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1.
Landmines continue to affect the lives of millions of people living in war-torn countries. One major challenge in humanitarian mine action (HMA) is finding new and integrated approaches to land release, which remains a slow and costly process. The use of geographic information systems (GIS) in HMA can improve the land release process by efficient mapping and prioritizing of landmine risk areas. This study explores the usage of aspatial and spatial regression techniques to construct a predictive geo-statistical model for landmine risk mapping in a small 160 km2 municipality in Bosnia and Herzegovina (BiH) and a large 4500 km2 region in Colombia. The first application of logistic geographically weighted regression to landmine risk mapping is presented. The results show that in the BiH study area, the effect of local parameters that influence the distribution of landmine risk varies significantly across the study area. Conversely, in the Colombia case study the effect of explanatory variables remains more homogeneous over the study area. We produced two landmine risk maps for each study area, based on aspatial and spatial regression models. Risk maps are classified into five classes, i.e. very low, low, medium, high, and very high risk. The landmine risk maps created through the usage of these innovative methodologies improve the assessment of risk and prioritization of the land release process in mine-contaminated areas, compared to existing approaches.  相似文献   

2.
The purpose of this study was to investigate the capabilities of different landslide susceptibility methods by comparing their results statistically and spatially to select the best method that portrays the susceptibility zones for the Ulus district of the Bart?n province (northern Turkey). Susceptibility maps based on spatial regression (SR), linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logistic regression (LR) method, and artificial neural network method (ANN) were generated, and the effect of each geomorphological parameter was determined. The landslide inventory map digitized from previous studies was used as a base map for landslide occurrence. All of the analyses were implemented with respect to landslides classified as rotational, active, and deeper than 5 m. Three different sets of data were used to produce nine explanatory variables (layers). The study area was divided into grids of 90 m × 90 m, and the ‘seed cell’ technique was applied to obtain statistically balanced population distribution over landslide inventory area. The constructed dataset was divided into two datasets as training and test. The initial assessment consisted of multicollinearity of explanatory variables. Empirical information entropy analysis was implemented to quantify the spatial distribution of the outcomes of these methods. Results of the analyses were validated by using success rate curve (SRC) and prediction rate curve (PRC) methods. Additionally, statistical and spatial comparisons of the results were performed to determine the most suitable susceptibility zonation method in this large-scale study area. In accordance with all these comparisons, it is concluded that ANN was the best method to represent landslide susceptibility throughout the study area with an acceptable processing time.  相似文献   

3.
This work deals with the identification of potentially contaminated areas using remote sensing, geographic information systems (GIS) and multi‐criteria spatial analysis. The identification of unknown illegal landfills is a crucial environmental problem in all developed and developing countries, where a large number of illegal waste deposits exist as a result of fast, and relatively unregulated, industrial growth over the past century. The criteria used to perform the spatial analysis are here selected by considering the characteristics which are ‘desirable’ for an illegal waste disposal site, chiefly related to the existence of roads for easy access and to a low population density which facilitates unnoticed dumping of illegal waste materials. A large dataset describing known legal and illegal landfills and the context of their location (population, road network, etc.) was used to perform a spatial statistical analysis to select factors and criteria allowing for the identification of the known waste deposits. The final result is a map describing the likelihood of an illegal waste deposit to be located at any arbitrary location. Such a probability map is then used together with remote sensing techniques to narrow down the set of possibly contaminated sites (Silvestri and Omri, 2008 Silvestri, S. and Omri, M. 2008. A method for the remote sensing identification of uncontrolled landfills: formulation and validation.. International Journal of Remote Sensing, 29(4): 975–989. [Taylor & Francis Online] , [Google Scholar]), which are candidates for further analyses and field investigations. The importance of the integration of GIS and remote sensing is highlighted and represents a key instrument for environmental management and for the spatially‐distributed characterization of possible uncontrolled landfill sites.  相似文献   

4.

