Оценка расстояний между российскими регионами с учетом транспортной инфраструктуры

One of the major challenges in engineering seismology is the reliable prediction of site-specific ground motion for particular earthquakes, observed at specific distances. For larger events, a special problem arises, at short distances, with the source-to-site distance measure, because distance metrics based on a point-source model are no longer appropriate. As a consequence, different attenuation relations differ in the distance metric that they use. In addition to being a source of confusion, this causes problems to quantitatively compare or combine different ground-motion models; for example, in the context of Probabilistic Seismic Hazard Assessment, in cases where ground-motion models with different distance metrics occupy neighboring branches of a logic tree. In such a situation, very crude assumptions about source sizes and orientations often have to be used to be able to derive an estimate of the particular metric required. Even if this solves the problem of providing a number to put into the attenuation relation, a serious problem remains. When converting distance measures, the corresponding uncertainties map onto the estimated ground motions according to the laws of error propagation. To make matters worse, conversion of distance metrics can cause the uncertainties of the adapted ground-motion model to become magnitude and distance dependent, even if they are not in the original relation. To be able to treat this problem quantitatively, the variability increase caused by the distance metric conversion has to be quantified. For this purpose, we have used well established scaling laws to determine explicit distance conversion relations using regression analysis on simulated data. We demonstrate that, for all practical purposes, most popular distance metrics can be related to the Joyner-Boore distance using models based on gamma distributions to express the shape of some “residual function.” The functional forms are magnitude and distance dependent and are expressed as polynomials. We compare the performance of these relations with manually derived individual distance estimates for the Landers, the Imperial Valley, and the Chi-Chi earthquakes.

Railway stations function as nodes in transport networks and places in an urban environment. They have accessibility and environmental impacts, which contribute to property value. The literature on the effects of railway stations on property value is mixed in its finding in respect to the impact magnitude and direction, ranging from a negative to an insignificant or a positive impact. This paper attempts to explain the variation in the findings by meta-analytical procedures. Generally the variations are attributed to the nature of data, particular spatial characteristics, temporal effects and methodology. Railway station proximity is addressed from two spatial considerations: a local station effect measuring the effect for properties with in 1/4 mile range and a global station effect measuring the effect of coming 250 m closer to the station. We find that the effect of railway stations on commercial property value mainly takes place at short distances. Commercial properties within 1/4 mile rang are 12.2% more expensive than residential properties. Where the price gap between the railway station zone and the rest is about 4.2% for the average residence, it is about 16.4% for the average commercial property. At longer distances the effect on residential property values dominate. We find that for every 250 m a residence is located closer to a station its price is 2.3% higher than commercial properties. Commuter railway stations have a consistently higher positive impact on the property value compared to light and heavy railway/Metro stations. The inclusion of other accessibility variables (such as highways) in the models reduces the level of reported railway station impact.

