Close Neighborhoods Research Articles

Flood shocks and post-disaster recovery of households: An empirical analysis from rural Odisha, India

This paper uses primary household survey data collected after the severe flooding that occurred in the Bhadrak region of Odisha in 2014. We contribute to the literature by examining the factors that enhance rural households’ disaster recovery capacities. We distinguish two main recovery outcomes: an income (economic) recovery and a housing structure (structural) recovery outcome. A Generalized Ordered Logit Model (GOLM) is employed where the recovery is determined by socioeconomic characteristics and coping mechanisms available to the households. The findings reveal that rural agricultural households adopted several measures in response to the flood. Migratory labor-based strategies significantly increased the likelihood of economic recovery one year after the disaster. Selling productive assets reduced the likelihood of recovery. Community-level coping strategies were not effective, as floods caused devastating effects on a large part of the village community. Poorer households were more constrained in their capacities to recover from disaster shock. However, there was evidence of community-level support in terms of labor exchange between households in close neighborhoods. Finally, the study finds that post-disaster public assistance was mainly provided to land-owning farmers rather than poor, landless agricultural households.

Detecting rumor outbreaks in online social networks

Social media platforms are broadly used to exchange information by milliards of people worldwide. Each day people share a lot of their updates and opinions on various types of topics. Moreover, politicians also use it to share their postulates and programs, shops to advertise their products, etc. Social media are so popular nowadays because of critical factors, including quick and accessible Internet communication, always available. These conditions make it easy to spread information from one user to another in close neighborhoods and around the whole social network located on the given platform. Unfortunately, it has recently been increasingly used for malicious purposes, e.g., rumor propagation. In most cases, the process starts from multiple nodes (users). There are numerous papers about detecting the real source with only one initiator. There is a lack of solutions dedicated to problems with multiple sources. Most solutions that meet those criteria need an accurate number of origins to detect them correctly, which is impossible to obtain in real-life usage. This paper analyzes the methods to detect rumor outbreaks in online social networks that can be used as an initial guess for the number of real propagation initiators.

Evidential Combination of Classifiers for Imbalanced Data

It remains an important research topic for the classification of imbalanced data. There exist some methods to solve this problem, such as hybrid-sampling, over-sampling, and under-sampling. Each method has its own advantage, and different methods generally provide some complementary knowledge. We want to combine these three methods at the decision level in an appropriate way for achieving as good as possible classification performance. Evidence theory is expert at representing and combining uncertain information. So a new method called an evidential combination of classifiers (ECC) is proposed for dealing with imbalanced data. The classification result generated by different strategies (i.e., hybrid-sampling, over-sampling, or under-sampling) may have different reliabilities for query patterns. A cautious reliability evaluation rule is developed for each classification result based on the close neighborhoods. After that, the classification result is revised with a new belief redistribution way according to the reliability evaluation, and the probability/belief of one class can be partially transferred to other classes as well as the total ignorance, which is defined by the whole frame of classes. By doing this, we can reduce the error risk of each classification method. Then, the revised classification results from different methods are combined by evidence theory to make the final class decision. The effectiveness of the ECC method has been demonstrated using several experiments, and it shows that ECC can effectively improve the classification performance comparing with other related methods.

Confidence estimation for t-SNE embeddings using random forest

Dimensionality reduction algorithms are commonly used for reducing the dimension of multi-dimensional data to visualize them on a standard display. Although many dimensionality reduction algorithms such as the t-distributed Stochastic Neighborhood Embedding aim to preserve close neighborhoods in low-dimensional space, they might not accomplish that for every sample of the data and eventually produce erroneous representations. In this study, we developed a supervised confidence estimation algorithm for detecting erroneous samples in embeddings. Our algorithm generates a confidence score for each sample in an embedding based on a distance-oriented score and a random forest regressor. We evaluate its performance on both intra- and inter-domain data and compare it with the neighborhood preservation ratio as our baseline. Our results showed that the resulting confidence score provides distinctive information about the correctness of any sample in an embedding compared to the baseline. The source code is available at https://github.com/gsaygili/dimred.

