Portraying Citizens’ Occupations and Assessing Urban Occupation Mixture with Mobile Phone Data: A Novel Spatiotemporal Analytical Framework

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.

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.

Assignment of a Synthetic Population for Activity-Based Modeling Employing Publicly Available Data

Agent-based modeling has the potential to deal with the ever-growing complexity of transport systems, including future disrupting mobility technologies and services, such as automated driving, Mobility as a Service, and micromobility. Although different software dedicated to the simulation of disaggregate travel demand have emerged, the amount of needed input data, in particular the characteristics of a synthetic population, is large and not commonly available, due to legit privacy concerns. In this paper, a methodology to spatially assign a synthetic population by exploiting only publicly available aggregate data is proposed, providing a systematic approach for an efficient treatment of the data needed for activity-based demand generation. The assignment of workplaces exploits aggregate statistics for economic activities and land use classifications to properly frame origins and destination dynamics. The methodology is validated in a case study for the city of Tallinn, Estonia, and the results show that, even with very limited data, the assignment produces reliable results up to a 500 × 500 m resolution, with an error at district level generally around 5%. Both the tools needed for spatial assignment and the resulting dataset are available as open source, so that they may be exploited by fellow researchers.

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.

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

Numerous studies have been devoted to uncovering the characteristics of resident density and urban mobility with multisource geospatial big data. However, little attention has been paid to the internal diversity of residents such as their occupations, which is a crucial aspect of urban vibrancy. This study aims to investigate the variation between individual and interactive influences of built environment factors on occupation mixture index (OMI) with a novel GeoDetector-based indicator. This study first integrated application (App) use and mobility patterns from cellphone data to portray residents’ occupations and evaluate the OMI in Guangzhou. Then, the mechanism of OMI distribution was analyzed with the GeoDetector model. Next, an optimized GeoDetector-based index, interactive effect variation ratio (IEVR) was proposed to quantify the variation between individual and interactive effects of factors. The results showed that land use mixture was the dominating factor, and that land use mixture, building density, floor area ratio, road density affected the OMI distribution directly. Some interesting findings were uncovered by IEVR. The influences of cultural inclusiveness and metro accessibility were less important in factor detector result, while they were found to be the most influential in an indirect way interacting with other built environment factors. The results suggested that both “hardware facilities” (land use mixture, accessibility) and “soft facilities” (cultural inclusiveness) should be considered in planning a harmonious urban employment space and sustainable city.