Urban Planning Applications Research Articles

From road centrelines to carriageways-A reconstruction algorithm.

Roads are important for many urban planning applications, such as traffic modelling and delivery vehicle routing. At present, most available datasets represent roads only as centrelines. This is particularily true for OpenStreetMap which provides, among many features, road networks at worldwide coverage. Furthermore, most approaches for creating more detailed networks, such as carriageways or lanes, focus on doing so from sources that are not easy to acquire, such as satellite imagery or LiDAR scans. In this paper we present a methodology to create carriageways based on OpenStreetMap's centrelines and open access areal representations (i.e. polygons) to determine which roads should be represented as two individual carriageways. We applied our methodology in five areas across four different countries with different built environments. We analysed the outcome in a delivery routing problem to evaluate the validity of our results. Our results suggest that this method can be effectively applied to create carriageways anywhere in the world, as long as there is sufficient coverage by OpenStreetMap and an areal representation dataset of roads.

High-Resolution Gridded Air Temperature Data for the Urban Environment: The Milan Data Set

Temperature is the most used meteorological variable for a large number of applications in urban resilience planning, but direct measurements using traditional sensors are not affordable at the usually required spatial density. On the other hand, spaceborne remote sensing provides surface temperatures at medium to high spatial resolutions, almost compatible with the needed requirements. However, in this case, limitations are represented by cloud conditions and passing times together with the fact that surface temperature is not directly comparable to air temperature. Various methodologies are possible to take benefits from both measurements and analysis methods, such as direct assimilation in numerical models, multivariate analysis, or statistical interpolation. High-resolution thermal fields in the urban environment are also obtained by numerical modelling. Several codes have been developed to resolve at some level or to parameterize the complex urban boundary layer and are used for research and applications. Downscaling techniques from global or regional models offer another possibility. In the Milan metropolitan area, given the availability of both a high-quality urban meteorological network and spaceborne land surface temperatures, and also modelling and downscaling products, these methods can be directly compared. In this paper, the comparison is performed using: the ClimaMi Project high-quality data set with the accurately selected measurements in the Milan urban canopy layer, interpolated by a cokriging technique with remote-sensed land surface temperatures to enhance spatial resolution; the UrbClim downscaled data from the reanalysis data set ERA5; a set of near-surface temperatures produced by some WRF outputs with the building environment parameterization urban scheme. The comparison with UrbClim and WRF of the cokriging interpolated data set, mainly based on the urban canopy layer measurements and covering several years, is presented and discussed in this article. This comparison emphasizes the primary relevance of surface urban measurements and highlights discrepancies with the urban modelling data sets.

Automatic Target Detection from Satellite Imagery Using Machine Learning.

Object detection is a vital step in satellite imagery-based computer vision applications such as precision agriculture, urban planning and defense applications. In satellite imagery, object detection is a very complicated task due to various reasons including low pixel resolution of objects and detection of small objects in the large scale (a single satellite image taken by Digital Globe comprises over 240 million pixels) satellite images. Object detection in satellite images has many challenges such as class variations, multiple objects pose, high variance in object size, illumination and a dense background. This study aims to compare the performance of existing deep learning algorithms for object detection in satellite imagery. We created the dataset of satellite imagery to perform object detection using convolutional neural network-based frameworks such as faster RCNN (faster region-based convolutional neural network), YOLO (you only look once), SSD (single-shot detector) and SIMRDWN (satellite imagery multiscale rapid detection with windowed networks). In addition to that, we also performed an analysis of these approaches in terms of accuracy and speed using the developed dataset of satellite imagery. The results showed that SIMRDWN has an accuracy of 97% on high-resolution images, while Faster RCNN has an accuracy of 95.31% on the standard resolution (1000 × 600). YOLOv3 has an accuracy of 94.20% on standard resolution (416 × 416) while on the other hand SSD has an accuracy of 84.61% on standard resolution (300 × 300). When it comes to speed and efficiency, YOLO is the obvious leader. In real-time surveillance, SIMRDWN fails. When YOLO takes 170 to 190 milliseconds to perform a task, SIMRDWN takes 5 to 103 milliseconds.

