高质量发展的测量和空间相关性分析水平:对中国长江三角洲城市聚集的案例研究
Yanlong Guo1, XueMei Jiang1, Yelin Zhu2
1Social Innovation Design Research Centre, Anhui University, Hefei, China.
Heliyon
|April 22, 2024
概括
这是长江三角洲.
科学领域:
- 城市和区域经济学
- 可持续发展研究 可持续发展研究
- 空间规划和空间分析
背景情况:
- 长江三角洲 (YRD) 城市集群面临经济放缓和环境压力.
- 高质量发展 (HQD) 是该地区的一个关键战略目标.
- 了解HQD的动态对于可持续城市规划至关重要.
研究的目的:
- 构建和应用一个全面的索引系统来评估YRD城市集群的HQD.
- 分析整个YRD的HQD的时间和空间变化.
- 确定实现HQD的区域优势和弱点.
主要方法:
- 开发一个24指标系统用于HQD评估.
- 应用权重的TOPSIS方法 (以理想解决方案相似度为优先顺序的技术).
- 空间相关性分析以了解区域发展模式.
主要成果:
- 从2010年到2021年,YRD中的HQD总体上有所改善,2017年是峰值年.
- 各省之间存在显著差异:上海在协调发展方面领先,江在绿色/经济方面领先,江苏在创新/生计方面领先,而安则落后.
- 上海 (0.511) 总体得分最高,其次是江 (0.484),江苏 (0.440) 和安 (0.435),表明发展不均.
结论:
- YRD城市集群呈现不均的HQD,具有明显的空间差异.
- 需要有针对性的政策来解决地区弱点,促进均衡增长.
- 该研究为优化YRD的城市发展战略提供了数据驱动的基础.
更多相关视频
04:35Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
Published on: July 3, 2020
3.3K
10:28Investigating the Relationship between Sea Surface Chlorophyll and Major Features of the South China Sea with Satellite Information
Published on: June 13, 2020
5.8K
相关概念视频
Design Example: Analyzing Capacity Contours for Flood Risk Assessment
46
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
46
Levels of Use of a GIS
49
Geographic Information Systems (GIS) operate across three levels of application, each representing an increasing degree of complexity: data management, analysis, and prediction. These levels reflect the expanding functionality and versatility of GIS technology in handling spatial data for diverse purposes.Data ManagementAt its foundational level, GIS serves as a tool for data management, enabling the input, storage, retrieval, and organization of spatial data. This level is often employed in...
49
Scatter Plot
6.8K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
6.8K
Mean Absolute Deviation
2.6K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
2.6K
Empirical Method to Interpret Standard Deviation
5.2K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
This rule is used widely in statistics to calculate the proportion of data values...
5.2K
Calculating and Interpreting the Linear Correlation Coefficient
5.9K
The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
5.9K
