A 3-DIMENSIONAL DATA MODEL FOR VISUALIZING CLOVERLEAF JUNCTION IN A CITY MODEL

The research work has been seldom done about cloverleaf junction expression in a 3-dimensional city model (3DCM). The main reason is that the cloverleaf junction is often in a complex and enormous construction. Its main body is bestraddle in air,and has aerial intersections between its parts. This complex feature made cloverleaf junction quite different from buildings and terrain, therefore, it is difficult to express this kind of spatial objects in the same way as for buildings and terrain. In this paper,authors analyze spatial characteristics of cloverleaf junction, propose an all-constraint points TIN algorithm to partition cloverleaf junction road surface, and develop a method to visualize cloverleaf junction road surface using TIN. In order to manage cloverleaf junction data efficiently, the authors also analyzed the mechanism of 3DCM data management, extended BLOB type in relational database, and combined R-tree index to manage 3D spatial data. Based on this extension, an appropriate data

Research on City Style Perception Based on Multi-source Data

City style is the characteristics of the city formed under the influence of natural geography,social economy,human history and other factors in the development process of the city.In the information age,the operation and development of cities are deeply affected.Technical platforms such as social networks,city data,and street view maps cover all levels of the city.The resulting multi-source data provided new ideas and methods for urban landscape research.The article pointed out through the study of urban landscape that the strong coupling between urban landscape and multi-source data was a very promising multi-field cross-over study.Finally,multi-source city data,using traditional data,urban POI data,urban street scene pictures,and Weibo sign-in data,were explored to conduct perceptual research on the overall urban style,urban spatial pattern,urban architectural style and urban humanistic emotions,and construct a framework for urban style perception driven by multi-source data.

Multi-source Data-driven Identification of Urban Functional Areas:A Case of Shenyang,China

Urban functional area(UFA)is a core scientific issue affecting urban sustainability.The current knowledge gap is mainly reflected in the lack of multi-scale quantitative interpretation methods from the perspective of human-land interaction.In this paper,based on multi-source big data include 250 m×250 m resolution cell phone data,1.81×105 Points of Interest(POI)data and administrative boundary data,we built a UFA identification method and demonstrated empirically in Shenyang City,China.We argue that the method we built can effectively identify multi-scale multi-type UFAs based on human activity and further reveal the spatial correlation between urban facilities and human activity.The empirical study suggests that the employment functional zones in Shenyang City are more concentrated in central cities than other single functional zones.There are more mix functional areas in the central city areas,while the planned industrial new cities need to develop comprehensive functions in Shenyang.UFAs have scale effects and human-land interaction patterns.We suggest that city decision makers should apply multi-sources big data to measure urban functional service in a more refined manner from a supply-demand perspective.

Study on Residential Water Use in North China:Analysis of Nationwide Statistical Data and Case Study of Taiyuan City

Residential water use is gradually becoming the focus in China's municipal water supply planning and management in recent years.Little is known,however,about the residential water use in modern China due to the transition of economy and enhancement of management on water conservation.In order to better understand the characteristics of residential water use in North China,a model for identifying the determinants of residential water use was established and analyzed by using panel data and cross-section data methodologies.Then Taiyuan city,the capital city of Shanxi Province in Northern China was selected as a case study.Both the analyses and field investigation indicate that the relatively slow increase of residential water use in recent years may result from the implementation of strict laws and regulations on water conservation.And through the investigation, first-hand information about water consumption pattern,water reuse/conservation,people's attitude toward water quantity and quality,etc.have been obtained.

On the Influence Factors of Second-hand House Prices in First-tier Cities——Empirical Analysis Based on Panel Data

With the arrival of the ＂housing stock＂ in first - tier cities, the second - handhousing^market will become the dominant property market. This ardcle aim to the first - tiercities of second - hand housing prices and new home price index for the empirical analysis, thedata related to the cointegration analysis found that the result of the first -tier cities real estatemarket in China, the new home price index is the significant factors influencing the second -hand house price indexi For Beijing, Shanghai second - hand housing and new home price in-dex time series johans test, found that there exists cointegration relationship between two varia-bles,the new city real estate market prices out of a line on the secondary market have clearguide. Therefore, the real estate market regulation aiming at the first -tier cities and the＂housing stock＂ should take the second - hand housing market as the main direction, startingwith the sale price and influencing factors of new houses. At the same time, in different cities,we should adhere to the city＇ s policies, reflect the policy differentiation, promote the reformof the real estate supply side, and promote the return of housing properties.

