Consistency Evaluation of Land Use Distribution Pattern Supported by Spatial Autocorrelation

LUO Fang; AI Tinghua; JIA Xiaobin

doi:10.13203/j.whugis20200179

Volume 47 Issue 7

Jul. 2022

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Geomatics and Information Science of Wuhan University > 2022 > 47(7): 1017-1024. > DOI: 10.13203/j.whugis20200179

LUO Fang, AI Tinghua, JIA Xiaobin. Consistency Evaluation of Land Use Distribution Pattern Supported by Spatial Autocorrelation[J]. Geomatics and Information Science of Wuhan University, 2022, 47(7): 1017-1024. DOI: 10.13203/j.whugis20200179

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Consistency Evaluation of Land Use Distribution Pattern Supported by Spatial Autocorrelation

1.
Planning and Natural Resources Survey Center of Shenzhen Municipality, Shenzhen 518034, China
2.
School of Resource and Environmental Sciences, Wuhan University, Wuhan 430079, China
3.
Huatu International Digital Intelligent Technology Shenzhen Co. Ltd, Shenzhen 518000, China

Funds:

The Open Fund of Key Laboratory of Urban Land Resources Monitoring and Simulation, Ministry of Natural Resources KF-2020-05-0076

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Author Bio:
LUO Fang, PhD, senior engineer, specializes in surveying and mapping data quality. E-mail: whulfgis@163.com
Corresponding author:
JIA Xiaobin, PhD. E-mail: jiaxiaobin_123@126.com
Received Date: April 06, 2021
Published Date: July 04, 2022

Graphical Abstract

Abstract

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.
- spatial autocorrelation,
- consistency evaluation,
- distribution pattern,
- visualization

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References (21)

References

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