Proximity Metrics Research Articles

Environmental Monitoring for Unconventional Oil and Gas Development

Background: Over 4 million people live within one mile of an unconventional oil and gas (UOG) well. Quantitative environmental monitoring data and models are needed to advance understanding of the complex inter-relationships between UOG activity, water and air quality, and human exposures.Methods: In a preliminary exposure study of 66 homes in Belmont County, Ohio, we measured the volatile organic compounds (VOCs) benzene, ethylbenzene, and toluene in air inside and outside homes with passive charcoal badges. We analyzed drinking water samples for 13 UOG-related VOCs, gasoline-range organics (GRO), and diesel-range organics. Using UOG well coordinates from the Ohio Department of Natural Resources, we calculated several metrics capturing residential proximity to multiple UOG wells.Results: Detection rates were low for the air samples; no associations between well proximity metrics and air VOC detection or concentrations were observed. Distance to nearest well was negatively correlated with drinking water concentrations of GRO, toluene, and certain disinfection byproducts linked with UOG wastewater.Research underway: To advance upon this work, we are formulating a model quantifying groundwater vulnerability to UOG contamination by augmenting proximity information with hydrological data. We are collecting water samples from hundreds of homes across Pennsylvania and Ohio and analyzing them for a range of inorganic and organic compounds using new analytical chemistry techniques to aid in source attribution.Conclusions: Low detection rates impeded air monitoring inferences; more sensitive techniques are needed at a greater number of homes. Our new water vulnerability model could be used to prioritize monitoring or assign exposures in epidemiologic studies. Our inter-disciplinary approach (hydrogeology, chemistry, exposure science) to UOG environmental monitoring has the potential to provide important insights into human exposure potential via the drinking water pathway.

Residential Proximity to Unconventional Oil and Gas Development and Air Volatile Organic Compound Concentrations in Ohio

Approximately 4 million Americans live within one mile of an unconventional oil and gas (UO&G or “fracking”) well, potentially placing them in the path of toxic air emissions. We conducted a community-based, exploratory study to evaluate associations between proximity to UO&G wells and presence of volatile organic compounds (VOCs) inside and outside 66 homes (n=132 samples) in Belmont County, Ohio, an area with an active UO&G industry. Well locations were obtained from the Ohio Department of Natural Resources. Passive charcoal badges were selected as low-cost samplers that could readily be deployed both indoors and outdoors over a one-week period. Samplers were used to collect indoor and outdoor air samples over approximately nine days and were analyzed for three VOCs using gas chromatography and flame ionization detection. Using multivariate logistic regression, we assessed relationships between inverse-distance-squared-weighted (ID2W) well counts and detection rates of benzene, ethylbenzene, and toluene. VOCs were generally not detectable in the outdoor air samples (detection rate ≤3%); indoor detection rates were 23% for benzene, 14% for ethylbenzene, and 49% for toluene. Three homes had indoor and one home had outdoor benzene concentrations exceeding health-based references. No associations between ID2W well count and detection rates of benzene, ethylbenzene, and toluene in residential air samples were observed. Though we had a large proportion of samples below the detection limit and did not observe any correlations, this study is among the first to compare UO&G proximity metrics with indoor and outdoor home measurements of air pollutants. Use of more sensitive sampling methods are needed for future efforts. The inclusion of additional attributes of UO&G wells and local meteorological and topographical data into proximity models could provide further insights into the potential for increased residential exposures to VOCs in communities with UO&G operations.

A Survey of Random Forest Based Methods for Intrusion Detection Systems

Over the past decades, researchers have been proposing different Intrusion Detection approaches to deal with the increasing number and complexity of threats for computer systems. In this context, Random Forest models have been providing a notable performance on their applications in the realm of the behaviour-based Intrusion Detection Systems. Specificities of the Random Forest model are used to provide classification, feature selection, and proximity metrics. This work provides a comprehensive review of the general basic concepts related to Intrusion Detection Systems, including taxonomies, attacks, data collection, modelling, evaluation metrics, and commonly used methods. It also provides a survey of Random Forest based methods applied in this context, considering the particularities involved in these models. Finally, some open questions and challenges are posed combined with possible directions to deal with them, which may guide future works on the area.

