With the development of computer technology, especially the comprehensive popularization of the Internet of things and mobile terminal, milestone changes have taken place in data production, data transmission, and data analysis, marking the arrival of the Big Data Era. At the same time, the popularity of digital social medias such as Facebook(2004), Twitter (2006), WeChat(2011), and online payment platforms such as PayPal(1999) and Alipay(2004) have promoted the radical change of human social activities, shopping, working and travel methods, and been completely digitized, algorithmized, and computerized. City, as the physical representation and space container of human society, will inevitable change with the transformation of human society, that is, the digitization, algorithmization, and computerization of urban form and urban planning theory and planning method. Therefore, the systematic study of the coupling relationship of cognitive mode between digital media space and real urban space has important theoretical value and practical significance for the urban planning discipline development in the Big Data Era. This paper takes Harbin, China as the specific research object to discuss the computational transformation of urban planning discipline in the Big Data Era. The main research contents as following: Frist, the study of digital media space pattern, with Python language, systematically collect data from commonly used digital media platforms such as WeChat Moments, Ctrip, Dianping, ect., and then use data mining methods to explore the methods and models of cognitive of the digital media space; Secondly, the study of real urban space pattern, using field surveys, interviews, etc., to explore the methods and models of cognitive of the real urban space under the face-to-face social interaction by drawing cognitive map. Thirdly, the coupling analysis of digital media space and real urban space, through the coupling analysis of the similarities and differences between digital media space and real urban space cognitive patterns, discussing the trends and motivations of urban space evolution in the Big Data Era, and the innovative patterns and approaches of urban planning and planning method, and then providing theoretical support for the computational transformation of the urban planning discipline in the Big Data Era, to better respond to the needs of the digital transformation of human society, and realize the common ideal of “Better city, better life”.

As in medicine science diagnosis is generally recognized as a bridge that links basic medicine and clinical medicine, in urban planning, diagnosis is also required as transition and connection between the planning principle and practice. The basic principles of planning and the data on the performance of all systems of a city serve as strong supports for setting urban planning goals and corresponding strategies. With the development of information technology, big data and related technologies have delivered new tools and approaches to urban diagnosis. They also provided data basis and technologies for formulating systematic and quantitative diagnostic standards, enabling urban diagnosis to be more scientific and accurate. The diagnosis in community renewal is an important application of urban diagnosis methodology on a micro-scale. The population base, historical data and realistic demands of old communities determine that the urban diagnosis methods based on big data are of great significance for guiding the formulation of community upgrading goals and strategies. Bajiao Community in Shijingshan District, Beijing, a complex for the working class whose building work started in the 1980s, is the first area that realized urbanization in the district, thus of specific historical and cultural significance. The community features complex traffic, diversified population and various buildings built in different years. In order to rejuvenate the old residential areas, achieve fine management and deliver quality life, the local government began to formulate a plan to upgrade this area in 2018, where city diagnostic methods and a variety of big data technologies were adopted. Besides traditional research, big data was employed to identify the community’s symptoms and their root causes, analyze its historical problems, and check corresponding urban systems so as to help explore the major demands of community development as well as the needs regarding facility supply, public space and public activities and made quantitative analysis of these issues. All this offered meaningful guidance for the establishment of community upgrading strategies and the design of implementation paths.

