A simulation model of home improvement with neighborhood spillover

This paper develops a simulation model of home improvement with neighborhood spillover. The goal is to explore how the household decisions between home improvement and moving shape urban land development and housing markets, and the role of neighborhood spillover. The model is implemented based on a monocentric city framework. The existence of neighborhood spillover effects slows down the pace of urban land development, while it also significantly increases average household duration of residence and amount of home improvement investment. In practice, the neighborhood spillover effects can be considered as a form of social capital which connects homeownership and neighborhood quality. Based on the simulation results and sensitivity analysis of key policy relevant parameters (social interaction strength, neighborhood size, transportation cost), the paper further explores implications for public policymaking related to transportation, housing markets, and land use. The dynamic simulation tool developed in this paper can also be found useful in other land use, urban and regional modeling.     

Label Propagation through Linear Neighborhoods

In many practical data mining applications such as text classification, unlabeled training examples are readily available, but labeled ones are fairly expensive to obtain. Therefore, semi supervised learning algorithms have aroused considerable interests from the data mining and machine learning fields. In recent years, graph-based semi supervised learning has been becoming one of the most active research areas in the semi supervised learning community. In this paper, a novel graph-based semi supervised learning approach is proposed based on a linear neighborhood model, which assumes that each data point can be linearly reconstructed from its neighborhood. Our algorithm, named linear neighborhood propagation (LNP), can propagate the labels from the labeled points to the whole data set using these linear neighborhoods with sufficient smoothness. A theoretical analysis of the properties of LNP is presented in this paper. Furthermore, we also derive an easy way to extend LNP to out-of-sample data. Promising experimental results are presented for synthetic data, digit, and text classification tasks.     

Neighborhood rough set based heterogeneous feature subset selection

Feature subset selection is viewed as an important preprocessing step for pattern recognition, machine learning and data mining. Most of researches are focused on dealing with homogeneous feature selection, namely, numerical or categorical features. In this paper, we introduce a neighborhood rough set model to deal with the problem of heterogeneous feature subset selection. As the classical rough set model can just be used to evaluate categorical features, we generalize this model with neighborhood relations and introduce a neighborhood rough set model. The proposed model will degrade to the classical one if we specify the size of neighborhood zero. The neighborhood model is used to reduce numerical and categorical features by assigning different thresholds for different kinds of attributes. In this model the sizes of the neighborhood lower and upper approximations of decisions reflect the discriminating capability of feature subsets. The size of lower approximation is computed as the dependency between decision and condition attributes. We use the neighborhood dependency to evaluate the significance of a subset of heterogeneous features and construct forward feature subset selection algorithms. The proposed algorithms are compared with some classical techniques. Experimental results show that the neighborhood model based method is more flexible to deal with heterogeneous data.     

A stochastically constrained cellular model of urban growth

Recent approaches to modeling urban growth use the notion that urban development can be conceived as a self-organizing system in which natural constraints and institutional controls (land-use policies) temper the way in which local decision-making processes produce macroscopic patterns of urban form. In this paper a cellular automata (CA) model that simulates local decision-making processes associated with fine-scale urban form is developed and used to explore the notion of urban systems as self-organizing phenomenon. The CA model is integrated with a stochastic constraint model that incorporates broad-scale factors that modify or constrain urban growth. Local neighborhood access rules are applied within a broader neighborhood in which friction-of-distance limitations and constraints associated with socio-economic and bio-physical variables are stochastically realized. The model provides a means for simulating the different land-use scenarios that may result from alternative land-use policies. Application results are presented for possible growth scenarios in a rapidly urbanizing region in south east Queensland, Australia.     

