measure the economic feasibility of implementing alternative management pathways. A multi-objective mixed-integer non-linear programming framework accounts for the selection of direct land application, and waste to energy technologies, spatial variation in manure supply, facility sizing, and the products’ consumption and end-of-life. This optimization framework is evaluated for a case study in New York state. Under an attributional life cycle optimization, anaerobic digestion minimizes FE with an impact of 0.002 kg P-eq/ton manure, while using a consequential life cycle optimization, hydrothermal carbonization minimizes FE with an impact of −0.96 kg P-eq/ton manure. For both life cycle methodologies, pyrolysis with land application of biochar minimizes GWP. At the attributional life cycle assessment tradeoff point for GWP and NPV, minimum carbon credits of US$33/ton CO2-eq are necessary for a pyrolysis plant to have a positive NPV, implying that harnessing economies of scale or robust carbon accounting is necessary for the widespread adoption of pyrolysis as a poultry manure management technology.

Sustainable urban road planning under the digital twin-MCDM-GIS framework considering multidisciplinary factors

Sustainable urban road planning necessitates the formulation of functional, economic, people-friendly, eco-friendly, and engineering-reasonable road schemes within the confines of limited urban space. It is challenging to meet traffic demands, stimulate economic growth, foster social progress, and minimize adverse impacts on the social and natural environment. This study proposes a comprehensive approach for sustainable urban road planning, employing the Digital Twin-MCDM-GIS framework. Digital twins play a pivotal role in converting diverse factors into comprehensible expressions. MCDM (multi-criteria decision making) evaluates and assigns weights to these multidisciplinary factors. GIS (geographic information system) offers an integrated digital space for analysis. Various digitalisation and parsing methods, including direct assignment methods, spatial analysis methods, and complex professional methods, are introduced for traffic, development, cost, social, environmental, and engineering factors. By assigning distinct weights to these factors, diverse raster data are established to generate multiple road schemes using the least-cost wide path analysis. To validate the proposed approach, a case study was conducted in the London Borough of Bromley, resulting in the generation of 14 road schemes with varying widths and factor considerations in limited urban space.

Spatial Analysis of Cafés and Smoking Pollutants in Assiut City Using GIS

Spatial Analysis of Cafés and Smoking Pollutants in Assiut City Using GIS

Neuroimaging meta regression for coordinate based meta analysis data with a spatial model.

Coordinate-based meta-analysis combines evidence from a collection of neuroimaging studies to estimate brain activation. In such analyses, a key practical challenge is to find a computationally efficient approach with good statistical interpretability to model the locations of activation foci. In this article, we propose a generative coordinate-based meta-regression (CBMR) framework to approximate a smooth activation intensity function and investigate the effect of study-level covariates (e.g. year of publication, sample size). We employ a spline parameterization to model the spatial structure of brain activation and consider four stochastic models for modeling the random variation in foci. To examine the validity of CBMR, we estimate brain activation on 20 meta-analytic datasets, conduct spatial homogeneity tests at the voxel level, and compare the results to those generated by existing kernel-based and model-based approaches. Keywords: generalized linear models; meta-analysis; spatial statistics; statistical modeling.

Horizontal and vertical distribution of Clarireedia spp., in asymptomatic and symptomatic creeping bentgrass cultivars.

Dollar spot is an important disease of both cool- and warm-season turfgrasses caused by six fungal species in the genus Clarireedia, yet the ecology and epidemiology of these pathogens remains poorly understood. The goal of this study was to determine the distribution of Clarireedia in asymptomatic and symptomatic creeping bentgrass (Agrostis stolonifera) in the field using a previously developed qPCR assay. To determine the horizontal distribution of the pathogen, the abundance of Clarireedia spp. was measured in leaf and crown tissue from 90, 1-cm diameter cores spaced 10-cm apart in May 2019 and 2020 (asymptomatic tissue) and August 2019 and July 2020 (symptomatic tissue). Thirty-seven to 69% of cores sampled from asymptomatic turfgrass and 77 to 95% of cores taken from symptomatic turfgrass yielded positive detections for Clarireedia. Spatial analysis indicated that Clarireedia was randomly distributed in the field in both asymptomatic and symptomatic turfgrass. To assess the vertical distribution of the pathogen, the abundance of Clarireedia was measured in the foliar, crown, and thatch layers of 39, 1-cm dia. x 2.5-cm deep cores of creeping bentgrass maintained at fairway height (9.5 mm) during 2019 and 2020. Clarireedia was most abundant in foliar tissue, followed by crown tissue, and thatch (lowest abundance) throughout the two-year study. Both studies provide evidence that Clarireedia is widely distributed in turfgrass swards prior to symptom development and that it can persist within turfgrass as an endophyte. These findings will improve our understanding of Clarireedia epidemiology and may lead to more sustainable dollar spot management.

Policy-Driven Vegetation Restoration in Qinghai Province: Spatiotemporal Analysis and Policy Evaluation

The Chinese government has implemented numerous ecological policies in Qinghai Province aimed at protecting and restoring the natural ecosystem. Yet, amid global climate change, the precise effects of these policies on ecological improvement remain ambiguous. There is an urgent need for a comprehensive evaluation of the effects of these policies at a regional scale and an analysis of the changes in policy implementation stages to optimize the strategic direction of regional ecological policies. In this study, using mathematical statistics and spatial analysis, we analysed the spatial and temporal characteristics of the Normalized Difference Vegetation Index (NDVI) in Qinghai Province from 2000 to 2023. Further, by systematically reviewing ten major ecological policies currently or previously implemented in the region, we explored the response of vegetation restoration to these policies through both horizontal and vertical evaluations by reasonably delineating the policy study sub-zones. The study identified distinct stages of policy implementation—regreening, stabilizing, and natural recovery—and correlated these stages with the efficacy of policy impacts. Our findings indicate significant vegetation coverage improvements across Qinghai Province over the past two decades, with all ecological policies positively influencing the environment. The main contribution of this study is that it comprehensively evaluates the impact of multiple ecological policies on vegetation restoration at the regional scale, providing a reference for the formulation and adjustment of subsequent ecological policies.

Environmental vulnerability applied to the territorial planning of a tropical semiarid basin.

Tropical semiarid regions are naturally prone to environmental damage. Human activity can worsen this situation. To understand how human actions affect the ecosystem, plan land use effectively, and establish targeted management practices, assessing environmental vulnerability is crucial. This study focuses on a sub-basin receiving water transfers from the São Francisco River in Brazil's semiarid region. Here, we map and evaluate how land use and occupation alter natural vulnerability. We also propose zoning strategies to support water resource management and implement sustainable development policies in the region. To achieve this, we conducted an integrated analysis of physical factors (soil types, geology, climate, vegetation, and landforms) and spatial land-use data using geographic information systems (GIS) and map algebra techniques. Map algebra allowed us to combine these various datasets within the GIS environment, enabling the creation of maps that synthesize both natural and environmental vulnerability across the study area. Following analysis of these vulnerability maps, our findings reveal a high level of vulnerability. The areas with high to very high degrees of natural vulnerability coincide with the places that have high slopes, high altitudes, Lithic Neosols, and thick vegetation. Furthermore, the interaction between environmental factors and human activity exacerbates vulnerability. Based on the environmental vulnerability assessment, we defined four environmental management zones. These zones require distinct protection measures and management approaches. As a method to potentially improve the basin's vulnerability scenario, soil conservation measures are recommended. This approach is highly relevant for managing land in tropical semiarid regions and, with adaptations to specific regional factors, can be applied globally.