Spatial Analysis Research Articles

Relationship between sun exposure and seasonal affective disorder symptoms in rural older people with different personalities: a cross-sectional study

ObjectivesEvidence suggests that environmental support, personality traits, and psychological factors can influence seasonal changes in human mood and behavior, particularly in rural middle-aged women and older people. This study aimed to quantify the associations between personality traits, seasonal affective disorder symptoms, and sun exposure in rural older people.MethodsThis study is a cross-sectional analytical study, the participants were 300 rural older persons from 12 natural villages and 5 geriatric service centers in 4 different cities in Jiangxi Province, China. The Eysenck Personality Questionnaire (EPQ), the Personal Inventory of Depression and Seasonal Affective Disorder (PIDS-SA-SimpChi), and the Sunlight Exposure Scale were used to conduct follow-up interviews throughout the year. Spatial analysis was performed using ArcGIS and Geodetic Probes. The data were analyzed using SPSS 21 and Amos 23.0 mediated models.ResultsRural older people with low sun exposure exhibited higher personality trait scores (p < 0.001). Personality traits were directly associated with seasonal affective disorder symptoms(p < 0.01); Sun exposure mediated this effect in rural older people (p < 0.05).ConclusionHigh-scoring personalities are more typical of rural older people with low sun exposure, and there is a greater risk of emotional and behavioral instability. Latitudinal differences are not a determinant of SAD. Increased sun exposure is associated with symptom relief. The promotion of light therapy devices in rural areas with low sunlight is warranted.

Genome-wide gene network uncover temporal and spatial changes of genes in auxin homeostasis during fruit development in strawberry (F. × ananassa)

BackgroundThe plant hormone auxin plays a crucial role in regulating important functions in strawberry fruit development. Although a few studies have described the complex auxin biosynthetic and signaling pathway in wild diploid strawberry (Fragaria vesca), the molecular mechanisms underlying auxin biosynthesis and crosstalk in octoploid strawberry fruit development are not fully characterized. To address this knowledge gap, comprehensive transcriptomic analyses were conducted at different stages of fruit development and compared between the achene and receptacle to identify developmentally regulated auxin biosynthetic genes and transcription factors during the fruit ripening process. Similar to wild diploid strawberry, octoploid strawberry accumulates high levels of auxin in achene compared to receptacle.ResultsGenes involved in auxin biosynthesis and conjugation, such as Tryptophan Aminotransferase of Arabidopsis (TAAs), YUCCA (YUCs), and Gretchen Hagen 3 (GH3s), were found to be primarily expressed in the achene, with low expression in the receptacle. Interestingly, several genes involved in auxin transport and signaling like Pin-Formed (PINs), Auxin/Indole-3-Acetic Acid Proteins (Aux/IAAs), Transport Inhibitor Response 1 / Auxin-Signaling F-Box (TIR/AFBs) and Auxin Response Factor (ARFs) were more abundantly expressed in the receptacle. Moreover, by examining DEGs and their transcriptional profiles across all six developmental stages, we identified key auxin-related genes co-clustered with transcription factors from the NAM-ATAF1,2-CUC2/ WRKYGQK motif (NAC/WYKY), Heat Shock Transcription Factor and Heat Shock Proteins (HSF/HSP), APETALA2/Ethylene Responsive Factor (AP2/ERF) and MYB transcription factor groups.ConclusionsThese results elucidate the complex regulatory network of auxin biosynthesis and its intricate crosstalk within the achene and receptacle, enriching our understanding of fruit development in octoploid strawberries.

