Joining and Relating tables part 2. This time in the state of Louisiana, we will go to the Lower Mississippi Buffer to see the section of Pits_75 but relating tables. Once after relating tables with the Pits and Metals to the Remedial Actions and IDNumbers. Witness, you can see the record points of 3 pits that contain lead.
Joining and Relating tables, a map of Louisiana showing parishes, navigable, waterways, and pits. But now with joining the tables of Remedial-Actions to thier IDNumbers it will show that Pits need to be repaired or removed that are moslty located in parishes of the southeastern tip of the state.
The Data Mining project was kind of difficult at first especially looking for the right data that needs to be downloaded. Here depicts like 3 types of spatial datasets that is used. For instance, DEM Data, Hydrography Data, Census Data and a Wetlands Data. All of them used the projection of North America 1983 11N to be visible and match with the corresponding datasets that are put together.
The depiction of this map is to review the proposal and the map needs to familiar with the geography of India. So that's why I inserted legends, scale bars, and north arrows because they are necessary for interpreting a map for geographic orentation.
Here we see a title that portrays the meaning of what the project is about. Before moving on the data frame on the right depicts the use of vegetation and forest for residential and commercial use. The frame on the left depicts Proposed reserves where high priorities are in red and low priorities are in blue.
First of all, this project consists of tiger conservation and existing reserves along vegetation corridors. I began to work with three Data Frames that were given to me but needed to be aligned. The bottom data frame is the Overview frame, the data frame on the left is the Existing reserves frame (Bio), and the one on the right will be the Proposed reserves frame (Tiger).