Project 6 GIS

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Diagnosing the Data: Project 6 Alternate: Graduated Colors and Raster Maps Steven McHugh School of Graduate, Online and Continuing Education, Fitchburg State University SP23_GIS and Decision Making-52 Dr. Davis February 27, 2023

Project Summary Throughout the progression of this course, we’ve learned about the significance of GIS and the data they could provide. As such, we have been how to extrapolate data and present it in a comprehensive and interactive manner. This week’s project presents another data analysis tool, raster analysis, of which we can utilize with the other topic at hand, imagery layers. To gain insight into this project, we must first define the concept of raster analysis. Raster “is a data format that consists of a matrix of cells (or pixels) organized into rows and columns (or a grid), in which each cells contains a value representing information. Raster data is advantageous because it consists of simple data structures, easy simulation due to the cells being the same size, and simple overlay and combination of maps and remote sensed images. Most importantly, it is cheap to perform (Sain, 2018). The main drawback to raster data analysis is its aesthetics as it may appear crude and its use of large cells may distort data causing a serious loss of information (Sain, 2018). In the context of ArcGIS, imagery “refers to any raster or cell-based data” (Fu, 2022, p.325) and “is often used as a basemap for reference” (Fu, 2022, p.326) and for different forms of spatial analysis. Imagery can then be implemented and published as imagery layers, the second major topic of this week’s reading. An imagery layer “provides access to raster data through a web service” (Fu, 2022, p.330). The use of web services is nothing new as we’ve been utilizing them all throughout this course for data interpretation and presentation. However, this week’s project had us not only present the provided data in a visually, interactive manner but it also gave us the chance to analyze it through a new means, that is raster analysis. My intent was to complete the primary project but for some reason, I was not able to access the option to create an imagery layer. Therefore, I focused on the alternate project which

focused on geocoded data and graduated symbols. This project’s feature layer pertained to COVID-19 cases in the United States. The purpose of Project 6 was to manipulate the map to change the symbology to reflect confirmed cases by incident rate as well as deaths. By doing so, each attribute is highlighted, and the data is easily presented. Proposed Map https://smfsu.maps.arcgis.com/home/item.html?id=54d48bf206ec473c9765fdd719a5ea22 https://smfsu.maps.arcgis.com/home/item.html?id=a176a1311e6f443db3d92f825accb656 The purpose of this map was to provide raster data pertaining to COVID-19 cases in the United States. As with previous projects, the basemap did not have to illustrate anything in particular and therefore, I went with the default topographic basemap. Next, I added the two predetermined layers for this map, USA States Generalized Boundaries and the main focus of this map, COVID-19 US Cases. I was able to acquire both of these layers through the ArcGIS Online database. Map 1.1 COVID-19 Cases in the US Figure 1.1 COVID-19 Cases in the US Legend

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As this was a comparative analysis based on the cases in each US state, I found first layer beneficial in providing definitive borders, allowing the data points in each state better context. The COVID-19 US Cases layer provided a lot of valuable data, consisting of more attributes than just cases per state. It also listed such information as people tested, people hospitalized, and the number of those who have required amongst other attributes. Additionally, this layer was geocoded with not only information for each state but also for each individual state’s counties as well. After creating this map with the necessary feature layers, the next step was to alter the symbology of the map to reflect confirmed cases by incident rate as well as by deaths. To do so, I created two new maps, following the same steps to create each albeit spotlighting a different attribute. Using the Style feature, I selected Confirmed Cases as the main attribute and then adjusted the style to Counts and Amounts (Size). Once in the Counts and Amounts menu, I was able to manipulate the data by dividing the Confirmed Cases attribute by the Incident Rate and Deaths Figures respectively. I did not change the orginal settings for these styles. I left the number of classes at 9. I did so as I felt more might clutter the map with too many sized symbols yet less classes would not provide small enough parameters to effectively display the data’s disparity. Additionally, due to similar reasons, I did not adjust the symbol sizes either rather leaving them at the defaults given. To distinguish these two maps from one another, I chose two different color schemes (blue for incident rate and green for deaths) and then I renamed them each by their style attributes i.e. US Covid-19 Confirmed/Incident Rate and US Covid-19 Confirmed/Deaths. From these maps, we can determine where the highest incident rates per confirmed cases as well as where the highest death percentages from confirmed cases are. The

graduated symbols provide visual depth to the statistical data that the base layer could not provide on its own. Map 1.2 Confirmed Cases Divided by Incident Rate Figure 1.2 Confirmed Cases Divided by Incident Legend Map 1.3 Confirmed Cases Divided by Death Figure 1.3 Confirmed Cases Divided by Deaths Legend Being able to present certain data points such as in the manner of the two above maps can prove to very important in determining the next course of action for whatever circumstance the data is portraying. For example, with the above maps, federal organizations can now have a

better understanding of where to allocate resources, limiting the spread of the virus and hopefully, eliminating the death factor. While this is only one particular circumstance, it showed us the ways in which we can use GIS to correlate data and find patterns which we can then use to analyze and possibly troubleshoot. Recommendations I would’ve liked to have done the primary project as it seemed more interactive, but like previously stated, I could not find the option to create an imagery layer. After researching through various ArcGIS forums, the only assumption I could make was that the version of ArcGIS Online we are using does not include the permissions to create such layers. With that being said, I had to opt for the alternate project. We’ve had much experience with layer styling throughout this course and that was one of the major aspects of this project. I didn’t want to deviate from the directions provided which stated I create two separate maps, once based on confirmed cases/incident rates and the other being confirmed cases/deaths. I feel it would’ve made more sense to add the COVID-19 layer twice, style each of them to the reflect the pertinent data and rename them. However, without attempting this, I’m not sure if it would be feasible. Other than this though, the project was straightforward and easy to complete. My only other issue with the alternate project was its lack of interactivity in comparison to what this week’s primary project would’ve provided. Nevertheless, it got the job done by displaying the raster date in a vector format in a clear and comprehensible manner. Conclusion This project emphasized the importance of GIS and their data’s interpretation by highlighting the important factors relevant to COVID-19 such as incident rates, deaths, and

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confirmed cases amongst others. Through the use of raster data, we were able to find ratios of death per confirmed cases as well as incident rates per confirmed cases. Having access to this data can give way to insight such as hotspots and where the virus has subsided for example. While this is only one example, it showed the ability GIS can have to isolate and manipulate certain attribute points to acquire specific data patterns. Although I was unable to create imagery layers, this week’s reading taught us the significance of them, and the different ways raster analysis can be applied to them. It provided us another means of visual and spatial analysis that although probably not used by the everyday consumer as much, still has its benefits in terms of deciphering data. Additionally, it further enforced the role of geographic information systems and how their relevance continues to blend into everyday life.

References Fu, P. (2022). Getting to Know Web GIS (5th Edition). ESRI Press. Sain. (2018, August 27). Advantages and Disadvantages of Raster & Vector Data. GIS RS GPS; Blogspot.

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