How the ecosystem extent is changing: A national-level accounting approach and application

Assessing the spatial and temporal changes in ecosystems is essential to account for natural capital contribution to human well-being. However, various methods to quantify these changes challenge the development of reliable values which can be integrated into national statistical accounts. Following the international system of environmental-economic accounting framework, which recently adopts an ecosystem accounting standard. We present a novel approach to develop an ecosystem extent account from existing ecosystem classifications. This study shows the spatial and statistical extent account of 26 ecosystems (i.e. forests, grasslands, croplands, and urban, among others) between 1970 and 2015 at the national scale. Extent accounts were developed at a resolution of 25 m and provided reliable information on how ecosystem types have changed over time in Spain. Our results reflect three main patterns in the extension account: (i) an increase in forest ecosystems, (ii) a considerable decrease in agroecosystems (especially annual croplands), and (iii) substantial development of urban areas. To the best of our knowledge, this method is the first attempt to develop a robust methodology to measure the extent of ecosystems at the national level. The proposed approach is crucial for a strong knowledge of ecosystem dynamics and their implications for ecosystem conditions and services at a national level. This has potential applications in urban planning, green infrastructure development, and multiple uses for territory management and policies, integrating natural capital into official statistics and mainstreaming ecosystems into national-level planning and monitoring processes.

Single Convolutional Neural Network With Three Layers Model for Crowd Density Estimation

Crowd density estimation is an important topic in computer vision due to its widespread applications in surveillance, urban planning, and intelligence gathering. Resulting from extensive analysis, crowd density estimation reflects many aspects such as similarity of appearance between people, background components, and inter-blocking in intense crowds. In this paper, we are interested to apply machine learning for crowd management in order to monitor populated area and prevent congestion situations. We propose a Single-Convolutional Neural Network with Three Layers (S-CNN3) model to count the number of people in a scene and conclude about the crowd estimation. Then, a comparative study for density counting establishes the performance of the proposed model against the convolutional neural networks with four layers (single-CNN4) and Switched Convolutional neural networks (SCNN). ShanghaiTech dataset, considered as the largest data base for crowd counting, is used in this work. The proposed model proves high effectiveness and efficiency for crowd density estimation with 99.88% of average test accuracy and 0.02 of average validation loss. These results achieve better performance than the existing state-of-the-art models.

Prospective for urban informatics

The specialization of different urban sectors, theories, and technologies and their confluence in city development have led to a greatly accelerated growth in urban informatics, the transdisciplinary field for understanding and developing the city through new information technologies. While this young and highly promising field has attracted multiple reviews of its advances and outlook for its future, it would be instructive to probe further into the research initiatives of this rapidly evolving field, to provide reference to the development of not only urban informatics, but moreover the future of cities as a whole. This article thus presents a collection of research initiatives for urban informatics, based on the reviews of the state of the art in this field. The initiatives cover three levels, namely the future of urban science; core enabling technologies including geospatial artificial intelligence, high-definition mapping, quantum computing, artificial intelligence and the internet of things (AIoT), digital twins, explainable artificial intelligence, distributed machine learning, privacy-preserving deep learning, and applications in urban design and planning, transport, location-based services, and the metaverse, together with a discussion of algorithmic and data-driven approaches. The article concludes with hopes for the future development of urban informatics and focusses on the balance between our ever-increasing reliance on technology and important societal concerns.