가상현실 시스템에서 높은 현실감과 직관적인 시각정보의 제공이 커다란 장점임에도 불구하고, 가상공간에서의 공간제시에 있어서 거리감각이 정확하게 전달되지 않는 문제점이 있다. 특히나 건축분야와 같은 공간을 취급하는 분야에서의 공간제시에 있어서의 왜곡문제는 시급히 해결해야할 필요성이 있다. 본 논문에서는 가상현실공간에서 거리감각을 고찰함에 있어서 거리감각을 조사하는 하나의 요소로서, 피실험자에 의한 이동거리추정판단에 관한 실험을 행하였다. 가상현실공간에서의 제시정보의 부족이 거리감각의 차이를 가져온다는 가정하에 몰입형 가상현실시스템에서 다양한 영상의 투영조건하에서 이동거리추정판단을 실험적으로 조사함으로써, 가상공간을 형성하는 시스템이 제공하는 정적, 동적 영상이미지의 특성에 의한 거리감각의 변화를 규명하고자 한다. 구체적인 실험방법으로는 실재 건축물의 복도를 모델링하여, 복도의 실재공간과 모델링된 가상공간을 피실험자에게 영상으로 제시하면서 이동거리추정판단의 심리실험을 수행하였다. 실험에 이용한 모델과 같은 실재의 복도에서도 이동거리추정판단의 실험을 동시에 수행함으로써, 실재환경과 가상현실환경과의 거리감각의 차이를 비교분석하고, 그 결과 실재환경과 비교하여 몰입형 가상현실시스템에서의 거리감각이 단축이 되는 경향이 나타남을 설명한다. 나아가, 몰입형 가상현실 시스템에 있어서 거리감각이 과소평가되는 경향이 나타나는 요인을 고찰하여, 영상의 해상도를 거리감각을 형성하는 중요한 요소로 판단하고 정적, 동적 이미지의 해상도의 변화에 따른 거리감각을 규명한다. The virtual reality (VR) technology has been used as the application of architectural presentation or simulation tool in the field of industry. The high immersion and intuitive visual information are the great merits of design evaluation or environmental simulation when we are using the virtual environments. But the distortion of distance perception in VR is still a big problem when the accuracy of distance presentation is strictly required. For example, distance estimation is especially important when the virtual environments are applied to the presentational tool for evaluation the space design or planning in the field of architecture. If there are some perception error between the built space in real and represented space in virtual, the accurate design evaluation or modification of design is hard to be carried out during the design development stage. In this paper, we have carried out some experiments about distance estimation in the immersive virtual environments to verify the factors and their influence. We made a hypothesis that the lack of the information for the user in VR causes the different distance estimation from the real world because users are usually comfortable with moving fast and long distance in VR environments compared with moving slow and short distance in real space. So, we carried out basic experiment to prove our hypothesis that the lack of information makes subjects estimate the distance of walking in VR shorter compared with the same distance in real. Also, among the factors that probably affect the distance estimation in VR, we have verified the influence of the image resolution. The influence of resolution degradation of image on the distance estimation was verified with the condition of static and dynamic images. The results showed that the resolution has deep relation with the distance estimation. For example, the subject underestimated the distance at the lower resolution condition. We also found the methods of the making the lower resolution image could affect on the visual perception of subjects.

People naturally seek an interpersonal distance that feels comfortable, striking a balance between not being too close or too far from others until reaching a state of equilibrium. Previous studies on interpersonal distance preferences among autistic individuals have yielded inconsistent results. Some show a preference for greater distance, while others indicate a preference for shorter distances, or reveal higher variance in preferences among autistic individuals. In a related vein, previous studies have also investigated the way autistics accurately judge distance, and these studies have received inconsistent results, with some showing superior spatial abilities and others indicating biases in distance estimations. However, the link between distance estimation and preference has never been examined. To address this gap, our study measured interpersonal distance preferences and estimations and tested the correlation between the two factors. The results indicate greater variance among autistic people in both the preference of distance and the ability to estimate distance accurately, suggesting that inconsistencies in previous studies originate from greater individual differences among autistics. Furthermore, only among autistic individuals were interpersonal distance preference and estimation bias associated in a manner that violated equilibrium. Underestimation bias (judging others as closer than they are) was linked to a preference for closer proximity, while overestimation bias (judging others as further away) was associated with a preference for maintaining a greater distance. This connection suggests that biases in the estimation of interpersonal distance contribute to extreme preferences (being too close or too far away). Taken together, the findings suggest that biases in the estimation of interpersonal distance are associated with socially inappropriate distance preferences among autistics.

Insect Navigation: Measuring Travel Distance across Ground and through Air

BackgroundSeveral methodological approaches have been used to estimate distance in health service research. In this study, focusing on cardiac catheterization services, Euclidean, Manhattan, and the less widely known Minkowski distance metrics are used to estimate distances from patient residence to hospital. Distance metrics typically produce less accurate estimates than actual measurements, but each metric provides a single model of travel over a given network. Therefore, distance metrics, unlike actual measurements, can be directly used in spatial analytical modeling. Euclidean distance is most often used, but unlikely the most appropriate metric. Minkowski distance is a more promising method. Distances estimated with each metric are contrasted with road distance and travel time measurements, and an optimized Minkowski distance is implemented in spatial analytical modeling.MethodsRoad distance and travel time are calculated from the postal code of residence of each patient undergoing cardiac catheterization to the pertinent hospital. The Minkowski metric is optimized, to approximate travel time and road distance, respectively. Distance estimates and distance measurements are then compared using descriptive statistics and visual mapping methods. The optimized Minkowski metric is implemented, via the spatial weight matrix, in a spatial regression model identifying socio-economic factors significantly associated with cardiac catheterization.ResultsThe Minkowski coefficient that best approximates road distance is 1.54; 1.31 best approximates travel time. The latter is also a good predictor of road distance, thus providing the best single model of travel from patient's residence to hospital. The Euclidean metric and the optimal Minkowski metric are alternatively implemented in the regression model, and the results compared. The Minkowski method produces more reliable results than the traditional Euclidean metric.ConclusionRoad distance and travel time measurements are the most accurate estimates, but cannot be directly implemented in spatial analytical modeling. Euclidean distance tends to underestimate road distance and travel time; Manhattan distance tends to overestimate both. The optimized Minkowski distance partially overcomes their shortcomings; it provides a single model of travel over the network. The method is flexible, suitable for analytical modeling, and more accurate than the traditional metrics; its use ultimately increases the reliability of spatial analytical models.

