City Level Research Articles

Is ward-level calculation of urban green space availability important?—A case study on Vellore city, India, using the histogram-based spectral discrimination approach

How much green space is available for individuals is a major question that city planners are generally interested in, and the present study aimed to address this issue in the context of Vellore, India, through two approaches, namely, the per capita and the geographical area approach. In existing studies, urban green space (UGS) was only calculated at the macro level, i.e., for the city as a whole. Micro-or ward-level analysis was not attempted before, and the present study carried out the same to get a clear picture of the amount of greenery available in each ward of a city. For this purpose, a two-step approach was proposed where the histograms of Google Earth (GE) images were analyzed first to check whether the green cover types such as trees, shrubs/grassland, and cropland were spectrally different. Then, classification techniques such as ISODATA, maximum likelihood, support vector machine (SVM), and object-based methods were applied to the GE images. It was found that SVM performed well in extracting different green cover types with the highest overall accuracy of 93% and Kappa coefficient of 0.881. It was found that when considering the city as a whole, the amount of UGS available is 42% of the total area, which is more than the recommended range of 20–40%. Similarly, the available UGS per person is 97.84 m2, which is far above the recommended 12 m2/person. However, the micro-level analysis revealed that some of the wards have not satisfied the criteria of per capita and percentage area, though the city as a whole has satisfied both the criteria. Thus, the results indicate the importance of calculating the urban green space availability at the ward level rather than the city level as the former gives a closer look at the surplus and deficit areas. The results of terrestrial LiDAR survey at individual tree level revealed that if trees are located adjacent to buildings or roads, it results in fewer heat islands compared to the case where there are no trees.

Deep learning-based approach for COVID-19 spread prediction

AbstractSpread prediction models are vital tools to help health authorities and governments fight against infectious diseases such as COVID-19. The availability of historical daily COVID-19 cases, in conjunction with other datasets such as temperature and humidity (which are believed to play a key role in the spread of the disease), has opened a window for researchers to investigate the potential of different techniques to model and thereby expand our understanding of the factors (e.g., interaction or exposure resulting from mobility) that govern the underlying dynamics of the spread. Traditionally, infectious diseases are modeled using compartmental models such as the SIR model. However, this model shortcoming is that it does not account for mobility, and the resulting mixing or interactions, which we conjecture are a key factor in the dynamics of the spread. Statistical analysis and deep learning-based approaches such as autoregressive integrated moving average (ARIMA), gated recurrent units, variational autoencoder, long short-term memory (LSTM), convolution LSTM, stacked LSTM, and bidirectional LSTM have been tested with COVID-19 historical data to predict the disease spread mainly in medium- and high-income countries with good COVID-19 testing capabilities. However, few studies have focused on low-income countries with low access to COVID-19 testing and, hence, highly biased historical datasets. In addition to this, the arguable best model (BiLSTM) has not been tested with an arguably good set of features (people mobility data, temperature, and relative humidity). Therefore, in this study, the multi-layer BiLSTM model is tested with mobility trend data from Google, temperature, and relative humidity to predict daily COVID-19 cases in low-income countries. The performance of the proposed multi-layer BiLSTM is evaluated by comparing its RMSE with the one from multi-layer LSTM (with the same settings as BiLSTM) in four developing countries namely Mozambique, Rwanda, Nepal, and Myanmar. The proposed multi-layer BiLSTM outperformed the multi-layer LSTM in all four countries. The proposed multi-layer BiLSTM was also evaluated by comparing its root mean-squared error (RMSE) with multi-layer LSTM models, ARIMA- and stacked LSTM-based models in eight countries, namely Italy, Turkey, Australia, Brazil, Canada, Egypt, Japan, and the UK. Finally, the proposed multi-layer BiLSTM model was evaluated at the city level by comparing its average relative error with the other four models, namely the LSTM-based model considering multi-layer architecture, Google Cloud Forecasting, the LSTM-based model with mobility data only, and the LSTM-based model with mobility, temperature, and relative humidity data for 7 periods (of 28 days each) in six highly populated regions in Japan, namely Tokyo, Aichi, Osaka, Hyogo, Kyoto, and Fukuoka. The proposed multi-layer BiLSTM model outperformed the multi-layer LSTM model and other previous models by up to 1.6 and 0.6 times in terms of RMSE and ARE, respectively. Therefore, the proposed model enables more accurate forecasting of COVID-19 cases and can support governments and health authorities in their decisions, mainly in developing countries with limited resources.

