scholarly journals Comparing two distance measures in the spatial mapping of food deserts: The case of Petržalka, Slovakia

2017 ◽  
Vol 25 (2) ◽  
pp. 95-103 ◽  
Author(s):  
Kristína Bilková ◽  
František Križan ◽  
Marcel Horňák ◽  
Peter Barlík ◽  
Pavol Kita
Keyword(s):  
Euclidean Distance ◽  
Large Scale ◽  
Food Deserts ◽  
Distance Measures ◽  
Spatial Mapping ◽  
Street Network ◽  
Network Distance ◽  
Food Stores ◽  
Gis Environment ◽  
Distance Approach

AbstractOver the last twenty years or so, researchers’ attention to the issue of food deserts has increased in the geographical literature. Accessibility to large-scale retail units is one of the essential and frequently-used indicators leading to the identification and mapping of food deserts. Numerous accessibility measures of various types are available for this purpose. Euclidean distance and street network distance rank among the most frequently-used approaches, although they may lead to slightly different results. The aim of this paper is to compare various approaches to the accessibility to food stores and to assess the differences in the results gained by these methods. Accessibility was measured for residential block centroids, with applications of various accessibility measures in a GIS environment. The results suggest a strong correspondence between Euclidean distance and a little more accurate street network distance approach, applied in the case of the urban environment of Bratislava-Petržalka, Slovakia.

Context-aware Distance Measures for Dynamic Networks

2022 ◽  
Vol 16 (1) ◽  
pp. 1-34
Author(s):  
Yiji Zhao ◽  
Youfang Lin ◽  
Zhihao Wu ◽  
Yang Wang ◽  
Haomin Wen
Keyword(s):  
Large Scale ◽  
Dynamic Networks ◽  
Distance Measures ◽  
General Definition ◽  
Mathematical Representation ◽  
Network Clustering ◽  
Context Aware ◽  
Network Distance ◽  
Practical Applications ◽  
Spectral Distance

Dynamic networks are widely used in the social, physical, and biological sciences as a concise mathematical representation of the evolving interactions in dynamic complex systems. Measuring distances between network snapshots is important for analyzing and understanding evolution processes of dynamic systems. To the best of our knowledge, however, existing network distance measures are designed for static networks. Therefore, when measuring the distance between any two snapshots in dynamic networks, valuable context structure information existing in other snapshots is ignored. To guide the construction of context-aware distance measures, we propose a context-aware distance paradigm, which introduces context information to enrich the connotation of the general definition of network distance measures. A Context-aware Spectral Distance (CSD) is then given as an instance of the paradigm by constructing a context-aware spectral representation to replace the core component of traditional Spectral Distance (SD). In a node-aligned dynamic network, the context effectively helps CSD gain mainly advantages over SD as follows: (1) CSD is not affected by isospectral problems; (2) CSD satisfies all the requirements of a metric, while SD cannot; and (3) CSD is computationally efficient. In order to process large-scale networks, we develop a kCSD that computes top- k eigenvalues to further reduce the computational complexity of CSD. Although kCSD is a pseudo-metric, it retains most of the advantages of CSD. Experimental results in two practical applications, i.e., event detection and network clustering in dynamic networks, show that our context-aware spectral distance performs better than traditional spectral distance in terms of accuracy, stability, and computational efficiency. In addition, context-aware spectral distance outperforms other baseline methods.

Linkage analysis using geographical proximity: a test of the efficacy of distance measures

2020 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Shumpei Haginoya ◽  
Aiko Hanayama ◽  
Tamae Koike
Keyword(s):  
Environmental Factors ◽  
Euclidean Distance ◽  
Distance Measure ◽  
Distance Measures ◽  
Manhattan Distance ◽  
Geographical Proximity ◽  
Discrimination Accuracy ◽  
Content Type ◽  
Shortest Route ◽  
The Impact

Purpose The purpose of this paper was to compare the accuracy of linking crimes using geographical proximity between three distance measures: Euclidean (distance measured by the length of a straight line between two locations), Manhattan (distance obtained by summing north-south distance and east-west distance) and the shortest route distances. Design/methodology/approach A total of 194 cases committed by 97 serial residential burglars in Aomori Prefecture in Japan between 2004 and 2015 were used in the present study. The Mann–Whitney U test was used to compare linked (two offenses committed by the same offender) and unlinked (two offenses committed by different offenders) pairs for each distance measure. Discrimination accuracy between linked and unlinked crime pairs was evaluated using area under the receiver operating characteristic curve (AUC). Findings The Mann–Whitney U test showed that the distances of the linked pairs were significantly shorter than those of the unlinked pairs for all distance measures. Comparison of the AUCs showed that the shortest route distance achieved significantly higher accuracy compared with the Euclidean distance, whereas there was no significant difference between the Euclidean and the Manhattan distance or between the Manhattan and the shortest route distance. These findings give partial support to the idea that distance measures taking the impact of environmental factors into consideration might be able to identify a crime series more accurately than Euclidean distances. Research limitations/implications Although the results suggested a difference between the Euclidean and the shortest route distance, it was small, and all distance measures resulted in outstanding AUC values, probably because of the ceiling effects. Further investigation that makes the same comparison in a narrower area is needed to avoid this potential inflation of discrimination accuracy. Practical implications The shortest route distance might contribute to improving the accuracy of crime linkage based on geographical proximity. However, further investigation is needed to recommend using the shortest route distance in practice. Given that the targeted area in the present study was relatively large, the findings may contribute especially to improve the accuracy of proactive comparative case analysis for estimating the whole picture of the distribution of serial crimes in the region by selecting more effective distance measure. Social implications Implications to improve the accuracy in linking crimes may contribute to assisting crime investigations and the earlier arrest of offenders. Originality/value The results of the present study provide an initial indication of the efficacy of using distance measures taking environmental factors into account.

