Hasil untuk "City planning"

Bingnan Dong, Shengqiang Li, Xing Fan et al.

The study used camera traps (2946 trap days, 60 sites) to investigate the diversity, habitat use, and activity rhythms of six sympatric ungulates in a montane ecosystem of southwestern China: tufted deer (<i>Elaphodus cephalophus</i>), Chinese goral (<i>Naemorhedus caudatus</i>), Chinese serow (<i>Naemorhedus griseus</i>), sambar (<i>Rusa unicolor</i>), wild boar (<i>Sus scrofa</i>), and blue sheep (<i>Pseudois nayaur</i>). Relative abundance indices indicated that sambar were most frequent, while blue sheep and Chinese goral were least common. Species showed distinct elevational, slope, and vegetation preferences, suggesting spatial niche segregation. Kernel density estimates revealed predominantly diurnal activity, with bimodal patterns for tufted deer, sambar, and Chinese goral, and unimodal peaks for blue sheep, wild boar, and Chinese serow. Temporal overlap was highest between sambar and tufted deer, and lowest between tufted deer and blue sheep. These results demonstrate spatial and temporal partitioning as key mechanisms enabling ungulate coexistence and underscore the importance of conserving heterogeneous montane habitats.

Betelhem Zemenu, Ephrem Getahun, Sisay Alemayehu et al.

Rapid urban expansion in developing countries has intensified land use and land cover (LULC) changes, resulting in significant environmental impacts that challenge sustainable urban development. Although Arba Minch City is one of the fastest-growing urban centers in southern Ethiopia, comprehensive studies integrating advanced geospatial techniques and quantitative models to assess urban expansion patterns and their environmental implications remain limited. This study analyzed urban expansion in Arba Minch, southern Ethiopia, between 2013 and 2023 using Geographic Information Systems (GIS), remote sensing, and the Shannon entropy model. The supervised classification, utilizing the maximum likelihood algorithm on Landsat 8 OLI/TIRS imagery, achieved high overall accuracies of 93% in 2013 and 94% in 2023. Additionally, Kappa coefficients of 0.91 for both years indicate a strong level of classification reliability. LULC change analysis revealed a notable increase in built-up areas by 1262.34 hectares, primarily at the expense of vegetation (-929.34 ha) and bare land (-359.19 ha). During 2013–2023, vegetation indices (NDVI) declined from an average of 0.31 to 0.2, while built-up index (NDBI) values increased from a mean of -0.17 to -0.09. Land Surface Temperature (LST) rose from 18.7 °C - 35.6 °C in 2013 to 19.6 °C - 44.0 °C in 2023, reflecting intensification of the urban heat island effect. The Shannon entropy analysis indicates dispersed patterns of urban growth, particularly toward the northwest (NW) and northeast (NE) directions. Furthermore, the urban expansion intensity index (UEII) uncovered a high level of urban growth intensity (UEII=1.64). The findings indicate substantial spatio-temporal changes in LULC and urban growth during 2013–2023. The study concludes that rapid and dispersed urban growth in Arba Minch City has led to considerable environmental degradation. It recommends the implementation of sustainable urban planning strategies, protection of remaining vegetation, and integration of geospatial tools into urban management policies to balance future urban development with environmental conservation.

Filippo Marchesani, Francesca Masciarelli, Andrea Bikfalvi

The rise of smart cities represents a significant trend in urban development. However, only in recent years has attention shifted toward the international promotion of these cities. Despite ongoing academic discussions on the impact of smart city development on urban environments, the global recognition of smart cities remains uncertain due to their multidisciplinary nature. To address this, we conducted a systematic literature review of articles published in top-tier peer-reviewed journals from 2008 to December 2021, offering a comprehensive analysis of the existing literature.

