scholarly journals Contribution of human and biophysical factors to the spatial distribution of forest fire ignitions and large wildfires in a French Mediterranean region

2017 ◽  
Vol 26 (6) ◽  
pp. 498 ◽  
Author(s):  
Julien Ruffault ◽  
Florent Mouillot
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Mediterranean Region ◽  
Fire Hazard ◽  
Regional Distribution ◽  
Boosted Regression Trees ◽  
Fire Weather ◽  
Explanatory Variables ◽  
Management Actions ◽  
Fire Ignitions

Identifying the factors that drive the spatial distribution of fires is one of the most challenging issues facing fire science in a changing world. We investigated the relative influence of humans, land cover and weather on the regional distribution of fires in a Mediterranean region using boosted regression trees and a set of seven explanatory variables. The spatial pattern of fire weather, which is seldom accounted for in regional models, was estimated using a semi-mechanistic approach and expressed as the length of the fire weather season. We found that the drivers of the spatial distribution of fires followed a fire size-dependent pattern in which human activities and settlements mainly determined the distribution of all fires whereas the continuity and type of fuels mainly controlled the location of the largest fires. The spatial structure of fire weather was estimated to be responsible for an average of 25% of the spatial patterns of fires, suggesting that climate change may directly affect the spatial patterns of fire hazard in the near future. These results enhance our understanding of long-term controls of the spatial distribution of wildfires and predictive maps of fire hazard provide useful information for fire management actions.

Predicting spatial patterns of fire on a southern California landscape

2008 ◽  
Vol 17 (5) ◽  
pp. 602 ◽  
Author(s):  
Alexandra D. Syphard ◽  
Volker C. Radeloff ◽  
Nicholas S. Keuler ◽  
Robert S. Taylor ◽  
Todd J. Hawbaker ◽  
...  
Keyword(s):  
Spatial Patterns ◽  
Southern California ◽  
Vegetation Type ◽  
Fire Hazard ◽  
Fire Regimes ◽  
Fire Frequency ◽  
Fuel Loading ◽  
Landscape Characteristics ◽  
Management Actions ◽  
Fire Ignitions

Humans influence the frequency and spatial pattern of fire and contribute to altered fire regimes, but fuel loading is often the only factor considered when planning management activities to reduce fire hazard. Understanding both the human and biophysical landscape characteristics that explain how fire patterns vary should help to identify where fire is most likely to threaten values at risk. We used human and biophysical explanatory variables to model and map the spatial patterns of both fire ignitions and fire frequency in the Santa Monica Mountains, a human-dominated southern California landscape. Most fires in the study area are caused by humans, and our results showed that fire ignition patterns were strongly influenced by human variables. In particular, ignitions were most likely to occur close to roads, trails, and housing development but were also related to vegetation type. In contrast, biophysical variables related to climate and terrain (January temperature, transformed aspect, elevation, and slope) explained most of the variation in fire frequency. Although most ignitions occur close to human infrastructure, fires were more likely to spread when located farther from urban development. How far fires spread was ultimately related to biophysical variables, and the largest fires in southern California occurred as a function of wind speed, topography, and vegetation type. Overlaying predictive maps of fire ignitions and fire frequency may be useful for identifying high-risk areas that can be targeted for fire management actions.

scholarly journals Modeling and mapping wildfire ignition risk in Portugal

2009 ◽  
Vol 18 (8) ◽  
pp. 921 ◽  
Author(s):  
Filipe X. Catry ◽  
Francisco C. Rego ◽  
Fernando L. Bação ◽  
Francisco Moreira
Keyword(s):  
Spatial Patterns ◽  
Regression Models ◽  
Good Accuracy ◽  
National Level ◽  
Risk Map ◽  
Wildfire Management ◽  
Logistic Regression Models ◽  
Explanatory Variables ◽  
Fire Ignitions ◽  
Southern European Countries

Portugal has the highest density of wildfire ignitions among southern European countries. The ability to predict the spatial patterns of ignitions constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and firefighting resources allocation. In this study, we analyzed 127 490 ignitions that occurred in Portugal during a 5-year period. We used logistic regression models to predict the likelihood of ignition occurrence, using a set of potentially explanatory variables, and produced an ignition risk map for the Portuguese mainland. Results show that population density, human accessibility, land cover and elevation are important determinants of spatial distribution of fire ignitions. In this paper, we demonstrate that it is possible to predict the spatial patterns of ignitions at the national level with good accuracy and using a small number of easily obtainable variables, which can be useful in decision-making for wildfire management.