Estimation of maximum mass velocity from macroseismic data: A new method and application to archeoseismological data

An important task in seismic hazard assessment is the estimation of intensity and frequency of rare strong seismic shaking, in particular, the long-term peak ground velocity values(PGVs). A recently proposed method is suitable for simply estimating PGVs based on the examination of the magnitude of displacements of rock blocks. The effectiveness of this method is demonstrated by results of studies on the source zones of two large earthquakes and a vicinity of one strong explosion. In this study, the method is applied to the examination of archeoseismological data from the ancient Rehovot-ba-Negev city and other ancient cities from the Negev desert(in Southern Israel) where numerous evidences of presumable seismic damage were found earlier. The cities and also a sophisticated irrigation system within the region, which existed in the Negev desert, were abandoned however in the middle of the seventh century. The abandonment could be caused by a combined effect, from not only the cessation of the state support from Byzantium as a result of the Arab conquest but also the severe destruction from the strong earthquake that hit the area at that time. The intensities of the seismic events that hit the cities were estimated earlier, which are within the range of 8 e9. Our estimates indicate that the PGV values are about 1.5 m/s. Hence, the magnitude of the causative earthquake could be in the range M z 6.5 e7.5, and the location of the epicenter might be at a distance of a few dozens of kilometers from the ancient Rehovot-ba-Negev city, while the other variants associated with the earthquake seem to be less probable.

Unified Data Model of Urban Air Pollution Dispersion and 3D Spatial City Model:Groundwork Assessment towards Sustainable Urban Development for Malaysia

Understanding the behavior of urban air pollution is important en route for sustainable urban development (SUD). Malaysia is on its mission to be a developed country by year 2020 comprehends dealing with air pollution is one of the indicators headed towards it. At present monitoring and managing air pollution in urban areas encompasses sophisticated air quality modeling and data acquisition. However, rapid developments in major cities cause difficulties in acquiring the city geometries. The existing method in acquiring city geometries data via ground or space measurement inspection such as field survey, photogrammetry, laser scanning, remote sensing or using architectural plans appears not to be practical because of its cost and efforts. Moreover, air monitoring stations deployed are intended for regional to global scale model whereby it is not accurate for urban areas with typical resolution of less than 2 km. Furthermore in urban areas, the pollutant dispersion movements are trapped between buildings initiating it to move vertically causing visualization complications which imply the limitations of existing visualization scheme that is based on two-dimensional (2D) framework. Therefore this paper aims is to perform groundwork assessment and discuss on the current scenario in Malaysia in the aspect of current policies towards SUD, air quality monitoring stations, scale model and detail discussion on air pollution dispersion model used called the Operational Street Pollution Model (OSPM). This research proposed the implementation of three-dimensional (3D) spatial city model as a new physical data input for OSPM. The five Level of Details (LOD) of 3D spatial city model shows the scale applicability for the dispersion model implementtation. Subsequently 3D spatial city model data commonly available on the web, by having a unified data model shows the advantages in easy data acquisition, 3D visualization of air pollution dispersion and improves visual analysis of air quality monitoring in urban areas.

Towards Urban Resource Flow Estimates in Data Scarce Environments: The Case of African Cities

Data sourcing challenges in African nations have led many African urban infrastructure developments to be implemented with minimal scientific backing to support their success. In some cases this may directly impact a city’s ability to reach service delivery, economic growth and human development goals, let alone the city’s ability to protect ecosystem services upon which it relies. As an attempt to fill this gap, this paper describes an exploratory process used to determine city-level demographic, economic and resource flow data for African nations. The approach makes use of scaling and clustering techniques to form acceptable and utilizable representations of selected African cities. Variables that may serve as the strongest predictors for resource consumption intensity in African nations and cities were explored, in particular, the aspects of the Koppen Climate Zones, estimates of average urban income and GDP, and the influence of urban primacy. It is expected that the approach examined will provide a step towards estimating and understanding African cities and their resource profiles.

