Spatial Logistic Regression Model Research Articles

Urban explosion and hotspots of forest loss in western Himalaya: Mapping land use/cover change trends since 1975

The Western Himalayan (WH) region is under constant threat from Land Use and Land Cover changes (LULCC) changes brought on by various socioeconomic activities such as urbanization, agricultural expansion, and population growth. Detailed research into the scope of these changes is necessary to comprehend the challenges they represent. This study aimed at (1) examining and quantifying spatiotemporal patterns of LULCC from 1975 to 2015, (2) mapping the hotspots of forest loss, and (3) modelling future LULCC for the year 2055 model using medium spatial resolution multispectral satellite imagery (MSS, TM, LISS-I, LISS-III, AWiFS), field data, visual interpretation and logistic regression model in the WH. Logistic regression integrated various drivers of LULCC, including climatic, topographic, and socio-economic factors and past LULCC tends to forecast potential LULC in WH region by 2055. The results highlight an outrageous 184% percentage increase in regional urbanization. Expansion of urban and cropland (12%) led to deforestation which reduced the area of natural habitats specifically forests (−11%), water bodies (−8%) and scrubland (−6%). A significant rise in barren land by 30% was noted, while there was a decrease of 20% in glacier/snow melt. The future projection for 2055 anticipates continuing current trends, emphasizing an intensified urban expansion by 63% and an approximate 9% reduction in overall forest coverage. The accuracy of the predicted 2015 LULC map was assessed using ROC/AUC (Receiver Operating Characteristic/Area under Curve) which was found to be 0.79.

Identifying local foci of tuberculosis transmission in Moldova using a spatial multinomial logistic regression model

BackgroundMultidrug resistant tuberculosis (MDR-TB) represents a major public health concern in the Republic of Moldova, with an estimated 31% of new and 56% of previously treated TB cases having MDR disease in 2022. A recent genomic epidemiology study of incident TB occurring in 2018 and 2019 found that 92% of MDR-TB was the result of transmission. The MDR phenotype was concentrated among two M. tuberculosis (Mtb) lineages: L2.2.1 (Beijing) and L4.2.1 (Ural). MethodsWe developed and applied a hierarchical Bayesian multinominal logistic regression model to Mtb genomic, spatial, and epidemiological data collected from all individuals with diagnosed TB in Moldova in 2018 and 2019 to identify locations in which specific Mtb strains are being transmitted. We then used a logistic regression model to estimate locality-level factors associated with local transmission. FindingsWe found differences in the spatial distribution and degree of local concentration of disease due to specific strains of Beijing and Ural lineage Mtb. Foci of transmission for four strains of Beijing lineage Mtb, predominantly of the MDR-TB phenotype, were located in several regions, but largely concentrated in Transnistria. In contrast, transmission of Ural lineage Mtb had less marked patterns of spatial aggregation, with a single strain (also of the MDR phenotype) spatially clustered in southern Transnistria. We found a 30% (95% credible interval 2%-80%) increase in odds of a locality being a transmission cluster for each increase of 100 persons per square kilometer, while higher local tuberculosis incidence and poverty were not associated with a locality being a transmission focus. InterpretationOur results identified localities where specific Mtb transmission networks were concentrated and quantified the association between locality-level factors and focal transmission. This analysis revealed Transnistria as the primary area where specific Mtb strains (predominantly of the MDR-TB phenotype) were locally transmitted and suggests that targeted intensified case finding in this region may be an attractive policy option.