Spatial patterns of wildfire ignitions in south-eastern Australia

2015 ◽  
Vol 24 (8) ◽  
pp. 1098 ◽  
Author(s):  
Kathryn M. Collins ◽  
Owen F. Price ◽  
Trent D. Penman
Keyword(s):  
Spatial Patterns ◽  
Linear Models ◽  
Important Variable ◽  
Explanatory Variables ◽  
Climate Change Effects ◽  
South Eastern ◽  
Management Actions ◽  
South Wales ◽  
Eastern Australia ◽  
South Eastern Australia

Wildfires can have devastating effects on life, property and the environment. Official inquiries following major damaging fires often recommend management actions to reduce the risk of future losses from wildfires. Understanding where wildfires are most likely to occur in the landscape is essential to determining where wildfires pose the greatest risk to people and property. We investigated the spatial patterns of wildfire ignitions at a bioregional scale in New South Wales and Victoria using generalised linear models. We used a combination of social and biophysical variables and examined whether different categories of ignitions respond to different explanatory variables. Human-caused ignitions are the dominant source of ignitions for wildfires in south-eastern Australia and our results showed that for such ignitions, population density was the most important variable for the spatial pattern of ignitions. In future years, more ignitions are predicted in the coastal and hinterland areas due to population increases and climate change effects.

Spatial Patterns of Urban Innovation and Their Evolution from Perspectives of Capacity and Structure: Taking Shenzhen as an Example

2021 ◽  
Vol 11 (1) ◽  
pp. 7
Author(s):  
Erjie Hu ◽  
Di Hu ◽  
Handong He
Keyword(s):  
Spatial Distribution ◽  
Spatial Pattern ◽  
Distribution Pattern ◽  
Spatial Patterns ◽  
Spillover Effect ◽  
Tertiary Industry ◽  
Innovation Capacity ◽  
Structure Index ◽  
Secondary Industry ◽  
Spatial Spillover Effect

Innovation is a key factor for a country’s overall national strength and core competitiveness. The spatial pattern of innovation reflects the regional differences of innovation development, which can provide guidance for the regional allocation of innovation resources. Most studies on the spatial pattern of innovation are at urban and above spatial scale, but studies at urban internal scale are insufficient. The precision and index of the spatial pattern of innovation in the city needs to be improved. This study proposes to divide spatial units based on geographic coordinates of patents, designs the innovation capability and innovation structure index of a spatial unit and their calculation methods, and then reveals the spatial patterns of innovation and their evolutionary characteristics in Shenzhen during 2000–2018. The results show that: (1) The pattern of innovation capacity of secondary industry exhibited a pronounced spatial spillover effect with a positive spatial correlation. The innovation capacity and innovation structure index of the secondary industry evolved in a similar manner; i.e., they gradually extended from the southwest area to the north over time, forming a tree-like distribution pattern with the central part of the southwest area as the “root” and the northwest and northeast areas as the “canopy”. (2) The pattern of innovation capacity of tertiary industry also had a significant spatial spillover effect with a positive spatial correlation. There were differences between the evolutions of innovation capacity and innovation structure index of tertiary industry. Specifically, its innovation capacity presented a triangular spatial distribution pattern with three groups in the central and eastern parts of the southwest area and the south-eastern part of the northwest area as the vertices, while its innovative structure showed a radial spatial distribution pattern with the southwestern part of the southwest area as the source and a gradually sparse distribution toward the northeast. (3) There were differences between the evolution modes of secondary and tertiary industries. Areas with high innovation capacity in the secondary industry tended to be more balanced, while areas with high innovation capacity in the tertiary industry did not necessarily have a balanced innovation structure. Through the method designed in this paper, the spatial pattern of urban innovation can be more precise and comprehensive revealed, and provide useful references for the development of urban innovation.

scholarly journals Spatially varying constraints of human-caused fire occurrence in British Columbia, Canada

2017 ◽  
Vol 26 (3) ◽  
pp. 219 ◽  
Author(s):  
Philip E. Camp ◽  
Meg A. Krawchuk
Keyword(s):  
British Columbia ◽  
Spatial Patterns ◽  
Fire Management ◽  
Fire Occurrence ◽  
Explanatory Variables ◽  
Human Footprint ◽  
Key Factor ◽  
Key Drivers ◽  
Spatially Varying

Human-caused wildfires are controlled by human and natural influences, and determining their key drivers is critical for understanding spatial patterns of wildfire and implementing effective fire management. We examined an array of explanatory variables that account for spatial controls of human-caused fire occurrence from 1990 to 2013 among six ecosystem zones that vary in human footprint and environmental characteristics in British Columbia, Canada. We found that long-term patterns of human-caused fire in ecosystem zones with a larger human footprint were strongly controlled by biophysical variables explaining conditions conducive to burning, whereas fire occurrence in remote ecosystem zones was controlled by various metrics of human activity. A metric representing the wildland–urban interface was a key factor explaining human-caused fire occurrence regardless of ecosystem zone. Our results contribute to the growing body of research on the varying constraints of spatial patterns of fire occurrence by explicitly examining human-caused fire and the heterogeneity of constraints based on human development.