Spatial Dynamics of Logistics Risk Management in Saudi Arabia: Using Spatial Panel Model Analysis

Spatial Dynamics of Logistics Risk Management in Saudi Arabia: Using Spatial Panel Model Analysis

Building resilient infrastructure, supporting sustainable industrialization, and fostering innovation (SDG 9) in selected European countries: spatial and taxonomic analysis

Building resilient infrastructure, supporting sustainable industrialization, and fostering innovation (SDG 9) in selected European countries: spatial and taxonomic analysis

Spatial Pattern Analysis and Conservation Assessment of Apiaceae in Mongolia

Spatial Pattern Analysis and Conservation Assessment of Apiaceae in Mongolia

Prevalence, spatial variation and determinants of zero-dose children in Ethiopia: Spatial and multilevel analyses

ObjectivesVaccination is a crucial public health intervention protecting individuals and communities from vaccine-preventable diseases. However, unvaccinated children in low- and middle-income countries pose a significant challenge. Ethiopia, a Global Alliance for Vaccines and Immunisation (GAVI)-supported country, ranks fifth in zero-dose immunisation burden, indicating concerning vaccine coverage gaps. Despite the severity of this issue, there is a dearth of research investigating the disparities, prevalence and contributing factors associated with zero-dose children in Ethiopia. This study aimed to assess the prevalence, spatial distribution and determinants of zero-dose children in Ethiopia. Study designA community-based cross-sectional study was conducted using data from the 2019 Ethiopian Mini Demographic and Health Survey (EMDHS). Data were collected from 21 March 2019 to 28 June 2019. MethodsThe study included a total of 1334 children aged 12–35 months (weighted sample). For spatial and multilevel analyses, ArcGIS 10.8 and Stata 17 software were used, respectively. The measure of association was determined by computing the adjusted odds ratio (AOR) at a 95 % confidence interval (95 % CI), and a p-value <0.05 was considered statistically significant. ResultsThe prevalence of zero-dose children in Ethiopia was 46.5 % (95 % CI: 43.8, 49.2). Southeast Amhara, Afar, Somali, Oromia and SNNPR (Southern Nations, Nationalities and Peoples’ Region) regions had high zero-dose proportions. Maternal age 15–19 years (AOR = 1.63; 95 % CI: 1.05, 2.64), lack of antenatal care (AOR = 1.77; 95 % CI: 1.34, 2.35), rural residence (AOR = 1.94; 95 % CI: 1.17, 3.19) and region were significantly associated to zero-dose status in Ethiopia. ConclusionsThe prevalence of zero-dose children in Ethiopia was high and the distribution exhibited significant variation across the country's clusters. Individual and community factors were key contributors. It is essential that areas with a high prevalence of zero-dose children have access to recommended childhood vaccines. This proactive approach can help protect children from morbidity and mortality caused by vaccine-preventable diseases.

Spatial patterns and associated factors of HIV testing and counselling (HTC) as a component of antenatal care services in Ethiopia

Background While HIV testing and counselling play a crucial role in preventing mother-to-child transmission, numerous pregnant women did not receive these services. Understanding the spatial variation of HIV testing and counselling and its associated factors during antenatal care in Ethiopia remains limited. Thus, this study was aimed at assessing the spatial patterns and factors associated with HIV testing and counselling during antenatal care visits in Ethiopia. Methods A cross-sectional study design was employed with a two-stage stratified cluster sampling technique. A total of 2,789 women who gave birth in the two years prior to the survey and had at least one antenatal care visit were included in the study. Stata version 16 and ArcGIS version 10.8 software were used for analysis. A multilevel robust Poisson regression model was fitted to identify significantly associated factors since the prevalence of HIV testing and counselling was higher than 10%. A statistically significant association was declared based on multivariable multilevel robust Poisson regression analysis using an adjusted prevalence ratio with its 95% confidence interval at a p-value &lt; 0.05. Spatial regression analysis was conducted, and the local coefficients of statistically significant spatial covariates were visualised. Results In Ethiopia, the overall prevalence of HIV testing and counselling during antenatal care visits was 29.5% (95% CI: 27.8%, 31.2%). Significant spatial clustering was observed (Global Moran’s I = 0.138, p-value &lt;0.001). In the spatial regression analysis, high and comprehensive knowledge related to HIV, and comprehensive knowledge on the prevention of mother-to-child transmission were significant explanatory variables for the spatial variation of HIV testing and counselling. In the multivariable multilevel robust Poisson regression analysis, education, household wealth, media exposure, number of antenatal care visits, comprehensive knowledge on mother-to-child transmission, comprehensive knowledge on prevention of mother-to-child transmission, and region were significantly associated factors. Conclusion The prevalence of HIV testing and counselling during antenatal care visits was low. Empowering women through education, promoting mass media exposure, increasing numbers of antenatal care visits, and enhancing women’s knowledge related to HIV and mother-to-child transmission by targeting cold spot areas could improve HIV testing and counselling service uptake among pregnant women in Ethiopia.

