Neighborhood Relations Research Articles

Exploring Factors Affecting Residential Satisfaction in Old Neighborhoods and Sustainable Design Strategies Based on Post-Occupancy Evaluation

Residential satisfaction has always been a crucial concern in urban planning and community management. This paper conducts a study of key factors affecting residential satisfaction in settlements with multiple build periods coexisting in old neighborhoods. This study aims to comprehensively analyze the influencing factors of residential satisfaction in order to obtain the post-occupancy evaluation of the residential environment by residents in different communities and propose sustainable design strategies based on the evaluation analysis. By constructing an evaluation system of residential satisfaction with 19 evaluation indicators and based on the questionnaire, the sample frequency analysis, reliability and validity test, IPA analysis, and evaluation factor analysis of the data, the IPA mean distribution results of residential satisfaction in the four communities are obtained. The study reveals that residents are most satisfied with the surrounding services in their communities but generally less satisfied with the internal residential environment. Among the factors, fitness facilities and space in the community have a significant impact on residents’ satisfaction; community activities and neighbor relationships are also key influencing factors, which become more prominent as the construction period of the community approaches. Additionally, community residents’ self-organizational governance will affect their satisfaction with community conventions and social capital. Therefore, this study proposes improvement suggestions, including planning community life circles to fully utilize available resources in the surrounding area, establishing community self-organization for community governance and improving the quality of community life, and implementing mobile service stations to flexibly adapt to community space and service needs. This study provides a valuable reference for urban community renewal and sustainable development. The research findings are expected to provide practical guidance for improving the residential environment within old neighborhoods and enhancing residents’ satisfaction.

The impact of household wealth gap on individual’s mental health

BackgroundImproving the individual’s mental health is important for sustainable economic and social development. Although some studies found that household wealth gap may affect individuals’ mental health, few studies have clarified the causal relationship between household wealth gap between mental health in China. This study examines the impact of the household wealth gap on individuals’ mental health using data from the 2012–2018 China Family Panel Survey.MethodsThis study first used the two-way fixed effects model to investigate the impact of household wealth gap on individuals’ mental health. Considering the endogeneity, the two-stage least square and propensity score matching were employed to examine the impact of household wealth inequality on individuals’ mental health.ResultsThe results show that the household wealth gap has negative impact on individuals’ mental health. A series of robustness tests support this conclusion. The results of heterogeneity analysis show that the impact of household wealth gap on mental health is more pronounced among middle-aged and elderly individuals, residents with lower education levels, and rural residents. The results of the mechanism analysis suggest that the household wealth gap may affect individuals’ mental health by influencing the individual’s health insurance investment and neighborhood relations. In addition, the household wealth gap not only significantly negatively affects individuals’ mental health in the short term but also in the medium- to long-term.ConclusionThese findings suggest that the government should take various measures to narrow the wealth inequality between families, which may effectively improve the mental health of residents.

THE DEVELOPMENT OF NEIGHBOR LAW IN INDONESIA

Neighbor law was born in the midst of community life to regulate and limit rights and obligations in neighborly relationships. One of the developments in neighbor law is the concept of servitude or property easement. Issues related to servitude right in Indonesia are still frequently encountered, often resulting in illegal actions that lead to losses for others. Therefore, based on several court decisions in Indonesia, it is necessary to conduct an in-depth study of property easements as a limitation on property rights and legal protection for those who are harmed in cases involving property rights restrictions. This research is a legal study that employs three approaches: legislative approach, conceptual approach, and case approach. The results reveal that servitude right is a restriction on property rights over land that is still applicable according to civil law provisions and is based on the social function found in agrarian law. Legal protection provided through the judicial system in Indonesia regarding cases involving property rights restrictions and violations of servitude right involves filing lawsuits by the injured party based on illegal actions. If it is proven that the defendant has committed an illegal act, the judge will issue a court order (aanmaning) to perform or cease a certain action, as well as award compensation to the harmed party for actions that violate the servitude right.