Effects of Impervious Surface on the Spatial Distribution of Urban Waterlogging Risk Spots at Multiple Scales in Guangzhou, South China

An impervious surface is considered one of main factors affecting urban waterlogging. Previous studies found that spatial pattern (composition and configuration) of impervious surfaces affected urban waterlogging. However, their relative importance remains unknown, and the scale-effect of the spatial pattern on urban waterlogging has been ignored. To move forward, our research studied the relationship between spatial patterns on the impervious surface and its subcategories (building and pavement) on urban waterlogging risk spots using Pearson correlation, partial redundancy analysis and performed at three grid scales (1 km × 1 km, 3 km × 3 km, 5 km × 5 km) and the catchment scale based on different spatial resolution land cover maps (2 m, 10 m and 30 m). We identified positively-correlated metrics with urban waterlogging risk spots, such as the composition of impervious surface (i.e., total impervious surface, building, pavement) and the aggregation metric of the total impervious surface at most scales, as well as two negatively correlated metrics (i.e., proximity metric of building, fragmentation metric of total impervious surface). Furthermore, the total variance of urban waterlogging risk spots explained by the spatial pattern of the total impervious surface and its subcategories increased with studied grid and catchment scales while decreasing from a fine to a coarse resolution. The relative contribution of the impervious surface composition and configuration to the variation of urban waterlogging risk spots varied across scales and among impervious surface types. The composition contributed more than the configuration did for the total impervious surface at both grid and catchment scales. Similar to total impervious surface, the composition of buildings was more important than its configuration was at all the grid scales, while the configuration of buildings was more important at the catchment scale. Contrary to the total impervious surface, the configuration of pavement at both the grid and catchment scales mattered more than their compositions did. Furthermore, the composition of the building was more important than that of pavement, but its configuration mattered less. Our study could provide a multi-scale landscape perspective with detailed suggestions for controlling the area of impervious surface and optimizing its spatial configuration in urban waterlogging risk mitigation and urban planning.

Toward space-time buffering for spatiotemporal proximity analysis of movement data

ABSTRACTSpatiotemporal proximity analysis to determine spatiotemporal proximal paths is a critical step for many movement analysis methods. However, few effective methods have been developed in the literature for spatiotemporal proximity analysis of movement data. Therefore, this study proposes a space-time-integrated approach for spatiotemporal proximal analysis considering space and time dimensions simultaneously. The proposed approach is based on space-time buffering, which is a natural extension of conventional spatial buffering operation to space and time dimensions. Given a space-time path and spatial tolerance, space-time buffering constructs a space-time region by continuously generating spatial buffers for any location along the space-time path. The constructed space-time region can delimit all space-time locations whose spatial distances to the target trajectory are less than a given tolerance. Five space-time overlapping operations based on this space-time buffering are proposed to retrieve all spatiotemporal proximal trajectories to the target space-time path, in terms of different spatiotemporal proximity metrics of space-time paths, such as Fréchet distance and longest common subsequence. The proposed approach is extended to analyze space-time paths constrained in road networks. The compressed linear reference technique is adopted to implement the proposed approach for spatiotemporal proximity analysis in large movement datasets. A case study using real-world movement data verifies that the proposed approach can efficiently retrieve spatiotemporal proximal paths constrained in road networks from a large movement database, and has significant computational advantage over conventional space-time separated approaches.

Distributed Partitioning Algorithms for Locational Optimization of Multiagent Networks in SE(2)

This paper is concerned with the development of distributed spatial partitioning algorithms for locational optimization problems involving networks of agents with planar rigid body dynamics subject to communication constraints. The domain of the problems we consider is a three-dimensional (3-D) nonflat manifold embedded in the state space of the agents, which we refer to as the terminal manifold. The approach we propose allows us to associate the partition of the 3-D terminal manifold, which is induced by a nonquadratic proximity metric and comprised of nonconvex cells, with a one-parameter family of partitions of 2-D flat manifolds, which are induced by (parametric) quadratic proximity metrics and comprised of convex polygonal cells. By exploiting the special structure of the parametric partitions, we develop distributed partitioning algorithms that converge in a finite number of steps. Subsequently, we utilize the solutions to the latter problems to solve a class of locational optimization problems over the terminal manifold. Numerical simulations that illustrate the capabilities of the proposed algorithms are also presented.

