Decision Support For Urban Planning Research Articles

HashGAT-VCA: A vector cellular automata model with hash function and graph attention network for urban land-use change simulation

Vector cellular automata (VCA) models, which excel at representing spatiotemporal dynamics of irregularly shaped land parcels, have been widely employed in land use change simulations. However, current research faces the following issues: (1) most VCA models neglect the spatial heterogeneity of driving factors within each land parcel when evaluating the environmental driving effects; (2) when calculating the neighborhood effects, simple statistics of land use types in neighboring parcels are often used, overlooking the influence of driving factors within neighboring parcels; (3) the ability to explore the interactions between land parcels is often limited. To address the aforementioned issues, this study proposes a HashGAT-VCA model for investigating urban land use changes. The model utilizes a Hash function to encode the non-uniform distribution of each driving factor within each irregularly shaped land parcel into a fixed-length vector, and constructs a graph structure between land parcels based on their spatial topological relationships. By employing a Graph Attention Network (GAT), the model explores the mechanisms of environmental driving effects and inter-parcel interactions to calculate the probability of land use change for each parcel. The proposed HashGAT-VCA model was applied to simulate urban land use changes in Shenzhen, China, from 2009 to 2012. Compared to other VCA models, the HashGAT-VCA demonstrated higher simulation accuracy. The results indicated that HashGAT-VCA can effectively capture the impacts of the heterogeneously distributed driving factors and the interactions between land parcels on land use changes. Additionally, this study simulated land use patterns for the years 2025 and 2030 under ecological control strategies, providing decision support for urban planning.

The suitability assessment for land territorial spatial planning based on ANN-CA model and the Internet of Things

This work aims to utilize Internet of Things (IoT) technology and the Artificial Neural Network - Cellular Automaton (ANN-CA) model to analyze the construction of indicators for territorial spatial planning and urban development suitability assessment. Firstly, the IoT technology is introduced, and its application potential in land planning is explored. Using the IoT technology, various data related to land use are collected, and these data are transmitted and summarized through IoT equipment to form a data base. Based on the collected data, the ANN-CA model and the “dual assessment” concept are employed to establish an indicator system for urban development suitability assessment, encompassing permanent basic farmland, ecological redlines, and current built-up areas. Through the combination of these two models, the future land use situation can be predicted more accurately. The trained model is evaluated, including simulation accuracy, error analysis, Kappa coefficient and other indicators. Compared with the actual data, the accuracy and credibility of the model are verified. Finally, according to the prediction results of the model, the land use situation is analyzed and interpreted to provide decision support for urban planning and development. The research results show that the combination of IoT technology and ANN-CA model can effectively analyze the evolution law of land use and the suitability of urban development. Through the reasonable setting and processing of model parameters and data, people can get high accuracy land use prediction results, which provides important reference and support for urban planning and sustainable development. The suitability for urban development within County M exhibits noticeable spatial disparities, with the central region being more suitable for development while the peripheral regions are relatively less favorable. This work provides valuable guidance for decision-makers and researchers in the field of territorial planning, and promotes orderly urban development and sustainable prosperity.

A Scoping Review of Urban Planning Decision Support Tools and Processes That Account for the Health, Environment, and Economic Benefits of Trees and Greenspace.

Compelling evidence shows that trees and greenspaces positively impact human well-being and the environment and offer economic benefits. Nevertheless, there exists a knowledge gap regarding the extent to which this evidence is efficiently incorporated into existing urban planning decision-making processes. This scoping review identified the extent to which urban planning decision-making frameworks, models, and tools consider the health, environmental, and economic benefits of trees and greenspace. Out of 28 reviewed studies, 11 (39%) reported on frameworks, models, and tools that take into account the health, environmental, and economic dimensions of trees and greenspace. Additionally, seven studies provided comprehensive coverage of at least one of the three key dimensions. However, none of the decision support frameworks, models, or tools comprehensively integrated all three dimensions, with only two tools (7%) scoring above 50% (five or more out of nine) in terms of comprehensiveness. This review highlights the urgent need to incorporate the true economic and monetary values of the health and environmental benefits of trees and greenspace to inform urban development decision making.

