Introduction

The rapid development of information and communication technologies (ICTs) has facilitated the emergence of a large amount of new data. A new data environment consisting of big data and open data is gradually taking shape, bringing brand new opportunities and challenges to urban research and planning design. In this paper, "new data" refers to data that was not widely used ten years ago, as opposed to traditional data such as statistical yearbooks.

While new data has become an important tool for the planning profession, it has also influenced innovation in planning concepts. Since 2013, research ideas about the application of data in planning have emerged, giving rise to such ideas as "big data and small planning", "crowdfunding crowdsourcing crowdsourcing creativity", "micro-age and cloud planning", "data and cloud planning", "data and cloud planning", "data and cloud planning", "data and cloud planning", "data and cloud planning", "data and cloud planning", and "data and cloud planning". ", "data-enhanced design", "big model" and other trends. Based on new data to carry out planning analysis, planning assessment, auxiliary design and simulation prediction has become the main direction of exploration of planning transformation from a technical perspective.

Due to space constraints, this paper mainly adopts research and applications conducted in recent years as case studies.

Data Acquisition, Management and Platform

2.1 Data Acquisition

2.1.1 Data Types and Characteristics

First of all, big data and open data are analyzed. Open data refers to a kind of selected and licensed data that is not restricted by copyrights, patents and other management mechanisms, and is open to the public for free publication and use. Big Data originated from the development of Internet information technology in the 21st century. At present, Big Data is widely recognized as having the characteristics of "5V" - Volume, Variety, Velocity, Veracity, Value. Value. Big data is diverse and of high value, but the types of data that are actually put to use in the planning industry are relatively few, mainly bus card data, LBS data, floating car data and cell phone data.

Big data and open data*** together constitute the new data environment today. Due to various reasons such as the cost and accessibility of data, there is currently a phenomenon of "big data not open, open data not big", which restricts the accessibility and utilization of data for urban research and planning practices.

2.1.2 The value of new data in urban planning

(1) Expansion of the coverage of basic urban data

(2) Innovation of the preparation means

(3) Expansion of the way of participation

2.1.3 Exploration of the acquisition of new data

2.2 Data Management

Different types of databases are needed to store different data sources. Structured data is mainly used in traditional relational databases such as Oracle, MySql, SQL Server, etc., semi-structured data is used in non-relational databases such as HBase, etc., and unstructured data is stored in Text, HDFS, etc.. For new data with large data volume, such as public transportation card data, LBS location data, etc., it is necessary to develop a special platform for management. There are also more common big data management platforms such as Hadoop and Spark that are widely used in the big data industry. However, these platforms are not data management platforms built for the planning industry, and thus need to be modified accordingly before application.

2.3 Data Platforms

Currently, there are a variety of planning data platforms used by different organizations, with similar organizational frameworks, including data processing platforms, planning application platforms, city image platforms, and planning support platforms (Table 1).

Currently, comprehensive data platforms specialized for the planning industry are emerging. The Human Traces Map planning and analysis platform, the Xu Xiake program, and the BCL data **** enjoyment platform are three of the more typical ones (Table 2). They use different data sources and have different application areas, but all have the functional significance of data processing, urban research and planning support.

Urban Research

3.1 A New Paradigm for Urban Research

Along with the expansion of spatial and temporal scales, the new data brings a new paradigm for urban research, which Long Ying et al. distill into a "big model". The "big model" is a relatively fine-scale urban and regional analysis and simulation model built on a large geographical area. Compared with traditional city and region models, big models are driven by large-scale data and can balance research accuracy and scale (Figure 2 right: large space, fine accuracy).

In terms of application, the "big model" currently has the following main directions: first, the analysis of large, medium, small, and various scales of the city (Figure 3); second, the refinement of the analysis and simulation from the perspective of the human (Figure 4); and third, the metrics aggregated at the intra-city scale and the macroscopic city indicators for measurement and analysis, in order to enrich the theory of the city (Figure 5). theory (Figure 5).

3.2 Research Based on Open Data

Aiming at different open data, urban researchers have launched different studies. For example, government open data, microblogging data, LBS data and so on. In general, although the industry has a certain accumulation of technology and academic results, but the research based on open data is still in the initial stage. Due to the problem of data accuracy and data size, the research in this field still has a lot of room for development and improvement.