A Bibliometric Review of Publication Trends in the Application of Landscape Metrics in Urban and Regional Planning

Landscape metrics have been gaining popularity in urban and regional planning since the early 2000s. Primarily used to assess landscapes’ ecological composition and configuration, the field has expanded to include studies on the environment, land use studies, ecosystem services, spatiotemporal changes in land uses, perception studies, and peri-urban studies. However, a summarization and review of published literature in landscape metrics and its application in urban and regional planning is missing. The present study proposes to familiarize researchers with an overview of the publications in the field. Hence, the review aims at identifying the most prominent authors, papers, journals, keywords, and countries conducting studies in the subject domain. The Web of Science database was selected owing to the broader coverage. The selected articles from the Web of Science database are analyzed using Bibliometrix and Vosviewer software to gather relevant publication trends and identify the most significant networks. The present paper is limited to bibliometric analysis and has identified advances in using landscape metrics in urban and regional planning. The paper also indicates the potential future research areas.

Spatio-temporal prediction and factor identification of urban air quality using support vector machine

Accurate air quality prediction can provide better supervision and reference for management policies. Due to difficulties in data acquisition, combined spatio-temporal prediction is still inconclusive. This study utilizes the support vector machine (SVM) method to predict air quality of unknown space and time. Extracted from a geographic information system (GIS), geographic features such as population, land use, economy, pollution sources, and terrain parameters were added to a time series. Temporal prediction was first executed in the reference stations, and the predicted air quality index (AQI) was then used to spatially infer the future AQI of unknown locations. Verification indicated high accuracy for short-term temporal prediction. Various meteorological and climatic effects were observed to be influential in seasonal difference. In the spatial inference stage, urbanization and city types were spatial features that appeared to impact air quality. Agriculture and forest use, transportation use, residential use, and economic factors were clearly correlated to AQIs, whereas population and labor force were not. This study establishes a prediction framework in northern Taipei based on SVM. Other locations can build their own models based on local actual data to achieve better decision-making, urban planning, or other applications.

An End-to-End Point of Interest (POI) Conflation Framework

Point of interest (POI) data serves as a valuable source of semantic information for places of interest and has many geospatial applications in real estate, transportation, and urban planning. With the availability of different data sources, POI conflation serves as a valuable technique for enriching data quality and coverage by merging the POI data from multiple sources. This study proposes a novel end-to-end POI conflation framework consisting of six steps, starting with data procurement, schema standardisation, taxonomy mapping, POI matching, POI unification, and data verification. The feasibility of the proposed framework was demonstrated in a case study conducted in the eastern region of Singapore, where the POI data from five data sources was conflated to form a unified POI dataset. Based on the evaluation conducted, the resulting unified dataset was found to be more comprehensive and complete than any of the five POI data sources alone. Furthermore, the proposed approach for identifying POI matches between different data sources outperformed all baseline approaches with a matching accuracy of 97.6% with an average run time below 3 min when matching over 12,000 POIs to result in 8699 unique POIs, thereby demonstrating the framework’s scalability for large scale implementation in dense urban contexts.

METHODS OF URBAN ENVIRONMENT DESIGN BY ART AND GRAPHIC MEANS

The article is devoted to the methods of urban environment design of the XXI centuries by artistic and graphic means. The process of their inclusion in the urban environment was gradual. It began with painting, graphics, sculpture, arts and crafts and monumental arts. A new wave of artistic and graphic means in the urban environment began to develop intensively in the late nineteenth - early twentieth century. It was due to social and cultural processes in specific cities and countries. The use of various techniques in design began to be introduced from this period. In particular, such as: ready-made, found objects, assemblage, carving, lettering, perforation and others. Currently, there is a problem of perfection of such decisions with the intensification of the processes of participation in them not only professionals but also ordinary citizens.
 Artistic and graphic elements adjust the urban space to a certain thematic (content) atmosphere. They become a reflection of certain trends in art, but already in the urban space, like works of fine art in the interior. The most common among them are the following: surrealism; conceptuality; contextual game; visual illusion (optical illusion), irony, self-irony, wit, humor; metaphor (metaphorical poetics).
 Works of art give a wide range of possibilities for their application in architectural and urban planning. In practice, new artistic methods were developed along with the already known ways of designing the visual image of the elements. As a result of the analysis of a number of domestic and foreign cities the methods of design of the architectural environment based on active use of the invented art and graphic means are revealed. Among them are deliberate exaggeration of the image (scale, hyperbolization) and change the traditional shape and size of artistic elements, increase contrast, copying and repetition of the real landscapes, imitation of perspective images, and enrichment of the urban space with architectural details, creation of the provocation.
 The variety of artistic methods requires their correct application in the urban environment. It is necessary in order for them to perform their assigned role properly. The realization of creative artistic ideas in the urban spaces of the XXI century is due to the introduction of new engineering and creative forms, innovative technologies; introduction of significant supergraphic and font compositions, new techniques of evening lighting and color; tolerance in the use of graphic arts in historical spaces.

