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Great discussions! The connection between spatial epidemiology and communicable diseases is well established. In this class, I’d like you to further explore the intersection between spatial epidemiology and non-communicable diseases. Mental health issues, for instance, have been on the rise, especially following the COVID-19 pandemic. For the class project, you will have the opportunity to analyze real data on suicide cases in London, UK. I hope this hands-on experience will deepen your understanding of how spatial analysis can be applied in epidemiology. 🙂
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The main task of the ‘maptools’ package is to use the function ‘readShapePoly’ to read a shapefile. If you encounter issues with ‘readShapePoly’ or the ‘maptools’ package, there are alternative packages and functions you can try. For instance, you can use the ‘shapefile’ function from the ‘raster’ package. Additionally, you can download the ‘maptools’ package directly from CRAN, as Ko Phyo did.
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Hello all,
The provided R code should work if all data and packages are downloaded properly. I suggest carefully checking the code, including spelling, packages,inputs, and data. If anyone can’t use the function ‘readShapePoly’, there are other functions you can try. Alternatively, you can try the ‘shapefile’ command in the ‘raster’ package.
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Thank you for your comment, Soe Htike. You’ve raised a good point regarding the R version affecting the installation and functionality of INLA. I might have used a different R version when I prepared the materials, which could explain why I didn’t encounter this issue.
For those experiencing similar issues, the command provided on the INLA website can be very helpful. Soe Htike, your shared command for installing INLA for R is also beneficial for classmates!
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Hello both,
Thank you for sharing your tips and tricks, Kansiri. Your detailed setup for displaying PV_2015 cases is helpful. I appreciate your creativity in setting dot sizes and background colors. It’s great to see this collaborative spirit in our class. Please continue to share your insights and experiences.
Have a great day!
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Hi Teerawat, Kansiri, and Nichcha,
Thank you for your feedback. I appreciate your suggestions to improve the QGIS instructions. Teerawat, incorporating bullets and more screenshots is a great idea. Kansiri and Nichcha, adding details on color settings and more visual cues will definitely help. I will work with the team on updating the document to incorporate your suggestions. Keep sharing your thoughts and challenges as they help us improve.
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Hi Kansiri and Soe Htike,
Thank you both for sharing your experiences and tips with QGIS! It’s great to see students helping each other navigate different versions of the software. It’s common for software interfaces to change with updates, and your shared solutions are valuable for everyone in the class. I encourage all of you to continue using this forum to share tips and solutions. This collaborative approach can greatly enhance our learning experience, especially in an online course.
Happy mapping!
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Thank you, Nichcha, for sharing your experiences and solutions with the class. Your detailed account of the issues you encountered and how you resolved them would be valuable for your classmates.
To all students, I encourage you to use this forum to share and communicate with each other. Your experiences can help create a supportive and resourceful online learning community. Don’t hesitate to post your questions, challenges, and solutions here. Together, we can make this course even more enriching and collaborative!
I am also glad to hear that the instructions were helpful! 🙂
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Hello TMHG 517 class…
It’s truly great to see the collaboration and support you’re providing to one another during this course. I hope that these connections will continue to thrive beyond the classroom.
Regarding the recent change in the INLA system, there is a recent change internally within INLA. Specifically, the marginals of the linear predictors, which typically demand a significant amount of storage space, are now omitted by default. Though these marginals are no longer displayed, they are still computed internally. Fortunately, this change doesn’t impact the accessibility or functionality of the summary statistics.
I’d also like to acknowledge the resourcefulness demonstrated by Boonyarat in finding a method to obtain the outcome/risk estimates. Your collective efforts reflect the collaborative spirit of our TMHG 517 class this year. Well done, everyone!
Keep up the excellent work! Cheers! 😀
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Hi class,
I would like to note that calculating fitted values in INLA can be approached differently based on the specific context. Departing from the class example, you might find it beneficial to explore the utilization of the ‘summary.fixed’ command, which offers an alternative method to determine the fitted values of relative risks. In regard to Moran’s I, both the default ‘equal’ hypothesis and the ‘greater’ hypothesis are valid options, as long as you explain your findings effectively to correspond with the selected approach. Please let me know if you have any questions!
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Hello everyone,
I’m glad to read your comments. Typically, spatial analysis in studies tends to concentrate on communicable diseases which you can see from TMHG549, GIS course. However, I think it would be also valuable to provide an alternative outlook from a view of non-communicable diseases. Despite the varying mode of transmission, the techniques covered in this class remain pertinent and can be applied to various health outcomes. Among non-communicable diseases, mental health is often overlooked and underrepresented, despite its recent recognition as a significant concern, especially in the wake of the COVID-19 pandemic. Nonetheless, our class will encompass comprehensive spatial analysis methods, which I hope you’ll be able to apply effectively to your specific areas of interest.
