Spatial Analysis Research Articles

Measuring and reducing the ecological risk of community tourism for ecosystem conservation

The escalating ecological risks associated with Community Tourism (CT) pose a significant threat to ecosystem conservation worldwide, yet methods to predict and prevent CT-induced problems remain underexplored. In the context of the pronounced emphasis on the economic advantages of CT and its extensive implementation, the spatial prediction of CT-induced ecological issues and the corresponding effective preventative strategies are crucial. This study integrates composite indicators, Public Participation Geographic Information Systems, spatial statistics, and scenario analysis to develop Community Tourism Ecological Risk Models (CTERMs). Using the Qinghai Lake region as a case study, this study quantifies the spatial distribution of ecological risks associated with CT, identify priority areas for intervention, and propose enhanced mitigation strategies. Results indicate that CTERMs effectively pinpoint sites of ecological concern, making them valuable tools for forecasting CT-induced problems. Although high-risk areas constitute a small portion of the study area, significant disparities among communities highlight three clusters requiring urgent preventive measures. The current tourism concession layout is inadequate for mitigating CT-induced ecological risks. Adjusting the locations of concessions, while maintaining their current number, can significantly reduce ecological threats and protect endangered species. These findings demonstrate that strategic concession placement offers considerable ecological benefits. As new frameworks for biodiversity conservation are being considered, conservation scientists and policymakers are presented with an opportunity to integrate community development, poverty reduction, and ecosystem conservation by utilizing CTERMs for the strategic allocation of concessions.

Organic farming is more related to topography than to soil characteristics in extensively and intensively managed grasslands in Switzerland

Organic farming and other agri-environmental schemes (AES) are important policy tools to support environmental-friendly agriculture. Often, AES require a direct reduction of actual management intensity to sustain biodiversity and non-marketable ecosystem services. In addition to lower management intensity, differences in topography and soils between AES and non-AES land can occur, driven by the targeted placement of AES in the landscape. Many of the latter effects of an AES are, however, widely unknown and frequently ignored, limiting a comprehensive understanding of how organic farming and other AES deliver environmental outcomes. We analysed pedological, topographical and other spatial characteristics of parcels under two grassland AES, i.e., organic farming (vs. conventional) and extensive management (vs. intensive). Thus, this study assessed whether organic farming is related to differences in topography and soil conditions in both extensively and intensively managed grasslands in the study region in the north of Switzerland. Therefore, we combined a regional-scale spatial analysis of permanent grassland parcels and a soil survey. Both AES were tested not only in interaction with each other but also within the two main harvest types, i.e., meadows (mainly mown) and pastures (mainly grazed), resulting in eight distinct grassland types that were studied. Results show both AES to be linked to differences in soil nutrients as well as topographical and other spatial characteristics. We found interactions of the two AES with the harvest type, i.e., meadow versus pasture. This was particularly pronounced for extensively managed conventional meadows, which were frequent at low elevation and on land potentially suitable for arable farming. Extensively managed pastures and all organic grasslands exhibited reduced production conditions (i.e., higher elevation, steeper slope, lower soil phosphorus concentrations). Yet, differences between organic and conventional grasslands were by tendency more pronounced in intensively than extensively managed grasslands. Our results show that farmers preferentially adopted both AES on land not ideal for intensive production, with the exception of many extensively managed meadows in low elevations. Our study therefore demonstrated that an assessment of the ecological outcomes of an AES must not only account for direct effects via management restrictions but also for indirect effects via spatial targeting by farmers. More research is still needed to assess and compare direct and indirect effects of AES to support evidence-based policymaking and improve spatial targeting of different land-use types.