Özge Deniz Toköz, Ali Berkay Avci, Hasan Engin Duran

The study focuses on the factors affecting visitor numbers to archaeological sites in Turkey. The aim is to investigate the geographical, economic, and demographic factors underlying the visits using statistical methods. The study covers 117 archaeological site visits in 2019. Although existing studies analysed determinants of visits to archaeological sites of different countries, the evidence needs to be explicit. Methodologically, the classical linear regression models are primarily applied in the literature, whereas the incorporation of spatial dependence has largely been ignored. This study contributes to the literature by employing demographic, economic, and climatic factors and spatial relations between the sites. Therefore, spatial autoregressive (SAR) and spatial error models (SEM) are developed in the analyses. According to the results, WHL inscription and distance to the city centre are crucial factors for the visits. In addition, the study emphasizes the significant negative effect of spatial dependence on visitor numbers of archaeological sites near each other.

Oh-Hyun Kwon, Inho Hong, Woo-Sung Jung et al.

The gravity model of human mobility has successfully described the deterrence of travels with distance in urban mobility patterns. While a broad spectrum of deterrence was found across different cities, yet it is not empirically clear if movement patterns in a single city could also have a spectrum of distance exponents denoting a varying deterrence depending on the origin and destination regions in the city. By analyzing the travel data in the twelve most populated cities of the United States of America, we empirically find that the distance exponent governing the deterrence of travels significantly varies within a city depending on the traffic volumes of the origin and destination regions. Despite the diverse traffic landscape of the cities analyzed, a common pattern is observed for the distance exponents; the exponent value tends to be higher between regions with larger traffic volumes, while it tends to be lower between regions with smaller traffic volumes. This indicates that our method indeed reveals the hidden diversity of gravity laws that would be overlooked otherwise.

Cristina Catalanotti

This article investigates the relationship between tourism-related economic activities and neighbourhood shops in the historical centre of Venice, in terms of both their spatial distribution and the conflictual uses of the city. In questioning how to inhabit and revitalise the city through commercial activities, the paper wishes to contribute to the discussion proposed in the special number, by presenting a specific yet paradigmatic context and by reflecting on urban regeneration and revitalisation bottom-up practices. The research first unfolds the landscapes of commerce in the city and identifies polarised geographies of tourism-related activities focusing on retail and catering businesses; secondly, it interrogates spatialised strategies that local actors are developing to reflect on their relation with urban planning and policy design processes.

Olusegun Aanuoluwapo Oguntona, Clinton Ohis Aigbavboa

The construction industry has been globally fingered as the major sector responsible for the continued deplorable state of the environment. The rising exploitation of the natural environment by the sector decapacitates the function of the flora and fauna to sustain life on earth. Therefore, the adoption and implementation of sustainability concepts in the construction industry are imperative to reduce the sector’s negative impacts on the environment. The growing field of biomimicry as a sustainability concept has increased global interest and call to maximize the numerous benefits offered by nature. This article is aimed at exploring biomimicry potentials in solving human challenges in a sustainable manner through responsible imitation, emulation, and drawing inspiration from nature. The first part of this paper explores the construction industry with rapt attention to its positive and negative impact on the human and natural environment. The second part provides a comprehensive overview of the biomimicry concept looking at its definitions, tenets, and sustainability standpoint. Finally, biomimicry inspiration, imitation, and emulation are discussed citing examples of their applications within and outside the built environment.

Chen Yang, Peng Liang, Liming Fu et al.

5G is the fifth generation wireless network, with a set of characteristics, e.g., high bandwidth and data rates. The scenarios of using 5G include enhanced Mobile Broadband (eMBB), massive Machine Type Communications (mMTC), and ultra-Reliable and Low-Latency Communications (uRLLC). 5G is expected to support a wide variety of applications. We conducted a systematic mapping study that covers the literature published between Jan 2012 and Dec 2019 regarding using 5G in smart cities. The scenarios, architecture, technologies, challenges, and lessons learned of using 5G in smart cities are summarized and further analyzed based on 32 selected studies, and the results are that: (1) The studies are distributed over 27 publication venues. 17 studies report results based on academic studies and 13 studies use demonstration or toy examples. Only 2 studies report using 5G in smart cities based on industrial studies. 16 studies include assumptions of 5G network design or smart city scenarios. (2) The most discussed smart city scenario is transportation, followed by public safety, healthcare, city tourism, entertainment, and education. (3) 28 studies propose and/or discuss the architecture of 5G-enabled smart cities, containing smart city architecture (treating 5G as a component), 5G network architecture in smart cities, and business architecture of using 5G in smart cities. (4) The most mentioned 5G-related technologies are radio access technologies, network slicing, and edge computing. (5) Challenges are mainly about complex context, challenging requirements, and network development of using 5G in smart cities. (6) Most of the lessons learned identified are benefits regarding 5G itself or the proposed 5G-related methods in smart cities. This work provides a reflection of the past eight years of the state of the art on using 5G in smart cities, which can benefit both researchers and practitioners.

