Hasil untuk "City population. Including children in cities, immigration"

Marie Reinbigler, Romain Rouffet, Peter Naylor et al.

The Urban Heat Island (UHI) effect, defined as a significant increase in temperature in urban environments compared to surrounding areas, is difficult to study in real cities using sensor data (satellites or in-situ stations) due to their coarse spatial and temporal resolution. Among the factors contributing to this effect are the properties of urban materials, which differ from those in rural areas. To analyze their individual impact and to test new material configurations, a high-resolution simulation at the city scale is required. Estimating the current materials used in a city, including those on building facades, is also challenging. We propose HeatMat, an approach to analyze at high resolution the individual impact of urban materials on the UHI effect in a real city, relying only on open data. We estimate building materials using street-view images and a pre-trained vision-language model (VLM) to supplement existing OpenStreetMap data, which describes the 2D geometry and features of buildings. We further encode this information into a set of 2D maps that represent the city's vertical structure and material characteristics. These maps serve as inputs for our 2.5D simulator, which models coupled heat transfers and enables random-access surface temperature estimation at multiple resolutions, reaching an x20 speedup compared to an equivalent simulation in 3D.

Tao Feng, Yunke Zhang, Huandong Wang et al.

Accurate origin-destination (OD) flow prediction is of great importance to developing cities, as it can contribute to optimize urban structures and layouts. However, with the common issues of missing regional features and lacking OD flow data, it is quite daunting to predict OD flow in developing cities. To address this challenge, we propose a novel Causality-Enhanced OD Flow Prediction (CE-OFP), a unified framework that aims to transfer urban knowledge between cities and achieve accuracy improvements in OD flow predictions across data-scarce cities. In specific, we propose a novel reinforcement learning model to discover universal causalities among urban features in data-rich cities and build corresponding causal graphs. Then, we further build Causality-Enhanced Variational Auto-Encoder (CE-VAE) to incorporate causal graphs for effective feature reconstruction in data-scarce cities. Finally, with the reconstructed features, we devise a knowledge distillation method with a graph attention network to migrate the OD prediction model from data-rich cities to data-scare cities. Extensive experiments on two pairs of real-world datasets validate that the proposed CE-OFP remarkably outperforms state-of-the-art baselines, which can reduce the RMSE of OD flow prediction for data-scarce cities by up to 11%.

Ke-ke Shang, Jiangli Zhu, Junfan Yi et al.

Complex networks are commonly used to explore human behavior. However, previous studies largely overlooked the geographical and economic factors embedded in collective attention. To address this, we construct attention networks from time-series data for the United States and China, each a key economic power in the West and the East, respectively. We reveal a strong macroscale correlation between urban attention and Gross Domestic Product (GDP). At the mesoscale, community detection of attention networks shows that high-GDP cities consistently act as core nodes within their communities and occupy strategic geographic positions. At the microscale, structural hole theory identifies these cities as key connectors between communities, with influence proportional to economic output. Overlapping community detection further reveals tightly connected urban clusters, prompting us to introduce geographic and topic-based metrics, which show that closely linked cities are spatially proximate and topically coherent. Of course, not all patterns were consistent across regions. A notable distinction emerged in the relationship between population size and urban attention, which was evident in the United States but absent in China. Building on these insights, we integrate key variables reflecting GDP, geography, and scenic resources into regression model to cross-verify the influence of economic and geographic factors on collective user attention, and unexpectedly discover that a composite index of population, access, and scenery fails to account for cross-city variations in attention. Our study bridges the gap between economic prosperity and geographic centrality in shaping urban attention landscapes.