Looking forward to an insightful learning experience together. Cheers!!
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Thank you, Chanapong, for your comment. I agree with you that spatial epidemiology’s interdisciplinary approach, which integrates advanced technologies and data from various scientific fields, allows us to gain valuable insights into disease patterns and their spatial context. Your point about considering the impact of location on disease factors is essential, as regional dietary habits and environmental factors can significantly influence disease prevalence. This emphasizes the importance of spatial analysis in understanding health disparities across different regions. In this course, we will explore various methods to model spatial health data, quantify uncertainty, and analyze disease clusters. Look forward to engaging in fruitful discussions with you all throughout the course. Cheers!!
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Thank you all for your contributions to this fruitful discussion! While communicable diseases are widely recognized for their spatial dimension, I also would like to emphasize that place can also serve as a proxy for non-communicable diseases, which we will further explore in this course. Understanding the geographic context and environmental factors of individuals’ living and working areas offers crucial insights to study spatial patterns and identify potential risk factors associated with specific areas. This knowledge is instrumental in designing effective public health interventions and shaping policies for both communicable and non-communicable diseases. 😀
Please feel free to share any further comments or engage in more discussions! Cheers!
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Hello both,
Thank you, Farhan, for assisting your fellow classmate. As Farhan mentioned, the R-INLA package is not available on CRAN due to its reliance on external C libraries, making it challenging to build binaries. To install the package, you must use install.packages() while adding the URL of the R-INLA repository.
In section 1.4 of the weekly lesson, you can find an example of how to install the stable version of the package using the provided R command.
Please re-visit: https://mu-informatics.org/courses/statistical-methods-in-spatial-epidemiology/lessons/introduction-to-r-inla-activities/
Hope this helps! Please let us know if you have further questions.
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Thank you for your feedback on Week 1 of the course. I’m glad to hear that you found it interesting on useful applications and data manipulation helpful. While you don’t have any questions at the moment, it’s great to see your interest in acquiring or creating vector and raster data for future use.
In upcoming modules, we will cover topics related to map creation. You will learn to incorporate vector data and create your own map. If you have any questions or need further clarification on any topics as we progress through the course, please don’t hesitate to reach out. We are here to assist you in expanding your skills and knowledge in GIS management.
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Thank you for raising an important point regarding the availability of spatial health data. You are absolutely correct that it is quite difficult to find public spatial health data, which can involve serious ethical concerns, particularly in terms of patient privacy and confidentiality. As a result, such data is generally challenging to locate in publicly accessible sources.
However, aggregate health data is more commonly available and can be obtained from the Ministry of Public Health and the National Statistics Office. These sources can provide valuable insights into population-level health indicators and trends. Additionally, public shapefiles of different administrative levels for various countries are available. These types of GIS data can still be valuable for analyzing the spatial distribution of diseases and identifying potential hotspots.
Hope this helps and if you have any further questions, please don’t hesitate to reach out to us!
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Great comment, Zarni. I completely agree with you. GIS is a powerful tool with extensive functionality. It’s however important to recognize that in resource-limited settings, not only Myanmar, data availability and quality can present significant challenges. The “garbage in, garbage out” principle applies to data analysis in any context, not only GIS, emphasizing the need for accurate and representative data to derive meaningful results from mapping and analysis.
I hope you find this course useful in your journey with GIS. If you need assistance or support for your future projects, please don’t hesitate to reach out to us and our team. Happy to help. 🙂
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Thank you for sharing your feedback regarding the course format. I understand that different individuals have varying preferences when it comes to learning mediums. While some prefer video formats, others find value in written materials. Although in the past there have been comments suggesting a preference for written tutorials, I appreciate your perspective, and I will take it into consideration for future improvements.
I understand that working through a lengthy PDF can be time-consuming. However, PDFs often provide detailed instructions and can be easily referenced for future use. In the future, we can explore ways to optimize the PDF content and make it more concise while still providing the necessary guidance.
Regarding the specific issues you mentioned:
1. I understand that as a Mac user, you might have different drive specifications. Creating a folder under the user directory is a suitable alternative.
2. It’s understandable that there might be discrepancies between the instructions and the example picture. In such cases, following the example picture closely or using a similar color as you did for Assignment 1.2 onwards is a reasonable approach. You are fine.