Cigarette and cigar sales in Hawaii before and after implementation of a Tobacco 21 Law

2020 ◽  
Vol 30 (1) ◽  
pp. 98-102 ◽  
Author(s):  
Rebecca Glover-Kudon ◽  
Doris G Gammon ◽  
Todd Rogers ◽  
Ellen M Coats ◽  
Brett Loomis ◽  
...  
Keyword(s):  
Large Scale ◽  
Geographical Area ◽  
Upward Trend ◽  
Food Stores ◽  
The Us ◽  
Before And After ◽  
Youth And Young Adults ◽  
The Usa ◽  
Product Sales ◽  
The Impact

IntroductionOn 1 January 2016, Hawaii raised the minimum legal age for tobacco access from 18 to 21 years (‘Tobacco 21 (T21)’) statewide, with no special population exemptions. We assessed the impact of Hawaii’s T21 policy on sales of cigarettes and large cigars/cigarillos in civilian food stores, including menthol/flavoured product sales share.MethodsCigarette and large cigar/cigarillo sales and menthol/flavoured sales share were assessed in Hawaii, California (implemented T21 in June 2016 with a military exemption), and the US mainland using the only Nielsen data consistently available for each geographical area. Approximate monthly sales data from large-scale food stores with sales greater than US$2 million/year covered June 2012 to February 2017. Segmented regression analyses estimated changes in sales from prepolicy to postpolicy implementation periods.ResultsFollowing T21 in Hawaii, average monthly cigarette unit sales dropped significantly (−4.4%, p<0.01) coupled with a significant decrease in menthol market share (−0.8, p<0.01). This combination of effects was not observed in comparison areas. Unit sales of large cigars/cigarillos decreased significantly in each region following T21 implementation. T21 policies in Hawaii and California showed no association with flavoured/menthol cigar sales share, but there was a significant increase in flavoured/menthol cigar sales share in the USA (7.1%, p<0.01) relative to Hawaii’s implementation date, suggesting T21 may have attenuated an otherwise upward trend.ConclusionsAs part of a comprehensive approach to prevent or delay tobacco use initiation, T21 laws may help to reduce sales of cigarette and large cigar products most preferred by US youth and young adults.

scholarly journals Joint Geosequential Preference and Distance Metric Factorization for Point-of-Interest Recommendation

2020 ◽  
Vol 2020 ◽  
pp. 1-14
Author(s):  
Chunyang Liu ◽  
Chao Liu ◽  
Haiqiang Xin ◽  
Jian Wang ◽  
Jiping Liu ◽  
...  
Keyword(s):  
Metric Space ◽  
Matrix Factorization ◽  
Euclidean Distance ◽  
Large Scale ◽  
Inner Product ◽  
Interaction Matrix ◽  
Distance Metric ◽  
Point Of Interest ◽  
Poi Recommendation ◽  
Real World Datasets

Point-of-interest (POI) recommendation is a valuable service to help users discover attractive locations in location-based social networks (LBSNs). It focuses on capturing users’ movement patterns and location preferences by using massive historical check-in data. In the past decade, matrix factorization has become a mature and widely used technology in POI recommendation. However, the inner product of latent vectors adopted in matrix factorization methods does not satisfy the triangle inequality property, which may limit the expressiveness and lead to suboptimal solutions. Besides, the extreme sparsity of check-in data makes it challenging to capture users’ movement preferences accurately. In this paper, we propose a joint geosequential preference and distance metric factorization framework, called GeoSeDMF, for POI recommendation. First, we introduce a distance metric factorization method that is capable of learning users’ personalized preferences from a position and distance perspective in the metric space. Specifically, we convert the user-POI interaction matrix into a distance matrix and factorize it into user and POI dense embeddings. Additionally, we measure users’ personalized preference for the POI by using the Euclidean distance metric instead of the inner product. Then, we model the users’ geospatial preference by applying a geographic weight coefficient and model the users’ sequential preference by using the Euclidean distance of continuous check-in locations. Moreover, a pointwise loss strategy and AdaGrad algorithm are adopted to optimize the positions and relationships of users and POIs in a metric space. Finally, experimental results on three large-scale real-world datasets demonstrate the effectiveness and superiority of the proposed method.

scholarly journals From trickle to flood: the large-scale, cryptic invasion of California by tropical fruit flies

