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News
May 25, 2026
HSE Scientists Train Neural Network to 'Hear' Faults in Electric Motors
Researchers at the AI and Digital Science Institute of the HSE Faculty of Computer Science have developed a new method—the Signature-Guided Data Augmentation (SGDA) framework—that achieves 99% accuracy in motor fault detection and 86% accuracy in fault classification. The application of this approach can reduce industrial equipment repair costs, minimise downtime, and improve production safety. The study results have been published in Engineering Applications of Artificial Intelligence.
May 25, 2026
'The Humanities Serve as a Conscience'
Maria Mizernaia studies Soviet literature and the history of book publishing. In this interview for the HSE Young Scientists project, she discusses plans to publish a novel about besieged Leningrad, AI-provoked reflections on what it means to be human, and how novels can help satisfy our dopamine hunger.
May 25, 2026
Is It Possible to Predict a Citys Life Based on the Shape of Its Neighbourhoods?
Is it possible to predict, based on the configuration of streets and buildings, where a café will open or where traffic congestion will occur? Participants in the Spatial Analysis and Modelling of Urban Processes research and study group use open data and machine learning to identify universal patterns. Alexander Sheludkov and Eduard Somov discuss the purpose of comparing cities, the need for new forms of urban statistics, and how open data is transforming approaches to urban studies.

 

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Все идет (не) по плану: влияние политики редлайнинга на социально-экономическую и политическую дифференциацию Чикаго

Городские исследования и практики. 2022. Т. 7. № 2. С. 61–70.
Alov I.

This article addresses the question of the continuing influence of the policy of redlining (dividing the city into quarters based on the risks of issuing mortgages), carried out in the 1930–1950s, on the racial, socioeconomic and electoral differentiation of Chicago. This city was chosen because the theoretical models of the researchers of the Chicago School of Sociology were developed using it as an example, and on the basis of which political decisions were made about the division—in fact, the segregation—of the city. Hoyt played a leading role in the implementation of these theoretical views. Within his model, permanent segregation by race was considered a logical consequence of the market economy, and not only did not need to be fought, but should have been fixed in planning decisions if this would lead to an increase in economic efficiency. Consequently, the Chicago area was divided into four categories: A (“the best” neighborhoods), B (“still desirable”), C (“definitely declining”), D (“hazardous”). As valuations fell, the likelihood of a mortgage (or refinancing an existing problem mortgage), the main form of supporting demand in the housing market after the Great Depression, also decreased. In “hazardous” areas, this probability was zero. This study checked whether racial and socio-economic differentiation obeys the logic set by the redlining policy, and whether this logic has implications for the last presidential election. For this, the Darden-Kamel composite socioeconomic index was calculated and the racial composition was analyzed in the context of the types of quarters identified in 1935.

The results of the 2020 presidential election were also analyzed. As a result, Chicago was found to be differentiated differently than the redlining pattern suggested. The growth of the population of two racial groups, practically unrepresented in the 1930s–1950s (Hispanics and Asian Americans), has strongly altered this pattern and disrupted the racial status quo.

Research target: Sociology (including Demography and Anthropology
Language: Russian
Full text
DOI
Keywords: дифференциацияdifferentiationЧикагоChicago SchoolЧикагская школаracial groupsрасовые группыredliningChicagoредлайнинг
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