STUDY ON AUTOMATIC REGISTRATION METHOD OF SOURCE DATA FOR CIM BUILDING MODEL CONSTRUCTION

Abstract. The City Information Model (CIM) is a digital model designed to describe, analyze, and manage urban spatial information. It integrates various urban elements, providing decision support for urban planning, design, and management. This study focuses on the construction of building models in CIM, particularly the issue of automatic registration of source data. We propose an automatic registration method based on DWG-format architectural design drawings and SHAPE-format remote sensing building outline information. This method extracts the axis line information of the building from the plan design drawings, indirectly obtains feature points, and selects registration base points, thereby achieving the registration of real estate subdivision data. Meanwhile, by automatically fitting the outer bounding rectangle of the building and dynamically adjusting the angle, this study realizes the automatic registration of the aligned multilayer real estate subdivision data with the building outline data obtained from remote sensing images. This research provides an effective method for the automatic and batch accurate alignment of data for the construction of CIM building models, thereby enhancing the precision and reliability of the modeling.

Identifying and Classifying Urban Data Sources for Machine Learning-Based Sustainable Urban Planning and Decision Support Systems Development

With the increase in the amount and variety of data that are constantly produced, collected, and exchanged between systems, the efficiency and accuracy of solutions/services that use data as input may suffer if an inappropriate or inaccurate technique, method, or tool is chosen to deal with them. This paper presents a global overview of urban data sources and structures used to train machine learning (ML) algorithms integrated into urban planning decision support systems (DSS). It contributes to a common understanding of choosing the right urban data for a given urban planning issue, i.e., their type, source and structure, for more efficient use in training ML models. For the purpose of this study, we conduct a systematic literature review (SLR) of all relevant peer-reviewed studies available in the Scopus database. More precisely, 248 papers were found to be relevant with their further analysis using a text-mining approach to determine (a) the main urban data sources used for ML modeling, (b) the most popular approaches used in relevant urban planning and urban problem-solving studies and their relationship to the type of data source used, and (c) the problems commonly encountered in their use. After classifying them, we identified the strengths and weaknesses of data sources depending on several predefined factors. We found that the data mainly come from two main categories of sources, namely (1) sensors and (2) statistical surveys, including social network data. They can be classified as (a) opportunistic or (b) non-opportunistic depending on the process of data acquisition, collection, and storage. Data sources are closely correlated with their structure and potential urban planning issues to be addressed. Almost all urban data have an indexed structure and, in particular, either attribute tables for statistical survey data and data from simple sensors (e.g., climate and pollution sensors) or vectors, mostly obtained from satellite images after large-scale spatio-temporal analysis. The paper also provides a discussion of the potential opportunities, emerging issues, and challenges that urban data sources face and should overcome to better catalyze intelligent/smart planning. This should contribute to the general understanding of the data, their sources and the challenges to be faced and overcome by those seeking data and integrating them into smart applications and urban-planning processes.

Spatial–temporal characteristics of surface thermal environment and its effect on Lake surface water temperature in Dianchi Lake basin

The surface thermal environment plays an important role in urban sustainable development and ecological environment protection. Existing researches mainly focus on the formation process and mechanism of the surface thermal environment and lack the analysis of its effect on the lake ecological environment under the influence of human activities. Therefore, based on the analysis of the variations in land surface temperature (LST) and lake surface water temperature (LSWT) of Dianchi Lake at multiple spatio-temporal scales, this study evaluated the response of LSWT by using the methods of spatial influence, the center of gravity migration trajectory, trend analysis, and correlation analysis. The results show that: (1) Urbanization has a greater warming effect on LSWT than on LST, and the warming effect at night is greater than that at daytime. From 2001 to 2018, the warming trend of LSWT in daytime and night was 0.01°C/a and 0.02°C/a, respectively, while the cooling trend of LST in daytime was −0.03°C/a and the warming trend of LST in night was 0.01°C/a. (2) Areas with high human activity are warming faster, both in the eastern and northern coastal areas of lake and the heavily urbanized sub-basins. (3) The spatial influence of LST and LSWT are highly correlated, and the response of the outer buffer in the range of 2 km is obvious, and the direction of gravity center migration trajectory is consistent. The results are of great significance for the control and improvement of urban heat island and ecological environment protection of Dianchi Lake in Kunming and can provide data support and decision support for urban planning, promoting the construction of the ecological civilization city in Kunming, and reducing the accumulation of urban surface heat.

