On July 8, 2023, the 7th "Chengyuan Cup - Planning Decision Support Model Design Competition" final evaluation meeting was held in Beijing, jointly sponsored by Beijing Chengyuan Digital Technology Company Limited, World Urban Planning Education Network (WUPEN), Homedale Urban Planning & Architects CO., LTD. Of BICP, and co-sponsored by BCL, China Unicom Smart Steps, Baidu Maps Smart Eyes, and GeoScene Information Technology Co. ,Ltd.

Two student teams from the School of Architecture of Tianjin University stood out from more than a hundred teams and won the second prize and the excellence award respectively.

01 Competition Profile

The competition not only attracted academic elites from Tsinghua University, Peking University, Tongji University, Tianjin University, Nanjing University, Wuhan University, Southeast University, Harbin Institute of Technology, Central South University, Shenzhen University and other famous domestic institutions, but also technical experts from the Peking University Institute of Territorial Spatial Planning and Design, Architectural Design and Research Institute of Tongji University, Shanghai Municipal Research Institute of Urban Planning and Design, Guangzhou Municipal Urban Planning and Design Institute, Beijing Centre for Urban and Engineering Safety and Disaster Reduction of Beijing Institute of Technology, Guangdong Guodi Planning Science and Technology Co.

At the Final Evaluation Conference, the Chairman of the Competition, Academician Wu Zhiqiang, delivered a speech in video format, Chief Planner Wang Yinyuan of Beijing Municipal Institute of Urban Planning and Design (BMIPD) delivered a welcome speech, and President Shi Xiaodong of BMIPD delivered a closing speech. Industry authoritative experts from domestic urban quantitative research and related fields formed the final evaluation expert group, with Prof Zhan Qingming and Prof Wu Lun as the executive chairmen of the competition, to select the 19 final evaluation shortlisted teams. The finalists presented their projects in turn, received questions from the experts, and carried out in-depth academic exchanges.

02 Second Prize Winners

An Exploration of the Relationship between Urban Open Space and Neighbourhood Change and the Construction of a Decision Model for its Governance.

Participants: Zhang Shiyun, Li Zhichao, Zhao Yamei, Bian Junjie, Pan Xiaomin, Liu Mengdi

Instructor: Mi Xiaoyan, Sun Delong

Course on "Design Software Practicum":

The course is a practical course on the application of technical methods of urban research and planning offered by the School of Architecture of Tianjin University for fourth-year undergraduate students majoring in urban and rural planning and landscape architecture. It has been offered by the Department of Urban and Rural Planning since 2016, and will be accepted by Architecture majors from 2020, and will be offered to Landscape Architecture majors from 2021 at the same time. The course was initiated by Dr He Jie and taken over by Dr Mi Xiaoyan in 2021. In the same year, the Department of Landscape Architecture was co-taught by Dr Xu Tao, now in its eighth year. The course faculty has been growing in line with the needs of the industry, and is experienced and technically solid. Dr Xu Tao of Landscape Architecture, Dr Xiaoyan Mi of Urban and Rural Planning, Dr Sinan Yuan of Architecture, and Dr Yuanyuan Guo of Urban and Rural Planning teach the course, and invite experts in the field of artificial intelligence from foreign universities to give lectures on the subject, while Dr Delong Sun of Architecture is invited to participate in the coursework guidance.

The course is oriented to practical project transformation, focusing on combining theoretical knowledge with practical projects, through learning and applying the knowledge and skills of big data mining and analysis, ArcGIS mapping and analysis, spatial syntactic analysis and visualisation, metrological and statistical analysis models, and artificial intelligence programming for image recognition, applied to practical cases and projects.

Project Profile:

The winning entry originated from the coursework of three undergraduate students, Shiyun Zhang, Zhichao Li, and Yamei Zhao, in "Design Software Internship".

