Urban Data Research Articles

Urban travel carbon emission mitigation approach using deep reinforcement learning.

The urbanization process has led to a significant increase in energy consumption and carbon emissions, which can be mitigated through scientific urban planning and management. This research proposes a bottom-up urban carbon emission mitigation strategy based on deep reinforcement learning (DRL). Using Ningbo City as a case study, multi-source urban data, including points of interest (POI) data and urban transportation system data, are utilized, along with varying carbon emission coefficients for different travel modes, to construct a comprehensive carbon emission environment for urban areas. The proposed DRL model adopts an Actor-Critic framework, which iteratively optimizes the land use configuration and building type proportions within the urban matrix to achieve the goal of mitigating travel carbon emissions. Experimental results demonstrate that this approach exhibits significant carbon reduction effects in urban scenario. By adjusting the discount rate of the reward function, various optimization strategies can be obtained, such as short-term and long-term strategies, achieving reductions of 0.47% and 0.61%, respectively, which are notably higher than the 0.39% reduction expected if travel emissions were uniformly distributed across the matrix. The findings highlight the potential of DRL-based approaches in urban planning to achieve adaptive and data-driven strategies for carbon emission mitigation.

Analysing trends of computational urban science and data science approaches for sustainable development

AbstractUrban computing with a data science approaches can play a pivotal role in understaning and analyzing the potential of these methods for strategic, short-term, and sustainable planning. The recent development in urban areas have progressed towards the data-driven smart sustainable approaches to resolve the complexities around urban areas. The urban system faces severe challenges and these are complicated to capture, predict, resolve and deliver. The current study advances an unconventional decision-support framework to integrate the complexities of science, urban sustainability theories, and data science, with a data-intensive science to incorporate grassroots initiatives for a top-down policies. This work will influence the urban data analytics to optimize the designs and solutions to enhance sustainability, efficiency, resilience, equity, and quality of life. This work emphasizes the significant trends of data-driven and model-driven decision support systems. This will help to address and create an optimal solution for multifaceted challenges of an urban setup within the analytical framework. The analytical investigations includes the research about land use prediction, environmental monitoring, transportation modelling, and social equity analysis. The fusion of urban computing, intelligence, and sustainability science is expected to resolve and contribute in shaping resilient, equitable, and future environmentally sensible eco-cities. It examines the emerging trends in the domain of computational urban science and data science approaches for sustainable development being utilized to address urban challenges including resource management, environmental impact, and social equity. The analysis of recent improvements and case studies highlights the potential of data-driven insights with computational models for promoting resilient sustainable urban environments, towards more effective and informed policy-making. Thus, this work explores the integration of computational urban science and data science methodologies to advance sustainable development.

Fermatean Fuzzy TOPSIS Method and Its Application in Ranking Business Intelligence-Based Strategies in Smart City Context

With the expansion of smart cities, the use of business intelligence (BI) has emerged as a crucial tool for resource optimization, increasing efficiency, and improving the citizens’ quality of life. BI enables companies to make better strategic decisions by analyzing vast amounts of urban data, helping them remain competitive in the dynamic smart city environment. This study utilizes content analysis and the Fermatean Fuzzy TOPSIS (FF-TOPSIS) method to rank the strategies based on business intelligence in the context of smart city. Initially, relevant criteria were identified through content analysis, and subsequently, five strategies were developed and ranked based on these criteria. The results revealed that the "Development of IOT-enabled smart networks (S2)" ranked highest, as it plays a significant role in optimizing resource management and enhancing urban service performance, thereby contributing greatly to the advancement of smart cities. "Process automation and the deployment of robotic systems (S5)" ranked second, as it enhances efficiency and reduces human errors. "Cloud platform integration for seamless access to data and services (S3)" also proved to be of considerable importance, ranking third, as it provides seamless access to data and services. " Artificial intelligence deployment for predictive analytics and process optimization (S4)" ranked fourth and was vital for predictive analytics and process optimization, while " Big data analytics for smart decision-making (S1)"—despite its importance—ranked fifth. Urban managers should prioritize the development of IOT networks to fully leverage their potential for resource management and efficiency gains. Following this, attention to process automation and AI integration can significantly enhance the quality of life for citizens and reduce urban costs.