Prediction of network public opinion features in urban planning based on geographical case-based reasoning

ABSTRACT As a significant part of sustainable urban development proposed by the United Nations, urban planning is related to the ecological environment and transportation, especially affecting quality of life and social well-being. In the process of urban planning, the public express their opinions on open network platforms, resulting in large quantities of network public opinion data, which has important implications for evaluating urban planning. Based on the idea of geographical case-based reasoning (CBR), this paper constructs an expression framework for urban planning cases in the form of a ‘case problem–case attribute–case result’ triad. On this basis, this paper proposes a similarity calculation method of urban planning cases that integrates case attribute. Finally, based on an improvement to the traditional k-nearest neighbors method, the proposed public opinion feature calculation model considers similarity weights, which allow us to predict network public opinion features, including viewpoint-level emotional tendency and concerned groups of urban planning cases. The experimental result shows similarity weights (SWs) model could effectively improve the prediction accuracy, where the average MIC-F1 score reached more than 74%. Based on CBR, the proposed method can predict the development trends of public opinion in future planning cases, and provide scientific and reasonable decision support for urban planning.

Estimated health impacts from maritime transport in the Mediterranean region and benefits from the use of cleaner fuels

Ship traffic emissions degrade air quality in coastal areas and contribute to climate impacts globally. The estimated health burden of exposure to shipping emissions in coastal areas may inform policy makers as they seek to reduce exposure and associated potential health impacts. This work estimates the PM2.5-attributable impacts in the form of premature mortality and cardiovascular and respiratory hospital admissions, from long-term exposure to shipping emissions. Health impact assessment (HIA) was performed in 8 Mediterranean coastal cities, using a baseline conditions from the literature and a policy case accounting for the MARPOL Annex VI rules requiring cleaner fuels in 2020. Input data were (a) shipping contributions to ambient PM2.5 concentrations based on receptor modelling studies found in the literature, (b) population and health incidence data from national statistical registries, and (c) geographically-relevant concentration-response functions from the literature. Long-term exposure to ship-sourced PM2.5 accounted for 430 (95% CI: 220–650) premature deaths per year, in the 8 cities, distributed between groups of cities: Barcelona and Athens, with >100 premature deaths/year, and Nicosia, Brindisi, Genoa, Venice, Msida and Melilla, with tens of premature deaths/year. The more stringent standards in 2020 would reduce the number of PM2.5-attributable premature deaths by 15% on average. HIA provided a comparative assessment of the health burden of shipping emissions across Mediterranean coastal cities, which may provide decision support for urban planning with a special focus on harbour areas, and in view of the reduction in sulphur content of marine fuels due to MARPOL Annex VI in 2020.

Simulating urban land use change by integrating a convolutional neural network with vector-based cellular automata

ABSTRACT Vector-based cellular automata (VCA) models have been applied in land use change simulations at fine scales. However, the neighborhood effects of the driving factors are rarely considered in the exploration of the transition suitability of cells, leading to lower simulation accuracy. This study proposes a convolutional neural network (CNN)-VCA model that adopts the CNN to extract the high-level features of the driving factors within a neighborhood of an irregularly shaped cell and discover the relationships between multiple land use changes and driving factors at the neighborhood level. The proposed model was applied to simulate urban land use changes in Shenzhen, China. Compared with several VCA models using other machine learning methods, the proposed CNN-VCA model obtained the highest simulation accuracy (figure-of-merit = 0.361). The results indicated that the CNN-VCA model can effectively uncover the neighborhood effects of multiple driving factors on the developmental potential of land parcels and obtain more details on the morphological characteristics of land parcels. Moreover, the land use patterns of 2020 and 2025 under an ecological control strategy were simulated to provide decision support for urban planning.

