Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area
2.2. Data Acquisition and Processing
2.3. Methods
2.3.1. Landscape Metrics Selection
2.3.2. Dynamic Change Transfer Matrix of Landscape Types
2.3.3. Stepwise Multiple Linear Regression
2.3.4. Path Analysis
2.3.5. Geographic Detector
3. Results
3.1. Spatial-Temporal Variation of Landscape Types from 2000 to 2020
3.2. Landscape Pattern Changes from 2000 to 2020
3.3. Driving Factors of Landscape Pattern Changes
3.3.1. Temporal Driving Forces
3.3.2. Spatial Driving Forces
4. Discussion
4.1. Landscape Pattern Dynamics of Karst Mountainous Cities
4.2. Driving Force of Landscape Pattern Dynamics
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Temporal Driving Factors | ||
Dimension | Name | Description |
Economic level | GDP (x1) | Gross Domestic Product, it is the result of the productive activity of all resident units in a country (or region) during a certain period of time. |
Per capita GDP (x2) | The ratio of the GDP of a region to the resident population of the region during the accounting period. | |
Indices of gross domestic product (x3) | It is a relative number that reflects the trend and extent of changes in GDP over a certain period of time. | |
Total investment in fixed assets (x4) | It is the workload of construction and acquisition activities of fixed assets expressed in monetary terms. | |
Total retail sales of consumer goods (x5) | Refers to the amount of physical goods sold by enterprises (units) to individuals and social groups through transactions, not for production or business use, and the amount of revenue earned from the provision of food and beverage services. | |
Local financial revenue (x6) | Consists of the region’s fiscal revenues. Local fiscal revenues include local budget revenues and extra-budgetary revenues. | |
Population size | Population density (x7) | Number of people per unit of land area. |
Natural population growth rate (x8) | Ratio of natural increase in population in a given period to the average total population in the same period. | |
Industrial structure | Ratio of primary industrial structure (x9) | Value added and employment in the primary industrial as a share of GDP and total labor force. |
Ratio of secondary industry structure (x10) | Value added and employment in the secondary industry as a share of GDP and total labor force. | |
Ratio of tertiary industry structure (x11) | Value added and employment in the tertiary industry as a share of GDP and total labor force. | |
Spatial Driving Factors | ||
Dimension | Name | Description |
Socioeconomic factors | GDP (y1) | Kilometer grid data to measure the level of economic development of a region. |
Population density (y2) | Kilometer grid data, indicating the population density of the area. | |
Natural factors | Slope (y3) | The degree of steepness of the surface unit, the ratio of the vertical height of the slope to the distance in the horizontal direction. |
Elevation (y4) | The vertical distance above sea level at a point on the ground. | |
Topographical relief (y5) | The difference between the elevation of the highest point and the elevation of the lowest point in a particular area. | |
Accessibility factors | Distance to river (y6) | Distance between urban construction land and river. |
Distance to railway (y7) | Distance between urban construction land and railway. | |
Distance to highway (y8) | Distance between urban construction land and highway. |
Level | Name | Acronym | Description |
---|---|---|---|
C | Patch density | PD | The proportion of the landscape area to the number of patches of the same patch type |
Largest patch index | LPI | The area occupied by the largest patch of each class as a percentage of the overall area | |
Number of patches | NP | The heterogeneity and fragmentation of landscape | |
Area-weighted mean fractal dimension index | FRAC_AM | The complexity and stability of landscape type shape | |
L | Aggregation index | AI | A metric for the degree of spatial pattern aggregation |
Contagion | CONTAG | When all patch types are considered, the measure can reflect the spatial distribution features and mixing state of all patch types at the same time | |
Shannon’s diversity index | SHDI | Relative patch diversity within a range of 0–1 | |
Shannon’s evenness index | SHEI | Relative patch evenness within a range of 0–1 |
Landscape Type | Equation of Regression | Driving Factors | Determination Coefficient | Residual Path Coefficient |
---|---|---|---|---|
Cultivated land | Population density (−) Ratio of tertiary industry structure (−) | 0.998 | 0.045 | |
Forestland | Ratio of tertiary industry structure (+) | 0.943 | 0.239 | |
Construction land | Ratio of tertiary industry structure (+) Ratio of primary industrial structure (−) | 0.999 | 0.032 |
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Luo, Y.; Wang, Z.; Zhou, X.; Hu, C.; Li, J. Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City. Sustainability 2022, 14, 8274. https://doi.org/10.3390/su14148274
Luo Y, Wang Z, Zhou X, Hu C, Li J. Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City. Sustainability. 2022; 14(14):8274. https://doi.org/10.3390/su14148274
Chicago/Turabian StyleLuo, Yuanhong, Zhijie Wang, Xuexia Zhou, Changyue Hu, and Jing Li. 2022. "Spatial-Temporal Driving Factors of Urban Landscape Changes in the Karst Mountainous Regions of Southwest China: A Case Study in Central Urban Area of Guiyang City" Sustainability 14, no. 14: 8274. https://doi.org/10.3390/su14148274