Investigating spatial and temporal characteristics and elements influencing running among residents of Nanchang city in green open areas

Understanding the exercise behaviors of urban residents in green open spaces is crucial for optimizing future urban spatial planning and improving residents’ quality of life. This study provides an in-depth analysis of residents’ running behaviors across 87 green open spaces in Nanchang City. Running data collected from the exercise platform Keep, coupled with urban geographic data and field research, reveals the spatial distribution and temporal variations of running activities, including running flow, distance, time, and pace. A set of indicator systems, such as total area, landscape form index, surrounding residential land area, and road conditions, is used to analyze its relationship with the environmental elements of green open spaces. The research results show that the running flow, running distance, and running time in green open spaces in Nanchang City are significantly influenced by different objective elements, while running pace shows little correlation with these environmental elements. Specifically, the study highlights the significant impact of elements such as total area and road length, as well as the number of nearby bus stops and population density, on residents’ running behaviors in Nanchang City’s green open spaces.

Digital-twin-based multi-scale simulation supports urban emergency management: a case study of urban epidemic transmission

ABSTRACT Improving emergency management capabilities is vital for sustainable cities. However, urban emergency management is a complex, giant system that interacts between urban virtual and real spaces, requiring simulation in the virtual space and feedback to the real space. The digital twin model can potentially play an important role in this process, but still faces significant challenges in terms of modeling, simulation, and visualization. In this study, we propose a digital-twin-based multi-scale simulation method and take epidemic transmission as a case study. First, an entity-oriented unified urban database integrating multi-source urban data was constructed as the digital twins of urban human settlements for emergency modeling. Then, a multi-scale urban emergency simulation method in 3D geographic environment was proposed to support emergency simulation for multi-level urban management units. Moreover, an adaptive fusion rendering of emergency information and 3D scene was proposed to support the refinement of emergency visualization. An experimental case study on epidemic risk estimation was conducted through thousands of outbreak scenarios. The results demonstrate that the proposed method can establish the digital twins of urban human settlements, where modeling, simulation, and visualization of emergency at multi-scales can be carried out, thus enhancing the efficacy and efficiency of emergency management.

The Power of the Tower: Nicolas Schöffer’s Tour Lumière Cybernétique for La Défense 1962–1973

This paper looks at Nicolas Schöffer’s unrealised Tour Lumière Cybernétique (1962–1973) to explore the interplay between urban data, participation, and political consensus in 1960s France. Envisioned for Paris’s business district La Défense, the purpose of the tower was to help keep the city in a technological equilibrium by employing a set of automated algorithms for collecting and visualising inputs received from the urban environment. Yet Schöffer’s ad-hoc disruptive interventions, the so-called perturbations that he deemed essential to the tower’s program, suggest that he regarded the outcome of data collection to be too monotonous to maintain that desired equilibrium.This article stands at the intersection of histories of architecture and urban data-processing. It aims to suggest that Schöffer’s TLC tower presents an indirect version of participation, one that, rather than fostering self-determinacy or proposing a political agenda, focuses on the extraction as well as abstraction of information ultimately framed as a participatory practice.