Automated detection of regions with persistently enhanced methane concentrations using Sentinel-5 Precursor satellite data

Abstract. Methane (CH4) is an important anthropogenic greenhouse gas, and its rising concentration in the atmosphere contributes significantly to global warming. A comparatively small number of highly emitting persistent methane sources are responsible for a large share of global methane emissions. The identification and quantification of these sources, which often show large uncertainties regarding their emissions or locations, are important to support mitigating climate change. Daily global column-averaged dry air mole fractions of atmospheric methane (XCH4) are retrieved from radiance measurements of the TROPOspheric Monitoring Instrument (TROPOMI) on board on the Sentinel-5 Precursor (S5P) satellite with a moderately high spatial resolution, enabling the detection and quantification of localized methane sources. We developed a fully automated algorithm to detect regions with persistent methane enhancement and to quantify their emissions using a monthly TROPOMI XCH4 dataset from the years 2018–2021. We detect 217 potential persistent source regions (PPSRs), which account for approximately 20 % of the total bottom-up emissions. By comparing the PPSRs in a spatial analysis with anthropogenic and natural emission databases, we conclude that 7.8 % of the detected source regions are dominated by coal, 7.8 % by oil and gas, 30.4 % by other anthropogenic sources like landfills or agriculture, 7.3 % by wetlands, and 46.5 % by unknown sources. Many of the identified PPSRs are in well-known source regions, like the Permian Basin in the USA, which is a large production area for oil and gas; the Bowen Basin coal mining area in Australia; or the Pantanal Wetlands in Brazil. We perform a detailed analysis of the PPSRs with the 10 highest emission estimates, including the Sudd Wetland in South Sudan, an oil- and gas-dominated area on the west coast in Turkmenistan, and one of the largest coal production areas in the world, the Kuznetsk Basin in Russia. The calculated emission estimates of these source regions are in agreement within the uncertainties in results from other studies but are in most of the cases higher than the emissions reported by emission databases. We demonstrate that our algorithm is able to automatically detect and quantify persistent localized methane sources of different source type and shape, including larger-scale enhancements such as wetlands or extensive oil- and gas-production basins.

Temporal and spatial changes to cropland cover on the Korean Peninsula over the past millennium

Temporal and spatial changes to cropland cover on the Korean Peninsula over the past millennium

School Closures and the Spatial Ecology of Education Access in 10 U.S. Cities

Public school closures are increasing in frequency, number and size in U.S. cities. This study examines the spatial distribution of public school closures occurring in 10 U.S. cities between 2010 and 2019. I employ Exploratory Spatial Data Analysis (ESDA) techniques to measure the spatial concentration of school closures, or the degree to which school closures cluster. I also develop a measure of spatial accessibility across neighborhoods based on the time it would take to travel to the nearest closed school, in every block group in the 10 study cities. To take into account that traffic, congestion and other factors may play a role, I compute and compare times by car and foot, and compare access based on neighborhood ethnoracial and socioeconomic composition. Findings show that school closures geographically cluster, and neighborhoods with a greater percent of Black residents, whether higher poverty or not, will have longer commute times to the next nearest open school after a school closure. The geographic clustering of closures and the increased commute times to the nearest open school suggest that school deserts may be formed after school closings.