基于模糊连通性的彩色图像切片序列分割方法

为了对中国虚拟人彩色图像切片序列中人体组织实现分割提取，提出了一种以模糊连通性理论为基础的三维分割方法，结合基于向量的聚类方法和各向异性扩散的局部邻域内数据采样策略，这种方法能够对彩色切片序列中人体组织实现精确定位和分割．本文也将展示一些分割结果和提取组织重建的图例．     

一种基于邻域粒度熵的离群点检测算法

离群点检测是数据挖掘领域的重要研究方向之一,其目的是找出数据集中与其他数据对象显著不同的一小部分数据。离群点检测在网络入侵检测、信用卡欺诈检测、医疗诊断等领域有着非常重要的应用。近年来,粗糙集理论被广泛用于离群点检测,然而,经典的粗糙集模型不能有效处理数值型数据。对此,本文利用邻域粗糙集模型来检测离群点,在邻域粗糙集中引入一种新的信息熵模型——邻域粒度熵。基于邻域粒度熵,提出一种新的离群点检测算法OD_NGE。实验结果表明,相对于已有的离群点检测算法,OD_NGE具有更好的离群点检测性能。     

Population-driven Urban Road Evolution Dynamic Model

In this paper, we propose a road evolution model by considering the interaction between population distribution and urban road network. In the model, new roads need to be constructed when new zones are built, and existing zones with higher population density have higher probability to connect with new roads. The relative neighborhood graph and a Fermat-Weber location problem are introduced as the connection mechanism to capture the characteristics of road evolution. The simulation experiment is conducted to demonstrate the effects of population on road evolution. Moreover, the topological attributes for the urban road network are evaluated using degree distribution, betweenness centrality, coverage, circuitness and treeness in the experiment. Simulation results show that the distribution of population in the city has a significant influence on the shape of road network, leading to a growing heterogeneous topology.     

Type-2 Fuzzy Markov Random Fields and Their Application to Handwritten Chinese Character Recognition

In this paper, we integrate type-2 (T2) fuzzy sets with Markov random fields (MRFs) referred to as T2 FMRFs, which may handle both fuzziness and randomness in the structural pattern representation. On the one hand, the T2 membership function (MF) has a 3-D structure in which the primary MF describes randomness and the secondary MF evaluates the fuzziness of the primary MF. On the other hand, MRFs can represent patterns statistical-structurally in terms of neighborhood system and clique potentials and, thus, have been widely applied to image analysis and computer vision. In the proposed T2 FMRFs, we define the same neighborhood system as that in classical MRFs. To describe uncertain structural information in patterns, we derive the fuzzy likelihood clique potentials from T2 fuzzy Gaussian mixture models. The fuzzy prior clique potentials are penalties for the mismatched structures based on prior knowledge. Because Chinese characters have hierarchical structures, we use T2 FMRFs to model character structures in the handwritten Chinese character recognition system. The overall recognition rate is 99.07%, which confirms the effectiveness of the proposed method.     

A General Selective Averaging Method for Piecewise Constant Signal and Image Processing

Piecewise constant signals and images, which are sampled from piecewise constant functions, are an important kind of data. Typical examples include bar code signals, images of texts, hand-written signatures, Quick Response codes (QR codes), logos and cartoons. Selective averaging method is a powerful technique for this kind of signal and image denoising. In this paper, we propose a general selective averaging method (GSAM) to use more flexible weights compared to the previous one. Some convergence results and a probabilistic interpretation are provided for its iterated version. For the choice of the weight parameter, we discuss its influence on the asymptotic rate of convergence. We also study its influence on the denoising results with a moderate number of iterations. Then, our method is compared to the iterated neighborhood filter in signal denoising. In 2D case, we propose a novel extension called the alternating GSAM (AGSAM). We similarly introduce an alternating neighborhood filter. Experimental results demonstrate that our method is especially effective for Gaussian noise removal from noisy piecewise constant signals and images.     

基于0-1整数规划交通信号控制优化模型及算法

基于城市交通拥堵的现实背景,主要研究了城市交通网络中信号灯的实时控制的优化问题。通过给出0-1整数规划的模型,定量研究了交通网络中路口信号相位之间的关系,并建立了交通信号控制适时优化模型对其进行优化。针对一组具有不同信号周期的路口信号灯,假设每个路口的相序已知,任意两个路口的相位差未知,综合考虑绿信比和相位差,寻找最优控制策略。在数学模型中,假定交通网络路口具有不同的信号周期和相位差预先未知,在各路口信号周期的最小公倍数的时间段内,通过决策信号灯在任意时间段内的状态来最小化总的车辆延迟时间。问题研究中涉及大量的0-1变量,通过定义内生、外生变量,形成了对各变量的有效约束,使模型在实际仿真实验中的计算复杂度大大减少。最后利用启发式算法对给出的算例进行仿真验证。     