New ways of collecting urban freight traffic data and applications for urban freight policies and research

The current quality of data on urban logistics traffic patterns is poor. Data are scarce, heterogeneous, often old and expensive to collect. This research looks at new ways to collect urban freight data and make that data available to both policy-makers and academia. Three case studies are examined: Rotterdam, the Netherlands; Brussels, Belgium; and Barcelona, Spain. They help identify new sources of data from traffic and parking policies: automated number plate recognition cameras, an on-street delivery smartphone app, and a truck pricing scheme. Both the context in which new data sources of interest to urban logistics are produced, and the level of appropriation of these data by local stakeholders and the research community are examined. We then construct an analysis grid to evaluate the diversity of systems (technical and socio-technical) producing data relevant to urban logistics. This analysis framework can be used to facilitate comparisons between data sources and thus help future case studies. In the three cases, we found that local governments use the data more than researchers do. The issue of data dissemination raises questions about costs (who is to bear the cost of storing, maintaining and extracting data sets?) as well as regulatory questions. We conclude that local administrations should be encouraged to work towards harmonizing data collection methods and data sharing conditions in order to improve the quality and comparability of the data. Cities in countries with little access to new sources of data, such as France for regulatory reasons, should try to find ways to create data source opportunities. In all countries, urban freight research communities should be encouraged to use the now increasingly available data.

The Mitigating Effect of Green Space's Spatial and Temporal Patterns on the Urban Heat Island in the Context of Urban Densification: A Case Study of Xi'an

Growing evidence highlights a strong link between urban densification and urban heat island (UHI) effects. However, there is a lack of research on time-based analyses of urban green space spatial patterns using urban densification indices to mitigate UHI effects. Addressing this gap, this study focuses on the central district of Xi'an, utilizing urban density data, MODIS LST products, and Landsat series imagery. The Coordination Coupling Degree Model (CCDM) is applied to longitudinally assess the mitigating effects of urban green space spatial patterns on UHI under different scales of spatial densification. The CCDM results show a significant overall improvement in the coordination coupling degree between green space spatial indices and LST in Xi'an's central district from 2013 to 2023. However, variations across administrative districts suggest that regional planning policies have differentially influenced green space patterns and UHI effects over time. Furthermore, a global negative correlation between the urban densification index and changes in the coordination coupling degree indicates that continued urbanization in Xi'an's central district could exacerbate the urban thermal environment. This study provides valuable insights into green space planning and UHI mitigation strategies in the context of urban densification.

Impact of Short-Haul High Speed Rail Commuting on Land Use and Spatial Structure in a Multi-Centre Metropolitan Area

Owing to the enhanced accessibility afforded by high-speed rail, the relationship between megacities and their neighboring cities has grown closer. Megacities exert a significant influence on the land use of neighboring cities. This study employs a spatial econometric model to investigate the direct impact and spatial spillover effects of high-speed rail on urban land values, based on urban panel data from China’s Yangtze River Delta region (2006-2020). The results revealed that shorter commuting times and improved frequency of service had a more substantial effect. Additionally, due to alterations in land values, there is a distinct preference tendency for urban land use attributes. Therefore, the city’s spatial organization has transformed, and its center of gravity has shifted as well. This study provides crucial theoretical justifications and empirical evidence to fill the gaps left by previous research on the effects of short-haul high-speed rail commuting on urban housing prices and land use. The study also offers valuable insights for strategic planning and implementation aimed at promoting urban development and urban agglomeration construction through HSR construction.

GPT-based data-driven urban building energy modeling (GPT-UBEM): Concept, methodology, and case studies

Achieving carbon neutrality is a critical global goal, with urban building energy modeling (UBEM) playing a pivotal role by providing data-driven insights to optimize energy consumption and reduce emissions. This paper introduces GPT-based urban building energy modeling (GPT-UBEM), a novel approach utilizing GPT’s advanced capabilities to address key UBEM challenges using GPT-4o. The study aimed to demonstrate the effectiveness of GPT-UBEM in performing UBEM tasks and to explore its potential in overcoming traditional limitations. Specifically, (1) basic analytics of urban data, (2) data analysis and energy prediction, (3) building feature engineering and optimization, and (4) energy signature analysis were conducted in four case studies. These analyses were applied to 2,000 buildings in Seoul and 31 buildings in Gangwon-do, South Korea. Through case study, the findings highlighted the ability of GPT-UBEM to integrate diverse data sources, optimize building features for high accuracy in prediction models, and provide valuable insights for urban planners and policymakers through the use of expert domain knowledge and intervention. Additionally, based on the results derived from GPT-UBEM in this study, the current limitations of GPT-UBEM (L1 to L3) and future research directions (F1 to F4) have been outlined.

