The use of Rough Set and Spatial Statistic in evaluating the Periurban Fringe

The distinction among urban, peri-urban and rural areas inside a territory represents a classical example of uncertainty in land classification. The transition among the three classes is not much clear and can be described with Sorites Paradox, considering the residential buildings and the settlements. Peri-urban fringe can be considered as a transition zone between urban and rural areas, as an area with its own intrinsic organic rules, as a built area without formal organisation or as an abandoned rural area contiguous to urban centres. In any case, concepts as density of buildings, services and infrastructures or the degree of rural, residential and industrial activities, will lead to uncertainty in defining classes, due to the uncertainty in combining some properties. One of the methods which can be utilized is the rough sets theory, which represents a different mathematical approach to uncertainty capturing the indiscernibility. The definition of a set is connected to information knowledge and perception about phenomena. Some phenomena can be classified only in the context of the information available about them. Two different phenomena can be indiscernible in some contexts and classified in the same way (Pawlak 83). The rough sets approach to data analysis hinges on two basic concepts, the lower approximation which considers all the elements that doubtlessly belong to the class, and the upper approximation which includes all the elements that possibly belong to the class. The rough sets theory furthermore takes into account only properties which are independent. This approach has been tested in the case of study of Potenza Province. This area, located in Southern Italy, is particularly suitable to the application of this theory, because it includes 100 municipalities with different number of inhabitants, quantity of services and distance from the main road infrastructures.

L'uso delle tecniche di analisi spaziale per la delimitazione delle aree periurbane del sistema insediativo della provincia di potenza

In questo lavoro si vuole presentare un’esperienza d’uso della geostatistica per l’individuazione degli ambiti periurbani definiti nella legge urbanistica 23/99 della Regione Basilicata. Questa ricerca è stata sviluppata nell’ambito territoriale della provincia di Potenza, che in perfetta tendenza con la maggior parte delle realtà territoriali, non si sottrae al fenomeno della dispersione insediativa. L’elevato consumo di suolo dovuto all’insediamento diffuso delinea l’ambito in cui sono concentrate le aspettative alla trasformazione urbana, per tale motivo è stata condotta un’analisi effettuando un rilievo puntuale del sistema insediativo e della viabilità minore, il lavoro procede attraverso l’impiego di tecniche di analisi spaziale in ambiente GIS. Le tecniche di geostatistica applicata agli elementi puntuali, in particolare la densità di Kernel basata su modelli Grid, hanno fornito una prima definizione degli “areali della casa sparsa”. Le operazioni di Map Algera tra Grid hanno consentito di definire, attraverso le regole di inclusione o esclusione di alcuni fattori legati agli aspetti fisici del territorio, il perimetro del periurbano per i centri urbani del territorio della provincia di Potenza. Emerge che la dispersione insediativa non è influenzata dai fattori come acclività, dissesti o presenza di nuclei rurali storici, ma essa si configura come un insediamento disperso a corona dell’area urbana o lungo le direttrici di connessione tra i vari comuni della provincia

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S

Density Analysis on Large Geographical Databases. Search for an Index of Centrality of Services at Urban Scale

Geographical databases are available to date containing detailed and georeferenced data on population, commercial activities, business, transport and services at urban level. Such data allow examining urban phenomena at very detailed scale but also require new methods for analysis, comprehension and visualization of the spatial phenomena. In this paper a density-based method for extracting spatial information from large geographical databases is examined and first results of its application at the urban scale are presented. Kernel Density Estimation is used as a density based technique to detect clusters in spatial data distributions. GIS and spatial analytical methods are examined to detect areas of high services\u2019 supply in an urban environment. The analysis aims at identifying clusters of services in the urban environment and at verifying the correspondence between urban centres and high levels of service

Atelier "Systèmes d'Information et de Décision pour l'Environnement" (SIDE 2009)

National audienceLes Systèmes d'Information et les Systèmes d'Aide à la Décision représentent des solutions de plus en plus performantes pour relever les récents challenges concernant la gestion et l'analyse des données environnementales. Souvent, le recueil ou l'acquisition des données environnementales restent dans le domaine environnemental un vrai challenge car les techniques mises en oeuvre (ex : enquêtes) ou l'instrumentation déployée (ex : satellite) sont lourdes et onéreuses. Or cette activité de recueil et d'acquisition est essentielle car sans information pertinente et de qualité les Systèmes d'Information ou d'Aide à la Décision deviennent rapidement inopérants. L'objectif de l'atelier est de présenter les dernières avancées dans le domaine des Systèmes d'Information mais aussi de présenter des outils et des méthodes permettant d'acquérir ou d'extraire de l'information d'une part et de mettre en forme cette information pour alimenter un système d'information d'autre part. L'atelier est ouvert aussi bien à la présentation de travaux déjà appliqués au contexte de l'environnement, qu'à des réflexions plus prospectives sur les possibilités d'utilisation d'un produit de la recherche en informatique pour une application environnementale. La journée d'atelier a été découpée en trois sessions. La première présente des méthodes et des outils permettant de mettre en forme des données sur l'eau, la seconde s'intéresse aux systèmes et méthodes pour la gestion des territoires et la troisième concerne les systèmes d'aide à la décision. Un premier article présente un système informatique pour la collaboration interdisciplinaire basée sur une théorie sociologique appliquée à des problématiques liées à l'eau. Un autre travail propose l'intégration d'outils d'analyse multicritères dans un S.I.G couplé à des modèles pour l'évaluation du potentiel aquifère des bassins versants. D'autres travaux portent sur une méthodologie de traitement d'images pour répondre à des besoins de modélisation hydrologique à différentes échelles. Les techniques multicritères et de statistiques spatiales sont au centre de deux autres articles, l'un pour la valorisation des continuums écologiques et l'autre pour l'analyse du développement urbain. La gestion des risques environnementaux est abordée par des recherches sur une infrastructure logicielle d'intégration à base d'agents ; une application est faite à l'évolution de la carte du risque d'incendie de forêt. Un papier présente une nouvelle architecture d'un système d'information pour la géolocalisation des animaux pour la prévention des risques sanitaires. Un article traite de l'écoulement des eaux et des polluants à l'échelle du bassin versant et introduit une méthode incrémentale et interactive d'apprentissage. Un dernier article décrit un outil d'extraction de connaissances pour l'aide à la qualification de l'état des milieux aquatiques. La qualité des travaux laisse présager une journée d'atelier particulièrement enrichissante. Nous remercions par ailleurs tous les membres du comité de programme pour leur excellent travail ainsi que les auteurs des articles