Miguel G. Folgado, Veronica Sanz, Johannes Hirn et al.

Traffic congestion is a major urban issue due to its adverse effects on health and the environment, so much so that reducing it has become a priority for urban decision-makers. In this work, we investigate whether a high amount of data on traffic flow throughout a city and the knowledge of the road city network allows an Artificial Intelligence to predict the traffic flux far enough in advance in order to enable emission reduction measures such as those linked to the Low Emission Zone policies. To build a predictive model, we use the city of Valencia traffic sensor system, one of the densest in the world, with nearly 3500 sensors distributed throughout the city. In this work we train and characterize an LSTM (Long Short-Term Memory) Neural Network to predict temporal patterns of traffic in the city using historical data from the years 2016 and 2017. We show that the LSTM is capable of predicting future evolution of the traffic flux in real-time, by extracting patterns out of the measured data.

Pedro Rito, Ana Almeida, Andreia Figueiredo et al.

This article presents the deployment and experimentation architecture of the Aveiro Tech City Living Lab (ATCLL) in Aveiro, Portugal. This platform comprises a large number of Internet-of-Things devices with communication, sensing and computing capabilities. The communication infrastructure, built on fiber and Millimeter-wave (mmWave) links, integrates a communication network with radio terminals (WiFi, ITS-G5, C-V2X, 5G and LoRa(WAN)), multiprotocol, spread throughout 44 connected points of access in the city. Additionally, public transportation has also been equipped with communication and sensing units. All these points combine and interconnect a set of sensors, such as mobility (Radars, Lidars, video cameras) and environmental sensors. Combining edge computing and cloud management to deploy the services and manage the platform, and a data platform to gather and process the data, the living lab supports a wide range of services and applications: IoT, intelligent transportation systems and assisted driving, environmental monitoring, emergency and safety, among others. This article describes the architecture, implementation and deployment to make the overall platform to work and integrate researchers and citizens. Moreover, it showcases some examples of the performance metrics achieved in the city infrastructure, the data that can be collected, visualized and used to build services and applications to the cities, and, finally, different use cases in the mobility and safety scenarios.

Thomas Louail, Marc Barthelemy

Characterizing the spatial organization of urban systems is a challenge which points to the more general problem of describing marked point processes in spatial statistics. We propose a non-parametric method that goes beyond standard tools of point pattern analysis and which is based on a mapping between the points and a "dominance tree", constructed from a recursive analysis of their Voronoi tessellation. Using toy models, we show that the height of a node in this tree encodes both its mark and the structure of its neighborhood, reflecting its importance in the system. We use historical population data in France (1876-2018) and the US (1880-2010) and show that the method highlights multiscale urban dynamics experienced by these countries. These include non-monotonous city trajectories in the US, as revealed by the evolution of their height in the tree. We show that the height of a city in the tree is less sensitive to different statistical definitions of cities than its rank in the urban hierarchy. The method also captures the attraction basins of cities at successive scales, and while in both countries these basin sizes become more homogeneous at larger scales, they are also more heterogeneous in France than in the US. Finally, we introduce a simple graphical representation - the height clock - that monitors the evolution of the role of each city in its country.

Emanuele Gabriel Margherita, Giovanni Esposito, Stefania Denise Escobar et al.