Deep sample clustering domain adaptation for breast histopathology image classification

Deep learning has been widely applied to the diagnosis of breast cancer. Most deep learning models require a large number of labeled samples for training, but a large number of datasets with detailed labels is impractical. Therefore, exploring the methods to accurately classify unlabeled data remains a crucial area of research. In this paper, a deep sample clustering unsupervised domain adaptation method based on deep fusion feature is proposed for breast histopathology image classification. Deep multi-level fusion feature has a strong ability to capture image information, allowing for accurate characterization of the structural features of cells, thus improving the clustering performance. Next, based on the criterion of adaptive clustering of samples, the similarity between different clustering centers in the source and target domains is adjusted jointly. Finally, the classification results were achieved by the nearest neighbor relationship between the clustering centers and samples in the target domain. The proposed method was tested on three different breast cancer datasets. The prediction results of all patches were aggregated to obtain the classification accuracy of the histopathological image. The experimental results demonstrate that the proposed method achieves high classification accuracy and has the potential for application in clinical settings.

TransRFT: A Knowledge Representation Learning Model Based on a Relational Neighborhood and Flexible Translation

The use of knowledge graphs has grown significantly in recent years. However, entities and relationships must be transformed into forms that can be processed by computers before the construction and application of a knowledge graph. Due to its simplicity, effectiveness, and great interpretability, the translation model lead by TransE has garnered the most attention among the many knowledge representation models that have been presented. However, the performance of this model is poor when dealing with complex relations such as one-to-many, many-to-one, and reflexive relations. Therefore, a knowledge representation learning model based on a relational neighborhood and flexible translation (TransRFT) is proposed in this paper. Firstly, the triples are mapped to the relational hyperplane using the idea of TransH. Then, flexible translation is applied to relax the strict restriction h + r = t in TransE. Finally, the relational neighborhood information is added to further improve the performance of the model. The experimental results show that the model has good performance in triplet classification and link prediction.

A spatially promoted SVM model for GRACE downscaling: Using ground and satellite-based datasets

Satellite-based terrestrial water storage changes have been recorded using the Gravity Recovery and Climate Experiment (GRACE) satellite which causing it an important dataset in hydrology and other related fields. GRACE dataset is widely utilized in many studies, but its coarse spatial resolution is a limiting drawback. Machine-learning approaches (e.g., ANN and SVM) are commonly applied in spatially downscaling. However, their input formation, which is in vector form, is a limitation of considering neighbor relations between the gridded-based inputs, specifically in spatial downscaling. Thus, developing an appropriate, simple, fast, and novel model to spatially downscale GRACE resolution is initially necessary for its utilizations. In this study, a Spatially Promoted Support Vector Machine (SP-SVM) model is innovatively proposed for GRACE downscaling from 0.5° to 0.25°. This promotion is investigated utilizing the distances between the unknown target points (with 0.25°) and their surrounding GRACE-valued points (0.5°), called their Distance Effect Coefficient (DEC), as the SP-SVM model input. In addition, the efficiencies of different in-situ and satellite-based datasets (fifteen variables from May 2005 to August 2020) are evaluated as the inputs of the GRACE downscaling models. After finding the most influential datasets, showing the best correlation with the GRACE, their best combinations in GRACE downscaling are identified. Based on the results, the set of PERSIANN-CDR without delay, the in-situ evaporation with a 1-month delay, and the soil moisture in 0–10 cm depth with a 1-month delay show the best performance in GRACE downscaling. The results of GRACE downscaling by the SP-SVM approach are also compared with the ones based on a usual statistical SVM (S-SVM) model, consisting of an intermediate bias interpolation to improve the estimations through a bias correction step. The results show that the SP-SVM model outperforms the common statistical SVM-based. Thus, compared with the usual S-SVM approach, the proposed SP-SVM (linear) model could be used as a simpler and more accurate model for downscaling any variable in a hierarchical process.