Multi-level context-driven interaction modeling for human future trajectory prediction

Human trajectory prediction is a challenging task with important applications such as intelligent surveillance and autonomous driving. We recognize that pedestrians in close and distant neighborhoods have different impacts on the person’s decision of future movements. Local scene context and global scene layout also affect the movement decision differently. Existing methods have not adequately addressed these interactions between humans and the multi-level contexts occurring at different spatial and temporal scales. To this end, we propose a multi-level context-driven interaction modeling (MCDIM) method for human future trajectory learning and prediction. Specifically, we construct a multilayer graph attention network (GAT) to model the hierarchical human–human interactions. An extra set of long short-term memory networks is designed to capture the correlations of these human–human interactions at different temporal scales. To model the human–scene interactions, we explicitly extract and encode the global scene layout features and local context features in the neighborhood of the person at each time step and capture the spatial–temporal information of the interactions between human and the local scene contexts. The human–human and human–scene interactions are incorporated into the multi-level GAT-based network for accurate prediction of future trajectories. We have evaluated the method on benchmark datasets: the walking pedestrians dataset provided by ETH Zurich (ETH) and the crowd data provided by the University of Cyprus. The results demonstrate that our MCDIM method outperforms existing methods, being able to generate more accurate and plausible trajectories for pedestrians. The average performance gain is 2 and 3 percentage points in terms of the average displacement error and final displacement error, respectively.

Impact of built environment on walking in the case of Tehran, Iran

IntroductionGrowth of population, economic activities, and travel demand has resulted in serious problems, including air pollution, particularly in the large cities of developing countries. Many metropolitan areas are thus now seeking strategies to encourage the use of sustainable travel modes, which is significantly affected by built environment (BE). This study examines the impact of BE on walking choice and distance for discretionary trips, namely recreational and shopping trips for the case of Tehran, Iran. Circular buffer is used to explore characteristics of close neighborhoods, while transport-analysis-zone (TAZ) for wider neighborhoods MethodThis study uses a Heckman selection model based on trip characteristics, socioeconomic variables, and objective BE. This two-stage model combines a regression model of walking distance and a binary probit model of walking choice, using Origin-Destination travel data of a clustered sample of 743 Tehran residents in 2014. ResultsResidential density, commercial density, and mixed land-use are observed to have a positive effect on walking distance. Highway density, average block size, and distance to the closest gym have a negative effect on the probability of walking, while the number of bus stations has a positive effect. Also, TAZs with lower–than-mean parking capacity (at destinations) are observed to have a strong tendency toward walking. ConclusionAs the first walking distance model (to our knowledge) to include a parking supply measure, this study can provide insight into walking behavior. Developing neighborhoods with lower highway density, smaller blocks, more gyms, higher mixed land-use, and higher residential and commercial density can encourage people to walk as long-term policies, while parking management like reducing parking capacity as short-term policies.