3. It’s common for command variations to exist between different operating systems. Utilizing the help function to find the necessary commands to complete the assignment is a resourceful way to overcome this challenge.Most of our course participants, including our team, are Windows users, and in terms of course management, it is quite difficult to create manuals for different platforms. However, I want to acknowledge your adaptability and quick learning. If you encounter any further obstacles or have any additional concerns throughout the course, please don’t hesitate to reach out. Your feedback helps us improve and tailor the course to better meet the needs of all students.
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It’s great to hear that you find QGIS to be more powerful, though more complex. Embracing the complexity will undoubtedly provide you with a deeper understanding and mastery of the software… Regarding your preference for video tutorials, I completely understand the value of visual learning resources. However, I want to highlight that in the past there have been comments suggesting a preference for written tutorials as they can be conveniently used for future reference. While we don’t have specific video tutorials using our class’s data examples (which I think this might also be a good idea to include video tutorials), I encourage you to explore platforms like YouTube or specialized GIS tutorial websites. They often offer a wealth of video content that can assist you in learning QGIS in a more interactive and dynamic manner.
Glad to hear that you appreciated the Week 0 explanation and the clear expectations laid out for the course. We thought that having a well-structured syllabus and clear guidelines can certainly enhance the learning experience. Best of luck with your QGIS journey, and don’t hesitate to reach out to our team if you have any questions or need further assistance throughout the course!
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Thanks Sri Budi Fajariyan for you comments. Alcohol consumption and substance use are much related to mental health which can well lead to suicide… Risk factors of mental health and suicide are generally multi-factorial. I agree economic variables are much related to suicide and depression…. Hope you find the tools you learn in this course are useful for your future research, perhaps on non-communicable diseases. 🙂
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Hello Karina, Thank you for your comments. Economic variables have been to known to relate to suicide. The results on suicide reflect that the culture in Thailand perhaps also has an effect on suicide rates. I agree that spatial analyses such as cluster detection can be applied to this issue which you’ll learn in next week! I just also learned that suicide is one of top five leading causes of death among adolescents globally… Hope that would be more research on mental health especially using spatial analysis.
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Hello Sri Budi Fajariyani and Karina, I agree that the spatial health data have limited availability due to various due to various factors; one of which is confidentiality. Moreover, spatial analyses for health data have been well developed and accessible for non-technical users for only in the last decades. Nonetheless, you will explore the methodology in this class! Hope you will enjoy! 🙂
Spatial epidemiology is an interdisciplinary field. Not only are health outcomes limited, risk factors at a fine resolution are sometimes unavailable partly due to infrastructure of surveillance system. So, the finding interpretation should be communicated carefully. We will study an example in this course of interpretation issues due to different scales of data. Thank you both of you for the fruitful discussion and look forward to discuss more with you in the class!
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Hello Hazem, you raised a couple of good points. Data confidentiality and completeness are main issues in spatial epidemiology. Most spatial health data are available at aggregate level. Although at a coarse scale, spatial health data can well be incomplete partly due to the infrastructure of surveillance system. However we will also analyze small-area (aggregate) health data as a case study in this course using hierarchical modeling. This kind of model accounts for spatial auto-correlation and generally make estimates more stable. Good comments! 🙂
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In this class we mainly focus on areal health data which have different names you may come across such as small area or aggregate data. Unfortunately the point data (with exact location) are not covered in this class. The models for those types are slightly different. However we can discuss if you need that tool for your research. Good comment!
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Great comments! I am aware of the issues appearing on the paper. Actually another type of modeling for counts and rates will be introduced next week and I also plan for each student to discuss the model assumptions and propose an alternative which are essentially what you just did! So perhaps it would be beneficial for the class to have informative discussions here for the week3 class project as well. Very well done!!! 😉
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Hello Napisa, There are no format requirements for weekly assignments, however you can find the general guideline for class project report on the course overview page: https://mu-informatics.org/courses/statistical-methods-in-spatial-epidemiology/lessons/course-orientation/#1593763833294-8e838bd2-a559
Please focus on the content and do not spend time much on formatting. Hope this helps and let me know if you have further questions. 🙂
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As you all mentioned and agreed, this field is interdisciplinary, there are lessons I have learned and continue to learn… That would be great and I would be honored if this class can be a part of what you need in your work. So please feel free to raise any concerns and hope your fellow classmates can help and support each other. 🙂
Like mentioned above about the Tobler’s First Law, the concept is the foundation of autocorrelation we will encounter in spatial modeling and cluster detection in next sessions… This is already a good start and I look forward to what you all will do in your assignments and class project! Best wishes!
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Great comments. I really enjoy what you mentioned!
