A review on “Landscape Genetics: Combining Landscape Ecology and Population Genetics”
The landscape genetics approach emerged as the result of researchers explaining observed spatial genetic patterns by using landscape variables and it is often seen as the combination of molecular population genetics and landscape ecology. In layman’s term, the landscape genetics approach aims to analyze the interaction between landscape features and micro evolutionary processes, such as gene flow, genetic drift etc. It is also useful in identifying population boundaries, making it useful across many disciplines.
There are two main-steps of landscape genetics according to the article. First, it detects genetic discontinuities, which can be described as geographic zone of sharp genetic change (Manel et al, 2003). Second, it detects the correlation of these genetic discontinuities and environmental features, such as barriers (mountains, lakes etc.). As mentioned above, this approach provides information that is useful for many disciplines. For one, in the field of evolutionary biology and ecology, it is crucial to understand how the movements of individuals influence the genetic structure of a population, which depends on the detection of genetic discontinuities. Also for conservation biologists and natural resource managers, landscape genetics approach allows for accurate detection of population subdivisions, which is central to the ability to delineate evolutionary significant units, management units, or conservation units (Manel et al, 2003).
Perhaps one of the greatest advantages of this approach is that it often considers the individual as the operational unit of study. By doing so, it avoids potential bias in identifying populations in advance and it allows the studies to be conducted at a finer scale. According to the article, there are six statistical methods for identifying spatial genetic patterns that are cornerstones in this approach. Those methods are: Mantel’s test and regression analysis, spatial autocorrelation methods, Bayesian clustering approaches, multivariate analyses and synthesis maps, Monmonier’s algorithm and the Womble approach (Manel et al, 2003). In addition to those six methods, there are also four tests that test for correlations with environmental or landscape variables, and those are: Mantel’s test, Canonical correspondence analysis, GIS, and testing correlation between two maps (Manel et al, 2003).
This article is very thorough in describing the landscape genetics approach, and it also did a good job in describing its applications, its advantages, and its methods. The use of diagrams and maps was a nice touch; it definitely helped me in understanding some of those cornerstone methods. However it barely touched on its weaknesses, specifically the weaknesses of each of the cornerstone methods. In addition, some of the examples provided to describe each of the methods are vague, with no additional details. The glossary on opening page was a nice touch, but I think that footnotes might be more appropriate, since I found myself constantly referring to the opening page. Overall this article does a good job in describing the landscape genetics approach, I would give it an 8 out of 10.
A review on “Health GIS and HIV/AIDS studies: Perspective and retrospective”
This article talks about the use of Geographic information systems (GIS) in health research, education, planning, monitoring and evaluation of health programs to eliminate and control diseases and epidemics (Kandwal et al., 2009).
The first half of the article mainly concerns with the applications of GIS in health research. Since human health is large affected by a variety of spatial factors (where people live, climate, environment, etc.), GIS can play a significant role in epidemiological surveillance, information management and analysis. Common applications of GIS in public health include: geographical distribution and variation of disease, analysis of spatial and temporal trends, mapping populations at risk and stratifying risk factors, and much more (Kandwal et al., 2009).
The article then went on to describe four main advantages of implementing GIS in public health. The first is GIS operations. The main objectives of GIS are data management, analysis and display of geographical data. In the most basic form, GIS can provide map-based point and click for an individual to view information regarding a feature (ex: hospital, origin of outbreak). More advanced users can use spatial analysis techniques like overlay, buffer, neighborhood analysis etc. to answer more complex health related questions. Second, GIS can provide a temporal dimension to medical analysis. In some occasions spatial analysis is not enough unless a temporal dimension is incorporated. For example, the implementation of temporal GIS (TGIS) and geographical visualizations (Gvis) can determine origins of disease, rate of infection and disease clustering more effectively (Kandwal et al., 2009). Third, data mining or “knowledge discovery in databases” is also important to public health; especially knowledge on health is ever growing. Database management system (DBMS) or relational DBMS (GIS) not only allow the rapid retrieval of critical information but also allow pattern recognition of existing data. Finally, GIS can be of tremendous help in establishing healthcare facilities and managing health services (Kandwal et al., 2009). Since people (especially those that require medical care) are not evenly distributed in space, GIS can determine the best locations of establishing healthcare facilities (hospitals, clinics, etc.).
The second part of the article discusses the application of GIS in the context of HIV/AIDS. The article suggests visualization, the simplest element of GIS, can help people be aware and understand the spatial spread of HIV/AIDS. Spatial Data Infrastructure (SDI), a combination of HIV/AIDS databases can also play a pivotal role in helping governmental institutions to better plan for intervention in preventing or containing HIV/AID outbreaks. Furthermore, modeling approaches involving GIS have proven relevant in HIV/AIDS studies; for example, Spatial and temporal models for AIDS cases in US are used for prediction, in Finland, researchers performed inductive modeling to study HIV diffusion (Kandwal et al., 2009).
Overall, this article is exceptionally useful in describing (or advertising) applications of GIS in the field of public health. It also provided many great examples (and links) to illustrate the usefulness of GIS in public health. I recommend this article to every one. 10/10.
