The Luojia 1–01 (LJ1-01) night lighting satellite's superior spatial information capture capability provides conditions for accurate assessment of regional wealth distribution inequality (RWDI) at a small scale. This paper evaluated the wealth Gini coefficient (WGC) of 2,853 counties and 31 provinces in mainland China to establish a comprehensive picture of inequalities at county-level regions in China as a whole, using data from LJ1-01 and the Suomi National Polar Orbiter Partnership Visible Infrared Imaging Radiometer Suite (NPP-VIIRS). The WGC values (LJ-Gini) calculated by the LJ1-01 data are always higher than those (NPP-Gini) based on NPP-VIIRS, and the mean of the ratio between them is 1.7. Compared with NPP-Gini, LJ-Gini showed sensitivity to low RWDI areas. The average county and provincial LJ-Gini are statistically consistent, 0.77 and 0.78; County LJ-Gini’s volatility is significantly higher than that of the provincial LJ-Gini, with standard deviations (SD) 0.13 and 0.096. The differences of RWDI in the regions within some provinces are more significant than in other provinces. For example, the SD of Tibet is 0.31, while all provinces' average SD is 0.13. In addition, this paper establishes a grading criterion based on the normal distribution abstracted from provincial LJ-Gini to reflect the corresponding relationship between the LJ-Gini value and the five inequality ranks. Totally, RWDI demonstrates heterogeneity at various spatial scales and regions, and it correlates negatively with economic development. The superior performance of LJ1-01 data in evaluating county-level RWDI demonstrates its potential to evaluate RWDI on a smaller scale, such as communities and streets.
Residential location choice is a crucial topic in transportation planning research since land use as well as residential land use can significantly affect a city's attractiveness for development and residence. Understanding the factors that influence households in their residential location choice is essential for policymakers to evaluate the effect of their decisions. In this study, the impact of transportation factors on the attractiveness of residential areas was investigated in Qazvin city, Iran, using the stated preference (SP) method and structural equation modeling (SEM). The results indicated that the type of housing and private house preference were significant factors influencing the residential location choice. Additionally, proximity to health centers, low pollution levels, and access to public transportation and taxi stations were the top priorities for residents when choosing a place to live. Notably, households with children in education had a greater emphasis on air pollution and the proximity to taxi stations, as these factors could affect their children's health and education. Overall, the findings suggested that transportation factors played a critical role in the residential location choice and that policymakers should prioritize public transportation and taxi services, as well as reduce pollution levels, to make residential areas more attractive and livable for Qazvin residents.
Gross Domestic Product (GDP) is significant for measuring the strength of national and global economies in urban profiling areas. GDP is significant because it provides information on the size and performance of an economy. The real GDP growth rate is frequently used to indicate the economy’s health. This paper proposes a new model called Pearson Correlation-Long Short-Term Memory-Recurrent Neural Network (PC-LSTM-RNN) for predicting GDP in urban profiling areas. Pearson correlation is used to select the important features strongly correlated with the target feature. This study employs two separate datasets, denoted as Dataset A and Dataset B. Dataset A comprises 227 instances and 20 features, with 70% utilized for training and 30% for testing purposes. On the other hand, Dataset B consists of 61 instances and 4 features, encompassing historical GDP growth data for India from 1961 to 2021. To enhance GDP prediction performance, we implement a parameter transfer approach, fine-tuning the parameters learned from Dataset A on Dataset B. Moreover, in this study, a preprocessing stage that includes median imputation and data normalization is performed. Mean Square Error, Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, Median Absolute Error, and determination coefficient (R2) evaluation metrics are utilized in this study to demonstrate the performance of the proposed model. The experimental results demonstrated that the proposed model gave better results than other regression models used in this study. Also, the results show that the proposed model achieved the highest results for R2, with 99.99%. This paper addresses a critical research gap in the domain of GDP prediction through artificial intelligence (AI) algorithms. While acknowledging the widespread application of such algorithms in forecasting GDP, the proposed model introduces distinctive advantages over existing approaches. Using PC-LSTM-RNN which achieves high R2 with minimum error rates.
Bangladesh is a disaster-prone area due to its geographic location, especially since it is affected by a tropical cyclone (TC) almost every year. TC causes severe damage to lives and livelihoods in this region of Bangladesh. TC prediction and monitoring are still based on the traditional statistical model. In general, the conventional statistical model has the limitation of not handling nonlinear datasets in a precious way. However, the country is gradually adopting modern technologies like artificial intelligence (AI), machine learning (ML), and Fourth Industrial Revolution (IR4) technology for disaster management. The purpose of this study is to identify the scope of adopting new technologies like machine learning and deep learning (DL) for cyclone prediction in countries like Bangladesh, which are cyclone-prone but have constraints on funds to invest in this field. To establish the idea, we examine the research work on the TC forecasting model used in the country from 2010 to 2022. This paper examines the TC forecasting model used to identify the scope of improvement in the current system based on AI and process a better cyclone prediction system using an AI-based model. This study intends to reveal the gaps in mainstream cyclone prediction methods and focus on cyclone prediction system improvement. Moreover, this work will summarize the current state of the TC prediction forecasting system in Bangladesh and how the incorporation of modern technology can increase its efficiency. Finally, as a final note, we conclude this paper with the answer of proximity to the proposal of including AI in cyclone detection and prediction systems. A workflow diagram to address cyclone prediction based on ML and DL has also been presented in this paper, which may augment the capacity of the Bangladesh Meteorological Department (BMD) in performing their responsibility. Moreover, some specific recommendations have been proposed to improve the cyclone prediction system in Bangladesh.