Multi-Hospital Management: Combining Vital Signs IoT Data and the Elasticity Technique to Support Healthcare 4.0

Smart cities can improve the quality of life of citizens by optimizing the utilization of resources. In an IoT-connected environment, people’s health can be constantly monitored, which can help identify medical problems before they become serious. However, overcrowded hospitals can lead to long waiting times for patients to receive treatment. The literature presents alternatives to address this problem by adjusting care capacity to demand. However, there is still a need for a solution that can adjust human resources in multiple healthcare settings, which is the reality of cities. This work introduces HealCity, a smart-city-focused model that can monitor patients’ use of healthcare settings and adapt the allocation of health professionals to meet their needs. HealCity uses vital signs (IoT) data in prediction techniques to anticipate when the demand for a given environment will exceed its capacity and suggests actions to allocate health professionals accordingly. Additionally, we introduce the concept of multilevel proactive human resources elasticity in smart cities, thus managing human resources at different levels of a smart city. An algorithm is also devised to automatically manage and identify the appropriate hospital for a possible future patient. Furthermore, some IoT deployment considerations are presented based on a hardware implementation for the proposed model. HealCity was evaluated with four hospital settings and obtained promising results: Compared to hospitals with rigid professional allocations, it reduced waiting time for care by up to 87.62%.

How do the 3D urban morphological characteristics spatiotemporally affect the urban thermal environment? A case study of San Antonio

Urban landscapes, characterized by intricate interactions within a three-dimensional (3D) framework, give rise to complex mechanisms that underlie the Surface Urban Heat Island phenomenon. While global case studies have emphasized the impact of 3D urban morphological components on urban thermal environments, conducting quantitative assessments to identify spatial and seasonal variations in these relationships poses significant challenges. Based on Lidar and land cover dataset, this study first investigated the 3D urban morphology characteristics across nine components, and then assessed their spatial and seasonal impact on the urban thermal environment through Geographically Weighted Regression (GWR). We found that Impervious Surface Fraction (ISF) has the greatest warming effect, whereas the Vegetation Coverage Fraction (VCF) exerts the strongest cooling influence on the thermal environment across all seasons. Building Height (BH) and Street Aspect Ratio (SAR) exhibit spatially varying cooling effects, while Building Number (BN) and Standard Deviation of Building Height (BH_SD) have minimal warming impacts. Additionally, the influence of BH on the thermal environment is significant primarily in areas characterized by higher building coverage. VCF has a stronger cooling effect in central regions with limited vegetation during spring and winter, but this effect diminishes compared to suburban ones in summer and autumn. These findings provide valuable insights into developing effective heat mitigation strategies at both city and community levels, ultimately fostering sustainable urban development.

Optimization of ecological network function and structure by coupling spatial operators and biomimetic intelligent algorithm

Ecological networks (ENs) can maintain regional ecological security and reconcile the contradiction between development and conservation. Optimization of ENs is crucial for promoting sustainable development and human well-being. Although various valuable methods have been explored to optimize ENs, collaborative optimization research on the EN function and structure through a quantitative simulation at the patch level is yet to be conducted. This study integrated spatial function and structural operators into a multi-type ant colony optimization model and constructed a high-performance land-use conversion framework for EN optimization. Results showed that: (1) the optimized ecological source exhibits heightened functionality and improved connectivity, resulting in a more stable and rational network structure; (2) the proposed ecological nodes emergence mechanism prevents fragmentation of local ENs and improves the overall structural integrity and resilience; and (3) the introduction of graphics processing unit (GPU) parallelization improves the efficiency of the model at city level by 13.49 times, resolving the paradox between the computational efficiency of high-precision land-use simulation and the construction of large-scale ENs. This study provides methodological support for patch-level landscape planning and reference for formulating reasonable ecological restoration strategies.

