Elisa Brini, Lars Dommermuth, Trude Lappegård
et al.
Understanding the motivations that underpin fertility decision-making can shed light on why people in low-fertility countries are increasingly having fewer or no children. Using data from the 2020 Norwegian Generations and Gender Survey, we examine 3,024 people of childbearing age and their childbearing motivations. We find that the childbearing motivations receiving the highest ratings are lifelong joy, fulfilling parental instinct, and the satisfaction of raising a child. Non-parents exhibit more negative motivations than parents, especially regarding care responsibilities. Gender differences in childbearing motivations emerge, with women giving a higher rating than men to the fulfilment of parental instinct, and men rating the time and energy burden of having children more highly. As expected, individuals with more positive and less negative childbearing motivations have higher fertility desires. However, on the whole, fertility desires appear to be influenced more by positive motivations than negative ones. This study emphasises the importance of individual perceptions and predispositions towards parenthood when examining fertility preferences.
Urban groups. The city. Urban sociology, City population. Including children in cities, immigration
Abstract This article theorizes everyday forms of uncertainty that immigrant and refugee populations negotiate as a form of violence. We argue that while the state has made immigrants and refugees an explicit target on which to exercise sovereignty through detention and deportation, sovereignty also operates through a necropolitics of uncertainty where immigrants and refugees face inconsistent and spatially diffuse states of exception that target their material, social, and psychological wellbeing. To support this argument, we draw on three case studies that illustrate how avoiding or opting out of social services and health care, or by “self-deporting,” are effects of targeted violence.
Lorena Izaguirre, Tanja Bastia, Matthew Walsham
et al.
Abstract While the integration of an intersectional perspective marks a significant advance in gender and migration literature over recent decades, this scholarship remains heavily dominated by studies focusing on South-North migration. Consequently, despite growing attention to gender within South-South migration from both researchers and policy-makers, key research areas applying an intersectional lens remain neglected. In this paper, we identify three such areas that remain significantly under-explored in South-South migration research: sexuality, disability, and ageing. We argue that migration scholarship in general, and gender and migration scholarship in particular, need to urgently move to encompass all types of migrations, including regional, South-South migration, as well as broaden the languages of the publications that are taken into account in the mainstream gender and migration literature. In this article, we put forward a framework for advancing the research agenda on intersectionality in South-South migration, one which departs from the common focus on English-speaking countries and publications. We take a first step towards implementing this approach by including publications in Spanish and Portuguese.
Accurate population flow prediction is essential for urban planning, transportation management, and public health. Yet existing methods face key limitations: traditional models rely on static spatial assumptions, deep learning models struggle with cross-city generalization, and Large Language Models (LLMs) incur high computational costs while failing to capture spatial structure. Moreover, many approaches sacrifice resolution by clustering Points of Interest (POIs) or restricting coverage to subregions, limiting their utility for city-wide analytics. We introduce UrbanPulse, a scalable deep learning framework that delivers ultra-fine-grained, city-wide OD flow predictions by treating each POI as an individual node. It combines a temporal graph convolutional encoder with a transformer-based decoder to model multi-scale spatiotemporal dependencies. To ensure robust generalization across urban contexts, UrbanPulse employs a three-stage transfer learning strategy: pretraining on large-scale urban graphs, cold-start adaptation, and reinforcement learning fine-tuning.Evaluated on over 103 million cleaned GPS records from three metropolitan areas in California, UrbanPulse achieves state-of-the-art accuracy and scalability. Through efficient transfer learning, UrbanPulse takes a key step toward making high-resolution, AI-powered urban forecasting deployable in practice across diverse cities.
Stina Sundstedt, Mattias Wingren, Susanne Hägglund
et al.
Preschool children with language vulnerabilities -- such as developmental language disorders or immigration related language challenges -- often require support to strengthen their expressive language skills. Based on the principle of implicit learning, speech-language therapists (SLTs) typically embed target morphological structures (e.g., third person -s) into everyday interactions or game-based learning activities. Educators are recommended by SLTs to do the same. This approach demands precise linguistic knowledge and real-time production of various morphological forms (e.g., "Daddy wears these when he drives to work"). The task becomes even more demanding when educators or parent also must keep children engaged and manage turn-taking in a game-based activity. In the TalBot project our multiprofessional team have developed an application in which the Furhat conversational robot plays the word retrieval game "Alias" with children to improve language skills. Our application currently employs a large language model (LLM) to manage gameplay, dialogue, affective responses, and turn-taking. Our next step is to further leverage the capacity of LLMs so the robot can generate and deliver specific morphological targets during the game. We hypothesize that a robot could outperform humans at this task. Novel aspects of this approach are that the robot could ultimately serve as a model and tutor for both children and professionals and that using LLM capabilities in this context would support basic communication needs for children with language vulnerabilities. Our long-term goal is to create a robust LLM-based Robot-Assisted Language Learning intervention capable of teaching a variety of morphological structures across different languages.
