Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects
Abstract
:1. Introduction
2. Data and Methods
2.1. Study Area
2.2. Cellphone Big Data and Processing Algorithm
2.3. Occupations Mixure Index
2.4. Geographical Detector Model
2.4.1. The Factor Detector Model (Individual Effect of Factors)
2.4.2. The Interactive Detector Model (Interactive Effect of Factors)
2.5. Interactive Effect Variation Ratio (IEVR)
2.6. Variable Selection
3. Results and Discussion
3.1. Spatial Distribution of Occupation Mixture Index
3.2. Individual Effects of Determinants on OMI
3.3. Interactive Effects of Determinants on OMI
4. Conclusions
- Results of GeoDetector (factor detector) model showed that Land was the factor with highest level of direct influence on OMI, and that the direct impacts of Land, BD, FAR, Road were at high level.
- Interactive detector model showed that the interactive effects were significant when land use mixture interacted with accessibility factors (Metro and Road) or building factors (BD and FAR).
- By proposing the IEVR, some interesting results were found: CI and Metro contributed less to the distribution of OMI from factor detector and interactive detector model, while their interactive effect enhancement were at a high level, with their IEVR ranking the first and second. That is to say, the roles of CI and Metro in affecting OMI distribution tended to be indirect, generating strong interactive effects with other factors, rather than direct effect. Though CI ranked the last in direct influence ranking list, it mattered in the interaction with other built environment factors. It was a reminder that both the direct individual effects and the indirect interactive effects of potential driving factors mattered, and that how different are the interactive effects from individual effect of each variable should be clarified in a quantitative way (IEVR).
- It should be noticed that the results of this study might shed light on urban planning and management. First, OMI was applied to evaluate the diversity of residents from the perspective of occupations in Guangzhou. Areas with high OMI were located in and around downtown, where is close to saturation relatively in land development and talent employment. Therefore, attention and planning policy should be paid to those low OMI areas (suburbs) with greater potential in the process of urban renewal and suburban development. First of all, the demands of “hardware facilities” should be met in local planning including land use mixture, building density and road density, which were found important and effective in promoting OMI. Second, the result of IEVR reminded planners that based on the solid “hardware facilities” foundation, cultural inclusiveness has great potential in increasing OMI when it interacts with other built environment factors. However, we have to realize that the function of CI is to measure the current status of cultural inclusiveness of the city, which was represented by cuisine POIs. That is, CI was a variable substituting the realistic cultural inclusiveness, which was irreversible. Therefore, densifying the restaurants of different cuisines might not help improving CI and OMI. To achieve it, reforming the household registration and talent introduction policy might be helpful to build a harmonious and friendly pluralistic society.
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Deng, X.; Liu, Y.; Gao, F.; Liao, S.; Zhou, F.; Cai, G. Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects. ISPRS Int. J. Geo-Inf. 2021, 10, 659. https://doi.org/10.3390/ijgi10100659
Deng X, Liu Y, Gao F, Liao S, Zhou F, Cai G. Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects. ISPRS International Journal of Geo-Information. 2021; 10(10):659. https://doi.org/10.3390/ijgi10100659
Chicago/Turabian StyleDeng, Xingdong, Yang Liu, Feng Gao, Shunyi Liao, Fan Zhou, and Guanfang Cai. 2021. "Spatial Distribution and Mechanism of Urban Occupation Mixture in Guangzhou: An Optimized GeoDetector-Based Index to Compare Individual and Interactive Effects" ISPRS International Journal of Geo-Information 10, no. 10: 659. https://doi.org/10.3390/ijgi10100659