CiteULike: neteler's Furlanello

Spatial aspects of disease dynamics

G Hess — 2007-01-24T17:34:47-00:00

(2002), pp. 102-118.

Predicting habitat suitability with machine learning models: The potential area of Pinus sylvestris L. in the Iberian Peninsula

Marta Garzon — 2006-05-01T09:45:24-00:00

Ecological Modelling, Vol. 197, No. 3-4. (August 2006), pp. 383-393.

We present a modelling framework for predicting forest areas. The framework is obtained by integrating a machine learning software suite within the GRASS Geographical Information System (GIS) and by providing additional methods for predictive habitat modelling. Three machine learning techniques (Tree-Based Classification, Neural Networks and Random Forest) are available in parallel for modelling from climatic and topographic variables. Model evaluation and parameter selection are measured by sensitivity-specificity ROC analysis, while the final presence and absence maps are obtained through maximisation of the kappa statistic. The modelling framework is applied at a resolution of 1 km with Iberian subpopulations of Pinus sylvestris L. forests. For this data set, the most accurate algorithm is Breiman's random forest, an ensemble method which provides automatic combination of tree-classifiers trained on bootstrapped subsamples and randomised variable sets. All models show a potential area of P. sylvestris for the Iberian Peninsula which is larger than the present one, a result corroborated by regional pollen analyses.

Boosting of Tree-based Classifiers for Predictive Risk Modeling in GIS

C Furlanello — 2006-08-03T16:11:22-00:00

(2000), pp. 220-229.

Boosting of tree-based classifiers has been interfaced to the Geographical Information System (GIS) GRASS to create predictive classification models from digital maps. On a risk management problem in landscape ecology, the performance of the boosted tree model is better than either with a single classifier or with bagging. This results in an improved digital map of the risk of human exposure to tick-borne diseases in Trentino (Italian Alps) given sampling on 388 sites and the use of several overlaying georeferenced data bases. Margin distributions are compared for bagging and boosting. Boosting is confirmed to give the most accurate model on two additional and independent test sets of reported cases of bites on humans and of infestation measured on roe deer. An interesting feature of combining classification models within a GIS is the visualization through maps of the single elements of the combination: each boosting step map focuses on different details of data distribution. In this problem, the best performance is obtained without controlling tree sizes, which indicates that there is a strong interaction between input variables.

Ixodes ricinus (Acari: Ixodidae) infestation on roe deer (Capreolus capreolus) in Trentino, Italian Alps.

C Chemini — 2006-08-02T15:34:06-00:00

Parassitologia, Vol. 39, No. 1. (March 1997), pp. 59-63.

The most important tick-deer system potentially supporting the epidemiology of Lyme disease in the Italian Alps is that regarding Ixodes ricinus (L.) and roe deer (Capreolus capreolus L.). In this study, the pattern of tick infestation on 562 male roe deer harvested in September 1994 in 56 game districts of Trentino, Northern Italy, was assessed. The prevalence and density of infestation by I. ricinus were analyzed by a model based on classification and regression trees (CART), using both discrete and continuous variables concerning environmental and host parameters. The model discriminated attitude and host density as the 2 variables having the greatest effect on the prevalence and density of infestation of deer; the levels of infestation were higher at an altitude below 1125 m or at roe deer densities over 8.5 head per 100 ha. The density of tick infestation tended to be higher in older roe deer.

Classification tree methods for analysis of mesoscale distribution of Ixodes ricinus (Acari:Ixodidae) in Trentino, Italian Alps.

S Merler — 2006-08-02T15:27:28-00:00

Journal of Medical Entomology, Vol. 33, No. 6. (November 1996), pp. 888-893.

Cases of Lyme disease and tick-borne encephalitis were recognized recently in the Province of Trento, Italian Alps. Assessment of areas of potential risk for these tick-borne diseases is carried out by a model based on classification and regression trees (CART), using both discrete and continuous variables. Data on Ixodes ricinus (L.) occurrence resulted from extensive sampling carried out by standard methods in 99 sites over an area of approximately 2,700 km2 in the Province of Trento. A series of environmental parameters were recorded from each site and population densities of roe deer, Capreolus capreolus (L.), were considered. The CART model discriminates 2 variables that appear to have the greatest effect on the mesoscale occurrence of ticks: altitude and geological substratum, with a drastic decrease of tick frequency above an altitude of approximately 1,100 m and on volcanic substrata. The model is effective in identifying the mesoscale areas at greater potential risk, with a relatively low sampling effort.

