空间依赖性对可预测性的普遍影响
Pervasive impact of spatial dependence on predictability
AI总结：
本研究通过对比6组环境与社会经济数据集的空间及非空间预测模型，发现空间依赖性普遍存在于地理及非地理属性维度且显著影响预测性能，提出复杂条件下捕捉空间依赖性的新方法，拓展了空间分析的跨学科应用场景。
摘要：
理解空间信息的复杂特性对于社会科学与环境科学领域的问题解决至关重要。本研究探究了空间数据的底层模式如何显著影响空间预测结果。研究团队认识到空间数据具备空间依赖性、空间异质性等独特特征，深入剖析了空间预测模型与非地理空间预测模型之间的根本异同。\n通过对6组包含环境变量与社会经济变量的不同数据集开展分析，对比地理空间模型与非地理空间模型的表现，本研究凸显了空间依赖性跨越地理边界的普遍属性。这一创新方法不仅识别出由经纬度定义的地理空间中的空间依赖性，还发现其存在于非地理的、基于属性的维度中。\n研究结果表明，空间依赖性对多个领域的预测结果存在普遍影响，且在所有类型的空间中均会显著作用于预测性能。研究发现，环境变量的最强空间依赖性通常出现在地理空间中，而这一趋势并不统一适用于社会经济变量。本研究不仅完善了空间数据分析的理论框架，还提出了在复杂条件下准确捕捉与表达空间依赖性的新方法。研究将空间分析拓展至社交网络、基因表达模式等非地理维度，强调了空间依赖性在提升预测精度方面的作用，可为地理信息科学、环境科学、经济学、社会学、生物信息学等领域的跨学科应用提供支撑。
英文摘要：
Understanding the complex nature of spatial information is crucial for problem solving in social and environmental sciences. This study investigates how the underlying patterns of spatial data can significantly influence the outcomes of spatial predictions. Recognizing unique characteristics of spatial data, such as spatial dependence and spatial heterogeneity, we delve into the fundamental differences and similarities between spatial and non-geospatial prediction models. Through the analysis of six different datasets of environment and socio-economic variables, comparing geospatial models with non-geospatial models, our research highlights the pervasive nature of spatial dependence beyond geographical boundaries. This innovative approach not only recognizes spatial dependence in geographic spaces defined by latitude and longitude but also identifies its presence in non-geographic, attribute-based dimensions. Our findings reveal the pervasive influence of spatial dependence on prediction outcomes across various domains, and spatial dependence significantly influences prediction performance across all spaces. Our findings suggest that the strongest spatial dependence is typically found in geographic space for environment variables, a trend that does not uniformly apply to socio-economic variables. This investigation not only advances the theoretical framework for spatial data analysis, but also proposes new methodologies for accurately capturing and expressing spatial dependence under complex conditions. Our research extends spatial analysis to non-geographic dimensions such as social networks and gene expression patterns, emphasizing the role of spatial dependence in improving prediction accuracy, thereby supporting interdisciplinary applications across fields such as geographic information science, environmental science, economics, sociology, and bioinformatics.
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。
