Drawing upon a panel dataset on China's state-owned construction land supply at the prefecture level, this paper adopts the spatial panel data model to investigate the existence of spatial dependence in the mechanisms behind China's urban land expansion from 2004 to 2014. The empirical results show that China's urban land expansion has been mainly influenced by a quadruple process of urbanization, industrialization, globalization and decentralization. The strong existence of spatial dependence in China's urban land expansion can be reflected by the significant coefficient estimates of the spatial lags of both the dependent variable and independent variables.

An urban dynamic spatial structure circle layer definition method, comprising the following steps: collecting mobile phone user positioning data, and cleaning and processing the mobile phone user positioning data, so as to obtain matched mobile phone user positioning data; by taking a base station as a unit, combining the matched mobile phone user positioning data at various moments into everyday mobile phone positioning data by date; for the combined base station information, performing spatial positioning on base stations with different numbers of users in the range of the whole city by means of a Thiessen polygon processing method; allocating, according to a land parcel area ratio, the user number data in various polygons into each parcel contained thereby, so as obtain the number of mobile phone users in each land parcel; and obtaining a mobile phone user distribution multi-circle layer value rule at each time point and each time date by using a spatial analysis method for a Kriging interpolation method in ArcGis, then finding out a value zero point, combining circle layers in the same area, and defining an urban dynamic spatial structure circle layer distribution diagram. The method fully depends on the current mobile communication network resource, and is low in cost, high in frequency, high in precision and big in sample size, and is automated.

A method for constructing an urban space holographic map based on multi-source big data fusion. Green vegetation, municipal engineer projects, the physical environment (comprising the urban noise environment, urban wind environment, urban heat environment, and heat island effect), industrial institution POI, human and traffic activities, human perception evaluations within the coverage of an entire urban space are mapped to a cloud database via different layers, different types of data and spatial features are combined with urban space patterns and intuitively and dynamically displayed and outputted, and made available for real-time detection by various urban systems, thus facilitating the implementation of urban planning and design projects. The present method, based on a cloud data end, is capable of handling the processing of massive data and performing real-time quick query and display; by superimposing multi-source big data and urban space pattern data under a same digital map system, implemented are urban coordinate system-based seamless and continuous collection of multi-source data interfaces and analog dynamic display of spatial features.

Despite a growing interest among policymakers and urban planners in promoting polycentric and compact development to mitigate traffic congestion, empirical studies have often documented mixed and indirect evidence on the impacts of polycentricity and compactness on congestion. Drawing upon a direct and big-data based measure of congestion and gridded (1 km x 1 km) population data of 98 Chinese cities, this study investigates how polycentricity and compactness may affect congestion in these cities. The degrees of poly centricity and compactness are measured through fine-grained identification of population centers. All else being equal, the empirical results show that congestion is positively associated with the degree of compactness but negatively associated with that of polycentricity. However, increasing the degree of polycentricity by developing more than four population centers may also lead to more congestion. Furthermore, the negative impact of polycentricity on congestion becomes weaker with the increase in a city's population and even turns positive for large cities with more than six million inhabitants within urban districts. The paper concludes with spatial planning implications.

As the world's largest carbon emitter, China is under great pressure to cut down carbon emissions. Understanding the evolution of carbon emissions across Chinese cities is important for policymakers when allocating carbon emission quota among these cities. This paper draws upon the Open-source Data Inventory for Anthropogenic CO2 to calculate city-level per capita carbon emissions in China from 2001 to 2016. Overall, we find that per capita carbon emissions of Chinese cities have been generally on the rise during the 2001-2016 period. However, there has been on average a modest decline in per capita carbon emissions of cities in China's Yangtze River Delta region and Pearl River Delta region from 2011 to 2016, after a remarkable increase during the 2001-2011 period. Besides, the average north-south gap has been enlarged, with northern cities having a relatively higher level of per capita carbon emissions.

Despite the growing number of foreign applications for patents in China, the spatial distribution of countries that have applied for patents in China, as well as its evolution, has yet to be investigated. By using cartograms, this paper aims to show the evolving distribution of countries that have applied for patents in China from 1987 to 2017. First, we find that the number of patents applied for in China has been far from evenly distributed across countries. Rather, it has been mainly concentrated in several countries such as Japan, the USA, and Germany. Second, the distribution pattern changed a lot during the first decade of the study period and has remained relatively stable over the last two decades. Third, recent years have also seen an increasing number of patent applications in China by some offshore financial markets like the Cayman Islands, where many high-tech companies are registered.