Urban Infrastructure Planning Research Articles

Integrating Policy Instruments for Enhanced Urban Resilience: A Machine Learning and IoT-Based Approach to Flood Mitigation

In the context of global urbanization, the interconnected architecture of economic, social, and administrative activities in modern cities cultivates a complex web of interdependencies. This intricacy amplifies the impacts of natural disasters such as urban flooding, presenting unprecedented challenges in risk management and disaster responsiveness. To address these challenges, this study defines the concept of urban flood resilience and outlines its practical applications in flood risk management, proposing an integrated resilience governance framework. The framework systematically enhances urban flood management by combining structural flood mitigation methods with advanced technologies, including the Internet of Things (IoT) and non-structural decision-support tools powered by Machine Learning Algorithms (MLAs). This integrated approach aims to improve early flood warning systems, optimize urban infrastructure planning, and reduce flood-related risks. The case study of the Cypress Creek watershed validates the framework’s effectiveness under specific scenarios, achieving reductions of 25% in inundation area, 30% in peak flow, and 20% in total flood volume. These results not only demonstrate the framework’s efficacy in mitigating flood impacts but also provide empirical support for developing resilient urban governance models, highlighting the essential role of adaptive policy instruments in urban flood management.

Prediction of electric vehicle charging demand using enhanced gated recurrent units with RKOA based graph convolutional network

Accurate forecasting of traffic patterns plays a crucial role in the effective management and planning of urban transportation infrastructure. In particular, predicting the availability of electric vehicle (EV) charging stations is essential for alleviating range anxiety among drivers and facilitating the adoption of electric vehicles. This study proposes a novel deep learning-based predictor model to approximate the demand for charging electric vehicles over the long term. The methodology integrates the Berkeley wavelet transform (BWT) to decompose input time series data while preserving its inherent characteristics. The proposed hybrid prediction model combines an enhanced gate recurrent unit with an optimized convolution kernel within a fusion graph convolutional network (GCN). The Red Kite Optimization Algorithm (RKOA) is employed to select the convolution kernel of the GCN effectively. Additionally, the construction of the graph leverages both adjacency and adaptive graphs to accurately represent the correlations among nodes in the EV network. The model extracts multi-level spatial correlations through stacked fusion graph convolutional elements and captures multi-scale temporal correlations via an improved gated recurrent unit. Furthermore, the incorporation of residual connection units allows for the fusion of extracted spatiotemporal features with direct data, enhancing predictive performance. The proposed neural predictor is evaluated using EV charging data from Georgia Tech in Atlanta, USA. The experimental results demonstrate the effectiveness of the prediction metrics generated by the proposed model compared to existing methods reported in the literature, showcasing its capability to accurately forecast EV charging demand.

The contribution of remote sensing and geographic information systems to the morphometric study of six watersheds in the tindouf region (extreme south-western Algeria)

Understanding the hydrological system and improving water resource management requires a thorough analysis of the morphometric characteristics of watersheds. This study seeks to examine the hydrological and morphometric features of the water drainage network in the Tindouf region's numerous watersheds. A digital elevation model (DEM), which is an essential digital asset in GIS, was created using a 30 m resolution SRTM satellite image. The DEM model was then used to produce a map of the drainage network for the basins in the study area. Additionally, various hydrological and geomorphological features were extracted. This research is significant in promoting economic development and improving the quality of life of residents by establishing better resource allocation and water management, determining appropriate areas for urban development, infrastructure planning, tourism, and entertainment.