City civilization, employment creation and talent agglomeration: Empirical evidence from “National Civilized City” policy in China

China's rapid urbanization has intensified urban employment pressure. The National Civilized City (NCC) policy is a significant initiative in China aimed at promoting high-quality urban development. We utilize the NCC policy as a quasi-natural experiment and employ the staggered difference-in-difference method to empirically analyze the impact of urban civilization development on full employment and human capital level. The empirical results demonstrate that NCC policy significantly promotes urban entrepreneurship and employment creation, boosts labor demand of local enterprises, and enhances the human capital level. The positive effect on employment is significant in both talent-intensive and low- and medium-skilled intensive fields. In addition, heterogeneity tests show that this positive effect is more pronounced in low-tier cities and previously polluted cities. Finally, the mechanism analysis indicates that the NCC policy can enhance employment creation through investment promotion at the city level and by expanding production scale at the firm level. The NCC policy's impact on human capital level is supported by technological advancement and innovation emphasis mechanisms from the demand side, as well as talent agglomeration mechanism from the supply side. This study provides policy recommendations for local governments to leverage the achievements of civilized cities in promoting employment.

Potential and Influencing Factors of Urban Spatial Development under Natural Constraints: A Case Study of the Guangdong-Hong Kong-Macao Greater Bay Area

As urbanization in China progresses, urban spatial development is transitioning from rapid expansion to more intensive and compact growth. This study examined the role of physical geography and environmental factors in shaping the urban spatial development in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA). Based on the current natural conditions, we selected evaluation indices from topography, hydrogeology, climatic conditions, and natural disasters. These indices were used to create a carrying capacity and suitability evaluation system for development land under natural constraints. Finally, the spatial development potential of the city was finalized by taking into account the current state of the built-up area of the city. Meanwhile, we employed the Optimal Parameters-based Geographical Detector and assessed the impact of 14 natural factors on the spatial development of urban built-up areas. In 2020, the GBA had 52,168.77 km2 of land suitable for construction, of which 34,241.13 km2 was highly suitable (61.29%) and 17,927.64 km2 was moderately suitable (32.09%). At the Bay Area level, 90.15% of the development potential remains untapped; at the city level, Zhaoqing City has the highest potential at 99.56%, while Macao has the lowest at 26.83%. Key factors influencing urban development include silty sand content, annual average relative humidity, and cumulative temperature above 0 °C, with varying impacts across different urban scales. At the Bay Area level, the silty sand content, annual average relative humidity, and cumulative temperature above 0 °C are the main influencing factors on the spatial development of urban built-up areas; at the city level, the main factors are annual average relative humidity and cumulative active temperature above 0 °C. This study reveals the important influence of natural environmental factors on urban spatial development, which is conducive to promoting sustainable development of land resources in GBA.

What is the Evidence for Gun Buyback Programs?

Gun buyback programs are popular in the United States. By offering citizens of a city cash for their guns, it is thought that the number of crimes involving guns in that city will be reduced. Despite their popularity, previous research has shown that gun buyback programs often do not reduce crime, at least at the city level. For the first time, the source article examines the impact of these programs at a more local level. The findings indicate that gun buybacks in Philadelphia, PA, did not reduce gun crimes or gun-related calls for service in the immediate vicinity surrounding specific buyback locations. Policy makers may need to consider whether resources allocated to gun buyback programs might be better spent on more promising prevention efforts.

Biophilic design in Macedonia: Examples and proposals for greater implementation

Biophilic design enables connection between man and nature to bring the natural environment closer to users, using different patterns such as visual and non-visual connections with direct and indirect nature. Implemented on an individual building or at a city level, this concept has advantages in terms of health, improvement of the environment, and the economy for both occupants of buildings and urban settings. Moreover, biophilic design reduces stress, enhances creativity and clarity of thought, improves well-being, and expedites the healing of the users. The presence of natural motives in historic structures and places suggests that biophilic design is not a new concept. Several examples in Macedonia show some patterns of biophilic design. Unfortunately, contemporary architecture in Macedonia is mostly oriented towards quantity and financial benefits, while taking care of qualitative aspects, is to some level, neglected. In this paper, various examples of biophilic design in Macedonia are presented. In addition, the benefits of this concept, as well as the ways of its greater involvement in the architecture in our country, are presented and proposed

