Mapping Block-Level Urban Areas for All Chinese Cities

Abstract

As a vital indicator for measuring urban development, urban areas are expected to be identified explicitly and conveniently with widely available data sets, thereby benefiting planning decisions and relevant urban studies. Existing approaches to identifying urban areas are normally based on midresolution sensing data sets, low-resolution socioeconomic information (e.g., population density) in space (e.g., cells with several square kilometers or even larger towns or wards). Yet, few of these approaches pay attention to defining urban areas with high-resolution microdata for large areas by incorporating morphological and functional characteristics. This article investigates an automated framework to delineate urban areas at the block level, using increasingly available ordnance surveys for generating all blocks (or geounits) and ubiquitous points of interest (POIs) for inferring density of each block. A vector cellular automata model was adopted for identifying urban blocks from all generated blocks, taking into account density, neighborhood condition, and other spatial variables of each block. We applied this approach for mapping urban areas of all 654 Chinese cities and compared them with those interpreted from midresolution remote sensing images and inferred by population density and road intersections. Our proposed framework is proven to be more straightforward, time-saving, and fine-scaled compared with other existing ones. It asserts the need for consistency, efficiency, and availability in defining urban areas with consideration of omnipresent spatial and functional factors across cities.

城市地区由于作为评估城市发展的生动指标, 因而被期待能够以广泛可及的数据集明确且便利地进行指认, 藉此加惠规划决策和相关的城市研究。指认城市地区的既有方法, 一般是根据中度辨识率的遥测数据集、空间中(例如具有数平方公里的区块, 甚至是更大的乡镇或行政区) 低度辨识率的社会经济信息(例如人口密度)。但这些方法鲜少关注透过纳入形态与功能之特徵, 以高度辨识率的微观数据为大型区域界定城市区域。本文探讨自动架构以描绘街廓层级的城市地区, 使用逐渐可及的地形测量以生产所有街廓(或地理单位) 与普遍存在的兴趣点 (POIs) 来推断各街廓的密度。本文採用向量细胞自动机模型, 从所有生成的街廓中指认城市街廓, 并将各街廓的密度、邻里条件与其他空间变异纳入考量。我们将此方法应用于绘製中国共六百五十四座城市的城市地区地图, 并将其与从中度辨识率的遥测影像、以及从人口密度与道路交口推断的地区进行比较。与其他既有的方法相较之下, 我们所提出的架构证实更为直接、省时、且尺度精密。该架构主张界定城市地区时必须具有一致性、效率与可及性, 并考量广佈各城市的无所不在的空间与功能因素。

Como indicador vital para medir el desarrollo urbano, se espera que las áreas urbanas sean identificadas explícita y convenientemente con conjuntos de datos ampliamente disponibles, fortaleciendo así las decisiones de planificación y los estudios urbanos relevantes. Los enfoques existentes para identificar las áreas urbanas normalmente se basan en conjuntos de datos de sensores a resolución intermedia, información socioeconómica de baja resolución (e.g., densidad de población) en el espacio (e.g., celdas de varios kilómetros cuadrados o incluso pueblos más grandes o distritos). No obstante, pocos de estos enfoques le prestan atención a definir las áreas urbanas con datos micro de alta resolución para áreas grandes, incorporando características morfológicas y funcionales. Este artículo investiga un marco automático para delinear áreas urbanas a nivel de manzanas o cuadras, usando los cada vez más comunes servicios de cartografía para generar todas las manzanas (o geounidades) y ubicuos punto de interés (POIs) para inferir la densidad de cada manzana. Se adoptó un modelo vector cellular autómata para identificar manzanas urbanas a partir de todas las manzanas generadas, tomando en cuenta la densidad, la condición del vecindario y otras variables espaciales de cada manzana. Aplicamos este enfoque para cartografiar las áreas urbanas de todas las 654 ciudades chinas, y las comparamos con las interpretadas de imágenes de percepción remota a resoluciones intermedias y deducidas de la densidad de población y las intersecciones de carreteras. El esquema que proponemos es probado como más directo, económico en tiempo y de escala fina en comparación con otros disponibles. Este esquema reivindica la necesidad de consistencia, eficiencia y disponibilidad para definir áreas urbanas con la consideración de los factores espaciales y funcionales presentes por doquier a través de las ciudades.

Key Words:

• China
• points of interest (POIs)
• road network
• urban block
• vector cellular automata

关键词：:

• 中国, 兴趣点 (POIs)
• 路网
• 城市街廓
• 向量细胞自动机。

Palabras clave:

• China
• puntos de interés
• red de carreteras
• manzana o cuadra urbana
• modelo vector celular autómata

Acknowledgments

The authors would like to thank Dr. Kang Wu and Dr. Dong Li for providing China data sets. Our thanks are also given to Stewart Scales for his advice on polishing the text. The mapping results are available at the Beijing City Lab Web site (www.beijingcitylab.com). Yao Shen is the author to whom all correspondence should be addressed.

Funding

This work was supported by a grant from the National Natural Science Foundation of China (No. 51408039).

Notes

1. “One book and two certificates”—containing a proposal of project location, the permit of land planning, and the permit of construction planning—refers to the construction approval files delivered by the government based on urban planning law.

2. Sansha in Hainan and Beitun in Xinjiang appearing in MOHURD (2013) were not included due to spatial data availability. Taiwan was not included in all analysis and results in this article.

Additional information

Notes on contributors

Ying Long

YING LONG is an interdisciplinary scholar with substantive planning experiences at the Beijing Institution of City Planning, Xicheng 100045, Beijing, China. E-mail: [email protected]. In the past few years, his research has focused on quantitative urban studies and their applications in urban planning. Familiar with planning practices in China and versed in the international literature, his research creatively integrates international methods and experiences with local planning practices.

Yao Shen

YAO SHEN is a PhD candidate in the Space Syntax Laboratory at the Bartlett School of Architecture, University College London, NW1 2BX, London, UK. E-mail: [email protected]. His research interests include geospatial analysis and modeling, data-driven urban design and planning, spatial econometrics, and urban morphology.

Xiaobin Jin

XIAOBIN JIN is an Associate Professor in the School of Geographic and Oceanographic Sciences at Nanjing University, Jiangsu Province, China 210023. E-mail: [email protected]. His research interests include land use change modeling, ecological effects of land use, and land planning.