Jussi P. Laine

Abstract Refugee integration in Uganda’s West Nile borderlands reveals how progressive national policy frameworks encounter systematic implementation constraints when deployed in complex territorial contexts. This study examines the gap between Uganda’s acclaimed refugee policies and localized realities in Yumbe and Nebbi districts along borders with South Sudan and the Democratic Republic of Congo, investigating how three intersecting dynamics complicate integration: hybrid governance arrangements combining state and customary authority, infrastructural fragmentation in resource-constrained contexts, and refugee agency operating alongside formal policy structures. Through qualitative fieldwork incorporating stakeholder interviews, ethnographic observation, and secondary data analysis, the research reveals significant disjunctures between policy aspirations and implementation capacities. Findings demonstrate how initial hospitality dynamics transform into resource competition as local infrastructure becomes strained, while formal state institutions coexist with customary governance structures where traditional authorities wield substantial influence over land allocation and conflict mediation. These hybrid arrangements both facilitate and complicate integration as documented refugees, undocumented migrants, and cross-border communities compete for water, healthcare, education, and housing. The study highlights gendered displacement vulnerabilities, with women migrants facing heightened exploitation risks. This analysis contributes to forced migration scholarship by demonstrating the limitations of state-centric governance approaches in borderland contexts, where spatial specificities and institutional layering fundamentally reshape policy implementation. Policy implications emphasize strengthening coordination between governmental and traditional institutions, formalizing land tenure rights, and transitioning toward sustainable development investments that recognize refugees’ economic contributions to borderland economies.

Peng Luo, Di Zhu

Urban spaces, though often perceived as discrete communities, are shared by various functional and social groups. Our study introduces a graph-based physics-aware deep learning framework, illuminating the intricate overlapping nature inherent in urban communities. Through analysis of individual mobile phone positioning data at Twin Cities metro area (TCMA) in Minnesota, USA, our findings reveal that 95.7 % of urban functional complexity stems from the overlapping structure of communities during weekdays. Significantly, our research not only quantifies these overlaps but also reveals their compelling correlations with income and racial indicators, unraveling the complex segregation patterns in U.S. cities. As the first to elucidate the overlapping nature of urban communities, this work offers a unique geospatial perspective on looking at urban structures, highlighting the nuanced interplay of socioeconomic dynamics within cities.

M. Haapanen, D. Vetrano, T. Mikkola et al.

BACKGROUND Early growth, stress, and socioeconomic factors are associated with future risk of individual chronic diseases. It is uncertain whether they also affect the rate of multimorbidity accumulation later in life. This study aimed to explore whether early life factors are associated with the rate at which chronic diseases are accumulated across older age. METHODS In this national birth cohort study, we studied people born at Helsinki University Central Hospital, Helsinki, Finland between Jan 1, 1934, and Dec 31, 1944, who attended child welfare clinics in the city, and were living in Finland in 1971. Individuals who had died or emigrated from Finland before 1987 were excluded, alongside participants without any registry data and who died before the end of the registry follow-up on Dec 31, 2017. Early anthropometry, growth, wartime parental separation, and socioeconomic factors were recorded from birth, child welfare clinic, or school health-care records, and Finnish National Archives. International Classification of Diseases codes of diagnoses for chronic diseases were obtained from the Care Register for Health Care starting from 1987 (when participants were aged 42-53 years) until 2017. Linear mixed models were used to study the association between early-life factors and the rate of change in the number of chronic diseases over 10-year periods. FINDINGS From Jan 1, 1934, to Dec 31, 2017, 11 689 people (6064 [51·9%] men and 5625 [48·1%] women) were included in the study. Individuals born to mothers younger than 25 years (β 0·09; 95% CI 0·06-0·12), mothers with a BMI of 25-30 kg/m2 (0·08; 0·05-0·10), and mothers with a BMI more than 30 kg/m2 (0·26; 0·21-0·31) in late pregnancy accumulated chronic diseases faster than those born to older mothers (25-30 years) and those with a BMI of less than 25 kg/m2. Individuals with a birthweight less than 2·5 kg (0·17; 0·10-0·25) and those with a rapid growth in height and weight from birth until age 11 years accumulated chronic diseases faster during their life course. Additionally, paternal occupational class (manual workers vs upper-middle class 0·27; 0·23-0·30) and wartime parental separation (0·24; 0·19-0·29 for boys; 0·31; 0·25-0·36 for girls) were associated with a faster rate of chronic disease accumulation. INTERPRETATION Our findings suggest that the foundation for accumulating chronic diseases is established early in life. Early interventions might be needed for vulnerable populations, including war evacuee children and children with lower socioeconomic status. FUNDING Finska Läkaresällskapet, Liv och Hälsa rf, the Finnish Pediatric Research Foundation, and Folkhälsan Research Center. TRANSLATIONS For the Finnish and Swedish translations of the abstract see Supplementary Materials section.

