Neighborhood Information System Research Articles

Weighted-Neighborhood-Information-Network-Enabled Anomaly Detection Method for Electronic Sensors and Sensor Networks

As electronic sensors and sensor networks advance, perception data are increasingly characterized by mixed attributes. Traditional anomaly detection methods predominantly focus on numerical attributes. In this paper, we introduce a weighted neighborhood information network (WNIN)-enabled anomaly detection method tailored for mixed-attribute data from electronic sensors and sensor networks. Firstly, we employ the analytic hierarchy process (AHP) to analyze the security of sensor networks, leveraging a hierarchical electronic sensor network model to construct a hierarchical perception security architecture for anomaly detection. Subsequently, a neighborhood information system is established to ascertain the relationships between data objects with mixed attributes. We then develop the WNIN to encapsulate the relationships, and a state-transferring probability matrix based on data object similarity is derived. Ultimately, a random wandering process within the WNIN is executed, and the importance of data objects is evaluated using the steady-state distribution vector, thereby determining the anomaly data. Simulation outcomes reveal that our proposed method attains superior anomaly detection rates compared with existing methods.

Pelatihan Pemeliharaan Website Sistem Informasi Rukun Warga

The rapid development of technology has made information dissemination not only through offline media, but also online media contribute to the provision of information. One of the areas that use information technology in the service process is Rukun Warga (RW). RW 13 is one of 22 RW’s in Cipinang Melayu Village, which currently uses a manual system in the service process. In addition, the problem faced by the administrators of RW 13 is the lack of knowledge in managing and maintaining the website. As a form of service to the surrounding community, a group of lecturers from Universitas Nusa Mandiri conducted a community service activity in the environment of RW 13 in the form of training activities on website maintenance of the Neighborhood Information System. This activity aims to make the participants understand how important it is to maintain the website so that the website will continue to run optimally. Participants in this community service activity are RT administrators, RW administrators, and youth from RW 13 youth organizations. The method used in this community service activity is interactive theoretical training, while the practical method uses simulation and question and answer methods. The evaluation results of the community service activities conducted based on the completion of questionnaires by 20 participants can be concluded that the participants are satisfied with the implementation of this training activity so that it can increase the knowledge, understanding and skills of the participants on the importance of website maintenance. In addition, the participants argue that this community service activity in the form of training has met the expectations of the participants, can increase the knowledge and skills of the participants, and can help solve existing problems.

Attribute reduction for incomplete mixed data based on neighborhood information system

In an era of data-based and information-centric Industry 4.0, extracting potential knowledge and valuable information from data is central to data mining tasks. Yet, the ambiguity, imprecision, incompleteness, and hybrid in real-world data pose tremendous challenges to critical information mining. Accordingly, we propose a new Max-Correlation Min-Redundant (MCMR) attribute reduction model from the uncertainty relation of attributes to avoid information loss in incomplete mixed data. Specifically, the neighbor relations are primarily developed based on the soft computing approach of the neighborhood information system, which divides the objects into neighborhood covers to maximize the utilization of the information in the incomplete mixed data. Then, we detailly analyze the internal and external consistency relationships of the four main uncertainty functions. Based on this, a new MCMR uncertain function is designed with maximum relevance and minimum redundancy. Experiments on nine real-world datasets validate the proposed model can improve data quality by mining critical information in classification tasks and achieving optimal performance with a minimum number of attributes.

Multigranulation-Based Granularity Selection for Intuitionistic Fuzzy Weighted Neighborhood IoT Data

Granular computing is to represent, construct, and process information granules formalized in many different approaches, and different formal approaches emphasize the same fundamental facet in different ways. In this paper, we present the multigranulation rough set models in the intuitionistic fuzzy neighborhood information system generated by Internet of Things (IoT) data with weighted features and develop the basic properties of the proposed models. Moreover, the multigranulation-based optimal granularity selection approach is established by introducing the concept of granularity significance. The experimental results on eight IoT datasets demonstrate that the proposed methods exhibit better efficiency by comparing three classical methods under the intuitionistic fuzzy weighted neighborhood information system.