The literature uses the phrases which you might come cross in the future, e.g., geographical epidemiology, spatial epidemiology, and medical geography, to describe a dynamic body of theory and analytic methods concerned with the study of spatial patterns of disease incidence and mortality. Interest in spatial epidemiology began with the recognition of maps as useful tools for illuminating potential “causes” of disease.
I totally agree with you all that it is clear where people live can be of great importance in identifying patterns of
disease. Nonetheless, investigations in public health need not pertain solely to geographical distributions of disease. The spatial distributions of the socioeconomic structure, occupational patterns, and environmental exposures of a population are also of particular importance.It is part of human nature to try to discover patterns from a seemingly arbitrary set of events. We are taught from an early age to “connect the dots,” learning that if we connect the right dots in the right way, a meaningful picture will emerge. Spatial information may be a ‘missing dot’ in public health research due to the various reasons you mentioned!, and as a component of the pattern observed, the location where an event happens may provide some indication as to why that particular event occurs.
In public health, identification and quantification of patterns in disease occurrence provide the first steps toward increased understanding and possibly, control of that particular disease. Next week we will conceptually discuss the next component of why we need analytical tools for discovering knowledge in spatial health data we start having in the past decades. Can’t wait to hear more from you all… Cheers!!! 🙂
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Hello all, shapefiles have mandatory and optional files. The mandatory file extensions needed for a shapefile are .shp, .shx and .dbf, and others are optional files. The main file is .shp that gives features of their geometry. Every shapefile has its own .shp file that represents spatial vector data. For example, it could be points, lines and polygons in a map.
However, when you move shapefile files, you should drag and drop all the mandatory and optional files. Hope this helps krub.
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Hello, the figure on page 27 is an example of the map you could get before changing the symbology. However I can see confusion in the outline color of West_boundary. The default was red for the version we used and so was the screenshot captured. However you can use black or red for the outline color of West_boundary. No worries, you won’t be penalized as long as you explain what/why you did 🙂 Good work! Keep up good comments!
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Hello all, the examples and tutorials in this course have been developed and tested with the long term version 3.10 (the most recent at the time) and things on your screen can look slightly different to what is shown in the images in each practical. However most of them should be sufficient you can follow. Please let us know though if you have difficulties following the practicals due to version differences.
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Hello class… Location or place can be considered as a surrogate for the health outcome of interest. It is recognized that the place where an individual lives or works can be considered as a potential disease determinant. For example, residents located in proximity of major traffic corridors are exposed to particulate matter and diesel exhausts, causing a variety of respiratory and cardiovascular diseases, or people who live in areas with high crime rate have higher stress. You will conduct analysis with real data for the class project and see how statistical modeling in this class can be used to quantify an association between the health outcome and potential risk factors.
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Hello Tullaya, there will be a reduction in overall performance part (maximum of 8%) for those who submitted late at each step. You can also choose to not grade (give a zero) presentations submitted late (after Thursday). The final grade of the JC project will be based on all evaluation forms and comments for each presentation. For the comments you are supposed to get, you can also choose to not respond to late comments (after Friday) and won’t be penalized by not responding late questions.
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Hello class, there is a concern about late submission of your classmates on each step which can affect the peer review process of the JC project. Please check out and follow the guideline under this topic: https://mu-informatics.org/forums/topic/deadline/
Let me know if you have any concerns regarding the issue.
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Hello class, there is a concern about late submission of your classmates on each step which can affect the peer review process of the JC project. Please check out and follow the guideline under this topic: https://mu-informatics.org/forums/topic/deadline/
Let me know if you have any concerns regarding the issue.
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Hello class, there is a concern about late submission of your classmates on each step which can affect the peer review process of the JC project. Please check out and follow the guideline under this topic: https://mu-informatics.org/forums/topic/deadline/
Let me know if you have any concerns regarding the issue.
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Hello class, there is a concern about late submission of your classmates on each step which can affect the peer review process of the JC project. Please check out and follow the guideline under this topic: https://mu-informatics.org/forums/topic/deadline/
Let me know if you have any concerns regarding the issue.
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Hello class, there is a concern about late submission of your classmates on each step which can affect the peer review process of the JC project. Please check out and follow the guideline under this topic: https://mu-informatics.org/forums/topic/deadline/
Let me know if you have any concerns regarding the issue.
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QGIS can also run on Mac or Linux, and you are welcome to use them; however, only Windows instructions are given in these lesson materials. If you get hung up and ain’t so familiar with the systems, you may be expected to spend sometime troubleshooting or default to a Windows machine in order to complete the exercises. I’m not a Mac/Linux expert but that is excellent for some of you to figure out a way around. If all you know is Windows, I suggest you stick with Windows for this course 🙂
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