Terms of Other Types of Data That They Might Represent
Samuel Kovach
1. Do the points and lines represent the data with the same level of abstraction? Discuss in terms
of their representation of the two data layers(cities, roads) that we have added so far, and in
terms of other types of data that they might represent.
In GIS, an abstraction is a simplified idea of something real. In our data layers the point is an abstraction of a city and a the line is an abstraction of roads. However both are the same level of abstraction as they are both a representation of a real spatial object onto a map. Points and lines can be the same level of abstraction or different levels. This is depending on what real object they represent on the map.
2. What happens when you use the identify tool? Is the option to change the layer(s) being
identified useful?
The identify tool gives a “Identify Results” window which has all of the different fields associated with the object as well as the value that is associated with the field. The option to change the layer is useful if there are multiple layers and you are trying to select data from certain layers. It makes clicking on the objects easier when they are overlapping or neighboring.
3. Why do you think the Field Definition requires that you differentiate between text and numeric
data types? Why do you need to specify the field width?
It is important to differentiate between text and numeric data types to establish what the data is and how it can be interpreted/ operated with. To analyze the data, ArcGIS must now if the data is numerical if it is to be used in any equations. Similarly text data can include numbers without numerical meaning, such as zip codes. This is why it is important to be differentiated. Specifying the field width is important for precision. By setting a length there is less of a deviation amongst fields.
4. What has changed in the table after joining?
The table for the states layer now has two extra columns. One of the columns is the name of the states in which were in the weather table. The names are repeats and the states that were not in the weather table have no data. The other column is the weather description. For the states in which weather descriptions were not added, there is no data.
5. How is the original attribute data from the States layer distinguished from the Weather data that
you joined?
The original attribute data from the States layer is still the same as it was before the joining. The weather data is placed to the far right of the table and contains lots of “null” data. Other then this there is little to distinguish that the two had been joined.
6. What would happen if you tried to join the attributes from the States layer to the Weather data
(rather than joining the Weather data to the States data as you just did)?
In the weather table there were only 10 states and the states layer contained all of the states. If the states layer was joined to the weather data there would be only the matching data would be displayed or all records would be displayed, depending on the selection options.
7. Print screen of selected record
8. Print screen of new attribute table
Part 2
9. What does the reclassification step in Step 1 accomplish?
The reclassification selected certain values, the distance from the major roads, and reassigned them a value, a color.
10. Please include a JPEG of roadscore (end of Step 1). This should be a completed map (i.e.
ready for display), exported into your student folder, and inserted as a picture into lab report.
11. Please include a JPEG of hydroscore (end of Step 2). This should be a completed map (i.e.
ready for display), exported into your student folder, and inserted as a picture into lab report.
12. At the end of Step 3, what does the map tell you in terms of the developer’s office buildingproject? What do the highest scores represent? What do the lowest scores represent?
The higher the score the more suitable the area is. The further from the major roads the lower the value was given. Similarly the further away from water, the higher the number was given. Thus the greatest sum would be the furthest from the water and closest to the roads. This is the desired location
13. What does Step 4 accomplish towards producing the final suitability data layer?
In order to find a suitable area, we must first take into account zoning restrictions. Step four takes into account zoning.
14. Please include a JPEG of final suitability layer (end of Step 4). This should be a completed map(i.e. ready for display), exported into your student folder, and inserted as a picture into labreport.
15. Prepare a brief executive summary (~2 paragraphs) to the developer, summarizing your results.Include a short description of the analysis you performed and indicate the locations you thinkwould be the best choices for her office project.
The first part in finding an adequate location was measuring the distances from the major roads. Because we wanted our office to be located near a major road, we made a raster overlay that showed the relative distances from the road. We reclassified these distances with a score. The closes locations were given a score of ten and the furthest locations were given a score of 1. There were five different intervals. Secondly we wanted to make sure we did not build the office building close to a river or stream in case of flooding. Similar to the road score, we made a raster showing the distances from the rivers. There were also 5 variables. However, the closest locations to the rivers were given a score of 1 and the furthest were given a score of 10.
To make a total suitability score, we added the hydrology score and the road score. The highest scores will be both close to major roads and also far enough away from rivers. The final factor we had to take into consideration was zoning restriction. We set acceptable zoning areas to a value of 1 and the unacceptable areas to a value of 0. Then by multiplying the zoning areas with the total suitability score only the areas in which offices are allowed, only the suitable areas had a score. The final map shows the areas which offices can be built. The higher the score the more suitable the location.