Urban-scale building energy consumption database: a case study for Wuhan, China

Building energy consumption accounts for 30% of the overall energy end use worldwide. This number is even much higher in urban areas. With rapid urbanization in China, cities are expanding with new constructions. It is essential to create an updated urban-scale building energy consumption database to represent energy use for different types of buildings in China, which could help urban planners, managers and decision makers to understand temporal and spatial building energy consumption distribution and ensure required electricity and/or gas supply. However, such urban-scale database is rarely found in China. This paper creates baseline EnergyPlus models for residential, small office and large office buildings and validates the baseline models using survey data from literature. Parametric simulations are conducted to consider different design factors, such as building enclosure, lighting power density, equipment power density, HVAC schedule, etc. In total 351 EnergyPlus models are generated to cover different energy use intensity scenarios. Data-driven regression analysis is conducted to predict building energy consumption using building price/rent. The prediction results are expected to provide design decision support for urban planning and power distribution for new constructions.

Analysis of residents' travel characteristics along Beijing rail transit line based on binary choice model

From ancient to modern times, in the historical process of urban development, urban transportation has been developing along with the development of the city's political, economic and cultural industries, and the relationship between each other has always been a topic constantly discussed by planning scholars. The development of urban transportation promotes the urban population gathering and industrial development, and promotes the development of urban geographical space. At the same time, it also brings endless convenience to urban residents, so that they can complete the displacement from the beginning to the destination with relatively low cost. However, with the rapid development of urban scale and the rapid growth of urban population, the problems of traffic congestion and land resource shortage in big cities of China seriously restrict the improvement of the quality of life of residents and the further development of the city. In this context, compact city is the inevitable choice for future urban development, while the transportation system supporting compact city form can only be public transportation. As a high-volume, efficient and rapid public transport mode, rail transit can not only solve the traffic congestion problem in high-density areas of cities, but also optimize the development and utilization of urban land and adjust the urban spatial layout, which is of great significance for the sustainable development of cities. The network of rail transit in Beijing becomes the backbone of public passenger transport system, and play an irreplaceable role in guiding the urban space layout adjustment, population migration and the transformation of traffic structure. The study of travel characteristics of the residents who live along the rail transit, in-depth analysis the relationship of the rail transit, the population migration and commuter travel, it is of great significance providing decision support for urban planning. Based on binary choice model, establishing the residents' travel choice model, rail transit impact model of different crowds. Study the relationship between rail transit and residents' travel characteristics, predict the rail transit to guide the trend of population migration.

海岛型城市生态安全格局研究——以平潭岛为例

PDF HTML阅读 XML下载 导出引用 引用提醒 海岛型城市生态安全格局研究——以平潭岛为例 DOI: 10.5846/stxb201611212363 作者: 作者单位: 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金项目（41401213）；福建省自然科学基金青年基金项目（2014J05051） A study on urban ecosecurity pattern of island city: a case study of Pingtan Island Author: Affiliation: Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:在快速城市化带来的城市生态安全严重威胁着城市可持续发展的这一时代背景下，构建城市生态安全格局是实现区域和城市生态安全的基本保障和重要途径。结合平潭综合实验区总体规划，从城市生态安全角度出发，在对平潭岛生态环境本底条件的调研基础上，选取与平潭岛的城市生态安全关系密切的6个关键生态因子进行深入分析，并通过层次分析法和GIS空间叠置法综合多要素分析了平潭岛的生态敏感性，最终有针对性地提出了面向新一轮大规模城市开发热潮的平潭岛城市生态安全格局。研究成果可为平潭岛的城市开发建设提供决策支持，对维护平潭岛城市生态安全、促进平潭岛可持续发展具有重要的理论和现实意义，同时也对其它海岛型城市的生态安全格局研究具有一定的借鉴意义。 Abstract:The world is undergoing the largest wave of urban growth in history. More than half of the world's population now lives in towns and cities, and the proportion of the global population in urban areas has increased conspicuously from 30% in 1950 to 54% in 2014. Urbanization is projected to increase even further to 60% in 2030. Though the process of urbanization provides comfortable and convenient living conditions for human beings, it also makes our country face a series of serious urban ecological security issues (such as environmental pollution and diminishing resources) because of the high-intensity of human activities and unreasonable land use. Nowadays, urban ecological security brought by the rapid urbanization is posing a serious threat to urban sustainable development. Against this background, constructing the urban ecological security pattern is the basic guarantee and important way to realize regional and urban ecological security. Based on the eco-environmental background conditions of Pingtan Island, this study first investigated the perspective of urban ecological security and considered the overall plan of Pingtan Comprehensive Pilot Zone to better understand six key ecological sensitive factors that were closely related to the urban ecological security of Pingtan Island. These six key ecological sensitive factors were drinking water resources, coastal shelterbelt, scenic spots and forest parks, natural mountain, cultivated land, and coastline resources. Thereafter, a comprehensive evaluation model was established by the analytical hierarchy process (AHP) and geographical information system (GIS) spatial overlay method to comprehensively evaluate the ecological sensitivity of Pingtan Island. The specific analysis process was as follows:First, an evaluation index system of Pingtan Island's ecological sensitivity by means of data collection, field investigation, remote sensing image interpretation and analysis, and expert reviews was established. These indices were classified according to their sensitivity. Second, the judgment matrix was constructed and consistency checks were performed in accordance with the AHP method, then the weight of each index was calculated according to the square root method. Finally, considering that scenic spots covered with natural mountain forest have the highest ecological sensitivity, and that other areas may not be ecologically sensitive or may be insensitive, the Pingtan Island was divided into five grades (extreme sensitivity, high sensitivity, moderate sensitivity, low sensitivity, and insensitive grades) according to their respective ecological sensitivity indices. We propose an ecological security pattern for Pingtan Island in order to adapt to the new round of large-scale urban development boom, and this pattern is mainly composed of ecological security barrier ring, ecological water system, ecological conservation area, ecological coastal zone, urban construction area, and the ecological promotion area. These components play different roles in maintaining ecological safety on Pingtan Island, and they must be rationally developed and utilized on the basis of their respective functions. Generally, the results of this study would help to provide decision support for urban planning, and could be of great theoretical and practical significance to maintain Pingtan Island's ecological security and promote the sustainable development of Pingtan Island. Moreover, these results may also be useful for ecosecurity pattern research on other island cities. 参考文献 相似文献 引证文献

