scholarly journals Measuring the circularity of congressional districts

2020 ◽  
Vol 42 (3) ◽  
pp. 298-312
Author(s):  
Balázs Nagy ◽  
Szilvia Szakál

AbstractShape analysis has special importance in the detection of manipulated redistricting, which is called gerrymandering. In most of the US states, this process is made by non-independent actors and often causes debates about partisan manipulation. The somewhat ambiguous concept of compactness is a standard criterion for legislative districts. In the literature, circularity is widely used as a measure of compactness, since it is a natural requirement for a district to be as circular as possible. In this paper, we introduce a novel and parameter-free circularity measure that is based on Hu moment invariants. This new measure provides a powerful tool to detect districts with abnormal shapes. We examined some districts of Arkansas, Iowa, Kansas, and Utah over several consecutive periods and redistricting plans, and also compared the results with classical circularity indexes. We found that the fall of the average circularity value of the new measure indicates potential gerrymandering.

Author(s):  
Yessi Jusman ◽  
Slamet Riyadi ◽  
Amir Faisal ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Zeehaida Mohamed ◽  
...  

2020 ◽  
Vol 40 (7) ◽  
pp. 570-574
Author(s):  
D. V. Kondusov ◽  
A. I. Sergeev ◽  
V. B. Kondusova

Author(s):  
Ahmed H. Asad ◽  
Ahmad Taher Azar ◽  
Aboul Ella Hassanien

Abnormality detection plays an important role in many real-life applications. Retinal vessel segmentation algorithms are the critical components of circulatory blood vessel Analysis systems for detecting the various abnormalities in retinal images. Traditionally, the vascular network is mapped by hand in a time-consuming process that requires both training and skill. Automating the process allows consistency, and most importantly, frees up the time that a skilled technician or doctor would normally use for manual screening. Several studies were carried out on the segmentation of blood vessels in general; however, only a small number of them were associated to retinal blood vessels. In this paper, an approach for segmenting retinal blood vessels is proposed using only ant colony system. Eight features are selected for the developed system; four are based on gray-level and the other features on Hu moment-invariants. The features are directly computed from values of image pixels, so they take about 90 seconds in computation. The performance of the proposed structure is evaluated in terms of accuracy, true positive rate (TPR) and false positive rate (FPR). The results showed that the overall accuracy and sensitivity of the presented approach achieved 90.28% and 74%, respectively.


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