Uptake of ecosystem service concept in urban blue-green infrastructure planning and decision-making in Russia

ABSTRACT In the scientific community, ecosystem services (ESs) are well-recognized as a promising concept to more actively consider and plan for the multitude of benefits provided by the green environment and thus promote sustainable urban development. Yet, to have an impact on decision-making, the concept must spread from academia to practice. To understand to what extent ES have been adopted in practice and decision-making in Russia, we conducted a series of interviews and an online survey with both practitioners and scientists and complemented it by document analysis. Our results showed that ES are not adequately reflected in urban decision-making and planning processes. Moreover, we found no explicit references to ES in strategic urban planning and landscape development projects, although there are few implicit references in several reviewed documents. Among the identified challenges for better uptake of the concept in practice were many disagreements associated with the lack of a unified terminology, typology, and methods. Stakeholders understand the potential of applying ES valuation in practice, which includes a necessity to develop methodological materials and different tools based on real-life cases. Finally, a list of proposals on possible ways to better include ES in urban planning and in decision-making processes was formed by the respondents.

What turns a bicycle street into a street for cyclists? A multimodal study on subjective safety of infrastructure measures on bicycle streets using an approach in virtual reality

In an increasingly urban and sustainable transportation-focused world, cycling emerges as a key environmentally friendly and health-promoting mode of travel. This paper addresses the gap in understanding cyclists' subjective safety, particularly in relation to bicycle streets in Germany. Utilizing a multimodal approach, two studies were conducted: an online questionnaire (Study I) and a Virtual Reality (VR) evaluation (Study II), focusing on the Reichenhainer Straße in Chemnitz. Study I involved N = 182 participants who regularly used the bicycle street. The questionnaire covered various aspects including mobility behavior, cycling experience, and subjective safety, using a 6-point Likert scale. Infrastructure characteristics were represented through images. Results showed that participants generally felt safe on the bicycle street, with key factors being the lane width and restrictions on motorized traffic. Study II, a pioneering effort in using VR and stereoscopic 360° images for evaluating cycling infrastructure safety, involved N = 32 participants. It provided a realistic evaluation of infrastructure elements and their impact on perceived safety. The study revealed significant differences in safety ratings based on the presence of cars and cyclists' experience levels. The use of VR allowed for a focused examination of infrastructure characteristics and their perceived safety, highlighting potential areas for improvement. Both studies demonstrated consistent results, underscoring the subjective safety of the Reichenhainer Straße, which was perceived as safer than the average bike-accessible infrastructure in Chemnitz. The VR method proved effective for detailed evaluation of subjective safety, independent of participants' familiarity with the specific infrastructure. This approach offers a structured, controlled, and resource-efficient way to evaluate cycling infrastructure under laboratory conditions. In conclusion, the findings align with existing literature and emphasize the importance of lane width and reduced motorized traffic for cyclists' safety. The innovative VR method, while still developing, offers promising implications for future citizen participation initiatives and infrastructure planning projects, enabling efficient post-completion evaluations of subjective safety. Such practices are rare in German planning offices, highlighting the paper's contribution to enhancing urban cycling infrastructure planning and safety assessment.

Urban Road Transport Infrastructure Planning and Development Implications on Socio-Economic Growth in the City of Douala, Cameroon

Cities are the major contributors to socio- economic growth with easy access to amenities which stem through road transport infrastructure development. This study was conducted to assess urban road transport infrastructure planning and examine development implications on socio-economic growth in the City of Douala. A descriptive research design was used with primary and secondary data collected and analyzed using descriptive and inferential statistical techniques. Results revealed that, transport infrastructure in Douala have been structured through urban development planning policy which influences surface spatial expansion of socio- economic activities. The planning policies have contributed to the rapid expansion and growth through the initiations of master plan, land use plan and sector plan. Infrastructural development on road transport depicts a physical expansion and construction of road networks which influences the socio-economic growth of the study area. Stakeholders’ actions on construction, reconstruction and rehabilitation, investments and maintenance of road infrastructure have helped to curb down some transportation challenges and creates easy access to facilities within different sectors of the study area. However, road transport infrastructure which is well-developed boasts socio-economic activities and growth. The state of road transport network, its diversity and characteristic features is defined by its prevalence within different areas in Douala. Contrarily, other transport infrastructures which are not functioning appropriately due to lapses in physical planning possess enormous implications on transportation in the study area. The conclusion drawn was that the manner in which urban planning policies are implemented determines the level of development in road transport infrastructure and socio-economic growth of Douala. The study recommends an effective application of planning policies to be integrated by implicating all the actors of urban managers and transportation planners for improved growth in the study area.