Foundation Treatment in Urban Underground Engineering Using Big Data Analysis for Smart City Applications

A core element of the sustainable approach to global living quality improvement can now become the intensive and organized usage of underground space.There is a growing interest in underground building and growth worldwide.The reduced consumption of electricity,effective preservation of green land,sustainable wastewater and sewage treatment,efficient reverse degradation of the urban environment,and reliable critical infrastructure management can improve the quality of life.At the same time,technological innovations such as artificial intelligence(AI),cloud computing(CC),the internet of things(IoT),and big data analytics(BDA)play a significant role in improved quality of life.Hence,this study aims to integrate the technological innovations in urban underground engineering to ensure a high quality of life.Thus,this study uses big data analytics to carry out the status quo of foundation treatment and proposes a conceptual framework named the BDA with IoT on urban underground engineering(BI0T-UUE).This framework connects hidden features with various high-level sensing sources and practical predictive model characterization to lower building costs,productive infrastructure management,preparedness for disasters,and modern community smart services.The IoT integration gives an optimum opportunity to work towards the functionality of‘‘digital doubles’’of secret infrastructure,both economical and scalable,with the increasing sophistication and tooling of the underworld.The simulation analysis ensures the highest efficiency and cost-effectiveness of the underground engineering with a value of 96.54%and 97.46%.

High Performance Priority Packets Scheduling Mechanism for Big Data in Smart Cities

Today,Internet of Things(IoT)is a technology paradigm which convinces many researchers for the purpose of achieving high performance of packets delivery in IoT applications such as smart cities.Interconnecting various physical devices such as sensors or actuators with the Internet may causes different constraints on the network resources such as packets delivery ratio,energy efficiency,end-to-end delays etc.However,traditional scheduling methodologies in large-scale environments such as big data smart cities cannot meet the requirements for high performance network metrics.In big data smart cities applications which need fast packets transmission ratio such as sending priority packets to hospitals for an emergency case,an efficient schedulingmechanism ismandatory which is the main concern of this paper.In this paper,we overcome the shortcoming issues of the traditional scheduling algorithms that are utilized in big data smart cities emergency applications.Transmission information about the priority packets between the source nodes(i.e.,people with emergency cases)and the destination nodes(i.e.,hospitals)is performed before sending the packets in order to reserve transmission channels and prepare the sequence of transmission of theses priority packets between the two parties.In our proposed mechanism,Software Defined Networking(SDN)with centralized communication controller will be responsible for determining the scheduling and processing sequences for priority packets in big data smart cities environments.In this paper,we compare between our proposed Priority Packets Deadline First scheduling scheme(PPDF)with existing and traditional scheduling algorithms that can be used in urgent smart cities applications in order to illustrate the outstanding network performance parameters of our scheme such as the average waiting time,packets loss rates,priority packets end-to-end delay,and efficient energy consumption.

Discussion on the Construction of Spatiotemporal Big Data Platform for Smart City

The so-called smart city is a new form of information technology that is achieved through the integration of the contemporary advanced big data technology,Internet of Things technology,cloud computing technology,and spatial geographic information.At present,the application of this technology is important for the urban construction,planning,and services as well as management.This technology also provides great convenience in these aspects,which has allowed the cities to develop and transform towards the direction of smart cities.Based on this situation,the construction of spatiotemporal big data platforms in smart cities was analyzed in this article,and this analysis may provide a reference for the construction and development of todays smart cities.

Exploring relations between city regions based on mobile phone data

City regions often have great diversity in form and function. To better understand the role of each region, the relations between city regions need to be carefully studied. In this work, the human mobility relations between regions of Shanghai based on mobile phone data is explored. By formulating the regions as nodes in a network and the commuting between each pair of regions as link weights, the distribution of nodes degree, and spatial structures of communities in this relation network are studied. Statistics show that regions locate in urban centers and traffic hubs have significantly larger degrees. Moreover, two kinds of spatial structures of communities are found. In most communities, nodes are spatially neighboring. However, in the communities that cover traffic hubs, nodes often locate along corridors.

