An imprecise region is referred to as a geographical area without a clearly-defined boundary in the literature. Previous clustering-based approaches exploit spatial information to find such regions. However, the prior studies suffer from the following two problems: the subjectivity in selecting clustering parameters and the inclusion of a large portion of the undesirable region (i.e., a large number of noise points). To overcome these problems, we present DIR-ST @math , a novel framework for delineating an imprecise region by iteratively performing density-based clustering, namely DBSCAN, along with not only spatio--textual information but also temporal information on social media. Specifically, we aim at finding a proper radius of a circle used in the iterative DBSCAN process by gradually reducing the radius for each iteration in which the temporal information acquired from all resulting clusters are leveraged. Then, we propose an efficient and automated algorithm delineating the imprecise region via hierarchical clustering. Experiment results show that by virtue of the significant noise reduction in the region, our DIR-ST @math method outperforms the state-of-the-art approach employing one-class support vector machine in terms of the @math score from comparison with precisely-defined regions regarded as a ground truth, and returns apparently better delineation of imprecise regions. The computational complexity of DIR-ST @math is also analytically and numerically shown.
. To delineate an imprecise region, researchers have acquired data either from human opinions @cite_25 @cite_20 or from computer applications @cite_13 @cite_1 @cite_27 @cite_29 @cite_3 . With the help of volunteers, an empirical study was conducted in @cite_25 by generating a probabilistic representation of the Downtown Santa Barbara area. Since the task of collecting such empirical data is often expensive and thus is not viable in a wide range of real applications, computer-based approaches have been developed as an alternative. The method in @cite_13 exploited the textual content on the web to discover the desired imprecise region. Another similar approach made use of online yellow pages to geo-locate business names inside a vernacular region, thus unfolding the region's location @cite_27 . With the rapid growth of online social media, geo-tagged points have been widely used for analyzing regions of interest, including imprecise regions @cite_1 @cite_29 @cite_3 . However, since the collected data may be textually heterogeneous, they inevitably contain noise in this context.
@cite_29 @cite_1 @cite_3 @cite_27 @cite_13 @cite_25 @cite_20
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Gazeteers and geographical thesauri can be regarded as parsimonious spatial models that associate geographical location with place names and encode some semantic relations between the names. They are of particular value in pro- cessing information retrieval requests in which the user employs place names to specify geographical context. Typically the geometric locational data in a gazetteer are cone ned to a simple footprint in the form of a centroid or a minimum bounding rectangle, both of which can be used to link to a map but are of limited value in determining spatial relationships. Here we describe a Voronoi diagram method for generating approximate regional extents from sets of centroids that are respectively inside and external to a region. The resulting approximations provide measures of areal extent and can be used to assist in answering geographical queries by evaluat- ing spatial relationships such as distance, direction and common boundary length. Preliminary experimental evaluations of the method have been performed in the context of a semantic modelling system that combines the centroid data with hierarchical and adjacency relations between the associated place names. Terms used to describe city centers, such as Downtown, are key concepts in everyday or vernacular language. Here, we explore such language by harvesting georeferenced and tagged metadata associated with 8 million Flickr images and thus consider how large numbers of people name city core areas. The nature of errors and imprecision in tagging and georeferencing are quantified, and automatically generated precision measures appear to mirror errors in the positioning of images. Users seek to ascribe appropriate semantics to images, though bulk-uploading and bulk-tagging may introduce bias. Between 0.5–2 of tags associated with georeferenced images analyzed describe city core areas generically, while 70 of all georeferenced images analyzed include specific place name tags, with place names at the granularity of city names being by far the most common. Using Flickr metadata, it is possible not only to describe the use of the term Downtown across the USA, but also to explore the borders of city center neighborhoods at the level of individual cities, whilst accounting for bias by the use of tag profiles. Spatial information takes different forms in different applications, ranging from accurate coordinates in geographic information systems to the qualitative abstractions that are used in artificial intelligence and spatial cognition. As a result, existing spatial information processing techniques tend to be tailored towards one type of spatial information, and cannot readily be extended to cope with the heterogeneity