O atual entendimento acerca da busca pessoal: uma revisão jurisprudencial e as atuais problemáticas

No atual cenário jurídico-penal brasileiro, a busca pessoal configura-se como uma medida de incontestável relevância no exercício da função policial. Neste contexto, a atuação das forças de segurança, notadamente da Polícia Militar, em suas atribuições de patrulhamento preventivo, adquire centralidade no controle da ordem social e na mitigação de riscos à coletividade. No entanto, a busca pessoal, enquanto instrumento típico da atuação policial, suscita profundas discussões nos tribunais superiores acerca de sua legalidade e adequação frente aos postulados constitucionais. As temáticas que circunscrevem a busca pessoal têm despertado acentuadas discussões jurisprudenciais e doutrinárias, trazendo diversas implicações jurídicas à atuação das polícias militares em todo o Brasil. Nesse sentido, a revisão jurisprudencial se torna imprescindível para avaliar a conformidade das decisões judiciais com a norma constitucional, sobretudo no que tange à interpretação de conceitos como “fundada suspeita”, a necessidade, ou não, de mandado judicial para a realização da busca pessoal e as implicações legais da apreensão de objetos pessoais, como aparelhos celulares, sob os limites do direito à privacidade. Ademais, problemáticas atuais, como a utilização seletiva da busca pessoal com base em critérios de raça, gênero e cor, agravam as tensões sociais, revelando-se como questões latentes a serem debatidas.

Developing an integrative management plan for urban and peri-urban forests: A case study of Ljubljana, Slovenia

Many cities worldwide have adopted management strategies for urban forests, typically focusing on forest parks, street trees and park trees. However, planning that concentrates on natural urban forest areas remains scarce. This study introduces an advanced planning framework for the integrative management of natural urban forests and applies it to Ljubljana's urban forests (2151 ha). The framework consists of an initiation phase and a six-step planning procedure. Through a comprehensive process lasting 20 months, with strong stakeholder involvement and political support, we developed a strategic plan for Ljubljana’s entire urban forest area and an operational plan for a specific block within this area. The approach employs the integrative management of forest stands within or near the city, incorporating harmonised activities in silviculture, recreation, protection, harvesting and communication. This planning method aims to bridge the gap between traditional forest management planning and urban green infrastructure planning.

Level of Population’s Satisfaction with Quality of Life Indicators in Krasnoyarsk

Background. The large city of Krasnoyarsk represents a unique urban environment where various indicators, such as ecology, infrastructure, healthcare, and education services, play an important role in shaping the level of residents’ satisfaction and impacting their quality of life. Purpose: To study the level of the population’s satisfaction with the quality of life indicators in Krasnoyarsk. Material and methods. A sociological survey was conducted using a questionnaire. The authors also applied the random sampling technique. The sample size was 687 respondents. Results. The study identified several key problems: low levels of satisfaction with environmental quality, quality of medical services, and accessibility of healthcare organizations; low income of the population; limited opportunities for recreation and entertainment. Overall, the study highlights the importance of further studying and improving quality of life indicators in Krasnoyarsk to increase residents' satisfaction and create a more comfortable urban environment. Conclusion. Based on the level of the population’s satisfaction, it is possible to consider public opinion in management decision-making and developing social programs for urban infrastructure planning.