Food deserts in Winnipeg, Canada: a novel method for measuring a complex and contested construct.

"Food deserts" have emerged over the past 20 years as spaces of concern for communities, public health authorities and researchers because of their potential negative impact on dietary quality and subsequent health outcomes. Food deserts are residential geographic spaces, typically in urban settings, where low-income residents have limited or no access to retail food establishments with sufficient variety at affordable cost. Research on food deserts presents methodological challenges including retail food store identification and classification, identification of low-income populations, and transportation and proximity metrics. Furthermore, the complex methods often used in food desert research can be difficult to reproduce and communicate to key stakeholders. To address these challenges, this study sought to demonstrate the feasibility of implementing a simple and reproducible method of identifying food deserts using data easily available in the Canadian context. This study was conducted in Winnipeg, Canada in 2014. Food retail establishments were identified from Yellow Pages and verified by public health dietitians. We calculated two scenarios of food deserts based on location of the lowest-income quintile population: (a) living ≥ 500 m from a national chain grocery store, or (b) living ≥ 500 m from a national chain grocery store or a full-service grocery store. The number of low-income residents living in a food desert ranged from 64 574 to 104 335, depending on the scenario used. This study shows that food deserts affect a significant proportion of the Winnipeg population, and while concentrated in the urban core, exist in suburban neighbourhoods also. The methods utilized represent an accessible and transparent, reproducible process for identifying food deserts. These methods can be used for costeffective, periodic surveillance and meaningful engagement with communities, retailers and policy makers.

Residential Proximity to Roadways and Ischemic Placental Disease in a Cape Cod Family Health Study.

Exposure to air pollution may adversely impact placental function through a variety of mechanisms; however, epidemiologic studies have found mixed results. We examined the association between traffic exposure and placental-related obstetric conditions in a retrospective cohort study on Cape Cod, MA, USA. We assessed exposure to traffic using proximity metrics (distance of residence to major roadways and length of major roadways within a buffer around the residence). The outcomes included self-reported ischemic placental disease (the presence of at least one of the following conditions: preeclampsia, placental abruption, small-for-gestational-age), stillbirth, and vaginal bleeding. We used log-binomial regression models to estimate risk ratios (RR) and 95% confidence intervals (CI), adjusting for potential confounders. We found no substantial association between traffic exposure and ischemic placental disease, small-for-gestational-age, preeclampsia, or vaginal bleeding. We found some evidence of an increased risk of stillbirth and placental abruption among women living the closest to major roadways (RRs comparing living <100 m vs. ≥200 m = 1.75 (95% CI: 0.82–3.76) and 1.71 (95% CI: 0.56–5.23), respectively). This study provides some support for the hypothesis that air pollution exposure adversely affects the risk of placental abruption and stillbirth; however, the results were imprecise due to the small number of cases, and may be impacted by non-differential exposure misclassification and selection bias.

Random Spectral Sampling for Compliance Enforcement in Dynamic Spectrum Access Networks

A similarity-based spectral stratification technique is proposed for automatic, real-time characterization of spectrum occupancy in wideband sensing, using random spectral sampling. Sub-bands with similar properties, as defined by proximity metrics, are grouped into strata during any statistically stationary interval. A subset of each stratum is then sampled for estimating properties of interest. This approach can reduce requirements for sampling and processing of the entire wideband. The technique is investigated in the context of spectrum monitoring for the compliance enforcement function, which is an essential component in spectrum management. The technique is explored, to estimate bandwidth occupancy using software defined radio hardware, in the ISM band as well as for 24 h of measurements spanning 800 MHz–1 GHz. Results suggest that the proposed technique improves on previous non-adaptive stratification techniques for random spectral sampling, and was comparable in clustering performance to k-means clustering, while executing in a deterministic time without the need for iterations or setting the initial number of segments to cluster, as required for k-means. The proposed stratification technique therefore offers an alternative approach for deployment in spectrum monitoring networks for compliance enforcement in dynamic spectrum access networks.