A statistical model for automated mapping of the spatial distribution of permafrost in the area of Corral del Veleta in south-east Spain (3703' N, 322' W; 3398 m a.s.l.) was developed and applied. The model uses a relationship between permafrost occurrence as indicated by BTS measurements, and variables such as altitude, solar radiation and summer snow cover. The model was implemented within a geographical information system (GIS) and determines the spatial distribution of probable permafrost in Corral del Veleta. Validation was achieved by comparing the predicted permafrost distribution with the results of recent fieldwork, such as geomorphic mapping, geophysical soundings and ground temperature logging.  相似文献   

5.
Seabed sediment textural parameters such as mud, sand and gravel content can be useful surrogates for predicting patterns of benthic biodiversity. Multibeam sonar mapping can provide near-complete spatial coverage of high-resolution bathymetry and backscatter data that are useful in predicting sediment parameters. Multibeam acoustic data collected across a ～1000 km2 area of the Carnarvon Shelf, Western Australia, were used in a predictive modelling approach to map eight seabed sediment parameters. Four machine learning models were used for the predictive modelling: boosted decision tree, random forest decision tree, support vector machine and generalised regression neural network. The results indicate overall satisfactory statistical performance, especially for %Mud, %Sand, Sorting, Skewness and Mean Grain Size. The study also demonstrates that predictive modelling using the combination of machine learning models has provided the ability to generate prediction uncertainty maps. However, the single models were shown to have overall better prediction performance than the combined models. Another important finding was that choosing an appropriate set of explanatory variables, through a manual feature selection process, was a critical step for optimising model performance. In addition, machine learning models were able to identify important explanatory variables, which are useful in identifying underlying environmental processes and checking predictions against the existing knowledge of the study area. The sediment prediction maps obtained in this study provide reliable coverage of key physical variables that will be incorporated into the analysis of covariance of physical and biological data for this area.  相似文献   

6.
Effects of spatial autocorrelation (SAC), or spatial structure, have often been neglected in the conventional models of pedogeomorphological processes. Based on soil, vegetation, and topographic data collected in a coastal dunefield in western Korea, this research developed three soil moisture–landscape models, each incorporating SAC at fine, broad, and multiple scales, respectively, into a non-spatial ordinary least squares (OLS) model. All of these spatially explicit models showed better performance than the OLS model, as consistently indicated by R2, Akaike’s information criterion, and Moran’s I. In particular, the best model was proved to be the one using spatial eigenvector mapping, a technique that accounts for spatial structure at multiple scales simultaneously. After including SAC, predictor variables with greater inherent spatial structure underwent more reduction in their predictive power than those with less structure. This finding implies that the environmental variables pedogeomorphologists have perceived important in the conventional regression modeling may have a reduced predictive power in reality, in cases where they possess a significant amount of SAC. This research demonstrates that accounting for spatial structure not only helps to avoid the violation of statistical assumptions, but also allows a better understanding of dynamic soil hydrological processes occurring at different spatial scales.  相似文献   

7.
Most of the literature to date proposes approximations to the determinant of a positive definite n × n spatial covariance matrix (the Jacobian term) for Gaussian spatial autoregressive models that fail to support the analysis of massive georeferenced data sets. This paper briefly surveys this literature, recalls and refines much simpler Jacobian approximations, presents selected eigenvalue estimation techniques, summarizes validation results (for estimated eigenvalues, Jacobian approximations, and estimation of a spatial autocorrelation parameter), and illustrates the estimation of the spatial autocorrelation parameter in a spatial autoregressive model specification for cases as large as n = 37,214,101. The principal contribution of this paper is to the implementation of spatial autoregressive model specifications for any size of georeferenced data set. Its specific additions to the literature include (1) new, more efficient estimation algorithms; (2) an approximation of the Jacobian term for remotely sensed data forming incomplete rectangular regions; (3) issues of inference; and (4) timing results.  相似文献   

