Module 1:
Basics of Data and Information
Problem: This module of Turning Data into Information focused on visualization and interaction because they are the primary methods for retrieving information and producing new information with spatial data. This module I had to study coffee shops and their customers, so a new coffee shop location can be built.
Analysis Procedures: To complete this exercise, I had to first analyze and create a distance layer of the coffee shops by utilizing the Euclidean Distance tool. This tool was used because it showed the closet and furthest coffee shops. Secondly, I had to create a new layer based on the customers location. The Kernel Density tool was used, to create the customers location, because it showed where customers spent most of their money on the map. Lastly, I then had to create a query that would find areas that were more than the specified distance from existing shops with high spending density.
Problem: This module of Turning Data into Information focused on visualization and interaction because they are the primary methods for retrieving information and producing new information with spatial data. This module I had to study coffee shops and their customers, so a new coffee shop location can be built.
Analysis Procedures: To complete this exercise, I had to first analyze and create a distance layer of the coffee shops by utilizing the Euclidean Distance tool. This tool was used because it showed the closet and furthest coffee shops. Secondly, I had to create a new layer based on the customers location. The Kernel Density tool was used, to create the customers location, because it showed where customers spent most of their money on the map. Lastly, I then had to create a query that would find areas that were more than the specified distance from existing shops with high spending density.
Results: Figure B is a
screen shot of the exercise map, and it shows the values of areas with
distances to an existing coffee shop greater than a specified distance with
a high spending density. The results are also accompanied by a work flow
diagram (Figure C).
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Application
and Reflection:
This application was very interesting because I was able to see how businesses
could get information from spatial analysis.
-New Problem: CrossFit gyms are very popular, and they can be found almost around every corner in Fayetteville, NC. However, a person could use spatial analysis to locate a suitable location for a new CrossFit facility.
-Data Needed: Data that would be needed are Fayetteville NC data layer, CrossFit gym data layer, and residential data layer.
-Analysis Procedures: To locate a suitable location for a new CrossFit gym facility, the analyst would need to develop a layer that can calculate the distance of the existing CrossFit facilities. Secondly, the analyst would identify the high and low income areas and what CrossFit facilities charge in those areas. Lastly, the analyst would query the distance and average income to be able to find a suitable location for a new CrossFit gym.
-New Problem: CrossFit gyms are very popular, and they can be found almost around every corner in Fayetteville, NC. However, a person could use spatial analysis to locate a suitable location for a new CrossFit facility.
-Data Needed: Data that would be needed are Fayetteville NC data layer, CrossFit gym data layer, and residential data layer.
-Analysis Procedures: To locate a suitable location for a new CrossFit gym facility, the analyst would need to develop a layer that can calculate the distance of the existing CrossFit facilities. Secondly, the analyst would identify the high and low income areas and what CrossFit facilities charge in those areas. Lastly, the analyst would query the distance and average income to be able to find a suitable location for a new CrossFit gym.
Module 2:
Cartography, Map
Production, and Geovisualization
Problem: In this
I explored how GIS
can be more realistic and informative versus a paper map. The objectives of
this module were to learn how a GIS map differs from a paper map, use
attributes to represent symbols, and identify ways that spatial properties of
objects can be altered to clarify a map's message. This exercise I had to experiment with symbols that represented features on a map, and the attribute associated with those features. At the end of the exercise, I saw how important map scales are with visualizing data.
Analysis
Procedure: To
complete the exercise I had to first explore the data so that I could see what
was symbolized and what was not according to the map. First, I had to categorize the polygon city boundary based on
unique values from the attribute table. Once this was complete, I had to
categorize the line features so that they are more distinguishable. Then I had
to symbolize the road and its types through the properties using the symbol
selector, and I had to label those roads on the map. Lastly, I had to classify the points on the
map with graduated symbols and define those values.
Results: The exercise showed how GIS is easy to work
with. GIS offered numerous ways to symbolize spatial data, view ranges of
scales, and filter attributes. A screen shot and work flow diagram are presented with this module.
