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

Abstract

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.

Advancing Urban Life: A Systematic Review of Emerging Technologies and Artificial Intelligence in Urban Design and Planning

“…Artificial intelligence in geographic design transforms complex qualitative descriptions in space into quantitative analysis and design models through intelligent mechanisms. The role of artificial intelligence technology in geographical design is mainly reflected in two aspects: (1) Using artificial intelligence algorithms and thinking to calculate and analyze the relevant data in geographical design research efficiently and accurately and mining knowledge and rules from it; (2) Aiming at complex and difficult problems in geospatial research, establish a spatial intelligence model to reveal the internal mechanism behind the phenomenon. Artificial intelligence applied to geographic design mainly refers to "weak artificial intelligence": execution ability is generally better than humans and can formulate and apply digital technology to achieve goals [8].…”

“…[49] Analyzes the built environment's impact on occupational diversity using GeoDetector-based indicators for in-depth analysis. [1] Employs deep learning for image segmentation and NDVI to measure urban greenness. [50] Utilizes a mix of text mining, image processing, clustering, kernel density estimation, and sentiment analysis to assess urban perceptions.…”

Study on the Spatial and Temporal Trends of Ecological Environment Quality and Influencing Factors in Xinjiang Oasis

“…It can be used to study spatial anisotropy, reveal the degree of influence and importance of each driver, detect the strength of interactions between factors, and perform risk detection. In this study, we used the interaction detection tool to analyze the drivers of ecological change in the region [40].…”

Observed Equity and Driving Factors of Automated External Defibrillators: A Case Study Using WeChat Applet Data

“…The company density and residential density were expressed by calculating the POI density of the company and residential community, which was obtained via Baidu Map API in December 2022. Population mobility is calculated based on cell phone signal data at the base station level, which was provided by the local operators with the largest market share [40,42,43]. The population mobility of a block is equal to the difference between the average maximum and average minimum population of all the base stations in the block (Figure 6).…”