Sampling design methods for making improved lake management decisions

AbstractThe ecological status of lakes is important for understanding an ecosystem's biodiversity as well as for service water quality and policies related to land use and agricultural run‐off. If the status is weak, then decisions about management alternatives need to be made. We assess the value of information of lake monitoring in Finland, where lakes are abundant. With reasonable ecological values and restoration costs, the value of information analysis can be compared with the survey's costs. Data are worth gathering if the expected value from the data exceeds the costs. From existing data, we specify a hierarchical Bayesian spatial logistic regression model for the ecological status of lakes. We then rely on functional approximations and Laplace approximations to get closed‐form expressions for the value of information of a sampling design. The case study contains thousands of lakes. The combinatorially difficult design problem is to wisely pick the right subset of lakes for data gathering. To solve this optimization problem, we study the performance of various heuristics: greedy forward algorithms, exchange algorithms and Bayesian optimization approaches. The value of information increases quickly when adding lakes to a small design but then flattens out. Good designs are usually composed of lakes that are difficult to manage, while also balancing a variety of covariates and geographic coverage. The designs achieved by forward selection are reasonably good, but we can outperform them with the more nuanced search algorithms. Statistical designs clearly outperform other designs selected according to simpler criteria.

Dynamics of Land Use/Land Cover Considering Ecosystem Services for a Dense-Population Watershed Based on a Hybrid Dual-Subject Agent and Cellular Automaton Modeling Approach

Land use/land cover represents the interactive and comprehensive influences between human activities and natural conditions, leading to potential conflicts among natural and human-related issues as well as among stakeholders. This study introduced economic standards for farmers. A hybrid approach (CA-ABM) of cellular automaton (CA) and an agent-based model (ABM) was developed to effectively deal with social and land use synergic issues to examine human–environment interactions and projections of land use conversions for a humid basin in south China. Natural attributes and socioeconomic data were used to analyze land use/land cover and its drivers of change. The major modules of the CA-ABM are initialization, migration, assets, land suitability, and land-use change decisions. Empirical estimates of the factors influencing the urban land use conversion probability were captured using parameters based on a spatial logistic regression (SLR) model. Simultaneously, multicriteria evaluation (MCE) and Markov models were introduced to obtain empirical estimates of the factors affecting the probability of ecological land conversion. An agent-based CA-SLR-MCE-Markov (ABCSMM) land-use conversion model was proposed to explore the impacts of policies on land-use conversion. This model can reproduce observed land-use patterns and provide links for forest transition and urban expansion to land-use decisions and ecosystem services. The results demonstrated land-use simulations under multi-policy scenarios, revealing the usefulness of the model for normative research on land-use management.

Prevalence and incidence of transfusion-transmissible infections among blood donors in Malawi: A population-level study.

Voluntary non-remunerated blood donors (VNRBDs) are essential to sustain national blood supplies. Expanding testing capacity for the major transfusion-transmitted infections (TTI) is crucial to ensure safe blood products. Understanding trends in TTIs can inform prioritisation of resources. We conducted a retrospective cohort data analysis of routine blood donation data collected from VNRBDs by the Malawi Blood Transfusion Service from January 2015 to October 2021. Variables included age, occupation; and screening results of TTIs (HIV, Hepatitis B and C, and syphilis). We estimated both prevalence and incidence per person-year for each TTI using longitudinal and spatial logistic regression models. Of the 213 626 donors, 204 920 (95.8%) donors were included in the final analysis. Most donors (77.4%) were males, baseline median age was 19.9 (IQR 18.0, 24.1), 70.9% were students, and over 80.0% were single at first donation. Overall TTI prevalence among donors was 10.7%, with HBV having the highest prevalence (3.4%), followed by syphilis (3.3%), then HIV (2.4%) and HCV (2.4%). Incidence per 1000 person-years for syphilis was 20.1 (19.0, 21.3), HCV was 18.4 (17.3, 19.5), HBV was 13.7 (12.8, 14.7), and HIV was 11.4 (10.6, 12.3). We noted geographical variations with the northern region having lower rates of both prevalence and incidence compared to central and southern regions. The individual TTI prevalence and incidence rates from this study are consistent with Southern African regional estimates. By identifying geographical variations of TTI prevalence and incidence, these findings could potentially inform prioritisation of blood collection efforts to optimise blood collection processes.

Geospatial variations and determinants of contraceptive utilization among married reproductive age women in Ethiopia: spatial and multilevel analysis of Ethiopian Demographic and Health Survey, 2019.