Meteorological effects of ventilation corridor in central urban areas: A case study of Wuhan

How ventilation corridors affect urban climate is attracting researchers' attention. Taking the inland Chinese city of Wuhan as an example, this paper first uses remote sensing image technology to evaluate the urban thermal environment. Additionally, based on the GIS/RS spatial analysis method, the ventilation corridors in the central urban area are identified and constructed. Finally, the mesoscale meteorological model WRF-UCM is used to simulate four cases with different corridor forms to explore the impact of different corridors on the climate environment in Wuhan during the summer. The results indicate that: (1) The WRF-UCM model, when coupled with LCZ classification, can significantly improve the accuracy of mesoscale urban canopy meteorological field simulations. (2) The water corridors located in the central urban area can effectively regulate the temperature and wind environment during summer. In the high-temperature period of the day, the average temperature in the central city decreases by 0.3–0.4 °C, the heat island proportion index decreases by 1.61 %, and the strong heat island proportion index decreases by 1.89 % in the afternoon. During the period of low temperature, the average wind speed in the central urban area increased by 0.05 m/s increase, and even increased by 0.1 m/s. (3) The specific humidity value of the green corridor is reduced by 0.0000136 kg/kg in comparison to the construction land in the corridor, while the water corridor can increase by 0.000133 kg/kg. If the two kinds of surface, water and green land, are organically combined in the corridor, it will be able to improve the hot and humid conditions in Wuhan in summer. (4)Low-rise and low-density construction land as the corridors in the central urban area can not improve the urban thermal and wind environment. Through an attempt to conduct a complete workflow of urban heat island analysis, ventilation corridor identification and setting, urban climate simulation, analysis and summary, the authors believe that it is an effective set of working methods in sustainable urban planning, design, and policy-making. The implementation of pertinent research findings in the domain of urban planning and design demonstrates its universal applicability and has the potential to extend to analogous research and practice.

Oxytenanthera abyssinica (A. Rich.) Munro land suitability evaluation in the Kurar watershed, Abay Gorge, Upper Blue Nile River Basin, Ethiopia

O. abyssinica, known in Ethiopia as lowland bamboo, is a solid-stemmed clump-forming bamboo species widely distributed in the western dry regions of Ethiopia. The versatility of the species means that it has enormous potential for land restoration in arid and semi-arid areas, in addition to bringing socio-economic benefits. It also displays remarkable adaptability, allowing it to thrive in the challenging ecological conditions of areas such as the Abay Gorge. The Abay Gorge, situated within the Upper Blue Nile River Basin of Northwestern Ethiopia is characterized by problems associated with soil erosion and land degradation. This study, therefore, aimed to evaluate and recommend a suitable spatial analysis for the adoption and development of lowland bamboo (O. abyssinica). Fifteen primary influencing factors were chosen according to the needs of O. abyssinica, the accessibility of data, and the financial implications associated with data analysis, particularly the expenses related to soil laboratory testing. Eight composite soil samples were taken directly by dividing the watershed into two categories (upper and lower catchment areas). These composite soil samples were analyzed. Data analysis was performed via Analytical Hierarchy Process (AHP) in conjunction with Multi-Criteria Decision-Making (MCDM) analysis and the use of Remote Sensing (RS) and Geographic Information System (GIS). The spatial analysis employed in this study was a weighted sum overlay analysis, which was applied by considering the criteria weight assigned to each factor. The analysis revealed that 21.2 % (666.5 ha) of the total area was highly suitable for the growth and development of O. abyssinica. About 56 % (1753 ha) of the land was moderately and marginally suitable for the desired land utilization type. However, 23 % (723.8 ha) of the study area was unsuitable for O. abyssinica. O. abyssinica has the potential to rehabilitate large parts of challenging areas such as the Abay Gorge due to its naturally invase rhizomes and drought-tolerance. It can also be introduced into agroforestry systems and used as living fences for villages.