Spatial correlation effect of haze pollution in the Yangtze River Economic Belt, China.

With the rapid development of industry, haze pollution has become an urgent environmental problem. This study innovatively utilizes network-based methods to investigate the spatial correlation effects of haze pollution transmission between urban clusters in the Yangtze River Economic Belt. A spatial correlation network of haze pollution in the Yangtze River Economic Belt was constructed using 328 urban meteorological data collection points as research samples, and its structural characteristics were examined. Main findings are as follows: (1) The spatial correlation network of PM2.5 in the Yangtze River Economic Belt urban agglomeration exhibits typical network structural characteristics: obvious spatial correlation within the network. (2) Chengdu, Chongqing, Wuhan, Nanchang, Yichang, Changsha and Yueyang are located at the center of the spatial network. They have more receiving and sending relationships. (3) 36 cities can be divided into four types: bilateral overflow, net beneficiary, net overflow and broker. Each type has different functional characteristics and linkage effects in the network. (4) Haze pollution positively correlates with the city's synergistic development capacity and urbanization rate, the higher the city's development level and the higher the Urbanization rate, the stronger its haze pollution capacity. This study provides new insights into the study of the spatial correlation and impact of haze pollution.

Urban health data in India: mapping gaps and solutions

Inadequacies of current health information systems in capturing the diverse health needs and vulnerabilities within urban populations, emphasizing the need to move beyond fragmented traditional data collection approaches. By incorporating emerging technologies and enhancing governance structures, such as strengthening coordination mechanisms and promoting transparency, urban health stakeholders can overcome data gaps and better address the evolving health landscape.

Advancing urban mobility in developing countries: A mobile RSU approach for sustainable transportation

AbstractThe rapid and uncontrolled urbanization of cities in developing countries has engendered a plethora of urban mobility issues, including traffic congestion, accidents, and air pollution. To address these challenges, contemporary urban mobility trends are incorporating innovative technologies and sustainable governance practices. This article investigates how urban managers can leverage the opportunities presented by cost‐effective technologies and the management of urban data to enhance urban mobility in developing countries. Within this discourse, an RSU‐based approach is proposed that employs motorbike taxis for inter‐vehicular communication, given their status as the most widely used form of public transportation. This approach substantially diminishes investment costs and reinforces the sustainability of urban mobility. Through the implementation of this solution, a noteworthy reduction is anticipated in the emission of gases such as CO2, and NOx, known contributors to climate change and various respiratory diseases. To validate the efficacy of the proposed solution, four distinct scenarios are scrutinized in a case study centered on the city of Douala in Cameroon, utilizing tools such as OMNET++, SUMO, Veins, and INET. The proposed framework offers significant benefits in terms of environmental sustainability and operational efficiency. It enables a 10% reduction in CO2 emissions, a 15% reduction in NOx emissions, an 11% drop in fuel consumption, and a 15% reduction in waiting time in traffic jams. The envisaged solution aims to aid urban managers in their decision‐making processes, specifically in advancing sustainable urban mobility. Through the adoption of this approach, cities in developing countries can mitigate challenges associated with urban mobility and enhance the overall well‐being of their residents.