scholarly journals INVESTIGATION AND EVALUATION OF SPATIAL PATTERNS IN TABRIZ PARKS USING LANDSCAPE METRICS

2017 ◽  
Vol 10 (2) ◽  
pp. 263-269
Author(s):  
Ali Majnouni Toutakhane ◽  
Mojtaba Mofareh
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Landscape Metrics ◽  
Shape Index ◽  
Quantitative Information ◽  
Green Spaces ◽  
Quantitative Results ◽  
Patch Density ◽  
Distribution Composition ◽  
Nearest Neighborhood

Nowadays, the green spaces in cities and especially metropolises have adopted a variety of functions. In addition to improving the environmental conditions, they are suitable places for spending free times and mitigating nervous pressures of the machinery life based on their distribution and dispersion in the cities. In this research, in order to study the spatial distribution and composition of the parks and green spaces in Tabriz metropolis, the map of Parks prepared using the digital atlas of Tabriz parks and Arc Map and IDRISI softwares. Then, quantitative information of spatial patterns of Tabriz parks provided using Fragstats software and a selection of landscape metrics including: the area of class, patch density, percentage of landscape, average patch size, average patch area, largest patch index, landscape shape index, average Euclidean distance of the nearest neighborhood and average index of patch shape. Then the spatial distribution, composition, extent and continuity of the parks was evaluated. Overall, only 8.5 percent of the landscape is assigned to the parks, and they are studied in three classes of neighborhood, district and regional parks. Neighborhood parks and green spaces have a better spatial distribution pattern compared to the other classes and the studied metrics showed better results for this class. In contrast, the quantitative results of the metrics calculated for regional parks, showed the most unfavorable spatial status for this class of parks among the three classes studied in Tabriz city.

scholarly journals Amplification and Suppression of Distinct Brain-wide Activity Patterns by Catecholamines

2018 ◽  
Author(s):  
RL van den Brink ◽  
S Nieuwenhuis ◽  
TH Donner
Keyword(s):  
Spatial Distribution ◽  
Human Brain ◽  
Spatial Patterns ◽  
Large Scale ◽  
Network Dynamics ◽  
Brain Network ◽  
Heterogeneous Distribution ◽  
Neurotransmitter Systems ◽  
The Brain ◽  
Catecholamine Levels

ABSTRACTThe widely projecting catecholaminergic (norepinephrine and dopamine) neurotransmitter systems profoundly shape the state of neuronal networks in the forebrain. Current models posit that the effects of catecholaminergic modulation on network dynamics are homogenous across the brain. However, the brain is equipped with a variety of catecholamine receptors with distinct functional effects and heterogeneous density across brain regions. Consequently, catecholaminergic effects on brain-wide network dynamics might be more spatially specific than assumed. We tested this idea through the analysis of functional magnetic resonance imaging (fMRI) measurements performed in humans (19 females, 5 males) at ‘rest’ under pharmacological (atomoxetine-induced) elevation of catecholamine levels. We used a linear decomposition technique to identify spatial patterns of correlated fMRI signal fluctuations that were either increased or decreased by atomoxetine. This yielded two distinct spatial patterns, each expressing reliable and specific drug effects. The spatial structure of both fluctuation patterns resembled the spatial distribution of the expression of catecholamine receptor genes: α1 norepinephrine receptors (for the fluctuation pattern: placebo > atomoxetine), ‘D2-like’ dopamine receptors (pattern: atomoxetine > placebo), and β norepinephrine receptors (for both patterns, with correlations of opposite sign). We conclude that catecholaminergic effects on the forebrain are spatially more structured than traditionally assumed and at least in part explained by the heterogeneous distribution of various catecholamine receptors. Our findings link catecholaminergic effects on large-scale brain networks to low-level characteristics of the underlying neurotransmitter systems. They also provide key constraints for the development of realistic models of neuromodulatory effects on large-scale brain network dynamics.SIGNIFICANCE STATEMENTThe catecholamines norepinephrine and dopamine are an important class of modulatory neurotransmitters. Because of the widespread and diffuse release of these neuromodulators, it has commonly been assumed that their effects on neural interactions are homogenous across the brain. Here, we present results from the human brain that challenge this view. We pharmacologically increased catecholamine levels and imaged the effects on the spontaneous covariations between brain-wide fMRI signals at ‘rest’. We identified two distinct spatial patterns of covariations: one that was amplified and another that was suppressed by catecholamines. Each pattern was associated with the heterogeneous spatial distribution of the expression of distinct catecholamine receptor genes. Our results provide novel insights into the catecholaminergic modulation of large-scale human brain dynamics.