Machine learning for predictions of road traffic accidents and spatial network analysis for safe routing on accident and congestion-prone road networks

Machine learning for predictions of road traffic accidents and spatial network analysis for safe routing on accident and congestion-prone road networks

Identification of priority conservation areas in Beijing-Tianjin-Hebei using multi-scenario trade-offs based on different spatial scales and their drivers

Identifying priority conservation areas (PCAs) on the basis of ecosystem services (ESs) trade-offs is a crucial step in achieving optimal ecosystem management in the BTH, noting the complex spatio-temporal and scaling effects inherent in these ecosystems. A cross-scale quantification of spatial and temporal changes in ecosystem services and their correlations was conducted in the BTH. Furthermore, the Ordered Weighting Algorithm (OWA) was introduced to simulate a variety of scenarios in order to determine the PCAs and to explore the drivers of ES in the PCAs. The results indicated that Food supply (FS), Water yield (WY), and Soil retention (SR) in the BTH increased by 16.4 × 104t/hm2, 81.81 mm, and 37081.29 t/hm2, respectively. In contrast, Carbon storage (CS) and Habit quality (HQ) exhibited a decrease of 0.01 t/hm2 and 0.03. Spatially, the high-value areas of ESs at the county scale exhibited greater clustering. At the county scale, there were two trade-offs and eight synergies in 2000, and four trade-offs and six synergies by 2020. In contrast, at the raster scale, there were two trade-offs and eight synergies from 2000 to 2020. The mechanisms of change were similar and exhibited spatial heterogeneity. Scenario 6 represents the optimal priority conservation area, exhibiting the most balanced conservation efficiency of each ecosystem service. This scenario enables the simultaneous conservation of multiple ESs. The natural factors within the PCAs exert a stronger influence on ESs than socio-economic factors. The inclusion of interacting factor effects will enhance the explanatory power of the drivers. The findings of this study can serve as a theoretical foundation for cross-scale ecosystem service management decisions.

Developing an annual global Sub-National scale economic data from 1992 to 2021 using nighttime lights and deep learning

The Gross Domestic Product (GDP) per capita is one of the most widely used socioeconomic indicators, serving as an integral component for climate change impact analysis. However, a national scale assessment may induce considerable bias because it conceals any internal variations within a country. The lack of a long-term sub-national scale GDP data is a substantive hinderance. Leveraging the close relationship between nighttime lights and GDP, we address this gap by developing a novel methodological framework in two steps. First, under the modeling philosophy of spatial statistics, we developed a novel approach based on deep and machine learning techniques to establish a complex mapping between two inconsistent nighttime lights (NTL) datasets: the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP) and the National Polar-Orbiting Partnership’s Visible Infrared Imaging Radiometer Suite (VIIRS). The models achieve accuracies ranging from 0.945 to 0.980 (correlation coefficients). By taking the estimations ensemble of the two techniques, the time series of DMSP data was extended to 2021. Next, a novel modeling strategy based on multi-layer perceptron was developed to derive the non-linear relationship between NTL and GDP per capita at sub-national scale to alleviate scale effects at this granularity, while explicitly capturing regional heterogeneity effect. The trained models achieve average accuracies of 0.967, 0.959, and 0.959 on the training, validation, and test sets, respectively. We evaluate the developed dataset at the global, national, and sub-national scales from various perspective, and the results offer solid evidence on the reliability of the estimated economic data. By linking to historical global climate change data, we quantify global economic losses attributed to extreme heat to demonstrate how the estimated GDP data can be useful in the climate change impact analysis.

Measuring G protein activation by spectrally resolved imaging fluorescence fluctuation spectroscopy

The activation of heterotrimeric G proteins through G-protein-coupled receptors (GPCRs) is a ubiquitous signaling mechanism in eukaryotic biology. The three principal molecular components of this cascade are the GPCR, Gα subunit, and Gβγ subunit. Measurement of interactions between these components and their downstream effectors in live cells is paramount to understanding how cells fine-tune their physiology in response to many external stimuli. Multicolor fluorescence fluctuation spectroscopy (FFS) approaches allow the sensitive detection of heteromeric interactions by using spectrally distinct fluorophores to label biomolecules of interest. We considered three imaging FFS approaches to measuring molecular interactions from the signals produced by a spectrally resolved confocal microscopy: raster spectral image correlation spectroscopy (RSICS), spectral spatial cumulant analysis, and native resolution spatial cumulant analysis. We characterized these approaches using simulation and experiments on heteromers with known stoichiometries. We found that RSICS had the best sensitivity for measuring heteromeric interactions and employed it to measure G protein complexes. As measured by RSICS, interactions between the G protein subunits Gαi1 and Gβ1γ2 were sensitive to the stimulation of two GPCRs, the D2 dopamine receptor and the α-2A adrenergic receptor. Interactions between GPCRs and G proteins were not detectable above background, supporting a collisional model of GPCR/G protein interactions in contrast to a preassembly model where strong interactions would be present. These data are uniquely available by this FFS framework, which is appropriate for not only multiplexed measurements of G protein biology but any dynamic protein complexes in the cell.