Contextual and socio-economic factors affected urban dwellers experiences of and vulnerability to ecosystem disservices

Peoples’ experiences of Ecosystem Disservices (EDS) are influenced by socio-economic and environmental characteristics, and this needs better understanding for informed planning and management of urban green infrastructure. The paper investigated the understudied area of how socio-economic and environmental factors influence people's experiences of EDS in urban green spaces, in four towns in South Africa. Employing a questionnaire survey, in 303 randomly selected households in three neighborhoods of different economic standing, alongside statistical analysis provided quantitative data on experiences and vulnerability to EDS. A list of reported EDS was compiled and the number of EDS/respondent was compared between towns, neighborhoods and places of experience, using Kruskal-Wallis test and nested ANOVAs. Respondents’ vulnerability to EDS was assessed by analyzing the magnitudes of experience and coping abilities. The influences of socio-economic and environmental characteristics on experiences of and vulnerability to EDS were assessed using PCAs. Twenty-three EDS were reported in total, with the highest numbers recorded in affluent areas and the lowest in the poorest areas. Irrespective of town and neighborhood, the reported EDS were mainly experienced in domestic gardens and surrounding streets. Consequently, only one EDS seems of high concern, while the others are of lower concern or well manageable by the respondents. Significant influences of socio-economic (education and income levels, source of income, upbringing, frequency of visits to green spaces (GS), length of residence in neighborhoods) and environmental (environmental worldview, garden ownership, membership to environmental NGO) characteristics were observed on the experiences and vulnerability to EDS. Therefore, such variations in and influences on peoples’ experiences and vulnerability to EDS should be considered for sustainable management of urban GS. Also, the creation of GS should be accompanied by actions to improve people relationships to nature as well as to ensure their maintenance and security, particularly public GS and commonages.

UB-FineNet: Urban building fine-grained classification network for open-access satellite images

Fine classification of city-scale buildings using satellite imagery is a crucial research area with significant implications for urban planning, infrastructure development, and population distribution analysis. However, the task faces great challenges due to low-resolution overhead images acquired from high-altitude space-borne platforms and the long-tailed sample distribution of fine-grained urban building categories, leading to a severe class imbalance problem. To address these issues, we propose a deep network approach to the fine-grained classification of urban buildings using open-access satellite images. A Denoising Diffusion Probabilistic Model (DDPM) based super-resolution method is first introduced to enhance the spatial resolution of satellite images, which benefits from domain-adaptive knowledge distillation. Then, a new fine-grained classification network with Category Information Balancing Module (CIBM) and Contrastive Supervision (CS) technique is proposed to mitigate the problem of class imbalance and improve the classification robustness and accuracy. Experiments on Hong Kong data set with 11 distinct building types revealed promising classification results with a mean Top-1 accuracy of 60.45%, which is on par with street-view image based approaches. A comprehensive ablation study demonstrates that the CIBM and CS modules improve Top-1 accuracy by 2.6% and 3.5%, respectively, over the baseline approach. In addition, these modules can be easily integrated into other classification networks, achieving similar performance improvements. This research advances urban analysis by providing an effective solution for detailed classification of buildings in complex mega-city environments using only open-access satellite imagery. The proposed technique can serve as a valuable tool for urban planners, aiding in the understanding of economic, industrial, and population distribution within cities and regions, ultimately facilitating informed decision-making in urban development and infrastructure planning. Data and code will be publicly available at https://github.com/ZhiyiHe1997/UB-FineNet.

CAG-NSPDE: Continuous adaptive graph neural stochastic partial differential equations for traffic flow forecasting

Traffic flow forecasting, which aims to predict future traffic flow given historical data, is crucial for the planning and construction of urban transportation infrastructure. Existing methods based on discrete dynamic graphs are unable to model the smooth evolution of spatial relationships and usually have high computational cost. In addition, they ignore the effect of stochastic noise on the modeling of temporal dependencies, which lead to poor predictive performance on complex intelligent transportation systems. To address these issues, we propose continuous adaptive graph neural stochastic partial differential equation (CAG-NSPDE). A continuous graph is constructed based on learnable continuous adaptive node embeddings for the modeling of evolution of spatial dependencies. To explicitly model the effect of noise, a novel architecture based on stochastic partial differential equation (SPDE) is proposed. It can simultaneously extract features from the temporal and frequency domains by reducing SPDEs to a system of ODEs in Fourier space. We perform extensive experiments on four real-world datasets and the results demonstrate the superiority of CAG-NSPDE compared with state-of-the-arts.