The interactive relation between transportation and urban spatial structure remains a significant yet challenging issue in transport engineering and urban planning. Most previous studies indicate that the coordination of transportation and urban structure is conducive to solve urban diseases and promote urban sustainable development. Grounded in the theory of city-region spatial structure, this study examines the spatiotemporal dynamics of urban spatial structure from 2006 to 2019 and investigates the impact of transportation on shaping urban spatial structure in prefecture-level cities in China using spatial Durbin model. Major findings include: first, the nighttime light remote sensing data is employed to characterize urban spatial structure with the mono-centricity index ranging from 0.26 to 0.48. The coastal cities tend to exhibit the polycentric structure, while the cities in western region often display the monocentric structure. Second, there is a gradual decline in mono-centricity structure in these cities. Spatial heterogeneity in urban spatial structure is evident in eastern, central, western and northeastern China. Third, transportation significantly and positively influences spatial structure, however, the impact varies across regions and city sizes. Finally, policy implications are proposed based on these findings, such as promoting the integrated land use-transportation development, implementing targeted regional policies, and enhancing land use spatial planning.
The COVID-19 pandemic has had profound adverse effects on public health and society, with increased mobility contributing to the spread of the virus and vulnerable populations, such as those with pre-existing health conditions, at a higher risk of COVID-19 mortality. However, the specific spatial and temporal impacts of health conditions and mobility on COVID-19 mortality have yet to be fully understood. In this study, we utilized the Geographical and Temporal Weighted Regression (GTWR) model to assess the influence of mobility and health-related factors on COVID-19 mortality in the United States. The model examined several significant factors, including demographic and health-related factors, and was compared with the Multiscale Geographically Weighted Regression (MGWR) model to evaluate its performance. Our findings from the GTWR model reveal that human mobility and health conditions have a significant spatial impact on COVID-19 mortality. Additionally, our study identified different patterns in the association between COVID-19 and the explanatory variables, providing insights to policymakers for effective decision-making.
The intensification of global heat wave events is seriously affecting residents' emotional health. Based on social media big data, our research explored the spatial pattern of residents' sentiments during heat waves (SDHW). Besides, their association with urban functional areas (UFAs) was analyzed using the Apriori algorithm of association rule mining. It was found that SDHW in Beijing were characterized by obvious spatial clustering, with hot spots predominately dispersed in urban areas and far suburbs, and cold spots mainly clustered in near suburbs. As for the associations with urban function areas, green space and park areas had significant effects on the positive sentiment in the study area, while a higher percentage of industrial areas had a greater impact on negative SDHW. When it comes to combined UFAs, our results revealed that the green space and park area combined with other functional areas was more closely related to positive SDHW, indicating the significance of promoting positive sentiment. Subdistricts with a lower percentage of residential and traffic areas may have a more negative sentiment. There were two main combined UFAs that have greater impacts on SDHW: the combination of residential and industrial areas, and the combination of residential and public areas. This study contributes to the understanding of improving community planning and governance when heat waves increase, building healthy cities, and enhancing urban emergency management.
Due to their importance in weather and climate assessments, there is significant interest to represent cities in numerical prediction models. However, getting high resolution multi-faceted data about a city has been a challenge. Further, even when the data were available the integration into a model is even more of a challenge due to the parametric needs, and the data volumes. Further, even if this is achieved, the cities themselves continually evolve rendering the data obsolete, thus necessitating a fast and repeatable data capture mechanism. We have shown that by using AI/graphics community advances we can create a seamless opportunity for high resolution models. Instead of assuming every physical and behavioral detail is sensed, a generative and procedural approach seeks to computationally infer a fully detailed 3D fit-for-purpose model of an urban space. We present a perspective building on recent success results of this generative approach applied to urban design and planning at different scales, for different components of the urban landscape, and related applications. The opportunities now possible with such a generative model for urban modeling open a wide range of opportunities as this becomes mainstream.
Severe weather events, such as heat waves, floods, pollution, and health threats, are becoming more common in metropolitan places across the world. Overcrowding, poor infrastructure, and fast, unsustainable urbanization are some of the problems that India faces, and the country is also susceptible to natural disasters. This research analyzes climatic variables affecting urban hazards in Bangalore (also known as Bengaluru) via a thorough review. Heat waves, urban floods, heat islands, and drought were identified in 156 qualifying publications using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method. Contributing variables were also considered. City development and urbanization were key to changing climate and increasing urban dangers. While long-term climatic variable distribution is uneven, warming is evident. The report promotes strong urban planning techniques, comprehensive policies, more green areas, and sustainable development beyond short-term heat response programs to boost urban climate resilience. This study shows how climate, land use, and urban dangers are interconnected. Future studies may benefit by categorizing urban risk studies and identifying climatic factors.
In the context of smart cities, ensuring road safety is crucial due to increasing urbanization and the interconnected nature of contemporary urban environments. Leveraging innovative technologies is essential to mitigate risks and create safer communities. Thus, there is a compelling imperative to develop advanced solutions to enhance road safety within smart city frameworks. In this article, we introduce a comprehensive vehicle safety framework tailored specifically for smart cities in the realm of Artificial Intelligence of Things (AIoT). This framework seamlessly integrates a variety of sensors, including eye blink, ultrasonic, and alcohol sensors, to bolster road safety. The utilization of eye blink sensor serves to promptly detect potential hazards, alerting drivers through audible cues and thereby enhancing safety on smart city roads. Moreover, ultrasonic sensors provide real time information about surrounding vehicle speeds, thereby facilitating smoother traffic flow. To address concerns related to alcohol consumption and its potential impact on road safety, our framework incorporates a specialized sensor that effectively monitors the driver’s alcohol levels. In instances of high alcohol content, the system utilizes GPS and GSM technology to automatically adjust the vehicle’s speed while simultaneously notifying pertinent authorities for prompt intervention. Additionally, our proposed system optimizes inter-vehicle communication in smart cities by leveraging Li-Fi technology, enabling faster and more efficient data transmission via visible light communication (VLC). The integration of Li-Fi enhances connectivity among connected vehicles, contributing to a more cohesive and intelligent urban transportation network. Through the structured integration of AIoT technologies, our framework lays a robust foundation for a safer, smarter, and more sustainable future in smart city transportation. It offers significant advancements in road safety and establishes the groundwork for further enhancement in intelligent urban transportation networks.