Robert Klar, Anna Fredriksson, Vangelis Angelakis

Ports are striving for innovative technological solutions to cope with the ever-increasing growth of transport, while at the same time improving their environmental footprint. An emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes is the digital twin. Although digital twins have been successfully integrated in many industries, there is still a lack of cross-domain understanding of what constitutes a digital twin. Furthermore, the implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin, keeping in mind the extent to which the respective findings can be applied to the port. It turns out that the digital twin of the port is most comparable to complex systems such as smart cities and supply chains, both in terms of its functional relevance as well as in terms of its requirements and characteristics. The conducted literature review, considering the different port processes and port characteristics, results in the identification of three core requirements of a digital port twin, which are described in detail. These include situational awareness, comprehensive data analytics capabilities for intelligent decision making, and the provision of an interface to promote multi-stakeholder governance and collaboration. Finally, specific operational scenarios are proposed on how the port's digital twin can contribute to energy savings by improving the use of port resources, facilities and operations.

Jana Kiralj Lacković, Dean Ajduković, Dana Abdel-Fatah et al.

Abstract The socio-psychological dimension of integration is based on relations between the refugees and receiving community members revealed through intergroup thoughts, perceptions, emotions and behaviours. This study aimed to investigate and interpret the differences in the indicators of socio-psychological integration among the receiving communities of Sweden, Germany, Croatia and Jordan—countries with diverse socio-economic, socio-political and cultural contexts, histories of inward migration, as well as differently preferred destinations in the migration of refugees from Syria in the 2010s. The contextual differences are reflected in the attitudes of the members of receiving communities towards refugees from Syria, perceptions of realistic and symbolic threats posed by refugees, frequency and valence of intergroup contact, support for the rights of refugees and readiness to assist them, social proximity, perception of refugees’ exposure to discrimination, and the assessment of how much the refugees are a part of the respective local communities.

Bin Lu, Xiaoying Gan, Weinan Zhang et al.

Spatio-temporal graph learning is a key method for urban computing tasks, such as traffic flow, taxi demand and air quality forecasting. Due to the high cost of data collection, some developing cities have few available data, which makes it infeasible to train a well-performed model. To address this challenge, cross-city knowledge transfer has shown its promise, where the model learned from data-sufficient cities is leveraged to benefit the learning process of data-scarce cities. However, the spatio-temporal graphs among different cities show irregular structures and varied features, which limits the feasibility of existing Few-Shot Learning (\emph{FSL}) methods. Therefore, we propose a model-agnostic few-shot learning framework for spatio-temporal graph called ST-GFSL. Specifically, to enhance feature extraction by transfering cross-city knowledge, ST-GFSL proposes to generate non-shared parameters based on node-level meta knowledge. The nodes in target city transfer the knowledge via parameter matching, retrieving from similar spatio-temporal characteristics. Furthermore, we propose to reconstruct the graph structure during meta-learning. The graph reconstruction loss is defined to guide structure-aware learning, avoiding structure deviation among different datasets. We conduct comprehensive experiments on four traffic speed prediction benchmarks and the results demonstrate the effectiveness of ST-GFSL compared with state-of-the-art methods.