Eight years of homicide evolution in Monterrey, Mexico: a network approach

Homicide is without doubt one of Mexico’s most important security problems, with data showing that this dismal kind of violence sky-rocketed shortly after the war on drugs was declared in 2007. Since then, violent war-like zones have appeared and disappeared throughout Mexico, causing unfathomable human, social and economic losses. One of the most emblematic of these zones is the Monterrey metropolitan area (MMA), a central scenario in the narco-war. Being an important metropolitan area in Mexico and a business hub, MMA has counted hundreds to thousands of casualties. In spite of several approaches being developed to understand and analyze crime in general, and homicide in particular, the lack of accurate spatio-temporal homicide data results in incomplete descriptions. In order to describe the manner in which violence has evolved and spread in time and space through the city, here we propose a network-based approach. For this purpose, we define a homicide network where nodes are geographical entities that are connected through spatial and temporal relationships. We analyzed the time series of homicides in different municipalities and neighborhoods of the MMA, to observe whether or not a global correlation appeared. We studied the spatial correlation between neighborhoods where homicides took place, to observe whether distance is a factor of influence in the frequency of homicides. We constructed yearly co-occurrence networks, by correlating neighborhoods with homicides happening within a same week, and counting the co-occurrences of these neighborhood pairs in 1 year. We also constructed a crime network by aggregating all data of homicides, eliminating the temporal correlation, in order to observe whether homicide clusters appeared, and what those clusters were distributed geographically. Finally, we correlated the location and frequency of homicides with roads, freeways and highways, to observe if a trend in the homicidal violence appeared. Our network approach in the homicide evolution of MMA allows us to identify that (1) analyzing the whole 86-month period, we observed a correlation between close cities, which decreases in distant places. (2) at neighborhood level, correlations are not distance-dependent, on the contrary, highest co-occurrences appeared between distant neighborhoods and a polygon formed by close neighborhoods in downtown Monterrey. Moreover, (3) An elevated number of homicides occur close to the 85th freeway, which connects MMA with the US border. (4) Some socioeconomic barriers determine the presence of homicide violence. Finally, (5) we show a relation between homicidal crime and the urban landscape by studying the distance of safe and violent neighborhoods to the closest highway and by studying the evolution of highway and crime distance over the cartel-related years and the following period. With this approach, we are able to describe the spatial and temporal evolution of homicidal crime in a metropolitan area.

Scale-dependent effects of neighborhood biodiversity on individual tree productivity in a coniferous and broad-leaved mixed forest in China.

The relationship between biodiversity and productivity has stimulated an increasing body of research over the past decades, and this topic still occupies a central place in ecology. While most studies have focused on biomass production in quadrats or plots, few have investigated the scale‐dependent relationship from an individual plant perspective. We present an analysis of the effects of biodiversity (species diversity and functional diversity) on individual tree growth with a data set of 16,060 growth records from a 30‐ha temperate forest plot using spatially explicit individual tree‐based methods. A significant relationship between species diversity and tree growth was found at the individual tree level in our study. The magnitude and direction of biodiversity effects varies with the spatial scale. We found positive effects of species diversity on tree growth at scales exceeding 9 m. Individual tree growth rates increased when there was a greater diversity of species in the neighborhood of the focal tree, which provides evidence of a niche complementarity effect. At small scales (3–5 m), species diversity had negative effects on tree growth, suggesting that competition is more prevalent than complementarity or facilitation in these close neighborhoods. The results also revealed many confounding factors which influence tree growth, such as elevation and available sun light. We conclude that the use of individual tree‐based methods may lead to a better understanding of the biodiversity‐productivity relationship in forest communities.

Biomass-dominant species shape the productivity-diversity relationship in two temperate forests

A negative productivity-diversity relationship was determined for biomass-dominant species at the community level. This study thus supports the hypothesis in which the effects of individual species on the productivity-diversity relationships at the community level are related to their biomass density, an important functional trait. The productivity-diversity relationships have been extensively studied in various forest ecosystems, but key mechanisms underlying the productivity-diversity relationships still remain controversial. The objective of this study is to explore the productivity-diversity relationships at the community level, and to investigate the roles of individual species in shaping the community-level relationships between productivity and diversity under different forest types. The study was conducted in two fully stem-mapped temperate mixed forest plots in Northeastern China: a natural secondary forest plot, and an old-growth forest plot. An individual-based study framework was used to estimate the productivity-diversity relationships at both species and community levels. A homogeneous Thomas point process was used to evaluate the significance of productivity-diversity relationship deviating from the neutral. At the species level, most of the studied species exhibit neutral productivity-diversity relationship in both forest plots. The percentage of species showing negative productivity-diversity relationship approaches linearly a peak value for very close neighborhoods (the secondary forest plot: r = 3 m, 38%; the old-growth forest plot: r = 4 m, 42%), and then decreases gradually with increasing spatial scale. Interestingly, only a few species displayed positive productivity-diversity relationship within their neighborhoods. Dominant species mainly exhibit negative productivity-diversity relationship while tree species with lower importance values exhibit neutral productivity-diversity relationship in both forests. At the community level, a consistent pattern of productivity-diversity relationship was observed in both forests, where tree productivity is significantly negatively associated with local species richness. Four biomass-dominant species (Juglans mandshurica Maxim., Acer mono Maxim.,Ulmus macrocarpa Hance and Acer mandshuricum Maxim.) determined a negative productivity-diversity relationship at the community level in the secondary forest plot, but only one species (Juglans mandshurica) in the old-growth forest plot. The productivity-diversity relationship is closely related to the dominance of individual species at the species level. Moreover, this analysis is the first to report the roles of biomass-dominant species in shaping the productivity-diversity relationship at the community level.