Sensitivity of sequence methods in the study of neighborhood change in the United States

There is a recent surge in research focused on urban transformations in the United States via empirical analysis of neighborhood sequences. The alignment-based sequence analysis methods have seen many applications in urban neighborhood change research. However, it is unclear to what extent these methods are robust in terms of producing consistent and converging neighborhood sequence typologies. This article sheds light on this issue by applying four sequence analysis methods to the same data set – 50 largest Metropolitan Statistical Areas (MSAs) of the United States from 1970 to 2010, and finds that these methods do not provide converging neighborhood sequence typologies, and their behavior varies across MSAs, thus prohibiting meaningful comparisons of similar studies. MSAs with higher average household income in 1970 tend to be less sensitive to the choice of the SA methods. In other words, when investigating neighborhood change in these MSAs, different SA methods tend to produce a more converging neighborhood sequence typology. Comparatively, for MSAs hosting neighborhoods which have experienced frequent changes during the period 1970–2010, they are less likely to produce similar typologies with different SA methods. In addition, there is a big difference in the neighborhood sequence typology between applying the classic SA methods with varying costs and using the SA variant focusing on the second-order sequence property. After comparing the behavior of these methods, we highlight one method (“OMecenter”) which leverages the socioeconomic similarities of neighborhoods and suggest researchers consider it as the building block towards designing a meaningful sequence analysis method for neighborhood change research.     

Predicting and mapping neighborhood-scale health outcomes: A machine learning approach

Estimating health outcomes at a neighborhood scale is important for promoting urban health, yet costly and time-consuming. In this paper, we present a machine-learning-enabled approach to predicting the prevalence of six common non-communicable chronic diseases at the census tract level. We apply our approach to the City of Austin and show that our method can yield fairly accurate predictions. In searching for the best predictive models, we experiment with eight different machine learning algorithms and 60 predictor variables that characterize the social environment, the physical environment, and the aspects and degrees of neighborhood disorder. Our analysis suggests that (a) the sociodemographic and socioeconomic variables are the strongest predictors for tract-level health outcomes and (b) the historical records of 311 service requests can be a useful complementary data source as the information distilled from the 311 data often helps improve the models' performance. The machine learning models yielded from this study can help the public and city officials evaluate future scenarios and understand how changes in the neighborhood conditions can lead to changes in the health outcomes. By analyzing where the most significant discrepancies between the predicted and the actual values are, we will also be ready to identify areas of best practice and areas in need of greater investment or policy intervention.     

A simplified approach to independent component analysis

Independent Component Analysis (ICA) is one of the fastest growing fields in the area of neural networks and signal processing. Blind Source Separation (BSS) is one of the applications of ICA. In this paper, ICA has been used for separating unknown source signals. BSS is used to extract independent signal components from their observed linear mixtures at an array of sensors. Various statistical techniques based on information theoretic and algebraic approaches exist for performing ICA. In this paper, we have used an objective function based on independence criterion of the signals. Optimisation of this objective function yields a neural algorithm along with a non-linear function for signal separation. Performance of the algorithm for artificially generated signals as well as audio signals has been evaluated.     

Unsupervised urban area extraction from SAR imagery using GMRF

This paper presents a new method for unsupervised urban area extraction from SAR imagery using two different GMRF models. One model is the T-based GMRF model proposed by Xavier Descombes specially for acquiring urban area in panchromatic SPOT imagery. When it is used for urban area extraction from SAR imagery, some missing detection occurs. The other model is the conventional GMRF model that requires training samples for urban area extraction. When it is used for SAR imagery, the extraction result includes all urban areas and some false detection. Three steps are made up in our method. First, we adopt a threshold for the T-based GMRF model parameter T to acquire the result of urban area extraction. Then, taking the result as training samples, we estimate the conventional GMRF model parameters and acquire a new result of urban area extraction. Finally, we fuse the two results above using a region-growing algorithm to form the final accurate urban area extraction. Experimental results show that the proposed unsupervised approach can obtain accurate urban area delineation. The text was submitted by the authors in English. Yang Yong. Born in 1978. Now a postgraduate in the Department of Communication and Information Systems, School of Electronic Information, Wuhan University. The research direction is Image Processing. Scientific interestsare SAR image segmentation and classification with the Markov random field approach. Hong Sun. Born in 1954. Graduated from the Huazhong University of Science and Technology of Electrical Engineering in 1982. Received a Doctoral degree in 1995. Author of Advanced Digital Signal Processing, which is widely used as a textbook for graduated students in China. Scientific interests include statistical signal processing, image analysis, and communication signal processing. Yongfeng Cao. Born in 1976. Graduated from Wuhan University of China in 1999. Assistant and doctoral candidate in the laboratory of Signal Processing and Modern Communication, School of Electronic Information, Wuhan University, China. Scientific interests include Markov random fields, Watershed transformation, and SAR image interpretation.     