The Beijing-Tianjin-Hebei integration plan rose to the status of a national-level strategy in 2014. This paper provides a deep analysis of the Beijing-Tianjin-Hebei area’s inter-city commuter big data. This research analyzed the overview of spatial structure, polycentric structure, hierarchical structure and clustering characteristics of the BTH based on network analysis methods. It reveals that the inter-city commuter network exhibits clear polycentric characteristics, with Beijing acting as the central hub. The degree of network correlation between cities in Tianjin and Hebei is notably low, indicating that the flow of people primarily revolves around Beijing, while interactions between other cities remain limited. Therefore, it is necessary to further decentralize Beijing's non-capital core functions. The level of connectedness among the areas surrounding the Bohai Rim is not very high, and it has not developed the coastal advantage. The cooperation could be strengthed among the cities within Bohai Rim. The polycentric structure has initially taken shape, but it exhibits obvious polarization characteristics. It is necessary to strengthen the interaction of talents between cities to form secondary central units in BTH.
Information on the depth of floodwater is crucial for rapid mapping of areas affected by floods. However, previous approaches for estimating floodwater depth, including field surveys, remote sensing, and machine learning techniques, can be time-consuming and resource-intensive. This paper presents an automated and rapid approach for estimating floodwater depth from on-site flood photos. A pre-trained large multimodal model, Generative pre-trained transformers (GPT-4) Vision, was used specifically for estimating floodwater. The input data were flood photos that contained referenced objects, such as street signs, cars, people, and buildings. Using the heights of the common objects as references, the model returned the floodwater depth as the output. Results show that the proposed approach can rapidly provide a consistent and reliable estimation of floodwater depth from flood photos. Such rapid estimation is transformative in flood inundation mapping and assessing the severity of the flood in near-real time, which is essential for effective flood response strategies.
Urban areas face increasing pressure due to densification, presenting numerous challenges involving various stakeholders. The impact of densification on human well-being in existing urban areas can be both positive and negative, which requires a comprehensive understanding of its consequences. Computational Urban Design (CUD) emerges as a valuable tool in this context, offering rapid generation and evaluation of design solutions, although it currently lacks consideration for human perception in urban areas. This research addresses the challenge of incorporating human perception into computational urban design in the context of urban densification, and therefore demonstrates a complete process. Using Place Pulse 2.0 data and multinomial logit models, the study first quantifies the relationship between volumetric built elements and human perception (beauty, liveliness, and safety). The findings are then integrated into a Grasshopper-based CUD tool, enabling the optimization of parametric designs based on human perception criteria. The results show the potential of this approach. Finally, future research and development ideas are suggested based on the experiences and insights derived from this study.
The challenge of spatial resource allocation is pervasive across various domains such as transportation, industry, and daily life. As the scale of real-world issues continues to expand and demands for real-time solutions increase, traditional algorithms face significant computational pressures, struggling to achieve optimal efficiency and real-time capabilities. In recent years, with the escalating computational power of computers, the remarkable achievements of reinforcement learning in domains like Go and robotics have demonstrated its robust learning and sequential decision-making capabilities. Given these advancements, there has been a surge in novel methods employing reinforcement learning to tackle spatial resource allocation problems. These methods exhibit advantages such as rapid solution convergence and strong model generalization abilities, offering a new perspective on resolving spatial resource allocation problems. Despite the progress, reinforcement learning still faces hurdles when it comes to spatial resource allocation. There remains a gap in its ability to fully grasp the diversity and intricacy of real-world resources. The environmental models used in reinforcement learning may not always capture the spatial dynamics accurately. Moreover, in situations laden with strict and numerous constraints, reinforcement learning can sometimes fall short in offering feasible strategies. Consequently, this paper is dedicated to summarizing and reviewing current theoretical approaches and practical research that utilize reinforcement learning to address issues pertaining to spatial resource allocation. In addition, the paper accentuates several unresolved challenges that urgently necessitate future focus and exploration within this realm and proposes viable approaches for these challenges. This research furnishes valuable insights that may assist scholars in gaining a more nuanced understanding of the problems, opportunities, and potential directions concerning the application of reinforcement learning in spatial resource allocation.
The configuration of public open spaces plays a crucial role in shaping how different people use them. Nevertheless, our understanding of how the physical features of public open spaces influence the activities conducted within them, and the extent to which this impact differs across various individuals and population groups, is currently limited. In this study, we explore how the physical characteristics of public open spaces influence the likelihood of use among individuals, spanning different age and gender groups. By employing crowdsourcing, street-level imagery, statistical comparisons, and reflexive thematic analysis we uncover significant variations in the suitability of public open spaces for distinct activities, such as socializing or exercising. Greenspaces emerge as the preferred choice for almost all activities, whereas streets are consistently rated as the least suitable. Additionally, we identified various characteristics that influence the activities people are likely to engage in. These include the size of the space, the presence of seating, natural elements such as vegetation or water bodies, and the proximity to transport infrastructure. Surprisingly, we do not observe statistically significant differences in preferences among most age and gender groups. Overall, our study underscores the need for providing a diverse range of public open spaces tailored to accommodate different individuals, population groups, and activities.
