Running Head: GIS MAP WEEK 7
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GIS Map Week 7
Bryan R LaPointe
Fitchburg State University
MGMT9150: GIS and Decision Making
October 27, 2023
Public Map Links: GIS Map - Week 7
GIS MAP WEEK 7
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Project Summary
In Project 7, a combination of hosted layers and analysis capabilities was used to generate
a buffered view of Acute Care Hospitals. This buffered layer was completed by using the “Use
Proximity” analysis capabilities within ArcGIS while being set for a 5 mile radius. For the
purposes of this project, these buffers were output onto another layer and then used to compare
against COVID-19 testing sites as shown from the hosted layer “GISCorps COVID-19 Testing
Locations in the United States Symbolized by Status”. Once compared, the tasks for this project
required that students add in map notes on 5 locations from the aforementioned hosted layer that
are not covered by the buffer output of the prior analysis. These locations indicate places that are
outside of a 5 mile radius of an Acute Care Hospital. In addition to the previously mentioned
hosted layers, 2 other layers were requested to be added. The first of which includes the
Massachusetts City/Town Boundaries (survey-derived) and the second of which was from
changing the basemap to “streets” giving street information for this map.
Students then had to add notes and details for each of the 5 pinned locations including the
city/town/state/population for the area. This map had pins placed at/on testing facility locations
that were found in Millis, Westborough, Billerica, North Reading, and Tewksbury but other
locations could have been used as well. No stylistic changes were specifically required and no
pop-over information, dashboards, or experiences were created or manipulated from their default
states. While adding these notes or pins it was found to be easier/quicker to switch from the
modern map viewer interface into the classic viewer. This was done because documentation and
screenshots were more readily available for the classic viewer showing how to create notes. Very
few if any information was easily found for the modern viewer and its comparable functionality.
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Proposed Maps
Figure 1. Statewide View (Unbuffered)
With these layers enabled and this zoom level set, viewers are presented with a default
view of the hosted layers. This view is not intended to be their final stop but helps understand the
underlying data before it is analyzed. As more and more layers are added, things become more
difficult to comprehend on the map. Colorized bubbles and “H” figures are used to identify
COVID testing sites and Acute Care Hospitals.
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Figure 2. Statewide View (Buffered)
An additional view is proposed with the buffer analysis results included. By including
this, the viewers are able to start making decisions and collecting data points for themselves. The
underlying layers have not been removed or hidden since they still contain important data for
viewers such as pop-overs containing hospital addresses and wards/capabilities.
Analysis and Recommendations
Viewers may use the data from this map for any number of analyses. In this project a
proximity analysis was conducted to create buffers around Acute Care Hospitals. Another
analysis could include looking at the density of hospitals in relation to their local populations. As
for recommendations, style and symbology changes are in dire need as the map in its current
state is far too busy and heavy to look at. These changes and analyses will be discussed below.
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Aesthetics
Most of the proposed style and symbology changes come from the need for simplifying
the map view for its viewers. The reason for improving map aesthetics is that users perceive
aesthetically pleasing sites and apps as more usable and thus have a higher perceived satisfaction
using them (Yablonski, 2023). As such, each layer could benefit from its own changes. Starting
with the basemap and assuming that street level data is needed, the “streets (night)” base map
removes some of the distracting colorations from the “streets” basemap and therefore is
suggested for use. Next, the Massachusetts City/Town Boundaries would have their boundaries
adjusted to a lighter color to stylistically pop against the newly selected darker and simpler
background. Since the symbology and iconography of the Acute Care Hospitals and Testing
Locations comes from hosted layers, their changes are more limited in nature unless their
iconography and existing coloring are tossed out. For this reason only size changes for the
symbols are proposed. On the buffer layer, the fill and border should be changed to have a more
vibrant brighter blue color for contrasting against the background. The border should be a
matching or similar blue hue with no transparency in order to help divide each bubble visibly
from one another while overlapping. Lastly, a legend should be enabled in order to clarify the
complex iconography of each hosted layer.
Datasets & Analysis
No other data sets are being proposed since the purpose of these two data sets can be
sufficiently achieved with what has been provided. If other analyses are to be conducted then
additional data sets may make sense. Take as an example the previously proposed analysis that
looks at hospital capacities and densities as they compare to local populations. One could add a
layer or subset of data to an existing layer for hospital bed capacities. An analysis could then be
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run to see what the population to available bed ratio is for a given area. A high ratio may indicate
an under-representation of hospitals or perhaps a bed shortage whereas a low ratio would
indicate the opposite.
Conclusion
Buffers “create polygons around input features to a specified distance” (ArcGIS, 2023).
These buffers can be used to identify features that lie within or without the bounds of a buffer
area. Essentially, buffering can be used to help identify features within a proximity to other
features. In the case of COVID test sites this means overlapping hospital locations within a 5
mile radius. Buffering can also be used for planning or mapping purposes such as in applying a
buffer around rivers or lakes. These buffers may serve to identify flood zones or wetland zones
around the features (Shivaji College, n.d.). This form of analysis is both powerful and useful and
should be considered a basic necessary skill for GIS users everywhere.
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References
Buffer (analysis). Buffer (Analysis)-ArcGIS Pro | Documentation. (2023).
https://pro.arcgis.com/en/pro-app/latest/tool-reference/analysis/buffer.htm
Data Query and Buffering. Shivaji College. (n.d.).
https://www.shivajicollege.ac.in/sPanel/uploads/econtent/a8ba7f36524748004c639f785d
2aa687.pdf
Yablonski, J. (2023). Aesthetic-usability effect. Laws of UX. https://lawsofux.com/aestheticusability-effect/