30 Minimum distance procedures

We present a new probabilistic technique of embedding graphs in Zd, the d-dimensional integer lattice, in order to find the shortest paths and shortest distances between pairs of nodes. In our method the nodes of a breath first search (BFS) tree, starting at a particular node, are labeled as the sites found by a branching random walk on Zd. After describing a greedy algorithm for routing (distance estimation) which uses the l1 distance (l2 distance) between the labels of nodes, we approach the following question: Assume that the shortest distance between nodes s and t in the graph is the same as the shortest distance between them in the BFS tree corresponding to the embedding, what is the probability that our algorithm finds the shortest path (distance) between them correctly? Our key result comprises the following two complementary facts: i) by choosing d = d(n) (where n is the number of nodes) large enough our algorithm is successful with high probability, and ii) d does not have to be very large - in particular it suffices to have d = O(polylog(n)). We also suggest an adaptation of our technique to finding an efficient solution for the all-sources all-targets (ASAT) shortest paths problem, using the fact that a single embedding finds not only the shortest paths (distances) from its origin to all other nodes, but also between several other pairs of nodes. We demonstrate its behavior on a specific nonsparse random graph model and on real data, the PGP network, and obtain promising results. The method presented here is less likely to prove useful as an attempt to find more efficient solutions for ASAT problems, but rather as the basis for a new approach for algorithms and protocols for routing and communication. In this approach, noise and the resulting corruption of data delivered in various channels might actually be useful when trying to infer the optimal way to communicate with distant peers.

Introduction: the inclusion of the language issue into the institutional agenda is a topical matter for Russia with its multiple ethnic groups speaking their native language. However, such an inclusion can be implemented in different forms and to a different degree, in particular, in the context of thematic government programs. The study attempts to determine the variability spectrum in the practices of including the ethnic minorities’ languages into thematic government programs texts in the multi-ethnic regions of the Russian Federation. Objectives: to identify models for including the language component into thematic government programs texts operating in multi-ethnic Russian regions, to understand the scale of the existing variability, taking into account the ethnolinguistic structure of the population in this regions. Methods: cross-regional comparative content analysis of thematic government programs in Russian multi-ethnic regions. Results: the presence of cross-regional variability was determined in the inclusion of the language component into thematic government programs texts of three types: the programs in the field of national policy, in education and in relation to the preservation, development and study of the regional languages. Four models of including the language component into thematic government programs texts have been identified: “all-encompassing”, “title-oriented”, “priority selective” and “ignoring”. No stable relationship between the specificity of the ethno-national structure of the Russian regions population and the variability of mentioning the languages in thematic government programs texts has been found. Conclusions: the variability of the language component inclusion into thematic government programs texts is manifested in the extent and depth of language topics coverage, therefore there is a need for an additional research through a qualitative comparative analysis of a selection of cases.