Rapid increasing urbanization and resource scarcity are global phenomena nowadays, leading to the urban transformation of cities into smart cities. This article explores sustainability by using the lens of the spirit of place (SOP) for smart city development by proposing a model for the transformation of the cities into smart cities and attainment of the sustainable development simultaneously based on Interpretive Structure Modelling (ISM) and Analytic Hierarchy Process (AHP). This study followed a systematic approach by utilizing an analytical framework that included an extensive literature review and urban experts' opinions for the identification of a pool of indicators and its evaluation for validity, pilot testing, and administration of a questionnaire to a population sample. The study utilizes a sample of 142 participants who have witnessed the transformation of their city over the years. The research showed that every place has its own identity known to be the ‘spirit of place’ that helps in assessing the sustainable characteristics and utilizing that in the path of planning and development for the attainment of sustainable development. It also showed that urban developers should consider local populations’ views and important aspects in designing and planning development projects to achieve sustainable development with resilient infrastructure. This study will help facilitate sustainability at a local level for urban developers, planners, and decision-makers while crafting strategic plans.
This paper reviews a large number of scholarly articles in the housing field spanning the last thirty years, from 1993 to 2022 by implementing bibliometric analysis method. We examine scientific outputs, identify influential articles, journals, international collaboration and evolution of research trends. Keywords such as “Housing price,” “Housing policy,” “Affordable housing,” “Homeownership,” “Housing market,” “Urban planning,” and “Neighborhood” have been identified as the most prevalently cited terms during this period. Furthermore, the prominence of terms such as “China,” “Gentrification,” “Public housing,” “Social housing,” “Homelessness,” “Migration,” “Urbanization,” “Energy,” “Inequality,” “Land use,” “Gender,” and “Foreclosure” have grown in importance, pointing to future research trends. The analysis also reveals that articles pertaining to the COVID-19 pandemic predominantly address the comprehensive effects of the virus on aspects of mental and physical health, consumer behavior, and economic and societal challenges.
In this paper, I advocate for a radical expansion of computational urban science to encompass a multidisciplinary, human-centered approach, addressing the inadequacies of traditional methodologies in capturing the complexities of urban life. Building on insights from Jane M. Jacobs and others, I argue that integrating computational tools with disciplines like sociology, anthropology, and urban planning can significantly enhance our understanding and management of urban environments. This integration facilitates a deeper analysis of cultural phenomena, improves urban policy design, and promotes more sustainable, inclusive urban development. By embracing qualitative research methods—such as ethnography and participatory observations—alongside computational analysis, I highlight the importance of capturing the nuanced social fabrics and subjective experiences that define urban areas. I also stress the necessity of including community stakeholders in the research process to ensure that urban science not only analyzes but also improves the lived experiences of urban populations. Furthermore, I underscore the need for ethical governance and the mitigation of biases inherent in computational tools, proposing rigorous model auditing and the inclusion of diverse perspectives in model development. Overall, this work champions a holistic approach to urban science, aiming to make cities smarter, more equitable, and responsive to their residents’ needs.
The outbreak of the COVID-19 Omicron variant in Shanghai in 2022 elicited complex emotions among Shanghainese during the two-month quarantine period. This paper aims to identify prevailing public themes and sentiments by analyzing social media posts from Weibo. Initially, we conducted research based on a dataset of 90,000 Weibo posts during the 2022 COVID-19 outbreak in Shanghai. By examining social media data that mirrors residents' emotional shifts and areas of focus during unforeseen circumstances, we have developed an analytical framework combining hotspot analysis and public sentiment assessment. Subsequently, we employed the Latent Dirichlet Allocation (LDA) method to conduct topic modeling on the Weibo text data. The SnowNLP sentiment classification method was then utilized to quantify sentiment values. Ultimately, we performed spatial visualization of sentiment and concern data, categorizing them into distinct time periods based on Shanghai's infection curve. This approach allowed us to investigate concern focal points, sentiment trends, and their spatiotemporal evolution characteristics. Our findings indicate that variations in public sentiment primarily hinge on the severity of the epidemic's spread, emerging events, the availability of essential resources, and the government's ability to respond promptly and accurately. It is evident that, while residents' concerns shift over time, their primary objective on social media remains expressing demands and releasing emotions. This research offers an avenue for leveraging public opinion analysis to enhance governance capacity during crises, fortify urban resilience, and promote public involvement in governmental decision-making processes.
The spatiotemporal mobility patterns and next location prediction of fake base stations (FBS) provide important technical support for the police to prevent spam messages from FBS. However, due to the difficulty in locating their real-time locations, our understanding of the mobility patterns and predictability of FBS is still limited. Based on the crowdsourced spam data, we extract the time and potential locations of FBS and propose a Tucker-MMC method that combines Tucker decomposition with a Mobility Markov Chain (MMC) model to investigate the mobility patterns and predictability of FBS sending spam messages. First, we utilize Tucker decomposition to reflect the spatial and temporal preferences during the movement of the corresponding FBS. Then the mobility regularity and the theoretical maximum predictability of the FBS trajectories with similar mobility preferences are analyzed by entropy and Fano's inequality. A Tucker-MMC is also established for the next location prediction. The results using the spam dataset in Beijing show that the accuracy of Tucker-MMC is more than double that of the MMC. The accuracy of the actual location prediction model is more likely to approach the theoretical maximum predictability when FBS send spam messages in a shorter time, shorter transfer distance, and smaller access range.
