Urban Planning Tools Research Articles

Salute, benessere e piano urbanistico: una sperimentazione incompiuta nell'Area Bazzanese

One knot to untangle when regenerating contemporary cities regards the possibility of institutions to recover cognitive, evaluative, and negotiating skills to respond to the social demand for quality of life. With reference to urban planning and design tools, confirming the centrality of the well-being and health issues may constitute a useful call to overcome an exclusively quantitative approach to planning standards in favour of a qualitative/performance-based approach. One pioneering experience was developed in the frame of the associated Municipal structural plan of the Municipalities in the Bazzanese Area, in Emilia Romagna: despite the failure implementation, it provides inputs to innovate planning processes and techniques.

Explaining the theoretical model of place branding: an asset-based approach to regeneration of the historic district of Tehran

Over recent years, place branding has come to vogue as a tool for urban planning and management projects such as regeneration of historic districts. In line with the context-oriented nature of regeneration, place branding should also be based on contextual conditions. Using a method based on grounded theory, therefore, the present study aims at developing a contextualized model for place branding of the historic district of Tehran. For this purpose, 32 in-depth interviews were conducted. The extracted codes were classified into 173 concepts and 47 categories. The final model, which was the result of modification of the paradigmatic model, moves place branding toward an asset-based approach. This model introduces the identity of place brand as the core category of place branding; an identity which is made up of three types of identity, namely tourism, inhabitation, and bazaar. In this model, place brand is not a phenomenon imposed from outside but the result of interaction among various agents on the basis of their shared interests that pave the way for place branding in a strategic process by virtue of the assets of the place. Therefore, this model creates added value for the historic district which guides the task of regeneration.

Estimation of Wastewater Discharges by Means of OpenStreetMap Data

For the optimization of sewer networks and integration of water management in urban planning, estimations of wastewater discharges at a high spatial resolution are a key boundary condition. In many cases, these data are not available or, for reasons of data protection and company secrecy, the data are not accessible for research purposes. Therefore, procedures are needed to determine the volume of wastewater with high spatial resolution, based on freely accessible data. The approach presented here uses mainly OpenStreetMap (OSM) data, combined with a dataset of the German official topographic–cartographic Information System (ATKIS), to estimate the volume of wastewater on a building level. By comparison with daily values of the dry weather inflow at pumping stations and sewage treatment plants, it is shown that the method can generate realistic results, if target inflows exceed 50 m³/d. Difficulties due to the effect of commuting and the individual use of the buildings have to be considered, as well as data-quality issues in the OSM dataset. As an application example, the generated wastewater discharges are spatially joined with land-use plans. The resulting wastewater yield factors serve as input data for decision-support tools in urban water planning or modeling tasks.

A framework for Air Quality Management Zones - Useful GIS-based tool for urban planning: Case studies in Antwerp and Gdańsk

There is a growing recognition of the importance of proper urban design in the improvement of air flow and pollution dispersion and in reducing human exposure to air pollution. However, a limited number of studies have been published so far focusing on the development of standard procedures which could be applied by urban planners to effectively evaluate urban conditions with respect to air quality. To fill this gap, a new approach for the determination of urban Air Quality Management Zones (AQMZs) was proposed and presented based on two case studies: Antwerp, Belgium and Gdańsk, Poland. The main objectives of the study were to 1) formulate a theoretical framework for the management of urban ventilation potential and human exposure to air pollution and to 2) develop methods for its implementation by means of a geographic information system (GIS). As a result of the analysis, the typologies that may be associated with decreased ventilation potential and the areas that require close monitoring due to potential human exposure to air pollution were identified for both cities. It is advocated that delimiting these typologies – combined with investigating local climate, wind and topography conditions and air pollution characteristics – could constitute a preliminary step in the urban planning process aimed at air quality improvement. These methods can be further applied to other urban areas in order to indicate where detailed studies are required and to facilitate the development of planning guidelines. Moreover, the directions for further research and urban planning strategies were discussed.

Mobile Phone Data for Urban Climate Change Adaptation: Reviewing Applications, Opportunities and Key Challenges

Climate change places cities at increasing risk and poses a serious challenge for adaptation. As a response, novel sources of data combined with data-driven logics and advanced spatial modelling techniques have the potential for transformative change in the role of information in urban planning. However, little practical guidance exists on the potential opportunities offered by mobile phone data for enhancing adaptive capacities in urban areas. Building upon a review of spatial studies mobilizing mobile phone data, this paper explores the opportunities offered by such digital information for providing spatially-explicit assessments of urban vulnerability, and shows the ways these can help developing more dynamic strategies and tools for urban planning and disaster risk management. Finally, building upon the limitations of mobile phone data analysis, it discusses the key urban governance challenges that need to be addressed for supporting the emergence of transformative change in current planning frameworks.

