The impacts in real estate of landscape values: Evidence from Tuscany (Italy)

Using spatial econometric techniques and local spatial statistics, this study explores the relationships between the real estate values in Tuscany with the individual perception of satisfaction by landscape types. The analysis includes the usual territorial variables such as proximity to urban centres and roads. The landscape values are measured through a sample of respondents who expressed their aesthetic-visual perceptions of different types of land use. Results from a multivariate local Geary highlight that house prices are not spatial independent and that between the variables included in the analysis there is mainly a positive correlation. Specifically, the findings demonstrate a significant spatial dependence in real estate prices. The aesthetic values influence the real estate price throughout more a spatial indirect effect rather than the direct effect. Practically, house prices in specific areas are more influenced by aspects such as proximity to essential services. The results seem to show to live close to highly aesthetic environments not in these environments. The results relating to the distance from the main roads, however, seem counterintuitive. This result probably depends on the evidence that these areas suffer from greater traffic jam or pollution or they are preferred for alternative uses such as for locating industrial plants or big shopping centres rather than residential use. Therefore, these effects decrease house prices

Visualisation of Origins, Destinations and Flows with OD Maps

We present a new technique for the visual exploration of origins (O) and destinations (D) arranged in geographic space. Previous attempts to map the flows between origins and destinations have suffered from problems of occlusion usually requiring some form of generalisation, such as aggregation or flow density estimation before they can be visualized. This can lead to loss of detail or the introduction of arbitrary artefacts in the visual representation. Here, we propose mapping OD vectors as cells rather than lines, comparable with the process of constructing OD matrices, but unlike the OD matrix, we preserve the spatial layout of all origin and destination locations by constructing a gridded two‐level spatial treemap. The result is a set of spatially ordered small multiples upon which any arbitrary geographic data may be projected. Using a hash grid spatial data structure, we explore the characteristics of the technique through a software prototype that allows interactive query and visualisation of 105‐106 simulated and recorded OD vectors. The technique is illustrated using US county to county migration and commuting statistics

Development of Bayesian Geostatistical Models with Applications in Malaria Epidemiology