Copernicus is the European Union's earth observation programme, providing information services that draw from satellite earth observation. The programme consists of a space segment and a ground segment. The ground segment provides access to earth observation data and services. One of the services is the Copernicus Land Monitoring Service (CLMS). The aim of CLMS is to address the data and information needs of a wide range of policies such as environment, including biodiversity, climate change, agriculture and forestry, regional and urban planning, as well as water management, transport, energy or neighbourhood policy, at global, European, national and local level. CLMS is a wide-ranging programme aiming to provide information to support regional and urban planning at the local level. Therefore, it is pertinent to study the user uptake and the correspondence between systems and data provided by CLMS and the needs and requirements in the planning sector. This research primary purpose is to identify the needs related to geospatial Land Cover, Land Use and Land Use-change information for urban and spatial planning in Poland and Norway and to examine the actual use of CLMS data in the planning system. Where previous studies mainly focus on what kind of data is needed for spatial planning, our attention is equally given to what kind of data management spatial planning requires. The broader context is to explore the preconditions for increased use of data from the CLMS in spatial planning in these two countries. It is assumed that a better understanding of the requirements in terms of metadata, ownership, and data management will have universal relevance. The research used data obtained from literature review, examination of practice in spatial planning and an online questionnaire on spatial planning needs concerning geospatial information on Land Cover and Land Use and Land Cover and Land Use change conducted in Poland. The cross-validation methods within the framework of scientific cooperation with experts and practitioners of spatial planning, spatial databases were also explored. The review of international documents, European acts and national legislation, and planning strategies were proceeded. The system of spatial planning in Poland and Norway was described systematically in order to enable comparison. Map data used in spatial planning in the two countries were analysed in order to define the problems and challenges of geospatial information on land cover and land use in Poland and Norway. Finally, a survey was conducted in the form of an online questionnaire. The survey was conducted from February 1 to February 28, 2021, among designers, researchers and people using spatial data. Official national data are reliable and usually very detailed. However, they do not consider the elements of development that were created without a proper building permit or are the own initiative of the property owner. The research confirmed that Copernicus Land Monitoring Services is an accurate tool supporting evidence-based spatial planning, especially in decision making. The following pressing planning needs concerning spatial data were identified: reliable, accurate, up-to-date, high-quality spatial databases showing actual land use and land cover. Finally, the most important is the need to introduce standard and integration of currently scattered geospatial databases for the purpose of spatial planning. The open, user-friendly geospatial data platform being developed by InCoNaDa team will enable viewing, visualising, and analysing CLMS and national data essential in effective spatial planning on a local level, is in line with these goals and needs.

City Information Modeling (CIM), on the basis of Building Information Modeling (BIM), Geographic Information System (GIS), Internet of Things (IoT) etc., has incorporated a full-factor spatial model with two and three-dimensional information and data of cities, and therefore, is a fundamental and critical platform for smart city construction. Based on CIM platform, simulation of scheme for city planning and design has brought it to fruition in the following aspects: it can carry out simulation in a world of digital twin to discover experiences and laws, moreover, it can effectively be implemented and executed in this physical world so as to cut costs in terms of trial-and-error; and a benign interaction of “virtual vs. real” nature is therefore achieved. From this perspective, the construction and application of CIM platform are of great practical significance to improve the comprehensive city management and operation capability. This research, based on the clarified construction principle and development of CIM, combining with the practice in Guangzhou city, has elaborated the specific application of CIM platform in different spatial scale, such as monomer construction, communities (campus) and cities, and also application at different operating stage, including city planning, design, construction, management and operation. Moreover, this research takes the representative application scenarios of “CIM+Smart Site”, “CIM + smart parking” and “CIM + city renewal” into account, based on which, analyses the chief role and its function as guiding framework concerning CIM platform in city management. Stemmed from these efforts, practical experience is therefore summarized, providing reference to further explore extensive “CIM +” application and promote the construction of city information digital platform for related sectors.

The central city of southern Yunnan is located in the Hani Yi Autonomous Prefecture of Honghe, Yunnan Province, and consists of three cities: Mengzi City, Kaiyuan City and Gejiu City. The central city of southern Yunnan has obvious characteristics of natural elements such as large altitude difference, obvious topography, and rich and diverse climate. Land simulation is one of the important technical means to predict the future trend and direction of urban development. Generally, by calculating the probability of land use conversion over the years, selecting factors influencing urban development, and constructing a land prediction model. This paper uses the 2009 and 2014 land use data as the base year data, and divides the land types into 8 categories: cultivated land, forest land, garden land, agricultural land, construction land, water area, unused land and grassland, using Marko in IDRISI 17.0 The husband model obtains the increase or decrease of each land use type and the conversion probability of each land use type, predicts the spatial distribution of the land use type in 2016 through the conversion matrix, and uses the 2016 land use type to verify the kappa coefficient to simulate and predict southern Yunnan The spatial distribution of land types in central cities in 2035. Through permanent basic farmland, ecological protection red line, water area and other prohibitive factors, and restrictive factors such as topography and landform, soil type, night light index and other restrictive factors, the central city of southern Yunnan will be simulated and predicted by multiple scenarios in 2035, and land use types under each scenario will be increased Analysis of the reduction situation, in-depth study of the impact of different scenario simulations on the future development direction and spatial pattern of the city, in order to provide a reference for the future development direction of the city and the layout of construction land.

