609 / 2019-04-19 17:26:21

Stimulating the Expansion of Urban and Rural Construction Land of Guangzhou Supported by Big Data

Urban and Rural Construction Land,Expansion Stimulation,Cellular Automata,Guangzhou,big data

全体主题 > Cellular Automata

Rapid urbanization in China has turned large amount non-construction land into construction land (urban and rural construction land take the major), providing abundant carrying capacity for population and economy. However, improvident development pattern has caused many problems, such as invading cultivated land, occupying ecological land and so on. Stimulating the expansion of urban and rural construction land (URCL) can help predict the change of a city’s spatial pattern, providing suggestions for solving the above problems. In this research, we first based on big data, like Weibo check-in data, night-light data and Gaode electronic map data, to build the indicators system about URCL expansion. Employing space syntax and kernel density estimating, we visualized the indicators. Then, with Logistic regression model, we calculated the expanding potentiality results of every spatial grid, and corrected the results. Finally, using cellular automata (CA) model, we stimulated the expansion process of URCL for different time intervals, and checked the stimulation results’ accuracy through Kappa coefficient. The findings including: (1)By comparing the impact of all indicators’ for URCL expansion, we found that economy development and population distribution ranked the top two, which means economy and population are main driving forces for URCL expansion; (2)In the time interval from 2014 to 2020, the Kappa coefficient of stimulation results and standard data reached 0.8743, however, there exist difference of results’ accuracy of different districts and land classes, the districts in central area have a higher accuracy than the outside districts in stimulation results. Besides, urban and town construction land were stimulated better than mining land and special-used land (like prison, cemetery land). Moreover, we calculated the gravity center of URCL of Guangzhou from the year 2005 to 2017, and found the gravity center of URCL continually moved to the South, which means Guangzhou’s “Development toward to South Part” was constantly implemented; (3) From the year 2021 to 2035, we found that the increment of URCL of central districts is limited, and the expansion pattern is filled type. By contrast, the outside districts possess much more amount of URCL, mainly distributed in Huadu district, Baiyun Airport Economic Zone, and Nansha district, and their expansion pattern is sprawling type instead. This research introduced big data into urban planning field, enriching big data’s application scenarios. Furthermore, our results can assist in anticipating Guangzhou’s spatial pattern in future, providing suggestions for land-use planning and arranging major projects.