Explicit Spatializing Heat-Exposure Risk and Local Associated Factors by coupling social media data and automatic meteorological station 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.

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

Mobile phone data is a typical type of big data with great potential to explore human mobility and individual portrait identification. Previous studies in population classifications with mobile phone data only focused on spatiotemporal mobility patterns and their clusters. In this study, a novel spatiotemporal analytical framework with an integration of spatial mobility patterns and non-spatial behavior, through smart phone APP (applications) usage preference, was proposed to portray citizens’ occupations in Guangzhou center through mobile phone data. An occupation mixture index (OMI) was proposed to assess the spatial patterns of occupation diversity. The results showed that (1) six types of typical urban occupations were identified: financial practitioners, wholesalers and sole traders, IT (information technology) practitioners, express staff, teachers, and medical staff. (2) Tianhe and Yuexiu district accounted for most employed population. Wholesalers and sole traders were found to be highly dependent on location with the most obvious industrial cluster. (3) Two centers of high OMI were identified: Zhujiang New Town CBD and Tianhe Smart City (High-Tech Development Zone). It was noted that CBD has a more profound effect on local as well as nearby OMI, while the scope of influence Tianhe Smart City has on OMI is limited and isolated. This study firstly integrated both spatial mobility and non-spatial behavior into individual portrait identification with mobile phone data, which provides new perspectives and methods for the management and development of smart city in the era of big data.

Characteristics of noise complaints and the associations with urban morphology: A comparison across densities

This study aimed to characterise spatial-temporal distribution of noise complaints across urban areas with different densities and to analyse the associations between urban morphology and noise complaints. Taking New York City as the study area, crowdsourced noise complaint and urban morphology datasets from the government's open data source were statistically analysed. The results suggest that between boroughs the characteristics of noise complaints are different, in terms of their spatial-temporal distribution, their relation to transport network, land use, and building morphology. Noise complaints were clustered around the highest density area (Manhattan). The rate of noise complaints showed a year-on-year increase, peaking in autumn and spring. The rate of noise complaints is higher in areas with higher densities and roads that are 20-40 m wide, closer to road crossings, and in enclosed blocks. The relationships between noise complaints and urban morphology are weaker in high-density boroughs than in other boroughs.