Surveying programs of study at institutions of higher learning throughout the world reveals that one natural disciplinary coupling is statistics and operations research, although these two specific disciplines currently lack an active synergistic research interface. Similarly, the development of spatial statistics and spatial optimization has occurred in parallel and nearly in isolation. This paper seeks to alter this situation by initiating transformative work at the interface of these two subdisciplines, encouraging considerably more future interaction between them. It outlines three ways spatial statistics can contribute to spatial optimization by exploiting spatial autocorrelation in georeferenced data: missing attribute value imputation (analogous to kriging); identifying colocations of local spatial autocorrelation hot spots and spatial medians; and, geographic tessellation stratified random sampling inputs to spatial optimization heuristics that successfully guide them to optimal location solutions. One contention emphasized throughout this paper is that this spatial statistics/optimization interface furnishes another vehicle for delivering spatial statistical benefits to society, which, in turn, benefits spatial statistics by providing better integration of it into novel interdisciplinary contexts.

对全世界高等教育机构的研究计划进行的调查显示，统计学和运筹学是一个自然的学科耦合，尽管这两个特定学科目前缺乏积极的协同研究界面。同样，空间统计和空间优化的发展是平行的，几乎是孤立的。本文试图通过在这两个子学科的接口处启动变革性工作来改变这种情况，鼓励它们之间更多的未来互动。它概述了空间统计通过利用地理参考数据中的空间自相关来促进空间优化的三种方式：缺失属性值插补（类似于克里金法）；识别局部空间自相关热点和空间中位数的共置；并且，地理镶嵌对空间优化启发式的随机抽样输入进行分层，成功地引导他们找到最佳位置解决方案。本文强调的一个论点是，这种空间统计/优化接口为向社会提供空间统计利益提供了另一种工具，而这反过来又通过将空间统计更好地整合到新的跨学科环境中来使空间统计受益。