Ke Wang, Yafei Zhao, Rajan Kumar Gangadhari et al.

Smart cities play a vital role in the growth of a nation. In recent years, several countries have made huge investments in developing smart cities to offer sustainable living. However, there are some challenges to overcome in smart city development, such as traffic and transportation man-agement, energy and water distribution and management, air quality and waste management monitoring, etc. The capabilities of the Internet of Things (IoT) and artificial intelligence (AI) can help to achieve some goals of smart cities, and there are proven examples from some cities like Singapore, Copenhagen, etc. However, the adoption of AI and the IoT in developing countries has some challenges. The analysis of challenges hindering the adoption of AI and the IoT are very limited. This study aims to fill this research gap by analyzing the causal relationships among the challenges in smart city development, and contains several parts that conclude the previous scholars work, as well as independent research and investigation, such as data collection and analysis based on DEMATEL. In this paper, we have reviewed the literature to extract key chal-lenges for the adoption of AI and the IoT. These helped us to proceed with the investigation and analyze the adoption status. Therefore, using the PRISMA method, 10 challenges were identified from the literature review. Subsequently, determination of the causal inter-relationships among the key challenges based on expert opinions using DEMATEL is performed. This study explored the driving and dependent power of the challenges, and causal relationships between the barriers were established.

Patrizio Vanella, Timon Hellwagner, Philipp Deschermeier

Since 2013, more than two million refugees have arrived in Germany and have been allocated across federal states and districts according to legal policies. A steadily increasing number of refugees is now entering the German labor market, albeit under varying economic and demographic contexts. However, regional differences in refugees’ labor market integration have received little attention both retrospectively and particularly prospectively, given the projected population decline across Germany. Addressing this apparent shortcoming in the literature, we collect data on refugee arrivals by gender, nationality, approval rates, and regional allocation from 1995 to 2019. Applying principal component analysis and time series analysis, we first analyze past patterns of refugee migration to Germany and project both arrivals and regional allocations by gender and nationality until 2030. Then, combining the collected migration figures for German labor market regions and official labor market statistics, we investigate past regional employment effects from 2008 to 2019. Next, we calculate corresponding future employment effects conditional on our projected refugee figures, our estimation results, and official regional demographic forecasts until 2030. Our findings suggest that refugee migration does not affect German labor market regions equally, but instead has and will continue to lead to distinct regional employment effects. Moreover, the labor market integration differs by gender and origin of the refugees. Consequently, the interaction of regional employment effects with projected population change gives rise to different regional mitigation potentials in view of the upcoming population decline. * This article belongs to a special issue on "Refugee Migration to Europe – Challenges and Potentials for Cities and Regions".

Sara Riva, Gerhard Hoffstaedter

Anatolie Coşciug

Whilst the extant scholarship offers a detailed exploration of why return migrants enter self-employment or engage in business initiatives in general, we know relatively little about their involvement in transnational economic activities which connect the previous destination coun-try with the origin one and how they compare to other kinds of entrepreneurial venture in this vein. This article aims to understand these motivations by using insights from 50 semi-structured interviews conducted with traders of used cars imported in Romania, a mass phe-nomenon in the Central and Eastern European area and beyond. An important result of this research is that entrepreneurs have to consider a multitude of factors in multiple locations when entering the used-car business. The article also suggests that entrepreneurial motivations among used-car traders are not fixed but, rather, can and do change over time.