8.
Terrain attributes such as slope gradient and slope shape, computed from a gridded digital elevation model (DEM), are important input data for landslide susceptibility mapping. Errors in DEM can cause uncertainty in terrain attributes and thus influence landslide susceptibility mapping. Monte Carlo simulations have been used in this article to compare uncertainties due to DEM error in two representative landslide susceptibility mapping approaches: a recently developed expert knowledge and fuzzy logic-based approach to landslide susceptibility mapping (efLandslides), and a logistic regression approach that is representative of multivariate statistical approaches to landslide susceptibility mapping. The study area is located in the middle and upper reaches of the Yangtze River, China, and includes two adjacent areas with similar environmental conditions – one for efLandslides model development (approximately 250 km2) and the other for model extrapolation (approximately 4600 km2). Sequential Gaussian simulation was used to simulate DEM error fields at 25-m resolution with different magnitudes and spatial autocorrelation levels. Nine sets of simulations were generated. Each set included 100 realizations derived from a DEM error field specified by possible combinations of three standard deviation values (1, 7.5, and 15 m) for error magnitude and three range values (0, 60, and 120 m) for spatial autocorrelation. The overall uncertainties of both efLandslides and the logistic regression approach attributable to each model-simulated DEM error were evaluated based on a map of standard deviations of landslide susceptibility realizations. The uncertainty assessment showed that the overall uncertainty in efLandslides was less sensitive to DEM error than that in the logistic regression approach and that the overall uncertainties in both efLandslides and the logistic regression approach for the model-extrapolation area were generally lower than in the model-development area used in this study. Boxplots were produced by associating an independent validation set of 205 observed landslides in the model-extrapolation area with the resulting landslide susceptibility realizations. These boxplots showed that for all simulations, efLandslides produced more reasonable results than logistic regression.  相似文献   

9.
The analysis of the spatial structure of animal communities requires spatial data to determine the distribution of individuals and their limiting factors. New technologies like very precise GPS as well as satellite imagery and aerial photographs of very high spatial resolution are now available. Data from airborne LiDAR (Light Detection and Ranging) sensors can provide digital models of ground and vegetation surfaces with pixel sizes of less than 1 m. We present the first study in terrestrial herpetology using LiDAR data. We aim to identify the spatial patterns of a community of four species of lizards (Lacerta schreiberi, Timon lepidus, Podarcis bocagei, and P. hispanica), and to determine how the habitat is influencing the distribution of the species spatially. The study area is located in Northern Portugal. The position of each lizard was recorded during 16 surveys of 1 h with a very precise GPS (error < 1 m). LiDAR data provided digital models of surface, terrain, and normalised height. From these data, we derived slope, ruggedness, orientation, and hill-shading variables. We applied spatial statistics to determine the spatial structure of the community. We computed Maxent ecological niche models to determine the importance of environmental variables. The community and its species presented a clustered distribution. We identified 14 clusters, composed of 1–3 species. Species records showed two distribution patterns, with clusters associated with steep and flat areas. Cluster outliers had the same patterns. Juveniles and subadults were associated with areas of low quality, while sexes used space in similar ways. Maxent models identified suitable habitats across the study area for two species and in the flat areas for the other two species. LiDAR allowed us to understand the local distributions of a lizard community. Remotely sensed data and LiDAR are giving new insights into the study of species ecology. Images of higher spatial resolutions are necessary to map important factors such as refuges.  相似文献   

10.
Gully erosion in the Black Soil Region of China has posed a threat to food security. This study aimed to determine the spatial distribution and morphologic characteristics of gullies in the region and their topographic thresholds. A 28 km2 watershed was surveyed and 117 gullies measured. The results showed that: (1) Gullies were distributed equally on both hillslope and valley floor positions, with a total gully density of .66 km/km2. (2) The mean depth, width, and cross-sectional area of gullies were .74 m, 2.39 m, and 2.43 m2, respectively. These characteristics varied among gullies according to their topographic positions and slope gradients. Individual gully volume (V) was well predicted from gully length (L) by V = 2.08L0.96 (r2 = .66). Total gully volume (V) of each sub-watershed was predicted from mean slope gradient (S) and drainage area (A) as V = 275800S ? 8600A (r2 = .73). (3) Gully erosion was more serious in steeper sub-watersheds and steeper hillslope positions. Gullies were wider in regions with relatively larger drainage areas, except for those developed in the main valley. The topographic threshold for gully initiation was S = .10A?0.34, which indicated gully erosion was dominated by surface runoff. (4) Human activities, such as road construction, played a significant role in gully erosion.  相似文献   