Meso-structural evaluation of asphalt mixture skeleton contact based on Voronoi diagram

The Voronoi diagram is a fundamental data structure for spatial neighborhood relations used in various fields related to geometric information. Taking four asphalt mixtures as the research object, this study established the skeleton contact Voronoi diagram model to characterize the contact meso-structure between coarse aggregates, and extracted the quantitative information of skeleton contact space. The results show that the skeleton contact Voronoi diagram model can represent important spatial information of asphalt mixtures from the meso-structure including the homogeneity of asphalt mixtures, the extrusion relationship between coarse and fine aggregates, and the skeleton contact distribution characteristics, etc. The shape of the connected tree reflects the coarse aggregate contact distribution and load transfer trend, and the order of the connected tree reflects the contact connectivity strength of the skeleton. We used Voronoi diagram area Vs, connected tree area TVs and the total length of contact points L to quantitatively evaluate the skeleton contact of asphalt mixtures. In the design of asphalt mixtures with dense skeletons, the evaluation criteria of skeleton contact characteristics include Vs (30–180 mm2) proportion ≥75 %, Tr-1 and Tr-2 proportion ≤75 % and L ≥70. The results provide new ideas to promote the quality of asphalt mixtures and pavement construction.

Multi-label borderline oversampling technique

Class imbalance problem commonly exists in multi-label classification (MLC) tasks. It has non-negligible impacts on the classifier performance and has drawn extensive attention in recent years. Borderline oversampling has been widely used in single-label learning as a competitive technique in dealing with class imbalance. Nevertheless, the borderline samples in multi-label data sets (MLDs) have not been studied. Hence, this paper deeply discussed the borderline samples in MLDs and found they have different neighboring relationships with class borders, which makes their roles different in the classifier training. For that, they are divided into two types named the self-borderline samples and the cross-borderline samples. Further, a novel MLDs resampling approach called Multi-Label Borderline Oversampling Technique (MLBOTE) is proposed for multi-label imbalanced learning. MLBOTE identifies three types of seed samples, including interior, self-borderline, and cross-borderline samples, and different oversampling mechanisms are designed for them, respectively. Meanwhile, it regards not only the minority classes but also the classes suffering from one-vs-rest imbalance as those in need of oversampling. Experiments on eight data sets with nine MLC algorithms and three base classifiers are carried out to compare MLBOTE with some state-of-art MLDs resampling techniques. The results show MLBOTE outperforms other methods in various scenarios.

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.

Query Path Generation via Bidirectional Reasoning for Multihop Question Answering From Knowledge Bases

Multi-hop question answering from knowledge bases (KBQA) is a hot research topic in natural language processing. Recently, graph neural network-based (GNN-based) methods have achieved promising results as the KB can be organized as a knowledge graph (KG). However, they often suffered from the sparsity of the KG which was detrimental to the structure-encoding and reasoning capabilities of GNN. Specifically, a KG is a sparse graph linked by directed relations and previous studies have paid scant attention to the directional characteristic of relations in the KG, limiting the patterns of relation path that GNN-based approaches could resolve. This study proposes a bidirectional recurrent graph neural network (BRGNN) to tackle these difficulties. To model the bidirectional information of relations, all adjacent relations of an entity are grouped by their directions, and they are separately aggregated into the entity representation in outward and inward directions. For the reasoning process, BRGNN simultaneously considers the neighbor relations in both directions to cover more patterns of relation paths and improve the recall of answers. Extensive experiments on three benchmarks: WebQuestionsSP, ComplexWebQuestions and MetaQA, verify that BRGNN can answer more questions by taking into account the directional information, and it is competitive to all state-of-the-art approaches.

