Definition Of Neighborhood Research Articles

Route Maps

A map is needed to move accurately and with confidence between widely separated points. This is true if widely separated means at such a remove that one requires a map. We can find our way unaided throughout some neighbourhood of our homes, through its extent varies from person to person and, for one person, with time and conditions. For any persons there are, moreover, a variety of partially overlapping neighborhoods, each associated with a particular mode of travel. A neighbourhood may be sketchy and primitive. In other cases neighbourhood are extensive and known. Within a neighbourhood, orientation is an expression of one's familiarity with the surroundings. Beyond it, some additional device is needed. Such devices are referred to here as maps, or more specifically route maps. The definitions of neighbourhood and map are thus intimately connected. It is concluded that a map is needed whenever we leave our neighbourhood.

A self-organizing algorithm for vector quantizer design applied to signal processing.

Vector quantization plays an important role in many signal processing problems, such as speech/speaker recognition and signal compression. This paper presents an unsupervised algorithm for vector quantizer design. Although the proposed method is inspired in Kohonen learning, it does not incorporate the classical definition of topological neighborhood as an array of nodes. Simulations are carried out to compare the performance of the proposed algorithm, named SOA (self-organizing algorithm), to that of the traditional LBG (Linde-Buzo-Gray) algorithm. The authors present an evaluation concerning the codebook design for Gauss-Markov and Gaussian sources, since the theoretic optimal performance bounds for these sources, as described by Shannon's Rate-Distortion Theory, are known. In speech and image compression, SOA codebooks lead to reconstructed (vector-quantized) signals with better quality as compared to the ones obtained by using LBG codebooks. Additionally, the influence of the initial codebook in the algorithm performance is investigated and the algorithm ability to learn representative patterns is evaluated. In a speaker identification system, it is shown that the the codebooks designed by SOA lead to higher identification rates when compared to the ones designed by LBG.

The application of thermodynamic principles to histochemical and morphometric tissue research: principles and practical outline with focus on the glycosciences.

Physicochemical terms such as entropy or current of entropy are commonly used to refer solely to the description of reactions in the realm of chemistry and physics. Since these thermodynamic terms have a predictive value for the further course of development of such reactions, e.g., extent of a chemical reaction or affinity of molecular interactions, it is tempting to introduce the respective algorithms to biological problems. By combining quantitative morphology with the histochemical visualization of distinct cellular and textural properties such as nuclear DNA contents or intensity of histochemical staining, equations from the general theory of thermodynamics can be adapted. They permit appropriate calculations to be performed which introduce the entropy concept to the processing of the information collected by analysis of structures formed by histochemically labeling cells. The theory of weighted graphs offers the appropriate mathematical tools. Nuclei are defined as vertices. Their DNA contents measured by the integrated optical density or additional cellular features (for example staining intensity of applied immuno-/ligandohistochemical probes) define the associated weights and the minimum spanning tree as a derivative from Voronoi's theorem for the definition of the geometrical neighborhood. This technique is equivalent to syntactic structure analysis as developed by Lu and Fu (1978), Sanfeliu et al. (1981), Kayser and Schlegel (1982), and Kayser (1988). Assuming that the texture of a healthy tissue is displayed in the energetically most efficient and stable manner to perform the required biological functions, i.e., to maintain the lowest level of entropy, deviations from this level are reflected in differences in distances and weights between neighboring nuclei or cells. The measure of textural differences in relation to the normal appearance of an organ or tissue is denoted as structural entropy. Since organisms or their compartments are thermodynamically open systems, they are insufficiently described by the (structural) entropy. This parameter only provides a snapshot, with no information about the status of entropy changes from a directed exchange with the environment. The current of entropy, which is equivalent to the amount of entropy exported or imported through a boundary, is an appropriate measure of the "thermodynamic distance" of the system under consideration from its environment, as is readily appreciated for tumors. A solid tumor is a biological system embedded in another one (healthy tissue). Its current of entropy can be calculated if its boundary and proliferative activity are known. This parameter can be measured by histochemical methods (Ki-67 antibody) or from the cytometric characteristics (percentage of S-phase-related tumor cells), and the boundary can be measured by the volume fraction of the internal vessels and the size of the external surface of the tumor. Since biochemical factors will contribute to the generation and establishment of these structural and thermodynamic features at the level of tissue organization, histochemical studies can uncover the correlation with these parameter alterations. Taking glycohistochemical determinants as an example for this hypothesis, the potential value of combining the results of immuno-/ligandohistochemistry with the data derived from the cytometric or syntactic structure analysis measurements and from the calculations based on thermodynamic theorems is illustrated.

Procudere for Definition of Neighborhood in Faulty Hypercube Multiprocessor

A procedure for definition of neighborhood and fault free subcubes in a faulty hypercube is developed in this study. The cube algebra operations are used for proposed procedure. With this, we can increase the number of faulty elements In the hypercube. Which is obstacle for effective data processing and communication processes in such a system.

Neighbourhood differences in diet: the Atherosclerosis Risk in Communities (ARIC) Study.

STUDY OBJECTIVE: To investigate whether neighbourhood characteristics are related to dietary patterns independently of individual level variables. DESIGN: A cross sectional analysis of the relation between neighbourhood median household income...