Feature selection using self-information uncertainty measures in neighborhood information systems

Feature selection using self-information uncertainty measures in neighborhood information systems

Feature selection in a neighborhood decision information system with application to single cell RNA data classification

A neighborhood information system (NIS) deals with an information system (IS) by means of neighborhoods. Sometimes it has some advantages over an IS. A neighborhood decision information system (NDIS) means a NIS with decision attributes. Single cell RNA (scRNA) data possess the characteristics of high dimensionality, small sample, unbalanced distribution, big noise and high redundancy. It has become an important research topic to select suitable and effective genes. This paper studies feature selection in a NDIS and considers its application for scRNA data classification. We first give the distance between information values on each attribute in a NDIS. Then, we present tolerance relations on the object set of a NDIS based on this distance. Next, we define the rough approximations in a NDIS by means of the presented tolerance relations. Furthermore, we put forward the notions of δ-dependence degree, δ-information entropy, δ-conditional information entropy and δ-joint information entropy in a NDIS. Based on Kryszkiewicz’s ideal, we introduce δ-generalized decision and consider feature selection in a consistent NDIS by decision. Finally, we study feature selection in a consistent NDIS by using dependence degree and information entropy, and design the relevant algorithms. The experimental results conducted several scRNA data demonstrate that the designed algorithms possess excellent performance.

Proposal for an application from the perspective of neighborhood to urban security in Turkey: the neighborhood information system

Mahalle, kent yaşamının en önemli unsurları arasındadır. Bir yerleşim mekânı olmanın yanında; toplumsal yaşamın düzenlenmesi, dayanışmanın güçlenmesi, kentsel güvenlik ve hemşehrilik bilincinin artması gibi olgular yönünden de büyük önem taşımaktadır. Diğer taraftan kentleşmenin hızlı ve düzensiz işleyen seyri, komşuluk ilişkilerinin zayıflaması gibi durumlar; kentsel güvenlik anlayışı ve kentlilik bilinci gibi kenti yaşanabilir kılan bileşenlere zarar vermektedir. Neticede kent yaşamında toplum sağlığından kent ekonomisine kadar birçok alanda olumsuz durumlar yaşanabilmektedir. Kent sakinlerinin yaşamlarını sürdürürken kendilerini güvende hissetmeleri şeklinde ifade edilebilecek olan “kentsel güvenlik” anlayışınım mahallelerin işlevselliğiyle orantılı olduğu söylenebilir. Bu bakımdan mahallelerin işlevinin arttırılması için de kent sakinlerinin ilgisinin mahalleye yönlendirilmesi önemlidir. Bu çalışmada Türkiye’de mahallelerin geçmişten bugüne sürecine değinilerek kentsel güvenlik açısından anlamı tartışılmaya çalışılmıştır. Bu bağlamda mahallelerin Osmanlı döneminden Türkiye Cumhuriyeti’ne sürecinin ele alınması ve kentsel güvenliğe yönelik muhtemel katkılarının değerlendirilmesi amaçlanmıştır. Çalışmanın son bölümünde mahallelerin işlevlerinin artırılması ve hemşehrilerin hayatlarının kolaylaştırılması amacıyla teknoloji tabanlı olarak tasarlanan “Mahalle Bilgi Sistemi” uygulamasına yönelik temel öngörüler sunulmuştur. Bu bağlamda Mahalle Bilgi Sistemi’nin işlevi, sistemin muhtemel faydaları, mahallelere ve kente yapacağı katkılar temel seviyede değerlendirmeye çalışılmıştır. Mahallelerin; merkezi yönetim, yerel yönetimler ve hemşehriler açısından öneminin arttırılmasına yönelik uygulama önerilerine yer verilmiştir.

Incremental updating knowledge in neighborhood multigranulation rough sets under dynamic granular structures

Neighborhood Multigranulation Rough Sets (NMGRS), constructed by a family of neighborhood relations, can effectively obtain required knowledge from Neighborhood Information Systems (NIS). In various practical situations, NIS may alter dynamically with time. Incremental learning is an alternative manner for maintaining knowledge by utilizing previous computational results under dynamic data contexts. In dynamic NIS with numerical data, potential useful knowledge, e.g., the positive, boundary and negative regions in NMGRS, needs to be updated for various applications. To address this issue, we present matrix-based incremental approaches to update knowledge in NMGRS with the addition or deletion of granular structures. First, a matrix-based representation of neighborhood multigranulation rough approximations is developed. Then, the matrix-based dynamic strategies are proposed to update the positive, boundary, and negative regions in the optimistic and pessimistic NMGRS under the variation of a granular structure. In accordance with the proposed updating strategies, the matrix-based dynamic algorithms are explored for maintaining the positive, boundary and negative regions while adding or deleting granular structures. Finally, comparative experiments are conducted to show that the matrix-based dynamic algorithms are feasible and effective.