Water index and Swin Transformer Ensemble (WISTE) for water body extraction from multispectral remote sensing images

ABSTRACT Automatic surface water body mapping using remote sensing technology is greatly meaningful for studying inland water dynamics at regional to global scales. Convolutional neural networks (CNN) have become an efficient semantic segmentation technique for the interpretation of remote sensing images. However, the receptive field value of a CNN is restricted by the convolutional kernel size because the network only focuses on local features. The Swin Transformer has recently demonstrated its outstanding performance in computer vision tasks, and it could be useful for processing multispectral remote sensing images. In this article, a Water Index and Swin Transformer Ensemble (WISTE) method for automatic water body extraction is proposed. First, a dual-branch encoder architecture is designed for the Swin Transformer, aggregating the global semantic information and pixel neighbor relationships captured by fully convolutional networks (FCN) and multihead self-attention. Second, to prevent the Swin Transformer from ignoring multispectral information, we construct a prediction map ensemble module. The predictions of the Swin Transformer and the Normalized Difference Water Index (NDWI) are combined by a Bayesian averaging strategy. Finally, the experimental results obtained on two distinct datasets demonstrate that the WISTE has advantages over other segmentation methods and achieves the best results. The method proposed in this study can be used for improving regional to continental surface water mapping and related hydrological studies.

Unraveling the role of informal mutual aid networks in maintaining urban farms in Lubumbashi, Democratic Republic of Congo

IntroductionThe multiple constraints of urban agriculture have prompted farmers in Lubumbashi to turn to informal mutual aid networks. The survey data collected from 88 farmers chosen at random from the 202 farmers previously interviewed enabled us to decipher the crucial role of mutual aid in maintaining urban farms.MethodsThe survey data collected from 88 farmers chosen at random from the 202 farmers previously interviewed enabled us to decipher the crucial role of mutual aid in maintaining urban farms.ResultsThe results show that 79.5% of the surveyed farmers resort to mutual aid to ensure the vegetable production cycle. More specifically, this mutual aid consists in sharing resources, for which the farmer would interact 15.1 times with other farmers, and in sharing agricultural know-how, for which the farmer would interact 11.6 times with other farmers. Four categories of resources are defined in this mutual aid network: highly exchanged resources with a high exchange intensity (hoes, watering cans, plant protection products and chicken droppings), highly exchanged resources with a low exchange intensity (land capital, spades, and seeds), lowly exchanged resources with a low exchange intensity (motor pumps and buckets) and non-exchanged resources (financing, labor, chemical fertilizers). Agricultural know-how such as soil preparation, soil fertility management, pest control and sales techniques are widely shared. Characteristics such as gender, age, experience, religious affiliation, and farmer status in the household are statistically significant explanatory factors of mutual aid. In addition, neighborhood relations, kinship and religious affiliation are social ties that enable farmers to help each other.DiscussionThe results provide useful information on the crucial role played by informal mutual aid networks in maintaining urban farms in the face of the negative consequences of chaotic urbanisation and climate change. The study recommends that policymakers and agricultural extension services take these networks into account when drawing up policies for disseminating innovations. For, although informal, they constitute powerful and inexpensive channels of communication in an inoperative institutional context of urban agriculture.

COĞRAFİ AĞIRLIKLI REGRESYON İLE YÜKSEK TEKNOLOJİ İHRACATININ MEKANSAL ANALİZİ

The development of High-Technology Exports (HTE) contributes to the increase of competitiveness and growth in national production, the development of commercial and economic cooperation with foreign countries, and the diversification of the economy. In addition, high technology exports reduce the current account deficit of the country's economy. Therefore, high-tech industries have become indispensable for economic development in a country. The aim of this study is to examine the existence of spatial (neighbourhood) relations of countries in high-technology exports. In this study, Exploratory Regression analysis was applied to determine the model that best explains HTE among the variables. Moran Index was used for spatial autocorrelation, Geographical Weighted Regression (GWR) method for spatial regression analysis, and Ordinary Least Squares (OLS) method, to compare with the GWR method. The GWR model established in this study showed that countries have a weak or strong spatial relationship with their neighboring countries in terms of HTE. It has been observed that this spatial relationship is strong in the countries neighboring China, which ranks first in HTE, while the spatial relationship is weak in countries where HTE is low.

