Spatial Pattern of Functional Urban Land Conversion and Expansion under Rapid Urbanization: A Case Study of Changchun, China

As populations continue to be concentrated in cities, the world will become entirely urbanized, and urban space is undergoing a drastic evolution. Understanding the spatial pattern of conversion and expansion of functional urban land, in the context of rapid urbanization, helps us to grasp the trajectories of urban spatial evolution in greater depth from a theoretical and practical level. Using the ESRI ArcGIS 9.3 software platform, methods, such as overlay analysis, transition matrix, and kernel density estimation, were used in order to analyze the spatiotemporal characteristics of different types of functional urban land conversion and expansion in the central city of Changchun. The results show that different types of functional urban land were often expanded and replaced, and the urban spatial structure was constantly evolving. The conversion and expansion of functional urban land show similar characteristics to concentric zone and sector modes and show dynamic changes in different concentric circles and directions at different periods. Our method can accurately identify the different types of functional urban land, and also explore the evolutionary trajectory of urban spatial structure. This study will help to coordinate the development of different functional urban spaces and to optimize the urban spatial structure in the future.

Toward comparative polycentricity scores: Assessing variations in regional delineation and subcenter identification

Urban spatial structure is increasingly characterized by polycentricity, the presence of multiple interconnected centers of similar size. Polycentricity indicators, which influence research and policies related to urban development, rely on three phases of analysis: (a) delineating regions, (b) identifying subcenters within these regions, and (c) operationalizing polycentricity; and each phase contains decision points for analysts. This paper argues that polycentricity methodologies should be thought of in terms of pathways, then systematically applies 15 such pathways to the case of German regional polycentricity and compares the results. Findings suggest that questions of polycentricity are more robustly measured by comparing across multiple regional delineation methods and selection of subcenters, then looking for signs of agreement or disagreement. When possible, constructing regions from larger areas through bottom-up methods tends to avoid the biases of administratively defined regions. When this is not possible, statistical approaches to subcenter identification can serve as a check to avoid forced selection of subcenters in poorly defined regions.

Identifying the Social Urban Spatial Structure of Vulnerability: Towards Climate Change Equity in Bogotá

The constant modification of land use, economic instability, environmental factors, and social behaviour changes among the inhabitants of big cities characterize current urbanism. In Colombia, land-use planning processes supported by geographical information systems are a recent phenomenon and the legal instruments of spatial planning are inadequate in most municipalities. Moreover, socio-spatial equity represents a challenge for Latin American cities in which there is increasing awareness of the role that spatial planning plays. Consequently, the question arises as to how the urban spatial structure and organization contribute to an inclusive and equitable socio-spatial evolution, considering climate change impacts. The case study analysed in this article focuses upon the northern limits of the city of Bogotá. Therefore, this research aims to define the ideal balance of urban land-use distribution between social stratum classification and the vulnerability of the communities seeking to better adapt to climate change. We propose a methodological approach of analysing spatial syntax and the (social) intensity of activities and infrastructure, which enables us to characterize the urban structure itself and identify vulnerable urban instances. As a result, we find that the urban network with low values presents spatial unpredictability in its pattern, constraining equitable development based on the urban morphology of the city. This research allows us to conclude that the degree of vulnerability encountered by the social urban spatial structure is higher in expansion areas than in central areas of the city.

Integrating the Eigendecomposition Approach and k-Means Clustering for Inferring Building Functions with Location-Based Social Media Data

Understanding the relationship between human activity patterns and urban spatial structure planning is one of the core research topics in urban planning. Since a building is the basic spatial unit of the urban spatial structure, identifying building function types, according to human activities, is essential but challenging. This study presented a novel approach that integrated the eigendecomposition method and k-means clustering for inferring building function types according to location-based social media data, Tencent User Density (TUD) data. The eigendecomposition approach was used to extract the effective principal components (PCs) to characterize the temporal patterns of human activities at building level. This was combined with k-means clustering for building function identification. The proposed method was applied to the study area of Tianhe district, Guangzhou, one of the largest cities in China. The building inference results were verified through the random sampling of AOI data and street views in Baidu Maps. The accuracy for all building clusters exceeded 83.00%. The results indicated that the eigendecomposition approach is effective for revealing the temporal structure inherent in human activities, and the proposed eigendecomposition-k-means clustering approach is reliable for building function identification based on social media data.

The Relationship between Urban Population Density Distribution and Land Use in Guangzhou, China: A Spatial Spillover Perspective

Urban population density distribution contributes towards a deeper understanding of peoples’ activities patterns and urban vibrancy. The associations between the distribution of urban population density and land use are crucial to improve urban spatial structure. Despite numerous studies on population density distribution and land use, the significance of spatial dependence has attained less attention. Based on the Baidu heat map data and points of interests data in the main urban zone of Guangzhou, China, the current paper first investigated the spatial evolution and temporal distribution characteristics of urban population density and examined the spatial spillover influence of land use on it through spatial correlation analysis methods and the spatial Durbin model. The results show that the urban population density distribution is characterized by aggregation in general and varies on weekends and weekdays. The changes in population density within a day present a trend of “rapid growth-gentle decline-rapid growth-rapid decline”. Furthermore, the spatial spillover effects of land use exist and play the same important roles in population density distribution as the direct effects. Additionally, different types of land use show diverse direct effects and spatial spillover effects at various times. These findings suggest that balancing the population density distribution should consider the indirect effect from neighboring areas, which hopefully provide implications for urban planners and policy makers in utilizing the rational allocation of public resources and regarding optimization of urban spatial structure.

Using Smart Card Data of Metro Passengers to Unveil the Urban Spatial Structure: A Case Study of Xi’an, China

This study adopted smart card data collected from metro systems to identify city centers and illustrate how city centers interacted with other regions. A case study of Xi’an, China, was given. Specifically, inflow and outflow patterns of metro passengers were characterized to measure the degree of population agglomeration of an area, i.e., the centricity of an area. On this basis, in order to overcome the problem of determining the boundaries of the city centers, Moran’s I was adopted to examine the spatial correlation between the inflow and outflow of ridership of adjacent areas. Three residential centers and two employee centers were identified, which demonstrated the polycentricity of urban structure of Xi’an. With the identified polycenters, the dominant spatial connections with each city center were investigated through a multiple linkage analysis method. The results indicated that there were significant connections between residential centers and employee centers. Moreover, metro passengers (commuters mostly) flowing into the identified employee centers during morning peak-hours mainly came from the northern and western area of Xi’an. This was consistent with the interpretation of current urban planning, which validated the effectiveness of the proposed methods. Policy implications were provided for the transport sector and public transport operators.