The COVID-19 pandemic brought unprecedented changes to various aspects of daily life, profoundly affecting human mobility. These changes in mobility patterns were not uniform, as numerous factors, including public health measures, socioeconomic status, and urban infrastructure, influenced them. This study examines human mobility changes during COVID-19 in San Diego County and New York City, employing Latent Profile Analysis (LPA) and various network measures to analyze connectivity and socioeconomic status (SES) within these regions. While many COVID-19 and mobility studies have revealed overall reductions in mobility or changes in mobility patterns, they often fail to specify ’where’ these changes occur and lack a detailed understanding of the relationship between SES and mobility changes. This creates a significant research gap in understanding the spatial and socioeconomic dimensions of mobility changes during the pandemic. This study aims to address this gap by providing a comprehensive analysis of how mobility patterns varied across different socioeconomic groups during the pandemic. By comparing mobility patterns before and during the pandemic, we aim to shed light on how this unprecedented event impacted different communities. Our research contributes to the literature by employing network science to examine COVID-19’s impact on human mobility, integrating SES variables into the analysis of mobility networks. This approach provides a detailed understanding of how social and economic factors influence movement patterns and urban connectivity, highlighting disparities in mobility and access across different socioeconomic groups. The results identify areas functioning as hubs or bridges and illustrate how these roles changed during COVID-19, revealing existing societal inequalities. Specifically, we observed that urban parks and rural areas with national parks became significant mobility hubs during the pandemic, while affluent areas with high educational attainment saw a decline in centrality measures, indicating a shift in urban mobility dynamics and exacerbating pre-existing socioeconomic disparities.
Bus Rapid Transit (BRT) proves its effectiveness in alleviating traffic congestion, especially in urban areas. The implementation of Transit Signal Priority (TSP) for BRT has shown a significant reduction in delays. However, in densely populated urban areas, this priority can inadvertently cause additional delays for other modes of transportation. In this paper, we propose a control strategy for Regionally Coordinating Bus Priority Signals Control (RCBPSC) at urban intersections. The aim is not only to reduce bus delays but also to consider minimizing delays for pedestrians and other vehicles. To achieve this, we modeled two consecutive intersections along the Amman BRT. Essentially, we evaluated three different control scenarios in addition to the current base scenario. These scenarios include adaptive traffic signal control, RCBPSC with no signal timing optimization, and RCBPSC with signal optimization. Simulation results indicate that the adaptive traffic signal timing has the worst operational performance in terms of average delay and Level of Service (LOS) compared to the base scenario. Additionally, the results show that BRT delays significantly decrease at both intersections when we implement RCBPSC scenarios. When implementing RCBPSC with optimization scenarios, the results show an average reduction of more than 60% in intersection delay, a decrease in emissions of more than 50%, and an improved LOS for system users compared to the base scenario. The findings of this work can help agencies improve the current operational condition of BRT when implementing RCBPSC.
Infectious diseases usually originate from a specific location within a city. Due to the heterogenous distribution of population and public facilities, and the structural heterogeneity of human mobility network embedded in space, infectious diseases break out at different locations would cause different transmission risk and control difficulty. This study aims to investigate the impact of initial outbreak locations on the risk of spatiotemporal transmission and reveal the driving force behind high-risk outbreak locations. First, we built a SLIR (susceptible-latent-infectious-removed)-based age-stratified meta-population model, integrating mobile phone location data, to simulate the spreading process of an infectious disease across fine-grained intra-urban regions (i.e., 649 communities of Shenzhen City, China). Based on the simulation model, we evaluated the transmission risk caused by different initial outbreak locations by proposing three indexes including the number of infected cases (CaseNum), the number of affected regions (RegionNum), and the spatial diffusion range (SpatialRange). Finally, we investigated the contribution of different influential factors to the transmission risk via machine learning models. Results indicate that different initial outbreak locations would cause similar CaseNum but different RegionNum and SpatialRange. To avoid the epidemic spread quickly to more regions, it is necessary to prevent epidemic breaking out in locations with high population-mobility flow density. While to avoid epidemic spread to larger spatial range, remote regions with long daily trip distance of residents need attention. Those findings can help understand the transmission risk and driving force of initial outbreak locations within cities and make precise prevention and control strategies in advance.
Rapid replacement of vegetated land with impermeable land (built-up areas) is a major factor in the increase in Land Surface Temperature (LST), while increased LST worsens the temperature in cities and creates the Surface Urban Heat Island (SUHI) effect. The study aims to measure vegetation loss and Land Surface Temperature of the Gazipur district between 2000 and 2020 and explore the relationship among Normalized Difference Vegetation Index (NDVI), LST, and Urban Thermal Field Variance Index (UTFVI). The Landsat TM/OLI images with minimum cloud coverage have been used to derive different indices. The mean NDVI values are 0.21, 0.16, and 0.22 in 2000, 2010, and 2020 respectively which indicates a general improvement in the health of the vegetation. Besides, the highest LST values throughout 20 years, represent a general increasing trend. As a consequence, different land covers have experienced fluctuations in mean temperature. The result shows that the mean temperature of bare land, buildup, vegetation, and waterbody has increased by 4.77, 2.01, 2.25, and 2.23 °C respectively from 2000 to 2020. The strongest SUHI zone’s area grew by about 28% between 2000 and 2020. Additionally, the highest index value of UTFVI was 0.39 in 2000 and grew to 0.43 in 2010. It changed to 0.49 in 2020, or ten years later. Thus, the SUHI effect’s increasing intensity is visible. Also, regression analysis has been used to explore the correlation between the derived indices. Stakeholders from different sectors like urban planners and policymakers may take insights from this study to work to promote greenery for a healthy urban environment.