The Role of Ab-Anbars in the Vernacular Architecture of Iran with Emphasis on the Performance of Wind-Catchers in Hot and Dry Climates

Vernacular and traditional Iranian architecture has always acted rationally, harmoniously, and climate-friendly to meet the needs of the people in dealing with the environment. In addition, without harming the environment, they have achieved the best initiatives with the least facilities. For example, we can mention that the Ab-Anbars in arid and desert areas of Iran, which are used to store water in seasons with precipitation for use in the rest of the year, has been an optimal way to use natural resources and provide climate comfort. The Ab-Anbars are realized with ventilated cisterns through openings on their roof or wind-catchers to keep the water cool and provide comfortable conditions for the occupants. In order to study the essential role of natural ventilation and cooling in the Ab-Anbars, thermal analysis with CFD software was carried out to assess the effectiveness of a typical wind-catcher according to different wind directions in Yazd city. The results showed that Ab-Anbars have played an important role in reducing cooling loads and supply the necessary ventilation rate of buildings and can be used in the future for application in contemporary architecture and urban planning.

A Stacking Ensemble Deep Learning Model for Building Extraction from Remote Sensing Images

Automatically extracting buildings from remote sensing images with deep learning is of great significance to urban planning, disaster prevention, change detection, and other applications. Various deep learning models have been proposed to extract building information, showing both strengths and weaknesses in capturing the complex spectral and spatial characteristics of buildings in remote sensing images. To integrate the strengths of individual models and obtain fine-scale spatial and spectral building information, this study proposed a stacking ensemble deep learning model. First, an optimization method for the prediction results of the basic model is proposed based on fully connected conditional random fields (CRFs). On this basis, a stacking ensemble model (SENet) based on a sparse autoencoder integrating U-NET, SegNet, and FCN-8s models is proposed to combine the features of the optimized basic model prediction results. Utilizing several cities in Hebei Province, China as a case study, a building dataset containing attribute labels is established to assess the performance of the proposed model. The proposed SENet is compared with three individual models (U-NET, SegNet and FCN-8s), and the results show that the accuracy of SENet is 0.954, approximately 6.7%, 6.1%, and 9.8% higher than U-NET, SegNet, and FCN-8s models, respectively. The identification of building features, including colors, sizes, shapes, and shadows, is also evaluated, showing that the accuracy, recall, F1 score, and intersection over union (IoU) of the SENet model are higher than those of the three individual models. This suggests that the proposed ensemble model can effectively depict the different features of buildings and provides an alternative approach to building extraction with higher accuracy.

Unmanned Aerial Vehicle (UAV)/ Drone Surveying & Its applications in Urban Planning

Abstract: The current advancement in technology of Unmanned Aerial Vehicles (UAV)/ drones has potential use in various sides of urban life, including in the Town planning sector. Compared with other alternatives for surveying, Drones could potentially provide a more proficient and cost-effective, adaptable, and exact information gathering instrument for planners. Presently, however, government regulations on the use of Drones in India, especially in town planning sector, are not precisely defined with a general focus on safety rather than stimulating technology towards a more efficient system. Town planners and surveyors need flexible, realistic; drone regulations that will deal with the complex issues related to the urban use of UAVs, to take the maximum possible public benefit from the use of this technology in Town planning. In this report current use of UAVs in different Urban Planning sectors are discussed.