Contraception is the most effective method of preventing unwanted pregnancies and their associated disadvantages. It is critical to recognize one's desire to utilize contraceptives before drafting and implementing a good family planning program, especially in developing nations like Ethiopia. This study aimed to identify the geospatial variations and determinants affecting the utilization of contraceptives among married reproductive age women in Ethiopia. This study was based on an extensive national survey, the Ethiopian Demographic and Health Survey. A total weighted sample of 5,743 married reproductive-age women was included. Because of the hierarchical nature of the DHS data, a spatial analysis multilevel logistic regression model was used to study individual and community-level factors that may influence contraceptives. The Bernoulli model was used by applying Kulldorff methods using the SaTScan software to analyze the purely spatial clusters of contraceptive usage. ArcGIS version 10.3 was used to visualize the distribution of contraceptives. A 95% confidence interval and a p-value of less than 0.05 were used to declare statistical significance. The overall utilization of contraceptives was discovered at 41.25% (39.98, 42.53). Participants age range of 25-34 years [AOR = 0.80, CI: (0.66, 0.96,)] and 35-49 years [AOR = 0.50, CI 95%:(0.66, 0.96)] times less likely to use contraceptives than 15-24 years old respectively. Having primary [AOR = 1.47, CI 95%: (1.25, 1.73)], secondary [AOR = 1.42, CI 95%: (1.09, 1.83)] and higher education level [AOR = 1.92, CI 95%: (1.41, 2.60)], middle wealth [AOR = 1.48, CI 95%: (1.14, 1.90)], richer [AOR = 1.41, CI 95%: (1.07, 1.86)] and richest [AOR = 2.17, CI 95%: (1.52, 3.11)], having 1-4 ANC follow up have [AOR = 1.60, CI 95%: (1.26, 2.03)], gave birth at age of 35-44 [AOR = 0.29, CI 95%: (0.22, 0.37)], having 3-5 children [AOR = 1.26, CI 95%: (1.03, 1.52)], being from community of high level women education [AOR = 1.61, CI 95%: (1.21, 2.15)] were associated positively. Participants from Amhara, Oromia, Benishangul and SNNPR regions have revealed [AOR = 2.40, CI 95%: (1.53, 3.77)], [AOR = 1.64, CI 95%: (1.05, 2.56)], [AOR = 1.62, CI 95%: (1.01, 2.62)] and [AOR = 2.04, CI 95: (1.31, 3.19)], in contrast, Somali and Afar regions have shown [AOR = 011, CI 95%: (0.05, 0.22)] and [AOR = 0.31, CI 95%: (0.18, 0.54)] times less likely to use contraceptive services than Tigray Region respectively. The spatial analysis of contraceptive usage discovered that the northern, central and southern parts of the country had higher utilization of contraceptives than the eastern and northeastern of the country. The study revealed that contraceptive usage among married women is comparatively low, with wide regional variation. Raising awareness among mothers about the importance of antenatal care and assisting mothers who are financially disadvantaged or do not have access to health facilities will aid in providing better family planning services. Improving contraceptive information dissemination at community and regional levels is key to averting potential barriers.

Federal Nutrition Assistance for Young Children: Underutilized and Unequally Accessed

The federal Child and Adult Care Food Program (CACFP) improves nutrition and reduces food insecurity among young children by helping cover the food costs for child care providers and families. This nationwide study evaluated the extent and predictors of the CACFP's utilization among licensed child care centers to identify opportunities for expanding CACFP nutrition support. Administrative data from the CACFP and child care licensing agencies in 47 states and District of Columbia were compiled and geocoded for 93,227 licensed child care centers. CACFP participation was predicted using a multivariable Bayesian spatial logistic regression model in the sample of low-income areas to target CACFP eligible child care centers. Data were collected in 2020-2021 and analyzed in 2022. Of all licensed child care centers, 36.5% participated in the CACFP, ranging from 15.2% to 65.3% across states; when restricted to low-income areas, 57.5% participated (range, 15.7%-85.7%). Income differences did not explain the large variation in CACFP participation rates across states. Having at least three CACFP sponsoring agencies per state predicted a 38% higher probability of CACFP participation (OR=1.38; 95% Credible Interval=1.08-1.78). Currently CACFP participation rates among licensed child care centers point to program underutilization and unequal access, particularly in some states and regions. Work at the federal and state levels is warranted to expand participation in the program, above all in low-income areas, so that more young children could eat healthfully with the CACFP.