Towards Sustainable Urban Mobility: Voronoi-Based Spatial Analysis of EV Charging Stations in Bangkok

This study leverages the efficacy of Voronoi diagram theory within a mixed-methods approach to thoroughly examine the spatial distribution, service coverage, and optimal locations for expanding electric vehicle (EV) charging infrastructure in Bangkok. Drawing on data from field surveys and public data providers, our analysis unfolds in four key stages. Firstly, we delve into the spatial distribution of charging stations, scrutinizing density, proximity to various road types, and land use through the lens of Voronoi diagrams. Secondly, the application of Voronoi diagrams informs the evaluation of service boundaries. Thirdly, utilizing this effective tool, we assess population density per parking slot or charging head to anticipate future EV adoption. Finally, the study introduces an approach to identify suitable locations for new charging stations through the application of overlapping Voronoi circles. Findings reveal a clustered distribution of charging stations along roads, particularly in the central business district, showcasing the efficiency of Voronoi diagrams in spatial analysis. Residential areas and urban commercial zones also host significant charging station concentrations. Notably, service coverage in inner Bangkok surpasses that of middle and outer areas, highlighting underserved regions. Prospective areas for new charging stations, identified through Voronoi analysis, include Bang Khae, Phra Khanong, Min Buri, and Huai Khwang. This research, rooted in the application of Voronoi diagram theory, offers vital insights for various stakeholders involved in urban infrastructure planning. By employing Voronoi diagrams within Geographic Information Systems (GIS), the study contributes to strategically placing charging stations, optimizing spatial understanding, and enhancing resource allocation. This GIS-based approach not only supports the rise of electric vehicles but also promotes sustainable urban development practices through the efficient utilization of spatial data and analysis techniques.

Scalar transport after a high-resolution solitary fractal tree based on large-eddy simulation: Implication to urban green infrastructure

Scalar transport is one of the most concerned issues in the urban environment (e.g., pollutants, pollens, and aeolian transport). Vegetations are commonly regarded as scalar-moderation structures in urban areas by influencing airflows, avoiding aeolian erosion and pollution. The morphological multi-scale branch structures of the single tree were usually neglected in previous studies, which, however, can lead to highly inhomogeneous wake. The turbulence generation through the multi-scale branches is not yet clear. The corresponding spatial details of scalar distribution and its mechanism need to be further examined. In this study, the wake after a single fractal tree is investigated using the large-eddy simulation (LES). The spatial characteristics of the wakes are illustrated, indicating susceptible zones in the canopy, first-generation levels of branches, and recirculation regions. Higher-order moments signify the rare, extreme events with strong upward acceleration, removing air masses likely to occur at the canopy shear layer within 3h downstream where h is the characteristic tree height. The quadrant analysis elucidates the scalar transport potential. The scalar distribution and its transport mechanism are visualized, showing dilution and accumulation by the streamwise and vertical velocities. Involving the multi-scale geometries of the tree can provide guidance for urban green infrastructure (UGI) planning, advancing environmental and sustainability development.