Nonlinear regression model for predicting environmental traffic noise

Transport is an essential activity for urban life and for the economy, however, bad planning of the city script causes some movement in some places, causing excessive noise levels generated by road traffic. We aim to present a mathematical model that represents the relationship between the equivalent level of sound and vehicle volume quantities using the nonlinear logistic regression model in order to facilitate decision-making in relation to urban planning. We collected the data of sound pressure and vehicle volume for ten days during five hours totaling a collection of 50 hours, with a total of 180 thousand data of sound pressure level. The place of data collection is a highway that passes through the city of Maringa, in Parana, being an intense traffic highway. The results show the performance of the proposed model were differences between measured and predicted traffic noise levels ranged from -0.3 dB (A) to +0.3 dB (A) and the mean difference was -0.006 dB (A). The proposed road traffic noise forecasting model can be used as a decision support tool in urban planning, considering the same boundary conditions as the road study.

Sound Levels Forecasting in an Acoustic Sensor Network Using a Deep Neural Network.

Wireless acoustic sensor networks are nowadays an essential tool for noise pollution monitoring and managing in cities. The increased computing capacity of the nodes that create the network is allowing the addition of processing algorithms and artificial intelligence that provide more information about the sound sources and environment, e.g., detect sound events or calculate loudness. Several models to predict sound pressure levels in cities are available, mainly road, railway and aerial traffic noise. However, these models are mostly based in auxiliary data, e.g., vehicles flow or street geometry, and predict equivalent levels for a temporal long-term. Therefore, forecasting of temporal short-term sound levels could be a helpful tool for urban planners and managers. In this work, a Long Short-Term Memory (LSTM) deep neural network technique is proposed to model temporal behavior of sound levels at a certain location, both sound pressure level and loudness level, in order to predict near-time future values. The proposed technique can be trained for and integrated in every node of a sensor network to provide novel functionalities, e.g., a method of early warning against noise pollution and of backup in case of node or network malfunction. To validate this approach, one-minute period equivalent sound levels, captured in a two-month measurement campaign by a node of a deployed network of acoustic sensors, have been used to train it and to obtain different forecasting models. Assessments of the developed LSTM models and Auto regressive integrated moving average models were performed to predict sound levels for several time periods, from 1 to 60 min. Comparison of the results show that the LSTM models outperform the statistics-based models. In general, the LSTM models achieve a prediction of values with a mean square error less than 4.3 dB for sound pressure level and less than 2 phons for loudness. Moreover, the goodness of fit of the LSTM models and the behavior pattern of the data in terms of prediction of sound levels are satisfactory.

Modelling multi-regional urban growth with multilevel logistic cellular automata

Simulation models based on cellular automata (CA) are useful for revealing the complex mechanisms and processes involved in urban growth and have become supplementary tools for urban land use planning and management. Although the urban growth mechanism is characterized by multilevel and spatiotemporal heterogeneity, most existing studies focus only on simulating the urban growth of singular regions without considering the heterogeneity of the urban growth process and the multilevel factors driving urban growth within regions that consist of multiple subregions. Thus, urban growth models have limited performance when simulating the urban growth of multi-regional areas. To address this issue, we propose a multilevel logistic CA model (MLCA) by incorporating a multilevel logistic regression model into the traditional logistic CA model (LCA). In the MLCA, multilevel driving factors are considered, and the multilevel logistic model allows the transition rules to not only vary in space, but also change when the subregional level factors change. To verify the MLCA's validity, it was applied to simulate the urban growth of Tongshan County, located in China's Xuzhou Prefecture. The results were compared with three comparative models, LCA1, which only considered grid cell-level factors; LCA2, which considered both grid cell- and subregional-level factors; and artificial neural network CA. Urban growth data for the periods 2000–2009 and 2009–2017 were used. The results show that the MLCA performs better on both visual comparison and indicators for accuracy verification. The Kappa of the results increased by <5%, but the improvement was significant, while increases for the accuracy of urban land and figure of merit were much higher than 5%. In addition, the results of MLCA had the smallest mean absolute percentage error when allocating new urban land areas to the various subregions. The results reveal that higher-level (e.g., town level) factors either strengthened or weakened the effects of grid cell-level factors on urban growth, which indirectly affected the spatial allocation of new urban land. The MLCA model is an effective step towards simulating nonstationary urban growth of multi-regional areas, using the comprehensive effects of multilevel driving factors.