Unveiling the link between rainfall, temperature, and childhood undernutrition in Bangladesh using spatial analysis

Child undernutrition is still a major public health concern in Bangladesh in spite of significant decline in the last few years. Climate change may impact the prevalence of undernutrition and its geographical variability through food security and recurring outbreaks of disease, as well as impede efforts to reduce the undernutrition burden. This study aims to evaluate rainfall and temperature associated with childhood malnutrition in Bangladesh. A spatial mixed effect logistic regression model was used to estimate the association between undernutrition (measured by the composite index of anthropometric failure) and residential area-level rainfall and temperatures, controlling for covariates and spatial effects of residential locations using national data from the 2017 to 2018 Bangladesh Demographic and Health Survey. Our findings indicate that an increase in rainfall was associated with increased odds of undernutrition [adjusted odds ratio (AOR) 1.15, 95% confidence interval (CI) 1.07–1.24], whereas an increase in temperature was associated with decreased odds of undernutrition (AOR 0.86, 95% CI 0.80–0.93). We also found statistically significant structured and unstructured spatial variations in undernutrition, indicating locational effects. Investing in infrastructure and education could be potential strategies for mitigating the negative effects of climate change. High-risk regions in terms of climatic change and malnutrition could be given priority for intervention implementation.

Compliance of biosecurity practices for compartmentalization to foot-mouth disease and classical swine fever viruses in commercial swine companies from southern Brazil.

Classical swine fever (CSF) and foot-mouth disease (FMD) are both highly contagious disease and disruptive to commercial trades, but they are examples of foreign animal diseases that biosecurity-based compartmentalization could be used to support trade in free zones in response to an outbreak. This study aimed to evaluate biosecurity compliance to the Federal Normative Instruction #44 from December 4th, 2017 (BRAZIL, 2017) in commercial swine farms located in southern Brazil. A total of 604 swine farms from 10 commercial swine companies were sampled, from which 28.5% were breeding farms, 29.1% nursery, 32.8% finishing, 6.8% multipliers, and 2.8% farrow-to-finish. Cluster analyses revealed that farms with high compliance (n = 303, Cluster 1) performed 71% of the practices, moderate (n = 219, Cluster 2) 47%, and the low (n = 82, Cluster 3) 33%. A spatial logistic regression model estimated that biosecurity compliance was highest in only one of 10 commercial swine companies, and within a company, multipliers (when present) obtained the highest biosecurity compliance (p-value < 0.01). These results suggest that major improvements in biosecurity practices are needed in breeding herds, nursery, and grow-finish farms to be compliant to the Federal Instruction #44. Based on the combination of these analyses, only one commercial swine company was more suitable to establish compartments for CSF and FMD with minimal investments. Still, this study revealed that the majority of commercial swine companies needs to improve biosecurity practice protocols to then target compartmentalization.

Modeling of occupant behavior considering spatial variation: Geostatistical analysis and application based on American time use survey data

Numerous occupant behavior (OB) models that simulate occupancy, activity and action at home have been developed to improve the accuracy and quality of energy demand estimations. Previous studies have revealed that the consideration of inter-occupant diversity improves the performance of OB models. However, existing models ignore spatial variation in OBs or partially consider it using a simple method without evaluating whether it is sufficient. Moreover, the modeling method to reproduce the spatial variation is missing. This study aims to develop a modeling method that can effectively reproduce spatial variation in OBs using American time use data. The global Moran’s index test confirmed that spatial variations exist in OBs; however, they differ by time of day and activities for studied population. Subsequently, two spatial variation representations were generated using the ordinary kriging and spatial autoregressive methods. Finally, three spatial logistic regression models that consider spatial variations were developed and evaluated. The developed models generated smaller errors and higher inter-occupant diversity than the conventional logistic regression models at the state level. The established method is applicable to any country and region. Using higher spatial resolution and richer time use datasets may further improve OB models to model region-specific characteristics of building energy demand.