Study on Urban Land Simulation under the Perspective of Local Climate Zoning—A Case Study of Guiyang City

The examination of land-use change simulations across a range of scenarios represents a pivotal research avenue for the advancement of sustainable development analysis. Nevertheless, the extant research merely categorises all building land in a land-use classification into a single category, which is unable to provide a detailed analysis of the dynamic internal spatial form of the city. This paper analyses 17 LCZ land-use types in Guiyang City in 2013 and 2022, and reclassifies them into 7 RLCZ land-use types based on the height of urban building sites. It also proposes three possible scenarios of BAU, WLC, and SPC in 2040 and simulates their land-use changes using the PLUS model. The results demonstrate that (1) the size of low-rise buildings in Guiyang has declined significantly over the past decade; (2) built-up land within cities is significantly affected by drivers such as night-time lighting, topography, elevation, and roads; (3) the SPC scenario emphasises a development pattern of land intensification and a focus on high density in urban built space. It also protects the stability of the ecosystem. The scenario can provide informative suggestions for spatial pattern changes in rapidly developing cities such as Guiyang.

Regional socioeconomic characteristics and density of general practitioners in Germany: A nationwide cross-sectional and longitudinal spatial analysis

ObjectivesLimited healthcare availability impacts population health. Regional disparities in GP density across Germany raise questions about their association with regional socioeconomic characteristics. Study designThis longitudinal nationwide ecological German study used regional data at the county level (n = 401) from 2015 to 2019 provided by the Federal Institute for Research on Building, Urban Affairs and Spatial Development (BBSR). The outcome was general practitioners (GPs) density, defined as the number of GPs per 10,000 inhabitants. MethodsUnivariate Moran's I, cluster analysis (LISA), and spatial lag of X (SLX) models were employed to analyse the spatial distribution of GP density and its correlation with various regional socioeconomic characteristics from a cross-sectional and longitudinal perspective. ResultsIn contrast to the univariate analysis, rural counties showed the highest GP density the multivariate model. Several counties were identified as embedded in low- or high-GP-density clusters. In 2015 and 2019, larger household size (2015: std. β = −2.31, p = 0.021; 2019: std. β = −4.14, p < 0.001) and higher unemployment rate (2015: std. β = −2.84, p = 0.005; 2019: std. β = −5.47, p < 0.001) were associated with lower GP density. In the longitudinal model, a greater increase in the unemployment rate was related to a greater decrease in GP density (std. β = −2.17, p = 0.030). ConclusionA higher regional unemployment rate is linked to lower GP availability in Germany, and a greater increase in the unemployment rate was related to a greater decrease in GP availability over time. This necessitates policy intervention to avoid socioeconomic disparities in GP care.

Environmental Fragility in Commercial Forest Plantations Aiming at Sustainable Management