3.3 Research Based on Big Data

The industry mainly uses big data such as public transportation card swipe record data, cell phone signaling data, and smartphone LBS data. This kind of data originates from the smart card-based automatic billing system for public **** transportation, which records the travel behavior of the cardholder and also reveals the usage pattern of urban space in the individual dimension. Next is cell phone signaling data. Cell phone signaling data can take into account a wider coverage based on the precision of spatial and temporal behaviors of individuals, and it is a kind of data that is nearly full sample and full coverage. In addition, there are LBS and user labeling data based on smartphone apps obtained through cooperation with TalkingData.

Planning Applications

4.1 Recognizing the Laws of Cities - Understanding

However, despite the many advantages of the new data, there are inevitable drawbacks such as obvious bias of the data and lack of individual socio-economic attributes. While new data is good at analyzing the relationships that characterize urban phenomena, it is not as good at revealing the reasons behind the phenomena.

4.2 Planning - Creation

Long Ying and Shen Yao proposed Data Augmented Design (DAD) on the basis of the Big Model. " (DAD, Data Augmented Design). It is a design model based on fine-scale design, while accurately understanding and evaluating the effects of each scale, and is a deepening and development of the "Big Model" concept in planning and design. It is driven by quantitative urban analysis, and through data analysis, modeling, and forecasting, it provides support tools for the whole process of planning and design, such as research, analysis, program design, evaluation, and tracking (Figure 6), in order to improve the scientific nature of the design, and to stimulate the creativity of the planners and designers.

DAD is essentially consistent with the past computer-aided design (CAD) and geographic information systems (GIS), and is a new way of planning and design assistance. Its design framework mainly consists of three dimensions of time, space and people for large-scale urban design (Figure 7). Each of these dimensions can be subdivided into two scales and granularity indicators (Figure 8), and can be freely dispatched between scales and granularity.

In practice, the DAD concept has been applied in the incremental design cases of Beijing Vice Center and Xiongan's overall urban design, as well as the stock design case of Shanghai Urban Design Challenge. For incremental design, DAD can play a role in exploring patterns and assisting design. Among them, the "City Growth Gene" method can be used for data-scarce sites, such as the Beijing Vice Center urban planning and design. Drawing on the "city growth gene", the project explores the "city growth gene" from historical data to quantitatively analyze and predict Beijing's future urban form and vitality. In addition, the project also explored other similarly targeted urban "growth genes" to summarize general patterns and extract patterns to support the evaluation and selection of design options for new districts (Figure 9).

For stock design, DAD can also provide an information platform that facilitates communication and collaboration. Take the work of Cao Zhejing et al. in the Shanghai Hengfu Urban Design Competition as an example. The project is based in the historic district of Hengfu, and involves the collision and integration of multiple subjects, property rights, and values. Therefore, the project builds a dynamic feedback platform for measuring spatial data with the help of multidimensional data, which communicates between the design subject and the object, and creates a feedback mechanism for spatial intervention.

4.3 Planning Design Evaluation - Assessment

Urban Planning Cloud Platform: New Planning Forms and Technological Foundations in the Era of Inventory Planning

5.1 Future Direction of the Planning Industry: Data Platform Supported by the Humanistic stock planning

The revolutionary significance of data and platformization for the planning industry is self-evident, and how to adapt to and take advantage of the DT (Data Technology) era has become a key proposition for the planning industry to upgrade and change itself. Nowadays, part of the business of traditional planning institutes has been equipped with the conditions for platformization, and cases with economic value have already appeared. CITYIF, the cloud-based urban planning platform built by the Beijing Municipal Institute of Urban Planning and Design, is one such example.

5.2 Content and Composition of the Urban Planning Cloud Platform

The Urban Planning Cloud Platform is a cloud platform built by the Beijing Institute of Urban Planning and Design (BIUPD) in 2014 to serve the government, citizens, and planners. The platform can be divided into three parts: data cloud platform, wisdom cloud platform and power cloud platform, aiming to realize the three functions of data pooling, wisdom pooling and power pooling.

(1) Data Cloud

The task of the data cloud is to realize the integration of tiny elements of the Internet, including all new data and the technologies, tools, and applications that provide the power to drive it.

