Влияние аддиктивных товаров на качество человеческого капитала потребителей: региональный аспект

Дата поступления 23 января 2020 г.; дата принятия к печати 12 марта 2020 г.Received January 23, 2020; accepted March 12, 2020.Research relevance. Consumption of addictive goods and its impact on the human capital is widely discussed in contemporary research literature, not only on the micro- and macro- but also on the meso-level. At the present stage of the ongoing transformations we are prompted to reassess current approaches to this problem and to re-evaluate its public significance; moreover, practical application of available research outcomes should also be reconsidered. In Russia, consumption of addictive goods is subject to significant regional variations determined by socio-economic and other factors. Research aim. The study is aimed at investigating the impact of consumption of addictive goods (alcohol) on the quality of Russian consumers’ human capital and at building a system of indicators to estimate this impact. Data and methods. The study uses the methods of comparative analysis, expert estimation, ranking, and economic-statistical analysis, it also proposes a spatial approach to problems associated with regional variations in human capital of consumers of addictive goods. The study relies on the Russian and international research evidence; the data of the Federal State Statistics Service and its regional offices; expert estimates and the authors’ own calculations. Results. The study demonstrates the connection between consumption of addictive goods and consumers’ human capital. It also describes a system of statistical indicators that can be used for estimating the impact of alcohol consumption on human capital and the criteria such indicators should meet. Based on the proposed indicator set, the study analyzes and compares the trends in human capital deterioration on the regional and national levels. As a result of cross-regional analysis, regions with the highest and lowest figures of human capital deterioration are identified. Conclusions. As their addiction progresses, alcohol consumers face an increasing devaluation of their human capital. This parameter varies significantly across Russian regions due to a range of climatic, regional, and socio-economic factors, which should be taken into account when devising and implementing regional alcohol policies. The existing system of statistical observations uses a limited set of indicators that needs to be expanded to allow for a more comprehensive cross-regional analysis.Актуальность. Разработка научной проблемы формирования человеческого капитала потребителей аддиктивных товаров приобретает все большую актуальность не только на микро- и макроуровне, но и на мезоуровне. Современный этап трансформационных процессов заставляет переосмысливать представления об указанной проблеме, ее общественной значимости, использовании прикладных результатов исследований. В российских условиях актуальность изучения территориальных аспектов дифференциации потребления аддиктивных товаров связана с высокой поляризацией социально-экономического положения регионов и многообразием факторов ее определяющих. Цель исследования. Выявить результаты влияния аддиктивных товаров на качество человеческого капитала потребителей в регионах России (на примере потребления алкогольной продукции) и сформировать систему показателей, определяющих тенденции указанного процесса. Данные и методы. В исследовании были использованы методы сравнительного анализа, экспертных оценок, ранжирования, методы экономико-статистического анализа. Предложен пространственный подход к исследованию проблем региональной дифференциации показателей человеческого капитала потребителей аддиктивных товаров. Информационную базу исследования составили результаты исследований отечественных и зарубежных экономистов потребления аддиктивных товаров; официальные данные Федеральной службы государственной статистики и ее территориальных органов, а также экспертные оценки и авторские расчеты. Результаты. Раскрыта связь между потреблением аддиктивных товаров и человеческим капиталом потребите- ля; обоснованы требования к показателям, которые целесообразно использовать для оценки влияния на человеческий капитал потребления алкоголя; с учетом данных требований предложена совокупность статистических показателей; проведена оценка динамики показателей деградации человеческого капитала на общероссийском уровне и межрегиональные сравнения. В ходе межрегиональных сравнений выделены регионы с наиболее высокими и наиболее низкими показателями деградации человеческого капитала. Выводы. Злоупотребление аддиктивными товарами сопровождается деградацией человеческого капитала индивида по мере роста зависимости. Уровень «деградации» человеческого капитала от потребления аддиктивных товаров в российских регионах значительно варьируется в силу многообразия климатических, религиозных, социально-экономических особенностей, которые необходимо учитывать при разработке и реализации дифференцированной антиалкогольной политики. Существующая система статистических наблюдений позволяет провести межрегиональные сравнения по ограниченному числу показателей и нуждается в совершенствовании.The research was supported by the Russian Foundation for Basic Research and Volgograd Region Administration (research project № 19-410-340006, r_a).Работа выполнена при поддержке Российского фонда фундаментальных исследований и Администрации Волгоградской области (исследовательский проект № 19-410-340006, р_а)