Habitat selection by free-roaming domestic dogs in rabies endemic countries in rural and urban settings

Domestic dogs can affect human health through bites and pathogen transmission, particularly in resource-poor countries where dogs, including owned ones, predominantly roam freely. Habitat and resource selection analysis methods are commonplace in wildlife studies but have not been used to investigate the environmental resource use of free-roaming domestic dogs (FRDD). The present study implements GPS devices to investigate habitat selection by FRDD from an urban site and a rural site in Indonesia, and one urban and two rural sites in Guatemala (N = 321 dogs). Spatial mixed effects logistic regression models, accounting for heterogeneous distribution of the resources, showed that patterns of habitat selection by FRDD were similar across study sites. The most preferred resources were anthropogenic, being buildings and roads, which implies selection for human proximity. Vegetation and open fields were less preferred and steep terrain was avoided, indicating that FRDD were synanthropic and that their space patterns likely optimised energy use. Results presented here provide novel data on FRDD habitat selection patterns, while improving our understanding of dog roaming behaviour. These findings provide insights into possible high-risk locations for pathogen transmission for diseases such as rabies, and can assist management authorities in the planning and deployment of efficient disease control campaigns, including oral vaccination.

Adaptive spatial designs minimizing the integrated Bernoulli variance in spatial logistic regression models - with an application to benthic habitat mapping

Constructing efficient spatial sampling designs is important for monitoring, understanding and characterizing environmental processes acting over large-scale spatial domain. Multiple types of devices can accomplish informative sampling in various ways, and time and computational limitations should be considered too. In-situ sampling is often accurate, but it tends to be very sparse in the vast spatial areas considered for mapping purposes, and careful constructions of experimental sampling designs are needed. A criterion for mapping spatial presence-absence variables is presented. The expected integrated Bernoulli variance criterion is used in order to explore regions with more uncertainty about the binary outcomes. This approach uses a hierarchical Bayesian logistic regression model for the binary variable and develops approximate closed form expressions for the expected integrated Bernoulli variance. The approximations are fast to compute for many designs. The expressions are extended to find adaptive designs in the setting of sequential spatial exploration, where there are limited computation resources and operational time restrictions. In simulation studies the approximations are compared with tedious Monte Carlo sampling methods. Approximations are shown to be accurate and to work for a large spatial grid. An application in benthic habitat mapping is presented. It considers a coral dataset from Australia to demonstrate the suggested sampling design approach.

A Spatial Logistic Regression Model Based on a Valid Skew-Gaussian Latent Field

A Spatial Logistic Regression Model Based on a Valid Skew-Gaussian Latent Field

Variable Selection for Spatial Logistic Autoregressive Models

When the spatial response variables are discrete, the spatial logistic autoregressive model adds an additional network structure to the ordinary logistic regression model to improve the classification accuracy. With the emergence of high-dimensional data in various fields, sparse spatial logistic regression models have attracted a great deal of interest from researchers. For the high-dimensional spatial logistic autoregressive model, in this paper, we propose a variable selection method with for the spatial logistic model. To identify important variables and make predictions, one efficient algorithm is employed to solve the penalized likelihood function. Simulations and a real example show that our methods perform well in a limited sample.

Bayesian spatial analysis of factors influencing neonatal mortality and its geographic variation in Ethiopia.