PRIORITY AREAS FOR FOREST CONSERVATION IN AN URBAN LANDSCAPE AT THE TRANSITION BETWEEN ATLANTIC FOREST AND CERRADO

ABSTRACT Urbanization and agriculture growth are some of the major causes of natural ecosystems depletion and biodiversity loss. Conservation efforts can be developed through the prioritization of areas for forest conservation in order to minimize this process. Here, we establish conservation strategies based on a spatial analysis of forest fragments in an urban landscape at the transition between two important Brazilian biodiversity hotspots: Atlantic Forest and Cerrado. A high-resolution mapping of forest patches was used to quantify forest cover and to provide spatial analysis. We developed a Forest Conservation Priority Index (FCPI) derived from landscape metrics to select priority areas for forest conservation efforts. We used area, shape and proximity metrics as landscape structure indicators. Landscape metrics were classified and we attributed a rank and weights for them to calculate the FCPI. Forest covers 17% of the study area. 60% of the forest patches comprise less than one hectare, 95% less than 10 ha and only 1% more than 50 ha. The largest fragments (&gt; 100ha) are all long and narrow. Also, fewer than half the patches are connected to others. Regions classified as high and very high priority for forest conservation are localized at greater slopes, along rivers and on private lands. Our index allowed the prioritization of forest fragments in an urban landscape, directing efforts of conservation. Creating protected areas and restoration plans are necessary for the better situation of the natural ecosystem. Land-use planning must resolve human demands and conservation of this important ecosystem.

Identification of category associations using a multilabel classifier

Description of the data using categories allows one to describe it on a higher abstraction level. In this way, we can operate on aggregated groups of the information, allowing one to see relationships that do not appear explicit when we analyze the individual objects separately. In this paper we present automatic identification of the associations between categories used for organization of the textual data. As experimental data we used a network of English Wikipedia articles and their associated categories, that have been preprocessed by a dedicated filtering method for noise reduction. The main contribution of the paper is the introduction of the method based on supervised machine learning for mining relations between these categories. We describe existing in the literature category proximity metrics as well as introduce three new ones, based on observing the properties of a multilabel Support Vector Machine classifier. The first metric uses classifier predictions, the second uses its errors, and the third is based on its model. Comparison to the existing state-of-the-art methods, and to manual assessments, confirm that the proposed methods are useful and are more flexible than typical approaches. We show how different metrics allow us to introduce new significant relations between categories. Aggregated results of mining categories’ associations have been used to build a semantic network that shows a practical application of the research. The proposed method for finding associations can be extended with using other approaches than SVM classification, and can find (other than presented in the paper) applications for mining categories in text repositories. Eg.: it can be used for extending the prediction of the rating in recommender systems or as a method of missing data imputation.

The Arbitrary Definition of the Current Atlantic Major Hurricane Landfall Drought

Abstract A major hurricane [96+ knots (kt; 1 kt = 0.51 m s–1) of maximum sustained wind] has not made landfall in the United States since Wilma (2005). Recent elegant stochastic–statistical modeling estimates the return period of a 9-yr streak for this metric as 177 yr, suggesting extraordinary rarity, especially in the context of the length of the record (1851–2014). Current awareness of the drought is increased given that the 2015 hurricane season is expected to be suppressed from El Niño and the recent anniversaries of several noteworthy landfalls. Yet, here we show that the significance or even existence of the current 9-yr drought is highly dependent on the metric used. Acknowledging that wind intensity estimates are binned every 5 kt and have approximate 10-kt uncertainty, we examine the same record using landfall thresholds of 95–105 kt. Using 105-kt landfall, 1993–2003 becomes a previously unreported yet more remarkable 11-yr drought and 1981–88 becomes an 8-yr drought. Further, landfall minimum sea level pressure is more reliably estimated than maximum sustained wind speed. For landfall intensities stronger than 960 hPa (a climatological threshold for 100 kt), the current drought disappears because of Irene (2011) and Sandy (2012). A coastline-independent yet nearby proximity metric is tested and reveals a nonexistent drought. Accordingly, this study suggests the following: 1) Caution is advised when identifying a hurricane drought and its historical significance. 2) Using hurricane landfall statistics to infer a climate signal is fraught with issues (threshold, coastline, and potentially nonscientific contributions), regardless of intensity metric. 3) From a societal context, human and financial losses matter most, and Irene [2011; $8 billion (U.S. dollars)] and Sandy (2012; $88 billion) occurred during the current drought.

