Lab 2 is mostly on understanding arc catalogue and arc map. Geospatial data is data that are given between space given on maps. Understanding geospatial data and concepts is important because they can allow you to get information give from within a raster file. Allows you to take photo and pull off data form that said photo. This lab uses geospatial to find heights of a map within a raster.
As seen in figure 1 files have to be organized in a way that a can be found an have correct data with them, next we established a folder connection that allows to drag files from one source to another and safely saves them.
figure 2
As seen in figure two files in Arc Catalog are easier to pull up and use. It also take a large amount of information and files and brings it down to a few important files. The icons in figure 2 show shape files: line and polygonal, there is also shape file point. The raster file is more or so an image and are different from shape files.
The next topic in the lab was metadata, which is numerical data that go with the files. in the raster file we can find elevation for surfaces which is important for anyone wanting to know heights or distances. For the raster we found the heights for max, 323.08865356445 then the Min,281.04708862305 and finally the mean,296.9669108356. The data analysis is important for the client to make sure we are finding to proper information for them and to share your findings. The infromation found withing the raster are as follows
In the lab the tornado extensions are; dbf, prj, sbn, sbx, shp, and shx.
As seen in figure 1 files have to be organized in a way that a can be found an have correct data with them, next we established a folder connection that allows to drag files from one source to another and safely saves them.
figure 2As seen in figure two files in Arc Catalog are easier to pull up and use. It also take a large amount of information and files and brings it down to a few important files. The icons in figure 2 show shape files: line and polygonal, there is also shape file point. The raster file is more or so an image and are different from shape files.
The next topic in the lab was metadata, which is numerical data that go with the files. in the raster file we can find elevation for surfaces which is important for anyone wanting to know heights or distances. For the raster we found the heights for max, 323.08865356445 then the Min,281.04708862305 and finally the mean,296.9669108356. The data analysis is important for the client to make sure we are finding to proper information for them and to share your findings. The infromation found withing the raster are as follows
o Cell Size:0.02077, 0.02077
o Format:TIFF
o XY Coordinate System:WGS_1984_UTM_Zone_16N
o Linear Unit:Meter (1.000000)
o Datum: D_WGS_1984
o Use the Linear unit and write the pixel size in square cm: 2cm squared
ArcMap
Next i opened a World topographic map which shows shows states and places and used that as a background.
this data is a raster file with vector line data over it. vector point data. The tornado tracts coordinate system is NAD_1983_UTM_Zone_16N. The other two coordinate systems for the image are, GCS_North_American_1983 and USA_Contiguous_Equidistant_Conic. All these coordinate systems are different because they were collected by different types of systems. 
figure 3
figure 3
Figure 3 is a Raster file with a format of .tiff, the projection for both the files are WGS_1984_UTM_Zone_16N which is good because then we can accurately compare the data. As seen in the figure 3 because they match up your now able to see the ground control points within the file.
After this we used the measure, swipe, and identify tools. they allow you to measure distances, compare images, and get specific information for a pixel.
In conclusion using Arc map and catalog we are able to derive information that that is useful and then compare to other data collected in the same way. The data does have some limitations but is still very useful. You can take these images over time and compare them in order to see the changes in data throughout time.
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