Hybrid data-driven outlier detection based on neighborhood information entropy and its developmental measures

The outlier relies on its distinctive mechanism and valuable information to play an important role in expert and intelligent systems, and thus outlier detection has already been extensively applied in relevant fields including the fraud detection, medical diagnosis, public security, etc. The outlier detection methods of rough sets recently gain in-depth research, because they are data-driven and never require additional knowledge. However, classical rough set-based methods consider only categorical data; furthermore, neighborhood rough sets adhere to numeric and heterogeneous data, but their outlier detection is mainly restricted to numeric data now. According to the hybrid data-driving, this paper investigates outlier detection by the neighborhood information entropy and its developmental measures, and the applicable data sets widely concern categorical, numeric, and mixed data; as a result, the new method extends both the traditional distance-based and rough set-based methods to enrich outlier detection. Concretely, the neighborhood information system is first determined by the heterogeneous distance and self-adapting radius, the neighborhood information entropy is then defined to implement whole uncertainty measurement, three gradual information measures are further constructed to describe each single object, and finally the neighborhood entropy-based outlier factor (NEOF) is integratedly established to detect outliers; moreover, the NEOF-based outlier detection algorithm (called the NIEOD algorithm) is designed and applied. By virtue of UCI data experiments, the NIEOD algorithm is compared with six existing detection algorithms (including the NED, IE, SEQ, FindCBLOF, DIS, KNN algorithms), and the concrete results generally reflect the better effectiveness and adaptability of the new method.

Collaborative governance and information technology innovation: public–nonprofit partnerships to build neighborhood information systems

This paper addresses the collaboration and partnership between public and nonprofit organizations under the new governance paradigm in public administration, particularly in building neighborhood information systems (NISs). An NIS is a hybrid application of geographical information system and web technologies that provides demographic, social, and economic information to community stakeholders. The NIS is an innovative information technology (IT) tool to enhance local governance. In developing an NIS, local governments, nonprofits, universities, and foundations must work together. This paper examines what types of partnership models work better for NIS development. The findings of this study suggest that developing an effective NIS requires a collaborative environment and strong managerial leadership. This paper also suggests that IT innovation tools in turn can facilitate collaborative governance.

Neighborhoods, Race, and Health: Examining the Relationship between Neighborhood Distress and Birth Outcomes in Pittsburgh

ABSTRACT:One of the major challenges in researching neighborhoods and birth outcomes today concerns access to neighborhood-level data. The present study combines local data from the Pittsburgh Neighborhood and Community Information System with data from the national American Community Survey to examine the relationship between adverse birth outcomes (i.e., low birth weight, preterm birth, and infant mortality) and neighborhood distress. We examine racial differences in neighborhood distress using the census-based measure of socioeconomic disadvantage as well as measures of abandonment from local data sources, including vacancy, violence, tax delinquency, property sales, and property conditions. Results reveal significant differences in neighborhood distress by race for every aspect examined. Furthermore, findings show that abandonment-related measures explain a substantial portion of variation in adverse birth outcomes, with tax delinquency explaining more variation than socioeconomic disadvantage. We discuss the implications of these findings for future research and for strategies to reduce racial disparities in birth outcomes.

Kenyan “Nyumba Kumi” Neighborhood Information System using Artificial Intelligence

our daily life, we need to engage with our neighbors on several issues ranging from social, security and general neighborhood wellbeing. In this regard, we have designed a nyumba kumi information system which uses artificial intelligence and the facts stored in its knowledge base to answer various nyumba kumi neighborhood queries. We collected a sample nyumba kumi concerns and converted them into facts and the system rules. Hence, we fed the system with sample facts converted into predicate logics, which we collected from a certain nyumba kumi neighborhood. The system is able to answer various neighborhood queries using artificial intelligence knowledge from the facts and rules fed into the system, which we designed using prolog. The system could be customized by adding as many facts and rules as possible as long as the facts and rules are not contradicting.

Environmental Correlates of Recreational Walking in the Neighborhood.