An evaluation of terrain‐based downscaling of fractional snow covered area data sets based on LiDAR‐derived snow data and orthoimagery

AbstractReliable maps of snow‐covered areas at scales of meters to tens of meters, with daily temporal resolution, are essential to understanding snow heterogeneity, melt runoff, energy exchange, and ecological processes. Here we develop a parsimonious downscaling routine that can be applied to fractional snow covered area (fSCA) products from satellite platforms such as the Moderate Resolution Imaging Spectroradiometer (MODIS) that provide daily ∼500 m data, to derive higher‐resolution snow presence/absence grids. The method uses a composite index combining both the topographic position index (TPI) to represent accumulation effects and the diurnal anisotropic heat (DAH, sun exposure) index to represent ablation effects. The procedure is evaluated and calibrated using airborne‐derived high‐resolution data sets across the Tuolumne watershed, CA using 11 scenes in 2014 to downscale to 30 m resolution. The average matching F score was 0.83. We then tested our method's transferability in time and space by comparing against the Tuolumne watershed in water years 2013 and 2015, and over an entirely different site, Mt. Rainier, WA in 2009 and 2011, to assess applicability to other topographic and climatic conditions. For application to sites without validation data, we recommend equal weights for the TPI and DAH indices and close TPI neighborhoods (60 and 27 m for downscaling to 30 and 3 m, respectively), which worked well in both our study areas. The method is less effective in forested areas, which still requires site‐specific treatment. We demonstrate that the procedure can even be applied to downscale to 3 m resolution, a very fine scale relevant to alpine ecohydrology research.

Price of anarchy and an approximation algorithm for the binary-preference capacitated selfish replication game

We consider the capacitated selfish replication (CSR) game with binary preferences, over general undirected networks. We study the price of anarchy of such games, and show that it is bounded above by 3. We develop a quasi-polynomial algorithm O(n2+lnD), where n is the number of players and D is the diameter of the network, which can find, in a distributed manner, an allocation profile that is within a constant factor of the optimal allocation, and hence of any pure-strategy Nash equilibrium (NE) of the game. Proof of this result uses a novel potential function. We further show that when the underlying network has a tree structure, every globally optimal allocation is an NE, which can be reached in only linear time. We formulate the optimal solutions and NE points of the CSR game using integer linear programs. Finally, we introduce the LCSR game as a localized version of the CSR game, wherein the actions of the players are restricted to only their close neighborhoods.

Relationships between tree biomass productivity and local species diversity

AbstractThis study evaluated the relationship between the productivity of aboveground coarse woody biomass and species richness at individual species and community levels in two large and fully stem‐mapped temperate forest plots in northeastern China. Although productivity–diversity relationships (PDRs) have been investigated for different forest ecosystems, specific patterns have rarely been documented for individual species. In our study, the PDR patterns were found to be scale dependent at the community level in both research forests. Productivity is positively linked with species richness at the 20 × 20 m sampling scale. At the 40 × 40 m scale, however, significantly positive PDRs were only observed in the mature forest. A summary statistic combining both productivity and richness characteristics was used to investigate whether and at which spatial scale individual species show positive, negative, or neutral PDRs. The results show that 66.7% of all focal species exhibited positive or negative PDRs in the near‐mature forest, while 64.3% exhibited positive or negative PDRs in the mature forest. Contrary to expectations, there were few species showing positive PDRs in either forest. PDR patterns were found to be scale dependent in both forest types: Negative PDRs dominate at close neighborhoods in the near‐mature forest, while both positive and negative PDRs were found in the mature forest. To our knowledge, this is the first analysis that evaluates the PDRs of individual species based on facilitative and competitive effects in their neighborhoods.

