Aspirations and realities of polycentric development: Insights from multi-source data into the emerging urban form of Shanghai

Abstract

The polycentric idea has recently been reinvigorated in many rapidly urbanising countries, but the paucity of reliable and disaggregated data has so far made it almost impossible to understand the policy feedback there. This paper capitalises on official statistical data, novel online data, and proprietary digital data, and builds a dynamic spatial equilibrium model for understanding the past lessons and future options for developing new urban sub-centres. Following the post-2010 situation of a negative sign of monocentric bid-rent curve and physical polycentric growth, we assemble, corroborate, and validate multi-source data sets within the model to explore possible polycentric development scenarios . The data analysis and model tests show that over time, the balance between jobs and housing provision appears to have been lost, and it is insufficient job opportunities in the planned sub-centres rather than any lack of housing development that has led to the loss of this balance. Localised circumstances have also played a role in this overarching pattern of evolution. The insights are cogent for current decision making regarding polycentric planning and growth, especially in developing countries.

City Branding Evaluation as a Tool for Sustainable Urban Growth: A Framework and Lessons from the Yangtze River Delta Region

“…Despite the limited data sources and information at the pilot stage, this research provides an exemplary case on how a sustainability-oriented city branding framework is constructed and how the evaluation results of city-level performance can be interpreted to inform policy-makers. Future research projects should introduce a more context-specific and dynamic sustainable urban branding framework based on the increasing availability of urban datasets (see, for example, [50][51][52]), and continuous updates on the factor weights.…”

Understanding commuting patterns and changes: Counterfactual analysis in a planning support framework

“…From a theoretical perspective, the standard economic analysis of where people choose to live and work hinges on a locational trade-off between commuting and housing costs (Anas et al, 1998). In high-employment areas, inadequate housing supply drives up rent prices and leads to longer commutes (Yang et al, 2019a). It is clear that urban spatial structure and travel are influenced by a variety of actors -such as firms, households and governments -in a dynamic relationship (Br€ ocker and Mercenier, 2011;Lim et al, 2015).…”

“…location-based services and cellular signalling data) that have great potential to illustrate individual travel behaviours across years (Jahani et al, 2017). These data are especially promising for low-and middle-income cities where established commuting data sources are not available (Yang et al, 2019a).…”

Confusion of Goals — Rethinking the Implication of Data Analytics and Modelling for Urban Planning and Design Industry

“…While some biases are amenable to statistical corrections, some are difficult to identify and may result in significant analytical errors. Linking data from multiple sources is an effective way to detect and potentially correct data biases [32] [33] . Another useful approach is to combine big data analytics with urban system models.…”

“…Recent policy initiatives in China to improve the national planning system through a unified territorial and spatial planning scheme is another driver of change: it includes a mandate for local governments to develop territorial data platforms by integrating multisource geographical data and applying advanced information technology for policy analysis and monitoring. The new data platform, if properly established and managed, can create a shared evidence base for improving policy coordination in 够反映出人与地理空间之间有趣的动态时空关系，但却仍无法从根本 上解释既有空间格局，需要利用多源数据并结合因果模型来分析其基 础行为机制。基于相关性而非因果关系的预测也存在一定问题，且尤 其不适用于中长期政策的制定。 其次，相较于结构化调查等传统数据来源，城市大数据往往存在 采样偏差和测量误差 [30] [31] 。虽然有时可以通过数据修正来纠偏，但有 些偏差难以识别，且可能会导致严重的分析失误。将多源数据彼此关 联起来能够有效甄别并有望修正数据偏差 [32] [33] ；另一种有效方法是将 大数据分析与城市系统模型相结合。万励等人 [34] 开展的北京通勤人流 预测研究便体现了城市大数据对于系统建模的提升和改进作用。 此外，中国现有的城市数据分析和建模实践往往推崇数据先行， 研究课题、方法和范围都是取决于可获取到的数据类型，而非政策问 题的本质。这种研究方式的基本风险在于，得到的政策结果并非基于 代表性样本的可信数据，因此无益于甚至可能误导政策制定。由此引 出的另一个关键问题是：针对特定的政策问题，应该采集哪些数据？ 本文将在第三章中对这一问题进行简要论述。 需要指出的是，DAD概念仍处于不断完善之中。正如龙灜等人 [35] 的 研究结果所示，中国已经通过一系列专业、学术和教育项目探索并检 验了DAD概念及其在设计实践中的应用。例如，一年一度的中国DAD 大会和清华大学新近开设的"大数据与城市规划"研究生课程。由此 看来，DAD已经产生了一定的影响力，并在城市规划与设计应用中初 见成效(如基于实证的决策制定重新引起重视)。此外，中国近年出 台的相关政策强调通过统一的国土空间规划方案改善国家规划体系， 要求地方政府开发国土数据平台，整合多源地理数据，运用先进信息 spatial planning. The DAD concept, as an early attempt to use big data in urban planning and design, is expected to provide useful insights for implementing the territory spatial planning scheme.…”