Objective: The objective of this study is to investigate the environmental fragility of two landscapes in which there is a predominance of commercial forest plantations, with the aim of analyzing this fragility through multicriterial evaluation, focusing on the planning of forest roads. Theoretical Framework: The theory of environmental fragility, multi-criteria evaluation methods, and principles of landscape management stand out, providing a solid basis for understanding the context of the investigation. These frameworks are crucial for analyzing the impact of forest roads on environmental variables such as water dynamics, sediment production, and soil erodibility. The integration of these theories allows for a comprehensive assessment of environmental fragility, essential for effective forest planning and road maintenance in forested areas Method: The methodology adopted involves a multi-criteria evaluation approach to assess environmental fragility in forest landscapes. Key criteria, including slope, distance from water bodies, soil erodibility, and topographic humidity, were integrated to create fragility maps. Data were collected through geographical and environmental analysis, processed with spatial analysis tools, and supplemented by principal component analysis to identify influential criteria. This approach enabled a detailed assessment of forest roads' environmental impact, supporting informed decisions in road planning and landscape management. Results and Discussion: The results revealed notable variations in environmental fragility. The analysis of forest roads highlighted their varying impact on environmental fragility. These results are framed within the context of environmental fragility and multi-criteria evaluation, emphasizing the role of criteria such as slope, proximity to water bodies, soil erodibility, and topographic humidity in assessing landscape degradation. The study underscores the need for careful forest road planning to mitigate environmental impacts and promotes the use of comprehensive evaluation methods in sustainable landscape management. Research Implications: The practical and theoretical implications of this research are discussed, providing insights into how the results can be applied or influence practices in the field of forest management and environmental conservation. These implications could encompass forest road planning, sustainable landscape management, and the development of environmental policies aimed at mitigating the impact of infrastructure on natural ecosystems.The study's findings emphasize the need for incorporating multi-criteria evaluation methods into forest planning processes to identify and mitigate areas of high environmental fragility. This approach can guide decision-makers in prioritizing areas for road maintenance, rehabilitation, or even rerouting to minimize ecological damage. Furthermore, the research underscores the importance of integrating environmental fragility assessments into broader forest management strategies, enhancing the sustainability of commercial forestry operations while preserving essential ecosystem services. Originality/Value: This study contributes to the literature by introducing an innovative application of multi-criteria evaluation in assessing environmental fragility within forest landscapes, specifically in the context of forest road planning. The research combines various environmental criteria into a comprehensive fragility map, offering a nuanced understanding of landscape vulnerability that is less commonly addressed in existing studies. Notably, this methodology focused on forest roads has not been previously explored in the literature, highlighting the unique contribution of this research to the field.The relevance and value of this research are evidenced by its potential to influence forest management practices, particularly in the sustainable planning and maintenance of forest roads.

Electroencephalography Neurofeedback Training with Focus on the State of Attention: An Investigation Using Source Localization and Effective Connectivity

Identifying brain activity and flow direction can help in monitoring the effectiveness of neurofeedback tasks that aim to treat cognitive deficits. The goal of this study was to compare the neuronal electrical activity of the cortex between individuals from two groups—low and high difficulty—based on a spatial analysis of electroencephalography (EEG) acquired through neurofeedback sessions. These sessions require the subjects to maintain their state of attention when executing a task. EEG data were collected during three neurofeedback sessions for each person, including theta and beta frequencies, followed by a comprehensive preprocessing. The inverse solution based on cortical current density was applied to identify brain regions related to the state of attention. Thereafter, effective connectivity between those regions was estimated using the Directed Transfer Function. The average cortical current density of the high-difficulty group demonstrated that the medial prefrontal, dorsolateral prefrontal, and temporal regions are related to the attentional state. In contrast, the low-difficulty group presented higher current density values in the central regions. Furthermore, for both theta and beta frequencies, for the high-difficulty group, flows left and entered several regions, unlike the low-difficulty group, which presented flows leaving a single region. In this study, we identified which brain regions are related to the state of attention in individuals who perform more demanding tasks (high-difficulty group).

Does transportation infrastructure contribute to urban entrepreneurship? Evidence from the high-speed railway in China

High-speed railways have a profound impact on entrepreneurship. Adopted China's city panel data, we use a time-varying DID model to estimate the influence of the high-speed railways on urban entrepreneurship and study its mechanism. The analysis results reveal that high-speed railways have improved the entrepreneurial vitality of cities. The mechanism test suggests that high-speed railway promotes entrepreneurship activities by accelerating human capital flow, strengthening producer services agglomeration, and promoting market integration. A range of robustness tests including a parallel trend test, placebo test, and PSM-DID model are used to ensure the reliability of the empirical results. Heterogeneity analysis and spatial analysis are used to deepen the research. Heterogeneity analysis reveals that high-speed railway construction is likely to motivate entrepreneurship in eastern cities, big cities, and cities with high-level business environments. The spatial analysis results show that the opening of the high-speed railways promotes the entrepreneurial level of surrounding cities. The research results help to evaluate the economic benefits of high-speed railway network expansion, and provide decision-making references for stimulating urban entrepreneurship.

