Geo_Referencing Rastar

The following map was created using the Geo-referencing toolbox to identify and match features of an unknown or unreferenced layer to the same features on a referenced control layer (for example: roads shapefile, buildings, DOQQ and Digital Raster. 

To Geo-reference a photo, data needed to be added first.  The Geo-referencing toolbox was open and the adding control box was used to take points which were needed in order for the buildings and roads to fit in the UWF_N shapefile it was then saved and recorded  with it RMS errors.  This was also done for UWF_S1 shapefile. After this was done the shapefile was updated and saved. 

Secondly, the editor toolbox was open to edit the data and new shapefile was created and named as Athletic_Fields, this was done in order to create new fields in the map. lastly, features and symbols (legend, text, scale bar, scale text) were then added; the file was then saved and exported as JPG.

Projections and Coordinates

The following map was created in an Albers type projection.   Cntbnd Shapefile was added to ArcMap and under the properties for the “Layer” data frame the coordinates was selected, it was then renamed as Albers and new coordinates was added. To Re-Project Datasets the Project Data Management toolbox was selected.  Once this done to complete the process, new sets of parameters were added and data was re-projected and saved as UTM and coordinates NAD_1983_To_HARN_Florida.  

Under “layer” data frame the shapefile UTM was moved to a new data frame to have both in full extent. After doing this, the attribute table for both were edited and a new field was added and named as Area, with this done the area was calculated for both, and then compared to see the difference which was more or less like 10-12 in value.

Lastly, the following counties were added: Alachua, Escambia, Miami-Dade, Polk by creating a query under properties of Albers and UTM. Features and Symbols were added to give the maps meaning. It was then saved and exported as JPG. ;)

University of Belize, Belmopan

Collecting GPS Data 

 

First, data was gathered using a GPS, then points were recorded.

Secondly, the data was added to Arcmap program, where symbols and labels were inserted.

lastly, a basemap was selected to give the map meaning. then it was exported and saved as JPG.

 

 

This is how the Map looks:

 

Stann_Creek. Belize

Data Search Collection

This activity was based on data search collection by finding existing GIS datasets on the web.

First of all, to begin the activity, we had to create and acquire GIS data, and to do gather data we had to explore and know how to find existing datasets on the web, which is a critical step of GIS projections. It is very important to make use of the resources available on the web. Belize is a small country and consists of 6 districts and in this activity we will only focus in 1. This map represents a district of Belize, which is Stann Creek.

The process …

After downloading data from the web, under layers on the Table of content the data Belize_Basemap_District was added to ArcMap. Then, under layer the attribute data table was open and Stann Creek District was selected and highlighted.

With this done, the district was exported. After exporting the selected District, under the analysis toolbox, clip toolbox was selected; this was done in order to isolate only the district of Stann Creek because we don’t need the whole country to show in our map, we only want to focus on the Stann Creek.

Once clipped, the rest of the data was added for example Urban_Areas, Roads, Protected Areas and Aquatic_Ecosystems found ONLY in the district of Stann Creek. Before adding the above data, each one was clipped to the District of Stann_Creek, keeping this in mind; we don’t want to show the entire country only the selected District. Features such as legend, text, and scale bar, North Arrow were inserted to make the map meaningful and useful.

Finally, the activity was saved and exported to JPEG. 

This is how the map looks after clipping and adding existing datasets on the web.