Lucia Nalbandian

Abstract The promise of artificial intelligence has been originally to put technology at the service of people utilizing powerful information processors and ‘smart’ algorithms to quickly perform time-consuming data analysis. It soon though became apparent that the capacity of artificial intelligence to scrape and analyze big data would be particularly useful in surveillance policies. In the wider areas of migration and asylum management, increasingly sophisticated artificial intelligence tools have been used to register and manage vulnerable populations without much concern about the potential misuses of the data collected and the overall ethical and legal underpinnings of these operations. This article examines three cases in point. The first case investigates the United Nations High Commissioner for Refugees’ decision to deploy a biometric matching engine engaging artificial intelligence to make accessing identification documents easier for both refugees and asylum seekers and the states and organizations they interact with. The second case focuses on the New Zealand government’s introduction of artificial intelligence to improve border security and streamline immigration. The third case looks at data scraping and biometric recognition tools implemented by the United States government to track (and eventually deport) undocumented migrants. The article first shows how states and international organizations are increasingly turning to artificial intelligence tools to support the implementation of their immigration policies and programs. Subsequently, the article also outlines how even despite well-intentioned efforts, the decision to use artificial intelligence tools to increase efficiency and support the implementation of migration or asylum management policies and programs often involves jeopardizing or altogether sacrificing individuals’ human rights, including privacy and security, and raises concerns about vulnerability and transparency.

Andika Wahab, Mashitah Hamidi

Abstract For decades, Malaysia has been heavily dependent on unskilled and temporarily contracted migrant workers to fulfil labour gaps in the country. While Malaysia’s economy continues to rely on migrant workers, the COVID-19 pandemic has further aggravated their precarious working and living conditions. In-depth interviews with Nepali migrant workers and community leaders in Malaysia and Nepal in 2021 revealed the incidence of labour rights violations, compounded by the lack of access to justice and effective remedies. Besides, workers are allegedly no longer benefiting from the competitive wages, subsequently limiting the value of their remittance to Nepal. We argue that these incidents serve as the drivers of the changing views of mobility, eventually influencing the emigration environment in which the social construction of migration exists in Nepal. This study examines the migratory realities in the Nepal–Malaysia migration corridor during the pandemic, subsequently contributing to current debate on the aspiration–ability model as a class of research.

K. McKenzie

Toronto, Ontario, Canada is home to 8% of Canada’s population and 11% of Canada’s coronavirus cases [1]. There is significant income inequality; 25% of children and 20% of adults live in poverty [2]. 52% of the population is racialized. Income and race are risk factors for covid-19 so a pandemic strategy needs to be equitable to be effective [1]. To flatten the curve, we needed to focus on who is under the curve, but, at the start of the pandemic, little routine socio-demographic data was being collected by public health. Reports of higher rates covid-19 in Black populations in the USA and UK and the rise of Black Lives Matter in spring 2020 led Toronto communities to question whether similar disparities were present locally. An open letter to the Government of Ontario calling for race based data collection [3], newspaper op-eds and multi-media interviews crystalized in the development of a backbone organization the Black Health Equity Working Group (BHEWG) which linked Black communities, academics, service providers and policy specialists. BHEWG developed a strategy for the collection and use of sociodemographic data including race/ethnicity and income in which initial analysis of existing area-based data was used as way of highlighting the need for individual level data collection at testing, tracing and hospitalization. A longer-term goal was for socio-demographic data collection when people renew their Ontario Health Insurance Plan cards. The strategy included suggested tools for data collection and the development of a data governance framework (available on request). The aim was to use data to improve equity by changing practice in all parts of the system involved in pandemic: public health units, City of Toronto, the Province of Ontario and Federal Government. Encouraging government analysts and policy organizations to use existing area based data from the census to map disparities was a vital first step. These analyses reported covid-19 rates 10 times higher in some areas and the best predictors were the percentage of racialized populations in an area and income [4].

Yibo Gao, Hongwei Wang, Suyan Yi et al.

