When, in the winter of 1994, under the supervision of my postdoc adviser André Journel, I started writing Geostatistics for Natural Resources Evaluation in the bedroom of a tiny Palo Alto apartment, little did I know that 25 years later I would be conducting National Institutes of Health (NIH)-funded research on medical geostatistics from a lakefront office nestled in the Irish Hills of Michigan. The professional and personal path that led me to trade the mapping of heavy-metal concentrations in the topsoil of the Swiss Jura for the geostatistical analysis of cancer data was anything but planned, yet André’s help and guidance were instrumental early on. Looking back, shifting scientific interest from the characterization of contaminated sites to human health made sense, as the field of epidemiology is increasingly concerned with the concept of exposome, which comprises all environmental exposures (e.g., air, soil, and drinking water) that a person experiences from conception throughout the life course. Although both environmental and epidemiological data exhibit space–time variability, the latter have specific characteristics that required the adaptation of traditional geostatistical tools, such as semivariogram and kriging. Challenges include: (i) the heteroscedasticity of disease rate data (i.e., larger uncertainty of disease rates computed from small populations), (ii) their uneven spatial support (e.g., rates recorded for administrative units of different size and shape), and (iii) the limitations of Euclidean metrics to embody proximity when dealing with data that pertain to human mobility. Most of these challenges were addressed by borrowing concepts developed in adjacent fields, stressing the value of interdisciplinary research and intellectual curiosity, something I learned as a fresh PhD in agronomical sciences joining André’s research group at the Stanford Center for Reservoir Forecasting in the early 1990s.

1994 年冬天，在我的博士后顾问 André Journel 的指导下，我开始撰写《用于自然资源评估的地质统计学》在帕洛阿尔托一间小公寓的卧室里，我几乎不知道 25 年后，我将在密歇根州爱尔兰山的湖滨办公室进行由美国国立卫生研究院 (NIH) 资助的医学地质统计学研究。引导我将瑞士汝拉表层土壤中的重金属浓度制图换成癌症数据的地质统计分析的专业和个人路径完全没有计划，但安德烈的帮助和指导在早期发挥了重要作用。回顾过去，将科学兴趣从污染场地的特征转移到人类健康是有道理的，因为流行病学领域越来越关注暴露的概念，它包括所有环境暴露（例如，空气、土壤、和饮用水）一个人在整个生命过程中从受孕开始所经历的。尽管环境和流行病学数据都表现出时空变异性，但后者具有需要采用传统地质统计工具（如半变异函数和克里金法）的特定特征。挑战包括：(i) 疾病发病率数据的异方差性（即从小群体中计算出的疾病发病率的更大不确定性），(ii) 它们不均匀的空间支持（例如，不同大小和形状的行政单位记录的发病率），以及（ iii) 在处理与人类流动性有关的数据时，欧几里得度量在体现接近度方面的局限性。大多数这些挑战都是通过借鉴相邻领域开发的概念来解决的，