Estimation and Inference under Weak Identification and Persistence in Dynamic Nonlinear Regression

The reaction coefficients in the forecast-based monetary policy reaction function are only weakly identified when the smoothing coefficient for the nominal interest rate is close to unity. This situation also causes the nominal interest rate to be highly persistent. Inference based on the standard strong identification assumption and recently developed identification robust procedures based on stationarity fail in this setting, and we propose modifications. In particular, based on nonlinear least squares (NLS) estimation, we develop valid confidence sets for reaction coefficients that are robust to a wide range of the smoothing coefficient, including close and distant neighborhoods of unity. Allowing for both potential weak identification and a nearly unit root constitutes the novelty of the suggested procedure. Our empirical application using recently available real-time data suggests that, whether the estimates are accurate enough to rule out the possibility of indeterminacy in US monetary policy depends on forecast horizons of expected inflations and output gaps used in the model.

Detecting Sybil attacks in vehicular networks

Abstract Abstract A Sybil attack consists of an adversary assuming multiple identities to defeat the trust of an existing reputation system. When Sybil attacks are launched in vehicular networks, the mobility of vehicles increases the difficulty of identifying the malicious vehicle location. In this paper, a novel protocol for Sybil detection in vehicular networks is presented. Considering that vehicular networks are cyber-physical systems, the technique exploits well grounded results in the physical (i.e., transportation) domain to detect the Sybil attacks in the cyber domain. Compared to existing works that rely on additional cyber hardware support, or complex cryptographic primitives for Sybil detection, the protocol leverages the theory of platoon dispersion that models the physics of naturally occurring vehicle dispersion. Specifically, the proposed technique employs a certain number of roadside units that periodically collect reports from vehicles regarding their physical neighborhood. Leveraging from existing models of platoon dispersion, a protocol was designed to detect anomalously close neighborhoods that are reflective of Sybil attacks. To the best of the authors’ knowledge, this paper is unique in integrating a well established theory in transportation engineering for detecting cyber space attacks in vehicular networks. The resulting protocol is simple, efficient, and robust in diverse attack environments.

Functional and genetic analysis of the colon cancer network.

Cancer is a complex disease that has proven to be difficult to understand on the single-gene level. For this reason a functional elucidation needs to take interactions among genes on a systems-level into account. In this study, we infer a colon cancer network from a large-scale gene expression data set by using the method BC3Net. We provide a structural and a functional analysis of this network and also connect its molecular interaction structure with the chromosomal locations of the genes enabling the definition of cis- and trans-interactions. Furthermore, we investigate the interaction of genes that can be found in close neighborhoods on the chromosomes to gain insight into regulatory mechanisms. To our knowledge this is the first study analyzing the genome-scale colon cancer network.

Spectral Difference in the Image Domain for Large Neighborhoods, a GEOBIA Pre-Processing Step for High Resolution Imagery

Contrast plays an important role in the visual interpretation of imagery. To mimic visual interpretation and using contrast in a Geographic Object Based Image Analysis (GEOBIA) environment, it is useful to consider an analysis for single pixel objects. This should be done before applying homogeneity criteria in the aggregation of pixels for the construction of meaningful image objects. The habit or “best practice” to start GEOBIA with pixel aggregation into homogeneous objects should come with the awareness that feature attributes for single pixels are at risk of becoming less accessible for further analysis. Single pixel contrast with image convolution on close neighborhoods is a standard technique, also applied in edge detection. This study elaborates on the analysis of close as well as much larger neighborhoods inside the GEOBIA domain. The applied calculations are limited to the first segmentation step for single pixel objects in order to produce additional feature attributes for objects of interest to be generated in further aggregation processes. The equation presented functions at a level that is considered an intermediary product in the sequential processing of imagery. The procedure requires intensive processor and memory capacity. The resulting feature attributes highlight not only contrasting pixels (edges) but also contrasting areas of local pixel groups. The suggested approach can be extended and becomes useful in classifying artificial areas at national scales using high resolution satellite mosaics.