Classification Tree Methods for Analysis of Mesoscale Distribution of Ixodes ricinus (Acari: Ixodidae) in Trentino, Italian Alps

S Merler — 2006-07-25T22:42:28-00:00

Journal of Medical Entomology, Vol. 33, No. 6. (June 1996), pp. 888-893.

Cases of Lyme disease and tick borne encephalitis were recently recognized in the province of Trento, Italian Alps. Assessment of areas of potential risk for these tick-borne diseases is carried out by a model based on CART (Classification and Regression Trees), using both discrete and continuous variables. Data on <em> Ixodes ricinus</em> (L.) occurrence resulted from samplings carried out by standard methods in 99 sites over an area of 2,700 km2 in the Province of Trento. A series of environmental parameters were recorded from each site and population densities of roe deer, <em> Capreolus capreolus</em> (L.), were considered. The CART model discriminates two variables which appear to have the greatest effect on the mesoscale occurrence of ticks: altitude and geological substratum with drastic decrease of tick frequency above 1,100 m a.s.l. or on volcanic substrata. The model is effective in identifying the mesoscale areas at greater potential risk, with a relatively low sampling effort.

Geographical information systems and bootstrap aggregation (bagging) of tree-based classifiers for Lyme disease risk prediction in Trentino, Italian Alps.

A Rizzoli — 2005-04-28T09:22:23-00:00

J Med Entomol, Vol. 39, No. 3. (May 2002), pp. 485-492.

The risk of exposure to Lyme disease in the province of Trento, Italian Alps, was predicted through the analysis of the distribution of Ixodes ricinus (L.) nymphs infected with Borrelia burgdorferi s.l. with a model based on bootstrap aggregation (bagging) of tree-based classifiers within a geographical information system (GIS). Data on L ricinus density assessed by dragging the vegetation in 438 sites during 1996 were cross-correlated with the digital cartography of a GIS, which included the variables altitude, exposure and slope, substratum, vegetation type and roe deer density. Ticks were more abundant at altitudes below 1,300 m a.s.l., in the presence of limestone and vegetation cover with thermophile deciduous forests and high densities of roe deer. A bootstrap aggregation procedure (bagging) was used to produce a model for the prediction of tick occurrence, the accuracy of which was tested on actual tick counts assessed by a further dragging campaign carried out during 1997 to determine infection prevalence and resulted in average 77%. Other tests of the model were made on additional and independent data sets. The prevalence of infection with Borrelia burgdorferi s.l, determined by polymerase chain reaction on 2,208 nymphs collected by random dragging in 245 transects selected within eight areas where the model predicted the occurrence of I. ricinus during 1997, was 17.5% and was positively correlated to tick abundance and roe deer density. These findings were used to relate the output of the bagged model (probability of tick occurrence) to the density of infected nymphs through a stepwise model selection procedure and thus to produce a GIS digital map of the probability distribution of infected nymphs in the Province of Trento at high resolution scale (50 by 50-m cell resolution). The application of the bagging procedure increased the accuracy of the prediction made by a single classification tree, a well-known classification method for the analysis of epidemiological data.

An integrated toolbox for image registration, fusion and classification

M Neteler — 2005-04-27T20:06:27-00:00

International Journal of Geoinformatics. Special Issue on FOSS/GRASS 2004 & GIS-IDEAS 2004, Vol. 1, No. 1. (March 2005), pp. 51-61.

In this paper we present a suite of new image processing tools for the GRASS Geographic Information System. New modules are suggested to support improved and semi-automated geocoding of vertical imagery. The ortho-rectification procedures have been extended to rectify oblique imagery from digital hand-held cameras for rendering purposes. Multi- and hyperspectral image analysis has been implemented to derive landuse/landcover maps at subpixel resolution. Image fusion with the Brovey transform is shown. We finally show high performance SMAP image classification on an openMosix cluster.

GIS and the Random Forest Predictor: Integration in R for Tick-Borne Disease Risk Assessment

Cesare Furlanello — 2005-04-27T19:43:30-00:00

We discuss how sophisticated machine learning methods may be rapidly integrated within a GIS for the development of new approaches in landscape epidemiology. A multitemporal predictive map is obtained by modeling in R, analyzing geodata and digital maps in GRASS, and managing biodata samples and weather data in PostgreSQL. In particular, we present a risk mapping system for tick-borne diseases, applied to model the risk of exposure to Lyme borreliosis and tick-borne encephalitis (TBE) in...