Tools to support sustainable entrepreneurship in energy positive neighbourhoods

In an Energy Positive Neighbourhood (EPN) the annual energy demand is lower than annual energy supply from local renewable energy sources. Short-term imbalances in energy supply and demand are corrected with national energy supplies. In this paper the early stages of the development - the specifications - of the tools for intelligent management of energy positive neighbourhoods are presented. These tools include an energy management tool for real-time management of the energy flows, user interfaces that support energy efficient behavior of the users in the neighbourhood and an urban planning decision support tool. The specifications and tools are the result of European co-operation, and are designed so that they can easily be adopted in different European countries with minimum changes.

Incorporating Bio-Physical Sciences into a Decision Support Tool for Sustainable Urban Planning

Deciding upon optimum planning actions in terms of sustainable urban planning involves the consideration of multiple environmental and socio-economic criteria. The transformation of natural landscapes to urban areas affects energy and material fluxes. An important aspect of the urban environment is the urban metabolism, and changes in such metabolism need to be considered for sustainable planning decisions. A spatial Decision Support System (DSS) prototyped within the European FP7-funded project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism), enables accounting for the urban metabolism of planning actions, by exploiting the current knowledge and technology of biophysical sciences. The main aim of the BRIDGE project was to bridge the knowledge and communication gap between urban planners and environmental scientists and to illustrate the advantages of considering detailed environmental information in urban planning processes. The developed DSS prototype integrates biophysical observations and simulation techniques with socio-economic aspects in five European cities, selected as case studies for the pilot application of the tool. This paper describes the design and implementation of the BRIDGE DSS prototype, illustrates some examples of use, and highlights the need for further research and development in the field.

Sustainable urban metabolism as a link between bio-physical sciences and urban planning: The BRIDGE project

Urban metabolism considers a city as a system with flows of energy and material between it and the environment. Recent advances in bio-physical sciences provide methods and models to estimate local scale energy, water, carbon and pollutant fluxes. However, good communication is required to provide this new knowledge and its implications to endusers (such as urban planners, architects and engineers). The FP7 project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) aimed to address this gap by illustrating the advantages of considering these issues in urban planning. The BRIDGE Decision Support System (DSS) aids the evaluation of the sustainability of urban planning interventions. The Multi Criteria Analysis approach adopted provides a method to cope with the complexity of urban metabolism. In consultation with targeted end-users, objectives were defined in relation to the interactions between the environmental elements (fluxes of energy, water, carbon and pollutants) and socioeconomic components (investment costs, housing, employment, etc.) of urban sustainability. The tool was tested in five case study cities: Helsinki, Athens, London, Florence and Gliwice; and sub-models were evaluated using flux data selected. This overview of the BRIDGE project covers the methods and tools used to measure and model the physical flows, the selected set of sustainability indicators, the methodological framework for evaluating urban planning alternatives and the resulting DSS prototype.