Study on the Impact of China’s Pension Insurance System on the Savings Rate of Urban Residents Based on the Preventive Saving Perspective

In the context of population aging, the study of the impact of the pension insurance system on the savings behavior of urban residents can help reduce preventive savings, increase current consumption, and promote the healthy development of the economy. This study constructs a two-period OLG model of the pension insurance system based on the preventive saving perspective using urban panel data of 31 provinces from 2002 to 2021 and conducts empirical analysis through the systematic generalized method of moments. The results show that the pension insurance participation rate was positively correlated with the savings rate, while the pension replacement rate was negatively correlated with the savings rate, and this effect varied significantly across income levels and different levels of aging. Increased participation in high-income areas promoted savings, while increased pension replacement rates in low-income areas reduced savings. The positive correlation between participation rates and savings rates was more pronounced in regions with higher levels of aging, while the opposite was true in regions with lower levels of aging. In addition, the negative correlation between pension replacement ratios and savings rates was particularly pronounced in high-aging regions.

The impact of data factor-driven industry on the green total factor productivity: evidence from the China

Data factors have become an essential factor of production in today’s digital era, and provided renewed energy for China’s green and high-quality development. This study analyses the impact of data factor-driven industries on green total factor productivity (GTFP) and the underlying mechanisms using panel data of 277 Chinese cities from 2008 to 2020. The results show that: First, the urban data factor-driven industry can enhance GTFP. Second, the data factor-driven industry can increase the input–output ratio and enhance GTFP by promoting AI technological innovation and AI technological entrepreneurship. Third, the heterogeneity analysis show that in regions with high levels of digital technological innovation, the market size of data factor-driven industries is larger and the effect of data factor-driven industries in enhancing GTFP is more significant. And in regions with low levels of savings, the data factor-driven industry has limited capital availability and is more efficient in using capital, which has a stronger effect on GTFP enhancement. This study provides valuable empirical evidence on the relationship between data factor-driven industries and urban GTFP, and important policy implications for fully leveraging the green economic effects of data factors, and promoting green and high-quality urban development.

Evolutive 3D Urban Data Representation through Timeline Design Space

AbstractCities are constantly changing to adapt to new societal and environmental challenges. Understanding their evolution is thus essential to make informed decisions about their future. To capture these changes, cities are increasingly offering digital 3D snapshots of their territory over time. However, existing tools to visualise these data typically represent the city at a specific point in time, limiting a comprehensive analysis of its evolution. In this paper, we propose a new method for simultaneously visualising different versions of the city in a 3D space. We integrate the different versions of the city along a new way of 3D timeline that can take different shapes depending on the needs of the user and the dataset being visualised. We propose four different shapes of timelines and three ways to place the versions along it. Our method places the versions such that there is no visual overlap for the user by varying the parameters of the timelines, and offer options to ease the understanding of the scene by changing the orientation or scale of the versions. We evaluate our method on different datasets to demonstrate the advantages and limitations of the different shapes of timeline and provide recommendations so as to which shape to chose.

Does the cross-border e-commerce comprehensive pilot area policy help to improve the level of urban technological innovation?

This study assesses the policy effects on the level of urban technological innovation (LUTI) in three pilot cities (Hefei, Zhengzhou, and Shenyang) that have implemented the cross-border e-commerce comprehensive pilot area (CECPA) policy. First, based on urban panel data from 2006 to 2021, we use the entropy-TOPSIS model to measure LUTI. Further, this study applies the synthetic control method (SCM) to evaluate the effect of policy treatment on three cities. Additionally, this study analyzes two mechanism variables-urbanization rate and average salary level of urban employees-to assess the internal mechanisms affecting LUTI. Lastly, we provide the corresponding countermeasure enlightenment. Empirical evidence suggests that the CECPA policy significantly improves the LUTI in cities. From 2016 to 2021, the LUTI in Hefei rose from 0.4508 to 0.8135, and in Zhengzhou from 0.5316 to 0.7296. Shenyang's LUTI increased from 0.7427 to 0.8307 from 2018 to 2021. Overall, the policy has had a positive effect on the development of technological innovation in cities. In particular, the CECPA policy has a positive impact on the LUTI by increasing the urbanization rate and increasing the average salary level of employees. These results provide a good example of cities implementing CECPA policy and carrying out technological innovation reforms.