The middle and lower reaches of the Yangtze River are frequently affected by the Western Pacific Subtropical High (WPSH) in summer. This leads to phenomena including air subsidence, high temperatures, low rainfall, and weak winds, all of which affect the urban heat island (UHI) effect. Currently, there are few studies on the influence of WPSH on the UHI effect. In this study, we analysed the temporal and spatial distributions of the influence of WPSH on the UHI effect by establishing two scenarios: with and without WPSH. We calculated the UHI intensity and the urban heat island proportion index (UHPI) to analyse the temporal and spatial distributions of the UHI effect. The geographical detector method was then used to analyse the factors influencing UHI. The results indicate the strong heat island effect during the day in provincial capitals and some developed cities. The area of high UHI intensity was larger under the influence of WPSH than in the years without WPSH. WPSH affected UHPI at both day and night, although the effect was more pronounced at night. The factors affecting daytime UHI intensity are mainly POP and NTL, O3 plays a large role in the years with WPSH control. The main factors affecting the UHI intensity at night are AOD, POP and NTL were mainly factors in the years without WPSH control, POP and WPSH were mainly factors in the years with WPSH control. The interactions of the factors are mainly POP and multi-factors during the daytime, and DEM and multi-factors during the nighttime. It was found that the UHI intensity was enhanced under the control of the WPSH, and the influencing factors of the diurnal UHI differed with and without the WPSH control, which ultimately provides realistic suggestions for mitigating the intensity of the UHI in areas affected by the WPSH.
Streets are an important component of urban public spaces and also a high-incidence area for urban crime. However, current research mainly involves adult crime, or fails to distinguish between adult and juvenile crime, which poses a severe challenge to the prevention of juvenile delinquency. Juveniles have lower self-control abilities and are more likely to be influenced by external environmental factors to trigger criminal behavior compared to adults. Therefore, this study uses New York’s Manhattan district as an example, based on CPTED and social disorganization theories, and utilizes street view data and deep learning techniques to extract street environment indicators. The GWR model is used to explore the influence mechanism of urban street environment on juvenile crime. The results of this study, considering spatial heterogeneity, demonstrate the impact of various physical environmental indicators of urban streets on juvenile delinquency, and reveal that some street indicators have differentiated effects on crime in different areas of the city. Overall, our research helps to uncover the relationship between juvenile delinquency and the built environment of streets in complex urban settings, providing important references for future urban street design and juvenile delinquency prevention.
This paper proposes a framework to examine how neighborhood factors influence criminal justice (CJ) contact and contribute to disparities across multiple stages of the justice process. By conceptualizing the punishment process as a dynamic set of decision-making points, this study highlights the role of neighborhood context in shaping offenders’ CJ trajectories and post-CJ residential inequality. Using Harris County, Texas, as a case study, this research considers individual-, neighborhood-, and event-level variables to understand the cumulative effects of neighborhood characteristics on CJ outcomes. This study underscores the critical need to investigate neighborhood mobility and its broader implications for community development and public policy. The findings can be supported by extensive data from the Federal Statistical Research Data Centers and the Criminal Justice Administrative Records System, offering a robust analysis of offenders’ spatial patterns and economic transitions.
Urban settings require a thorough understanding of traffic patterns to best manage traffic, be prepared for emergency scenarios and to guide future infrastructure investments. In addition to analyzing collected traffic data, traffic modeling is an important tool that often requires detailed simulations that can be computationally intensive and time-consuming. A well-known comprehensive simulation framework is MATSim. On the other hand, simpler shortest-path routing systems that compute trips on an individual basis promise faster computations. The primary focus of this study is to assess the viability of a fast shortest path routing system as a method of traffic simulation. This study compares the MATSim with the Graphhopper routing system. Key metrics include travel time accuracy, congestion levels, route similarity, vehicle miles traveled, and average travel time. By analyzing these metrics, this study shows that a shortest-path routing system can serve as an effective and expedient approximation of more resource intensive simulation frameworks. This has significant implications for authorities and planners, as it offers a quick and efficient tool for traffic management and decision-making during critical events, enhancing their ability to respond quickly and effectively to dynamic traffic conditions.
Urban 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.
The rapid growth of marginal settlements in the Global South, largely fueled by the resettlement of millions of internally displaced people (IDPs), underscores the urgent need for tailored housing solutions for these vulnerable populations. However, prevailing approaches have often relied on a one-size-fits-all model, overlooking the diverse socio-spatial realities of IDP communities. Drawing on a case study in Medellin, Colombia, where a significant portion of the population consists of forced migrants, this interdisciplinary study merges concepts from human geography and urban theory with computational methods in remote sensing and exploratory spatial data analysis. By integrating socio-spatial theory with quantitative analysis, we challenge the conventional housing paradigm and propose a novel framework for addressing the housing needs of IDPs. Employing a three-phase methodology rooted in Lefebvre’s theoretical framework on the production of space, including participatory mapping, urban morphology characterization, and similarity analysis, we identify distinct patterns within urban IDP settlements and advocate for culturally sensitive housing policies. Our analysis, focusing on Colombia, the country with the largest IDP population globally, reveals the limitations of standardized approaches and highlights the importance of recognizing and accommodating socio-cultural diversity in urban planning. By contesting standardized socio-spatial practices, our research aims not only to promote equality but also to foster recognition and inclusivity within marginalized communities.
Sustainable transportation is vital to climate justice and social equity. Despite the efforts to achieve sustainability, there is still a lack of adequate measurement that integrates land use and transportation systems, which can be barriers to planning implementation. With methodological improvements in fuzzy theory application, this study develops an integrated index to measure the sustainability of multimodal accessibility. We do so by defining a fuzziness degree based on the different trip purposes and modes of transportation with a case study in Isfahan, Iran. Sustainable accessibility indicators were developed for walking, biking, and public transportation to represent the performance of each transportation system, considering the integration with land-use patterns. We analyze transportation modes and the accessibility to five main urban activities, including employment opportunities, education, healthcare, shopping, and recreation services, based on the travel distances, followed by a statistical integration method with Principal Components Analysis (PCA) for each travel mode. The outcome provides insights for urban planners and transportation planners to effectively evaluate the degree of integration between transportation and land-use systems and contribute to enhancing sustainable accessibility.