scholarly journals Impacts of spatial mismatch on commuting time of urban residents in China

2014 ◽  
Vol 26 (3) ◽  
pp. 227-233 ◽  
Author(s):  
Danwen Bao ◽  
Tangyi Guo ◽  
Hongshan Xia
Keyword(s):  
Spatial Distribution ◽  
Spatial Patterns ◽  
Measurement Methods ◽  
Urban Residents ◽  
Spatial Mismatch ◽  
Chinese Cities ◽  
Job Accessibility ◽  
Nanjing City ◽  
Commuting Time ◽  
Job Opportunity

In much of studies on spatial mismatch between residential and employer locations, job accessibility has been measured. However, the apparent disadvantages of the traditional measurement methods on the studies of Chinese cities have been noted.  This paper proposed an optimized method for job accessibility measurement by introducing the weigh coefficient of job opportunity, which quantifies the degree of uneven distribution of job opportunity in the Chinese cities. Take Nanjing city for example, this new method was used to measure the spatial distribution of job opportunity, investigate the spatial patterns and analyze the influences of job accessibility on commuting behavior. The results show that the distribution of job accessibility in Nanjing exhibits the different spatial patterns and mechanisms compared with US cases.

Mining Spatial Patterns of Distribution of Uranium in Surface and Ground Waters in Ukraine

2017 ◽  
pp. 520-546
Author(s):  
Michael Govorov ◽  
Viktor Putrenko ◽  
Gennady Gienko
Keyword(s):  
Factor Analysis ◽  
Spatial Distribution ◽  
Spatial Patterns ◽  
Large Scale ◽  
Chemical Elements ◽  
Environmental Indicators ◽  
Local Factor ◽  
Correlation And Regression Analysis ◽  
Weighted Factor ◽  
Major Factors

A variety of geovisualization and spatial statistical methods can reveal spatial patterns in the distribution of chemical elements in surface and groundwater, and also identify major factors which define those patterns. This chapter describes a combination of modeling techniques to enhance understanding of large-scale spatial distribution of uranium in groundwater in Ukraine, by linking spatial patterns of several indicators and predictors. Factor, correlation, and regression analysis, including their spatial implementations, were used to describe the impacts of several environmental variables on spatial distribution of uranium. Local factor analysis (or Geographically Weighted Factor Analysis, GWFA) was proposed to identify major environmental factors which define the distribution of uranium, and to discover and map their spatial relationships. The study resulted in a series of maps to help visualize and explore the relationships between uranium and several environmental indicators.

scholarly journals Spatiotemporal analysis of rabies in cattle in central Mexico

2019 ◽  
Vol 14 (2) ◽  
Author(s):  
Isabel Bárcenas-Reyes ◽  
Diana Paulina Nieves-Martínez ◽  
José Quintín Cuador-Gil ◽  
Elizabeth Loza-Rubio ◽  
Sara González-Ruíz ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Minimum Temperature ◽  
Spatial Epidemiology ◽  
Risk Estimation ◽  
Regional Distribution ◽  
Spatiotemporal Analysis ◽  
Maximum Temperature ◽  
Spatial Continuity ◽  
Simple Kriging ◽  
Annual Minimum Temperature

Spatial epidemiology of bat-transmitted rabies in cattle has been limited to spatial distribution of cases, an approach that does not identify hidden patterns and the spread resulting in outbreaks in endemic and susceptible areas. Therefore, the purpose of this study was to determine the relationship between the three variables average annual maximum, annual minimum temperature and precipitation in the region on the one hand, and the spatial distribution of cases on the other, using geographic information systems and co-Kriging considering that these environmental variables condition the existence of the rabies vector Desmodus rotundus. A stationary behaviour between the primary and the secondary variables was verified by basic statistics and moving window statistics. The directions of greater and lesser spatial continuity were determined by experimental cross-semivariograms. It was found that the highest risk for bovine paralytic rabies occurs in areas known as La Huasteca Potosina and La Sierra Gorda that are characterized by a maximum temperature of 29.5 °C, a minimum temperature of 16.5 °C and precipitation of 1200 mm. A risk estimation map was obtained for the presence of rabies with a determination coefficient greater than 95%, and a correlation coefficient greater than 0.95. Our conclusion is that ordinary co- Kriging provides a better estimation of risk and spatial distribution of rabies than simple Kriging, making this the method recommended for risk estimation and regional distribution of rabies.

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