Paper
28 January 2025 Spatial autocorrelation analysis of employment deprivation research on local labour markets
Yan Gao, Xuefei Ma, Chi Zhang
Author Affiliations +
Proceedings Volume 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024); 135061S (2025) https://doi.org/10.1117/12.3057545
Event: Sixth International Conference on Geoscience and Remote Sensing Mapping (ICGRSM 2024), 2024, Qingdao, China
Abstract
Liverpool has the highest proportion of highly deprived neighbourhoods in England. To explore the spatial pattern of employment deprivation, data from the 2011 Census, and IMD (Index of Multiple Deprivation) from the government are used. Spatial autocorrelation analysis (Moran's I and LISA methods) is applied to determine if there is a systematic pattern in the spatial distribution of the variable and to identify clusters. Time series analysis is conducted to illustrate unemployment levels in Liverpool. The results indicate a positive spatial autocorrelation for employment deprivation in Liverpool, suggesting that neighbouring areas are similar, potentially due to a lack of schools, job opportunities, and labour force demand in certain locations. This pattern of employment deprivation is concentrated, with clusters found in the northern part, southern part, and inner urban cores. Despite improvements in the employment situation, it still falls behind the overall level of the United Kingdom.
(2025) Published by SPIE. Downloading of the abstract is permitted for personal use only.
Yan Gao, Xuefei Ma, and Chi Zhang "Spatial autocorrelation analysis of employment deprivation research on local labour markets", Proc. SPIE 13506, Sixth International Conference on Geoscience and Remote Sensing Mapping (GRSM 2024), 135061S (28 January 2025); https://doi.org/10.1117/12.3057545
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