Influencing factors of industrial transformation in resource-based cities: Comprehensive measurement method based on resource price distortion

The rational allocation of industrial factors in resource-based cities is a necessary condition for promoting high-quality economic development. To systematically measure the mismatch of industrial factors, this study integrates the production function model and accounting framework of Aoki (2012). The capital, labor, and energy factors are integrated into the overall measurement framework, and the factor mismatch calculation model is innovatively constructed at the city level. To effectively reflect the factor mismatch of different cities, the relative distortion coefficient of factor price is introduced to establish the relationship between the distortion of factor-use cost and effectiveness of resource allocation. The degree and direction of factor mismatch are effectively measured. This study takes China's resource-based cities as the research object. To avoid the impact of the COVID-19 pandemic, data on 114 resource-based cities in China from 2003 to 2018 are selected, and 164 non-resource-based cities are used as the control group to evaluate the factors affecting the transformation of such cities. Regarding the diversity of resource-based cities in China, this study compares the characteristics of factor misallocation in these cities for different growth stages, geographical regions, and dominant resources. Finally, this study finds that on average, the cities' degree of energy misallocation is the most serious, and the degree of labor misallocation ranks second. The degree of capital misallocation ranks the lowest, but the misallocation of various factors is very different in different situations. Therefore, different types of cities should make detailed policy adjustments in various transformation directions according to the calculation results.

Barriers and opportunities (B&O) for developing low carbon transportation (LCT) policies in Turkey

From a global perspective, climate change mitigation requires countries to consider Low-Carbon Development (LCD) policies, including “Low-Carbon Transport (LCT)” and decarbonization options, which should be ideally developed within a place-based scope. Due to the complex structure of the transportation sector, mitigation actions require not only technological but also behavioral shifts, which can be challenged by the existing legislative and socio-economic structure of a region. Therefore, it is essential to identify and assess barriers and opportunities (B&Os) as a necessary initial phase in the development of national mitigation strategies. Turkey voluntarily started climate change mitigation by preparing the legislative and political infrastructure as a part of the European Union Harmonization Process. Detection of B&Os in other dimensions required an in-depth evaluation of existing LCT barriers and instruments (B&Is) in the IPCC literature in terms of their applicability and priority for Turkey. A focus group study with transport sector representatives was conducted to evaluate 15 barriers and 19 instruments (proposing 285 B&I matrix combinations), followed by a brain-writing exercise that revealed 12 main barriers (i.e., a lack of measurable targets, an imbalanced modal split, a lack of institutional coordination and data archiving, etc.). Various opportunities have been identified, such as the increased utilization of Intelligent Transportation Systems and the timely adoption of vehicle and fuel technologies in the EU region. These opportunities would not only contribute to the development of a national climate change mitigation strategy but also facilitate the implementation of place-based action plans, including climate change action plans and sustainable urban mobility plans at the city level.

Seismic Risk Model for the Beijing–Tianjin–Hebei Region, China: Considering Epistemic Uncertainty from the Seismic Hazard Models

This study presents a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China. The model comprises a township-level residential building exposure model, a vulnerability model derived from the Chinese building taxonomy, and a regional probabilistic seismic hazard model. The three components are integrated by a stochastic event-based method of the OpenQuake engine to assess the regional seismic risk in terms of average annual loss and exceedance probability curve at the city, province, and regional levels. The novelty and uniqueness of this study are that a probabilistic seismic risk model for the Beijing–Tianjin–Hebei region in China is developed by considering the impact of site conditions and epistemic uncertainty from the seismic hazard model.

Staffi ng of St. Petersburg industry through modernization of the vocational education system

A brief description of the state of industry in St. Petersburg is given on the basis of data from open statistical sources. The problem of personnel shortage of enterprises has been fixed, since a significant part of graduates of institutions of higher and secondary vocational education are employed in the service sector. The causes of the problem of staffing the industry of St. Petersburg with young specialists are identified, existing support measures at the city level are analyzed, and an idea is proposed aimed at overcoming the dysfunction between the ecosystem of vocational education and the demands of employers. The essence of the idea is the modernization of the vocational education system in industry, the results of its implementation and the roadmap up to 2030 are described.