Abstract Despite the prevalence of ambiguous citizenship policies that say one thing in law and another in implementing regulations, few studies have focused on systematically studying this type of implementation gap, particularly in contexts beyond North America and Europe. This largely has remained the case despite research on discursive policy gaps, which occur between a policy’s stated objectives and its laws, efficacy gaps, which describe when a policy’s outcomes fail to meet its goals, and compliance gaps, which reflect disparities between a state’s commitments to international law and its corresponding domestic policies. How can we advance conceptualizations of law-regulation implementation gaps? This paper proposes one approach by focusing on the content of domestic laws, on the one hand, and the content of related implementing regulations, on the other. When law-regulation discrepancies occur, they illustrate the agency of senior officials in writing this intentional ambiguity into different levels of legislation, challenging assumptions about institutional weakness and lower-level bureaucratic discretion as chief drivers of implementation gaps. The paper illustrates this concept by analyzing discrepancies between Jordan’s nationality and passports laws and their related implementing regulations, particularly regarding Gaza refugees’ access to passports, investors’ access to nationality, and Palestinian-Jordanians’ subjection to nationality withdrawals. These diverse cases of intentional ambiguity demonstrate that such gaps can serve to partially exclude or include a group and can occur with noncitizen and citizen as well as more or less vulnerable groups.
Abstract Based on a qualitative study of Zimbabwean migrants based in South Africa, who regularly remitted goods and money to Zimbabwe between 2010 and 2020, this paper suggests that at a local level, remittances alleviated poverty with very limited if any transformation of the political economy at the national level. Such remittances promoted consumerism without sustainable investment that can structurally transform the economy. In addition, the dependence on remittances entrenches the culture of migration at the local level, which also contributes to or promotes ethno-tribal fissiparity. In rethinking diaspora remittances in the post-Mugabe era, it is advanced that the seemingly intractable economic and political quagmire in Zimbabwe must be resolved to inspire confidence in the diaspora to pull remittances together for a national socio-economic cause and not local-level band-aid accomplishments which remittances currently do.
Abstract Migration forecasts are crucial for proactive immigration and integration management. While the demand for accurate migration forecasts continues to grow, the current state of migration forecasting is still unsatisfactory, because they tend to lack precision. We introduce an alternative method to forecast migration movements: prediction markets. While prediction markets are mainly unknown in migration studies, they are established in the political economy of forecasting election outcomes. For its application to a complex phenomenon in a more constrained information environment such as migration movements, we argue that prediction markets allow to balance complementarities of current qualitative and quantitative approaches if they provide solutions to avoid thin trading and integrate expert knowledge into the market. We apply the prediction market to forecast immigration in four West European countries in 2020 and find encouraging results. We discuss the strengths and limitations of prediction markets to migration forecasting, including ethical considerations, and guide its future application.
Istianah Maghfirotul Qiromah, Pascalian Hadi Pradana, Hisbiyatul Hasanah
Today’s age, many children seem to be less interested in learning at school. This could be because they arespending more time playing with gadgets at home instead of studying. As a result, their creativity andcritical thinking skills are not being optimally developed. To address this issue, researchers have tried toimprove children’s creative thinking abilities by using nature-based loose part media. They conductedan action classroom research with 20 children to evaluate the impact of this approach on their creativethinking skills. The researchers observed indicators such as the children’s curiosity, imagination, ability toproduce forms, and sense of responsibility. The results showed that using nature-based loose part mediacan significantly enhance young children’s creative thinking abilities. In pre-cycle, 11 children showedhigh achievement in creative thinking abilities, but this number increased to 15 children in cycle I and 19children in cycle II. Therefore, it is crucial for educational institutions to use nature-based loose part mediaduring early childhood learning activities to promote children’s creativity and critical thinking skill
Education, City population. Including children in cities, immigration
Lucy Temple, Gabriela Viale Pereira, Lukas Daniel Klausner
This short paper represents a systematic literature review that sets the basis for the future development of a framework for digital twin-based decision support in the public sector, specifically for the smart city domain. The final aim of the research is to model context-specific digital twins for aiding the decision-making processes in smart cities and devise methods for defining the policy agenda. Overall, this short paper provides a foundation, based on the main concepts from existing literature, for further research in the role and applications of urban digital twins to assist decision- and policy-making in smart cities. The existing literature analyses common applications of digital twins in smart city development with a focus on supporting decision- and policy-making. Future work will centre on developing a digital-twin-based sustainable smart city and defining different scenarios concerning challenges of good governance, especially so-called wicked problems, in smaller-scale urban and non-urban contexts.