Enhancing Digital Twins with Human Movement Data: A Comparative Study of Lidar-Based Tracking Methods

Digitals twins, used to represent dynamic environments, require accurate tracking of human movement to enhance their real-world application. This paper contributes to the field by systematically evaluating and comparing pre-existing tracking methods to identify strengths, weaknesses and practical applications within digital twin frameworks. The purpose of this study is to assess the efficacy of existing human movement tracking techniques for digital twins in real world environments, with the goal of improving spatial analysis and interaction within these virtual modes. We compare three approaches using indoor-mounted lidar sensors: (1) a frame-by-frame method deep learning model with convolutional neural networks (CNNs), (2) custom algorithms developed using OpenCV, and (3) the off-the-shelf lidar perception software package Percept version 1.6.3. Of these, the deep learning method performed best (F1 = 0.88), followed by Percept (F1 = 0.61), and finally the custom algorithms using OpenCV (F1 = 0.58). Each method had particular strengths and weaknesses, with OpenCV-based approaches that use frame comparison vulnerable to signal instability that is manifested as “flickering” in the dataset. Subsequent analysis of the spatial distribution of error revealed that both the custom algorithms and Percept took longer to acquire an identification, resulting in increased error near doorways. Percept software excelled in scenarios involving stationary individuals. These findings highlight the importance of selecting appropriate tracking methods for specific use. Future work will focus on model optimization, alternative data logging techniques, and innovative approaches to mitigate computational challenges, paving the way for more sophisticated and accessible spatial analysis tools. Integrating complementary sensor types and strategies, such as radar, audio levels, indoor positioning systems (IPSs), and wi-fi data, could further improve detection accuracy and validation while maintaining privacy.

Integrating Metro Stations with the Adjacent Urban Fabric Using TOD Principles: A Case of Agargaon Metro Station, Dhaka

Transit-Oriented Development (TOD) principles offer a promising framework for integrating metro stations with their surrounding urban fabric, promoting sustainable urbanization and efficient transportation systems. Dhaka, one of the fastest-growing cities globally, faces significant challenges in traffic congestion, air pollution, and urban sprawl. Introducing metro systems offers a promising solution to alleviate these issues and enhance urban mobility. This study presents a case study of Agargaon Metro Station in Dhaka City, examining its integration with the adjacent urban fabric using TOD principles. Through a combination of field observations, spatial analysis, and stakeholder interviews, the study evaluates the current state of Agargaon Metro Station. On-site assessments examined the station's physical infrastructure, accessibility, and connectivity with nearby areas, while Geographic Information Systems (GIS) analyzed spatial data, including land use patterns and transportation networks. Semi-structured interviews with urban planners, government officials, and community members provided insights into the challenges and opportunities for implementing TOD at Agargaon. The findings reveal that the station is underutilized as a TOD hub, with inadequate pedestrian infrastructure and mixed-use developments, leading to poor connectivity and accessibility. The study highlights the necessity of improved land use planning, policy support, and community engagement to enhance the station's role in fostering TOD. These recommendations, if implemented, could alleviate traffic congestion, improve air quality, and create more livable urban spaces, thereby enhancing Dhaka's overall quality of life. Additionally, the research contributes to the social and economic dimensions of urbanization by offering a framework that can be adapted to similar metro stations in Dhaka and other rapidly urbanizing cities.

Driving the Evolution of Land Use Patterns: The Impact of Urban Agglomeration Construction Land in the Yangtze River Delta, China