The Urban Productive Safety Net Program is one of Africa’s most ambitious social protection initiatives, and it has achieved measurable successes. However, existing literature focusing on the role of programs in improving people’s lives, especially in ensuring food security and income diversification, gives more focus to rural areas and depends on data from individuals beyond those directly targeted by the program. Hence, this study examines beneficiaries’ perceptions of the program’s contribution to food security and livelihood diversification in the South Ethiopia Regional State. The study used a mixed research approach that included a questionnaire, interviews, observation, and focus group discussions. To select 310 survey household heads, a multistage sampling procedure was employed. We analyzed the quantitative data using SPSS version 27, while the qualitative data was analyzed through narration and summarization. The findings indicate that the selection process for beneficiaries, activities performed by public work groups, and beneficiaries graduating with reliable sources of income are generally positive, whereas negative assumptions exist regarding the adequacy and timeliness of cash transfers and the overall living status of residents. Therefore, the program managers and zonal-level team leaders should collaborate closely to directly engage with beneficiaries, monitor the support system, and raise awareness.
This study explores the complex interconnections among global population growth, energy consumption, CO2 production, and disparities in service access through the lens of a single case study. Rapid population growth in many major cities has created significant challenges related to equitable access to services and socio-economic development, thereby impacting both their energy consumption patterns and environmental impacts. The case investigated in this study, like many other cases in developing countries, exhibits differences in service provision, infrastructure development, and energy usage, particularly between the northern and southern regions, which significantly affect the quality of life, environmental sustainability, and economic development. Previous efforts to narrow these geographic disparities have yielded limited success and exhibited several shortcomings. By employing a GIS Analytical Network Process method, this study examines service accessibility patterns in a single-case city, with a particular emphasis on green spaces, food services, and educational facilities and services. This GIS-based approach seeks to achieve sustainable levels of access to multiple land uses by evaluating their accessibility and identifying areas of overlap between them. The study endeavors to increase access and density of service standards when planning the placement of new facilities based on these standards in new locations. The method developed in this study represents a critical stride toward achieving these key objectives. The findings reveal that only 47% of city population blocks enjoy high service accessibility, while 40% have moderate accessibility, and 2.6% experience poor accessibility. These insights are of significant value to urban planners, researchers, and policymakers striving to reduce energy shortages and promote sustainable energy and transportation strategies to mitigate environmental impact in urban areas.
With the rapid development of indoor Location Based Services (LBS), a growing volume of textual data is being generated in indoor environments. Consequently, indoor spatial keyword query holds significant potential for development in the coming years. However, existing methods for indoor spatial keyword queries often neglect the personalized needs of users. To solve this problem, we propose an Indoor Spatial Keyword Personalized Query (ISKPQ) method. First, a novel index structure called ISKIR-tree has been designed. This index integrates Hilbert encoding techniques and introduces Bloom filters and distance matrices to enhance the efficiency of processing indoor spatial keyword problems. Subsequently, an efficient pruning algorithm based on the ISKIR-tree index is proposed to refine the dataset effectively. Finally, a comprehensive scoring function that considers text similarity, spatial proximity, and user preferences is introduced to score and rank the pruned data points, thereby filtering out the optimal query results that meet users’ personalized requirements. Theoretical analysis and experimental studies demonstrate the outstanding performance of the proposed method in terms of both efficiency and accuracy.
China’s economic growth is increasingly being driven by the contemporary service industry in the context of a new economy. This study aims to examine the spatial heterogeneous relationship between various service industry activities and street network design configurations by integrating multisource big data and geospatial analysis to provide insightful implications for human-centered design for compact cities by taking the case study of an inland megacity in central China, Wuhan. Street configurations under the walking/driving modes including closeness, betweenness, severance and efficiency, are characterized from the perspective of spatial design network analysis and angular distance to effectively reflect network shapes and subjective perceptions when navigating through the streets. The point-like, point-axis and ring patterns of various service activities are identified using the kernel density estimation (KDE). Then two sets of densities are analyzed to investigate whether various service activities are spatially associated with specific street metrics and whether spatial stratified heterogeneity exists. The results show that severance and efficiency are two promising indicators to represent the human-scale street design besides the conventional street centrality indices. The spatial mismatch is mainly observed between street metrics and the tourism sector whereas spatial clusters are detected in other types of service activities. Diverse service activities have distinct location preferences for street designs under different transport modes. The walking mode values global closeness and betweenness, while the driving mode values severance and efficiency.
Point-of-interest (POI) is a fundamental data type of maps. Anomalous POIs would make maps outdated and lead to user-unfriendly location-based services, and thus should be discovered as fast as possible. Traditional POI anomaly detection methods are inefficient owing to high investigation costs. The emergence of massive human activity data provides a new insight into monitoring POI states through time series modeling. When a POI turns into an anomaly, the associated human activity would disappear. However, human activity data have complicated temporal patterns and noises. It is challenging for existing time series methods to model human activity dynamics. More importantly, there is a lag between the time a POI becomes anomalous and the time we discover it. In this research, we develop a temporal state regression network (TSRNet) model for fast POI anomaly detection. The model can extract temporal features in human activity data, and predict POI state scores as anomaly indicators. Meanwhile, an inference approach is proposed to generate state score sequences as inexact labels for model training. Such weak labels enable TSRNet to identify abnormal temporal patterns as soon as they appear, so that POI outliers can be detected at an early time. Experiments on real-word datasets from AMAP validate the feasibility of our method.
