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This book offers an essential introduction to a new urban planning and design methodology called Data Augmented Design (DAD) and its evolution and progresses, highlighting data driven methods, urban planning and design applications and related theories. The authors draw on many kinds of data, including big, open, and conventional data, and discuss cutting-edge technologies that illustrate DAD as a future-oriented design framework in terms of its focus on multi-data, multi-method, multi-stage and multi-scale sustainable urban planning. In four sections and ten chapters, the book presents case studies to address the core concepts of DAD, the first type of applications of DAD that emerged in redevelopment-oriented planning and design, the second type committed to the planning and design for urban expansion, and the future-oriented applications of DAD to advance sustainable technologies and the future structural form of the built environment. The book is geared towards a broad readership, ranging from researchers and students of urban planning, urban design, urban geography, urban economics, and urban sociology, to practitioners in the areas of urban planning and design.

Shrinking cities have become increasingly prevalent worldwide due to various factors, which pose serious challenges to affected areas in terms of population decline, economic decline, and spatial deterioration. While existing research studies have focused on identifying shrinking cities, there is a need for global projections to mitigate uncertainties in their growth trajectories. Spatially explicit population grids offer a new approach to identifying potentially shrinking cities with sufficient spatial resolution. By utilizing a global gridded population dataset from 2020 to 2100 under the SSP2 (Middle of the Road) scenario, we produce a global projection map for future shrinking cities. Among the total 19,024 natural cities, 9682 cities (50.9%) will face population decline and 1751 cities (9.2%) may lose more than half population by 2100. Cities in East Asia and East Europe may face serious population decline.

As the world's largest developing country, most Chinese cities are experiencing both population shrinkage and spatial expansion in the context of rapid urbanisation. To address the negative impacts of urban shrinkage in a targeted manner, it is necessary to accurately identify shrinking cities in China. In this study, we utilise the sixth and latest seventh population census data to identify and map shrinking cities in China that are experiencing population loss between 2010 and 2020. As a result, we identified 1507 shrinking districts and counties (52% of all 2896 districts and counties in China), with a total area of 4.4 million km2, covering almost 46% of China's territory. In addition, we further found that from 2010 to 2020, there are 266 shrinking cities in China, an increase of 86 (12%) compared with 2000–2010. This indicates that the situation of shrinking cities in China has become more critical. From the perspective of spatial distribution, Chinese shrinking cities are clustered, mainly in the northeast and central regions. We also plot the proportion of shrinkage in a deformed form to reveal which prefecture-level cities are losing population from 2010 to 2020. Our findings provide policymakers and urban planners with a reference and warning that measures should be taken to adjust the growth-oriented planning paradigm to reverse the negative impact of population loss on urban development.

The influence of urban design on economic vitality has been analyzed by a number of researchers and is also a key focus of many planning/design theories. However, most quantitative studies are based on just one city or a small set of cities, rather than a large number of cities that are representative of an entire country. With the increasing availability of new data, we aim to alleviate this gap by examining the impact of urban design upon economic vitality for the 286 largest cities in China by looking at a grid of geographical units that are 1 km by 1 km. We use these units and a set of new data (emerging big data and new data that reflecting urban developments and human mobility) to look at the impact of urban form indicators, such as intersection density (urban design), level of mixed use, and access to amenities and transportation, on economic vitality represented by activities using social media data. Our results show that these urban design indicators have a significant and positive relationship with levels of economic vitality for cities at every administrative level. The results contribute to a holistic understanding of how to improve economic vitality in cities across China at a detailed level, particularly at a time when China's economic growth will depend largely on growth of the service sector in urban areas. We think these results can help decision makers, developers, and planners/designers to improve economic vitality in cities across China.

When optimising the overall commuting pattern for a city or a region, there are often winners and losers among commuters at the subdivision level. Losers are those who are burdened with longer commutes than before the optimisation. Knowing who or where losers are is of interest to both researchers and policy-makers. The information would help them efficiently locate losers and compensate them. Few, however, pay attention to such losers. By revisiting 'excess commuting' in the economic framework, we show that optimising the commuting pattern is comparable to restoring Pareto optimality in commuting. Using Beijing as a case study, we identify and geo-visualise the losers when the city's bus commuting pattern is optimised. We examine the severity of the loss among the losers, the spatial pattern of the losers and their influencing factors. We find that most losers are located around the epicenter. The severity of the loss is independent of jobs/housing ratio but is associated with the commute distance before the optimisation. Workers whose commute distance is less than the global average are more likely to become losers. Places where losers reside have significantly lower employment density in a few industries than where non-losers reside. A low jobs/housing ratio in individual subareas does not necessarily increase the average trip length of commuters therein. A low jobs/housing ratio of one or several subareas, however, could influence the average trip length of all the commuters in the area. Locating diverse jobs and housing opportunities around or along transit corridors could compensate the losers and reduce the overall commuting cost.

Against the paucity of information on urban parcels in China, we propose a method to automatically identify and characterize parcels using OpenStreetMap (OSM) and points of interest (POI) data. Parcels are the basic spatial units for fine-scale urban modeling, urban studies, and spatial planning. Conventional methods for identification and characterization of parcels rely on remote sensing and field surveys, which are labor intensive and resource consuming. Poorly developed digital infrastructure, limited resources, and institutional barriers have all hampered the gathering and application of parcel data in China. Against this backdrop, we employ OSM road networks to identify parcel geometries and POI data to infer parcel characteristics. A vector-based cellular automata model is adopted to select urban parcels. The method is applied to the entire state of China and identifies 82 645 urban parcels in 297 cities. Notwithstanding all the caveats of open and/or crowd-sourced data, our approach can produce a reasonably good approximation of parcels identified using conventional methods, thus it has the potential to become a useful tool.

Land-use pattern is one of the key issues in the compilation of urban master plans. In China, government, planners, and residents, all with various requirements and preferences, are the main agents participating in this process. Among them, planners play a role in negotiating with related agents and then establishing land-use patterns. In this paper we propose a planner agent framework to support land-use pattern scenario analysis (LUPSA), based on existing planning support system (PSS) research. Planner agents are divided into three types: nonspatial planner agent (NPA), spatial planner agent (SPA), and chief planner agent (CPA). The NPA is responsible for formulating special plans (such as transport, municipal public facilities, or nature reserve plans) that correspond to available data (such as road network, public facilities, and nature reserve patterns) from LUPSA. The SPA is responsible for establishing land-use patterns. The SPA considers constraints of local development conditions and communicates and coordinates with the NPAs to confirm formulated special plans that can support the implementation of the established land-use pattern. The CPA is responsible for negotiating with the government agent to ensure the reasonability of comprehensive constraints, establishing the final land-use pattern based on an evaluation of established scenarios by several SPAs, then determining it after a public participation process involving local residents. We initially tested this framework in a hypothetical city, then did an experiment in Beijing. Results show that the proposed planner agent framework is suitable for LUPSA.