Urban Infrastructure Innovation: The Contribution Ratio-Based Distance-to-Target Prioritization Method for Advanced Systems

As urban infrastructure increasingly benefits from adopting advanced systems, several approaches for prioritizing their utilization in urban infrastructure planning have been proposed. However, more research focusing on prioritizing advanced systems in urban infrastructure is required, and many existing prioritization methods rely on subjective inputs to determine the weights (i.e., relative importance) of evaluation criteria. This paper introduces a contribution ratio-based distance-to-target (CR-DTT) method for prioritizing advanced systems to assist urban infrastructure innovation. The novel prioritization method combines DTT normalization and ratio-based weights, and consists of three modules: (a) hierarchy table preparation; (b) input of advanced systems information; and (c) advanced systems prioritization. Each module involves multiple steps that generate outputs, which serve as inputs for the subsequent modules. We applied the prioritization method to a case study involving urban roads to demonstrate its effectiveness in real-world settings. The newly presented CR-DTT prioritization method contributes to the current knowledge in the area of developing prioritization methods by developmentally combining the DTT normalization and the ratio-based weights approaches into a practical data-driven prioritization method, which addresses the temporal aspect of the input parameter and provides objective insights into how best to prioritize advanced systems to assist transparent and credible decision-making in relation to investment in urban infrastructure innovation.

Optimal site selection strategies for urban parks green spaces under the joint perspective of spatial equity and social equity.

Urban park green spaces (UPGS) are a crucial element of social public resources closely related to the health and well-being of urban residents, and issues of equity have always been a focal point of concern. This study takes the downtown area of Nanchang as an example and uses more accurate point of interest (POI) and area of interest (AOI) data as analysis sources. The improved Gaussian two-step floating catchment area (G2SFCA) and spatial autocorrelation models are then used to assess the spatial and social equity in the study area, and the results of the two assessments were coupled to determine the optimization objective using the community as the smallest unit. Finally, the assessment results are combined with the k-means algorithm and particle swarm algorithm (PSO) to propose practical optimization strategies with the objectives of minimum walking distance and maximum fairness. The results indicate (1) There are significant differences in UPGS accessibility among residents with different walking distances, with the more densely populated Old Town and Honggu Tan areas having lower average accessibility and being the main areas of hidden blindness, while the fringe areas in the northern and south-western parts of the city are the main areas of visible blindness. (2) Overall, the UPGS accessibility in Nanchang City exhibits a spatial pattern of decreasing from the east, south, and west to the center. Nanchang City is in transition towards improving spatial and social equity while achieving basic regional equity. (3) There is a spatial positive correlation between socioeconomic level and UPGS accessibility, reflecting certain social inequity. (4) Based on the above research results, the UPGS layout optimization scheme was proposed, 29 new UPGS locations and regions were identified, and the overall accessibility was improved by 2.76. The research methodology and framework can be used as a tool to identify the underserved areas of UPGS and optimize the spatial and social equity of UPGS, which is in line with the current trend of urban development in the world and provides a scientific basis for urban infrastructure planning and spatial resource allocation.

Mapping pollen allergenicity from urban trees in Valencia: A tool for green infrastructure planning

Urban trees provide many benefits to citizens but also have associated disservices such as pollen allergenicity. Pollen allergies affect 40% of the European population, a problem that will be exacerbated with climate change by lengthening the pollen season. The allergenic characteristics of the urban trees and urban parks of the city of Valencia (Spain) have been studied. The Value of Potential Allergenicity (VPA) was calculated for all species. The most abundant allergenic trees with a very high VPA were the cypresses, followed by Platanus x hispanica and species of genera Morus, Acer and Fraxinus, with a high VPA. On the contrary, Citrus x aurantium, Melia azedarach, Washingtonia spp., Brachychiton spp. and Jacaranda mimosifolia were among the most abundant low allergenic trees. VPA was mapped for the city and a hot spot analysis was applied to identify areas of clustering of high and low VPA values. This geostatistical analysis provides a comprehensive representation of the VPA patterns which is very useful for urban green infrastructure planning. The Index of Urban Green Zone Allergenicity (IUGZA) was calculated for the main parks of the city. The subtropical and tropical flora component included many entomophilous species and the lowest share of high and very high allergenic trees in comparison with the Mediterranean and Temperate components. Overall, a diversification of tree species avoiding clusters of high VPA trees, and the prioritization of species with low VPA are good strategies to minimize allergy-related impacts of urban trees on human health.

