## What is/are Spatial Risk?

Spatial Risk - (2) The spatial risk distribution showed a descending gradient from west Dongting area to the east and an overall pattern of high in the periphery of large lakes and low in other areas.^{[1]}Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors.

^{[2]}Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales.

^{[3]}The scan statistic analysis resulted in 14 spatial risk clusters for COVID-19 among persons deprived of liberty; the highest-risk cluster was in the Federal District.

^{[4]}Subnational variation is of particular interest, with geographically-indexed data being used to understand the spatial risk of detrimental outcomes and to identify who is at greatest risk.

^{[5]}The introduced framework is illustrated using a cross-sectional study linked with a national cohort dataset in Switzerland, we examine differences in underlying spatial risk patterns between respiratory disease and lung cancer.

^{[6]}The aim of this study was to assess the influence of different individual, environmental and spatial risk factors on the dog exposure to L.

^{[7]}However, integrated spatial risk assessments, for the purpose of mapping cyclone risk at subnational geographic scales, have not yet been developed in this area.

^{[8]}The model output was used to generate spatial risk predictions for the study area to aid in risk assessment, environmental investigations, and targeted public health interventions.

^{[9]}High spatial risk clusters have been identified by the scan statistics technique.

^{[10]}Several spatial risk factors have possible influence on its dispersal trajectory.

^{[11]}Subsequently, three different types of weaving lengths with 350 m, 450 m, and 550 m were set to conduct the sensitivity analysis based on four performance indexes of mean acceleration and deceleration, acceleration range, deceleration range, and speed standard deviation as the representative variables of spatial risk distribution.

^{[12]}This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy.

^{[13]}Finally, we investigate the potential advantages of this approach to assess the spatial risk of collision across a large region.

^{[14]}used space–time scan statistics and the maximum entropy model for spatial risk analysis of the global distribution of Bd.

^{[15]}In this study, we proposed a multiple hydro-meteorological spatial risk mapping in Surabaya city using a two-phase Fuzzy system.

^{[16]}Although this work is only a general approach to spatial risk modeling at a regional scale, it provides interesting information.

^{[17]}The case study illustrates the effects of spatial risk consideration, elderly priority, and coverage distance on the optimal solution of shelter locations.

^{[18]}Understanding is still developing about spatial risk factors for COVID-19 infection or mortality.

^{[19]}Generalised linear and non-linear models, specifically logistic regression, classification tree and random forest, were used to indicate the most relevant risk factors, to quantify their association with HPAI outbreak occurrence, and to develop a map depicting spatial risk distribution.

^{[20]}In conclusion, our results provide crucial clues for understanding geographic variance of plant invasion in China’s nature reserves and spatial risk assessment.

^{[21]}In this exploratory study, we identify the spatial risk factors associated with gang membership and gang crime in New Zealand using social disorganization as a theoretical framework.

^{[22]}We classified the data into three temporal phases (HEC1: 2001–2006, HEC2: 2007–2012, and HEC3: 2013–2018), in order to (1) derive spatial patterns of HEC; (2) identify the hotspots of HEC and its different types along with the number of people living in the high-risk zones; and (3) assess the temporal change in the spatial risk of HEC.

^{[23]}In this study we introduce the concept of a universal geo-spatial risk measure, denoted as the Universal Influenza-like Transmission (UnIT) score, to quantify the risk phenotype of US counties facilitating flu-like transmission mechanisms.

^{[24]}Conclusions To our knowledge, this study is the first to objectively identify spatial risks for falls in hospitals within in a large multihospital system.

^{[25]}This study proposes an integrated approach to assess risk of sediment hazard on the road network by borrowing concepts from (a) transport vulnerability studies, (b) disaster risk assessment, and (c) spatial risk analysis and applying it to an identified vulnerable road network in Kure, Japan.

^{[26]}Belém, the capital of Pará, presented the highest spatial risk for HIV/AIDS and was the only city to present spatiotemporal risk from, 2014 to 2018.

^{[27]}4% in Mirap) and spatial risk heterogeneity was less apparent compared to P.