A decision-support system for sustainable urban metabolism in Europe

Urban metabolism components define the energy and material exchanges within a city and, therefore, can provide valuable information on the environmental quality of urban areas. Assessing the potential impact of urban planning alternatives on urban metabolism components (such as energy, water, carbon and pollutants fluxes) can provide a quantitative estimation of their sustainability performance. Urban metabolism impact assessment can, therefore, contribute to the identification of sustainable urban structures with regards, for example, to building types, materials and layout, as well as to location and capacity of transportation and infrastructural developments. In this way, it enables the formulation of planning and policy recommendations to promote efficient use of resources and enhance environmental quality in urban areas.The European FP7 project BRIDGE (sustainaBle uRban plannIng Decision support accountinG for urban mEtabolism) has developed a decision-support system (DSS) that systematically integrates urban metabolism components into impact assessment processes with the aim of accurately quantifying the potential effects of proposed planning interventions. The DSS enables integration of multiple spatial and non-spatial datasets (e.g. physical flows of energy and material with variables of social and economic change) in a systematic manner to obtain spatially defined assessment results and to thus inform planners and decision-makers. This multi-criteria approach also enables incorporation of stakeholders' perceptions in order to prioritise decisive assessment criteria. This paper describes the methodological framework used to develop the DSS and critically examines the results of its practical application in five European cities.

A privacy-aware trajectory tracking query engine

Advances in telecommunications and GPS sensors technology have made possible the collection of data like time series of locations, related to the movement of individuals. The analysis of this, so-called trajectory data, is beneficial both for the individuals (e.g., through location-based services) and for the community as a whole (e.g., decision support for urban planning or traffic control). However, because of the very nature of this data, strict safeguards must be enforced to ensure the privacy of the individuals, whose movement is recorded. In this paper, we present a privacy-aware trajectory tracking query engine that offers strict guarantees about what can be observed by untrusted third parties. Through the query engine, subscribed users can gain restricted access to an in-house trajectory data warehouse, to perform certain analysis tasks. In addition to regular queries involving non-spatial non-temporal attributes, the engine supports a variety of spatiotemporal queries, including range queries, nearest neighbor queries and queries for aggregate statistics. The query results are augmented with fake trajectory data (dummies) to fulfil the requirements of K -anonymity. Through qualitative analysis, we prove the effectiveness of our approach towards blocking certain types of attacks, while minimally distorting the dataset.

Forecasting the pattern of urban growth with PUP: a web-based model interfaced with GIS and 3D animation

This paper describes a model (PUP — Projections for Urban Planning) implemented to forecast the location of housing development and population growth on the fringes of large cities. The model couples the land use and housing unit methods of population forecasting in a GIS framework that delivers a seamless interface between data assembly, modelling, visualisation and analysis. Work has focussed on incorporating spatial relationships, calibration, and visualisation. Three factors are accounted for: land availability; accessibility to facilities and other stimuli; and adjacency to existing development. These are weighted and combined to determine the probable location of future development. Forecasts are available as: GIS compatible files; tables; or 3D animations with interactive querying. The model has been implemented in Adelaide but its generic structure yields a flexible, interactive forecasting system that can be adapted to provide decision support for urban planning in a range of urban settings.

An integrated knowledge-based system for urban planning decision support

More applications that integrate knowledge-based decision support systems and artificial neural networks are starting to appear, and interest in such integrated systems is growing rapidly. The paper presents an integrated system in which a knowledge-based decision support system is integrated with a multilayer artificial neural network for urban planning. By integrating decision support systems, knowledge-based systems and artificial neural networks, the system achieves improvements in the implementation of each as well as increases the scope of the application. This approach is very rewarding in its synergism of three technologies to solve complex problems. The paper discusses the structure of the integrated system, as well as providing an example of application.