methods enables model fit. A common assumption in geostatistical modeling of malaria data is the stationarity, that is the spatial correlation is a function of distance between locations and not of the locations themselves. This hypothesis does not always hold, especially when modeling malaria over large areas, hence geostatistical models that take into account non-stationarity need to be assessed. Fitting geostatistical models requires repeated inversions of the variance-covariance matrix modeling geographical dependence. For very large number of data locations matrix inversion is considered infeasible. Methods for optimizing this computation are needed. In addition, the relation between environmental factors and malaria risk is often not linear and parametric functions may not be able to determine the shape of the relationship. Nonparametric geostatistical regression models that allow the data to determine the form of the environment-malaria relation need to be further developed and applied in malaria mapping. The aim of this thesis was to develop appropriate models for non-stationary and large geostatistical data that can be applied in the field of malaria epidemiology to produce accurate maps of malaria distribution. The main contributions of this thesis are the development of methods for: (i) analyzing non-stationary malaria survey data; (ii) modeling the nonlinear relation between malaria risk and environment/climatic conditions; (iii) modeling geostatistical mortality data collected at very large number of locations and (iv) adjusting for seasonality and age in mapping heterogeneous malaria survey data. Chapter 2 assessed the spatial effect of bednets on all-cause child mortality by analyzing data from a large follow-up study in an area of high perennial malaria transmission in Kilombero Valley, southern Tanzania. The results indicated a lack of community effect of bednets density possibly because of the homogeneous characteristic of nets coverage and the small proportion of re-treated nets in the study area. The mortality data of this application were collected over 7, 403 locations. To overcome large matrix inversion a Bayesian geostatistical model was developed. This model estimates the spatial process by a subset of locations and approximates the location-specific random effects by a weighted sum of the subset of location-specific random effects with the weights inversely proportional to the separation distance. In Chapter 3 a Bayesian non-stationary model was developed by partitioning the study region into fixed subregions, assuming a separate stationary spatial process in each tile and taking into account between-tile correlation. This methodology was applied on malaria survey data extracted from the MARA database and produced parasitaemia risk maps in Mali. The predictive ability of the non-stationary model was compared with the stationary analogue and the results showed that the stationarity assumption influenced the significance of environmental predictors as well as the the estimation of the spatial parameters. This indicates that the assumptions about the spatial process play an important role in inference. Model validation showed that the non-stationary model had better predictive ability. In addition, experts opinion suggested that the parasitaemia risk map based on the nonstationary model reflects better the malaria situation in Mali. This work revealed that non-stationarity is an essential characteristic which should be considered when mapping malaria. Chapter 4 employed the above non-stationary model to produce maps of malaria risk in West Africa considering as fixed tiles the four agro-ecological zones that partition the region. Non-linearity in the relation between parasitaemia risk and environmental conditions was assessed and it was addressed via P-splines within a Bayesian geostatistical model formulation. The model allowed a separate malaria-environment relation in each zone. The discontinuities at the borders between the zones were avoided since the spatial correlation was modeled by a mixture of spatial processes over the entire study area, with the weights chosen to be exponential functions of the distance between the locations and the centers of the zones corresponding to each of the spatial processes. The above modeling approach is suitable for mapping malaria over areas with an obvious fixed partitioning (i.e. ecological zones). For areas where this is not possible, a nonstationary model was developed in Chapter 5 by allowing the data to decide on the number and shape of the tiles and thus to determine the different spatial processes. The partitioning of the study area was based on random Voronoi tessellations and model parameters were estimated via reversible jump Markov chain Monte Carlo (RJMCMC) due to the variable dimension of the parameter space. In Chapter 6 the feasibility of using the recently developed mathematical malaria transmission models to adjust for age and seasonality in mapping historical malaria survey data was investigated. In particular, the transmission model was employed to translate age heterogeneous survey data from Mali into a common measure of transmission intensity. A Bayesian geostatistical model was fitted on the transmission intensity estimates using as covariates a number of environmental/climatic variables. Bayesian kriging was employed to produce smooth maps of transmission intensity, which were further converted to age specific parasitaemia risk maps. Model validation on a number of test locations showed that this transmission model gives better predictions than modeling directly the prevalence data. This approach was further validated by analyzing the nationally representative malaria surveys data derived from the Malaria Indicator surveys (MIS) in Zambia. Although MIS data do not have the same limitations with the historical data, the purpose of the analyzes was to compare the maps obtained by modeling 1) directly the raw prevalence data and 2) transmission intensity data derived via the transmission model. Both maps predicted similar patterns of malaria risk, however the map based on the transmission model predicted a slightly higher lever of endemicity. The use of transmission models on malaria mapping enables adjusting for seasonality and age dependence of malaria prevalence and it includes all available historical data collected at different age groups

Modeling Of Socio-economic Factors And Adverse Events In An Active War Theater By Using A Cellular Automata Simulation Approach

Department of Defense (DoD) implemented Human Social Cultural and Behavior (HSCB) program to meet the need to develop capability to understand, predict and shape human behavior among different cultures by developing a knowledge base, building models, and creating training capacity. This capability will allow decision makers to subordinate kinetic operations and promote non-kinetic operations to govern economic programs better in order to initiate efforts and development to address the grievances among the displeased by adverse events. These non-kinetic operations include rebuilding indigenous institutions’ bottom-up economic activity and constructing necessary infrastructure since the success in non-kinetic operations depends on understanding and using social and cultural landscape. This study aims to support decision makers by building a computational model to understand economic factors and their effect on adverse events. In this dissertation, the analysis demonstrates that the use of cellular automata has several significant contributions to support decision makers allocating development funds to stabilize regions with higher adverse event risks, and to better understand the complex socio-economic interactions with adverse events. Thus, this analysis was performed on a set of spatial data representing factors from social and economic data. In studying behavior using cellular automata, cells in the same neighborhood synchronously interact with each other to determine their next states, and small changes in iteration may yield to complex formations of adverse event risk after several iterations of time. The modeling methodology of cellular automata for social and economic analysis in this research was designed in two major implementation levels as follows: macro and micro-level. In the macro-level, the modeling framework integrates iv population, social, and economic sub-systems. The macro-level allows the model to use regionalized representations, while the micro-level analyses help to understand why the events have occurred. Macro-level subsystems support cellular automata rules to generate accurate predictions. Prediction capability of cellular automata is used to model the micro-level interactions between individual actors, which are represented by adverse events. The results of this dissertation demonstrate that cellular automata model is capable of evaluating socio-economic influences that result in changes in adverse events and identify location, time and impact of these events. Secondly, this research indicates that the socioeconomic influences have different levels of impact on adverse events, defined by the number of people killed, wounded or hijacked. Thirdly, this research shows that the socio-economic, influences and adverse events that occurred in a given district have impacts on adverse events that occur in neighboring districts. The cellular automata modeling approach can be used to enhance the capability to understand and use human, social and behavioral factors by generating what-if scenarios to determine the impact of different infrastructure development projects to predict adverse events. Lastly, adverse events that could occur in upcoming years can be predicted to allow decision makers to deter these events or plan accordingly if these events do occur