Hand, foot, and mouth disease (HFMD) remains a serious health threat to young children. Urumqi is one of the most severely affected cities in northwestern China. This study aims to identify the spatiotemporal distribution characteristics of HFMD, and explore the relationships between driving factors and HFMD in Urumqi, Xinjiang. Methods: HFMD surveillance data from 2014 to 2018 were obtained from the China Center for Disease Control and Prevention. The center of gravity and geographical detector model were used to analyze the spatiotemporal distribution characteristics of HFMD and identify the association between these characteristics and socioeconomic and meteorological factors. Results: A total of 10,725 HFMD cases were reported in Urumqi during the study period. Spatially, the morbidity number of HFMD differed regionally and the density was higher in urban districts than in rural districts. Overall, the development of HFMD in Urumqi expanded toward the southeast. Temporally, we observed that the risk of HFMD peaked from June to July. Furthermore, socioeconomic and meteorological factors, including population density, road density, GDP, temperature and precipitation were significantly associated with the occurrence of HFMD. Conclusions: HFMD cases occurred in spatiotemporal clusters. Our findings showed strong associations between HFMD and socioeconomic and meteorological factors. We comprehensively considered the spatiotemporal distribution characteristics and influencing factors of HFMD, and proposed some intervention strategies that may assist in predicting the morbidity number of HFMD.

Linfang Tian, Kai Zhao, Jiaming Yin et al.

It has been found that human mobility exhibits random patterns following the Levy flight, where human movement contains many short flights and some long flights, and these flights follow a power-law distribution. In this paper, we study the social-economical development trajectories of urban cities. We observe that social-economical movement of cities also exhibit the Levy flight characteristics. We collect the social and economical data such as the population, the number of students, GDP and personal income, etc. from several cities. Then we map these urban data into the social and economical factors through a deep-learning embedding method Auto-Encoder. We find that the social-economical factors of these cities can be fitted approximately as a movement pattern of a power-law distribution. We use the Stochastic Multiplicative Processes (SMP) to explain such movement, where in the presence of a boundary constraint, the SMP leads to a power law distribution. It means that the social-economical trajectories of cities also follow a Levy flight pattern, where some years have large changes in terms of social-economical development, and many years have little changes.

Alexander Krause, Malte Hansen, Wilhelm Hasselbring

The 3D city metaphor in software visualization is a well-explored rendering method. Numerous tools use their custom variation to visualize offline-analyzed data. Heat map overlays are one of these variants. They introduce a separate information layer in addition to the software city's own semantics. Results show that their usage facilitates program comprehension. In this paper, we present our heat map approach for the city metaphor visualization based on live trace analysis. In comparison to previous approaches, our implementation uses live dynamic analysis of a software system's runtime behavior. At any time, users can toggle the heat map feature and choose which runtime-dependent metric the heat map should visualize. Our approach continuously and automatically renders both software cities and heat maps. It does not require a manual or semi-automatic generation of heat maps and seamlessly blends into the overall software visualization. We implemented this approach in our web-based tool ExplorViz, such that the heat map overlay is also available in our augmented reality environment. ExplorViz is developed as open source software and is continuously published via Docker images. A live demo of ExplorViz is publicly available.

Laura Szabó, Igor Kiss, Branislav Šprocha et al.

We analyse Roma fertility in four neighbouring countries in Central and Eastern Europe with a large Roma minority: in Hungary, Slovakia, Romania and Serbia. The sources of data are the respective national population censuses from 2011. Fertility is measured at the birth cohort level as the average number of children ever born. We make an international comparison of the fertility of Roma and non-Roma majority population women on the basis of completed education. In the case of Hungary, we also explore how the correlation between fertility and ethnic identity is modified when completed education and ethnic residential segregation are controlled. The fertility of Roma women is far above the majority population average in all birth cohorts and in each country. Educational attainment modifies this relationship. The fertility of highly educated Roma and majority population women is converging. The exposure to majority behaviour also has an effect. The lower the level of ethnic residential segregation, the smaller the difference between the fertility of Roma and majority population women. Completed education and residential segregation may exert different forces at the two ends of the educational hierarchy when their joint effect is explored. At the upper end of the social hierarchy, neither segregation nor ethnicity matters; at the lower end, however, both exposure to ethnic majority behaviour and ethnicity matter.