BackgroundEthiopia is a Sub-Saharan country with very high neonatal mortality rates, varying across its regions. The rate of neonatal mortality reduction in Ethiopia is slow, and Ethiopia may not meet the third United Nations sustainable development target by 2030. This study aimed to investigate the spatial variations and contributing factors for neonatal mortality rates in Ethiopia.MethodsWe analysed data from the 2016 Ethiopian Demographic and Health Survey (EDHS), which used a two-stage cluster sampling technique with a census enumeration area as primary and households as secondary sampling units. A Bayesian spatial logistic regression model using the Stochastic Partial Differential Equation (SPDE) method was fitted accounting for socio-economic, health service-related and geographic factors.ResultsHigher neonatal mortality rates were observed in eastern, northeastern and southeastern Ethiopia, and the Somali region had higher risks of neonatal mortality. Neonates from frequently drought-affected areas had a higher mortality risk than less drought-affected areas. Application of traditional substances on the cord increased the risk of neonatal mortality (Adjusted Odds Ratio (AOR) = 2.07, 95% Credible Interval (CrI): 1.12 to 4.30) and getting health facility delivery services had a lower odds of neonatal mortality (AOR = 0.60, 95% CrI: 0.37, 0.98).ConclusionsResiding in drought-affected areas, applying traditional substances on the umbilical cord and not delivering at health facilities were associated with a higher risk of neonatal mortality. Policy-makers and resource administrators at different administrative levels could leverage the findings to prioritise and target areas identified with higher neonatal mortality rates.

The importance of fine-scale predictors of wild boar habitat use in an isolated population.

Predicting the likelihood of wildlife presence at potential wildlife–livestock interfaces is challenging. These interfaces are usually relatively small geographical areas where landscapes show large variation over small distances. Models of wildlife distribution based on coarse data over wide geographical ranges may not be representative of these interfaces. High‐resolution data can help identify fine‐scale predictors of wildlife habitat use at a local scale and provide more accurate predictions of species habitat use. These data may be used to inform knowledge of interface risks, such as disease transmission between wildlife and livestock, or human–wildlife conflict.This study uses fine‐scale habitat use data from wild boar (Sus scrofa) based on activity signs and direct field observations in and around the Forest of Dean in Gloucestershire, England. Spatial logistic regression models fitted using a variant of penalized quasi‐likelihood were used to identify habitat‐based and anthropogenic predictors of wild boar signs.Our models showed that within the Forest of Dean, wild boar signs were more likely to be seen in spring, in forest‐type habitats, closer to the center of the forest and near litter bins. In the area surrounding the Forest of Dean, wild boar signs were more likely to be seen in forest‐type habitats and near recreational parks and less likely to be seen near livestock.This approach shows that wild boar habitat use can be predicted using fine‐scale data over comparatively small areas and in human‐dominated landscapes, while taking account of the spatial correlation from other nearby fine‐scale data‐points. The methods we use could be applied to map habitat use of other wildlife species in similar landscapes, or of movement‐restricted, isolated, or fragmented wildlife populations.

Autocorrelation testing on residual spatial logistic regression model with Euclidean distance matrix approach

The existence of spatial effects should not be ignored because it will reduce the goodness of the model. One type of regression analysis that is quite widely used is logistic regression analysis. Spatial logistic regression modelling incorporates spatial effects into the logistic regression model with the expectation that the residuals generated from the model are independent or there is no autocorrelation. The purpose of this study was to obtain the results of spatial autocorrelation testing using a spatial logistic regression model with a Euclidean matrix approach. The results of the study were applied to natural disaster mitigation data in Kupang Regency, Nusa Tenggara Timur Province in 2020, where the distribution of areas in Kupang Regency by village/urban village has spatial autocorrelation. Spatial autocorrelation testing was carried out with Moran's I test to determine the presence of spatial autocorrelation. In this study, a standardized Euclidean distance matrix approach was used to accommodate this spatial effect. The results of the autocorrelation test of the binary spatial logistic model with the Euclidean distance matrix approach were able to accommodate the spatial effect.