Services oriented architecture of foundation data platform of digital city

Digital city, a large complex system, is comprised many subsystem of different fields. It involves common field data, flexible field processes, seamless intra-organization and inter-organization collaborations, and increasing connectivity and interoperability between distributed or decentralized systems. All above these are involved Foundation Data of Digital city. To promote the sustainable development of Digital City, this paper investigates to the construction of Foundation Data Platform (FDP) from the view of the architecture. The paper introduces the conceptions, characteristics and underlying problems of the FDP, and discusses the key issues of the architecture paradigms what FDP might adopted via comparing Object-Oriented Architecture (OOA), Component-Based Architecture (CBA), and Service-based Architecture (SBA). The Process Embedded Service-Oriented Architecture (SOA), one of the recent SOA Application Architecture is introduced. The results indicate that the Process Embedded SOA could promote the sustainable development of Digital City and accommodate the dynamic development environments of different fields which constantly changes requirements.

Optimal social welfare:A many-to-many data transaction mechanism based on double auctions

With the development of Big Data and the Internet of Things(IoT),the data value is more significant in both academia and industry.Trading can achieve maximal data value and prepare data for smart city services.Due to data's unique characteristics,such as dispersion,heterogeneity and distributed storage,an unbiased platform is necessary for the data trading market with rational trading entities.Meanwhile,there are multiple buyers and sellers in a practical data trading market,and this makes it challenging to maximize social welfare.To solve these problems,this paper proposes a Social-Welfare-Oriented Many-to-Many Trading Mechanism(SOMTM),which integrates three entities,a trading process and an algorithm named Many-to-Many Trading Algorithm(MMTA).Based on the market scale,market dominated-side and market fixed-side,simulations verify the convergency,economic properties and efficiency of SOMTM.

Multi-Layer Fog-Cloud Architecture for Optimizing the Placement of IoT Applications in Smart Cities

In the smart city paradigm, the deployment of Internet of Things(IoT) services and solutions requires extensive communication and computingresources to place and process IoT applications in real time, which consumesa lot of energy and increases operational costs. Usually, IoT applications areplaced in the cloud to provide high-quality services and scalable resources.However, the existing cloud-based approach should consider the above constraintsto efficiently place and process IoT applications. In this paper, anefficient optimization approach for placing IoT applications in a multi-layerfog-cloud environment is proposed using a mathematical model (Mixed-Integer Linear Programming (MILP)). This approach takes into accountIoT application requirements, available resource capacities, and geographicallocations of servers, which would help optimize IoT application placementdecisions, considering multiple objectives such as data transmission, powerconsumption, and cost. Simulation experiments were conducted with variousIoT applications (e.g., augmented reality, infotainment, healthcare, andcompute-intensive) to simulate realistic scenarios. The results showed thatthe proposed approach outperformed the existing cloud-based approach interms of reducing data transmission by 64% and the associated processingand networking power consumption costs by up to 78%. Finally, a heuristicapproach was developed to validate and imitate the presented approach. Itshowed comparable outcomes to the proposed model, with the gap betweenthem reach to a maximum of 5.4% of the total power consumption.

Efficient Network Selection Using Multi-Depot Routing Problem for Smart Cities

Smart cities make use of a variety of smart technology to improve societies in better ways.Such intelligent technologies,on the other hand,pose sig-nificant concerns in terms of power usage and emission of carbons.The suggested study is focused on technological networks for big data-driven systems.With the support of software-defined technologies,a transportation-aided multicast routing system is suggested.By using public transportation as another communication platform in a smart city,network communication is enhanced.The primary objec-tive is to use as little energy as possible while delivering as much data as possible.The Attribute Decision Making with Capacitated Vehicle(CV)Routing Problem(RP)and Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used in the proposed research.For the optimum network selection,a Multi-Attribute Decision Making(MADM)method is utilized.For the sake of reducing energy usage,the Capacitated Vehicle Routing Problem(CVRP)is employed.To reduce the transportation cost and risk,Half Open Multi-Depot Heterogeneous Vehicle Routing Problem is used.Moreover,a mixed-integer programming approach is used to deal with the problem.To produce Pareto optimal solutions,an intelligent algorithm based on the epsilon constraint approach and genetic algorithm is cre-ated.A scenario of Auckland Transport is being used to validate the concept of offloading the information onto the buses for energy-efficient and delay-tolerant data transfer.Therefore the experiments have demonstrated that the buses may be used effectively to carry out the data by customer requests while using 30%of less energy than the other systems.

