دورية أكاديمية
Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality
| العنوان: | Geovisual analytics to enhance spatial scan statistic interpretation: an analysis of U.S. cervical cancer mortality |
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| المؤلفون: | Lengerich Eugene J, Naito Adam T, Roth Robert E, Chen Jin, MacEachren Alan M |
| المصدر: | International Journal of Health Geographics, Vol 7, Iss 1, p 57 (2008) |
| بيانات النشر: | BMC |
| سنة النشر: | 2008 |
| المجموعة: | Directory of Open Access Journals: DOAJ Articles |
| مصطلحات موضوعية: | Computer applications to medicine. Medical informatics, R858-859.7 |
| الوصف: | Background Kulldorff's spatial scan statistic and its software implementation – SaTScan – are widely used for detecting and evaluating geographic clusters. However, two issues make using the method and interpreting its results non-trivial: (1) the method lacks cartographic support for understanding the clusters in geographic context and (2) results from the method are sensitive to parameter choices related to cluster scaling (abbreviated as scaling parameters), but the system provides no direct support for making these choices. We employ both established and novel geovisual analytics methods to address these issues and to enhance the interpretation of SaTScan results. We demonstrate our geovisual analytics approach in a case study analysis of cervical cancer mortality in the U.S. Results We address the first issue by providing an interactive visual interface to support the interpretation of SaTScan results. Our research to address the second issue prompted a broader discussion about the sensitivity of SaTScan results to parameter choices. Sensitivity has two components: (1) the method can identify clusters that, while being statistically significant, have heterogeneous contents comprised of both high-risk and low-risk locations and (2) the method can identify clusters that are unstable in location and size as the spatial scan scaling parameter is varied. To investigate cluster result stability, we conducted multiple SaTScan runs with systematically selected parameters. The results, when scanning a large spatial dataset (e.g., U.S. data aggregated by county), demonstrate that no single spatial scan scaling value is known to be optimal to identify clusters that exist at different scales; instead, multiple scans that vary the parameters are necessary. We introduce a novel method of measuring and visualizing reliability that facilitates identification of homogeneous clusters that are stable across analysis scales. Finally, we propose a logical approach to proceed through the analysis of SaTScan results. ... |
| نوع الوثيقة: | article in journal/newspaper |
| اللغة: | English |
| تدمد: | 1476-072X |
| العلاقة: | http://www.ij-healthgeographics.com/content/7/1/57Test; https://doaj.org/toc/1476-072XTest; https://doaj.org/article/f4172ab9594f4bd48b2185a5d5a0e696Test |
| DOI: | 10.1186/1476-072X-7-57 |
| الإتاحة: | https://doi.org/10.1186/1476-072X-7-57Test https://doaj.org/article/f4172ab9594f4bd48b2185a5d5a0e696Test |
| رقم الانضمام: | edsbas.F9D3E0F7 |
| قاعدة البيانات: | BASE |
Full Text Finder | تدمد: | 1476072X |
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| DOI: | 10.1186/1476-072X-7-57 |