Housing Rent Dynamics and Rent Regulation in St. Petersburg (1880-1917)

This article studies the evolution of housing rents in St. Petersburg between 1880 and 1917 covering an eventful period of Russian and world history. We collect and digitize over 5,000 rental advertisements from historic newspapers, which we use together with geo-coded addresses and detailed structural characteristics to construct a quality-adjusted rent price index in continuous time. We provide the first pre-war and pre-Soviet index based on market data for any Russian housing market. In 1915, one of the world's earliest rent control and tenant protection policies was introduced as a response to soaring prices following the outbreak of World War I. We analyze the impact of this policy: while before the regulation rents were increasing at a similar rapid pace as other consumer prices, the policy reversed this trend. We find evidence for official compliance with the policy, document a rise in tenure duration and strongly increased rent affordability among workers after the introduction of the policy. We conclude that the immediate prelude to the October Revolution was indeed characterized by economic turmoil, but rent affordability and rising rents were no longer the prevailing problems.Series: Department of Economics Working Paper Serie

Tietokierto ilmakehäfysiikassa : mitatusta millivoltista ilmakehän ymmärtämiseen

In this thesis the concept of data cycle is introduced. The concept itself is general and only gets the real content when the field of application is defined. If applied in the field of atmospheric physics the data cycle includes measurements, data acquisition, processing, analysis and interpretation. The atmosphere is a complex system in which everything is in a constantly moving equilibrium. The scientific community agrees unanimously that it is human activity, which is accelerating the climate change. Nevertheless a complete understanding of the process is still lacking. The biggest uncertainty in our understanding is connected to the role of nano- to micro-scale atmospheric aerosol particles, which are emitted to the atmosphere directly or formed from precursor gases. The latter process has only been discovered recently in the long history of science and links nature s own processes to human activities. The incomplete understanding of atmospheric aerosol formation and the intricacy of the process has motivated scientists to develop novel ways to acquire data, new methods to explore already acquired data, and unprecedented ways to extract information from the examined complex systems - in other words to compete a full data cycle. Until recently it has been impossible to directly measure the chemical composition of precursor gases and clusters that participate in atmospheric particle formation. However, with the arrival of the so-called atmospheric pressure interface time-of-flight mass spectrometer we are now able to detect atmospheric ions that are taking part in particle formation. The amount of data generated from on-line analysis of atmospheric particle formation with this instrument is vast and requires efficient processing. For this purpose dedicated software was developed and tested in this thesis. When combining processed data from multiple instruments, the information content is increasing which requires special tools to extract useful information. Source apportionment and data mining techniques were explored as well as utilized to investigate the origin of atmospheric aerosol in urban environments (two case studies: Krakow and Helsinki) and to uncover indirect variables influencing the atmospheric formation of new particles.Tässä työssä esitellään konsepti - tietokierto ilmakehätieteissä. Tietokierto on sinänsä yleinen käsite ja ei liity mihinkään tiettyyn tieteenalaan. Tietokierto huomioi jokaisen vaiheen raa asta mittausarvosta datan soveltamiseen, ymmärtämiseen ja tulkintaan. Ilmakehäfysiikassa tietokierto sisältää vaiheet signaalin havainnoinnista, datan keräämiseen, esikäsittelyyn, ja työstämiseen sekä sitä kautta tulkintaan. Ilmakehä on monimutkainen kokonaisuus, jossa kaikki on jatkuvasti muuttuvassa tasapainossa keskenään. Tiedeyhteisö on yksimielisesti sitä mieltä, että kiihtyvä ilmastonmuutos on ihmisen toiminnan seurausta. Tarkalleen sitä prosessia ei kuitenkaan tunneta. Suurin epävarmuus ymmärryksessä on pienhiukkasten aiheuttama vaikutus ilmastomuutokseen. Pienhiukkasia päätyy ilmakehään joko suoraan päästölähteistä tai ne muodostuvat nukleaation eli kaasu-hiukkasmuuntuman kautta. Viimeksi mainittu ilmiö on havaittu vasta hiljattain ja sen yksityiskohtainen ymmärrys vielä puuttuu. Ilmiön monimutkaisuus on kiehtonut ja motivoinut tutkijoita kehittämään uusia mittalaitteistoja, mittausmenetelmiä, datan analysointimenetelmiä ja uusia tapoja suodattaa tietoa jo kerätystä datasta - toisin sanoen täydentää ja parantaa tietokiertoa. Aikaisemmin on ollut mahdotonta mitata suoraan kaasu-hiukkasmuuntumisessa osallistuvien kaasujen kemiallista koostumusta. Tässä työssä käytetty laitteisto (ilmakehäpaineliitännäinen lentoaikamassaspektrometri, APiTOF) pystyy havaitsemaan kyseisiä kaasuja suoraan ilman esikäsittelyä. Koska laitteisto on uusi ja sen tuottama data määrä on iso, kehitettiin tässä työssä tehokas raakadatan esikäsittelymenetelmä ja työkalu. Kun yhdistetään prosessoitu data useista laitteista, informaation sisältö kasvaa, mutta sen esille saaminen hankaloituu. Tässä työssä kehitettiin ja käytettiin menetelmiä ilmamassojen päästölähdekartoitukseen, tarkoituksena selvittää kaupunginympäristön pahimmat saastuttajat ja päästölähteet. Datan louhintaa hyödynnettiin löytämään kaasu-hiukkasmuuntumaan vaikuttavia tekijöitä