Achieving Accessibility through Promoting Non-Motorized Transport within the Neighborhood Level of City: A Case Study of Ward 25 & 27, Bhubaneswar

Achieving Accessibility through Promoting Non-Motorized Transport within the Neighborhood Level of City: A Case Study of Ward 25 & 27, Bhubaneswar

Factors for a successful transition to the labor market for persons with disabilities: The perspective of special education teachers in Saudi Arabia

BACKGROUND: Despite legislation that promotes the transition of young people from school to work and the increasing knowledge of the factors that contribute to successful transitions, young people with disabilities face lower rates of participation in employment, education, and vocational training compared to their non-disabilities peers. OBJECTIVE: The paper aims to examine the perspective of special education teachers (SET) regarding the success factors of the transition to the labor market for persons with disabilities after graduating from secondary school in Riyadh, Saudi Arabia. METHODS: To conduct the research, we used a questionnaire; participants were sampled intentionally, and they were a representative sample at the level of Riyadh city (N = 180) of secondary school SET in the 2022/2023 school year. RESULTS: Educators have identified several factors critical to success in the labor market transition which can be separated into three categories; 1) person-centered factors, 2) family-specific factors, and 3) community instruction-specific factors. There were no statistically significant differences between the responses of participating SET according to their demographic characteristics (gender, educational level, and years of teaching experience) and the factors for successful labor market transition for persons with disability questionnaire. The results were interpreted in light of the theoretical framework and previous studies, and many recommendations were made for those that employ persons with disabilities. CONCLUSION: The findings highlight some of the areas that are critical to the labor market transition: employment and vocational skills’ training in secondary school, and families and community members’ attitudes and high expectations for the future of persons with disabilities.

Can COVID-19 herd immunity be achieved at a city level?

We propose a new approach to estimate the vaccination rates required to achieve herd immunity against SARS-COV2 virus at a city level. Based on information obtained from the Israeli Ministry of Health, we estimate two separate quadratic models, one for each dose of the BNT162b2 mRNA Pfizer vaccine. The dependent variable is the scope of morbidity, expressed as the number of cases per 10,000 persons. The independent variables are the first and second vaccination rates and their squares. The outcomes corroborate that herd immunity is achieved in the case that 71 percent of the urban population is vaccinated, and the minimum anticipated scope of morbidity is approximately 5 active COVID-19 cases per 10,000 persons, compared to 53-67 cases per 10,000 persons for zero vaccination rate. Findings emphasize the importance of vaccinations and demonstrate that urban herd immunity may be defined as a situation in which people continue to interact, yet the COVID-19 spread is contained. This, in turn, might prevent the need for lockdowns or other limitations at the city level.

Spatial analysis of digital economy and its driving factors: A case study of the Yangtze River Delta City Cluster in China.

The digital economy (DE) has become a major breakthrough in promoting industrial upgrading and an important engine for high-quality economic growth. However, most studies have neglected the important driving effect of regional economic and social (RES) development on DE. In this paper, we discuss the mechanism of RES development promoting the development of DE, and establish a demand-driven regional DE development model to express the general idea. With the help of spatial analysis toolbox in ArcGIS software, the spatial development characteristics of DE in the Yangtze River Delta City Cluster (YRDCC) is explored. We find the imbalance of spatial development is very significant in YRDCC, no matter at the provincial level or city level. Quantitative analysis reveals that less than 1% likelihood that the imbalanced or clustered pattern of DE development in YRDCC could be the result of random chance. Geographically weighted regression (GWR) analysis with publicly available dataset of YRDCC indicates RES development significantly promotes the development of DE.