In modern times, cities around the world have grappled with the challenges of racial and ethnic segregation. In Nairobi city, with its diverse ethnic makeup, there is widening inequalities and emerging patterns of ethnic segregation, where the five main ethnic groups - Kamba, Luo, Kikuyu, Luhyia, and Kisii - experience varying levels of spatial concentration. This study analysed the spatial patterns of ethnic segregation in Nairobi, using geocoded questionnaire data from the 2019 Kenya population and housing census data. We used the Index of Dissimilarity in STATA software and Geo-segregation Analyzer and Anselin’s Local Moran I method in GIS to map ethnic segregation patterns. Our findings uncovered a striking socio-spatial divide based on ethnicity. Anselin Local Moran’s I indicators further pinpointed areas with the highest levels of segregation and spatial clustering of specific ethnic groups. These findings offer crucial insights for urban planners and policymakers. By pinpointing areas experiencing the most severe spatial segregation, our research could inform spatially targeted interventions and resource allocation. This could inform policies that foster inclusivity, reduce spatial inequalities, and build a more equitable and socially cohesive city.
The imbalance between supply and demand is a pressing issue in the development of the tourism industry. Understanding the coupling coordination relationship and impact mechanism of supply-demand in the tourism system can help achieve high-quality tourism development. This study focuses on the Yangtze River Economic Belt (YREB) as the research area, quantifies the tourism supply index (TSI) and tourism demand index (TDI) from 2011 to 2020 using the tourism development index model, calculates the coupling coordination degree (CCD) of TSI and TDI based on the coupling coordination model, and explores the factors influencing the CCD using the geographic detector. The findings indicate that: (1) The TSI and TDI in various provinces show fluctuation but exhibit an overall upward trend. (2) There were apparent spatial disparities of the CCD, with a distribution characteristic of high in the east and low in other regions. The CCD gradually improved, with its gravity center slowly shifting toward the southwest. All provinces entered the intermediate stage of tourism development since 2014. (3) Basic service guarantee, consumption drive, and innovation drive are identified as the dominant factors influencing the CCD. The study can provide valuable insights for tourism coordination and sustainable development.
This study explores the future of Urban Digital Twin (UDT) in urban planning systems of developing countries, with a focus on Iran. Despite UDT's growing popularity, its implementation in developing countries is limited. The research identifies critical factors influencing UDT development, including organisational acceptance, urban infrastructure, policy and legislation, and technology and innovation. Using a futures studies approach, the study employs the Delphi method, MICMAC (Matrix Impact Cross-Reference Multiplication Applied to a Classification) technique, and SISMW (Strategic Uncertainties and Strengths Weaknesses Opportunities and Threats Matrix) methodologies to analyse these factors. The study reveals that international sanctions, organisational factors, technological factors, and infrastructure limitations hinder UDT development in Iran. However, UDT technology has the potential to transform urban planning in developing countries. The study provides a roadmap for collaboration between public and private sectors and research institutes to facilitate UDT implementation, highlighting the importance of legislative frameworks, digital infrastructure, innovation, and stakeholder engagement. Policy implications suggest that governments should prioritise supportive policies, investments in digital infrastructure, and collaborative efforts to address data privacy, security, and ownership issues. By addressing these challenges, developing countries can leverage UDT technology to improve urban planning, resource management, and quality of life.
This paper provides a baseline understanding on the anatomy of car-based ride-hailing (CBRH) and motorcycle-based ride-hailing (MBRH) trips in emerging economies, using the case of the Jakarta Metropolitan Area (JMA). Leveraging innovative urban data collection technologies, as manifested in an app-based travel survey with high granularity, this study unravels the spatial patterns of ride-hailing trips, trip-level characteristics (purpose, distance, time of day, duration), and their interaction with other modes, particularly transit. Based on recorded ride-hailing trips and a suite of descriptive analyses, findings suggest that: 1) ride-hailing is primarily a central city phenomenon, with most trips occurring to and from dense and spatially mixed neighborhoods; 2) there are substantial differences in trip characteristics between CBRH and MBRH; and 3) a predominant share of ride-hailing trips are stand-alone trips, coupled with insights that nearly 40% of ride-hailing trips likely fill the gap where quality transit services are lacking.
Outdoor jogging is increasingly recognized as a crucial component of urban active transport strategies aimed at improving public health. Despite growing research on the influence of both natural and built environmental factors on outdoor jogging, less is known about the relative importance of these factors. Moreover, the spatial heterogeneity effects of environmental factors remain unclear. Failing to consider these varying effects regarding impact intensity and spatial scale results in inefficient planning policies aimed at promoting active transport. This study addresses these gaps by analyzing crowdsourced jogging trajectory data in Shenzhen using a computational framework that combines Random Forest Variable Importance (RF-VI) and Multi-Scale Geographically Weighted Regression (MGWR). The analysis identifies hierarchical environmental effects and the varying impacts of twelve key determinants across different spatial scales. Results reveal that natural environmental factors are most contributing to outdoor jogging, while density-related built environment factors contribute the least. Additionally, environmental effects vary in scale, direction, and intensity, with seven variables exerting global impacts and five showing localized effects. Notably, the central and suburban areas of Shenzhen display considerable spatial heterogeneity in environmental influences. The findings inform the importance of integrating green infrastructure, mitigating over-dense urban development, and enhancing pedestrian-accessible road networks to promote outdoor jogging. These insights advocate for context-sensitive urban planning that balances natural and built environments to to foster healthier mobility.