Examining Pedestrian Accessibility to Opportunities in Four New Deal Villages

This study focuses on accessibility as an essential performance factor in city planning and urban development. The automobile-oriented designs that characterize and organize most modern United States cities, since the 1950s, have degraded pedestrian mobility and accessibility, causing people to be largely dependent on cars rather than walk, bike, and/or use public transit to reach essential and complementary daily destinations. This pervasive condition not only hinders community and sense of place, but also negatively affects people’s health and environment. We as planners should not forget that cities should be designed to serve people rather than cars. The more our cities are pedestrian accessible, the more they will draw people in, and potentially bring about other positive qualities, like safety, that could result in a better place to live in. This study explores how pedestrian accessibility to key destinations might be influenced by: 1-land-use spatial structure and 2- urban design, using transportation network topology as proxy. As a case study, the study compares the pedestrian accessibility afforded by four New Deal villages (Greenbelt (MD), Greendale (WI), Greenhills (OH), and Eleanor Roosevelt (P.R.)). These towns, or villages as they were originally conceived, were planned, and developed during the late 1930s as part of a comprehensive Federal economic revitalization policy program known as the New Deal (Figure 1). All four New Deal villages cases share the same age; similar mix of architectural typologies and densities; and similar original land-use programming where most services and opportunities for socio-economic interactions were located at and near a village center. Yet, these case studies differ slightly, with one of them differing significantly in terms of urban design and transportation network layout. These villages present a convenient quasi-experimental framework to evaluate how urban design, as expressed in the scaling and design of neighborhood blocks and in the disposition of their transportation networks (pedestrian and vehicular) might influence levels of accessibility to opportunities in each village and possibly corresponding aggregate travel behavior. Results from this investigation could inform recommendations to improve pedestrian accessibility in lower-ranking neighborhoods, according to the calculations and analysis of this study brought in the table of result (page 39) as well as inform methodologies and best-practices for the planning, design, and assessment of pedestrian accessibility in other existing or proposed neighborhoods. Sampling strategy motivated the selection of the four New Deal Town cases in this investigation and relates to their common policy and ideological (communitarian) origins, overarching planning, and spatial design paradigms inspired by Howard’s Garden City Model, which emphasized self-sufficiency and pedestrian accessibility; similar socio-economy profile and purpose as communities geared for the working class and lower-income families; and all being publicly subsidized in pursuit of housing affordability and job creation in a time of economic crisis. Despite all the similarities, only Eleonor Roosevelt Village exhibits a traditional web-like grid street network, in contrast to the larger and more organic superblock morphology of the other three New Deal villages located in the US mainland that reflect a distinct suburban neighborhood design tradition influenced by Clarence Stein and Henry Wright’s Radburn development, in New Jersey. Comparing these four neighborhoods could provide me insight into the influence of urban design and land-use patterns on pedestrian accessibility as a key factor in the planning and design for more sustainable neighborhood patterns that promote more walking. The insights and methods explored in this terminal project could also inform assessment protocols to evaluate existing and future developments as a standard practice in city planning, management of the built environment, and urban design

Recommendation Systems: An Insight Into Current Development and Future Research Challenges