Urban Air Quality Assessment Using Integrated Artificial Intelligence Algorithms and Geographic Information System Modeling in a Highly Congested Area, Iraq

Pollutant emissions are considered to be a major threat to air quality and human health in urban areas. Therefore, accurate modeling and assessment tools are required. In this study, a model was done by the integration of machine learning algorithms and a geographic information system model. This model included the optimization of the support vector regression model by using the principal component analysis algorithm. Then, the integration of the regression model with spatial analysis modeling via a grid (100 x 100 m) was done in order to generate prediction maps during holidays and workdays in the daytime and at nighttime in a highly congested area in Baghdad city, Iraq. The data used in this study categorized into two categories. The first category is the data acquired through field surveying that includes temperature, humidity, wind speed, wind direction, and traffic flow data (e.g., the number of light and heavy vehicles), as well as carbon monoxide samples by using mobile equipment. The second category is the information derived from geographic information system data, such as land use, road network, and building height. The accuracy of the proposed model is 81%, and the lowest value of root mean square error was 0.067 ppm. The integration between air pollution models and geographic information system techniques could be a promising tool for urban air quality assessment and urban planning. These tools effectively utilized by stakeholders and decision-makers to outline proper plans and strategies to mitigate air pollutants in urban areas.

A cellular automata-based land-use model as an integrated spatial decision support system for urban planning in developing cities: The case of the Bogotá region

Bogotá and the 17 neighboring municipalities make up one of the biggest metropolitan areas in Latin America. However, despite strong functional interactions within the area, there is no official government body at this level in charge of coordinating authorities and providing solutions to the wide variety of issues arising in the regional urban land system. Aiming at providing an insight on future land-use developments linked to new transport infrastructures and at offering a tool to support territorial decision-making, this paper presents a cellular automata-based (CA) model based in Metronamica® software, that allows testing different scenarios based on potential land-use policies, environmental suitability and transport alternatives.There has not been, so far, an urban planning tool that accounts for the complexities of this region. CA-based land-use simulations constitute a useful approach to understanding the impacts of urban planning policies and regulations. This tool can help to improve inter-territorial and inter-institutional coordination, which through planning and management policies seek a spatially integrated development, with a long-term perspective.The CA-based model proposed was calibrated to reproduce land-use changes between 2007 and 2016 using different methods and indicators. The model was used to simulate and analyze eight scenarios with different policy directions of transport infrastructure in the future of the region. The results of the simulations reflect the dynamics of territorial occupation. The calibration indices in the experiment indicate a high degree of suitability for the CA Bogotá model, proving its effectiveness and potential as a useful tool for decision-making. The results show that occupation scenarios with restricted developable zones within the city, tend to have the greater dispersion rate in the study area, compared to scenarios where land development plans are promoted in Bogotá, which representing a more compact development.

Identifying Urban Structure Based on Transit-Oriented Development

The fast development of urbanization has led to imbalances in cities, causing congestion, pollution, and urban sprawl. In response to the growing concern over the distribution of demand and supply, a more coordinated urban structure is addressed in comprehensive planning processes. In this study, we attempt to identify urban structure using a Network–Activity–Human model under the Transit-Oriented Development (TOD) concept, since TOD is usually regarded as an urban spatial planning tool. In order to explore the strengths and weaknesses of the urban structure, we define the TOD index and unbalance degree and then classify the urban areas accordingly. We take the city of Beijing as a case study and identify nine urban types. The results show a hierarchical urban structure: the city center covers most of the hotspots which display higher imbalances, the surroundings of the city center are less developed, and the city edges show higher potentials in both exploitation and transportation development. Moreover, we discuss the extent to which the spatial scale influences the unbalance degree and apply a sensitivity analysis based on the goals of different stakeholders. This methodology could be utilized at any study scale and in any situation, and the results could offer suggestions for more accurate urban planning, strengthening the relationship between TOD and spatial organization.

