数据驱动的社会经济剥夺预测：扩散映射的降维威力
Data-Driven Socio-Economic Deprivation Prediction via Dimensionality Reduction: The Power of Diffusion Maps
AI总结：
本研究利用扩散映射降维人口普查数据，以特征向量预测城市贫困区域，皮尔逊系数超0.7，正确识别38/52个最贫困区域，助力政府资源分配。
摘要：
本研究提出了一种利用人口普查数据预测城市中最贫困区域位置的模型。人口普查数据维度极高，需要简化。我们采用扩散映射算法进行降维并发现模式。特征由定义扩散映射的拉普拉斯矩阵的特征向量确定。对应最小特征值的特征向量揭示了人口的特定特征。先前工作已定性发现，在英国布里斯托尔，描述人口普查数据的第二重要维度与剥夺相关。在本研究中，我们通过将该维度与公认的衡量标准进行比较，分析其作为预测剥夺模型的优劣。皮尔逊相关系数大于0.7。我们提取了英国最贫困的前10%区域（这些区域也位于布里斯托尔）来测试模型的准确性。共有52个最贫困区域，其中38个区域通过与模型比较被正确识别。与模型不相关的IMD领域得分以及非贫困输出区域的特征向量2条目影响了模型对14个贫困区域的预测。该模型在预测项目区域未来剥夺方面表现出强大性能，预计将极大地协助政府资源分配和资金支持。代码可在此访问：https://github.com/junegoo94/diffusion_maps
英文摘要：
This research proposes a model to predict the location of the most deprived areas in a city using data from the census. Census data is very high-dimensional and needs to be simplified. We use the diffusion map algorithm to reduce dimensionality and find patterns. Features are defined by eigenvectors of the Laplacian matrix that defines the diffusion map. The eigenvectors corresponding to the smallest eigenvalues indicate specific characteristics of the population. Previous work has found qualitatively that the second most important dimension for describing the census data in Bristol, UK is linked to deprivation. In this research, we analyse how good this dimension is as a model for predicting deprivation by comparing it with the recognised measures. The Pearson correlation coefficient was found to be greater than 0.7. The top 10 per cent of deprived areas in the UK, which are also located in Bristol, are extracted to test the accuracy of the model. There are 52 of the most deprived areas, and 38 areas are correctly identified by comparing them to the model. The influence of scores of IMD domains that do not correlate with the models and Eigenvector 2 entries of non-deprived Output Areas cause the model to fail the prediction of 14 deprived areas. The model demonstrates strong performance in predicting future deprivation in the project areas, which is expected to assist in government resource allocation and funding greatly. The codes can be accessed here: https://github.com/junegoo94/diffusion_maps
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
