Xinyue Ye

Geospatial Analysis of Regional Development in China: The Case of Zhejiang Province and the Wenzhou Model

The paper, based on extensive field work and surveys, analyzes in a GIS environment the multiscalar patterns and emerging clusters of regional development in Zhejiang Province (one of Chinas most rapidly growing) and Wenzhou Municipality (known for the Wenzhou Model of development based on private enterprise and rural industrialization). The authors first investigate the extent to which a traditional northeast-southwest divide has been replaced by emerging coastal-interior divide and whether intraprovincial inequality in Zhejiang, especially rural intercounty inequality, has intensified as new development clusters have emerged. At a finer scale of investigation, they analyze the Wenzhou Model and explain the resulting patterns of change by addressing the role of localities, the state, and globalization. Journal of Economic Literature, Classification Numbers: O10, O14, O18, O20. 10 figures, 1 table, 36 references.

Urbanization, urban land expansion and environmental change in China

China’s economic reforms and unprecedented growth have generated many fascinating issues for scholarly research. An understanding of urbanization and land use change in China is required for appropriate strategies and policies to facilitate future sustainable development. This paper reviews the literature on urbanization, land use and sustainable development in China with a focus on land use change. We argue that land use and environmental research are embedded in the complex economic-geographical processes and multiple trajectories of development and urbanization in China. This paper highlights the important role of space–time modeling in a multi-disciplinary setting in the study of urbanization, land use and sustainable development. It also points out potential areas for future research.

Patterns of Near-Repeat Gun Assaults in Houston

The study assesses the extent to which gun assaults are clustered in space and time using crime data from Houston, Texas. The analysis examines patterns of gun assaults at the city-level as well as more localized levels in order to understand the spatial distribution of near-repeats within the city. Consistent with prior research, the city-level analysis shows significant and meaningful near-repeat patterns. The localized analysis indicates that the risk of near-repeats is not evenly distributed across space within the city, but is concentrated among a small portion of incidents and four relatively small spatial clusters. In addition, an examination of crime types, locations, and gang involvement shows slight differences between gun assaults with and without near-repeat follow-up shootings.

Editorial: human dynamics in the mobile and big data era

Human dynamics is a term that has been used and investigated by researchers in various fields from very different perspectives. Barabasi’s (2005) publication of ‘The origin of bursts and heavy tail...

TrajGraph: A Graph-Based Visual Analytics Approach to Studying Urban Network Centralities Using Taxi Trajectory Data

We propose TrajGraph, a new visual analytics method, for studying urban mobility patterns by integrating graph modeling and visual analysis with taxi trajectory data. A special graph is created to store and manifest real traffic information recorded by taxi trajectories over city streets. It conveys urban transportation dynamics which can be discovered by applying graph analysis algorithms. To support interactive, multiscale visual analytics, a graph partitioning algorithm is applied to create region-level graphs which have smaller size than the original street-level graph. Graph centralities, including Pagerank and betweenness, are computed to characterize the time-varying importance of different urban regions. The centralities are visualized by three coordinated views including a node-link graph view, a map view and a temporal information view. Users can interactively examine the importance of streets to discover and assess city traffic patterns. We have implemented a fully working prototype of this approach and evaluated it using massive taxi trajectories of Shenzhen, China. TrajGraphs capability in revealing the importance of city streets was evaluated by comparing the calculated centralities with the subjective evaluations from a group of drivers in Shenzhen. Feedback from a domain expert was collected. The effectiveness of the visual interface was evaluated through a formal user study. We also present several examples and a case study to demonstrate the usefulness of TrajGraph in urban transportation analysis.

Spatial heterogeneity of economic development and industrial pollution in urban China

The relationship between economic development and environmental pollution has been widely studied in the context of the environmental Kuznets curve. This study applies the three-dimension framework of density, division, and distance proposed by the World Bank to identify the spatial heterogeneity of development and pollution in urban China. An inverted U relationship is detected between density and industrial SO2 emission, while a cubic relationship is found between density and industrial SO2/soot emission intensity. The statistical significance of division indicates that the pollution haven hypothesis holds in the western region and cities in the periphery. The environmental implication of distance is that the industrial pollution is largely concentrated in the national and regional cores.