Smart Planning: From City to Territorial System

Actual cities have become smart cities divided by zones. In the past, various innovative actions have been carried out to facilitate smart cities. The projects that were carried out, in response to the needs expressed by inhabitants and driven by individualistic growth, have gone beyond the regulations defined by the urban plan. In fact, territorial contexts reflect the needs of inhabitants, which always shift faster and are more reactive than traditional urban planning tools. The territory is a complex system and it works well if the design choices for its sustainable development are structured and coordinated by a strategic planning tool. The objective of this research is to propose a smart planning process in order to pursue the sustainable development of complex territorial systems. The paper has a particular interest in observing the integrated coastal-marine system as a possible example of real smart development. Smart planning applied to this type of landscape has a positive influence on the development of the environment, landscapes, infrastructure, mobility, services, energy, research, and innovation.

Biodiversity constraint indicator establishment and its optimization for urban growth: framework and application

Urbanization causes tremendous pressure on biodiversity and ecosystems at the global scale. China is among the countries undergoing the fastest urban expansion. For a long time, ecological environment protection has not been a priority in China’s urban planning process. Current urban growth optimization research has some limitations regarding the selection of more scientific ecological constraint indicators and the interaction between urban expansion and ecological factors. This paper at first aimed to establish a reasonable comprehensive biodiversity constraint indicator based on the indicators of net primary productivity, habitat connectivity and habitat quality, and then conducted a case study in Beijing and compared biodiversity loss and urban growth patterns under different developing situations. The integrated valuation of ecosystem services and trade-offs model and GIS-related methods were used to obtain biodiversity and ecological spatial distribution layers. Then an ecological priority-oriented urban growth simulation method was proposed to search for minimum biodiversity loss. The results showed that the important biodiversity security areas were mostly distributed in the western part of the study area and that the ecological degradation in 2000 had a radial pattern and was well in line with the urban construction and ring road distribution patterns. Meanwhile, biodiversity loss with the biodiversity constraint was much less than actual urban growth in 2000–2010. Under the guidance of ecological optimization, urban growth in the research results reflects decentralized and multi-center spatial development characteristics. This type of urban growth not only provides a new model for breaking the inertia of urban sprawl but also proposes ‘biodiversity security’ as an applicable regulatory tool for urban planning and space governance reforming.

A Spatial-Socioeconomic Urban Development Status Curve from NPP-VIIRS Nighttime Light Data

Urban development status is closely related to the urban economy, environment, ecology, and health. Spatial and socioeconomic processes are the two key aspects of urban development, so the absence of any of them will affect the assessment of urban development status. In this study, using both spatial and socioeconomic information from land cover data and nighttime light data, respectively, we proposed an exponential model, Spatial–Socioeconomic Urban Development Curve (SSUDC), to provide a quantitative expression of the relationship between the two key processes of urban development and analyze urban development status. The SSUDC was calculated from the artificial surface ratio at 1% intervals obtained from Globeland30 land cover data and the corresponding average NPP-VIIRS nighttime light radiance data, using a nonlinear least-squares method. We generated SSUDCs for 330 prefecture-level cities in Mainland China, 208 of which had coefficients of determination (R2) greater than 0.6. Taking Ordos and Guiyang as two typical examples, we analyzed the importance and advantages of SSUDC. The coefficients α and β of the exponential SSUDC were shown to indicate the base intensity socioeconomic activity and the concentration of socioeconomic activities, respectively, and can be used to reveal the urban socioeconomic development status and functional type of cities. At the internal urban level, the residuals of SSUDC can imply the demand for urban physical or economic construction in different areas of the city, and even the urban growth type, together with the distribution of the artificial surface ratio. In summary, the proposed SSUDC provides a simple way to combine the spatial and socioeconomic processes of urban development, which is beneficial to the analysis of urban development at different scales and a rewarding tool for urban planning.

The Role of Green Infrastructures in Urban Planning for Climate Change Adaptation

The population that lives in cities has surpassed the one that lives in the countryside. Cities are recognized as apriority source of pollution. The degradation of air quality and the phenomenon of Urban Heat Island (UHI) are some of the most well-known consequences of urban development. The adaptation of the cities is emerging as one of the greatest challenges that urban planners will face in this century. Urban Green Infrastructures (GIs) could help cities adapt to climate change, and the strategy of expansion of greening in urban planning could play an important role in enhancing the sustainability and resilience of cities and communities. Many studies have shown the benefits of GIs to climate change mitigation and adaptation in urban areas and their role as an important urban planning tool to satisfy environmental, social, and economic needs of urban areas. The objective of this article is to propose a methodological approach to evaluate the social perception of citizens regarding urban green areas. The proposed methodology, applied to the reality of the “urban green system” of Catania, is based on an integrated approach between participatory planning and the methods social multi criteria evaluation to guiding the city’s government to realize a new urban resilient development.