Interactive Graphics and Local Statistics

Exploratory spatial data analysis has a series of aims including determining spatial structure in the data, describing and visualizing geographical distributions, exploring spatial dependencies, measuring heterogeneity and identifying outliers. To quantify these phenomena a rich variety of statistics has been proposed. Standard methods use all the data for the entire area under study, yet this area has usually been arbitrarily bounded and may include quite distinctive geographic features. Local statistics are relatively independent of the global boundaries and they attempt to quantify how close a given datum is to the values in its neighbourhood. Since each local statistic focuses on slightly different aspects of the data the use of more than one is suggested. Interactive graphics methods help to link the information from different local statistics and dynamic tools can be used to visualize the effects of changing the neighbourhood definition.

The flow shop with parallel machines: A tabu search approach

A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a flow shop with parallel machines is presented. The algorithm is based on a tabu search technique with a specific neighborhood definition which employs notions of a critical path in a graph and a block of jobs. This is the first improvement algorithm for the considered problem. A special advanced method of implementation improves the local search significantly and increases the speed of the algorithm. Computational experiments (up to 3000 operations and 60 machines) show its excellent numerical properties. The proposed approach can be used for modeling and solving a broad class of flexible flow line scheduling problems.

Automatic neighbourhood identification from population surfaces

The definition of coherent social neighbourhoods has long been a pursuit of social geographers. There has been renewed interest in the identification of neighbourhood and community boundaries for market analysis and service delivery, with the widespread availability of digital spatially referenced population data. Traditional computational approaches to neighbourhood identification have typically involved aspatial multivariate statistical techniques or statistical regionalization approaches which are fundamentally limited by the predefined boundaries in the spatial data. Incorporation of greater spatial analytical capabilities in GIS software has led to a widespread recognition that the nature of the underlying spatial data model places fundamental limitations on the analyses which can be performed. Surface models of population provide an alternative data structure for the development of explicitly geographical analysis techniques. This paper considers the application of computationally intensive regionalization methods to population surface models, and presents a prototype methodology for the automatic identification of social neighbourhoods without the constraints of preexisting zone boundaries.

On the use of neighbourhood-based non-parametric classifiers

Alternative non-parametric classification schemes, which come from the use of different definitions of neighbourhood, are introduced. In particular, the Nearest Centroid Neighbourhood along with the neighbourhood relation derived from the Gabriel Graph and the Relative Neighbourhood Graph are used to define the corresponding ( k-)Nearest Neighbour-like classifiers. Experimental results are reported to compare the performance of the approaches proposed here to the one obtained with the k-Nearest Neighbours rule.

Hyperspherical neighbourhoods and pattern recognition using neural networks

A definition of maximal neighbourhoods in the form of hyperspheres around points in metric spaces is presented. An approximate algorithm to compute the radii of these hyperspheres is described. An exact algorithm to solve the same problem is described next. This exact algorithm is shown to be capable of handling two simple generalisations of the problem scenario. Usefulness of hyperspherical neighbourhoods around points representing patterns in a neural network model is demonstrated by means of numerical simulations.

A Fast Taboo Search Algorithm for the Job Shop Problem

A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in a job shop is presented. The algorithm is based on a taboo search technique with a specific neighborhood definition which employs a critical path and blocks of operations notions. Computational experiments (up to 2,000 operations) show that the algorithm not only finds shorter makespans than the best approximation approaches but also runs in shorter time. It solves the well-known 10 × 10 hard benchmark problem within 30 seconds on a personal computer.

A fast tabu search algorithm for the permutation flow-shop problem

A fast and easily implementable approximation algorithm for the problem of finding a minimum makespan in the permutation flow shop is presented. The algorithm is based on a tabu search technique with a specific neighborhood definition which employs a block of jobs notion. Computational experiments (up to 500 jobs and 20 machines) show its excellent numerical properties.

A new definition of neighborhood of a point in multi-dimensional space

Given a set of points in multi-dimensional space, we propose a new definition for the neighbors of an arbitrary point P. The definition tries to capture the idea that the neighbors should be as near to P and as symmetrically placed around P as possible. In contrast, the conventional nearest neighborhood considers only nearness as the criterion for neighborhood. We propose an iterative procedure to compute the neighbors where the first neighbor is the nearest neighbor. The second and other neighbors are chosen so that at any stage the distance between the centroid of the neighbors and P is as small as possible. The centroid criterion takes care of symmetrical placement of the neighbors. One can use median instead of centroid to define the neighbors. The new definition is free from any user-specified parameter and can be used for pattern classification, clustering and low-level description of dot patterns.

A proximity-based approach to assessing habitat

Planning for either a single species, multiple species, or ecosystems is greatly dependent on spatial interactions in the landscape. Problems exist for evaluating wildlife habitat changes over large ranges of space and time. This paper illustrates the use of Geographic Information Systems (GIS) to evaluate habitat for a single species, ruffed grouse (Bonasa umbellus), following a time series of forest harvests. A habitat suitability model for ruffed grouse is utilized on a two-township study area in north-central Minnesota to assess the habitat suitability changes over time using an even-aged area-control harvesting plan. The results are presented as a habitat quality change map and a contingency table, representing the movement of habitat class areas between time periods resulting from the proposed harvesting. We developed a neighborhood definition to allow for spatially varying habitat values. This work illustrates the ability to ‘look ahead’ and ‘around’ in estimating the impact on wildlife habitat resulting from alternative future management activities.