Abstract This paper investigates the integration of immigrants and refugees by drawing on Bauman's conceptual distinction of tourists and vagabonds. Through qualitative interviews with immigrants and refugees in Istanbul, the study highlights differences in their networks, perceptions of the city, the nature and conditions of their stay, and their sense of being welcomed. The study illustrates, differences in resources, status, and the host society's ethno-racial hierarchy result in different adaptation processes. The study's findings contribute to scholarship on ethnicity and migration by comparatively revealing potential variations in refugee integration.
Existing neural radiance field-based methods can achieve real-time rendering of small scenes on the web platform. However, extending these methods to large-scale scenes still poses significant challenges due to limited resources in computation, memory, and bandwidth. In this paper, we propose City-on-Web, the first method for real-time rendering of large-scale scenes on the web. We propose a block-based volume rendering method to guarantee 3D consistency and correct occlusion between blocks, and introduce a Level-of-Detail strategy combined with dynamic loading/unloading of resources to significantly reduce memory demands. Our system achieves real-time rendering of large-scale scenes at approximately 32FPS with RTX 3060 GPU on the web and maintains rendering quality comparable to the current state-of-the-art novel view synthesis methods.
Smart cities transform urban landscapes with interconnected nodes and sensors. The search for seamless communication in time-critical scenarios has become evident during this evolution. With the escalating complexity of urban environments, envisioning a future with a blend of autonomous and conventional systems, each demanding distinct quality-of-service considerations, services in smart cities vary in criticality levels and necessitate differentiated traffic handling, prioritizing critical flows without compromising the network's reliability or failing on hard real-time requirements. To tackle these challenges, in this article, we discuss a time-sensitive networking approach, which presents multi-faceted challenges, notably interoperability among diverse technologies and standards at the scale of a smart city network. TSN emerges as a promising toolkit, encompassing synchronization, latency management, redundancy, and configuration functionalities crucial for addressing smart city challenges. Moreover, the article scrutinizes how TSN, predominantly utilized in domains like automotive and industry, can be tailored to suit the intricate needs of smart cities, emphasizing the necessity for adaptability and scalability in network design. This survey consolidates current research on TSN, outlining its potential in fortifying critical machine-to-machine communications within smart cities while highlighting future challenges, potential solutions, and a roadmap for integrating TSN effectively into the fabric of urban connectivity.
Yisheng Alison Zheng, Abdallah Lakhdari, Amani Abusafia
et al.
Human mobility patterns refer to the regularities and trends in the way people move, travel, or navigate through different geographical locations over time. Detecting human mobility patterns is essential for a variety of applications, including smart cities, transportation management, and disaster response. The accuracy of current mobility prediction models is less than 25%. The low accuracy is mainly due to the fluid nature of human movement. Typically, humans do not adhere to rigid patterns in their daily activities, making it difficult to identify hidden regularities in their data. To address this issue, we proposed a web platform to visualize human mobility patterns by abstracting the locations into a set of places to detect more realistic patterns. However, the platform was initially designed to detect individual mobility patterns, making it unsuitable for representing the crowd in a smart city scale. Therefore, we extend the platform to visualize the mobility of multiple users from a city-scale perspective. Our platform allows users to visualize a graph of visited places based on their historical records using a modified PrefixSpan approach. Additionally, the platform synchronizes, aggregates, and displays crowd mobility patterns across various time intervals within a smart city. We showcase our platform using a real dataset.