Determination of World's Humid Tropical Deforestation Rates during the 1990's. Methodology and Results of the TREES-II Research Programme.

Abstract not availableJRC.H-Institute for environment and sustainability (Ispra

Islamic Area Studies with Geographical Information Systems

In this volume the contributors use Geographical Information Systems (GIS) to reassess both historic and contemporary Asian countries and traditionally Islamic areas. This highly illustrated and comprehensive work highlights how GIS can be applied to the social sciences. With its description of how to process, construct and manage geographical data the book is ideal for the non-specialist looking for a new and refreshing way to approach Islamic area studies

Geoinformatic methodologies and quantitative tools for detecting hotspots and for multicriteria ranking and prioritization: application on biodiversity monitoring and conservation

Chi ha la responsabilità di gestire un’area protetta non solo deve essere consapevole dei problemi ambientali dell’area ma dovrebbe anche avere a disposizione dati aggiornati e appropriati strumenti metodologici per esaminare accuratamente ogni singolo problema. In effetti, il decisore ambientale deve organizzare in anticipo le fasi necessarie a fronteggiare le prevedibili variazioni che subirà la pressione antropica sulle aree protette. L’obiettivo principale della Tesi è di natura metodologica e riguarda il confronto tra differenti metodi statistici multivariati utili per l’individuazione di punti critici nello spazio e per l’ordinamento degli “oggetti ambientali” di studio e quindi per l’individuazione delle priorità di intervento ambientale. L’obiettivo ambientale generale è la conservazione del patrimonio di biodiversità. L’individuazione, tramite strumenti statistici multivariati, degli habitat aventi priorità ecologica è solamente il primo fondamentale passo per raggiungere tale obiettivo. L’informazione ecologica, integrata nel contesto antropico, è un successivo essenziale passo per effettuare valutazioni ambientali e per pianificare correttamente le azioni volte alla conservazione. Un’ampia serie di dati ed informazioni è stata necessaria per raggiungere questi obiettivi di gestione ambientale. I dati ecologici sono forniti dal Ministero dell’Ambiente Italiano e provengono al Progetto “Carta della Natura” del Paese. I dati demografici sono invece forniti dall’Istituto Italiano di Statistica (ISTAT). I dati si riferiscono a due aree geografiche italiane: la Val Baganza (Parma) e l’Oltrepò Pavese e Appennino Ligure-Emiliano. L’analisi è stata condotta a due differenti livelli spaziali: ecologico-naturalistico (l’habitat) e amministrativo (il Comune). Corrispondentemente, i risultati più significativi ottenuti sono: 1. Livello habitat: il confronto tra due metodi di ordinamento e determinazione delle priorità, il metodo del Vettore Ideale e quello della Preminenza, tramite l’utilizzo di importanti metriche ecologiche come il Valore Ecologico (E.V.) e la Sensibilità Ecologica (E.S.), fornisce dei risultati non direttamente comparabili. Il Vettore Ideale, non essendo un procedimento basato sulla ranghizzazione dei valori originali, sembra essere preferibile nel caso di paesaggi molto eterogenei in senso spaziale. Invece, il metodo della Preminenza probabilmente è da preferire in paesaggi ecologici aventi un basso grado di eterogeneità intesa nel senso di differenze non troppo grandi nel E.V. ed E.S. degli habitat. 2. Livello comunale: Al fine di prendere delle decisioni gestionali ed essendo gli habitat solo delle suddivisioni naturalistiche di un dato territorio, è necessario spostare l’attenzione sulle corrispondenti unità amministrative territoriali (i Comuni). Da questo punto di vista, l’introduzione della demografia risulta essere un elemento centrale oltre che di novità nelle analisi ecologico-ambientali. In effetti, l’analisi demografica rende il risultato di cui al punto 1 molto più realistico introducendo altre dimensioni (la pressione antropica attuale e le sue tendenze) che permettono l’individuazione di aree ecologicamente fragili. Inoltre, tale