A Trailblazing Framework of Security Assessment for Traffic Data Management

Connected and autonomous vehicles are seeing their dawn at this moment.They provide numerous benefits to vehicle owners,manufacturers,vehicle service providers,insurance companies,etc.These vehicles generate a large amount of data,which makes privacy and security a major challenge to their success.The complicated machine-led mechanics of connected and autonomous vehicles increase the risks of privacy invasion and cyber security violations for their users by making them more susceptible to data exploitation and vulnerable to cyber-attacks than any of their predecessors.This could have a negative impact on how well-liked CAVs are with the general public,give them a poor name at this early stage of their development,put obstacles in the way of their adoption and expanded use,and complicate the economic models for their future operations.On the other hand,congestion is still a bottleneck for traffic management and planning.This research paper presents a blockchain-based framework that protects the privacy of vehicle owners and provides data security by storing vehicular data on the blockchain,which will be used further for congestion detection and mitigation.Numerous devices placed along the road are used to communicate with passing cars and collect their data.The collected data will be compiled periodically to find the average travel time of vehicles and traffic density on a particular road segment.Furthermore,this data will be stored in the memory pool,where other devices will also store their data.After a predetermined amount of time,the memory pool will be mined,and data will be uploaded to the blockchain in the form of blocks that will be used to store traffic statistics.The information is then used in two different ways.First,the blockchain’s final block will provide real-time traffic data,triggering an intelligent traffic signal system to reduce congestion.Secondly,the data stored on the blockchain will provide historical,statistical data that can facilitate the analysis of traffic conditions according to past behavior.

Embracing the Future:AI and ML Transforming Urban Environments in Smart Cities

This research explores the increasing importance of Artificial Intelligence(AI)and Machine Learning(ML)with relation to smart cities.It discusses the AI and ML’s ability to revolutionize various aspects of urban environments,including infrastructure,governance,public safety,and sustainability.The research presents the definition and characteristics of smart cities,highlighting the key components and technologies driving initiatives for smart cities.The methodology employed in this study involved a comprehensive review of relevant literature,research papers,and reports on the subject of AI and ML in smart cities.Various sources were consulted to gather information on the integration of AI and ML technologies in various aspects of smart cities,including infrastructure optimization,public safety enhancement,and citizen services improvement.The findings suggest that AI and ML technologies enable data-driven decision-making,predictive analytics,and optimization in smart city development.They are vital to the development of transport infrastructure,optimizing energy distribution,improving public safety,streamlining governance,and transforming healthcare services.However,ethical and privacy considerations,as well as technical challenges,need to be solved to guarantee the ethical and responsible usage of AI and ML in smart cities.The study concludes by discussing the challenges and future directions of AI and ML in shaping urban environments,highlighting the importance of collaborative efforts and responsible implementation.The findings highlight the transformative potential of AI and ML in optimizing resource utilization,enhancing citizen services,and creating more sustainable and resilient smart cities.Future studies should concentrate on addressing technical limitations,creating robust policy frameworks,and fostering fairness,accountability,and openness in the use of AI and ML technologies in smart cities.

基于夜光数据的昆明市城市扩张格局演变分析

基于夜光数据采用POI&NPP综合指数提取建成区,然后进行建成区面积精度评定,从定性和定量两个角度进行城市扩张空间形态及格局演变分析。结果表明:1)POI融入夜光数据后,提高了提取的精度,提取边界与实际的分布范围更为接近。2)扩张速度和强度较快区域主要集中在呈贡区、嵩明县等,而较慢区域集中在北部的禄劝县、东川区、寻甸县等;嵩明县的动态变化率最高,中心城区的区域动态变化率较低,北部的寻甸县、东南部的宜良县、石林县动态变化率反而较高。3)昆明市整体上经济重心经度方向为向东偏移,纬度方向为向南偏移。