^{[28]}In this study, US Army Toposheet of 1955, multi-temporal Landsat data from 1972 to 2017, ALOS-PALSAR DEM, high-resolution Google Earth images, and cadastral maps were incorporated in remote sensing and GIS environment to assess the drainage density, morphological characteristics of creeks, spatial risk of flood inundation, and proximity analysis of households along the drainage channels.

^{[29]}We investigate both requirements by the implementation of disease outbreak modeling and exploring its induced dynamic spatial risk in form of risk assessment, along with its real-time integration back into the disease model.

^{[30]}Analysis of geographic interconnections of public facilities yielded locations by different classes of potential spatial risk.

^{[31]}Methods: the spatial risk of contamination for the Napo River basin was based on the calculation of a friction surface and the accessibility of possible oil contamination.

^{[32]}This quantification of spatial risk reduction can help to prioritize joint actions in flood management and environmental conservation, opening new opportunities to support reef management with hazard mitigation funding.

^{[33]}We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases.

^{[34]}As a result, the effect of preventive measures on the risk perception of visiting hair salons is that, first, spatial risk perception factors affect the definition of air quarantine, personal quarantine, and personnel control factors affect the higher the risk factor.

^{[35]}Spatial risk modelling is a useful tool to gain the understanding of wildlife damage and mitigate conflicts.

^{[36]}Therefore, spatial risk indices have been developed to evaluate the intrinsic risk from pesticide pollution.

^{[37]}We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer.

^{[38]}The results indicated differences in the level of collapse risk depending on the spatial scale, whereby the national scale hid the most important risk dynamics of ecoregions and biotic scale overestimated the spatial risk.

^{[39]}In this study we introduce the concept of a universal geospatial risk phenotype of individual US counties facilitating flu-like transmission mechanisms.

^{[40]}We employed a Bayesian geostatistical model to investigate both measured and unmeasured spatial risk factors for child stunting, comparing the performance of non-spatial (adjusting for selected covariates without spatial correlation), spatial (including spatial correlation), and null spatial (without the selected covariates) models.

^{[41]}A risk map was also built by mapping the unexplained spatial risk (residual) using R.

^{[42]}Exact AED locations can be determined using optimisation methods, but they do not incorporate known spatial risk factors for OHCA, such as income and demographics.

^{[43]}MethodsWe conducted a systematic review and meta-analysis on spatial risk factors for tick-borne disease and tick bites in eastern North America.

^{[44]}Test results for blood samples submitted by veterinary clinics for the years 2007-2016 were used to conduct a spatial risk analysis of heartworm among domestic dogs in Ontario.

^{[45]}The multivariate real time platform regressed the spatial risk of human exposure to Ae.

^{[46]}Boosted regression tree (BRT) models were used to quantify the association between spatial risk factors and HPAI H5N8 infection in poultry holdings and to generate predictive maps for HPAI infection.

^{[47]}This is particularly true for environmental/geospatial risk factors, which might contribute to these missing incidents.

^{[48]}ABSTRACT This study seeks to identify the spatial risk pattern of households (HHs) exposed to arsenic contamination in Bangladesh by adjusting potential socio-economic, demographic factors.

^{[49]}aegypti was modeled under an ecological niche approach using the maximum entropy technique with the aim of determining the spatial risk distribution of dengue.

^{[50]}

## Unmeasured Spatial Risk

We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer.^{[1]}We employed a Bayesian geostatistical model to investigate both measured and unmeasured spatial risk factors for child stunting, comparing the performance of non-spatial (adjusting for selected covariates without spatial correlation), spatial (including spatial correlation), and null spatial (without the selected covariates) models.

^{[2]}

## spatial risk factor

The aim of this study was to assess the influence of different individual, environmental and spatial risk factors on the dog exposure to L.^{[1]}Several spatial risk factors have possible influence on its dispersal trajectory.

^{[2]}Understanding is still developing about spatial risk factors for COVID-19 infection or mortality.

^{[3]}In this exploratory study, we identify the spatial risk factors associated with gang membership and gang crime in New Zealand using social disorganization as a theoretical framework.

^{[4]}We used multiple data streams to examine spatial risk factors associated with this outbreak, combining maps collected with an unmanned aerial vehicle (UAV), an entomological survey, a community census, and active surveillance of febrile cases.

^{[5]}We employed a Bayesian geospatial model to investigate both measured and unmeasured spatial risk factors for prostate cancer.