Do Declining Neighborhood Economic Conditions Trump Hoped for School Renovation Renewal Benefit?

The purpose of this study was to determine 1) individual student achievement, 2) teacher mobility rates, 3) perceptions of safety at school, and 4) student enrollment patterns, over time, in two recently renovated, same city, urban, No Child Left Behind compliant, Title I elementary school buildings located in close proximity neighborhoods one with improving the other with declining economic conditions. Achievement results indicated that fifth-grade students (n = 18) who attended a renovated school second-grade through fifth-grade in a neighborhood with improving economic conditions compared to fifth-grade students (n = 15) who attended a renovated school second-grade through fifth-grade in a neighborhood with declining economic conditions had statistically greater high stakes Reading Total and Math Total but not Language Total achievement test score improvement frequencies over time and statistically greater posttest-posttest Normal Curve Equivalent high stakes achievement test score comparisons for Reading Total, Math Total, and Language Total. Differing neighborhood economic conditions had no statistical effect on reported teacher, student, and parent perceptions of school safety or teacher mobility rates, however, enrollment in the renovated school in the neighborhood with declining economic conditions dropped significantly. We conclude that declining neighborhood economic conditions trumped hoped for school renovation renewal benefit. School closing policy and student open enrollment transfer options are discussed.

Local Networks, Local Topics: Structural and Semantic Proximity in Blogspace

Blog networks are often described as "small world" social networks where links are primarily created towards similar-minded individuals and well-connected bloggers. Examining a portion of the US blogosphere on several months, we find that bloggers relate to each other in a relatively local fashion, overwhelmingly and preferentially establishing links towards a limited neighborhood rather than the whole network. Furthermore, while long-distance interactions may indeed be dominated by homophily and authority effects, we show that close neighborhoods feature significantly more horizontal and diversified interactions — thereby challenging the conjecture of a widespread balkanization of Internet communities. We shed further light on this issue by describing the dual evolution of social and semantic proximity before and after two individuals interact with each other. We discuss in particular whether interactions are preceded or followed by a structural "contraction" and/or by an increasing similarity of the surrounding local social network.

Las redes sociales en Madrid y la congregación de San Fermín de los Navarros, siglos XVII Y XVIII

From the end of 16th Century, Madrid, as the home of the court and the main recipient of the landed revenues of the monarchy, aristocracy and Church , rapidly increased in size and attracted huge migration, thereby becoming a significant consumption market and financial center. Those who migrated set up complex social networks as well as hospitals and brotherhoods for mutual help. One of those, the Congregacion de San Fermin de los Navarros, linked those coming from Navarre, who held important positions in commerce, banking and royal bureaucracy through strong kinship and patronage networks. The building of a new church for the fraternity shows up the strong economic position of its members. Several internal debates point out how consistent the reciprocal relationship was between these brotherhoods and the close but separate neighborhoods fo the Navarrese and Basque communities in Madrid.

Do Cory’s shearwaters Calonectris diomedea synchronize laying among close neighborhoods? A reappraisal using data from artificial nest sites

Previous studies of Cory’s shearwater nesting biology suggest that pairs nesting at very close range to one another lay their eggs more synchronously than the colony as a whole. However, such apparent synchrony could be confounded by the existence of spatial structure in the quality of nesting sites and, hence, quality of the birds occupying the nests. If laying dates differ between sites of different quality, then synchrony is just a by-product of the spatial arrangement of nest sites. In this study, we show that when studying laying dates in artificial nests of uniform quality, no local synchronization of laying could be detected. We point to other shortcomings of previous analyses and conclude that, although Cory’s shearwaters show remarkably synchronized attendance behaviors at the nesting sites, there is no conclusive evidence showing that laying dates are influenced by the behavior of near neighbors.