Adaptive Neighborhood Extended Contrast Enhancement and Its Modifications

A new technique for contrast enhancement, namely adaptive neighborhood extended contrast enhancement, and several original modifications on the same are proposed. Developing from the adaptive contrast enhancement algorithm of Beghdadi and Negrate ( Comput. Vision Graphics Image Process. 46, 1989, 162-174), and the adaptive neighborhood histogram equalization algorithm of Paranjape et al. ( CVGIP Graphical Models Image Process. 54(3), 1992, 259-267), the above techniques have been evolved in order to make image enhancement more adaptive and context sensitive. The adaptive neighborhood extended contrast enhancement algorithm, without its other modifications, only combines the features of these two existing algorithms. This algorithm can be used for both image enhancement and de-enhancement and can also be combined with existing procedures such as power variation. To make the algorithm computationally efficient, the computationally efficient adaptive neighborhood extended contrast enhancement procedure is proposed. This modification achieves significant computational speedup without much loss of image quality. The adaptive neighborhood definition extended contrast enhancement procedure is next proposed in order to make the algorithm further adaptive by making its performance independent of its most sensitive parameter. This procedure achieves better identification of different gray level regions by an analysis of the histogram in the locality of every pixel in the image. The experimental results aptly demonstrate the efficacy of the procedure. This technique can be well applied to other contrast enhancement algorithms for improvement of the quality of the enhanced image. Finally, a correction mechanism called repulsion correction is evolved to correct for a specific inability of contrast enhancement algorithms in separating adjacent regions of nearly equal brightness from each other when surrounded by a very large, brighter or darker region.

Relative sensitivity of a family of closest-point graphs in computer vision applications

Various graph-theoretic definitions of neighborhood have been used in the past in computer vision and clustering applications. In this paper we study the properties of a set of four related closest-point graphs using Monte Carlo methods: (i) the Delaunay triangulation (DT) and its dual, Voronoi tessellation, (ii) the Gabriel graph (GG), (iii) the relative neighborhood graph (RNG), and (iv) the minimum spanning tree (MST). We examine the suitability of these graphs in the detection of structure in noisy images using the Hough transform. We also explore each graph's structural stability under random positional noise. Delaunay triangulation is shown to be the least sensitive to such noisy conditions.

Variants of self-organizing maps

Self-organizing maps have a bearing on traditional vector quantization. A characteristic that makes them more closely resemble certain biological brain maps, however, is the spatial order of their responses, which is formed in the learning process. A discussion is presented of the basic algorithms and two innovations: dynamic weighting of the input signals at each input of each cell, which improves the ordering when very different input signals are used, and definition of neighborhoods in the learning algorithm by the minimal spanning tree, which provides a far better and faster approximation of prominently structured density functions. It is cautioned that if the maps are used for pattern recognition and decision process, it is necessary to fine tune the reference vectors so that they directly define the decision borders.

The existence and capitalization of neighborhood externalities: A reassessment

An anomalous result in the empirical literature on residential property values has been the inability to measure the capitalization of negative neighborhood externalities using disaggregate data. This result has persisted, with few exceptions, across data sets and across definitions of externality and neighborhood.’ This paper argues that the literature may have misspecified the theoretical relationship between residential property values and neighborhood externalities and that this could explain the lack of observed capitalization. A new specification is proposed and given a preliminary test. The conventional literature on neighborhood externalities can be summarized by the hedonic price equation,

URBAN PLANNING AND RESIDENTIAL SEGREGATION: THE EFFECT OF A NEIGHBORHOOD-BASED CITIZEN PARTICIPATION PROJECT

A community development process oriented toward involving the public is encouraged, but the details of public participation have been more recommended than prescribed. City authorities have used a variety of techniques to seek public participation, ranging from public hearings and appointed citizen advisory committees to local neighborhood elections of representatives for community development decisionmaking. Birmingham, Alabama chose the localized electoral method of citizen participation. The Birmingham plan is evaluated every two years, and an important part of this evaluation is citizen review of neighborhood boundaries. As a result of regular review, citizens have an opportunity to determine by boundary definition the racial and class compositions of their own neighborhoods. The current study assesses the effect of citizen definition of neighborhood planning areas on the level of segregation in the communities.

People or Place

Urban policy is often premised on the assumption that neighborhoods are viable units for decision making. However, policymakers and community activists may not agree on the subjective definition of "neighborhood," which may stress either its physical setting or the characteristics of its residents or some combination of both. A set of neighborhood association presidents were asked to describe their communities. Their answers revealed much diversity; the choice of physical rather than human referents to define the neighborhood varied systematically with the sex, race, age, socioeconomic status, and length of community service of each individual. Their disagreement suggests that priorities for community improvements vary with perceptions of neighborhood definition.