^{[6]}We employed a Bayesian geostatistical model to investigate both measured and unmeasured spatial risk factors for child stunting, comparing the performance of non-spatial (adjusting for selected covariates without spatial correlation), spatial (including spatial correlation), and null spatial (without the selected covariates) models.

^{[7]}Exact AED locations can be determined using optimisation methods, but they do not incorporate known spatial risk factors for OHCA, such as income and demographics.

^{[8]}MethodsWe conducted a systematic review and meta-analysis on spatial risk factors for tick-borne disease and tick bites in eastern North America.

^{[9]}Boosted regression tree (BRT) models were used to quantify the association between spatial risk factors and HPAI H5N8 infection in poultry holdings and to generate predictive maps for HPAI infection.

^{[10]}This is particularly true for environmental/geospatial risk factors, which might contribute to these missing incidents.

^{[11]}ResultsSpatial risk factors of NiV transmission in pigs were identified by experts as being of three types, including i) natural host factors (bat preferred areas and distance to the nearest bat colony), ii) intermediate host factors (pig population density), and iii) environmental factors (distance to the nearest forest, distance to the nearest orchard, distance to the nearest water body, and human population density).

^{[12]}In this increasingly complex social business environment, college maker space has attracted the attention and research of many experts and scholars, but at the same time, there is still a significant gap in the study on the spatial risk factors of college maker space.

^{[13]}Diffusion of cholera and other diarrheal diseases in an informal settlement is a product of multiple behavioral, environmental and spatial risk factors.

^{[14]}However, our results also suggest the presence of additional, country-specific, spatial risk factors which modulate the variation in TT risk.

^{[15]}

## spatial risk assessment

However, integrated spatial risk assessments, for the purpose of mapping cyclone risk at subnational geographic scales, have not yet been developed in this area.^{[1]}In conclusion, our results provide crucial clues for understanding geographic variance of plant invasion in China’s nature reserves and spatial risk assessment.

^{[2]}In this paper the datasets utilised in the SPARE ('Spatial risk assessment framework for assessing exotic disease incursion and spread through Europe') project are described and discussed in terms of key criteria: accessibility, availability, completeness, consistency and quality.

^{[3]}The developed spatial risk assessment of HPAI occurrence provides a valuable source of information for risk managers and can contribute to early detection of potential outbreaks of HPAI in poultry.

^{[4]}Satellite derived urban heat island data and community profiles were integrated by a spatial risk assessment methodology to highlight potential heat health risk areas and build the foundations for mitigation and adaptation plans.

^{[5]}Drought hotspot identification requires continuous drought monitoring and spatial risk assessment.

^{[6]}

## spatial risk analysi

Remotely sensed data products can be combined with existing invasive species occurrence data via machine learning models to provide the proactive spatial risk analysis necessary for implementing coordinated and agile management paradigms across large scales.^{[1]}used space–time scan statistics and the maximum entropy model for spatial risk analysis of the global distribution of Bd.

^{[2]}This study proposes an integrated approach to assess risk of sediment hazard on the road network by borrowing concepts from (a) transport vulnerability studies, (b) disaster risk assessment, and (c) spatial risk analysis and applying it to an identified vulnerable road network in Kure, Japan.

^{[3]}Test results for blood samples submitted by veterinary clinics for the years 2007-2016 were used to conduct a spatial risk analysis of heartworm among domestic dogs in Ontario.

^{[4]}This article proposes an online spatial risk analysis capable of providing an indication of the evolving risk of power systems regions subject to extreme events.

^{[5]}

## spatial risk distribution

(2) The spatial risk distribution showed a descending gradient from west Dongting area to the east and an overall pattern of high in the periphery of large lakes and low in other areas.^{[1]}Subsequently, three different types of weaving lengths with 350 m, 450 m, and 550 m were set to conduct the sensitivity analysis based on four performance indexes of mean acceleration and deceleration, acceleration range, deceleration range, and speed standard deviation as the representative variables of spatial risk distribution.

^{[2]}Generalised linear and non-linear models, specifically logistic regression, classification tree and random forest, were used to indicate the most relevant risk factors, to quantify their association with HPAI outbreak occurrence, and to develop a map depicting spatial risk distribution.