In this position paper, we explore the adoption of a Smart City with a socio-technical perspective. A Smart city is a transformational technological process leading to profound modifications of existing urban regimes and infrastructure components. In this study, we consider a Smart City as a socio-technical system where the interplay between technologies and users ensures the sustainable development of smart city initiatives that improve the quality of life and solve important socio-economic problems. The adoption of a Smart City required a participative approach where users are involved during the adoption process to joint optimise both systems. Thus, we contribute to socio-technical research showing how a participative approach based on press relationships to facilitate information exchange between municipal actors and citizens worked as a success factor for the smart city adoption. We also discuss the limitations of this approach.

Meiyi Ma, Ezio Bartocci, Eli Lifland et al.

With the development of the Internet of Things, millions of sensors are being deployed in cities to collect real-time data. This leads to a need for checking city states against city requirements at runtime. In this paper, we develop a novel spatial-temporal specification-based monitoring system for smart cities. We first describe a study of over 1,000 smart city requirements, some of which cannot be specified using existing logic such as Signal Temporal Logic (STL) and its variants. To tackle this limitation, we develop SaSTL -- a novel Spatial Aggregation Signal Temporal Logic -- for the efficient runtime monitoring of safety and performance requirements in smart cities. We develop two new logical operators in SaSTL to augment STL for expressing spatial aggregation and spatial counting characteristics that are commonly found in real city requirements. We define Boolean and \newcontent{quantitative semantics}~for SaSTL in support of the analysis of city performance across different periods and locations. We also develop efficient monitoring algorithms that can check a SaSTL requirement in parallel over multiple data streams (e.g., generated by multiple sensors distributed spatially in a city). Additionally, we build a SaSTL-based monitoring tool to support decision making of different stakeholders to specify and runtime monitor their requirements in smart cities. We evaluate our SaSTL monitor by applying it to three case studies with large-scale real city sensing data (e.g., up to 10,000 sensors in one study). The results show that SaSTL has a much higher coverage expressiveness than other spatial-temporal logic, and with a significant reduction of computation time for monitoring requirements. We also demonstrate that the SaSTL monitor improves the safety and performance of smart cities via simulated experiments.

Dimitrios Amaxilatis, Evangelos Lagoudianakis, Georgios Mylonas et al.

We briefly present the design and architecture of a system that aims to simplify the process of organizing, executing and administering crowdsensing campaigns in a smart city context over smartphones volunteered by citizens. We built our system on top of an Android app substrate on the end-user level, which enables us to utilize smartphone resources. Our system allows researchers and other developers to manage and distribute their "mini" smart city applications, gather data and publish their results through the Organicity smart city platform. We believe this is the first time such a tool is paired with a large scale IoT infrastructure, to enable truly city-scale IoT and smart city experimentation.

Merlito Villa, Roel F. Ceballos

Fire incidence is a big problem for every local government unit in the Philippines. The two most detrimental effects of fire incidence are economic loss and loss of life. To mitigate these losses, proper planning and implementation of control measures must be done. An essential aspect of planning and control measures is prediction of possible fire incidences. This study is conducted to analyze the historical data to create a forecasting model for the fire incidence in Davao City. Results of the analyses show that fire incidence has no trend or seasonality, and occurrences of fire are neither consistently increasing nor decreasing over time. Furthermore, the absence of seasonality in the data indicate that surge of fire incidence may occur at any time of the year. Therefore, fire prevention activities should be done all year round and not just during fire prevention month.

Gennaro Angiello

Starting from the relationship between urban planning and mobility management, TeMA has gradually expanded the view of the covered topics, always following a rigorous scientific in-depth analysis. This section of the Journal, Review Notes, is the expression of a continuous updating of emerging topics concerning relationships among urban planning, mobility and environment, through a collection of short scientific papers. The Review Notes are made of four parts. Each section examines a specific aspect of the broader information storage within the main interests of TeMA Journal. In particular, the Urban practices section aims at presenting recent advancements on relevant topics that underlie the challenges that the cities have to face. The present note provides an overview of the policies and initiatives undertaken in four global cities in response to the Covid-19 outbreak: New York City (US), Beijing (CN), Paris (FR) and Singapore (SG). A cross-city analysis is used to derive a taxonomy of urban policy measures. The contribution discusses the effectiveness of each measures in providing answers to epidemic threats in urban areas while, at the same time, improving the sustainability and resilience of urban communities.