Classification of the tree for aerial image using a deep convolution neural network and visual feature clustering

In recent years, over-exploitation has led to the accelerated destruction of rural and natural environments for urban development, making an understanding of land use and land cover changes, one of the most urgently required and important tools for urban land planning. To this end, before any land planning begins, the distribution ratio of trees for a given piece of land is determined by calculating the area of that land covered by trees. This study proposes the use of supervised machine learning methods to classify treed areas and combines unsupervised color clustering techniques to achieve optimum classification results. First, the YOLO (you only look once) classification model is used to obtain tree features and location information. The ‘K-means’ and ‘Flood fill algorithm’ methods were tested with tree classification experiments, measuring precision rate, accuracy rate, and recall rate, with shape, illumination, and angle of tree species, and color differences affecting classification results.

Developing standard exposure-response relationships to inform protected area management

The Schultz Curve, which was embodied in ANSI Standard S12.9 Part 4, has been a central informative tool for urban planning and noise impact analyses. Numerous field studies have been conducted by the Volpe Transportation Center and others to develop the foundation for the same type of tool for national parks. I will review what these studies have revealed, and how they compare with the urban noise standard. Park settings vary more widely than urban environments, so one challenge for devising a standard is addressing the potential variety of receptors and the contexts in which effects could be analyzed. The complexity of this issue increases dramatically when considering the nonhuman receptors of noise in national parks, and the effects of noise on human-wildlife interactions.

L’aménagement des parkour-parks : les espaces dédiés pour les activités ludo-sportives comme outils d’inclusion? Études de cas à Nantes et à Rennes

Skating sports have dominated the field of studies in human and social sciences regarding sports-leisure practices for a long time, particularly because of their visibility in cities by the establishment of open spaces: skate parks. However, since the 1990s, these activities that invest in urban spaces have been diversifying. Parkour is one of them and presents the originality of questioning the (skate and/or parkour) parks, as it is an urban planning tool and it is also dedicated to the development of open and public facilities for this activity. Based on a literature review and a study conducted in Rennes and Nantes, we question this fact of the park and show that, beyond its urban planning issues, it constitutes (1) a lever of the emergence of renewed youth citizenship; and (2) a vehicle for building not only a sporty, but more broadly recreational, urbanity.

Conception of Green Infrastructure as a Tool of City Development Planning

The role of the greenery in the urban environment is priceless – both for maintaining ecological balance, and for enhancing visually aesthetic quality, as well as home for small habitats and the key to urban identity. The development of greenery structures in a city or part of a city is often chaotic and spontaneous – developing greenery only for certain objects and not linking it with one another. Planning of greenery in the urban environment is influenced by several regulatory documents, which mainly focus on the preservation of the existing greenery structure and the restrictions on the planning of the greenery. Unfortunately, the detailed guidelines for the planning of the greenery are not sufficiently reflected in the existing normative documents. The concept of greenery is one of the tools for urban planning that, together with other planning tools (infrastructure, spatial planning, architectural design, urban planning, economic urban development planning, etc.), forms a single framework for urban planning tools that helps to develop urban environment smoothly and identify priorities. The aim of the research is to develop a model of the concept of greenery of urban areas, which in practice can be used on the Latvian scale, as the planning document of greenery in local governments. The development of greenery plans and concepts has become topical in recent years and more and more municipalities are interested in this planning tool, which helps to plan the distribution of finances for maintaining the structure of greenery and developing new structure. In the last ten years, four concepts of greenery for medium-sized cities have been developed in Latvia.

A review and reframing of participatory urban dashboards

Abstract Urban dashboards form part of a wider ecosystem of urban data platforms and products being created and used in an age of ever-increasing digitalisation of city services and governance. The design of these dashboards typically consolidates information into a single view for government and industry to efficiently and effectively monitor the performance of urban systems – such as those of energy, built form, transport, culture, performance, innovation and health. Many of these dashboards focus on a one-way flow of data and thus do not cater specifically to the functions of public participation, community engagement and collaboration. With growing concerns in digital democracy, there is an increasing demand for software and tools that maximise human-centred design elements and include such participatory and collaborative features. The current trajectory of their development can be reframed to support this. This review frames a more porous definition of the term, integrating the concept of the urban dashboard among other existing digital urban planning methods and tools. It highlights the potential for further work to integrate with emerging media such as virtual, augmented and mixed realities. Reflecting on a diverse range of literature, the result of this paper is a classification system and key recommendations for development and understanding participatory urban dashboards as they fall within the existing lexicon of Smart City platforms and technologies.