Uncertainty and Context in Geography and GIScience: Reflections on Spatial Autocorrelation, Spatial Sampling, and Health Data

Abstract

One of the conference themes for the 2017 American Association of Geographers (AAG) annual meeting was “Uncertainty and Context in Geography and GIScience.” It included a triplet of special sessions cosponsored by the Spatial Analysis and Modeling (SAM) and the Health & Medical Geography (HMG) specialty groups. One session dealt with spatial autocorrelation, another featured spatial sampling, and a third focused on public health data. A conceptual framework and overviews of these three sessions emphasize research frontiers and advances in theory, method, and research practice that address challenges of uncertainty and context in geography and GIScience. This article summarizes these three sessions.

2017 年美国地理学家协会 (AAG) 的年会主题之一便是 “地理学与地理信息系统科学中的不确定性与脉络”。该主题包含由空间分析与模式化 (SAM) 和健康与医疗地理学 (HMG) 专业团体共同赞助的三大特别会议场次。其中一个场次应对空间自相关, 另一场次以空间抽样为主题, 第三个场次则聚焦公共健康数据。这三大场次的概念架构与综览, 强调处理地理学与地理信息科学中的不确定性与脉络挑战的理论、方法与研究实践之研究前沿与进展。本文将概括总结这三大会议场次。

Uno de los temas de conferencia para la reunión anual del 2017 de la Asociación Americana de Geógrafos (AAG) fue “La incertidumbre y el contexto en Geografía y SIGciencia”. Lo anterior se desarrolló en un terceto de sesiones especiales copatrocinados por los grupos especializados de Análisis Espacial y Modelado (SAM) y el de Geografía de la Salud & Médica (HMG). Una de las sesiones trató sobre autocorrelación espacial, otra versó sobre muestreo espacial y la tercera se enfocó en datos sobre salud pública. Un marco conceptual y reseñas de estas tres sesiones enfatizan las fronteras de investigación y avances en teoría, método y práctica investigativa que abocan los retos de la incertidumbre y el contexto en geografía y SIGciencia. Este artículo resume estas tres sesiones.

Key Words:

• error
• health data
• spatial autocorrelation
• spatial sampling
• uncertainty

关键词：:

• 误差, 健康数据, 空间自相关, 空间抽样, 不确定性。

Palabras clave:

• error, datos de salud, autocorrelación espacial, muestreo espacial, incertidumbre

Acknowledgment

The author appreciates the considerable input to the context of this article by his colleague, Dr. Yongwan Chun, who not only served as a member of the Scientific Committee for the conference featured theme “Uncertainty and Context in Geography and GIScience” but also co-organized the three special sessions discussed in this article. Of course, any errors or mistakes are the author's alone.

Note

Notes

1. Early LANDSAT MSS images have 57 × 79 m pixels, later LANDSAT MSS images have 30 × 30 m pixels, and more contemporary hyperspectral sensor images have pixels with a dimension of < 3 m.

Additional information

Notes on contributors

Daniel A. Griffith

DANIEL A. GRIFFITH is Ashbel Smith Professor of Geospatial Information Sciences in the School of Economic, Political, and Policy Sciences at the University of Texas at Dallas, Richardson, TX 75080. E-mail: [email protected] His research interests include quantitative geography, spatial statistics, urban economics, and urban public health.