2013 ◽  
Vol 280 (1768) ◽  
pp. 20131466 ◽  
Author(s):  
Nikos T. Papadopoulos ◽  
Richard E. Plant ◽  
James R. Carey
Keyword(s):  
Large Scale ◽  
Invasion Biology ◽  
Fruit Fly ◽  
Fruit Flies ◽  
Federal Agencies ◽  
Spatial Mapping ◽  
Tropical Fruit ◽  
Cryptic Invasion ◽  
Tephritid Species ◽  
Melon Fruit

Since 1954, when the first tropical tephritid fruit fly was detected in California, a total of 17 species in four genera and 11 386 individuals (adults/larvae) have been detected in the state at more than 3348 locations in 330 cities. We conclude from spatial mapping analyses of historical capture patterns and modelling that, despite the 250+ emergency eradication projects that have been directed against these pests by state and federal agencies, a minimum of five and as many as nine or more tephritid species are established and widespread, including the Mediterranean, Mexican and oriental fruit flies, and possibly the peach, guava and melon fruit flies. We outline and discuss the evidence for our conclusions, with particular attention to the incremental, chronic and insidious nature of the invasion, which involves ultra-small, barely detectable populations. We finish by considering the implications of our results for invasion biology and for science-based invasion policy.

Multivariate Multiple Regression Prediction Models: A Euclidean Distance Approach

2003 ◽  
Vol 92 (3) ◽  
pp. 763-769 ◽  
Author(s):  
Paul W. Mielke ◽  
Kenneth J. Berry
Keyword(s):  
Prediction Model ◽  
Multiple Regression ◽  
Effect Size ◽  
Euclidean Distance ◽  
Prediction Models ◽  
Regression Coefficients ◽  
Multiple Response ◽  
Multivariate Multiple Regression ◽  
Euclidean Distances ◽  
Distance Approach

An extension of a multiple regression prediction model to multiple response variables is presented. An algorithm using least sum of Euclidean distances between the multivariate observed and model-predicted response values provides regression coefficients, a measure of effect size, and inferential procedures for evaluating the extended multivariate multiple regression prediction model.

Aesthetic evaluation of wind turbines in stochastic setting: Case study of Tinos island, Greece

2020 ◽  
Author(s):  
Eleni Manta ◽  
Romanos Ioannidis ◽  
Georgios-Fivos Sargentis ◽  
Andreas Efstratiadis
Keyword(s):  
Wind Turbines ◽  
Large Scale ◽  
Local Community ◽  
Target Population ◽  
Field Analysis ◽  
3D Representation ◽  
Gis Environment ◽  
Visual Impacts ◽  
Stochastic Setting ◽  
Landscape Transformations

&lt;p&gt;Wind turbines are large-scale engineering infrastructures that may cause significant social reactions, due to the anticipated aesthetic nuisance. On the other hand, aesthetics is a highly subjective issue, thus any attempt towards its quantification requires accounting for the uncertainty induced from subjectivity. In this work, taking as example the Aegean island of Tinos, Cyclades, Greece, we present a stochastic-based methodology for evaluating the feasibility of developing wind parks in terms of their aesthetic impacts. At first, a field analysis is been conducted along with photographic surveying, 3D representation and the opinion of the target population regarding the development of wind parks across the island. Subsequently, the landscape transformations that will be caused from the wind turbines are assessed according to the theory of aesthetics, which are depicted by using suitable spatial analysis tools in GIS environment. The 3D representation images along with the maps are finally assessed through stochastic analysis, in order to quantify the visual impacts to the landscape and the nuisance to local community.&lt;/p&gt;

scholarly journals Euclidean distance as a measure to distinguish ventral and dorsal white matter connectivity in the human brain

2016 ◽  
Author(s):  
Philipp Kellmeyer ◽  
Magnus-Sebastian Vry
Keyword(s):  
White Matter ◽  
Human Brain ◽  
Cognitive Processing ◽  
Euclidean Distance ◽  
Large Scale ◽  
Diffusion Tensor ◽  
Large Body ◽  
Coordinate Space ◽  
Fiber Tractography

AbstractFiber tractography based on diffusion tensor imaging (DTI) has become an important research tool for investigating the anatomical connectivity between brain regions in vivo. Combining DTI with functional magnetic resonance imaging (fMRI) allows for the mapping of structural and functional architecture of large-scale networks for cognitive processing. This line of research has shown that ventral and dorsal fiber pathways subserve different aspects of bottom-up- and top-down processing in the human brain.Here, we investigate the feasibility and applicability of Euclidean distance as a simple geometric measure to differentiate ventral and dorsal long-range white matter fiber pathways tween parietal and inferior frontal cortical regions, employing a body of studies that used probabilistic tractography.We show that ventral pathways between parietal and inferior frontal cortex have on average a significantly longer Euclidean distance in 3D-coordinate space than dorsal pathways. We argue that Euclidean distance could provide a simple measure and potentially a boundary value to assess patterns of connectivity in fMRI studies. This would allow for a much broader assessment of general patterns of ventral and dorsal large-scale fiber connectivity for different cognitive operations in the large body of existing fMRI studies lacking additional DTI data.

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