Maosong Liu

Developing a quantitative landscape regionalization framework integrating driving factors and response attributes of landscapes

Regionalization plays an important role in delineating landscape heterogeneity and providing spatial frameworks for environmental management. In this study, we developed a comprehensive regionalization approach that integrated multiple quantitative techniques and differentiated two distinct types of landscape variables, i.e., response attributes and driving factors. This approach was applied to the regionalization of surface-water resource in the Huai River Basin (HRB), China. In the regionalization scheme, the 25 subwatersheds of the HRB were adopted as the basic spatial unit; surface-water capacity, runoff depth, and drainage system density were used to characterize surface-water distribution (i.e., response attributes). The HRB subwatersheds were classified into a certain number of groups using the k-means cluster analysis based on the three response attributes. A goodness-of-fit index, calculated as the ratio of between-group/within-group variation, was employed as a quantitative criterion to assess the statistical performance of the classification results. Ultimately, the 25 subwatersheds were partitioned into five distinct regions. Results from redundancy analysis suggested that such regional pattern of surface-water resource was primarily correlated with driving factors representing climate conditions; soil and geological properties also had significant influences. Overall, our approach presents two advantages over previous regionalization frameworks: (1) It improves objectivity of landscape regionalization and reveals underlying mechanisms, generating landscape patterns by integrating response attributes and driving factors; (2) Goodness-of-fit evaluation can substantially reduce subjectivity in determining regionalization results.

Spatial Patterns of Distinct Urban Growth Forms in Relation to Roads and Pregrowth Urban Areas: Case of the Nanjing Metropolitan Region in China

Urban growth can present distinct geometric forms, the patterns and determinants of which remain little understood. The aim of this study is to explore the spatial patterns of different urban growth forms (UGFs) in relation to roads and pregrowth urban areas (PUAs) in the Nanjing metropolitan region of China. Three basic UGFs—infilling, edge-expansion, and spontaneous growth—were distinguished using a topological quantitative criterion. Results from the UGF composition showed the growth of Nanjing City, China, tended to be less compact during the accelerated urbanization. The three UGFs generally showed exponential attenuation with increasing distance to roads and PUAs, while spontaneous form showed a lognormal relationship with PUAs. Results from the logistic regression suggested that PUAs had a stronger tendency to attract infilling and edge-expansion growth than roads, whereas roads were a stronger attractor of spontaneous growth. Overall, this study can provide better understandings on the evolution of urban morphology at the landscape scale as well as useful implications for urban planning.An alert of exacerbated urban sprawl in Nanjing, China, was raised and the importance of road planning in the effort to control sprawl was highlighted.

Linking greenhouse gas emissions to urban landscape structure: the relevance of spatial and thematic resolutions of land use/cover data

ContextEmissions of greenhouse gases in urban areas play an important role in climate change. Increasing attention has been given to urban landscape structure–emission relationships (SERs). However, it remains unknown if and to what extent SERs are dependent on observational scale.ObjectiveTo assess how changing observational scales (in terms of spatial and thematic resolutions) of urban landscape structure affect SERs.MethodsWe examined correlations between 16 landscape metrics and greenhouse gas emissions across 52 European cities, through (1) systematic manipulation of spatial and thematic resolutions of the urban land use/cover (ULUC) dataset, and (2) comparison between available standard ULUC datasets with different spatial resolutions.ResultsOur analyses showed that the observed SERs significantly depend on both thematic and spatial resolutions of the ULUC data. For the 16 landscape metrics, we found diverse spatial/thematic scaling relations exhibiting monotonic, hump-shaped or scale-invariant trends. For different landscape metrics, the SERs were strongest at different spatial scales, suggesting that there is no consistent scaling relation over those observational scales.ConclusionsSERs are highly sensitive to spatial and thematic resolutions of landscape data. To avoid the problem of ‘ecological fallacy,’ important caveats should be taken for interpretations based on single landscape metrics. Particular consideration should be paid to metrics that are easily interpretable, predictable in scaling behaviors, and important for shaping SERs, such as PLAND, ED, and LPI. Systematic investigations on scaling behaviors of SERs over well-defined scale domains are encouraged in future studies linking greenhouse gas emissions and urban landscape structure.

Open big data from ticketing website as a useful tool for characterizing spatial features of the Chinese high-speed rail system

ABSTRACT China now has the largest high-speed rail system in the world. However, due to data limitations, understanding of this system remains incomplete. Here we combined open big data, complex network indicators and spatial analyses to reveal the hierarchical and modular structure of the system. Many spatial features well coincide with the planning, while some interesting features may result from self-organization, including discrepancies between the actual and planned structure, displacement of regional railway hubs and mismatch between the spatial and network dimensions of system modularity. Open big data are to be further mined for better understanding this unique transportation system.