Machine Learning Algorithms for Urban Land Use Planning: A Review

Urbanization is persistent globally and has increasingly significant spatial and environmental consequences. It is especially challenging in developing countries due to the increasing pressure on the limited resources, and damage to the bio-physical environment. Traditional analytical methods of studying the urban land use dynamics associated with urbanization are static and tend to rely on top-down approaches, such as linear and mathematical modeling. These traditional approaches do not capture the nonlinear properties of land use change. New technologies, such as artificial intelligence (AI) and machine learning (ML) have made it possible to model and predict the nonlinear aspects of urban land dynamics. AI and ML are programmed to recognize patterns and carry out predictions, decision making and perform operations with speed and accuracy. Classification, analysis and modeling using earth observation-based data forms the basis for the geospatial support for land use planning. In the process of achieving higher accuracies in the classification of spatial data, ML algorithms are being developed and being improved to enhance the decision-making process. The purpose of the research is to bring out the various ML algorithms and statistical models that have been applied to study aspects of land use planning using earth observation-based data (EO). It intends to review their performance, functional requirements, interoperability requirements and for which research problems can they be applied best. The literature review revealed that random forest (RF), deep learning like convolutional neural network (CNN) and support vector machine (SVM) algorithms are best suited for classification and pattern analysis of earth observation-based data. GANs (generative adversarial networks) have been used to simulate urban patterns. Algorithms like cellular automata, spatial logistic regression and agent-based modeling have been used for studying urban growth, land use change and settlement pattern analysis. Most of the papers reviewed applied ML algorithms for classification of EO data and to study urban growth and land use change. It is observed that hybrid approaches have better performance in terms of accuracies, efficiency and computational cost.

RETRACTED ARTICLE: Prediction of mountain landslide susceptibility and influencing factors of green landscape design on both sides of roads

By collecting basic data about the geological environment of the study area, this article introduces the geological environment of the study area in mountainous areas. Use data model, logistic regression model, and spatial logistic regression model to estimate the landslide susceptibility of the area and divide the research area. Based on the GIS platform and the regression model data model, seven evaluation factors were selected-altitude, rock characteristics, distance to the river, distance to the road, distance to the fault zone, vegetation and rainfall, etc. Using spatial logistic regression model and information model to carry out landslide risk prediction and regional assessment in this area, it shows that the development of mountain landslide disasters in the study area has considerable spatial effects. The areas with extremely high and high susceptibility to mountain landslides are mainly distributed in the study area. The southwest region is mainly distributed along rivers, both sides of roads, and near fault zones; most of the low- and medium-risk areas are scattered in higher altitude areas, with large slopes, low human activities, and high vegetation coverage. At the same time, the issue of green landscape setting on both sides of the highway is also mentioned. The green landscape system is composed of the highway itself and the attached green spaces on both sides. At present, many studies on the landscape system of the two roads are focused on improving the engineering technology of the main body of the highway and improving the landscape effect. The attached green space on both sides of the road is taken as the main focus of the research. The green space on both sides of the road is an important part of the ecological zone and plays an irreplaceable ecological role. Therefore, this paper conducts a more in-depth study on the use of disaster-prone prediction technology to study landslides and the current green landscape design on both sides of the road.

Visiting nearby natural settings supported wellbeing during Sweden’s “soft-touch” pandemic restrictions

The coronavirus pandemic entailed varying restrictions on access, movement and social behavior in populations around the world. Knowledge about how people coped with “soft-touch” restrictions can inform urban spatial planning strategies that enhance resilience against future pandemics. We analyzed data from an online place-based survey on 2845 places across Sweden that respondents abstained from visiting, visited with similar frequency, or visited more frequently in spring 2020 as compared to before the pandemic. In spatial logistic regression models, we relate geographical and sociodemographic properties of places (fields, forests, water, residential population density and daytime population density) to self-perceived changes in wellbeing from visiting the given place less or more often, respectively. Abstaining from visiting places with natural features located in areas of high residential density was associated with a self-perceived negative influence on wellbeing. Yet, fields, forests and water were strongly associated with places people claimed wellbeing benefits from during pandemic restrictions. The further a visited place was from the respondent’s home, the more likely it was to have a positive wellbeing influence. As an illustrative case, we map our models onto the landscape of Stockholm, showing that some neighborhoods are likely more resilient than others when coping with pandemic restrictions. Both the most and least resilient neighborhoods span the socio-economic spectrum. Urban planning will do well to enable equitable, easy access to natural settings by foot or bike, to increase pandemic preparedness as well as support climate change mitigation and biodiversity protection.