approccio individua chiaramente le responsabilità ambientali di ogni singolo ente territoriale nei riguardi della difesa della biodiversità. In effetti un ordinamento dei Comuni sulla base delle caratteristiche ambientali e demografiche, chiarisce le responsabilità gestionali di ognuno di essi. Un’applicazione concreta di questa necessaria quanto utile integrazione di dati ecologici e demografici viene discussa progettando una Rete Ecologica (E.N.). La Rete cosi ottenuta infatti presenta come elemento di novità il fatto di non essere “statica” bensì “dinamica” nel senso che la sua pianificazione tiene in considerazione il trend di pressione antropica al fine di individuare i probabili punti di futura fragilità e quindi di più critica gestione.Who has the responsibility to manage a conservation zone, not only must be aware of environmental problems but should have at his disposal updated databases and appropriate methodological instruments to examine carefully each individual case. In effect he has to arrange, in advance, the necessary steps to withstand the foreseeable variations in the trends of human pressure on conservation zones. The essential objective of this Thesis is methodological that is to compare different multivariate statistical methods useful for environmental hotspot detection and for environmental prioritization and ranking. The general environmental goal is the conservation of the biodiversity patrimony. The individuation, through multidimensional statistical tools, of habitats having top ecological priority, is only the first basic step to accomplish this aim. Ecological information integrated in the human context is an essential further step to make environmental evaluations and to plan correct conservation actions. A wide series of data and information has been necessary to accomplish environmental management tasks. Ecological data are provided by the Italian Ministry of the Environment and they refer to the Map of Italian Nature Project database. The demographic data derives from the Italian Institute of Statistics (ISTAT). The data utilized regards two Italian areas: Baganza Valley and Oltrepò Pavese and Ligurian-Emilian Apennine. The analysis has been carried out at two different spatial/scale levels: ecological-naturalistic (habitat level) and administrative (Commune level). Correspondingly, the main obtained results are: 1. Habitat level: comparing two ranking and prioritization methods, Ideal Vector and Salience, through important ecological metrics like Ecological Value (E.V.) and Ecological Sensitivity (E.S.), gives results not directly comparable. Being not based on a ranking process, Ideal Vector method seems to be used preferentially in landscapes characterized by high spatial heterogeneity. On the contrary, Salience method is probably to be preferred in ecological landscapes characterized by a low degree of heterogeneity in terms of not large differences concerning habitat E.V. and E.S.. 2. Commune level: Being habitat only a naturalistic partition of a given territory, it is necessary, for management decisions, to move towards the corresponding administrative units (Communes). From this point of view, the introduction of demography is an essential element of novelty in environmental analysis. In effect, demographic analysis makes the goal at point 1 more realistic introducing other dimensions (actual human pressure and its trend) which allows the individuation of environmentally fragile areas. Furthermore this approach individuates clearly the environmental responsibility of each administrative body for what concerns the biodiversity conservation. In effect communes’ ranking, according to environmental/demographic features, clarify the responsibilities of each administrative body. A concrete application of this necessary and useful integration of ecological and demographic data has been developed in designing an Ecological Network (E.N.).The obtained E.N. has the novelty to be not “static” but “dynamic” that is the network planning take into account the demographic pressure trends in the individuation of the probable future fragile points

