Spatial Sorting of Binary Metadata Documents via Nature-Inspired Agents in Grids

2008 ◽  
pp. 63-72
Author(s):  
Agostino Forestiero ◽  
Carlo Mastroianni ◽  
Giandomenico Spezzano
Keyword(s):  
Spatial Sorting

scholarly journals Between-School Ability Tracking and Ethnic Segregation in Secondary Schooling

Social Forces ◽  
2018 ◽  
Vol 98 (1) ◽  
pp. 119-146 ◽  
Author(s):  
Hanno Kruse
Keyword(s):  
Secondary School ◽  
School System ◽  
Regional Variation ◽  
Unique Feature ◽  
School Reforms ◽  
School Systems ◽  
Ethnic Segregation ◽  
Secondary Schooling ◽  
Spatial Sorting ◽  
Prior Achievement

Abstract Between-school ability tracking—the assignment of students to different school types based on their prior achievement—is usually associated with increased ethnic segregation across schools. This article argues that stronger between-school ability tracking is not only associated with stronger ethnic sorting into school types, thus increasing segregation. At the same time, it hampers majority flight, majority members’ avoidance of exposure to minority members, thus decreasing segregation. To identify the two-fold effect of tracking the article exploits a unique feature of the German secondary school system: regional variation in the strength of between-school ability tracking. Analyses rely on administrative data entailing geocoded information on all secondary schools in Germany in 2008/2009. Results corroborate expectations of a two-fold effect: there is indication that between-school ability tracking increases segregation via more sorting into tracks while at the same time decreasing it via less school sorting within each track and via less spatial sorting. This suggests that school reforms changing tracked school systems into more comprehensive school systems may have a weaker desegregating impact than expected.

scholarly journals Macroevolutionary consequences of "spatial sorting"

2011 ◽  
Vol 108 (31) ◽  
pp. E347-E347 ◽  
Author(s):  
M. S. Y. Lee
Keyword(s):  
Spatial Sorting

Political Segregation of the Metropolis: Spatial Sorting by Partisan Voting in Metropolitan Minneapolis–St Paul

2013 ◽  
Vol 12 (1) ◽  
pp. 35-55 ◽  
Author(s):  
Kyle E. Walker
Keyword(s):  
Metropolitan Area ◽  
Spatial Clustering ◽  
Central City ◽  
The United States ◽  
Twin Cities ◽  
Spatial Analyses ◽  
Individual Level ◽  
Spatial Sorting ◽  
The Individual

Recent electoral research has claimed that individuals in the United States are self–segregating along political lines. In this paper, I use the Twin Cities, Minnesota, metropolitan area as a case study to test for the presence of political segregation through statistical and spatial analyses of electoral data from 1992 to 2012. I find that while segregation by partisan voting at the individual level is comparatively low, it has increased during the study period, and there exists substantial spatial clustering in voting patterns at aggregate levels. These distinct electoral divides between central city and exurb suggest spatial sorting of the electorate in the metropolitan area.

Wage distribution and the spatial sorting of workers

2011 ◽  
Vol 12 (2) ◽  
pp. 379-408 ◽  
Author(s):  
A. Matano ◽  
P. Naticchioni
Keyword(s):  
Wage Distribution ◽  
Spatial Sorting

Spatial sorting with heterogeneous firms and heterogeneous sectors

2014 ◽  
Vol 46 ◽  
pp. 42-56 ◽  
Author(s):  
Rikard Forslid ◽  
Toshihiro Okubo
Keyword(s):  
Heterogeneous Firms ◽  
Spatial Sorting

Performance comparisons of bonding box-based contact detection algorithms and a new improvement technique based on parallelization

2016 ◽  
Vol 33 (1) ◽  
pp. 7-27
Author(s):  
Mahmoud Yazdani ◽  
Hamidreza Paseh ◽  
Mostafa Sharifzadeh
Keyword(s):  
Large Scale ◽  
Distinct Element ◽  
Detection Algorithm ◽  
Computational Technique ◽  
Contact Detection ◽  
Content Type ◽  
Running Time ◽  
Spatial Sorting ◽  
Bounding Boxes ◽  
Contact Detection Algorithm

Purpose – The purpose of this paper is to find a convenient contact detection algorithm in order to apply in distinct element simulation. Design/methodology/approach – Taking the most computation effort, the performance of the contact detection algorithm highly affects the running time. The algorithms investigated in this study consist of Incremental Sort-and-Update (ISU) and Double-Ended Spatial Sorting (DESS). These algorithms are based on bounding boxes, which makes the algorithm independent of blocks shapes. ISU and DESS algorithms contain sorting and updating phases. To compare the algorithms, they were implemented in identical examples of rock engineering problems with varying parameters. Findings – The results show that the ISU algorithm gives lower running time and shows better performance when blocks are unevenly distributed in both axes. The conventional ISU merges the sorting and updating phases in its naïve implementation. In this paper, a new computational technique is proposed based on parallelization in order to effectively improve the ISU algorithm and decrease the running time of numerical analysis in large-scale rock mass projects. Originality/value – In this approach, the sorting and updating phases are separated by minor changes in the algorithm. This tends to a minimal overhead of running time and a little extra memory usage and then the parallelization of phases can be applied. On the other hand, the time consumed by the updating phase of ISU algorithm is about 30 percent of the total time, which makes the parallelization justifiable. Here, according to the results for the large-scale problems, this improved technique can increase the performance of the ISU algorithm up to 20 percent.

scholarly journals Measuring the geography of opportunity

2016 ◽  
Vol 41 (1) ◽  
pp. 3-25 ◽  
Author(s):  
Michael C. Lens
Keyword(s):  
Neighborhood Effects ◽  
School Quality ◽  
Structural Characteristics ◽  
Spatial Segregation ◽  
Neighborhood Violence ◽  
Concentrated Disadvantage ◽  
Job Accessibility ◽  
Empirical Justification ◽  
Demographic Groups ◽  
Spatial Sorting

Quantitative segregation research focuses almost exclusively on the spatial sorting of demographic groups. This research largely ignores the structural characteristics of neighborhoods – such as crime, job accessibility, and school quality – that likely help determine important household outcomes. This paper summarizes the research on segregation, neighborhood effects, and concentrated disadvantage, and argues that we should pay more attention to neighborhood structural characteristics, and that the data increasingly exist to include measures of spatial segregation and neighborhood opportunity. The paper concludes with a brief empirical justification for the inclusion of data on neighborhood violence and a discussion on policy applications.

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