GIS, Health & Hazards Lab, Department of Geography, Kent State University, Kent, OH 44242, USA.
Int J Health Geogr. 2013 Apr 15;12:21. doi: 10.1186/1476-072X-12-21.
Fine-scale and longitudinal geospatial analysis of health risks in challenging urban areas is often limited by the lack of other spatial layers even if case data are available. Underlying population counts, residential context, and associated causative factors such as standing water or trash locations are often missing unless collected through logistically difficult, and often expensive, surveys. The lack of spatial context also hinders the interpretation of results and designing intervention strategies structured around analytical insights. This paper offers a ubiquitous spatial data collection approach using a spatial video that can be used to improve analysis and involve participatory collaborations. A case study will be used to illustrate this approach with three health risks mapped at the street scale for a coastal community in Haiti.
Spatial video was used to collect street and building scale information, including standing water, trash accumulation, presence of dogs, cohort specific population characteristics, and other cultural phenomena. These data were digitized into Google Earth and then coded and analyzed in a GIS using kernel density and spatial filtering approaches. The concentrations of these risks around area schools which are sometimes sources of diarrheal disease infection because of the high concentration of children and variable sanitary practices will show the utility of the method. In addition schools offer potential locations for cholera education interventions.
Previously unavailable fine scale health risk data vary in concentration across the town, with some schools being proximate to greater concentrations of the mapped risks. The spatial video is also used to validate coded data and location specific risks within these "hotspots".
Spatial video is a tool that can be used in any environment to improve local area health analysis and intervention. The process is rapid and can be repeated in study sites through time to track spatio-temporal dynamics of the communities. Its simplicity should also be used to encourage local participatory collaborations.
在具有挑战性的城市地区，对健康风险进行精细尺度和纵向的地理空间分析通常受到限制，即使有病例数据，也缺乏其他空间层。除非通过后勤困难且通常昂贵的调查来收集，否则通常会缺少基础人口统计数据、居住环境以及相关的致病因素（如积水或垃圾位置）。缺乏空间背景也会阻碍对结果的解释和设计围绕分析见解构建的干预策略。本文提供了一种普遍的空间数据收集方法，使用空间视频，可以改善分析并涉及参与式合作。将使用海地沿海社区的三个健康风险的街道尺度映射案例研究来说明这种方法。
使用空间视频收集街道和建筑物尺度的信息，包括积水、垃圾堆积、狗的存在、特定队列的人口特征和其他文化现象。这些数据被数字化到谷歌地球中，然后使用核密度和空间过滤方法在 GIS 中进行编码和分析。由于儿童集中和卫生习惯不同，这些风险在学校周围的浓度会显示该方法的实用性，学校有时是腹泻病感染的来源。此外，学校提供了霍乱教育干预的潜在地点。
以前无法获得的精细健康风险数据在整个城镇的浓度不同，一些学校附近的地图风险浓度更高。空间视频还用于验证编码数据和这些“热点”内的特定地点风险。
空间视频是一种可以在任何环境中使用的工具，可以改善局部地区的健康分析和干预。该过程快速，并且可以通过时间在研究地点重复，以跟踪社区的时空动态。它的简单性也应该用于鼓励当地的参与式合作。