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Application &
Reflection:
The module was interesting because it
showed me how GIS data can be easily manipulated for projects. Also, it showed me how GIS makes cartography more useful.
-New Problem: The CrossFit gym that I work at has a small parking lot
that can only hold up to 24 cars. Currently the website does not show customers
where to park, nor does it show the overflow parking lot. I will need to
produce a map that shows customers where parking is available.
-Data Needed: Data that would be needed are shapefiles or geodatabase files of buildings polygons, parking polygons, and road line feature classes.
-Analysis Procedure: Once the data has been downloaded, I would first explore the files. Exploring the files is useful because this allows for better visualization. Second, I would want to map out the facility that I work at, and buildings that are nearby. I would have to label the building appropriately so that customers know what the buildings are. Thirdly, I would have to show the parking lot, and the overflow parking lot with labels. Lastly, I would add roads, with labels, so that customers can get a better spatial reference for what they are looking at.
-Data Needed: Data that would be needed are shapefiles or geodatabase files of buildings polygons, parking polygons, and road line feature classes.
-Analysis Procedure: Once the data has been downloaded, I would first explore the files. Exploring the files is useful because this allows for better visualization. Second, I would want to map out the facility that I work at, and buildings that are nearby. I would have to label the building appropriately so that customers know what the buildings are. Thirdly, I would have to show the parking lot, and the overflow parking lot with labels. Lastly, I would add roads, with labels, so that customers can get a better spatial reference for what they are looking at.
Module 3: Query and
Measurement
Problem: This module,
Query and Measurement, taught how to take data and turn it into valuable information. Query and Measurement is a starting point for further analysis. In this exercise, I had to
combine attribute and spatial queries with graphs and attribute calculations.
The exercise focused on developing a watershed awareness program with the local
school district.
Analysis Procedure: To complete this exercise, I had to open the land use acreages graph so that I could determine the dominant land use within each watershed. I selected land use polygons that fell within the northern watershed, and I had to explore how much land was devoted to it. Then I selected the land use polygons in the southern watershed and the census tracts within the southern watershed. I calculated and symbolized the census tracts by growth rate. Lastly, I had to select schools within the watershed, assign the watershed code to the selected schools, and symbolize the schools by their watershed value.
Analysis Procedure: To complete this exercise, I had to open the land use acreages graph so that I could determine the dominant land use within each watershed. I selected land use polygons that fell within the northern watershed, and I had to explore how much land was devoted to it. Then I selected the land use polygons in the southern watershed and the census tracts within the southern watershed. I calculated and symbolized the census tracts by growth rate. Lastly, I had to select schools within the watershed, assign the watershed code to the selected schools, and symbolize the schools by their watershed value.
Results: Figure F is a
screen shot of the results from the exercise. The screen shot only shows the
schools that are located within the northern and southern watersheds. The
results are accompanied by the work flow diagram.
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Application & Reflection:
This module was interesting because I learned that querying and measuring are
considered the most common operations used in GIS. In most cases, analysts will
depend on these skills to derive information from spatial data.
-New Problem: An example of a new problem could be a Police department interested in developing a campaign to minimize break-ins in the area they operate within.
-Data Needed: Residential information as polygons, Break-in Points, lines
-Analysis Procedure: I would want to explore the data through a graph so that I could see if the break-ins are concentrated in certain neighborhoods, certain days of the week, or times in a day. Once this was done, I would want to summarize the data and select only those areas with highest break-ins. Then I would want to symbolize the break-ins by occurrence in those areas. Lastly, I would want to assign the break-in occurrences with the neighborhoods.
-New Problem: An example of a new problem could be a Police department interested in developing a campaign to minimize break-ins in the area they operate within.
-Data Needed: Residential information as polygons, Break-in Points, lines
-Analysis Procedure: I would want to explore the data through a graph so that I could see if the break-ins are concentrated in certain neighborhoods, certain days of the week, or times in a day. Once this was done, I would want to summarize the data and select only those areas with highest break-ins. Then I would want to symbolize the break-ins by occurrence in those areas. Lastly, I would want to assign the break-in occurrences with the neighborhoods.