To assess environmental correlates of neighborhood recreational walking. The study used a cross-sectional survey. The study was conducted in the local community. Participants were adults who recently relocated and walk for recreation in their current neighborhood. The outcome measure was participant-reported neighborhood recreational walking in participants' prior neighborhood. Exposure measures were (1) participant-reported social and environmental characteristics of the prior neighborhood and (2) geographic information system-derived environmental characteristics assessed within a buffer around participant's prior address. Participants reporting current neighborhood recreational walking (n = 231) were characterized by whether they walked for recreation in their prior neighborhood. Associations between neighborhood characteristics and neighborhood recreational walking were assessed using logistic regression. Neighborhood recreational walking was associated with perceptions of the presence of recreational facilities (odds ratio [OR] = 2.49, 95% confidence interval [CI] = 1.29-4.84), interesting things to see (OR = 2.82, 95% CI = 1.46-5.45), and others being active (OR = 3.56, 95% CI = 1.80-7.05), and was inversely associated with concerns about crime (OR = .40, 95% CI = .20-.77) and traffic (OR = .43, 95% CI = .22-.87). This behavior was associated with objectively measured presence of walking trails (OR = 3.58, 95% CI = 1.07-4.46), percentage of street length with speed limits ≤25 mph (OR = 1.31 for 10% increase, 95% CI = 1.08-1.61), and percentage of tree canopy coverage (OR = 1.55 for 10% increase, 95% CI = 1.12-2.14). Recreational walking may be influenced by environmental factors that support a safe, enjoyable, and social experience, attributes that are not necessarily prioritized in transportation walking. Outcome and exposure specificity are important when studying recreational walking.

An entropy-based uncertainty measurement approach in neighborhood systems

Uncertainty measures can provide us with principled methodologies to analyze uncertain data and unveil the substantive characteristics of the data sets. Accuracy and roughness proposed by Pawlak are two main tools to deal with uncertainty measurement issue in rough set theory. Many uncertainty measure methodologies for discrete-valued information systems or discrete-valued decision systems have been developed. However, there are only limited on the uncertainty measurement for neighborhood systems. In this paper, we address the issues of uncertainty of a neighborhood system and extend the traditional accuracy and roughness measures to deal with neighborhood systems. In particular, a concept called neighborhood entropy is first introduced to evaluate the uncertainty of a neighborhood information system. Consequently, the entropy-based roughness and approximation roughness measures of neighborhood system are presented. Theoretical analysis indicates that entropy-based measures can be used to evaluate the uncertainty in neighborhood systems. Experiments are conducted on artificial data sets and standard UCI data sets to test our proposed methodologies. Results demonstrate that the entropy-based measures are effective and valid for evaluating the uncertainty of neighborhood systems.

In pursuit of the effective neighborhood information system: User-friendliness and training

The goal of this research is to address the effectiveness of Neighborhood Information Systems (NIS) as an Information Technology (IT) tool in assisting community development. NISs are hybrid applications of Geographical Information Systems (GIS) and web technologies that provide demographic, social, and economic information to community stakeholders. Government agencies, nonprofit organizations, and community organizations engage in decision-making process for community development, and NISs are designed to help information-sharing and effective and participatory decision-makings for community development stakeholders. This study finds that NISs have been successful in developing user-friendly features but can improve their effectiveness by providing training to the community users. While it is important to craft user-friendly NISs, it is more important to understand user needs and provide task-specific training to make NISs more effective. Additional findings of this research support the proposition that NISs promote data-sharing and empowerment among community stakeholders and is used in the decision-making process.