Javad Artin, Amin Valizadeh, Mohsen Ahmadi et al.

Traffic prediction is critical to expanding a smart city and country because it improves urban planning and traffic management. This prediction is very challenging due to the multifactorial and random nature of traffic. This study presented a method based on ensemble learning to predict urban traffic congestion based on weather criteria. We used the NAS algorithm, which in the output based on heuristic methods creates an optimal model concerning input data. We had 400 data, which included the characteristics of the day’s weather, including six features: absolute humidity, dew point, visibility, wind speed, cloud height, and temperature, which in the final column is the urban traffic congestion target. We have analyzed linear regression with the results obtained in the project; this method was more efficient than other regression models. This method had an error of 0.00002 in terms of MSE criteria and SVR, random forest, and MLP methods, which have error values of 0.01033, 0.00003, and 0.0011, respectively. According to the MAE criterion, this method has a value of 0.0039. The other methods have obtained values of 0.0850, 0.0045, and 0.027, respectively, which show that our proposed model has a minor error than other methods and has been able to outpace the other models.

Josemanuel Luna-Nemecio

El presente estudio guarda un doble propósito; en primer lugar se establecen los lineamientos teóricos conceptuales para entender la compleja relación entre la sustentabilidad y la resiliencia. Estas dimensiones se muestran como ejes de la producción del espacio urbano de cara a diagnosticar, analizar y proponer alternativas a la serie de problemas derivadas de la devastación ambiental contemporánea. En segundo lugar, el presente estudio cumple con la meta de presentar los artículos del Dossier "Los avatares de la ciduad en el siglo XXI". Siguiente una metodología aproximativa de corte cualitativo se llega a concluir que la sustentabilidad y resiliencia urbana son parte de las capacidades socioterritoriales para enfrentar las grandes adversidades e incertidumbres de nuestro tiempo.

Najla Al-Taleb, Nazar Abbas Saqib, Atta-ur-Rahman et al.

Smart city improved the quality of life for the citizens by implementing information communication technology (ICT) such as the internet of things (IoT). Nevertheless, the smart city is a critical environment that needs to secure it is network and data from intrusions and attacks. This work proposes a hybrid deep learning (DL) model for cyber threat intelligence (CTI) to improve threats classification performance based on convolutional neural network (CNN) and quasi-recurrent neural network (QRNN). We use QRNN to provide a real-time threat classification model. The evaluation results of the proposed model compared to the state-of-the-art models show that the proposed model outperformed the other models. Therefore, it will help in classifying the smart city threats in a reasonable time.

Gyorgy Csomos

Since the mid-1970s, it has become highly acknowledged to measure and evaluate changes in international research collaborations and the scientific performance of institutions and countries through the prism of bibliometric and scientometric data. Spatial bibliometrics and scientometrics (henceforward spatial scientometrics) have traditionally focused on examining both country and regional levels; however, in recent years, numerous spatial analyses on the city level have been carried out. While city-level scientometric analyses have gained popularity among policymakers and statistical/economic research organizations, researchers in the field of bibliometrics are divided regarding whether it is possible to observe the spatial unit 'city' through bibliometric and scientometric tools. After systematically scrutinizing relevant studies in the field, three major problems have been identified: 1) there is no standardized method of how cities should be defined and how metropolitan areas should be delineated, 2) there is no standardized method of how bibliometric and scientometric data on the city level should be collected and processed and 3) it is not clearly defined how cities can profit from the results of bibliometric and scientometric analysis focusing on them. This paper investigates major challenges ahead of spatial scientometrics, focusing on the city level and presents some possible solutions.