Analysis of Vehicle Use Patterns during Military Field Exercises to Identify Potential Roads

Military training is an intensive land use and can cause negative environmental effects. Many studies conducted under Integrated Training Area Management (ITAM) for quantifying the impact resulted from the military training exercise found that off-road vehicular activities during training exercises cause the major impact to the training land. Vehicle land use patterns at a certain location affect the impact severity: concentrated and repeated traffic create more serious damage to the land compared to the dispersed offroad vehicle movements. Those areas heavily disturbed by off-road traffic may require a longer period of time or special treatments for the land to return to its pre-disturbed status. Based on the impact severity and the shape of the disturbed area, some areas can be considered as potential roads, defined as the roads newly formed by concentrated offroad traffic during the military training exercises, or the roads currently exist but have not been mapped. Potential roads need to be rehabilitated, have traffic dispersed to return the land to its natural status, or to be included in the established road construction and maintenance programs. As Global Positioning System (GPS) has been used for monitoring vehicles\u27 activities during military training exercises; it enables the analysis of vehicle movement patterns. The vehicle movement patterns are characterized as the percentage of vehicle travel every day, vehicles\u27 on and off road travel, the frequencies of vehicle\u27s off-road velocity and turning radius. GPS vehicle tracking data collected during an eight-day reconnaissance training exercises in Yakima Training Center (YTC) in October 2001 were analyzed for vehicle movement patterns. Comparison of the on-road and off-road movement patterns indicates that potential roads may exist on the locations where the concentrated traffic or a high speed movement occurred. Based on the analysis of the movement patterns, factors were extracted to characterize the special movement patterns that indicate the vehicles moved on a potential road. The YTC was divided into small study units, and a multicriteria method was developed to determine if a study unit is a portion of a potential road. The multicriteria method was evaluated by comparing the predictions to the site visit results on 34 selected road segments that met different criteria levels. Results show that locations met higher criteria levels have higher possibilities to be roads: the location met all five criteria has an approximately 91% possibility for road existence; those met four criteria has an approximately 55% possibility; and for those met criteria level two or three, there is an approximately 14% probability for road existence. The analysis of updated off-road shows the percentage of vehicle off-road movement drops from 20.0% to 15.8% after excluding the potential road moving data. As an alternative method, a neural network approach for identifying the potential roads was introduced and compared to the multicriteria method. The neural network method obtained an approximately 85% accuracy when tested by on-road grids, successfully identified the high-way segment as road, and predicted approximately 31% off-road grids as potential road grids. Results show that the neural network method, although emphasized in factors different from the multicriteria method, has approximately 78% accuracy for identifying the potential road locations. The prediction from the neural network method was found highly correlated to the one of the criterion: vehicles travel in different directions. Simplified methods were also developed to identify potential roads by investigating the GPS point density, vehicle velocity, and the number of passes within a study unit. A simple linear relationship was found between the number of passes and the possibility for road existence. Although using vehicle velocity for identifying the potential roads may not be the best choose, velocity is still considered as one of the most important features to characterize vehicle movements and to locate special movement patterns. Considering the discrete situation in the predicted potential road areas, a kernel smoothing technique was introduced and applied to smooth the results to improve the continuity of the potential roads. The application found the kernel smoothing technique was able to obtain continuous potential road grids by selecting reasonable bandwidth

Understanding Geo-Social Network Patterns: Computation, Visualization, and Usability

Geo-social networks are formed by flows of physical entities (e.g., humans, vehicles, sensors, animals), and communication (e.g., information, ideas, innovation) that connect places to places and individuals to individuals. Several major problems remain to be addressed for understanding the complex patterns in geo-social networks. This dissertation makes the following contributions to the theory and methodologies that aim at understanding complex geo-social data by integrating methods of computation, visualization and usability evaluation. Chapter 2 introduces a novel network-based smoothing approach that addresses the size-difference and small area problem in calculating and mapping locational (graph) measures in spatial interaction networks. The new approach is a generic framework that can be used to smooth various graph measures which help examine multi-space and multi-scale characteristics of geo-social data. Chapter 3 introduces a space-time visualization approach to discover spatial, temporal and relational patterns in a dynamic geo-social network embedded in space and time. By developing and visualizing a measure of connectedness across space and time, the new approach facilitates the discovery of hot spots (hubs, where connectedness is strong) and the changing patterns of such spots across space and time. Chapter 4 introduces a series of user evaluations to obtain knowledge on how map readers perceive information presented with flow maps, and how design factors such as flow line style (curved or straight) and layout characteristics may affect flow map perception and users’ performance in addressing different tasks for pattern exploration. The findings of this study have significant implications for iterative design, interaction strategies and further user experiments on flow mapping