(2) Wisdom Cloud

The task of the Wisdom Cloud is to build a platform for the pooling and sharing of wisdom among planners. At present, there are microblogging, micro letter, network forums, virtual communities and other sharing platforms. The task of the Wisdom Cloud is to build a knowledge base and think tank on the basis of these platforms.

(3) Power Cloud

The Power Cloud aims to realize the full socialization and process of planning crowdsourcing, power pooling and public participation. The cloud platform will enable more bottom-up micro-dynamics through aggregation. It is a platform for top-down and bottom-up connections, as well as a platform for the aggregation of all aspects and levels of power.

Conclusions and Recommendations

6.1 Takeaways and Lessons Learned

As mentioned earlier, new data has been applied to all aspects of urban planning and research. For urban planning, new data is not only a resource exploration, but also a research paradigm, and has triggered conceptual innovations and formed application systems in practice. The following is a brief summary of the gains and experiences in these aspects in recent years:

(1) Resource Exploration

New data for urban planning and research is first of all a resource. Compared to traditional data, new data has a large sample size, dynamics, timeliness, refinement, diversification and other characteristics, in individual behavior capture, sample capacity, research scale, trend prediction, law discovery and other aspects of more advantageous. After several years of exploration, the industry has mastered certain technical methods for intelligently acquiring multi-source data. For the planning industry how to cooperate with the Internet company and other real problems also have certain thinking results.

(2) Research Paradigm

In addition to resources, new data also brings paradigm innovation to urban research. Based on the new data environment, the author summarizes the new paradigm of urban research driven by new data as "Big Model".

(3) Conceptual Innovation

Along with the application of new data in urban planning practice, some new concepts were born, such as Data Augmented Design (DAD), human-scale urban form, Street Urbanism and Picture Urbanism. These concepts have been applied in actual planning and design, such as the urban planning and design of the Beijing Vice Center, the overall urban design of Xiongan, and the Shanghai Urban Design Challenge for the renewal and renovation of the Hengfu Historic District. In practice, the concept of "urban growth genes" has been born, which can be directly applied to the planning and design of the conceptual subdivisions.

(4) Application System

The planning comprehensive application platform based on new data is also the harvest and accumulation of the planning industry embracing new data. Among them, CITYIF, the urban planning cloud platform built by the North Planning Institute, is a typical example. This system realizes the online interaction between planners, government and citizens, and is a real public participation platform based on "Internet+" and multi-source data.

6.2 Lessons Learned

While new data has brought new perspectives and impetus to urban planning and research, there are also a series of lessons to be learned from the unregulated use of data. In the case of urban research, the main lessons are the "big mistake" of using data inappropriately and a series of moral and ethical issues. The first is the "big error" of data quality and use, such as distortion of data collection, missing data, lack of representativeness, consistency and reliability of data, and the second is the issue of ethics and personal privacy. In the field of urban planning practice, there is also the phenomenon of using data blindly, using data for the sake of data, and the lack of methodological rigor makes the final results mixed.

Overall, these problems are related to unscientific and unstandardized data collection and processing. If we cannot use data in a scientific way, we cannot guide urban planning research and application in a scientific way. Therefore, we must learn the following lessons from this: (1) the scientific nature of data processing and analyzing methods; (2) the mode of cooperation of data suppliers; (3) the traditional data collection methods cannot be completely discarded; (4) the multi-source calibration of data; and (5) the review of research ethics.

6.3 Judgment on Future Development

The new data environment supports refined humanistic planning in the era of stock planning, and for the first time, the planning industry is equipped for humanistic planning practice. However, urban research and planning applications utilizing new data are still at the stage of phenomenon description and feature extraction. Further utilization of new data in the future will require further advances in data mining and analysis methods. In the next phase, machine learning and artificial intelligence intervention in the urban planning industry is imperative.

Thus, in this context, the reform of the planning industry is imminent. This reform is different from the introduction of CAD, GIS and other technological innovations, but from the methodology to re-construct the theoretical basis of urban planning, preparation methods, technical indicators and evaluation system. But this reform is difficult to realize by the planner group alone, so the whole planning industry should be ready to open up and unite resources outside the industry.

I have reason to believe that in the near future, big data will no longer be the exclusive property of a small number of urban researchers with data science skills, but rather, like CAD and topographic maps, every planner will be able to use, and can be put into the production of production data. And this is the result of the self-transformation and renewal of the urban planning industry in the age of data.