Traffic forecasting is a critical service in Intelligent Transportation Systems (ITS). Utilizing deep models to tackle this task relies heavily on data from traffic sensors or vehicle devices, while some cities might lack device support and thus have few available data. So, it is necessary to learn from data-rich cities and transfer the knowledge to data-scarce cities in order to improve the performance of traffic forecasting. To address this problem, we propose a cross-city few-shot traffic forecasting framework via Traffic Pattern Bank (TPB) due to that the traffic patterns are similar across cities. TPB utilizes a pre-trained traffic patch encoder to project raw traffic data from data-rich cities into high-dimensional space, from which a traffic pattern bank is generated through clustering. Then, the traffic data of the data-scarce city could query the traffic pattern bank and explicit relations between them are constructed. The metaknowledge is aggregated based on these relations and an adjacency matrix is constructed to guide a downstream spatial-temporal model in forecasting future traffic. The frequently used meta-training framework Reptile is adapted to find a better initial parameter for the learnable modules. Experiments on real-world traffic datasets show that TPB outperforms existing methods and demonstrates the effectiveness of our approach in cross-city few-shot traffic forecasting.
The world has been experiencing rapid urbanization over the last few decades, putting a strain on existing city infrastructure such as waste management, water supply management, public transport and electricity consumption. We are also seeing increasing pollution levels in cities threatening the environment, natural resources and health conditions. However, we must realize that the real growth lies in urbanization as it provides many opportunities to individuals for better employment, healthcare and better education. However, it is imperative to limit the ill effects of rapid urbanization through integrated action plans to enable the development of growing cities. This gave rise to the concept of a smart city in which all available information associated with a city will be utilized systematically for better city management. The proposed system architecture is divided in subsystems and is discussed in individual chapters. The first chapter introduces and gives overview to the reader of the complete system architecture. The second chapter discusses the data monitoring system and data lake system based on the oneM2M standards. DMS employs oneM2M as a middleware layer to achieve interoperability, and DLS uses a multi-tenant architecture with multiple logical databases, enabling efficient and reliable data management. The third chapter discusses energy monitoring and electric vehicle charging systems developed to illustrate the applicability of the oneM2M standards. The fourth chapter discusses the Data Exchange System based on the Indian Urban Data Exchange framework. DES uses IUDX standard data schema and open APIs to avoid data silos and enable secure data sharing. The fifth chapter discusses the 5D-IoT framework that provides uniform data quality assessment of sensor data with meaningful data descriptions.
Hamed Vahdat-Nejad, Tahereh Tamadon, Fatemeh Salmani
et al.
With the emergence of the Internet of things (IoT), human life is now progressing towards smartification faster than ever before. Thus, smart cities become automated in different aspects such as business, education, economy, medicine, and urban areas. Since smartification requires a variety of dynamic information in different urban dimensions, mobile crowdsourcing has gained importance in smart cities. This chapter systematically reviews the related applications of smart cities that use mobile crowdsourcing for data acquisition. For this purpose, the applications are classified as environmental, urban life, and transportation categories and then investigated in detail. This survey helps in understanding the current situation of smart cities from the viewpoint of crowdsourcing and discusses the future research directions in this field.
Athanasios Batakis, Thi-Thuy-Nga Nguyen, Michel Zinsmeister
In the first part of this paper we propose a new theoretical model of city growth based on percolation. The second half oh the paper is devoted to a concrete application of the model, namely to the city of Montargis. It appears that the embedded algorithm is quite efficient in terms of computational time and allows to exploit big data type ressources such as individual land lots.
This paper aims to examine the determinants of smart-city commitment across individuals from Bahía Blanca, Argentina. Literature has identified different factors explaining citizens’ commitment to smart cities, such as education, age, labor condition, and other more subjective factors, such as trust and awareness about the smart-city concept. A mediator factor of smart commitment is e-readiness or digital readiness, that is, the level of preparedness to properly exploit internet opportunities such as e-government and e-commerce. To achieve this goal, we used a survey conducted on 97 citizens (followers of the Moderniza Bahía Facebook) from the city of Bahía Blanca, Argentina. By estimating a structural equation model, we found that higher levels of ICT use are associated with higher levels of smart-city commitment and that higher awareness of the smart-city concept is related to higher levels of smart-city commitment. Sociodemographic factors such as age and labor condition also explain ICT use.