Graph matching with a dual-step EM algorithm

This paper describes a new approach to matching geometric structure in 2D point-sets. The novel feature is to unify the tasks of estimating transformation geometry and identifying point-correspondence matches. Unification is realized by constructing a mixture model over the bipartite graph representing the correspondence match and by affecting optimization using the EM algorithm. According to our EM framework, the probabilities of structural correspondence gate contributions to the expected likelihood function used to estimate maximum likelihood transformation parameters. These gating probabilities measure the consistency of the matched neighborhoods in the graphs. The recovery of transformational geometry and hard correspondence matches are interleaved and are realized by applying coupled update operations to the expected log-likelihood function. In this way, the two processes bootstrap one another. This provides a means of rejecting structural outliers. We evaluate the technique on two real-world problems. The first involves the matching of different perspective views of 3.5-inch floppy discs. The second example is furnished by the matching of a digital map against aerial images that are subject to severe barrel distortion due to a line-scan sampling process. We complement these experiments with a sensitivity study based on synthetic data

Islamic Area Studies with Geographical Information Systems

In this volume the contributors use Geographical Information Systems (GIS) to reassess both historic and contemporary Asian countries and traditionally Islamic areas. This highly illustrated and comprehensive work highlights how GIS can be applied to the social sciences. With its description of how to process, construct and manage geographical data the book is ideal for the non-specialist looking for a new and refreshing way to approach Islamic area studies

Regular Hierarchical Surface Models: A conceptual model of scale variation in a GIS and its application to hydrological geomorphometry

Environmental and geographical process models inevitably involve parameters that vary spatially. One example is hydrological modelling, where parameters derived from the shape of the ground such as flow direction and flow accumulation are used to describe the spatial complexity of drainage networks. One way of handling such parameters is by using a Digital Elevation Model (DEM), such modelling is the basis of the science of geomorphometry. A frequently ignored but inescapable challenge when modellers work with DEMs is the effect of scale and geometry on the model outputs. Many parameters vary with scale as much as they vary with position. Modelling variability with scale is necessary to simplify and generalise surfaces, and desirable to accurately reconcile model components that are measured at different scales. This thesis develops a surface model that is optimised to represent scale in environmental models. A Regular Hierarchical Surface Model (RHSM) is developed that employs a regular tessellation of space and scale that forms a self-similar regular hierarchy, and incorporates Level Of Detail (LOD) ideas from computer graphics. Following convention from systems science, the proposed model is described in its conceptual, mathematical, and computational forms. The RHSM development was informed by a categorisation of Geographical Information Science (GISc) surfaces within a cohesive framework of geometry, structure, interpolation, and data model. The positioning of the RHSM within this broader framework made it easier to adapt algorithms designed for other surface models to conform to the new model. The RHSM has an implicit data model that utilises a variation of Middleton and Sivaswamy (2001)’s intrinsically hierarchical Hexagonal Image Processing referencing system, which is here generalised for rectangular and triangular geometries. The RHSM provides a simple framework to form a pyramid of coarser values in a process characterised as a scaling function. In addition, variable density realisations of the hierarchical representation can be generated by defining an error value and decision rule to select the coarsest appropriate scale for a given region to satisfy the modeller’s intentions. The RHSM is assessed using adaptions of the geomorphometric algorithms flow direction and flow accumulation. The effects of scale and geometry on the anistropy and accuracy of model results are analysed on dispersive and concentrative cones, and Light Detection And Ranging (LiDAR) derived surfaces of the urban area of Dunedin, New Zealand. The RHSM modelling process revealed aspects of the algorithms not obvious within a single geometry, such as, the influence of node geometry on flow direction results, and a conceptual weakness of flow accumulation algorithms on dispersive surfaces that causes asymmetrical results. In addition, comparison of algorithm behaviour between geometries undermined the hypothesis that variance of cell cross section with direction is important for conversion of cell accumulations to point values. The ability to analyse algorithms for scale and geometry and adapt algorithms within a cohesive conceptual framework offers deeper insight into algorithm behaviour than previously achieved. The deconstruction of algorithms into geometry neutral forms and the application of scaling functions are important contributions to the understanding of spatial parameters within GISc