GIScience 2016 Short Paper Proceedings Measuring Distance “As the Horse Runs”: Cross-Scale Comparison of Terrain-Based Metrics BP Buttenfield 1 , M Ghandehari 1 , S Leyk 1 , LV Stanislawski 2 , ME Brantley 1 , and Yi Qiang 1 Department of Geography, University of Colorado, Boulder CO 80309 Email: {babs; mehran.ghandehari; stefan.leyk; margaret.brantley; yi.qiang} @colorado.edu U.S. Geological Survey (USGS), Center of Excellence for Geospatial Information Science, Rolla MO 65401 Email: lstan @usgs.gov Disclaimer: Any use of trade, firm, or product names is for descriptive purposes and does not imply endorsement by the U.S. Government. 1. Introduction Distance metrics play significant roles in spatial modeling tasks, such as flood inundation (Tucker and Hancock 2010), stream extraction (Stanislawski et al. 2015), power line routing (Kiessling et al. 2003) and analysis of surface pollutants such as nitrogen (Harms et al. 2009). Avalanche risk is based on slope, aspect, and curvature, all directly computed from distance metrics (Gutierrez 2012). Distance metrics anchor variogram analysis, kernel estimation, and spatial interpolation (Cressie 1993). Several approaches are employed to measure distance. Planar metrics measure straight line distance between two points (“as the crow flies”) and are simple and intuitive, but suffer from uncertainties. Planar metrics assume that Digital Elevation Model (DEM) pixels are rigid and flat, as tiny facets of ceramic tile approximating a continuous terrain surface. In truth, terrain can bend, twist and undulate within each pixel. Work with Light Detection and Ranging (lidar) data or High Resolution Topography to achieve precise measurements present challenges, as filtering can eliminate or distort significant features (Passalacqua et al. 2015). The current availability of lidar data is far from comprehensive in developed nations, and non-existent in many rural and undeveloped regions. Notwithstanding computational advances, distance estimation on DEMs has never been systematically assessed, due to assumptions that improvements are so small that surface adjustment is unwarranted. For individual pixels inaccuracies may be small, but additive effects can propagate dramatically, especially in regional models (e.g., disaster evacuation) or global models (e.g., sea level rise) where pixels span dozens to hundreds of kilometers (Usery et al 2003). Such models are increasingly common, lending compelling reasons to understand shortcomings in the use of planar distance metrics. Researchers have studied curvature-based terrain modeling. Jenny et al. (2011) use curvature to generate hierarchical terrain models. Schneider (2001) creates a ‘plausibility’ metric for DEM-extracted structure lines. d’Oleire- Oltmanns et al. (2014) adopt object-based image processing as an alternative to working with DEMs; acknowledging the pre-processing involved in converting terrain into an object model is computationally intensive, and likely infeasible for some applications. This paper compares planar distance with surface adjusted distance, evolving from distance “as the crow flies” to distance “as the horse runs”. Several methods are compared for DEMs spanning a range of resolutions for the study area and validated against a 3 meter (m) lidar data benchmark. Error magnitudes vary with pixel size and with the method of surface adjustment. The rate of error increase may also vary with landscape type (terrain roughness, precipitation regimes and land settlement patterns). Cross-scale analysis for a single study area is reported here. Additional areas will be presented at the conference. 2. Data and Study Area The study area is 7,885.94 square kilometers (sq km), located in western North Carolina, (35.798 degrees N and 81.473 degrees W) spanning the Pisgah National Forest. Its location at

This paper examines the spatial patterns of unemployment in Chicago between 1980 and 1990. We study unemployment clustering with respect to different social and economic distance metrics that reflect the structure of agents' social networks. Specifically, we use physical distance, travel time, and differences in ethnic and occupational distribution between locations. Our goal is to determine whether our estimates of spatial dependence are consistent with models in which agents' employment status is affected by information exchanged locally within their social networks. We present non‐parametric estimates of correlation across Census tracts as a function of each distance metric as well as pairs of metrics, both for unemployment rate itself and after conditioning on a set of tract characteristics. Our results indicate that there is a strong positive and statistically significant degree of spatial dependence in the distribution of raw unemployment rates, for all our metrics. However, once we condition on a set of covariates, most of the spatial autocorrelation is eliminated, with the exception of physical and occupational distance. Racial and ethnic composition variables are the single most important factor in explaining the observed correlation patterns. Copyright © 2002 John Wiley & Sons, Ltd.

Estimation of Distances between Russian Regions with an Account for Transport Infrastructure

In these few years, Wi-Fi technology is widely used for indoor localization to overcome the limitation of global positioning system (GPS) accessibility within the indoor environment. The GPS accuracy is affected with an approximated error rate of 5 to 10 meters because the GPS signal will decrease while penetrating through the building walls or obstacles. This paper describes a low-cost and simple development of a group monitoring system with a single Wi-Fi access point using Raspberry Pi. Distance estimation methods based on the free-space propagation model and received signal strength indicator (RSSI) are implemented. The received signal strength indicator is collected during active scanning for indoor group monitoring in public spaces, such as shopping malls, retail shops, airports, railway stations, hotels, etc. A Group monitoring system with Wi-Fi active scanning opens the possibilities for the utilization of a single Wi-Fi access point to analyze the distance estimation for multiple audiences within the indoor public spaces. The functionalities of the proposed system include device detection, distance estimation, group monitoring, and social distancing alert.

Geostatistical analysis of adult Rhyzopertha dominica (F.) (Coleoptera: Bostrichidae) in wheat stored at constant temperatures

GMM estimation with cross sectional dependence