Identifying different types of urban land use dynamics using Point-of-interest (POI) and Random Forest algorithm: The case of Huizhou, China

Cities ◽  
2021 ◽  
Vol 114 ◽  
pp. 103202
Author(s):  
Rong Wu ◽  
Jieyu Wang ◽  
Dachuan Zhang ◽  
Shaojian Wang
2021 ◽  
Vol 10 (8) ◽  
pp. 503
Author(s):  
Hang Liu ◽  
Riken Homma ◽  
Qiang Liu ◽  
Congying Fang

The simulation of future land use can provide decision support for urban planners and decision makers, which is important for sustainable urban development. Using a cellular automata-random forest model, we considered two scenarios to predict intra-land use changes in Kumamoto City from 2018 to 2030: an unconstrained development scenario, and a planning-constrained development scenario that considers disaster-related factors. The random forest was used to calculate the transition probabilities and the importance of driving factors, and cellular automata were used for future land use prediction. The results show that disaster-related factors greatly influence land vacancy, while urban planning factors are more important for medium high-rise residential, commercial, and public facilities. Under the unconstrained development scenario, urban land use tends towards spatially disordered growth in the total amount of steady growth, with the largest increase in low-rise residential areas. Under the planning-constrained development scenario that considers disaster-related factors, the urban land area will continue to grow, albeit slowly and with a compact growth trend. This study provides planners with information on the relevant trends in different scenarios of land use change in Kumamoto City. Furthermore, it provides a reference for Kumamoto City’s future post-disaster recovery and reconstruction planning.


2008 ◽  
Vol 22 (9) ◽  
pp. 943-963 ◽  
Author(s):  
C. M. Almeida ◽  
J. M. Gleriani ◽  
E. F. Castejon ◽  
B. S. Soares‐Filho

2020 ◽  
Vol 12 (15) ◽  
pp. 2488 ◽  
Author(s):  
Shouzhi Chang ◽  
Zongming Wang ◽  
Dehua Mao ◽  
Kehan Guan ◽  
Mingming Jia ◽  
...  

Understanding urban spatial pattern of land use is of great significance to urban land management and resource allocation. Urban space has strong heterogeneity, and thus there were many researches focusing on the identification of urban land use. The emergence of multiple new types of geospatial data provide an opportunity to investigate the methods of mapping essential urban land use. The popularization of street view images represented by Baidu Maps is benificial to the rapid acquisition of high-precision street view data, which has attracted the attention of scholars in the field of urban research. In this study, OpenStreetMap (OSM) was used to delineate parcels which were recognized as basic mapping units. A semantic segmentation of street view images was combined to enrich the multi-dimensional description of urban parcels, together with point of interest (POI), Sentinel-2A, and Luojia-1 nighttime light data. Furthermore, random forest (RF) was applied to determine the urban land use categories. The results show that street view elements are related to urban land use in the perspective of spatial distribution. It is reasonable and feasible to describe urban parcels according to the characteristics of street view elements. Due to the participation of street view, the overall accuracy reaches 79.13%. The contribution of street view features to the optimal classification model reached 20.6%, which is more stable than POI features.


2003 ◽  
Vol 27 (5) ◽  
pp. 481-509 ◽  
Author(s):  
Cláudia Maria de Almeida ◽  
Michael Batty ◽  
Antonio Miguel Vieira Monteiro ◽  
Gilberto Câmara ◽  
Britaldo Silveira Soares-Filho ◽  
...  

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