Assessing Neighborhood Conditions using Geographic Object-Based Image Analysis and Spatial Analysis (Short Paper)

Traditionally, understanding urban neighborhood conditions heavily relies on time-consuming and labor-intensive surveying. As the growing development of computer vision and GIScience technology, neighborhood conditions assessment can be more cost-effective and time-efficient. This study utilized Google Earth Engine (GEE) to acquire 1m aerial imagery from the National Agriculture Image Program (NAIP). The features within two main categories: (i) aesthetics and (ii) street morphology that have been selected to reflect neighborhood socio-economic (SE) and demographic (DG) conditions were subsequently extracted through geographic object-based image analysis (GEOBIA) routine. Finally, coefficient analysis was performed to validate the relationship between selected SE indicators, generated via spatial analysis, as well as actual SE and DG data within region of interests (ROIs). We hope this pilot study can be leveraged to perform cost- and time- effective neighborhood conditions assessment in support of community data assessment on both demographics and health issues

Nonsteroidal Anti-Inflammatory Drugs for Wounds: Pain Relief or Excessive Scar Formation?

The inflammatory process has direct effects on normal and abnormal wound healing. Hypertrophic scar formation is an aberrant form of wound healing and is an indication of an exaggerated function of fibroblasts and excess accumulation of extracellular matrix during wound healing. Two cytokines—transforming growth factor-β (TGF-β) and prostaglandin E2 (PGE2)—are lipid mediators of inflammation involving wound healing. Overproduction of TGF-β and suppression of PGE2 are found in excessive wound scarring compared with normal wound healing. Nonsteroidal anti-inflammatory drugs (NSAIDs) or their selective cyclooxygenase-2 (COX-2) inhibitors are frequently used as a pain-killer. However, both NSAIDs and COX-2 inhibitors inhibit PGE2 production, which might exacerbate excessive scar formation, especially when used during the later proliferative phase. Therefore, a balance between cytokines and medication in the pathogenesis of wound healing is needed. This report is a literature review pertaining to wound healing and is focused on TGF-β and PGE2

The Map is Not Which Territory?: Speculating on the Geo-Spatial Diffusion of Ideas in the Arab Spring of 2011

The process by which social movements move through time and space can be understood as a process of innovation diffusion of memes or ideas. This process of diffusion may be traceable through computational linguistics and map geocoding of the linguistic memes employed by such movements. A Visualizing Information Space In Ontological Networks (VISION) method is described and illustrated with web-based search results of keywords relevant to Arab Spring. Using map algebra, and with the potential for using computational linguistics, the intent is to demonstrate the feasibility of both the theoretical model of diffusion, as well as the relevance of the geospatial dimension in understanding another dimension of diffusion—the meaning space of ideas as they spread through new media. Such methodology holds substantial promise for understanding the communicative dynamics of social movements and social influence

Results from the centers for disease control and prevention's predict the 2013-2014 Influenza Season Challenge

Background: Early insights into the timing of the start, peak, and intensity of the influenza season could be useful in planning influenza prevention and control activities. To encourage development and innovation in influenza forecasting, the Centers for Disease Control and Prevention (CDC) organized a challenge to predict the 2013-14 Unites States influenza season. Methods: Challenge contestants were asked to forecast the start, peak, and intensity of the 2013-2014 influenza season at the national level and at any or all Health and Human Services (HHS) region level(s). The challenge ran from December 1, 2013-March 27, 2014; contestants were required to submit 9 biweekly forecasts at the national level to be eligible. The selection of the winner was based on expert evaluation of the methodology used to make the prediction and the accuracy of the prediction as judged against the U.S. Outpatient Influenza-like Illness Surveillance Network (ILINet). Results: Nine teams submitted 13 forecasts for all required milestones. The first forecast was due on December 2, 2013; 3/13 forecasts received correctly predicted the start of the influenza season within one week, 1/13 predicted the peak within 1 week, 3/13 predicted the peak ILINet percentage within 1 %, and 4/13 predicted the season duration within 1 week. For the prediction due on December 19, 2013, the number of forecasts that correctly forecasted the peak week increased to 2/13, the peak percentage to 6/13, and the duration of the season to 6/13. As the season progressed, the forecasts became more stable and were closer to the season milestones. Conclusion: Forecasting has become technically feasible, but further efforts are needed to improve forecast accuracy so that policy makers can reliably use these predictions. CDC and challenge contestants plan to build upon the methods developed during this contest to improve the accuracy of influenza forecasts. © 2016 The Author(s)