Settlement research in the Maya lowlands has struggled to reconcile its goals to model a tropical forest civilization in ecological terms with the logistical constraints imposed by the forest itself. In this paper, we argue that the methodological challenges facing settlement research in this tropical lowland setting limited researchers’ confidence in the representativeness of their data, nudging the discipline toward community-scale analysis and away from quantitative macro-scale settlement pattern research. As a result, many basic facts of human geography have remained unsettled. These challenges can now be overcome thanks to advances in remote sensing. Here, we use lidar-derived settlement and topographic data from the Corona-Achiotal region of northwestern Guatemala to develop a settlement suitability model that reveals patterns in the distribution of archaeological remains vis-à-vis landforms. Applying this model to a much larger published settlement dataset, we demonstrate how it is not only widely applicable in the interior Maya Lowlands, but also capable of identifying historical contingencies in the distribution of settlement, namely the crowding of less-suitable areas of the landscape, linked to urban densification.

玛雅低地的定居研究一直在努力协调其从生态角度模拟热带森林文明的目标与森林本身强加的后勤限制。在本文中，我们认为，在这种热带低地环境中聚落研究面临的方法论挑战限制了研究人员对其数据代表性的信心，促使该学科转向社区规模分析并远离定量的宏观规模聚落模式研究。结果，人文地理学的许多基本事实仍未得到解决。由于遥感技术的进步，这些挑战现在可以克服。这里，我们使用来自危地马拉西北部 Corona-Achiotal 地区的激光雷达衍生的定居点和地形数据来开发定居点适宜性模型，该模型揭示了考古遗迹相对于地貌的分布模式。将此模型应用于更大的已发布聚落数据集，我们展示了它如何不仅广泛适用于玛雅低地内部，而且还能够识别聚落分布中的历史突发事件，即不太适合的景观区域的拥挤，与城市密集化有关。