Distributed partitioning algorithms for multi-agent networks with quadratic proximity metrics and sensing constraints

We consider a class of Voronoi-like partitioning problems, in which a multi-agent network seeks to subdivide a subset of an affine space into a finite number of cells in the presence of sensing constraints. The cell of this subdivision that is assigned to a particular agent consists exclusively of points that can be sensed by this agent and are closer to it than to any other agent that can also sense them. The proximity between an agent and an arbitrary point is measured in terms of a non-homogeneous quadratic (generalized) distance function, which does not, in general, enjoy the triangle inequality and the symmetry property. One of the consequences of this fact is that the structure of the sublevel sets of the utilized proximity metric does not conform with that of the sensing region of an agent. Due to this mismatch, it is possible that a point may be assigned to an agent which is different from its “nearest” agent simply because the nearest agent cannot sense this point, unless special care is taken. We propose a distributed partitioning algorithm that enables each agent to compute its own cell independently from the other agents when the only information available to it is the positions and the velocities of the agents that lie inside its sensing region. The algorithm is based on an iterative process that adjusts the size of the sensing region of each agent until the associated cell of the latter corresponds to the intersection of its sensing region with the cell that would have been assigned to it in the absence of sensing constraints. The correctness of the proposed distributed algorithm, which successfully handles the aforementioned issues, is studied in detail.

Zoning Evaluation for Improved Coordinated Automatic Voltage Control

Hierarchically structured automatic voltage control (AVC) architecture has attracted increased interest as networks operate closer to their capacity limits. Hierarchical AVC enables wide-area coordinated voltage regulation (CVR). Due to the inherent complexity of the task, it is based on reduced control models, i.e., simplified models of the system suitable for voltage control. It is a fact however that a single reduced control model (static RCM) cannot be optimal for all network configurations and operating conditions. In pursuit of an improved CVR, this paper investigates the applicability of zoning methodologies in adaptively determined RCM. It further argues that the selection of a zoning methodology affects not only the CVR operation, but also its robustness to erroneous data and proposes a comprehensive generic framework for evaluating its performance. Lastly, it extends and evaluates several zoning-based control model reduction methodologies: namely, hierarchical clustering employing two different proximity metrics, spectral ${\rm k}$ -way and fuzzy ${\rm C}$ -means, on both static and adaptive schemes.

Computational Fact Checking from Knowledge Networks.

Traditional fact checking by expert journalists cannot keep up with the enormous volume of information that is now generated online. Computational fact checking may significantly enhance our ability to evaluate the veracity of dubious information. Here we show that the complexities of human fact checking can be approximated quite well by finding the shortest path between concept nodes under properly defined semantic proximity metrics on knowledge graphs. Framed as a network problem this approach is feasible with efficient computational techniques. We evaluate this approach by examining tens of thousands of claims related to history, entertainment, geography, and biographical information using a public knowledge graph extracted from Wikipedia. Statements independently known to be true consistently receive higher support via our method than do false ones. These findings represent a significant step toward scalable computational fact-checking methods that may one day mitigate the spread of harmful misinformation.

Partitioning algorithms for multi-agent systems based on finite-time proximity metrics

We address a generalized Voronoi partitioning problem for a team of mobile agents with nonlinear dynamics with respect to a state-dependent proximity metric. In particular, the proximity (pseudo-) metric corresponds to the reduction of a generalized energy metric that occurs during the transfer of an agent to an arbitrary destination with zero terminal velocity, in finite time. The realization of every finite-time state transition takes place by means of a class of continuous feedback control laws that render the closed loop dynamics of each mobile agent non-Lipschitzian. The arrival time also turns out to be a state-dependent quantity, whose functional description is not prescribed a priori. We show that the partitioning problem studied in this work can admit a decentralized solution, that is, each agent can compute its own cell independently from its teammates provided that is aware of the positions and velocities of its neighboring agents. Numerical simulations that illustrate the theoretical developments are also presented.