Spatial and Temporal Changes in Land Use and Landscape Pattern Evolution in the Economic Belt of the Northern Slope of the Tianshan Mountains in China

Spatial and Temporal Changes in Land Use and Landscape Pattern Evolution in the Economic Belt of the Northern Slope of the Tianshan Mountains in China

Spatial Pattern and Distribution of Malaria Cases in Owo Local Government Area, Nigeria

This study investigates the spatial pattern and distribution of malaria cases in Owo Local Government Area, Nigeria. A multi-stage sampling technique was used, such that 50% of the 7 urban political wards were selected. The selected 4 urban political wards comprise of 12,466 household heads. Also, 10% of the 127 rural settlements were randomly selected making 14 rural settlements with 9,095 household heads. In all, 1.5% of the total household heads, comprising 187 in the urban areas and 136 in the rural settlements were interviewed using systematic random sampling techniques. A secondary data source obtained from relevant healthcare facilities was used for spatial analysis. Specifically, 59 selected residential settlements were used as the input feature class, while the records on malaria cases in each residential settlement during the reviewed year serve as the input field for the analyses. Spatial statistical functions provided by ArcGIS 10.8 and Geospatial tool are utilized for data analysis. Findings revealed preponderance of malaria within the urban core area of Owo. This can be attributed to inadequate wastewater management, poor waste disposal methods, and inadequate environmental education and awareness. On the other hand, malaria cases in rural areas were relatively low due to the use of alternative disease treatment methods. To ameliorate these challenges, there is a need for improved access to a safe and high-quality environment, implement suitable sanitation facilities and solid waste disposal methods, promote community-based educational initiatives and awareness campaigns, and encouraging hygienic behaviors. Through these, the health challenges of malaria can be mitigated.

Spatial analysis of the concentrations of criteria air pollutans in Bogotá's Sunday ciclovias

Spatial analysis of the concentrations of criteria air pollutans in Bogotá's Sunday ciclovias

Childhood acute lymphoblastic leukemia survival and spatial analysis of socio-environmental risks in Mexico

Childhood acute lymphoblastic leukemia survival and spatial analysis of socio-environmental risks in Mexico

Spatial Analysis of Infant Mortality and Hazardous Sites across US States

Spatial Analysis of Infant Mortality and Hazardous Sites across US States

Advancing Public Health Surveillance: Integrating Modeling and GIS in the Wastewater-Based Epidemiology of Viruses, a Narrative Review.

This review article will present a comprehensive examination of the use of modeling, spatial analysis, and geographic information systems (GIS) in the surveillance of viruses in wastewater. With the advent of global health challenges like the COVID-19 pandemic, wastewater surveillance has emerged as a crucial tool for the early detection and management of viral outbreaks. This review will explore the application of various modeling techniques that enable the prediction and understanding of virus concentrations and spread patterns in wastewater systems. It highlights the role of spatial analysis in mapping the geographic distribution of viral loads, providing insights into the dynamics of virus transmission within communities. The integration of GIS in wastewater surveillance will be explored, emphasizing the utility of such systems in visualizing data, enhancing sampling site selection, and ensuring equitable monitoring across diverse populations. The review will also discuss the innovative combination of GIS with remote sensing data and predictive modeling, offering a multi-faceted approach to understand virus spread. Challenges such as data quality, privacy concerns, and the necessity for interdisciplinary collaboration will be addressed. This review concludes by underscoring the transformative potential of these analytical tools in public health, advocating for continued research and innovation to strengthen preparedness and response strategies for future viral threats. This article aims to provide a foundational understanding for researchers and public health officials, fostering advancements in the field of wastewater-based epidemiology.