^{[3]}aegypti was modeled under an ecological niche approach using the maximum entropy technique with the aim of determining the spatial risk distribution of dengue.

^{[4]}This book chapter applies data science methods to analyze storm surge induced flood risks along the Mississippi Gulf Coast by presenting the spatial risk distribution of the study area using the Geographic Information System (GIS) based visualization and quantifying the flood risk in statistical relationships with the risk related factors using multiple linear regression analysis models.

^{[5]}

## spatial risk modeling

Although this work is only a general approach to spatial risk modeling at a regional scale, it provides interesting information.^{[1]}To promote proactive CyanoHAB management, geospatial risk modeling can act as a predictive mechanism to supplement current mitigation efforts.

^{[2]}Very few studies explore the associated factors and spatial risk modeling together for E.

^{[3]}Considering this context, the spatial risk modeling along the cities can help public health programs in finding solutions to reduce the frequency of respiratory diseases.

^{[4]}

## spatial risk pattern

Based on four geographical detectors (risk detector, factor detector, ecological detector, and interaction detector) provided by the novel Geographical Detector technique, we assessed the spatial risk patterns of COVID-19 mortality and identified the effects of these factors.^{[1]}The introduced framework is illustrated using a cross-sectional study linked with a national cohort dataset in Switzerland, we examine differences in underlying spatial risk patterns between respiratory disease and lung cancer.

^{[2]}ABSTRACT This study seeks to identify the spatial risk pattern of households (HHs) exposed to arsenic contamination in Bangladesh by adjusting potential socio-economic, demographic factors.

^{[3]}

## spatial risk index

Therefore, spatial risk indices have been developed to evaluate the intrinsic risk from pesticide pollution.^{[1]}To model the effect of the natural disasters on resilient based distribution network planning, the geographical data for hurricane as a natural disaster is combined to create a spatial risk index map.

^{[2]}The effect of the natural disasters on resilient MG-based distribution network planning, the geographical data for disasters is modelled to give a geographical map that joins the spatial risk index with distribution network component location.

^{[3]}

## spatial risk cluster

The scan statistic analysis resulted in 14 spatial risk clusters for COVID-19 among persons deprived of liberty; the highest-risk cluster was in the Federal District.^{[1]}High spatial risk clusters have been identified by the scan statistics technique.

^{[2]}We used standard, isotonic scan statistics for the detection of spatial risk clusters.

^{[3]}

## spatial risk mapping

In this study, we proposed a multiple hydro-meteorological spatial risk mapping in Surabaya city using a two-phase Fuzzy system.^{[1]}The work presents the results of the RRA tested in the NA region, discussing how spatial risk mapping can be used to establish relative priorities for intervention, to identify hot-spot areas and to provide a basis for the definition of coastal adaptation and management strategies.

^{[2]}

## spatial risk model

As the predictive ability of spatial risk models may be limited where spatial patterns of carnivore depredation of livestock do not statistically differ from random, explicitly assessing such patterns is an important component of conflict mitigation efforts.^{[1]}Hence, remotely sensed (RS) data are commonly utilized in spatial risk models intended to inform control strategies.

^{[2]}

## spatial risk modelling

Spatial risk modelling is a useful tool to gain the understanding of wildlife damage and mitigate conflicts.^{[1]}We developed a rapid spatial risk modelling methodology that is theoretically comprehensive and practically simple.

^{[2]}

## spatial risk heterogeneity

4% in Mirap) and spatial risk heterogeneity was less apparent compared to P.^{[1]}By providing reliable short-term PUUV evolutionary rate estimates, this work facilitates the evaluation of spatial risk heterogeneity starting from timed phylogeographic reconstructions based on virus sampling in its animal reservoir, thereby side-stepping the need for difficult-to-collect human disease incidence data.

^{[2]}

## spatial risk prediction

The model output was used to generate spatial risk predictions for the study area to aid in risk assessment, environmental investigations, and targeted public health interventions.^{[1]}This study aims to compare the predictive performance of prevalence maps generated using Bayesian decision network (BDN) models and multilevel logistic regression models (a type of generalized linear model, GLM) in terms of malaria spatial risk prediction accuracy.

^{[2]}