The rapid increase in population and economic activities has greatly influenced land use and spatial development. In urban agglomerations where socioeconomic activities are densely concentrated, the clash between ecological protection and economic growth is becoming more evident. Therefore, a thorough quantitative assessment of spatial changes driven by land use dynamics, alongside an examination of temporal and spatial driving factors, is crucial in offering scientific backing for the long-term and sustainable growth of urban agglomerations. This paper focuses on the major urban agglomerations in China’s Yangtze River Delta region, examining the spatiotemporal evolution of land use and landscape patterns from 2000 to 2020. By employing the standard deviation ellipse technique, coupled with multiple linear regression and the geographical detector model, we conduct a quantitative assessment of the directional trends in urban construction land expansion as well as the diverse impacts of temporal and spatial factors on this expansion across various periods and regions. The findings indicate that over the past 20 years, construction land in the Yangtze River Delta Urban Agglomeration expanded in concentrated patches, showing significant scale effects with relatively intact farmland and forest land being increasingly encroached upon. Landscape-type transitions predominantly occurred in cities around Taihu Lake and Hangzhou Bay, with the most significant transition being farmland converted to construction land, resulting in a greater number of patches and more pronounced land fragmentation. Throughout the 20 years, the standard deviation ellipse of construction land in the Yangtze River Delta Urban Agglomeration expanded and shifted, with the predominant expansion trending from the northwest toward the southeast, and the EN orientation being the most intense expansion area, covering 1641.24 km2. The influence of temporal and spatial driving factors on the expansion of urban construction land differed across various periods and regions. This study thoroughly examines the driving factors that affect the evolution of urban construction land in the region, offering valuable scientific evidence and references for future planning and development of the Yangtze River Delta Urban Agglomeration, aiding in the formulation of more precise and efficient urban management and land use strategies.

Advancing spatial analysis of invasive species movement data to improve monitoring, control programs and decision making: feral cat home range as a case study

Context Many invasive animals are typically active across large areas, making monitoring and control programs expensive. To be efficacious, monitoring devices and control tools need to be strategically located to maximise the probability of encounter. This requires an understanding of how the target species uses the landscape, through identifying key habitat or landscape features that are preferred and used disproportionately more frequently by the species. Spatial analysis of animal movements can help identify high use areas. Aims The variability introduced by different range calculation methods can lead to uncertainty in subsequent habitat analyses. We aimed to determine which method is superior for accurate delineation of core areas for feral cats. Methods We analysed spatial data from 35 collared feral cats across four Australian study sites between 2016 and 2019, and compared the core areas generated using seven commonly used home range estimation methods. Key results We found that the α-hull method provided a higher precision of polygon placement, resulting in lower Type I and II errors and higher conformity to landscape features than other methods. The α-hull used a single default parameter and required no subjective input, making it a more objective, superior method. Conclusions We recommend that the α-hull method be used to define core activity areas for feral cats, enabling more robust habitat analysis, and identification of key habitat and landscape features to strategically target for monitoring and control programs. Implications This strategic approach could significantly improve cost efficiencies, particularly where existing management is widely dispersed, and core activity areas are clumped.

Spatial distribution characteristics and influencing factors of tourism resources based on point of interest data.

The agglomeration and dispersion of tourist attractions in space greatly affect the development of regional tourism resources and the consumption choice of tourism market. At present, the research on the spatial distribution characteristics of tourist attractions and their influencing factors mainly adopts induction and investigation, and there is a lack of effective statistical models for the research on the spatial distribution of tourist attractions and their influencing factors in some historical and cultural ancient cities. This paper uses Internet technology to obtain the spatial distribution data of tourist attractions in Shaoxing city, and uses mean nearest neighbor analysis, nuclear density analysis, imbalance index analysis, standard deviation ellipse and other spatial statistical analysis techniques and geographical detector methods to study the spatial distribution characteristics and influencing factors of tourist attractions in Shaoxing City. This paper studied the distribution characteristics of tourist attractions in Shaoxing city, such as spatial aggregation, distribution equilibrium and spatial orientation, and applied geographical detector to study the influencing factors of the spatial distribution of scenic spots. It was concluded that the spatial distribution pattern of scenic spots was affected by various factors such as natural environment, social environment and economic environment. The explanatory power of two-factor interaction is obviously stronger than that of single factor. The research results provide scientific basis for the planning, layout and development of tourist attractions in Shaoxing and its similar cities, and then promote the high-quality development of tourism in Shaoxing and its similar historical and cultural ancient cities.