Acquaintances or Familiar Strangers? How Similarity and Spatial Proximity Shape Neighbour Relations within Residential Buildings

ABSTRACT While scholars have long established that city dwellers choose with whom to develop relationships on the basis of social proximity, spatial proximity remains the basis for neighbour relations involving greetings, social conversation, and the exchange of services. Few studies have systematically compared the respective roles of spatial and social proximity in neighbour relations. In this paper, we investigate these two factors through statistical analysis of four social network datasets representing relationships within four rented apartment buildings in Geneva, Switzerland. Using a measure of distance that takes into account how the layout and materiality of buildings shape relationships through accessibility, visibility and audibility, we compare the effects of spatial proximity with the effects of individual determinants and similarity. Our study also breaks new ground by comparing weak ties – between people who interact regularly – and “invisible ties”, or ties to familiar strangers. Our study confirms that spatial proximity increases the likelihood of weak ties and questions the underlying mechanisms. It also shows that in addition to sociability, familiarity and anonymity are constitutive dimensions of neighbouring, even at the scale of buildings.

Performance Environment, Contract Binding, and the Contract Structure of the Farmland Transfer Market

This study discusses the mechanism of social trust and legal institutions and their impact on farmers’ contract selection in the farmland transfer market from the perspective of contract governance. Using data from a survey of 128 villages in Heilongjiang, Henan, Zhejiang, and Sichuan provinces, this study empirically tests the impact of social trust and legal institutions on the binding force of contracts, and the proportions of paper and long-term contracts in the farmland transfer market. The results showcase, first, that improvement in social trust and legal institutions can strengthen the binding force of farmland contracts. The strength of legal institutions, as embodied in regulation files and execution, and of social trust, as embodied in village neighborhood relations and loan relations, have significant positive impacts on the binding force of contracts in the farmland transfer market. Second, the binding force of contracts positively impacts both paper and long-term contracts in the farmland transfer market. Whether contract execution or dispute resolution rates are selected as the proxy variables for the binding force of contracts, the stronger the contract binding force, the higher the proportion of both paper and long-term contracts in the farmland transfer market. Therefore, improving formal and informal social systems to enhance contractual binding force is of great importance in standardizing contracts and improving the efficiency of market resource allocation.

Social Impact Assessment of Transitional Social Housing and Service Interventions for Low-Income Families: The Case of Hong Kong

Hong Kong is a typical global city with growing levels of substandard housing and a reduction in basic living quality. The government implemented a policy of transitional social housing (TSH) in 2018 to enhance the livelihood of low-income families. Nam Cheong 220 (NC220) is the first structural steel TSH project in Hong Kong. This study aims to evaluate the social impact and effectiveness of the project, and quantitative data were collected from individuals in three stages between 2020 and 2022. The first stage (T0) involved interviewing 106 successful applicants of NC220, followed by 91 participants in the second stage (T1) and 88 in the third stage (T2). Longitudinal analysis was implemented to scrutinize the changes observed over time, namely that the living area per capita substantially increased and housing expenses per capita decreased significantly due to residents’ relocation to NC220. Housing conditions and community problems also showed significant improvement. Living satisfaction, neighbour and family relationships also demonstrated positive changes. The most difficult issue for residents was finding suitable housing after staying at NC220. The findings reveal that the first TSH in Hong Kong as an intervention significantly improved the housing circumstances of residents. The social dimensions of housing for enhancing residents’ well-being is worth additional attention.

Factors Affecting Neighbourhood Preferences for Income and Hukou Segregation: Evidence from Shanghai, China

Abstract: This article analyses the factors that affect neighbourhood preferences for income and hukou segregation by using an ordered probit model on a sample of 482 residents in Shanghai. It finds that the overall preference for residential segregation among respondents is not very strong. However, for high-income groups with Shanghai hukou, their income level and household size increase their tendency to be reluctant to have neighbours with different income levels or hukou status. The effects of various influencing factors differ among other groups with different hukou and income levels. Neighbourhood relationship and xiaoqu quality are two important factors that affect all types of neighbourhood preferences for residential segregation. The results of this study show that developing mixed residential areas, improving inclusiveness over residential segregation, narrowing the income gap and reducing education inequality may reduce the probability of neighbourhoods accepting residential segregation.