11.
Tropical cyclones (hurricanes and typhoons) produce high winds that can generate waves capable of damaging coral reefs. As cyclones frequently pass through northeast Australia's Great Barrier Reef (GBR), it is important to understand how the spatial distribution of reef damage changes over time. However, direct measurements of wave damage, or even wave heights or wind speeds, are rare within the GBR. An important factor in estimating whether cyclone damage was possible is the magnitude and duration of high‐energy wind and waves. Thus, before the spatio‐temporal dynamics of past cyclone damage can be modelled, it is necessary to reconstruct the spread, intensity, and duration of high‐energy conditions during individual cyclones. This was done every hour along the track taken by each of 85 cyclones that passed near the GBR from 1969 to 2003, by implementing a cyclone wind hindcasting model directly within a raster GIS using cyclone data available from the Australian Bureau of Meteorology. Three measures of cyclone energy (maximum wind speed—MAX, duration of gales—GALES, and continuous duration of gales—CGALES) were derived from these data. For three cyclones, where field data documenting actual reef damage from cyclone‐generated waves were available, the predictive ability of each measure was assessed statistically. All three performed better in predicting reef damage at sites surveyed along the high‐energy reef front than those surveyed along the more protected reef back. MAX performed best for cyclone Joy (r 2 = 0.5), while CGALES performed best for cyclones Ivor (r 2 = 0.23) and Justin (r 2 = 0.48). Using thresholds for MAX and GALES obtained via comparison with field data of damage, it was possible to produce a preliminary prediction of the risk of wave damage across the GBR from each of the 85 cyclones. The results suggest that while up to two‐thirds of the GBR was at risk from some damage for 30–50% of the time series (～18 out of 35 years), only scattered areas of the region were at risk more frequently than that.  相似文献   

12.
Luoto Miska  Hjort Jan 《Geomorphology》2005,67(3-4):299-315
Predictive models are increasingly used in geomorphology, but systematic evaluations of novel statistical techniques are still limited. The aim of this study was to compare the accuracy of generalized linear models (GLM), generalized additive models (GAM), classification tree analysis (CTA), neural networks (ANN) and multiple adaptive regression splines (MARS) in predictive geomorphological modelling. Five different distribution models both for non-sorted and sorted patterned ground were constructed on the basis of four terrain parameters and four soil variables. To evaluate the models, the original data set of 9997 squares of 1 ha in size was randomly divided into model training (70%, n=6998) and model evaluation sets (30%, n=2999).In general, active sorted patterned ground is clearly defined in upper fell areas with high slope angle and till soils. Active non-sorted patterned ground is more common in valleys with higher soil moisture and fine-scale concave topography. The predictive performance of each model was evaluated using the area under the receiver operating characteristic curve (AUC) and the Kappa value. The relatively high discrimination capacity of all models, AUC=0.85–0.88 and Kappa=0.49–0.56, implies that the model's predictions provide an acceptable index of sorted and non-sorted patterned ground occurrence. The best performance for model calibration data for both data sets was achieved by the CTA. However, when the predictive mapping ability was explored through the evaluation data set, the model accuracies of CTA decreased clearly compared to the other modelling techniques. For model evaluation data MARS performed marginally best.Our results show that the digital elevation model and soil data can be used to predict relatively robustly the activity of patterned ground in fine scale in a subarctic landscape. This indicates that predictive geomorphological modelling has the advantage of providing relevant and useful information on earth surface processes over extensive areas, such data being unavailable through more conventional survey methods.  相似文献   

13.
Gambling using electronic gaming machines (EGMs) has emerged as a significant public health issue. While social impact assessments are required prior to the granting of new gaming machine licenses in Australia, there are a few established techniques for estimating the spatial distribution of a venue’s clientele. To this end, we calibrated a Huff model of gambling venue catchments based on a geocoded postal survey (n = 7040). We investigated the impact of different venue attractiveness measures, distance measures, distance decay functions, levels of spatial aggregation and venue types on model fit and results. We then compared model estimates for different behavioural subgroups. Our calibrated spatial model is a significant improvement on previously published models, increasing R2 from 0.23 to 0.64. Venue catchments differ radically in size and intensity. As different population subgroups are attracted to different venues, there is no single best index of venue attractiveness applicable to all subpopulations. The calibrated Huff model represents a useful regulatory tool for predicting the extent and composition of gambling venue catchments. It may assist in decision-making with regard to new license applications and evaluating the impact of health interventions such as mandated reductions in EGM numbers. Our calibrated parameters may be used to improve model accuracy in other jurisdictions.  相似文献   