Research on recommendation systems is swiftly producing an abundance of novel methods, constantly challenging the current state-of-the-art. Inspired by advancements in many related fields, like Natural Language Processing and Computer Vision, many hybrid approaches based on deep learning are being proposed, making solid improvements over traditional methods. On the downside, this flurry of research activity, often focused on improving over a small number of baselines, makes it hard to identify reference methods and standardized evaluation protocols. Furthermore, the traditional categorization of recommendation systems into content-based, collaborative filtering and hybrid systems lacks the informativeness it once had. With this work, we provide a gentle introduction to recommendation systems, describing the task they are designed to solve and the challenges faced in research. Building on previous work, an extension to the standard taxonomy is presented, to better reflect the latest research trends, including the diverse use of content and temporal information. To ease the approach toward the technical methodologies recently proposed in this field, we review several representative methods selected primarily from top conferences and systematically describe their goals and novelty. We formalize the main evaluation metrics adopted by researchers and identify the most commonly used benchmarks. Lastly, we discuss issues in current research practices by analyzing experimental results reported on three popular datasets

Spatio-temporal patterns of human mobility from geo-social networks for urban computing: Analysis, models & applications

The availability of rich information about fine-grained user mobility in urban environments from increasingly geographically-aware social networking services and the rapid development of machine learning applications greatly facilitate the investigation of urban issues. In this setting, urban computing emerges intending to tackle a variety of challenges faced by cities nowadays and to offer promising approaches to improving our living environment. Leveraging massive amounts of data from geo-social networks with unprecedented richness, we show how to devise novel algorithmic techniques to reveal underlying urban mobility patterns for better policy-making and more efficient mobile applications in this dissertation. Building upon the foundation of existing research efforts in urban computing field and basic machine learning techniques, in this dissertation, we propose a general framework of urban computing with geo-social network data and develop novel algorithms tailored for three urban computing tasks. We begin by exploring how the transition data recording human movements between urban venues from geo-social networks can be aggregated and utilised to detect spatio-temporal changes of local graphs in urban areas. We further explore how this can be used as a proxy to track and predict socio-economic deprivation changes as government financial effort is put in developing areas by supervised machine learning methods. We then study how to extract latent patterns from collective user-venue interactions with the help of a spatio-temporal aware topic modeling approach for the benefit of urban infrastructure planning. After that, we propose a model to detect the gap between user-side demand and venue-side supply levels for certain types of services in urban environments to suggest further policymaking and investment optimisation. Finally, we address a mobility prediction task, the application aim of which is to recommend new places to explore in the city for mobile users. To this end, we develop a deep learning framework that integrates memory network and topic modeling techniques. Extensive experiments indicate that the proposed architecture can enhance the prediction performance in various recommendation scenarios with high interpretability. All in all, the insights drawn and the techniques developed in this dissertation make a substantial step in addressing issues in cities and open the door to future possibilities in the promising urban computing area

Popularity, novelty and relevance in point of interest recommendation: an experimental analysis

AbstractRecommender Systems (RSs) are often assessed in off-line settings by measuring the system precision in predicting the observed user's ratings or choices. But, when apreciseRS is on-line, the generated recommendations can be perceived as marginally useful because lacking novelty. The underlying problem is that it is hard to build an RS that can correctly generalise, from the analysis of user's observed behaviour, and can identify the essential characteristics of novel and yet relevant recommendations. In this paper we address the above mentioned issue by considering four RSs that try to excel on different target criteria: precision, relevance and novelty. Two state of the art RSs called and follow a classical Nearest Neighbour approach, while the other two, and are based on Inverse Reinforcement Learning. and optimise precision, tries to identify the characteristics of POIs that make them relevant, and , a novel RS here introduced, is similar to but it also tries to recommend popular POIs. In an off-line experiment we discover that the recommendations produced by and optimise precision essentially by recommending quite popular POIs. can be tuned to achieve a desired level of precision at the cost of losing part of the best capability of to generate novel and yet relevant recommendations. In the on-line study we discover that the recommendations of and are liked more than those produced by . The rationale of that was found in the large percentage of novel recommendations produced by , which are difficult to appreciate. However, excels in recommending items that are both novel and liked by the users