이웃정보시스템을 이용한 공간 소지역 추정량 비교

최근 격자자료(lattice data) 분석 방법을 이용한 소지역 추정(small area estimation)이 연구되고 있으며 좋은 결과를 주고 있는 것으로 알려져 있다. 소지역 추정에 주로 사용되는 격자자료(lattice data) 분석의 경우 가장 자료를 잘 설명할 수 있는 이웃정보시스템을 사용하여야 분석의 효율을 향상시킨 수 있다. 최근 이강석과 신기일 (2008)은 지리정보시스템을 이용하여 만들어진 여러 이웃정보시스템을 비교, 분석하였다. 본 논문에서는 이강석과 신기일(2008)이 제안한 여러 이웃정보시스템이 소지역 추정에 얼마나 영향을 미치는지를 MSE, 커버리지, 캘리브레이션 그리고 회귀분석 방법 등을 이용하여 비교하였다. 2001년 경제활동인구조사의 실업자수 자료가 비교에 사용되었다. Recently many small area estimation methods using the lattice data analysis have been studied and known that they have good performances. In the case of using the lattice data which is mainly used for small area estimation, the choice of better neighborhood information system is very important for the efficiency of the data analysis. Recently Lee and Shin (2008) compared and analyzed some neighborhood information systems based on GIS methods. In this paper, we evaluate the effect of various neighborhood information systems which were suggested by Lee and Shin (2008). For comparison of the estimators, MSE, Coverage, Calibration, Regression methods are used. The number of unemployment in Economic Active Population Survey(2001) is used for the comparison.

Numerical Attribute Reduction Based on Neighborhood Granulation and Rough Approximation

To deal with numerical features, a neighborhood rough set model is proposed based on the definitions of δ neighborhood and neighborhood relations in metric spaces. Each object in the universe is assigned with a neighborhood subset, called neighborhood granule. The family of neighborhood granules forms a concept system to approximate an arbitrary subset in the universe with two unions of neighborhood granules: lower approximation and upper approximation. Thereby, the concepts of neighborhood information systems and neighborhood decision tables are introduced. The properties of the model are discussed. Furthermore, the dependency function is used to evaluate the significance of numerical attributes and a forward greedy numerical attribute reduction algorithm is constructed. Experimental results with UCI data sets show that the neighborhood model can select a few attributes but keep, even improve classification power.

격자자료분석을 위한 이웃정보시스템의 비교

최근 공간통계를 이용한 많은 연구가 진행되고 있고 공간통계학을 접목한 소지역 추정(small area estimation) 방법이 좋은 결과를 주고 있는 것으로 알려져 있다 소지역 추정에 사용되는 격자자료(lattice data) 분석에서 이웃정보를 정의하는 것은 자료 분석의 성패를 결정짓는 매우 중요한 부분이다. 그러나 기존에 사용된 대부분의 이웃정보시스템은 경계선을 공유할 때 이웃으로 정하는 방법을 사용하고 있다. 이에 본 논문에서는 경계선 공유를 이용한 이웃정보시스템 뿐 아니라 다른 여러 이웃정보시스템을 구하는 방법을 설명하고 2001년 경제활동자료를 이용하여 이 시스템들을 비교하였다 Recently many researches on data analysis using spatial statistics have been studied in various field and the studies on small area estimations using spatial statistics are in actively progress. In analysis of lattice data, defining the neighborhood information system is the most crucial procedure because it also determines the result of the analysis. However the used neighborhood informal ion system is generally defined by sharing the common border lines of small areas. In this paper the other neighborhood information systems are introduced and those systems are compared with Moran's I statistic and for the comparisons, Economic Active Population Survey (2001) is used.

Removing Barriers to the Use of Community Information Systems

Community information systems (CINS) are emerging as important tools for community, government, and educational organizations. This paper considers the training, evaluation, and outreach efforts relating to the Philadelphia Neighborhood Information System (NIS), a collection of online applications that integrate and distribute housing and demographic data. It presents an overview of the types of NIS users and uses and some specific examples ofhow the NISisbeing used for individual property inquiries, community surveys, needs assessments, and research. Finally, the paper discusses the barriers to more analytical uses and offers recommendations for social work education aimed at preparing social workers to support community organizations in their efforts to harness the potential of CINS for social change.

Predicting Housing Abandonment with the Philadelphia Neighborhood Information System

ABSTRACT:Several large US cities, including Chicago, Los Angeles, New York, and Philadelphia, have developed information systems to distribute property-level housing data to community organizations and municipal agencies. These early warning systems are also intended to predict which properties are at greatest risk of abandonment, but they have rarely used statistical modeling to support such forecasts. This study used logistic regression to analyze data from the Philadelphia Neighborhood Information System in order to determine which properties were most likely to become imminently dangerous. Several different characteristics of the property, including whether it was vacant, had outstanding housing code violations, and tax arrearages as well as characteristics of nearby properties were identified as significant predictors. Challenges common to the development of early warning systems—including integrating administrative data, defining abandonment, and modeling temporal and spatial data—are discussed along with policy implications for cities like Philadelphia that have thousands of vacant and abandoned properties.