14.
人口分布影响因素研究有利于揭示人口分布规律,预判人口分布趋势。基于陕西省区县级人口、经济社会、自然地理等数据,通过因子分析方法和空间计量建模解析人口分布的影响因素。研究发现,人口地域别比率不仅取决于一个特定区县内可观测的经济社会、历史基础、自然地理等外在特征,还取决于该区县不可观测的、模型遗漏的其他共有特征,其中经济与公共服务因子、人口基底因子对人口分布具有最显著的正向解释力,其他因素影响较弱或统计不显著;城市等级可显著强化产业结构、人均收入和地形因素对人口分布的影响。研究认为,经济与公共服务因素是优化人口分布的关键,同时需考虑自然地理因素的限制作用。研究对人口分布优化政策的制定具有参考价值。  相似文献   

15.
The complexity of hydrological processes and lack of data for modeling require the use of specific tools for non-linear natural phenomenon. In this paper, an effort has been made to develop a conjunction model – wavelet transformation, data-driven models, and genetic algorithm (GA) – for forecasting the daily flow of a river in northern Algeria using the time series of runoff. This catchment has a semi-arid climate and strong variability in runoff. The original time series was decomposed into multi-frequency time series by wavelet transform algorithm and used as inputs to artificial neural networks (ANN) and adaptive neuro-fuzzy inference system (ANFIS) models. Several factors must be optimized to determine the best model structures. Wavelet-based data-driven models using a GA are designed to optimize model structure. The performances of wavelet-based data-driven models (i.e. WANFIS and WANN) were superior to those of conventional models. WANFIS (RMSE = 12.15 m3/s, EC = 87.32%, R = .934) and WANN (RMSE = 15.73 m3/s, EC = 78.83%, R = .888) models improved the performances of ANFIS (RMSE = 23.13 m3/s, EC = 54.11%, R = .748) and ANN (RMSE = 22.43 m3/s, EC = 56.90%, R = .755) during the test period.  相似文献   

16.
Accurately mapping the spatial distribution of soil total nitrogen is important to precision agriculture and environmental management. Geostatistical methods have been frequently used for predictive mapping of soil properties. Recently, a local regression method, geographically weighted regression (GWR), got the attention of environmentalists as an alternative in spatial modeling of environmental attributes, due to its capability of incorporating various auxiliary variables with spatially varied correlation coefficients. The objective of this study is to compare GWR and ordinary cokriging (OCK) in predictive mapping of soil total nitrogen (TN) using multiple environmental variables. 353 soil Samples within the surface horizon of 0–20 cm in a study area were collected, and their TN contents were measured for calibrating and validating the GWR and OCK interpolations. The environmental variables finally chosen as auxiliary data include elevation, land use types, and soil types. Results indicate that, although OCK is slightly better than GWR in global accuracy of soil TN prediction (the adjusted R2 for GWR and OCK are 0.5746 and 0.6858, respectively), the soil TN map interpolated by GWR shows many details reflecting the spatial variations of major auxiliary variables while OCK smoothes out almost all local details. Geographically weighted regression could account for both the spatial trend and local variations, whilst OCK had difficulties to capture local variations. It is concluded that GWR is a more promising spatial interpolation method compared to OCK in predicting soil TN and potentially other soil properties, if a suitable set of auxiliary variables are available and selected.  相似文献   

17.
Abstract

The transport phase is an often neglected element in the risk assessment of non-nuclear hazardous waste life cycles. Data on special and hazardous waste movements are difficult to acquire, but information collected by the London Waste Regulation Authority during the 1980s gives details of waste consignments from cradle to grave, including U.K. grid references for waste producer and disposal sites. A GIS was used to model the routing of aqueous waste cargoes and assess the potential impacts of such movements. Deficiencies in the consignment records required many assumptions to be made and various scenarios were explored. Roads predicted to see passage of these wastes, together with the estimated levels of tanker traffic, were integrated with the distribution of population, groundwater vulnerability and accident probabilities to evaluate the transportation risks for different localities. Comparisons and evaluations of the implications of different routing scenarios across the study region were made.  相似文献   