Spatial network analysis and driving forces of urban carbon emission performance: Insights from Guangdong Province

As the primary contributor to carbon emissions, how cities enhance their carbon emission performance and mitigate emissions is crucial for achieving low-carbon urban environments in China. However, existing research often overlooks the spatial interconnectedness of carbon emission performance, neglecting reciprocal influences among cities. This study examines the network structure of carbon emission performance among Guangdong's cities from 1997 to 2019, using a super-efficient SBM model and social network analysis, and measures spatial impacts of network factors with the spatial Durbin model. Findings reveal that: (1) The overall network of carbon emission performance is relatively loose with minimal changes in connectivity and efficiency but shows significant local clustering. (2) Shenzhen, Guangzhou, and Zhuhai have high centrality, dominating carbon emission performance resources and acting as key transmission nodes, while most other regions have low centrality, indicating network polarization and potential vulnerabilities. (3) Enhancing a region's centrality, economic development, industrial structure, openness, and attraction of talent and technology can boost local carbon emission performance, but may also lead to the displacement of emissions to neighboring areas and outflow of low-carbon and innovative elements, negatively affecting surrounding regions through spatial spillover effects. This research advances regional carbon emission reduction strategies by highlighting the interplay between spatial networks and carbon emission performance, fostering synergies in reduction efforts.

Molecular Identification and Functional Characterization of LC-PUFA Biosynthesis Elongase (elovl2) Gene in Chinese Sturgeon (Acipenser sinensis).

Elongases of very-long-chain fatty acids (Elovls) are critical rate-limiting enzymes that are involved in LC-PUFA biosynthesis through catalyzing the two-carbon elongation of a pre-existing fatty acyl chain. Thus far, several Elovls have been extensively studied in teleost. However, the functional and physiological roles of Elovls in chondrichthyans have rarely been reported. In this study, we identified and characterized elovl2 from the endangered Chinese sturgeon (Acipenser sinensis) by whole genome scanning. The results show that the coding sequence of elovl2 was 894 bp in length, for a putative protein of 297 amnio acids. Comparative genomic analyses indicated that Chinese sturgeon elovl2 was evolutionarily conserved. Functional characterization in yeast demonstrated that the Chinese sturgeon Elovl2 could efficiently elongate C20 (ARA and EPA) and C22 (22:4n-6 and 22:5n-3) substrates, confirming its critical roles in LC-PUFA biosynthesis. Spatial and temporal expression analyses showed high elovl2 mRNA levels were detected in the liver and brain and showed an increase trend both in embryonic and post-hatching stages. Interestingly, diets with vegetable oils as lipid sources could significantly induce the high expression of elovl2 in Chinese sturgeon, implying that the endogenous LC-PUFA biosynthesis pathway was stimulated by lack of LC-PUFA in their diets. Our findings will enhance our understanding about the evolutionary and functional roles of elovl2 and provide novel insights into the LC-PUFA biosynthesis mechanism in vertebrates.

Spatiotemporal transcriptome atlas reveals gene regulatory patterns during the organogenesis of the rapid growing bamboo shoots.