of spatial information that often arises in practice. In applications such as geographic information retrieval, on the other hand, approximate boundaries of spatial regions need to be constructed, using whatever spatial information that can be obtained. Motivated by this observation, we propose a novel methodology for generating spatial scenarios that are compatible with available knowledge. By suitably discretizing space, this task is translated to a combinatorial optimization problem, which is solved using a hybridization of two well-known meta-heuristics: genetic algorithms and ant colony optimization. What results is a flexible method that can cope with both quantitative and qualitative information, and can easily be adapted to the specific needs of specific applications. Experiments with geographic data demonstrate the potential of the approach. John Shelton Reed's maps of the South and Dixie in 1970s and 1980s indicated the shrinking boundaries of these two vernacular regions. This study revisits the South and Dixie. Using electronic telephone directories, this study collected all business names with "Southern" and "Dixie" in all the cities in the US. A univariate local indicator of spatial association (LISA) analysis was used to identify the clusters of high and low values of normalized values of the terms. These analyses helped identify the current core regions of Dixie land and the South. The results indicate that "the South" and "Dixie" boundary erosion is noticeable. The study identified a previously unnoticed island of "Dixie" in Utah. Southern and Dixie identities are stronger in non-metropolitan counties compared to metropolitan and micropolitan counties. Southern and Dixie identities are eroding gradually: while the erosion of southern identity is very slow, the erosion of Dixie identity seems to be faster. Overall, it may be more appropriate to refer "Dixieland" as "Dixie islands" today, but the South is still the South. Abstract: Los mapas del Sur y Dixie en los 70s y 80s de John Shelton Reed indicaron una reduccion en los limites de esas dos regiones autoctonas. Este analisis estudia al Sur y Dixie. Usando directorios telefonicos electronicos, este estudio recopila los nombres de negocios con "Southern" y "Dixie" en todas las ciudades de Los Estados Unidos. Un analisis univariable LISA (Indicador Local de Asociacion Espacial) fue usado para identificar los conglomerados de valores altos y bajos de los valores normalizados de los terminos. Este analisis ayudo a identificar hoy las regiones de Dixie Land y el Sur. Los resultados indican que la erosion de los limites del "Sur" y "Dixie" es notable. El estudio identifico en Utah una "Isla de Dixie" que no habia sido notada anteriormente. Las identidades de Southern y Dixie son mas fuertes en los condados no-metropolitanos que en los condados metropolitanos y micropolitanos. Las identidades de Southern y Dixie se estan deteriorando gradualmente; mientras que el deterioro de las identidades del sur es bastante lento, el deterioro de Dixie parece ser mas rapido. En general, seria mas apropiado referirse hoy a "Dixieland" como "Islas Dixie", pero el Sur todavia es el Sur. This paper describes several steps in the derivation of boundaries of imprecise regions using the Web as the information source. We discuss how to use the Web to obtain locations that are part of and locations that are not part of the region to be delineated, and then we propose methods to compute the region algorithmically. The methods introduced are evaluated to judge the potential of the approach. Humans think and talk about regions and spatial relations imprecisely, in terms of vague concepts that are fuzzy or probabilistic (e.g., downtown, near). The functionality of geographic information systems will be increased if they can interpret vague queries. We discuss traditional and newer approaches to defining and modeling spatial queries. Most of the research on vague concepts in information systems has focussed on mathematical and computational implementation. To complement this, we discuss behavioral-science methods for determining the referents of vague spatial terms, particularly vague regions. We present a study of the empirical determination of downtown Santa Barbara. We conclude with a discussion of prospects and problems for integrating vague concepts into geographic information systems. Cognitive regions are regions in the mind, reflecting informal ways individuals and cultural groups organize their understanding of earth landscapes. Cognitive region boundaries are typically substantially vague and their membership functions are substantially variable – the transition from outside to inside the region is imprecise or vague, and different places within the region are not equally strong or clear as exemplars of the region. Methods for assessing and cartographically depicting cognitive regions, as with other vague geographic regions, have traditionally implied an inappropriate level of boundary sharpness and membership uniformity, such as when boundaries are mapped as precise lines. Research in recent decades has explored methods for assessing and depicting boundary vagueness and membership variability, either within or across individuals, but has still assumed homogeneity and regularity in the vagueness and variability. In this article, we present two studies that assess the cognitive regi...