Assessing spatial pattern of urban thermal environment in Shanghai, China

The aggravating urban thermal environment has considerable adverse effects on urban physical environment, energy consumption, and public health. Due to the complexity of factors contributing to the urban thermal environment, traditional statistical methods are insufficient for acquiring data and analyzing the impacts of human activities on the thermal environment, especially for identifying dominant factors. Based on thermal remote sensing imageries and Geographic Information System analysis, we assessed spatial pattern of urban thermal environment in Shanghai in 2008, and analyzed the factors contributing to the generation of urban heat island (UHI) using principal component analysis (PCA). We found that Shanghai had obvious UHI with uneven spatial pattern in 2008. Further, we identified three most important components leading to the variances of Shanghai’s UHI: the gradient from man-made to natural land cover, landscape configuration, and anthropogenic heat release. A linear model has thus been successfully constructed, implying that PCA is helpful in identifying major contributors to UHI. The findings are of significance for policy implication to urban thermal environment mitigation.

Spatial, temporal, and content analysis of Twitter for wildfire hazards

Social media data are increasingly being used for enhancing situational awareness and assisting disaster management. We analyzed the wildfire-related Twitter activities in terms of their attributes pertinent to space, time, content, and network, so as to gain insights into the usefulness of social media data in revealing situational awareness. Findings show that social media data can characterize the wildfire across space and over time, and thus are applicable to provide useful information on disaster situations. Second, people have strong geographical awareness during wildfire hazards and are interested in communicating situational updates related to wildfire damage (e.g., containment percentage and burned acres), wildfire response (e.g., evacuation), and appreciation to firefighters. Third, news media and local authorities are opinion leaders and play a dominant role in the wildfire retweet network.

Spatiotemporal dynamics of carbon intensity from energy consumption in China

The sustainable development has been seriously challenged by global climate change due to carbon emissions. As a developing country, China promised to reduce 40%-45% below the level of the year 2005 on its carbon intensity by 2020. The realization of this target depends on not only the substantive transition of society and economy at the national scale, but also the action and share of energy saving and emissions reduction at the provincial scale. Based on the method provided by the IPCC, this paper examines the spatiotemporal dynamics and dominating factors of China’s carbon intensity from energy consumption in 1997–2010. The aim is to provide scientific basis for policy making on energy conservation and carbon emission reduction in China. The results are shown as follows. Firstly, China’s carbon emissions increased from 4.16 Gt to 11.29 Gt from 1997 to 2010, with an annual growth rate of 7.15%, which was much lower than that of GDP (11.72%). Secondly, the trend of Moran’s I indicated that China’s carbon intensity has a growing spatial agglomeration at the provincial scale. The provinces with either high or low values appeared to be path-dependent or space-locked to some extent. Third, according to spatial panel econometric model, energy intensity, energy structure, industrial structure and urbanization rate were the dominating factors shaping the spatiotemporal patterns of China’s carbon intensity from energy consumption. Therefore, in order to realize the targets of energy conservation and emission reduction, China should improve the efficiency of energy utilization, optimize energy and industrial structure, choose the low-carbon urbanization approach and implement regional cooperation strategy of energy conservation and emissions reduction.

Coarse-grained parallel genetic algorithm applied to a vector based land use allocation optimization problem: the case study of Tongzhou Newtown, Beijing, China

A Coarse-Grained Parallel Genetic Algorithm (CGPGA) is utilized to search for near-optimal solutions for land use allocation optimization problems in the context of multiple objectives and constraints. Plans are obtained based on the trade-off among three spatial objectives including ecological benefit, accessibility and compatibility. The Multi-objective Optimization of Land Use model integrates these objectives with the fitness function assessed by reference point method (goal programming). The CGPGA, as the first coupling in land use allocation optimization problems, is tested through the experiments with one processor, two processors and four processors to pursue near-optimal land use allocation scenarios and the comparison to these experiments based on Generic Genetic Algorithm (GGA), which clearly shows the robustness of the model we proposed as well as its better performance. Furthermore, the successful convergent (near-convergent) case study utilizing the CGPGA in Tongzhou Newtown, Beijing, China evinces the capability and potential of CGPGA in solving land use allocation optimization problems with better efficiency and effectiveness than GGA.