18.
《Urban geography》2013,34(2):263-300
Negative spatial autocorrelation (NSA), the tendency for dissimilar neighboring values to cluster on a map, may go undetected in statistical analyses of immature Anopheles gambiae s.l., a leading malaria mosquito vector in Sub-Saharan Africa. Unquantified NSA generated from an inverse variance-covariance matrix may generate misspecifications in an An. gambiae s.l. habitat model. In this research, we used an eigenfunction decomposition algorithm based on a modified geographic connectivity matrix to compute the Moran's I statistic, to uncover hidden NSA in a dataset of georeferenced An. gambiae s.l. habitat explanatory predictor variables spatiotemporally sampled in Malindi and Kisumu, Kenya. The Moran's I statistic was decomposed into orthogonal synthetic map patterns. Global tests revealed that |zMC|s generated were less than 1.11 for the presence of latent autocorrelation. The algorithm captured NSA in the An. gambiae s.l. habitat data by quantifying all non-normal random variables, space-time heterogeneity, and distributional properties of the spatial filters.  相似文献   

19.
Fine-scale population distribution data at the building level play an essential role in numerous fields, for example urban planning and disaster prevention. The rapid technological development of remote sensing (RS) and geographical information system (GIS) in recent decades has benefited numerous population distribution mapping studies. However, most of these studies focused on global population and environmental changes; few considered fine-scale population mapping at the local scale, largely because of a lack of reliable data and models. As geospatial big data booms, Internet-collected volunteered geographic information (VGI) can now be used to solve this problem. This article establishes a novel framework to map urban population distributions at the building scale by integrating multisource geospatial big data, which is essential for the fine-scale mapping of population distributions. First, Baidu points-of-interest (POIs) and real-time Tencent user densities (RTUD) are analyzed by using a random forest algorithm to down-scale the street-level population distribution to the grid level. Then, we design an effective iterative building-population gravity model to map population distributions at the building level. Meanwhile, we introduce a densely inhabited index (DII), generated by the proposed gravity model, which can be used to estimate the degree of residential crowding. According to a comparison with official community-level census data and the results of previous population mapping methods, our method exhibits the best accuracy (Pearson R = .8615, RMSE = 663.3250, p < .0001). The produced fine-scale population map can offer a more thorough understanding of inner city population distributions, which can thus help policy makers optimize the allocation of resources.  相似文献   

20.
Over the last 20 years there has been a surge of interest in paleolimnology and as a result a large accumulation of lake sedimentation records. This emerging archive has allowed us to develop empirical models to describe which variables explain significant variation in sedimentation rates over the past ∼150 years across large spatial scales. We hypothesized that latitude would be a significant explanatory variable of profundal zone lake sedimentation rates across a temperate to polar gradient. We further hypothesized that along a more longitudinally-constrained dataset (i.e. east coast of North America), latitude would explain a greater proportion of the variance. To test these hypotheses, we collated data from 125 natural, average-sized lakes (with surface area <500 km2) by recording authors’ estimates of sedimentation rates (measured as mm/year) or by digitizing recent sediment profiles and calculating sedimentation rates over the past ∼150 years. We found that, at both scales, latitude was the strongest predictor of lake sedimentation rates (full dataset: r 2 = 0.28, P = 0.001, n = 125; east coast dataset: r 2 = 0.58, P < 0.001, n = 43). By conducting a multiple linear regression analysis, we found that 70% of the variance in sedimentation rates from the east coast transect was explained by latitude and elevation alone. This latter model is of sufficient strength that it is a robust predictive tool. Given that climate and land-use strongly co-vary with latitude and that both of these factors have previously been shown to influence lake sedimentation rates, it appears that latitude is a surrogate measure for climate and land-use changes. We also show support for land-use as an important variable influencing sedimentation rates by demonstrating large increases in recent versus Holocene accumulation rates. These results indicate that it is possible to make generalizations about sedimentation rates across broad spatial scales with even limited geographic data.  相似文献   

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