Urban public space is an important part of citizens' lives, and its environmental quality has an important impact on citizens' activities and behaviors. During the epidemic, both the citizens and the government are more fully aware of people’s desire to enjoy urban public spaces and the importance of the environmental quality of these spaces. At the same time, researchers are aware of the need to build more multi-dimensional, large-scale and Accurate methods for measuring the quality of urban public space, and the establishment of a system that can detect and feedback the quality of urban space in real time, and carry out precise control of urban public space on weekdays and in the case of possible outbreaks. Traditional quantitative research on the quality of urban public space is limited by technology and data sources, mostly small-scale questionnaire surveys and costly and time-consuming. In contrast, the application of some new technologies and new data can bring this situation different. This article summarizes, analyzes and reviews new technologies represented by machine learning, virtual reality, physiological sensors, eye tracking, etc. and new data represented by multi-source city data that have emerged in recent years. Analyze the possibility of more precise analysis and more intuitive presentation of these new technologies and new data in research methods, and propose the possibility of these new data and new technologies to innovate existing research paradigms, and how they can better help research The author responds to basic questions in the fields of architecture, urban design and landscape - how do people perceive and use space, and to what extent and how does the physical space environment affect people's perception and behavior?

Based on the theoretical elaboration of livable cities, this essay found that the traditional livable city evaluation methods are mainly based on statistical indicators through research on the livable city evaluation index system proposed by domestic and foreign scholars. However, in the era of big data, the construction of smart cities requires Realize high-dimensional breakthroughs with the help of "Internet +". Taking the evaluation indicators of Zhuhai's livable city as an example, 49 of the 70 indicators released by the Zhuhai City Science Association are based on data reported by departmental statistics, 7 are data from the research group's questionnaire survey, and 14 are data calculated through the city's basic information. Based on the big data such as cell phones, cabs and network information, together with the basic urban information data such as urban land use and planning management, this study focuses on the calculation method of dynamic livability index based on urban spatial big data, providing a new perspective and method for livability evaluation, and realizing the change of urban livability index from "one number for the whole city, one number for the whole year" to "one number for the whole year". It will provide support for the construction of a livable city in Zhuhai by changing from "one number for the whole city and one number for the whole year" to "different from place to place and different from time to time". This study focuses on building a system of ten livelihood indicators for citizens in four aspects: ecology, space, travel and services, including pleasant natural environment, close green space, convenient neighborhood activities, reasonable functional layout, comfortable commuting distance, smooth road travel, convenient public transportation use, convenient compulsory education, convenient medical services, and satisfactory housing prices. Considering the typicality of indicators and data availability, 2-3 representative sub-indicators were selected for each aspect, and the sub-indicators were calculated separately based on big data, and then the livelihood-related single indicators and Zhuhai comprehensive livability index were calculated through the steps of standardization of indicators, determination of indicator weights, and spatial superposition of multiple elements of indicators. By evaluating the results, it will serve as an important basis for evaluating the existing planning and construction of the city and point out the direction for the subsequent construction of a livable city in Zhuhai. On the other hand, by exploring a personalized platform that regularly announces Zhuhai's dynamic livability indicators to the public, individual indicators related to people's livelihood and Zhuhai's comprehensive livability index will be released to the public on a regular basis to provide support for the city's refined governance and intelligent management.