Explicit Modeling of Personal Space for Improved Local Dynamics in Simulated Crowds

Crowd simulation demands careful consideration in regard to the classic trade-off between accuracy and efficiency. Particle-based methods have seen success in various applications in architecture, military, urban planning, and entertainment. This method focuses on local dynamics of individuals in large crowds, with a focus on serious games and entertainment. The technique uses an area-based penalty force that captures the infringement of each entity's personal space. This method does not need a costly nearest-neighbor search and allows for an inherently data-parallel implementation capable of simulating thousands of entities at interactive frame rates. The algorithm reproduces personal space compression around motion barriers for moving crowds and around points of interest for static crowds.

With a Little Help From AI

Exploring the phenomenon of artificial intelligence (AI) applications in urban planning and governance, this article reviews most current smart city developments and outlines the future potential of AI, especially in the context of participatory urban design. It concludes that especially the algorithmic analysis and synthesis of large data sets generated by massive user participation projects present a beneficial field of application that enables better design decision making, project validation, and evaluation.

ESPAC-based 2D mini-array microtremor method and its application in urban rail transit construction planning

Conventional geophysical methods face many challenges in urban areas from limited site accessibility to severe industrial interferences. With the fast-paced development in urban area and urban light rail transit system, it is highly desirable to have a high-resolution geophysical method, which is suitable for the complex urban condition with strong background noises and, at the same time, is environment-friendly.In this paper, we introduce a 2D mini-array microtremor survey method, based on the extended spatial auto-correlation (ESPAC) method. It first uses the ESPAC to extract the dispersion curve of the Rayleigh wave from the microtremor data recorded using the mini array, then reconstructs the apparent S-wave velocity (Vx) structure of the stratigraphic rock formations by the Vr–Vx transformation, and finally, the 2D Vx profile is obtained by using Vx velocity imaging. This method not only makes full use of the multi-mode Rayleigh waves of dispersion curve, but also reduces the uncertainty during inversion caused by the potential pattern misjudgment when inverting the dispersion curve directly. Since the Vx is generally sensitive to the change of rock mass density, its profile is a natural and reliable reflection of the changes in rock properties. This technique provides an accurate and cost-effective tool for mapping complex near-surface geological anomalies such as karst cavities, fractured zone, and the soil/bedrock interface, which are frequently encountered in many urban engineering projects, such as urban rail transit development. The successful application of the microtremor survey in Guangzhou Metro Line 10 project demonstrates that this method can be very effective in identifying the soil/bedrock interface, mapping the severely weathered rock formations, and detecting the abandoned underground structures.

МЕТОД РОЗВ’ЯЗАННЯ ДВОВИМІРНОЇ ЗАДАЧІ РОЗТАШУВАННЯ ТОЧОК НА РЕГУЛЯРНИХ СІТКАХ

Point location problems often arise in areas such as object visibility analysis, sensor location, human flow simulation, urban planning, mobile control, and paint and film application. In many such problems, it is often necessary to define a set of points that cover given positions, such as viewpoints on the observed surface. This problem can be considered in terms of finding the set of sensor positions, which provides maximum coverage of the points of the set by some criterion of reachability, which is formulated in the paper on the basis of the Euclidean metric. Usually, the object location problem does not have an exact solution, so in practice, optimization methods are used to solve it, which do not guarantee finding a global optimum solution. This makes it necessary to find more effective methods for solving point location problems. The paper presents a method of solving the problem of location of a set of points, which provides a given coverage of the input point set by the criterion of reachability based on drawing circles on a regular (pixel) grid, and determining the grid points that belong to such circles. The presented method allows to solve such problems in a linear time from the number of points of the input set. The main iterations of the algorithm for finding a set of points covering a given set are presented, an experimental study on test point sets on a plane is performed, and examples of the obtained solutions are given. The presented approach consists of the following steps: formation of the criterion of mutual reach of points; determining the dimensions of the pixel grid and the battery array; drawing circles of a given radius on the grid; determination of points belonging to the applied circles; increment of values in the accumulator.Keywords: placement problem, point set, mutual reachability of points, pixel grid, coverage of point set.