Evolutionary Multiobjective Clustering Over Multiple Conflicting Data Views

Multi-view data analysis provides an effective means to integrate the distinct information sources which are inherent to many applications. Data clustering in a multi-view setting specifically aims to identify the most appropriate grouping for a collection of entities, where those entities (or their relationships) can be described from multiple perspectives. Leveraging recent advances in multi-objective clustering, we propose a new evolutionary method to tackle this challenge. Designed around a flexible and unbiased solution representation, together with strategies based on the minimum spanning tree and neighborhood relations, our algorithm optimizes multiple objectives simultaneously to effectively explore the space of candidate trade-offs between the data views. Through a series of experiments, we investigate the suitability of our proposal in the context of a bioinformatics application, clustering of plausible protein structures, and a diverse set of synthetic problems. The specific case of two data views is considered in this paper. The evaluation with respect to a variety of reference approaches demonstrates the effectiveness of our method in discovering high-quality partitions in a multiview setting. Robustness against unreliable data sources and the ability to automatically determine the number of clusters, are additional advantages evidenced by the results obtained.

Learning to Locate: Adaptive Fingerprint-Based Localization With Few-Shot Relation Learning in Dynamic Indoor Environments

WiFi fingerprint-based localization has been intensive studies as a promising technology of ubiquitous location-based services. Two main concerns for its wide spread applications are to tackle with the cumbersome efforts of site survey and to combat vulnerable environment changes. To address these issues comprehensively, we propose a novel approach on adaptive fingerprint-based localization with less site survey, named as LESS, by exploring a new paradigm of radio map construction and adaptation with few-shot relation learning. Firstly, we extend sparsely collected fingerprints with the fingerprint augmentation method which produces new related data and derives their location information based on local proximity property in a low-dimensional manifold space. Then, LESS designs deep relation networks to learn not only the appropriate features but also a transferable deep-distance metric for modeling the fundamental relationships of the neighborhood fingerprints. Finally, once trained, LESS can quickly establish the neighborhood relationships among new fingerprints in the changed surroundings to realize adaptive location estimations, even without the network updating. The extensive experimental results demonstrate that LESS can achieve an attractive trade-off between the system overhead and the location performance with the superiorities over others in dynamic indoor environments.

SGCN: A multi-order neighborhood feature fusion landform classification method based on superpixel and graph convolutional network

To address key issues with traditional landform classification methods that impact the integrity and continuity of the analysis area, as well as neglecting neighboring features, We presents a novel approach called superpixel-based graph convolutional network (SGCN) for automatic classification of digital elevation model (DEM) landform. The SGCN uses superpixel segmentation and graph convolutional networks (GCNs). Specifically, we employ the Simple Linear Iterative Clustering (SLIC) algorithm to perform superpixel segmentation on the DEM image and convert the resulting distribution of superpixels in the 2D image into a graph structure, where nodes are labeled. We then analyze the geographic features and external neighborhood relationships within each DEM superpixel to obtain corresponding node information for the graph structure. Finally, the GCN model is trained according to the graph structure, node feature information, and node label, enabling automatic recognition of node landform types. Importantly, SGCN accounts for the multi-order neighborhood features of the basic analysis unit and expresses the DEM micro-features relatively macroscopically. Experimental results demonstrate that compared with pixel-level semantic segmentation methods like U-Net, FCN, and SegNet, the SGCN significantly improves DEM landform classification accuracy by 17.84%, 33.22%, and 30.60%, respectively. Moreover, SGCN better preserves the integrity and continuity of features inside and outside the analysis area, overcoming the limitation of traditional methods that use regular grids as the basic analysis unit, which can compromise landform edge continuity.