| 空间自相关支撑下的地类分布模式一致性评价 |
| |
| 引用本文： | 罗芳,艾廷华,贾小斌.空间自相关支撑下的地类分布模式一致性评价[J].武汉大学学报(信息科学版),2022,47(7):1017-1024. |
| |
| 作者姓名： | 罗芳 艾廷华 贾小斌 |
| |
| 作者单位： | 1.深圳市规划和自然资源调查测绘中心，广东 深圳，518034 |
| |
| 基金项目： | 自然资源部城市国土资源监测与仿真重点实验室开放基金KF-2020-05-0076 |
| |
| 摘 要： | 空间分布模式是否保持一致是土地利用数据综合质量评价的一项重要内容。针对当前的研究缺少量化分析和位置表达的现状，提出了一种新的空间数据特有的自相关性评价方法。首先利用语义距离建立空间权重矩阵，随后通过莫兰指数（Moran’s I）计算数据处理前后全局和局部自相关度，最后利用莫兰（Moran）散点图和空间关联的局部指标（local indicators of spatial association，LISA）集聚图相结合的方法对综合前后的土地利用分布模式进行可视化对比。相较传统评价方法，所提方法顾及数据语义关系，计算可量化聚集程度，以直观可视化方法对比展示，更好地对土地利用数据在综合前后的全局空间分布模式一致性进行了评价。认知实验结果符合人类认知，表明所提方法切实有效。
|
| 关 键 词： | 空间自相关 一致性评价 分布模式 可视化 |
| 收稿时间： | 2021-04-07 |
Consistency Evaluation of Land Use Distribution Pattern Supported by Spatial Autocorrelation |
| |
| Affiliation: | 1.Planning and Natural Resources Survey Center of Shenzhen Municipality, Shenzhen 518034, China2.School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China3.Huatu International Digital Intelligent Technology Shenzhen Co. Ltd, Shenzhen 518000, China |
| |
| Abstract: | Objectives The consistency of spatial distribution pattern before and after generalization is an important factor to evaluate the quality of land use data. Considering the lack of quantitative analysis and visualization in spatial distribution model evaluation of data, we put forward a new evaluation method based on the unique autocorrelation of spatial data. Methods Firstly, we establish the spatial weight matrix using the semantic distance of data and calculate global and local autocorrelation of data before and after processing by Moran's I index. Secondly, we use Moran scatter plot and LISA (local indicators of spatial association) aggregation map to visualize the quality of spatial distribution patterns. Results Compared with the traditional data quality evaluation method, the proposed method can better evaluate the consistency of spatial distribution pattern of global map by taking into account the semantic relation of the data, obtaining the quantifiable aggregation degree and contrasting by the visual method. Conclusions In cognitive experiments, we take the land type data been scale transformed which cause data quality problems mostly as an example. The experimental results accord with human cognitions and the experimental method is practical. |
| |
| Keywords: | |
|
| 点击此处可从《武汉大学学报(信息科学版)》浏览原始摘要信息 |
|
点击此处可从《武汉大学学报(信息科学版)》下载全文 |