Exploring city propensity for the market success of micro-electric vehicles

As a subset of increasing EV growth and micro-mobility technology trends, micro-electric vehicles (micro-EVs) have the potential to address many transport system issues. Little research quantifies micro-EV potential, increasing investment risks. This paper builds an index to explore city propensity for micro-EV market success by incorporating differentiation, implementation, commercialisation, consumer and manufacturer requirements, and economic stability and viability aspects. The results highlight that micro-EV market success could most likely be influenced by the lock-in of the city’s transport system. Despite inherent difficulties due to lock-in, our index suggests that there could be windows of opportunity for micro-EVs to be successful. Although both cities show a propensity for market success, Shanghai scores higher than London, highlighting that these opportunities may exist particularly in developing countries as they experience less lock-in and have more consumer incentives. Implementing micro-EVs in cities with higher propensity could have the domino effect of motivating change in other locations

Computational intelligence for measuring macro-knowledge competitiveness

The aim of this research is to investigate the utilisation of Computational Intelligence methods for constructing Synthetic Composite Indicators (SCI). In particular for delivering a Unified Macro-Knowledge Competitiveness Indicator (UKCI) to enable consistent and transparent assessments and forecasting of the progress and competitiveness of Knowledge Based Economy (KBE). SCI are assessment tools usually constructed to evaluate and contrast entities performance by aggregating intangible measures in many areas such as economy, education, technology and innovation. SCI key value is inhibited in its capacity to aggregate complex and multi-dimensional variables into a single meaningful value. As a result, SCIs have been considered as one of the most important tools for macro-level and strategic decision making. Considering the shortcomings of the existing SCI, this study is proposing an alternative approach to develop Intelligent Synthetic Composite Indicators (iSCI). The suggested approach utilizes Fuzzy Proximity Knowledge Mining technique to build the qualitative taxonomy initially, and Fuzzy c-mean is employed to form the new composite indicators

A protocol for developing a complex needs indicator for veterans (CNIV) in the UK

Introduction: The veteran population in the UK has been decreasing, however, there remains a proportion of veterans and their families who continue to experience multiple and complex health, financial, and social needs. The complex problems tend to exacerbate each other and deepen over time if appropriate support is not provided. Identifying the veterans with complex needs is crucial for effective support by military charities and health and social care services. The present research aims to develop a complex needs indicator for the veteran population (CNIV) that will quantify complexity and help to identify the risk of having or developing complex needs. Methods: The development of the CNIV will be informed by the guidance for constructing composite indicators. The data on grant support received by veterans’ beneficiaries from the UK Royal Marine and SSFA charities will be used for designing the indicator and evaluating its robustness. The crucial step in constructing the indicator is assigning weights to different needs and risk factors associated with complex cases. Factor analysis (FA) and analytical network process (ANP) will be used as weighting methods for the analysed variables. Conclusion: The development of CNIV has important implications for research and practice, such as the potential to be used as a screening tool for identifying complex cases, improved provision of the targeted support to veterans, assessing the scope of complex problems among veterans within the country and informing policy makers and a more general audience of the complexity of need within the sector

IEEE Access Special Section Editorial: Big Data Technology and Applications in Intelligent Transportation

During the last few years, information technology and transportation industries, along with automotive manufacturers and academia, are focusing on leveraging intelligent transportation systems (ITS) to improve services related to driver experience, connected cars, Internet data plans for vehicles, traffic infrastructure, urban transportation systems, traffic collaborative management, road traffic accidents analysis, road traffic flow prediction, public transportation service plan, personal travel route plans, and the development of an effective ecosystem for vehicles, drivers, traffic controllers, city planners, and transportation applications. Moreover, the emerging technologies of the Internet of Things (IoT) and cloud computing have provided unprecedented opportunities for the development and realization of innovative intelligent transportation systems where sensors and mobile devices can gather information and cloud computing, allowing knowledge discovery, information sharing, and supported decision making. However, the development of such data-driven ITS requires the integration, processing, and analysis of plentiful information obtained from millions of vehicles, traffic infrastructures, smartphones, and other collaborative systems like weather stations and road safety and early warning systems. The huge amount of data generated by ITS devices is only of value if utilized in data analytics for decision-making such as accident prevention and detection, controlling road risks, reducing traffic carbon emissions, and other applications which bring big data analytics into the picture

KIPP Middle Schools: Impacts on Achievement and Other Outcomes

The Knowledge Is Power Program (KIPP) is a rapidly expanding network of public charter schools whose mission is to improve the education of low-income children. As of the 2012 -- 2013 school year, 125 KIPP schools are in operation in 20 different states and the District of Columbia (DC). Ultimately, KIPP's goal is to prepare students to enroll and succeed in college.Prior research has suggested that KIPP schools have positive impacts on student achievement, but most of the studies have included only a few KIPP schools or have had methodological limitations. This is the second report of a national evaluation of KIPP middle schools being conducted by Mathematica Policy Research. The evaluation uses experimental and quasi-experimental methods to produce rigorous and comprehensive evidence on the effects of KIPP middle schools across the country.The study's first report, released in 2010, described strong positive achievement impacts in math and reading for the 22 KIPP middle schools for which data were available at the time. For this phase of the study, we nearly doubled the size of the sample, to 43 KIPP middle schools, including all KIPP middle schools that were open at the start of the study in 2010 for which we were able to acquire relevant data from local districts or states. This report estimates achievement impacts for these 43 KIPP middle schools, and includes science and social studies in addition to math and reading. This report also examines additional student outcomes beyond state test scores, including student performance on a nationally norm-referenced test and survey-based measures of student attitudes and behavior