As China's urbanisation moves towards high-quality development, first-tier cities seek to reduce quantity and improve quality. Urban vacant land has become an important stock resource. This study aims to explore the possibility of urban vacant land as an important spatial indicator to measure neighbourhood change. The central urban areas of Beijing, Shanghai, Guangzhou and Shenzhen are selected for this study, and 314 streets in the four cities are used as the basic research units, with the time range from 2010 to 2020. This study applies the semantic segmentation model to identify urban vacant land in remote sensing images, uses image semantic segmentation to process streetscape images and extract the corresponding patches, and performs systematic clustering of neighbourhood change data. The correlation between urban vacant land and neighbourhood change factors is calculated by spatial measurement model, and a logistic regression model is constructed to establish a formula for predicting the type and degree of neighbourhood change through urban vacant land data, and to analyse the distribution differences among the four cities. This study builds a regulation model from the street unit, senses the response of urbanisation evolution in human living space in real time, and formulates management decisions on urban open space based on the impact on neighbourhoods, so as to provide a basic judgement for fair and orderly urban regeneration and fine-tuned development.

Innovation point 1: Constructing a quantitative indicator system and classification basis for neighbourhood change in the Chinese context

Neighbourhood office is a micro-unit of social governance with Chinese characteristics, and the changes in its economic, social and spatial factors profoundly reflect the operating laws of the urban system. This study takes the street as a unit, analyses the stages and categories of neighbourhood change, and determines the causes of neighbourhood change, in order to provide lessons and references for the governance of other similar cities.

Innovation point 2: integrated use of remote sensing technology and image analysis

In this study, a framework for automatic identification of large-scale urban open spaces developed based on DeepLabv3+ semantic segmentation model is adopted. The framework is able to identify open spaces quickly and accurately; it also introduces the semantic segmentation results of street view images as spatial change elements, which are incorporated into the indicator system to comprehensively assess the spatial changes in the process of neighbourhood change. This research methodology, which integrates remote sensing technology and image analysis methods, provides new means and tools for research, making it possible to conduct in-depth investigations into the relationship between urban open space and neighbourhood change.

Innovation point 3: Introduction of urban open space as a key spatial indicator.

While traditional neighbourhood change studies usually focus on the analysis of demographic and social factors, this study introduces urban open space as a key spatial indicator on this basis. By combining urban open space with the process of neighbourhood change, the efficiency and sustainability of land use in the development of urbanisation and its impact on neighbourhood change are explored in depth.

03 Award of Excellence Winners

Participants: Zhou Keqin, Zhao Yamei, Wang Yihan, Fu Yiqing, Ren Hangxuan, Wang Huazhao

Instructor: Xu Tao, Wang Miao

Project Profile:

Ecological governance is an important issue in China's development transformation, and it is also an inevitable requirement for realising high-quality control and sustainable development of land space. In this study, we constructed a coupled Natural Geography-Socio Economic-Ecological Environment (NGSEE) model for multi-scale ecological governance based on multi-source data and machine learning methods. The model consists of indicators of physical-geographical, Socio Economic and ecological systems, and it is applicable to different regional divisions, such as administrative boundaries, Socio Economic boundaries and physical-geographical boundaries. The study collects and calculates the data of each research unit from 2001 to 2020, uses popular learning algorithms to reduce the dimensionality of the indicators of each system, explores the degree of coupling and coordination among the three systems in each research sample, and proposes corresponding ecological management strategies for different scales of analyses.

Innovative point 1: The model perspective is unique and the algorithm is novel.

This study explores the coupling relationship between "Natural Geography-Socio Economic-Ecological Environment" systems from a multi-scale perspective, and empirically investigates it using SDV algorithms, MMM semi-supervised models, and machine learning regression, which provides innovative perspectives and methods.

Innovative point 2: The model is highly accurate and can better serve the analysis of the coupling relationship between the "Physical Geography-Socio Economic-Ecological Environment" system.

In this study, we compared a number of common machine learning methods, and selected AutoGluon to achieve higher efficiency and accuracy of machine learning, and Neo4j to construct a complex knowledge graph with high overall recognition accuracy.

Innovation point 3: The model is applicable to the way of regional division of different natures, such as administrative boundaries, Socio Economic boundaries and Physical Geographical boundaries.

This study builds a model with a sample of 2,850 county (district)-level administrative units, 12 metropolitan areas, and nine watersheds across the country (excluding Hong Kong, Macao, and Taiwan) from 2001 to 2020, with a large time span, a wide geographic scope, and many scales involved.