The role of municipal ownership for urban net-zero path creation

Particularly within recent years, the rising number of city actors worldwide declaring climate emergencies and pledging commitments to carbon neutrality has sparked a spatial turn in broader sustainable transition thinking, noting the importance of the local level for governing sustainable transitions. Using a Geographical Political Economy approach, this paper critically engages with path creation theory and sustainable transitions literature to explore the potential of urban net-zero carbon transitions in a multi-scalar governance framework. The main argument is that municipal ownership across sectors has a fundamental role in sustainable transitions at the city level. This paper makes a distinct conceptual contribution to sustainable transitions literature by drawing on path creation theory to illustrate how municipal ownership is a central tenet of path creation by bringing together local actors and enabling political capacity and agency to control and strategize integrated sustainable urban pathways. Using the transport and energy sectors in Nottingham in the UK, it applies the Path Creation Framework in an urban setting to illustrate that municipal ownership stimulates a positive path creation through three main arguments. First, municipal ownership enables a positive embeddedness and historical legacy in the provision of sustainable urban energy and transport services; second, it facilitates the establishment of skills and expertise that positively reinforces urban political capacity for the pursuit of urban sustainability; and third, it stimulates the creation of innovative urban projects for sustainable and equitable pathways.

Association between ambient temperature and influenza prevalence: A nationwide time-series analysis in 201 Chinese cities from 2013 to 2018

BackgroundTemperature affects influenza transmission; however, currently, limited evidence exists about its effect in China at the national and city levels as well as how temperature can be integrated into influenza interventions. MethodsMeteorological, pollutant, and influenza data from 201 cities in mainland China between 2013 and 2018 were analyzed at both the city and national levels to investigate the relationship between temperature and influenza prevalence. We examined the impact of temperature on the time-varying reproduction number (Rt) using generalized additive quasi-Poisson regression models combined with the distributed lag nonlinear model. Threshold temperatures were determined for seven regions based on the early warning threshold of serious influenza outbreaks, set at Rt = 1.2. A multivariate random-effects meta-analysis was employed to assess region-specific associations. The excess risk (ER) index was defined to investigate the correlation between Rt and temperature, modified based on seasonal and regional characteristics. ResultsAt the national level and in the central, northern, northwestern, and southern regions, temperature was found to be negatively correlated with relative risk, whereas the shapes of the data curves for the eastern, southwestern, and northeastern regions were not well defined. Low temperatures had an observable effect on influenza prevalence; however, the effects of high temperatures were not obvious. At an Rt of 1.2, the threshold temperatures for reaching a warning for serious influenza outbreaks were − 24.3 °C in the northeastern region, 16.6 °C in the northwestern region, and between 1℃ and 10 °C in other regions. ConclusionThe study findings revealed that temperature had a varying effect on influenza transmission trends (Rt) across different regions in China. By identifying region-specific temperature thresholds at Rt = 1.2, more effective early warning systems for influenza outbreaks could be tailored. These findings emphasize the significance of the region-specific adaptation of influenza prevention and control measures.

Decomposition, decoupling and dynamic prediction of carbon emissions from city-level building operations: a case study of the Yangtze River Delta

Reducing carbon emissions in the building sector is crucial to achieving carbon peak and carbon neutrality goals. Identifying the influencing factors of carbon emissions from building operations at the city level and predicting carbon peaks is of great significance for developing locally adapted policies. This article examines the spatial and temporal trends of carbon emissions at the building operation stage in the Yangtze River Delta (YRD) region over the last 15 years. This article analyzes the driving factors at a city level using the GDIM method and evaluates the decoupling status of income growth and carbon emissions with the GDIM-based decoupling model. The peak time and peak value of operational carbon emissions in the YRD region are predicted through a combination of scenario analysis and the Monte Carlo method. The research shows that: (1) the total carbon emissions from the building operational stage in the YRD region increased by 163.63%, of residents’ disposable income and energy consumption contributed the most. (2) In terms of decoupling relationship between carbon emissions and income growth, rural residential buildings (RR) outperform urban residential buildings (UR) and public buildings (PB). (3) According to the dynamic simulation, the operational carbon emission in the YRD region is expected to peak at 498.18–504.83 Mt MtCO2 between 2030 and 2035. (4) The peak of PBs and URs in Nanjing, Suzhou and some third- and fourth-tier cities, and RRs in eastern and central cities in the YRD will occur at the latest, necessitating close monitoring. These findings can serve as a basis for developing scientific and reasonable building emission reduction policies in a multilevel governance context.