Missing precipitation data estimation using optimal proximity metric-based imputation, nearest-neighbour classification and cluster-based interpolation methods

New optimal proximity-based imputation, K-nearest neighbour (K-NN) classification and K-means clustering methods are proposed and developed for estimation of missing daily precipitation records. Mathematical programming formulations are developed to optimize the weighting, classification and clustering schemes used in these methods. Ten different binary and real-valued distance metrics are used as proximity measures. Two climatic regions, Kentucky and Florida, (temperate and tropical) in the USA, with different gauge density and network structure, are used as case studies to evaluate the new methods. A comprehensive exercise is undertaken to compare the performances of the new methods with those of several deterministic and stochastic spatial interpolation methods. The results from these comparisons indicate that the proposed methods performed better than existing methods. Use of optimal proximity metrics as weights, spatial clustering of observation sites and classification of precipitation data resulted in improvement of missing data estimates.Editor D. Koutsoyiannis; Associate editor C. Onof

Field observations into the environmental soul: spatial configuration and social life for people experiencing dementia.

This article focuses on the important, facilitating role architectural design plays in social interaction within long-term care facilities (LTCFs) serving people with dementia. Here, we apply space syntax, a set of theories and techniques for the analysis of spatial configurations, as an objective measure of environmental characteristics. Almost 150 rounds of behavioral observations were collected in the social spaces of 3 LTCFs. Using the visibility and proximity metrics of space syntax, the locations of occurrence of various social activities in relation to the furniture and spatial layout on architectural floor plans have been identified. The results did not confirm the space syntax hypothesis that spaces with greater visibility and proximity promote more social interaction. Further analysis revealed that when in settings with better visibility and accessibility, the residents were more likely to engage in low levels of interaction. High-level social interactions actually were more likely to occur in settings providing greater privacy (eg, less visibility and accessibility). The findings suggest an important nuance that architectural configuration factors impact not only the likelihood but also the type of conversations likely to occur in certain locations. This would have implications for both design and staff training on how best to utilize social spaces for therapeutic effect, particularly within the context of person-centered care.

Using clustering coefficient to construct weighted networks for supervised link prediction

Link prediction problem has drawn much attention in the analysis of complex networks. A lot of previous works are devoted for performing link prediction both on weighted and un-weighted networks. Clustering coefficient is a well-studied attribute in graph theory. It measures the degree to which nodes in a graph tend to cluster together. In this paper, we apply normalized clustering coefficient as a weighting scheme to construct weighted networks for supervised link prediction. Unlike the weighting approaches used in some existing literatures which have to consider properties outside the scope of network topology, we take only the topological properties into consideration. Several proximity metrics like Common Neighbors, Jaccard’s coefficient and Adamic–Adar coefficient are chosen as predictor attributes. Learning algorithms like Naive Bayes, J48 and SVM available in the WEKA environment are used to perform binary classification experiments on the constructed networks. Both weighted and un-weighted versions of networks are used and experimental results show that the proposed weighting approach could bring benefits to link prediction.

Solar irradiance forecasting using spatio-temporal empirical kriging and vector autoregressive models with parameter shrinkage

As renewable energy sources proliferate and start to represent significant contributors to grid electricity generation, it becomes increasingly important to manage their inherent variability. Moving clouds are the primary cause of variability in spatio–temporal solar irradiance random processes. A key objective of this work is to reduce the number of parameters in the characterization of the process. Properly designed shrinkage models can improve forecast accuracies from a model that uses irrelevant information. Proximity metrics, such as correlation coefficient and spatial dispersion, are often used to identify the spatio–temporal dynamics in a given framework. In the literature, correlation–based forecasting studies aim to derive quantities such as cloud speeds and directions in a deterministic way. However, due to the sparsity of many irradiance networks, the day–to–day along–wind correlation measures may not be observable. In this work, we propose two novel methods to quantify the long–term threshold distance of the spatio–temporal irradiance process, namely, deriving the threshold distance from the distributions of maximum daily cross–correlation and from the isotropic dispersion. We also apply two spatio–temporal forecasting methods, namely, space–time kriging and vector autoregressive models. We find that the forecasting accuracies do not increase by including more stations beyond the threshold distance. This result allows for the design of simplified monitoring networks and improved forecasting techniques.