With the advancement of urban development, the relationship between cities has gradually evolved from a hierarchical structure to a network structure. This change accelerates the flow of various elements between cities and benefits to the sharing of social development results. “Shanghai 2035” emphasizes on strengthening collaboration with surrounding cities and develop a metropolitan area with global influence. This study selects 9 cities (Shanghai, Suzhou, Wuxi, Changzhou, Jiaxing, Nantong, Huzhou, Ningbo, Zhoushan) in the Shanghai metropolitan area mentioned in the “Shanghai 2035” as the preliminary research scope. In recent years, due to the development of geographic big data, there have been many researches on geographic big data based on platforms such as Tencent and Baidu. The results prove that this type of big data has a strong verification effect for the analysis of metropolitan areas and urban agglomerations. This study use python to obtain population migration data of AutoNavi Map to promote research on urban network characteristics.This study refers to some mature urban network construction methods, and uses migration index to reflect the degree of connection between cities. Combine social network analysis and other methods to build a city relationship network based on migration index to analyze urban network characteristics of Shanghai metropolitan area. Data processing, analysis and calculation are carried out through the database, and visual expression is carried out through ArcGIS. This study's purpose is to reveal the urban network structure of the Shanghai metropolitan area. To analyze the urban connection strength and the urban centrality of Shanghai metropolitan area. Identify the radiation range of the Shanghai metropolitan area and discuss the development of Shanghai Metropolitan Area in the Yangtze River Delta. Provide some advise on the further development of Shanghai Metropolitan Area.

At present, China's urban construction has been transformed to the historical stage of fine adjustment and improvement, which means that the focus of urban planning and construction has begun to shift from the concept of speed first to quality pursuit, and gradually pay attention to people's life comfort and happiness in cities. In the face of urban fine design needs, there is an urgent need for a comprehensive and rapid evaluation system to evaluate the quality of street space. Thinking about the quality of street space is not new. The pioneers represented by Jacobs and Lefebvre began to discuss the spatial quality and its social and economic effects as early as 1960s. Gehl, Whyte and Lynch deeply studied the characteristics of urban space, and constantly summarized the methods to improve the quality and vitality of urban space, in which street space is the research focus. These research results are still of guiding significance for the current research, but due to the lack of quantitative basic data and evaluation index construction, it is not conducive to further assist the fine design practice and management. This paper selects the street space of Lujiazui core area with dense traffic flow in Shanghai as the research object, and uses multi-source data such as POI data, road network data, spatial morphology data, social media data and street view images to establish the basic database for quantitative evaluation of street space quality. The evaluation index system of street space quality was constructed by using spatial syntax analysis, panoramic segmentation analysis of street view images, functional clustering analysis, POI density mixing degree and heat analysis, spatial and temporal variation of population density distribution and other analysis methods. The index system relies on the two dimensions of material space composition and subjective space perception of urban street space quality, including five indicators, 18 evaluation factors in total: street space carrying capacity (construction intensity, space form, street green coverage rate, street height width ratio, street space identifiability), street space vitality (street function, street scene type analysis, street facade color analysis, business hours facing the street analysis), street environment comfort (walking convenience, landscape beauty, facilities perfection), street travel safety (street brightness, vehicle interference index, completeness of marking facilities), crowd social interaction (Baidu heat map, crowd concentration, social interface index). The evaluation results show that the richness of various service formats in Lujiazui core area in Shanghai is low, the slow traffic system is broken, the degree of street humanization is low, the street space lacks the connection between streamline and style, and the street service level does not match the high-density crowd. Then we put forward promotion strategies from three aspects of street public space, street public facilities and street support system. It is pointed out that in the street planning and design, different types of squares and commercial facilities should be set up in combination with the diversified needs of residents, so as to make the street have diversified functions, improve the accessibility of traffic and the comfort of the street environment, as well as for the purpose of accumulating popularity and making the street more energetic. In this study, the evaluation of traffic trunk roads is relatively low, but as functional roads, they fully meet the requirements. Therefore, this study is applicable to the spatial quality evaluation of living streets and has high universal applicability. At the same time, limited by data sources and research technology, this study still has some limitations. In the follow-up study, we can carry out a more comprehensive evaluation by integrating the street's own attributes.

