Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects

Revealing disparities in different types of park visits based on cellphone signaling data in Guangzhou, China

Urban parks play a crucial role in promoting the urban ecological environment and the health and well-being of dwellers. However, existing research on park visits and drivers has largely ignored the classification of parks. Using the four-level park system in Guangzhou as a case, this study first measured park visits based on cellphone signaling data. Then, the independent and interactive influences of driving factors on the visits of four types of parks were investigated and compared comprehensively based on the geographical detector model. The factor detector model preliminarily distinguished the functional and role differences of various park types. Nature and urban parks are more functional, and community and pocket parks mainly provide nearby residents with convenient relaxation spaces. The interaction detector further revealed the disparities in park visit drivers between four types of parks. The most significant finding is that nearby recreational facility is the key to the use of natural and urban parks, while the determining factor for the visits of community and pocket parks is the surrounding population. Based on these findings, the study recommends tailored strategies for each type of park, to promote effective management and increased utilization. In particular, the study highlights the importance of understanding the differences between park types and developing customized strategies to maximize the benefits of urban parks and foster a healthy and sustainable urban environment.

Exploring spatiotemporal pattern and agglomeration of road CO2 emissions in Guangdong, China

Road transport is a prominent source of carbon emissions. However, fine-grained regional estimations on road carbon dioxide (CO2) emissions are still lacking. This study estimates road CO2 emissions in Guangdong Province, China, at high spatiotemporal resolution, with a bottom-up framework leveraging massive vehicle trajectory data. We unveil the spatiotemporal pattern of regional road CO2 emissions and highlight the contrasts among cities. The Greater Bay Area (GBA) is found to produce 76 % of the total emissions, wherein Guangzhou emits the most while Shenzhen has the highest emission intensity. Emission agglomeration is still an under-explored field, which we advance in this paper. We propose Quantile-based Hierarchical DBSCAN (QH-DBSCAN) to explore road CO2 emission agglomeration in GBA. Our method is the first one to identify the specific location and scope of emission hotspots. Emission hotspots exhibit significant concentration on major urban centers. Considering emission characteristics from multiple perspectives, we derive six emission categories, including four emission zones and two emission connectors. The density-based property of our method results in spatially contiguous regions with similar emission patterns. Accordingly, we divide policy zones and propose targeted strategies for road carbon reduction. The study provides new technologies and insights to achieve regional sustainable development.

Spatially explicit carbon emissions by remote sensing and social sensing

Scientific simulation of carbon emissions is an important prerequisite for achieving low-carbon green development and carbon peak and carbon neutralization. This study proposed a carbon emissions spatialization method based on nighttime light (NTL) remote sensing and municipal electricity social sensing. First, the economics-energy comprehensive index (EECI) was proposed by integrating the NTL and municipal electricity consumption (EC) data. Second, the carbon emissions were spatialized at a fine scale based on NTL, EC, and EECI, respectively. Finally, the geographical detector model was applied to quantify the influencing factors on carbon emissions from the perspectives of individuals and interactions. Results show that combining remote sensing and social sensing data helps depict carbon emissions accurately. The factor analysis found that GDP and population were the basis of carbon emissions, while the secondary industry and urbanization rate were the direct factors. This study is expected to provide constructive suggestions and methods for emission reduction, carbon peak, and carbon neutrality in high-density cities in China.

Exploring the Interactive Associations between Urban Built Environment Features and the Distribution of Offender Residences with a GeoDetector Model

Offender residences have become a research focus in the crime literature. However, little attention has been paid to the interactive associations between built environment factors and the residential choices of offenders. Over the past three decades, there has been an unprecedented wave of migrant workers pouring into urban centers for employment in China. Most of them flowed into urban villages within megacities. Weak personnel stability and great mobility have led to the urban villages to be closely related to decreased public safety and the deterioration of social order. The YB district in China was selected as the study area, which is located in one of the most developed cities in Southern China and has an area of approximately 800 km2 and a population of approximately four million people. This study aims to explore the associations between the neighborhood environment and the offender residences by using the geographical detector model (GeoDetector) from the perspective of interaction. The conceptual framework is based on the social disorganization theory. The results found that urban villages were the most important variable with a relatively high explanatory power. In general, taking the urban village as the carrier, various places (hotels, entertainment places, and factories) within the urban village may be more likely to include offender residences. This study also found the social disorganization theory applicable in the non-Western context. These findings may have important implications for offender residences identification, crime prevention, and the management of urban villages in Chinese cities.

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.