The universal visitation law of human mobility.

Human mobility impacts many aspects of a city, from its spatial structure1-3 to its response to an epidemic4-7. It is also ultimately key to social interactions8, innovation9,10 and productivity11. However, our quantitative understanding of the aggregate movements of individuals remains incomplete. Existing models-such as the gravity law12,13 or the radiation model14-concentrate on the purely spatial dependence of mobility flows and do not capture the varying frequencies of recurrent visits to the same locations. Here we reveal a simple and robust scaling law that captures the temporal and spatial spectrum of population movement on the basis of large-scale mobility data from diverse cities around the globe. According to this law, the number of visitors to any location decreases as the inverse square of the product of their visiting frequency and travel distance. We further show that the spatio-temporal flows to different locations give rise to prominent spatial clusters with an area distribution that follows Zipf's law15. Finally, we build an individual mobility model based on exploration and preferential return to provide a mechanistic explanation for the discovered scaling law and the emerging spatial structure. Our findings corroborate long-standing conjectures in human geography (such as central place theory16 and Weber's theory of emergent optimality10) and allow for predictions of recurrent flows, providing a basis for applications in urban planning, traffic engineering and the mitigation of epidemic diseases.

Citizen sensors for smart city planning and traffic management: crowdsourcing geospatial data through smartphones in Jeddah, Saudi Arabia

With more than 84% of the population residing in cities, the Kingdom of Saudi Arabia is among the highly urbanized countries in the world. New strategic policies support the development of smart cities across the kingdom. Though non-availability of data is a major hurdle in the way of planning smart cities. We used a mixed method approach for this study including a qualitative review of various urban planning applications in Jeddah and worldwide in combination with a pilot survey to understand what type and mode of data reporting people prefer. Based on the initial findings a data crowdsourcing application—‘SMART JEDDH’ was developed and tested. Around 177 crowdsourced reports detecting 15 hotspots of car parking dearth, while 47 PM2.5 measurements at King Abdulaziz University campus was recorded. Results were visualized by heat maps in an open-source GIS environment. Furthermore, we model developed ordinal logistic regression (OLR) models with the 1581 responses on 5-point Likert scale to answer 3 key questions including importance of smartphone-based participation in resolving location specific urban challenges, importance of public participation through smartphones in the improvement of the quality of smart city plans, and the importance of the role of the authorities in mobilising people for smartphone-based participation. OLR results revealed that there is a significant difference of public opinions based on their nationality, gender, education, profession, and income levels in the city of Jeddah. Finally, we recommend that crowdsourced smartphone applications should be promoted for data collection because of inherent benefits through increased public participation. Abundant data though public participation might be helpful in realizing success of smart cities under Vision 2030 of Saudi Arabia of which maximizing ‘quality of life’ is a key target.

EVALUATION OF URBAN INTERACTIVE KIOSKS CENTERED ON INTERFACE ERGONOMY AND USER EXPERIENCE

As a result of technological progress, interactive kiosks maintain various services with consistency for public use. In contemporary life, kiosks provide services that speed up daily operations with their informative, explanatory and regulative content. The study centers on interactive service kiosks as examples of technology based applications in urban planning as well as being hybrid interactive products of an interdisciplinary approach consisting of industrial design, service design and urban design. The study based on a qualitative approach covers the evaluation of the interaction types, service goals and content related information of kiosks used for public services evaluating the products in terms of interface ergonomics and aesthetical features forming user experience. Following the explanation of essential literature input that constitutes the study, concepts related to interface ergonomics and virtual ergonomic features of kiosk are elaborated with the inclusion of the project discussed. In the discussion section aligned with the goals of the study, the service kiosk designed for the Istanbul Aquarium with the participation of the author is presented, explained and evaluated in order to create a connection and comparison with the examples provided. By achieving an essential foundation for the comparison, the study aims to determine the similarities and differences between the kiosks built for public and private use by applying the same evaluation criteria on the service kiosk selected for the laboratory process.