Incorporation of extended neighborhood mechanisms and its impact on urban land-use cellular automata simulations

Urban cellular automata (CA) models are broadly used in quantitative analyses and predictions of urban land-use dynamics. However, most urban CA developed with neighborhood rules consider only a small neighborhood scope under a specific spatial resolution. Here, we quantify neighborhood effects in a relatively large cellular space and analyze their role in the performance of an urban land use model. The extracted neighborhood rules were integrated into a commonly used logistic regression urban CA model (Logistic-CA), resulting in a large neighborhood urban land use model (Logistic-LNCA). Land-use simulations with both models were evaluated with urban expansion data in Xiamen City, China. Simulations with the Logistic-LNCA model raised the accuracies of built-up land by 3.0%–3.9% in two simulation periods compared with the Logistic-CA model with a 3 × 3 kernel. Parameter sensitivity analysis indicated that there was an optimal large window size in cellular space and a corresponding optimal parameter configuration.     

Classification of mental tasks using stockwell transform

In recent years, various physiological signal based rehabilitation systems have been developed for the physically disabled in which electroencephalographic (EEG) signal is one among them. The efficiency of such a system depends upon the signal processing and classification algorithms. In order to develop an EEG based rehabilitation or assistive system, it is necessary to develop an effective EEG signal processing algorithm. This paper proposes Stockwell transform (ST) based analysis of EEG dynamics during different mental tasks. EEG signals from Keirn and Aunon database were used in this study. Three classifiers were employed such as k-means nearest neighborhood (kNN), linear discriminant analysis (LDA) and support vector machine (SVM) to test the strength of the proposed features. Ten-fold cross validation method was used to demonstrate the consistency of the classification results. Using the proposed method, an average accuracy ranging between 84.72% and 98.95% was achieved for multi-class problems (five mental tasks).     

Markov random fields for catadioptric image processing

Images obtained with catadioptric sensors contain significant deformations which prevent the direct use of classical image treatments. Thus, Markov random fields (MRF) whose usefulness is now obvious for projective image processing, cannot be used directly on catadioptric images because of the inadequacy of the neighborhood. In this paper, we propose to define a new neighborhood for MRF by using the equivalence theorem developed for central catadioptric sensors. We show the importance of this adaptation for segmentation, image restoration and motion detection.     

Contrast of a fuzzy relation

In this paper we address a key problem in many fields: how a structured data set can be analyzed in order to take into account the neighborhood of each individual datum. We propose representing the dataset as a fuzzy relation, associating a membership degree with each element of the relation. We then introduce the concept of interval-contrast, a means of aggregating information contained in the immediate neighborhood of each element of the fuzzy relation. The interval-contrast measures the range of membership degrees present in each neighborhood. We use interval-contrasts to define the necessary properties of a contrast measure, construct several different local contrast and total contrast measures that satisfy these properties, and compare our expressions to other definitions of contrast appearing in the literature. Our theoretical results can be applied to several different fields. In an Appendix A, we apply our contrast expressions to photographic images.     

几种常用三角网格滤波方法的比较

滤波作为一种信号处理的方法,广泛地运用在许多不同领域。在逆向工程中,由测量数据建立的三角网格模型常常包含着各种噪声点。为了提高网格模型质量,常需要对网格模型进行光顺、除噪。滤波是一种快速而有效方法,被广泛应用于光顺和除噪的过程中。该文介绍了3种常用的网格滤波方法,并且比较了这3种不同方法在处理不同网格模型的滤波效果,并给出了它们的适用范围。