Name
Abstract | ||
|---|---|---|
Rapid economic and social development has led to significant regional land use changes in depth and breadth. There is a strong and urgent need to explore and understand the driving forces and mechanisms behind coordinated development of intensive urban land use, especially in rapid urbanization regions, and examine the degree of coordination development among different systems or sectors. In this paper, we took Panyu District of Guangzhou City, China as an example, and applied the principle of coordination degree model to explore the driving mechanisms of coordinated development of intensive urban land use. Results indicate that Panyu is in the base intensive use stage with an urban land use intensity index of 0.3389. The region is seriously imbalanced among economic, social, and ecological systems because their coordination index was only 0.1307. Coordinated development index among economic, social, and ecological systems was 0.2546, placing it to the moderately imbalanced recessional economy-driven type. Urban land use intensity, coordination, coordination development, and intensity coordination mechanisms varied greatly among subregions or towns. |
Year | DOI | Venue |
|---|---|---|
2010 | 10.1109/GEOINFORMATICS.2010.5567813 | Geoinformatics |
Keywords | Field | DocType |
china,statistical analysis,urban land utilization,regional land use,regional planning,land use planning,coordinated development degree,rapid urbanization region,guangzhou city,panyu district,coordination degree model,coordination degree,intensive urban land use,economic indicators,indexes,indexation,force,social development,land use change | Ecological systems theory,Data mining,Urbanization,Computer science,Environmental planning,China,Regional planning,Economic indicator,Social change,Land-use planning,Land use | Conference |
ISBN | Citations | PageRank |
978-1-4244-7301-4 | 0 | 0.34 |
References | Authors | |
0 | 6 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yueming Hu | 1 | 17 | 7.92 |
| Zesheng Que | 2 | 0 | 0.34 |
| Junping Zhang | 3 | 1 | 0.73 |
| Shuguang Liu | 4 | 55 | 11.79 |
| Yuan Tian | 5 | 3 | 5.43 |
| Lun Wu | 6 | 26 | 6.94 |