Contradiction Hidden in Values of Urban Raster Data

Abstract. This paper discusses that raster data identified by the independent largest fraction method may include both manifest and hidden contradictions, and describes the underlying mechanisms of contradictions. Using the examples of zones of land use and floor-area ratio, we demonstrate that the cells including more than three different zone category combinations might be identified with a non-existing combination of the zone categories. For the zones in which the ratio of contradictory cells is comparatively large, this problem can be quite significant. We model, therefore, the probability of contradictory identification using the adjacency relationship of different categories, and demonstrate the good fitness of the model using actual urban raster data. The results reveal that there can be a significant proportion of hidden contradictions.

Urban Color Perception and Sentiment Analysis Based on Deep Learning and Street View Big Data

The acceleration of urbanization has resulted in a heightened awareness of the impacts of urban environments on residents’ emotional states. This present study focuses on the Lixia District of Jinan City. By using urban street view big data and deep learning methods, we undertook a detailed analysis of the impacts of urban color features on residents’ emotional perceptions. In particular, a substantial corpus of street scene image data was extracted and processed. This was performed using a deep convolutional neural network (DCNN) and semantic segmentation technology (PSPNet), which enabled the simulation and prediction of the subjective perception of the urban environment by humans. Furthermore, the color complexity and coordination in the street scene were quantified and combined with residents’ emotional feedback to carry out a multi-dimensional analysis. The findings revealed that color complexity and coordination were significant elements influencing residents’ emotional perceptions. A high color complexity is visually appealing, but can lead to fatigue, discomfort, and boredom; a moderate complexity stimulates vitality and pleasure; high levels of regional harmony and aesthetics can increase perceptions of beauty and security; and low levels of coordination can increase feelings of depression. The environmental characteristics of different areas and differences in the daily activities of residents resulted in regional differences regarding the impacts of color features on emotional perception. This study corroborates the assertion that environmental color coordination has the capacity to enhance residents’ emotions, thereby providing an important reference point for urban planning. Planning should be based on the functional characteristics of the region, and color complexity and coordination should be reasonably regulated to optimize the emotional experiences of residents. Differentiated color management enhances urban aesthetics, livability, and residents’ happiness and promotes sustainable development. In the future, the influences of color and environmental factors on emotions can be explored in depth, with a view to assist in the formulation of fine urban design.

A Hybrid Model Combined Deep Neural Network and Beluga Whale Optimizer for China Urban Dissolved Oxygen Concentration Forecasting

The dissolved oxygen concentration (DOC) is an important indicator of water quality. Accurate DOC predictions can provide a scientific basis for water environment management and pollution prevention. This study proposes a hybrid DOC forecasting framework combined with Variational Mode Decomposition (VMD), a convolutional neural network (CNN), a Gated Recurrent Unit (GRU), and the Beluga Whale Optimization (BWO) algorithm. Specifically, the original DOC sequences were decomposed using VMD. Then, CNN-GRU combined with an attention mechanism was utilized to extract the key features and local dependency of the decomposed sequences. Introducing the BWO algorithm solved the correction coefficients of the proposed system, with the aim of improving prediction accuracy. This study used 4-h monitoring China urban water quality data from November 2020 to November 2023. Taking Lianyungang as an example, the empirical findings exhibited noteworthy enhancements in performance metrics such as MSE, RMSE, MAE, and MAPE within the VMD-BWO-CNN-GRU-AM, with reductions of 0.2859, 0.3301, 0.2539, and 0.0406 compared to a GRU. These results affirmed the superior precision and diminished prediction errors of the proposed hybrid model, facilitating more precise DOC predictions. This proposed DOC forecasting system is pivotal for sustainably monitoring and regulating water quality, particularly in terms of addressing pollution concerns.