Landscape and soundscape quality promote stress recovery in nearby urban nature: A multisensory field experiment

Cities have different benefits and risks, but are often stressful environments to live in. Everyday contact with nearby nature can be a crucial way to alleviate stress and increase the well-being of citizens. However, there is still limited evidence on how nature-health benefits vary according to the type and quality of natural environments. This study integrated multiple landscape and soundscape objective and perceived assessments to examine stress recovery in different types of neighbourhood nature. We used a field randomised experiment (n=45) to analyse effects of various random and fixed factors on restoration including: environmental conditions (e.g. temperature, wind, air quality), personal characteristics (e.g. age, gender, perceived health, nature connectedness), presence of other people and environmental quality (e.g. Perceived Environmental Aesthetic Qualities Scale and Perceived Sound Affective Quality scale). We found that physiological and psychological restoration is significantly greater in sites with higher visual (% visual natural elements) and acoustic (Normalized Difference Soundscape Index (NDSI)) naturalness i.e. the beach and forest, compared to the urban park (control site). Perceived landscape and soundscape quality were strongly associated with stress recovery, and these results were more pronounced for the soundscape. This highlights that soundscape quality assessments deserve more systematic attention in urban green infrastructure research and planning. Finally, we found important early evidence of reduction in Electrodermal activity (EDA) only within 3 minutes of nature exposure especially in the forest.

Water Supply Systems in Urban Slum Communities in Lagos, Nigeria: Between Self-supply and Co-production

By investigating the contribution of the ‘informal sector’, this article explores the resourcefulness of place-based practices and how they shape the urban water provision in Lagos. It also provides a detailed and critical view of the enormous efforts of inhabitants in urban slums in the daily water provision. Analysing the qualitative field data collected from observations, 89 semi-structured interviews (field notes), 6 focus group discussions and 12 expert interviews, water supply systems, the utilisation in slum communities and power and politics around it are exposed. Even if people can afford to pay the bills, the provision of urban services by state actors (or commissioned firms) is erratic and often not available. The daily infrastructure provision remains the responsibility of individuals and groups, mostly organised in complex yet fragile and changing networks. Drawing from the research on the existing, largely informal water supply in slum communities, this article argues for the recognition and pro-active implementation of hybrid approaches in the planning, implementation and administration of urban infrastructure. It calls for co-production and ‘hybrid water governance’.

Optimize Urban Infrastructure Planning Based on Big Data and Enhance Xi'an's Urban Image

Infrastructure is an important support for urban operation, and city image is directly related to the quality of life of citizens and the construction of city brand. Urban infrastructure distribution, equipment types, network switching, and other issues have always restricted the development of communication in Xi'an city, and the addition of big data technology has further increased the communication pressure in Xi’an city and affected the image of Xi'an city. In this paper, we take Xi'an urban infrastructure as the research object, and combine the phyon method to obtain the big data information in the network and the data in the wireless self-organizing sensor. Then, the incomplete data was eliminated, and the data was mapped to the 0~1 interval in a logarithmic manner, and a standardized processing set was formed. set up wireless ad hoc sensor devices, collect infrastructure-related data, and summarize data through big data analysis. At the same time, based on social urban image data, public demand data, and urban infrastructure evaluation results, the content of urban planning is adjusted to better meet the expectations of the public and provide targeted planning solutions. Finally, according to the data fitting, the matching of wireless ad hoc sensor network and city image improvement is realized, and the reasonable planning of infrastructure is promoted. The results of urban image analysis show that wireless ad hoc sensor network and big data technology can simplify the steps of urban infrastructure planning, reduce urban planning costs, and enhance the functionality of the infrastructure,reduce the public complaint rate, and meet the requirements of Xi'an urban image improvement.