Cities are complex and incrementally developing systems, but current urban planning and data collection systems follow rigid parameters. Parameterising developments seems essential to encompass a comprehensive multidimensional understanding. The pace and incremental urban growth pose daunting challenges, particularly in low- and middle-income countries, such as deprived urban areas (DUA), reflecting the concentration of poverty. When communities are unmapped, governments can easily ignore them (Gevaert et al., 2019). Consequently, they miss out on vital resources that they should be able to access. A consistent and timely mapping is important not only to identify the most DUA or to design renewal strategies including supplying basic services, but also to carry out spatial analysis, e.g., overlaying maps of DUA from datasets such as socio-economic, environmental, morphological indicators in different scales to support upgrading programs (Snel and Henninger, 2002). There is no agreement on the methodology to conceptualise, quantify or to map deprived areas (Thomson et al., 2020). Most methods stay at a simplification of slum versus non-slum areas and at best provide spatial information on their boundaries. We observe a fundamental lack of a conceptual framework that allows combining household-based measures of slums (e.g. expressed as the slum definition of UN-Habitat) and area level-based measurements of deprivation. In this contribution, we present the IDEAMAPS-framework responding to this gap. The IDEAMAPS-framework has been developed based on a literature review and stakeholder workshops in several African cities. However, most frameworks are based on employing census data, thus household data and lack an appropriate understanding of area-level aspects of deprivation. Such area-level deprivations relate to additional burdens communities face when living in areas that are prone to hazards, unsafe or stigmatised. Upgrading programs would strongly benefit from combining both understandings (i.e., household and area level). Existing programs drawn up with general objectives aligned with the Sustainable Development Goals (SDGs). However, they typically lack a conceptual framework with measurable indicators to tackle urban deprivation. On the one hand, there exists a large body of literature on household level deprivation studies, such as the UK deprivation indices (Dymond-Green, 2020). On the other hand, Earth Observation (EO) and geo-spatial modelling studies have stressed the importance of area level mapping of deprivation (Taubenböck et al., 2018). However, no fully combined conceptualisation exists that can be supported with open data in an increasingly geo-spatial world. A framework was developed based on a review of existing literature that used a systematic coding of employed indicators, data sources, and scales of analysis. The analysis of the large body of literature was conceptualised in collaboration with local stakeholders (in the form of workshops) to develop domains of deprivation and indicator groups that allow their operationalisation. The framework was designed to improve data availability, quality, consistency, timeliness and disaggregation. The IDEAMAPS-framework emphasised the need for an area-level conceptualisation to encompass deprivation levels within a broader scope. The framework is divided in three levels: household level, within area level and area-connect level. IDEAMAPS-framework allows us to understand the spatial patterns of deprivation as well as urban changes in its spatial dimension, e.g., where and how changes took place and how these physical changes relate to the economy, welfare or health outcomes. It provides the flexibility to evaluate and describe the city beyond the restrictions inherent in census data (e.g., low temporal frequency). Thus the IDEAMAPS-framework supports the provision of systematic spatial knowledge on deprivation combining open geo-data and EO data. Responding also to the demand of the UN for frequently updated data for the SDGs (UN 2015).

Conceptualisation and design functions in the built environment is set to be revolutionised by system innovations propagated by the 4th Industrial Revolution (4IR). Related technological advances constitute a gamechanger in building design and construction, altering the nature of skills requirements for roleplayers in the architecture industry. As system innovations may significantly disrupt the sectoral status quo of service providers and consumers, so too may the proliferation of advanced Building Information Modelling (BIM) and parametric design technology alter the modus operandi of architecture professionals. In the developing country context, the need exists to ensure the relevant roleplayers are empowered with the required skills to ensure timely and continuous sectoral adaptation to retain competitiveness. The objective of this research is to evaluate the content inherent to knowledge transfer in the architecture schools of South Africa’s tertiary education institutions, with the aim of determining the exposure of prospective professionals to technology-related system innovations. A quantitative research methodology is utilised where the curricula of selected architecture schools are assessed based on their incorporation of themes relating to 4IR. In this evaluation, criteria is developed based on input from the relevant Sector Education and Training Authority (SETA) and subsequently applied on selected curricula using a self-ranking method. The research findings illustrate that there is a relatively limited incorporation of 4IR-related themes in selected South African architecture curricula. Recommendations include, inter alia, a knowledge-based infusion of potential technological applications in said curricula to foster ongoing sectoral adaptation and competitiveness in South Africa.