Sensing Mixed Urban Land-Use Patterns Using Municipal Water Consumption Time Series

Abstract

The biased population coverage and short temporal lengths of newly emerged data sets (e.g., data sets of social media, mobile phones, and smart cards) obstruct the effective analysis of long-term dynamics of landuse patterns, particularly in small and developing cities. This study proposed a framework to delineate and analyze mixed land-use patterns and their evolution using municipal water consumption data. A two-step classification strategy was designed based on the rotation forest scheme to differentiate the socioeconomic types of customers (e.g., residence, commerce, public facility, manufacturing, and recreation) using multiple features extracted from the various forms of water consumption time series. The spatial distributions of the socioeconomic functions were then derived, and the mixed land use was measured using a diversity index based on information entropy. Such an approach was applied to Changshu, a typical developing county-level city in China, for the period 2004 to 2013. The results showed that the urbanization of Changshu experienced both spatial expansion and intensification, with a slightly declining rate of growth in recent years. Apart from the city center, two subcenters have emerged for industrial development. The degree of land-use mixture has increased with urban growth, indicating a maturing of urbanization. This study explored the approach of identifying individual socioeconomic functions by the consumption patterns of municipal services and demonstrated that municipal service data sets can reveal land-use patterns and dynamics at a fine spatial resolution to evaluate urban planning and management, with the advantages of large population coverage and long-term temporal lengths.

4新的数据集（如社交媒体、手机、智能卡等）存在着人口覆盖面不全、数据时间短的问题, 阻碍了（尤其是小城市和发展中城市的）土地利用长期变化的分析。本文提出了一个框架, 从城市用水数据中, 提取和分析混合土地利用的模式和演变。基于旋转森林方法, 本文设计了两步分类法, 可以从多种城市用水时间序列数据中提取特征值, 区分不同社会经济类型的用户（居民, 商业, 公共设施, 制造业, 娱乐业等）。本文提取了社会经济功能的空间分布, 计算了基于信息熵的混合土地利用多样性指数。采用的案例为2004年至2013年的中国常熟市, 该市是中国典型的县级市。结果显示, 常熟的城市化经历了空间扩展和集约化, 而近几年的发展速度略有下降。工业发展带来了两个新的城市副中心。土地利用的混合程度随着城市发展而提高, 表明城市化趋于成熟。本文探索了利用城市服务的消费模式来确定社会经济功能, 展示了城市服务数据的人口覆盖面广、数据时间长的优势, 可以揭示高分辨率的土地利用模式及其变化, 进而评估城市规划和管理。

La cobertura sesgada de la población y las longitudes temporales cortas de los conjuntos de datos de reciente aparición (e.g., conjuntos de datos de los medios sociales, de teléfonos móviles y tarjetas inteligentes) obstruyen el análisis efectivo de las dinámicas a largo plazo en los patrones de uso del suelo, en particular en ciudades en desarrollo pequeñas. Este estudio propone un marco para delinear y analizar patrones mixtos de uso del suelo y su evolución por medio del uso de datos sobre consumo de agua a nivel municipal. Se diseñó una estrategia de clasificación de dos etapas con base en el esquema de bosque de rotación para diferenciar la tipología socioeconómica de los usuarios (e.g., residencial, comercio, servicios públicos, manufacturas y recreación) usando múltiples rasgos extraídos de las diversas formas de las series de tiempo del consumo de agua. Luego se derivaron las distribuciones espaciales de las funciones socioeconómicas, y el uso mixto del suelo se midió usando un índice de diversidad basado en la entropía de la información. Tal enfoque se aplicó a Changshu, una típica ciudad en desarrollo a nivel de condado, en China, para el período 2004-2013. Los resultados mostraron que el proceso de urbanización de Changshu experimentó tanto expansión como intensificación, con una tasa de crecimiento ligeramente declinante en los últimos años. Además del centro de la ciudad, han emergido dos subcentros para desarrollo industrial. El grado de mezcla en el uso del suelo se ha incrementado con el crecimiento urbano, lo cual indica madurez de la urbanización. Este estudio exploró el enfoque de identificar funciones socioeconómicas individuales a través de los patrones de consumo de los servicios municipales y demostró que los conjuntos de datos de los servicios municipales pueden revelar los patrones y dinámicas de uso del suelo a una resolución espacial fina para evaluar la planificación y administración urbanas, con las ventajas de una cobertura de población grande y longitudes temporales de largo plazo.

Key Words:

• land-use patterns
• mixed land use
• municipal water consumption
• rotation forest
• social sensing

关键词:

• ：土地利用模式
• 混合土地利用
• 城市用水
• 旋转森林
• 社会感知。

Palabras clave::

• bosque de rotación
• consumo municipal de agua
• patrones de uso del suelo
• sentido social
• uso mixto del suelo

Acknowledgments

We sincerely thank the editors and anonymous reviewers for their constructive comments and suggestions that significantly strengthened this article. Dr. Wen Zeng ([email protected]) serves as the corresponding author for this article.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China (Grant Nos. 41671408, 41801306), Natural Science Foundation of Hubei Province (Grant No. 2017CFA041), and Special Fund for Foundation and Frontier of Applications of Wuhan (Grant No. 2018010401011293).

Notes on contributors

Qingfeng Guan

QINGFENG GUAN is a Professor in the School of Geography and Information Engineering at China University of Geosciences, Wuhan, China. E-mail: [email protected]. His research interests include big spatio-temporal data analytics and mining, spatio-temporal modeling, spatial computational intelligence, and high-performance spatial computing.

Sijing Cheng

SIJING CHENG received her Master degree from the School of Geography and Information Engineering at China University of Geosciences, Wuhan, China, and she is currently a Software Engineer in the PowerChina Huadong Engineering Co., Ltd., Hangzhou, China. E-mail: [email protected]. Her research interests include big spatio-temporal data analytics and mining, and urban computing.

Yongting Pan

YONGTING PAN is a PhD candidate in the School of Geography and Information Engineering at China University of Geosciences, Wuhan, China. E-mail: [email protected]. Her research interests include big spatio-temporal data analytics and mining, and urban computing.

Yao Yao

YAO YAO is an Associate Professor in the School of Geography and Information Engineering at China University of Geosciences, Wuhan, China. E-mail: [email protected]. His research interests include big spatio-temporal data analytics and mining, urban computing, and social sensing.

Wen Zeng

WEN ZENG (corresponding author) is a Professor in the School of Geography and Information Engineering at China University of Geosciences, Wuhan, China. E-mail: [email protected]. His research interests include analytical and modeling algorithms for urban networks, big spatio-temporal data analytics and mining.