Bamboo with its remarkable growth rate and economic significance, offers an ideal system to investigate the molecular basis of organogenesis in rapidly growing plants, particular in monocots, where gene regulatory networks governing the maintenance and differentiation of shoot apical and intercalary meristems remain a subject of controversy. We employed both spatial and single-nucleus transcriptome sequencing on 10× platform to precisely dissect the gene functions in various tissues and early developmental stages of bamboo shoots. Our comprehensive analysis reveals distinct cell trajectories during shoot development, uncovering critical genes and pathways involved in procambium differentiation, intercalary meristem formation, and vascular tissue development. Spatial and temporal expression patterns of key regulatory genes, particularly those related to hormone signaling and lipid metabolism, strongly support the hypothesis that intercalary meristem origin from surrounded parenchyma cells. Specific gene expressions in intercalary meristem exhibit regular and dispersed distribution pattern, offering clues for understanding the intricate molecular mechanisms that drive the rapid growth of bamboo shoots. The single-nucleus and spatial transcriptome analysis reveal a comprehensive landscape of gene activity, enhancing the understanding of the molecular architecture of organogenesis and providing valuable resources for future genomic and genetic studies relying on identities of specific cell types.

Socioeconomic spatial analysis through fuzzy system as a tool for territorial planning applied to watersheds

ABSTRACT The watersheds are fundamental in the management of social policies, allowing for the measurement of physical and socioeconomic characteristics through indicators such as the HDI and SII. This study aims to develop the Socioeconomic Analysis Indicator (SAI) for the sub-basins of the Sorocabuçu River (SRB), using a fuzzy system and geoprocessing tools for zoning information by sub-basins. The HDI of the SRB resulted in high values, with income showing the lowest indices, followed by education and longevity. The SII showed low values, with income and literacy significantly contributing to socioeconomic vulnerability. The SAI results indicated reasonable socioeconomic conditions, with lower values in the sub-basins farther from urban and urbanized areas. This study provides data for the establishment of public policies aimed at reducing social and economic inequalities, prioritizing the most vulnerable sub-basins. The data obtained serve as an effective tool to support public management, promoting decisions that improve the quality of life of the local population through the integration of socioeconomic variables for a more holistic and sustainable approach to watershed management.

Regional drivers of industrial decarbonisation: a spatial econometric analysis of 238 EU regions between 2008 and 2020

ABSTRACT The European context of socio-economic integration and physical proximity likely plays an essential role in explaining the decarbonisation outcomes of industrial sectors. However, there is hardly any spatial regional analysis on CO2 emissions drivers in European countries. This study investigates the role of geographical space and regional determinants in industrial decarbonisation by analysing how socio-economic drivers and their interregional relationships impact industrial carbon emissions in European regions. We employ a spatial panel data econometric model to a novel panel dataset comprising 13 years (2008–20) of carbon emissions from hard-to-abate industrial sectors from 238 NUTS-2 regions across 27 European Union countries. Results indicate the presence of endogenous spatial interactions and high-time persistence between CO2-eq emissions in European Union regions. As such, industrial carbon emissions of regions follow similar patterns to their neighbours, supporting the evolutionary economic geography and growth theory assumptions of the spatial interaction of carbon emissions between regions. Furthermore, the use of a spatial econometric model illustrates the negative direct and spillover effects that higher levels of education and regional investment in research and development have on industrial CO2-eq emissions.

A simple method to assess flood regulation supply in urban lawns

Floods have an important impact on life and loss of goods. Urban green spaces are crucial to mitigating flood impact. However, their capacity to prevent floods depends on their condition, especially in areas highly affected by human activities such as lawns. Here, we developed a simple method to assess flood regulation using soil penetration resistance as a proxy and tested it on an urban lawn in Vilnius (Lithuania) in winter. We developed an experimental design using an app for collecting data and working with it in a GIS environment. To understand their spatial relations, geostatistical (e.g., semi-variogram model and ordinary kriging mapping) and spatial statistics ((Moran's global autocorrelation index and Cluster and Outlier Analysis (Anselin Local Moran's I)) tools were applied. The preliminary results from the tested method showed that the lawn studied has different capacities to retain floods due to the management practices. Nevertheless, it is essential to be applied in different soil moisture conditions since flood regulation (soil penetration resistance) can be variable throughout the year.•A novel method was developed to estimate flood regulation using soil penetration resistance as a proxy;•An urban lawn was used to test the method and identify areas with low and high capacity for flood regulation;•The method quickly assesses lawn flood retention capacity in different environments.