A Review on “Urban 3D GIS Modeling of Terrorism Sniper Hazards”
This article talks about the use of GIS and 3D modeling software to conduct viewshed analysis using line of sight (LoS) and base raster to combat and prevent possible sniper threat on individuals (2010). The term viewshed refers to the area that can be seen from a given vantage point (from a sniper’s position for example), and the term line of sight refers to a straight line along which an observer has unobstructed vision (Mosurinjohn & VanHorn, 2010). In this article, VanHorn and Mosurinjohn first digitally reconstruct the urban space of downtown Grand Rapids, Michigan, and then conduct viewshed analysis to reveal all possible enemy sniper positions (2010). In addition, they also attempt to narrow down the shooter’s positions by creating weapon potential domes (WPDs) using weapon specifications (2010). The context of this study is the funeral of Gerald Ford, the 38th President of the United States, who was laid to rest on the library of the same name in Grand Rapids, Michigan.
The first step of the viewshed methodology is to create a digital reconstruction of the area. In this case, the area in question is Grand Rapids, around the Gerald Ford Library. The software used in this reconstruction are ArcGIS Desktop with 3D analyst extension and Google SketchUp Pro. First, spatially referenced high-resolution aerial photographs are imported from ArcGIS to SketchUp Pro, where the footprints of buildings are drawn (spatially referenced). Building heights were then obtained from either online databases or from building enthusiasts, and buildings were also textured and shaped using field-based photographs. Next, to place the buildings at a correct base height above sea level, a triangulated irregular network (TIN) model is built using a DEM obtained from the U.S. Geological Survey through ArcMap, and later imported into SketchUp Pro. Next, the 3D environment built using SketchUp is moved to ArcGIS as Microsoft database files, which allows ArcGIS to render 3D structures. Finally, to conduct viewshed analysis, both observation points (the point/area you want to protect) and a raster-based surface model (constructed 3D environment) are inputted into ArcGIS’s viewshed algorithm. Since ArcGIS can only evaluate raster surfaces, a digital surface model (DSM) need created by combining both raster and vector surfaces.
To further narrow down sniper positions, VanHorn and Mosurinjohn incorporated weapon potential domes (WPDs) using weapon specifications. WPDs are basically the 3D version of ArcMap’s buffer (by adding elevation variables) (2010). By using both viewshed analysis and WPDs, law enforcement and protection details can further pinpoint possible sniper locations, thus accommodating budgetary restraints and making protection details more effective.
Overall, this was a very good read and in theory, the viewshed method can be applied virtually in any location. However, the viewshed method does have certain constraints, which are mentioned in the article. The viewshed method does not incorporate wind, which can play a factor in a bullet’s trajectory, especially in longer distances. Also it does not account for tree lines, which can affect the shooter’s line of sight as well as the bullet’s trajectory. Thus further research is recommended on those parts. I give this article 8 out of 10.
A review on the “Identification of spatial and cohort clustering of tuberculosis using surveillance data from British Columbia, Canada” (Darren Schouls)
This is a very interesting article, since tuberculosis (TB) is the “leading global infectious disease killer (Roth et al. 2016). The article talks about using GIS to identify spatial and cohort clustering of all TB cases in British Columbia. The researchers of this project first obtained TB records from Public Health Agency of Canada and geocoded each case based on postal code. Then each case were examined and separated into two categories (domestic born and foreign born). After that, the data is mapped using QGIS and spatial clusters were identified using SATScan. Cohort clustering was calculated using Lorenz curve plots and Gini coefficients. The Lorenz curves displays the distribution of the disease amongst a population while Gini coefficient calculates the degree of clustering. The resulting map shows greater concentrations of TB cases among Canadian born communities while lower concentrations are seen among communities dominated by foreign born Canadians. There were issues with this research however as some TB cases lacked metadata, and therefore somewhat skewed the results. Another limitation is the use of the Forward Sortation Area as their primary form of delineation area. The FSA’s worked very well in urban regions where population is more dense, however for the rest of the province the size of the FSA’s may have affected the accuracy of the results.
Overall, this is a very well written article for a very interesting research. The review provided by Darren Schouls does a good job in summarizing and critiquing the article. His presentation was also well executed. I give the article 8/10 and Darren’s review/presentation 9/10.
Bibliography
Kandwal, Rashmi, P. K. Garg, and R. D. Garg. “Health GIS and HIV/AIDS Studies: Perspective and Retrospective.” Journal of Biomedical Informatics. 42 (2009): 748-55. Web.
Manel, Stephanie, Michael K. Schwartz, Gordon Luikart, and Pierre Taberlet. “Landscape Genetics: Combining Landscape Ecology and Population Genetics.” Trends in Ecology & Evolution 18.4 (2003): 189-97. Web.
Roth, D., Otterstatter, M., Wong, J., Cook, V., Johnston, J., & Mak, S. (2016). Identification of spatial and cohort clustering of tuberculosis using surveillance data from British Columbia, Canada, 1990–2013. Social Science & Medicine,168, 214-222. doi:10.1016/j.socscimed.2016.06.047
Vanhorn, J. E., & Mosurinjohn, N. A. (2010). Urban 3D GIS Modeling of Terrorism Sniper Hazards. Social Science Computer Review,28(4), 482-496. doi:10.1177/0894439309360836